Apr 5, 2016 - He offered me the freedom to pursue a number of ..... 5.5.5 Variabilities of total thermodynamical forcing at mixed layer . ...... south Asian monsoon rainfall with a 9 day lag period (Krishnamurti and Bhalme, 1976). 8 ...... hindcast mode is essential to delineate the role of ocean dynamics in individual basin on.
Role of Ocean-Atmosphere Coupling in the Seasonal Prediction of South Asian Monsoon
a thesis submitted to Savitribai Phule Pune University Pune 411007, India
for the award of degree of Doctor of Philosophy (Ph.D.) in Atmospheric and Space Sciences Faculty of Science
submitted by Mr. Gibies George under the guidance of Dr A. Suryachandra Rao
Research Centre Indian Institute of Tropical Meteorology
April, 2016
Role of Ocean-Atmosphere Coupling in the Seasonal Prediction of South Asian Monsoon
a thesis submitted to Savitribai Phule Pune University Pune 411007, India
for the award of degree of Doctor of Philosophy (Ph.D.) in Atmospheric and Space Sciences Faculty of Science
submitted by Mr. Gibies George under the guidance of Dr A. Suryachandra Rao
Research Centre Indian Institute of Tropical Meteorology
April, 2016
.
Certificate of the Guide Certified that the work incorporated in the thesis entitled Role of Ocean-Atmosphere Coupling in the Seasonal Prediction of South Asian Monsoon submitted by Mr. Gibies George is an authentic record of the research work carried out by him at Indian Institute of Tropical Meteorology, Pune-411008, under my supervision and that no part thereof presented earlier for any other degree. Such material has been obtained from other sources has been duly acknowledged in the thesis.
05 April 2016 Pune
(Dr. A. Suryachandra Rao) Research Guide Scientist F, Indian Institute of Tropical Meteorology Pune 411008, India
Declaration by the candidate
I declare that the thesis entitled Role of Ocean-Atmosphere Coupling in the Seasonal Prediction of South Asian Monsoon submitted by me for the degree of Doctor of Philosophy is an authentic record of the research work carried out by me at Indian Institute of Tropical Meteorology, Pune-411008 during the period October, 2011 to September, 2015 under the guidance of Dr A. Suryachandra Rao and has not formed the basis for the award of any degree, diploma, associateship, fellowship, title in this or any other university or institution of higher learning. I further declare that the material obtained from other sources has been duly acknowledged in the thesis.
05 April 2016 Pune
(Gibies George) Candidate
Declaration under rule 4(E) of the rules for the degree of Doctor of Philosophy (PhD) of Savitribai Phule Pune University.
It is certified that in the case of all joint papers incorporated in the thesis, the major contribution to the papers is by the candidate. Contributions by the coauthors other than the guide have been only in respect of providing supports like processing of data and assistance in computations.
Gibies George (Candidate)
05 April 2016 Pune
Dr. A. Suryachandra Rao (Research Guide)
Acknowledgements
This thesis is more than the outcome of seven years of my research work at Indian Institute of Tropical Meteorology (IITM), Pune. It is the beginning of my research carrier and the fruit of my ambition seeded from my friends’words sowed in my heart during my schooldays in the early 1990s. Further, my life would have been different if I did not get an opportunity to interact with a few personalities at the Center for Science in Society (C-SiS), Cochin University of Science and Technology (CUSAT) during the period 2005-2007. It is my pleasure to convey my wholehearted gratitude to all the people who supported, encouraged and motivated me throughout my academic and personal life. First of all, I acknowledge my research guide Dr A. Suryachandra Rao
with great
pleasure and gratitude for all guidance and motivation. He offered me the freedom to pursue a number of my own ideas, which often down the wrong path, was invaluable to me for building a professional research career. He always encouraged me to think differently and solve research problems independently. As I mentioned above, my days at C-SiS are very precious in my life. There I realized the importance of science in the daily life. My heart is full of gratitude towards Prof. K. G. Nair, Director, C-SiS, CUSAT. I was first informed about Earth Sciences from the words of Emeritus Professor Dr. P. V. Joseph while I was working at C-SiS, CUSAT and that pointed my way to a scientific carrier. I may not be able to fulfill this research work without the fellowship provided by the Council of Scientific and Industrial Research (CSIR), Govt. of India for the period 2009-2014. Further, I extend my sincere gratitude for all the facilities provided by IITM, including the library, the super computation facility and the quarters. My research work may not be realized without the High Performance Computer facilities (Nimbus, Prithvi, and Adithya) at IITM and the cooperation of the technical supporting team for them. I should mention a couple of names such as Mr. Parthipan and Mr. Rajamanikam. I should specially thank Mr Ashish who showed me the way to run Climate Forecast System (CFSv1 and CFSv2) models for the first time. Since my work involve some modifications in the source code of CFSv2 model, I was honored with some inspiring moments of interactions with senior scientists at National Center for
Environmental Prediction (NCEP), through the Monsoon Desk as well as personal email communications. National Center for Atmospheric Research (NCAR) developed NCAR Command Language (NCL) and make it freely available with very good user support through dedicated mail-list. Almost all the visualizations in this thesis are made using the NCL software. Various committees such as RF/RA review committee of IITM, committee for the reviewing the research progress of CSIR fellows at IITM, committee to evaluate the PhD proposal/synopsis for Savitribai Phule Pune University, constituting of senior scientists from IITM, IMD and the University have helped me by evaluating my research progress and providing constructive comments. I extend my thanks to all members of those committees and I wish to specially mention some of those names such as, Prof. B. N. Goswami, Dr. M. Rajeevan, Dr. R. Krishnan, Dr P. S. Salvekar, Dr. H. P. Borgaonkar, Dr. C. Gyanaseelan, Dr. P. Mokhopadhyay, Dr. A. K. Sahai, Dr. K. Ashok, Dr. S. Datta Dr. D. S. Pai, Dr. Ananth K. My interactions with Prof. Raghu Murtugudde build confidence in me to approach challenging research problems patiently. He is a faculty member of the University of Maryland and frequently visit IITM, Pune and IISER, Pune to give series of lectures on Earth System Sciences. My colleges and co-authors of various research articles enriched my research experience with their previous experiences and thereby support my career development. I am not mentioning any of their names because the list is continuously growing and thereby it is difficult to sort those names. I also extend my thanks to the anonymous reviewers of various journals who reviewed our research articles and provide their comments for the improvement of those manuscripts. Once I started writing my thesis, I received constructive suggestions from Dr. Vinu Valsala and Dr. Prasanth A Pillai, who have read it throughout for many times. I also express my sincere gratitude to Mr. Abhay S.D. Rajput for reading my thesis and doing language editing, which not only improving the language of the thesis, but also helped me to get an insight about the frequently repeated errors in my writing. I have a lot of my seniors from CUSAT, who give me confidence in all facets of my life inside and outside academia, by creating a homely atmosphere along with their families at IITM, Pune. I wish to mention some of their names such as, Rahul, Sabeerali, Hamza, Abhilash, Sabin, Suhas, Manoj, Prasanth, Roxy, Sooraj, Preethi, Neena, Susmitha. Further, I have my friends who provide me unconditional support during both ups and downs of my life with friendly suggestions, love and motivations. Ruchith, Priya, Sujith, Prajeesh, Sakharam, Sachin, Anil,
Nagarjuna are some among them. As mentioned above, my childhood friends always express immense trust in my abilities, and their confidence in me is the stepping stone of my ambition to become a scientist and that is the most valuable treasure in my life. I feel this is the right place to mention some of their names such as, Anish M.P., Dhanesh Vijayan, Kiran George, John Varghese. The cheering from all my friends help me so much to face all the difficult situations with courage and confidence. The innocent smile of my son Joan erases all the stress and tensions in my daily life and fill my heart with positive energy. My wife Femy shared my stressful moments without any complaint, while her love and support empower me to face difficult situations with confidence. I have no words to express the continuous support and encouragement extended by all members in my family including my parents and my in-law parents. Their understanding, sacrifices, patience, encouragement, love and affection build confidence in me. My younger brother Ginil assumed the maturity to meet my responsibilities to my parents and he always encourages and supports me. My younger sister Feby considers me as a role model and always being proud of me. The moral and spiritual support given by all my friends and relatives are thankfully remembered at this moment. Overall I thank the supreme spiritual power who gifted me with good friends, teachers, relatives as well as healthy and peaceful environment since the very beginning of my life in this world till this moment.
Gibies George
Contents
List of publications
i
List of Figures
iii
List of Tables
xi
Abstract
xii
1 Introduction
1
1.1
Global monsoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.2
Dynamics of monsoon . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.3
Unique features of south Asian monsoon . . . . . . . . . . . . . . . . . . .
6
1.4
Unique circulation features of the Indian Ocean . . . . . . . . . . . . . . .
9
1.5
Monsoon variability and prediction . . . . . . . . . . . . . . . . . . . . . . 11
1.6
Tropical teleconnection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1.6.1
El-Ni˜ no southern oscillation . . . . . . . . . . . . . . . . . . . . . . 12
1.6.2
Indian ocean dipole . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.7
Extratropical teleconnections
. . . . . . . . . . . . . . . . . . . . . . . . . 14
1.8
Internal interannual variability . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.9
Seasonal prediction of monsoon . . . . . . . . . . . . . . . . . . . . . . . . 20
1.10 Dynamical simulations of monsoon . . . . . . . . . . . . . . . . . . . . . . 22 1.10.1 Atmospheric general circulation model experiments . . . . . . . . . 24 1.10.2 Limitations of tier-2 modeling strategy . . . . . . . . . . . . . . . . 26
Contents 1.10.3 Tier-1 modeling of monsoon . . . . . . . . . . . . . . . . . . . . . . 26 1.10.4 Commonly used decoupling strategies . . . . . . . . . . . . . . . . . 27 1.11 Gaps in the current understanding . . . . . . . . . . . . . . . . . . . . . . 27 1.12 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2 Experiment design and data
30
2.1
Observed and reanalysis datasets . . . . . . . . . . . . . . . . . . . . . . . 31
2.2
Model description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3
2.2.1
Atmosphere model . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2.2
Ocean model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.3
Land surface model . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.2.4
Sea ice model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.2.5
Coupler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Initial conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.3.1
Atmospheric initial conditions . . . . . . . . . . . . . . . . . . . . . 40
2.3.2
Ocean initial conditions . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3.3
Precipitation, snow depth and sea ice concentration . . . . . . . . . 41
2.4
Hindcast initialization from February . . . . . . . . . . . . . . . . . . . . . 42
2.5
Experiment design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.6
2.5.1
Sea surface temperature . . . . . . . . . . . . . . . . . . . . . . . . 43
2.5.2
Slab ocean model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.5.3
Sponge boundary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.5.4
Control run . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.5.5
Indian Ocean slab . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.5.6
Pacific Ocean slab
. . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Work flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3 Identify the key regions
49
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2
Global SST pattern favorable for monsoon . . . . . . . . . . . . . . . . . . 51
Contents 3.3
Coupled dynamics of the tropical oceans . . . . . . . . . . . . . . . . . . . 53
3.4
Simulation of mean features in CFSv2
3.5
Mixed layer heat budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.6
Time evolution of regional heat budget . . . . . . . . . . . . . . . . . . . . 78
3.7
Subsurface thermal structure . . . . . . . . . . . . . . . . . . . . . . . . . . 89
3.8
Ocean dynamics and SST evolution in CFSv2 . . . . . . . . . . . . . . . . 94
3.9
Monsoon related Indo-Pacific SSTs in CFSv2 . . . . . . . . . . . . . . . . . 95
. . . . . . . . . . . . . . . . . . . . 56
3.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4 Seasonal Prediction and Simulation of AISMR
98
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.2
Observed convection, circulation and tropospheric temperature . . . . . . . 100
4.3
Model simulation of convection, circulation and tropospheric temperature . 105
4.4
Sensitivity experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.4.1
Indian Ocean slab experiment . . . . . . . . . . . . . . . . . . . . . 108
4.4.2
Convection, circulation and tropospheric temperature in ISLAB . . 109
4.4.3
SST precipitation lead lag relationship . . . . . . . . . . . . . . . . 109
4.4.4
Pacific Ocean slab experiment . . . . . . . . . . . . . . . . . . . . . 110
4.4.5
Convection, circulation and tropospheric temperature in PSLAB . . 111
4.5
Interannual variation of all India summer monsoon rainfall . . . . . . . . . 111
4.6
Seasonal prediction skill of all India summer monsoon rainfall . . . . . . . 114
4.7
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5 Relationship among ENSO, IOD and AISMR
116
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.2
Indo-Pacific Climate Indices . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.3
Teleconnections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.3.1
Sea Surface Temperature favorable for monsoon . . . . . . . . . . . 119
5.3.2
Precipitation relationship with ENSO . . . . . . . . . . . . . . . . . 124
5.3.3
Precipitation relationship with IOD-EP . . . . . . . . . . . . . . . . 126
Contents 5.4
Thermocline variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
5.5
Variabilities in Mixed Layer Budget . . . . . . . . . . . . . . . . . . . . . . 131 5.5.1
Variabilities of shortwave radiation at surface . . . . . . . . . . . . 134
5.5.2
Variabilities of longwave radiation at surface . . . . . . . . . . . . . 136
5.5.3
Variabilities of latent heat flux . . . . . . . . . . . . . . . . . . . . . 138
5.5.4
Variabilities of sensible heat flux . . . . . . . . . . . . . . . . . . . . 141
5.5.5
Variabilities of total thermodynamical forcing at mixed layer . . . . 143
5.5.6
Variabilities of zonal advection of heat . . . . . . . . . . . . . . . . 147
5.5.7
Variabilities of meridional heat advection . . . . . . . . . . . . . . . 149
5.5.8
Variabilities of vertical heat advection . . . . . . . . . . . . . . . . . 151
5.5.9
Variabilities of entrainment . . . . . . . . . . . . . . . . . . . . . . 153
5.5.10 Variabilities of total dynamical forcing at mixed layer . . . . . . . . 155 5.6
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
6 Summary
159
6.1
Identification of Key Regions . . . . . . . . . . . . . . . . . . . . . . . . . . 159
6.2
Simulation and Prediction of Monsoon . . . . . . . . . . . . . . . . . . . . 160
6.3
Relationship among ENSO, IOD and Monsoon . . . . . . . . . . . . . . . . 162
6.4
Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
6.5
Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
List of publications 1. Gibies George, D.N. Rao, C.T. Sabeerali, A. Srivastava, S.A. Rao (2016) Indian summer monsoon prediction and simulation in NCEP CFSv2 model.Atmospheric Science Letter 17(1):57-64. doi:10.1002/asl.599 2. Chattopadhyay R., S.A. Rao, C.T. Sabeerali, Gibies George, D.N. Rao, A. Dhakate and K. Salunke (2016) Large scale teleconnection and seasonal prediction of Indian summer monsoon in climate forecast system (CFSv2) model. International Journal of Climatology 36(9):3297–3313 doi:10.1002/joc.4556 3. Ramu D.A., C.T. Sabeerali, R. Chattopadhyay, D.N. Rao, Gibies George, A. Dhakate, K. Salunke, A. Srivastava, S.A. Rao (2016) Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: impact of high resolution in the atmospheric model. Journal of Geophysical Research-Atmosphere 121(5):2205–2221 doi:10.1002/2015JD024629 4. Pillai P.A., S.A. Rao, Gibies George, D.N. Rao, A. Dhakate, K. Salunke, S. Mahapatra (2016) How distinct are the two flavors of El Ni˜ no in retrospective forecasts of Climate Forecast System version 2 (CFSv2)? Climate Dynamics doi:10.1007/s00382016-3305-2 5. Ramu D.A., S.A. Rao, P.A. Pillai., M. Pradhan, Gibies George, D.N. Rao, S. Mahapatra, D.S. Pai, M. Rajeevan (2017) Prediction of Seasonal Summer Monsoon Rainfall over Homogenous Regions of India using Dynamical Prediction System. Journal of Hydrology doi:10.1016/j.jhydrol.2017.01.010
i
Chapter 0. List of publications 6. “Understanding the sea surface temperature biases in CFSv2 coupled model using mixed layer heat budget” (under preparation) 7. “Callenges in the seasonal prediction of early developing phase of IOD and its teleconnection with monsoon” (under preparation) 8. ”Role of Ocean Atmosphere coupled dynamic for the evolution of ENSO flavours in CFSv2 coupled model” (under preparation)
ii
List of Figures 1.1
The regions where at least one month is having a climatological mean rainfall greater than 7 mm/day. Shading indicate the rainfall intensity (mm/day) of the peak month. . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.2
Climatology of 850 hPa Wind (a) and rainfall (b) for the month of July . .
3
1.3
Schematic view of the semi-permanent features during Asian summer monc soon season (Krishnamurti and Bhalme, 1976, American Meteorological Society. Used with permission.) . . . . . . . . . . . . . . . . . . . . . . . .
1.4
8
Global SST pattern (blue: cool and red: warm) during June through September (JJAS) favorable for the south Asian monsoon. Various climate modes are labeled at their respective locations in the map . . . . . . . . . 18
2.1
Zonal vertical cross section along the equator showing the role of the SLAB ocean in the sensitivity experiments. . . . . . . . . . . . . . . . . . . . . . 47
3.1
Observed global SST correlated with All India Summer Monsoon Rainfall Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2
Observed correlation between SST and (a) rainfall, (b) MLD, (c) D20 at each grid point for the summer monsoon season (JJAS) . . . . . . . . . . . 55
3.3
(a) Seasonal (JJAS) climatology of precipitation from GPCP dataset (Xie et al., 2003), (b) CFSv2 T126 simulation of seasonal (JJAS) precipitation climatology and (c) seasonal precipitation bias of CFSv2 T126 . . . . . . . 57
iii
List of Figures 3.4
(a) Seasonal (JJAS) climatological SST in ERSSTv3 dataset (Smith et al., 2008), (b) CFSv2 T126 simulation of seasonal (JJAS) SST climatology and (c) seasonal SST bias of CFSv2 model. . . . . . . . . . . . . . . . . . . . . 58
3.5
(a) Seasonal (JJAS) climatology of MLD in IFREMER dataset (Mont´egut, 2004), (b) CFSv2 T126 simulation of seasonal (JJAS) MLD climatology and (c) seasonal MLD bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . 60
3.6
The temperature tendency of mixed layer ( W/m3 ) is represented as (a) observed seasonal (JJAS) climatology, (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . 61
3.7
(a) Observed seasonal (JJAS) climatology of Shortwave radiation (positive towards ocean), (b) CFSv2 T126 simulation and (c) the seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.8
(a) Observed seasonal (JJAS) climatology of longwave radiation (positive towards ocean), (b) CFSv2 T126 simulation of seasonal (JJAS) LW climatology and (c) seasonal LW bias of CFSv2 T126 . . . . . . . . . . . . . . . 63
3.9
(a) Observed seasonal (JJAS) climatology of latent heat flux (positive away from ocean), (b) CFSv2 T126 simulation of seasonal (JJAS) latent heat flux climatology and (c) seasonal latent heat flux bias of CFSv2 T126
. . . . . 64
3.10 (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) sensible heat flux (positive away from ocean) climatology and (c) seasonal sensible heat flux bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . . . . . 65 Qnet ; W/m3 3.11 Surface heat flux forcing on unit volume of the mixed layer ( M LD
) (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . 67 M LD 3.12 Short wave radiation peneterate below the mixed layer ( Qsw ; W/m3 ) M LD
(a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . 68
iv
List of Figures M LD 3.13 Thermodynamical forcing on the mixed layer ( Qnet−Qsw ; W/m3 ) (a) M LD
observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . 70 3.14 Zonal heat transport forcing on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . . . . 71 3.15 Meridional heat transport forcing on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . 72 3.16 Vertical heat transport forcing on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . 73 3.17 Entrainment due to MLD variations on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . 75 3.18 Seasonal mean tendency of SST resulting from all the dynamical terms in the MLD budget (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . 76 3.19 The budget total forcing (right hand side of equation 3.7) on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 . . . . . . . . . . 77 3.20 Monthly time evolution of heating terms in Arabian Sea for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 . . . 80 3.21 Monthly time evolution of heating terms in Bay of Bengal for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 . . . 81 3.22 Monthly time evolution of heating terms in west pole of IOD for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 83 3.23 Monthly time evolution of heating terms in east pole of IOD for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 . . . 84
v
List of Figures 3.24 Monthly time evolution of heating terms in Ni˜ no 3.4 region for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 . . . 85 3.25 Monthly time evolution of heating terms in northeastern tropical Pacific (region with warm Sea Surface Temperature (SST) bias in CFSv2) for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 3.26 Monthly time evolution of heating terms in southeastern tropical Pacific (region with warm SST bias in CFSv2) for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 . . . . . . . . . . . . . 87 3.27 (a) Observed seasonal (JJAS) climatology of temperature, (b) CFSv2 T126 simulation of seasonal (JJAS) temperature climatology and (c) seasonal temperature bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . . . 90 3.28 (a) Observed seasonal (JJAS) climatology of D20, (b) CFSv2 T126 simulation of seasonal (JJAS) D20 climatology and (c) seasonal D20 bias of CFSv2 T126 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 3.29 CFSv2 simulated correlation between SST and (a) rainfall, (b) MLD, (c) D20 at each grid point for the summer monsoon season (JJAS) . . . . . . . 94 3.30 CFSv2 simulated correlation of SST with All India Summer Monsoon Rainfall Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4.1
Seasonal (JJAS) Mean, Observed Climatology (top row), CTL bias (second row), difference between ISLAB and CTL (third row) and difference between PSLAB and CTL (bottom row) for SST (left panel) and Precipitation (right panel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.2
(a) Observed seasonal (JJAS) climatology of 850 hPa wind, (b) seasonal 850 hPa wind bias of CFSv2 T126 (c) ISLAB and (d) PSLAB runs. . . . . 101
vi
List of Figures 4.3
Seasonal (JJAS) mean, observed climatology (top row), CTL bias (second row), difference between ISLAB and CTL (third row) and difference between PSLAB and CTL (bottom row) for Walker (zonal-vertical) circulation averaged between 5◦ S to 5◦ N (left) and Hadley (meridional-vertical) circulation averaged between 70◦ E to 90◦ E (right) . . . . . . . . . . . . . 103
4.4
Tropospheric temperature averaged between 600 hPa and 200 hPa (shaded) overlaid with wind vector at 850 hPa for (a) observations, (b) CTL run, (c) ISLAB run and (d) PSLAB run . . . . . . . . . . . . . . . . . . . . . . 104
4.5
M LD ; W/m3 ) on an off line 50m uniThermodynamical forcing ( Qnet−Qsw M LD
form slab ocean forced with seasonal (JJAS) climatology fluxes from (a) observations and (b) CFSv2 T126 simulation . . . . . . . . . . . . . . . . . 107 4.6
Lead lag (-20 day to +20 day) correlation between SST and precipitation at each grid point for Observations (left panel), CTL run (middle panel) and ISLAB (right panel) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.7
Interannual variation of All India Summer Monsoon Rainfall in observations, CTL, ISLAB and PSLAB . . . . . . . . . . . . . . . . . . . . . . . . 112
4.8
Taylor diagram shows the all India summer monsoon rainfall prediction skill in CTL, ISLAB and PSLAB run . . . . . . . . . . . . . . . . . . . . . 112
5.1
Seasonal (JJAS) mean SST correlated with All India Summer Monsoon Rainfall Index for (a) observations, (b) CTL, (c) ISLAB and (d) PSLAB . 120
5.2
Seasonal (JJAS) mean wind at 850 hPa regressed with All India Summer Monsoon Rainfall Index for (a) observations, (b) CTL, (c) ISLAB and (d) PSLAB
5.3
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
(a) Observed and (b) simulated correlation of precipitation with Ni˜ no 3.4 SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.4
(a) Observed and (b) simulated correlation of SST with Ni˜ no 3.4 SST index 125
5.5
(a) Observed and (b) simulated regression of 850 hPa wind with Ni˜ no 3.4 SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
vii
List of Figures 5.6
(a) Observed and (b) simulated correlation of precipitation with IOD-EP SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
5.7
(a) Observed and (b) simulated correlation of SST with IOD-EP SST index 127
5.8
(a) Observed and (b) simulated regression of 850 hPa wind with IOD-EP SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.9
(a) Observed and (b) simulated correlation of D20 with All India Summer Monsoon Rainfall Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
5.10 (a) Observed and (b) simulated correlation of D20 with Ni˜ no 3.4 SST index 130 5.11 (a) Observed and (b) simulated correlation of D20 with IOD-EP SST index 131 5.12 (a) Observed and (b) simulated correlation of MLD with All India Summer Monsoon Rainfall Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.13 (a) Observed and (b) simulated correlation of MLD with Ni˜ no 3.4 SST index133 5.14 (a) Observed and (b) simulated correlation of MLD with IOD-EP SST index133 5.15 (a) Observed and (b) Simulated Seasonal (JJAS) short wave radiation flux correlated with AISMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 5.16 (a) observed and (b) Simulated short wave radiation flux correlated with Nino3.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 5.17 (a) observed and (b) Simulated short wave radiation flux correlated with IOD-EP SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 5.18 (a) Observed and (b) Simulated Seasonal (JJAS) long wave radiation flux correlated with AISMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5.19 (a) observed and (b) Simulated long wave radiation flux correlated with Nino3.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5.20 (a) observed and (b) Simulated long wave radiation flux correlated with IOD-EP SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.21 (a) Observed and (b) Simulated Seasonal (JJAS) latent heat flux correlated with AISMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.22 (a) observed and (b) Simulated latent heat flux correlated with Nino3.4 . . 140
viii
List of Figures 5.23 (a) observed and (b) Simulated latent heat flux correlated with IOD-EP SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 5.24 (a) Observed and (b) Simulated Seasonal (JJAS) sensible heat flux correlated with AISMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 5.25 (a) observed and (b) Simulated sensible heat flux correlated with Nino3.4 . 142 5.26 (a) observed and (b) Simulated sensible heat flux correlated with IOD-EP SST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 5.27 (a) Observed and (b) simulated correlation of mixed layer thermodynamical M LD ; W/m3 ) with All India Summer Monsoon Rainfall Index143 forcing ( Qnet−Qsw M LD
5.28 (a) Observed and (b) simulated correlation of mixed layer thermodynamical M LD forcing ( Qnet−Qsw ; W/m3 ) with Nino3.4 Index . . . . . . . . . . . . . . 145 M LD
5.29 (a) Observed and (b) simulated correlation of mixed layer thermodynamical M LD forcing ( Qnet−Qsw ; W/m3 ) with IOD-EP SST Index . . . . . . . . . . . 146 M LD
5.30 (a) Observed and (b) simulated correlation of mixed layer zonal heat advection with All India Summer Monsoon Rainfall Index . . . . . . . . . . . 147 5.31 (a) Observed and (b) simulated correlation of mixed layer zonal heat advection with Nino3.4 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 5.32 (a) Observed and (b) simulated correlation of mixed layer zonal heat advection with IOD-EP SST Index . . . . . . . . . . . . . . . . . . . . . . . . 148 5.33 (a) Observed and (b) simulated correlation of mixed layer meridional heat advection with All India Summer Monsoon Rainfall Index . . . . . . . . . 149 5.34 (a) Observed and (b) simulated correlation of mixed layer meridional heat advection with Nino3.4 Index . . . . . . . . . . . . . . . . . . . . . . . . . 150 5.35 (a) Observed and (b) simulated correlation of mixed layer meridional heat advection with IOD-EP SST Index . . . . . . . . . . . . . . . . . . . . . . 150 5.36 (a) Observed and (b) simulated correlation of mixed layer vertical heat advection with All India Summer Monsoon Rainfall Index . . . . . . . . . 151 5.37 (a) Observed and (b) simulated correlation of mixed layer vertical heat advection with Nino3.4 Index . . . . . . . . . . . . . . . . . . . . . . . . . 152
ix
List of Figures 5.38 (a) Observed and (b) simulated correlation of mixed layer vertical heat advection with IOD-EP SST Index . . . . . . . . . . . . . . . . . . . . . . 152 5.39 (a) Observed and (b) simulated correlation of mixed layer entrainment with All India Summer Monsoon Rainfall Index . . . . . . . . . . . . . . . . . . 153 5.40 (a) Observed and (b) simulated correlation of mixed layer entrainment with Nino3.4 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5.41 (a) Observed and (b) simulated correlation of mixed layer entrainment with IOD-EP SST Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 5.42 (a) Observed and (b) simulated correlation of mixed layer dynamical forcing with All India Summer Monsoon Rainfall Index . . . . . . . . . . . . . . . 155 5.43 (a) Observed and (b) simulated correlation of mixed layer dynamical forcing with Nino3.4 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 5.44 (a) Observed and (b) simulated correlation of mixed layer dynamical forcing with IOD-EP SST Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
x
List of Tables 1.1
Details of the earlier sensitivity experiments. . . . . . . . . . . . . . . . . . 23
2.1
Main features of the atmosphere model . . . . . . . . . . . . . . . . . . . . 34
2.2
Main features of the ocean model . . . . . . . . . . . . . . . . . . . . . . . 36
2.3
Main features of the land surface model . . . . . . . . . . . . . . . . . . . . 37
2.4
Main features of sea-ice model . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5
Experiments and model domain. . . . . . . . . . . . . . . . . . . . . . . . . 46
3.1
SST forcing terms of Tropical Indian Ocean (top 500 m) averaged for June through September (JJAS). . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.1
Simulation of all India summer monsoon rainfall in CTL, ISLAB and PSLAB experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.1
Model simulation of ENSO and IOD . . . . . . . . . . . . . . . . . . . . . 118
5.2
Relationship among ENSO, IOD and Monsoon . . . . . . . . . . . . . . . . 119
xi
Abstract The Monsoon, characterized by seasonal reversal of wind and associated seasonal variations in the convection and precipitation, is an important factor in the climate of south Asian region. The regular seasonal cycle of the monsoon system is modulated by low frequency variations at the interannual and longer timescales. These modulations result in flood and drought conditions in some years, which affect the agriculture and the economy of the south Asian countries. An early and accurate prediction of seasonal mean performance of monsoon and efficient planing based on these forecasts are essential for the socio-economic well being of the people of this region. The seasonal prediction of monsoon is based on the slowly varying factors over Ocean, Land and Cryosphere, which provide long time memory for the climate system. This study focuses on the role of ocean-atmosphere coupling in the seasonal prediction of south Asian monsoon. A broad rectangular box is not considered to represent the south Asian monsoon, mainly because there is land-ocean contrast in the precipitation biases of state-of-the-art coupled models. The south Asian monsoon in this thesis is represented by All India Summer Monsoon Rainfall (AISMR) index, considering the fact that Indian subcontinent is the land region which receives maximum rainfall of the south Asian monsoon. The thesis is organized into six chapters. Chapter 1 provides a brief discussion about the south Asian monsoon, its simulation and seasonal prediction and the importance of tier one modeling strategy. The thesis is mainly focused on identification of key regions of ocean-atmosphere coupled dynamics and their influence on the seasonal prediction of south Asian monsoon. Chapter 2 describes the observed and reanalysis datasets used, details of the Climate Forecast System version 2 (CFSv2) coupled model and the strategy
xii
Chapter 0. Abstract for the sensitivity experiments. A pair of sensitivity experiments, one having uniform slab in the Indian Ocean (ISLAB) and the other having uniform slab in the Pacific Ocean (PSLAB), were performed along with the control run, in hindcast mode. The present study uses tier one modeling strategy which allows SST to evolve within the coupled system through thermodynamic processes, to switch off ocean dynamics without using external SST forcing. Chapter 3 deals with identification of key regions according to the importance of ocean dynamics and ocean-atmosphere coupling on the seasonal prediction of monsoon. The key regions of ocean dynamics and ocean-atmosphere coupling are identified based on thermocline-SST and SST-rainfall relationships respectively. The identified key regions, where SST evolution is largely influenced by the ocean dynamics, are the eastern equatorial Pacific Ocean, the western tropical Pacific Ocean, the southeastern Indian Ocean and the western tropical Indian Ocean. Where as the atmospheric forcing plays an important role over the Bay of Bengal and the South China Sea. The CFSv2 model realistically simulated the dynamics and coupling over the key regions. The SST pattern teleconnected with the All India Summer Monsoon Rainfall (AISMR) is reasonable in the tropical Pacific Ocean, while it is misrepresented in the Indian Ocean. Chapter 4 addresses the sensitivity of seasonal prediction skill of the monsoon rainfall in CFSv2 coupled model. The sensitivity of ocean dynamics is quantified by performing the ISLAB and PSLAB experiments, in which the Indian Ocean and the Pacific Ocean dynamics are switched off in respective experiments. This study demonstrates that the cold bias in the northern Indian Ocean is significantly reduced in ISLAB, which resulted in improved mean precipitation over the Indian land mass. This is also reflected in the atmospheric circulation and tropospheric temperature. Both the Indian Ocean and the Pacific Ocean coupled dynamics contribute significantly to interannual variance of AISMR. The seasonal prediction skill of AISMR is mainly contributed by the Pacific Ocean coupled dynamics, due to the misrepresentation of the relationship between Indian Ocean SST and monsoon. Chapter 5 provides a detailed discussion on the monsoon teleconnections with Indo-
xiii
Chapter 0. Abstract Pacific SSTs. The relationship among El Ni˜ no Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and monsoon are analyzed. Seasonal prediction skill of Nino 3.4 is very good while the skill of IOD east pole SST is poor. The ENSO-monsoon relationship is overestimated in the model. The model-simulated strong monsoon condition is associated with warming in IOD east pole, in contrast to the observation. The Indian Ocean coupled dynamics associated with the La-Ni˜ na teleconnected winds forces unrealistic deepening of thermocline in the eastern equatorial Indian Ocean which leads to warming of IOD east pole and hence negative IOD conditions in the model. The significant opposite relationship is removed by switching off either the Indian Ocean or the Pacific Ocean dynamics in the model. Hence, the discrepancy in Indian Ocean SST-monsoon relationship is due to the combined effect of ENSO-teleconnections and its response on the Indian Ocean coupled dynamics. The relationship between monsoon and Indian Ocean SST is to be improved by reducing the ENSO forcing on Indian Ocean thermocline. This can also improve the seasonal prediction skill of monsoon in the model. The mixed layer response to the wind pattern modulates temperature over the southern central Indian Ocean and thereby, misrepresents the spatial structure of the SST related to IOD and hence, reduces the seasonal prediction skill of the early developing IOD at boreal summer season. Chapter 6 summarizes the results and describes the scope for future studies. This study point out the requirement for the improvement of Indian Ocean coupled dynamics and its relationship with AISMR in CFSv2 model. It demonstrates that removel of the cold SST bias over the northern Indian Ocean not only reduces the dry bias over the Indian land region, but also improves the tropospheric temperature profile and the monsoon-Hadley circulation. ENSO-monsoon teleconnection is the major contributor for the seasonal prediction skill in the model. Misrepresentation in the IOD-monsoon teleconnection and absence of the ENSO-Modoki flavor of monsoon teleconnection are to be corrected to get a quantum improvement in the seasonal prediction skill of monsoon. Since most of the limitations are common for almost all state-of-the-art coupled models, focused development activities are required to overcome these challenges.
xiv
Chapter 1 Introduction Climate is a system of complex interactions among its various components such as atmosphere, ocean, land, sea ice, etc. There are physical, chemical, biological, hydrological, dynamical and thermodynamical components involved in a wide range of feedbacks which make the Earth-System. The Earth’s atmosphere is transparent to most of the solar radiation and hence, the radiant energy is deposited at the lower boundary (Earth surface). Convection and latent heat release are the major factors heating up the Earth’s atmosphere. The strongest convection zones of Earth’s atmosphere are aligned along a belt known as Inter-Tropical Convergence Zone (ITCZ). The seasonal migration of ITCZ results in wet summer and dry winter. This is also associated with seasonal reversal of wind. These unique seasonal features of tropics are known as Monsoon. The word Monsoon is derived from an Arabic word mausam meaning season.
1.1
Global monsoon
The monsoon domain is primarily identified by the wet summer climate with wide spread heavy precipitation. The climatological rainfall intensity of the peak precipitating month is shaded in figure 1.1, which shows the regions where there is a minimum of 7 mm/day monthly climatological rainfall for the most wet calendar month. The shading extend from India through Maritime continent region, Equatorial Pacific Ocean, South
1
Chapter 1. Introduction
Figure 1.1: The regions where at least one month is having a climatological mean rainfall greater than 7 mm/day. Shading indicate the rainfall intensity (mm/day) of the peak month. America, Equatorial Atlantic Ocean, West Africa, East Africa and up to Madagascar Island. The narrow belt along the Pacific Ocean and the Atlantic Ocean are associated with the quasi-stationary location of ITCZ, while the broad domains represent world’s famous monsoon domains (figure 1.1). The world’s largest monsoon system known as Afro-Asian Monsoon System (25◦ S to 30◦ N and 30◦ E to 170◦ W; Ramage, 1971) is associated with the world’s largest off-equatorial heat Island in the upper troposphere. Significant changes in the wind direction between winter and summer seasons is a remarkable peculiarity of the Monsoon domain (Ramage, 1971; Gadgil, 2003). Afro-Asian Monsoon domain defined by Ramage (1971) includes South Asia, East Asia, West Africa, East Africa, Maritime Continent and Australia. The meridional extent of monsoon domain is from east Asia (30◦ N) to north Australia (20◦ S). In South Asia, the wet season starting in June and extending up to September (boreal summer) is associated with southwesterly trade winds. Sri-Lanka and Southern Peninsular India have an extended wet season from October to December (boreal fall) with
2
Chapter 1. Introduction
Figure 1.2: Climatology of 850 hPa Wind (a) and rainfall (b) for the month of July northeasterly trade winds. The Australian region has wet season during boreal winter, while southern and eastern Asia has dry season. The land region from 20◦ N to 20◦ S and 50◦ W to 110◦ W is defined as American Monsoon domain. Brazilian and Mexican monsoon systems are located in this domain. The Pacific and Atlantic convergence zones are not considered as Monsoon domain, since the seasonal migration of ITCZ, reversal of wind and dryness of winter are not prominent over there (Ramage, 1971). The South Asian monsoon region hosts the world’s strongest monsoon system where ITCZ migrates from 5◦ S latitude during early summer to about 20◦ N latitude in July (figure 1.2). The rainfall pattern of the month of July shown in figure 1.2 as well as the amplitude of rainfall annual cycle shown in figure 1.1 clearly indicate that Indian subcontinent is the land region which receives the maximum rainfall of south Asian monsoon. Hence All India Summer Monsoon Rainfall (AISMR) is used here after to represent the south Asian monsoon.
1.2
Dynamics of monsoon
The solar radiation is the basic source of energy for the entire earth system. The inclination of solar radiation received on Earth’s surface has a seasonal variation because Earth’s rotation axis is inclined with an angle of 23.4◦ to the axis of the Earth’s orbital disk (ecliptic plane). Therefore, the summer hemisphere receives more energy compared
3
Chapter 1. Introduction to the winter hemisphere. The non-uniform reception of energy on the Earth surface is redistributed through general circulation of Atmosphere and Ocean. Global Monsoon is an integral part of the planetary wind system (Kurashima, 1968) which carries energy in the atmosphere and redistributes it. Many studies were carried out in the past to understand the physics and the mechanism of the monsoon (Webster, 1972; Godbole, 1973; Gill, 1980; Geisler, 1981). Godbole (1973) used a zonally symmetric model and identified the importance of heating distribution (meridional gradient of temperature), presence of Himalayas, moist convection etc for the simulation of monsoon system. Separate sensitivity experiments were conducted using dry and moist models with and without Himalayan orography. The moist atmosphere with Himalayan orography simulates the mean features associated with monsoon while all the other runs failed to do so. The zonal asymmetry of the atmospheric pressure were not considered in the symmetric monsoon models. The orographic forcing on a westerly zonal flow can simulate zonal asymmetry of isobars on a geopotential surface (or geopotential height on an isobaric surface; Charney and Eliassen, 1949). The properties of Rossby waves and gravity waves on an equatorial beta plane were identified (Matsuno, 1966) using quasi geostrophic model. Steady state solutions in response to heating varied linearly with latitude, were obtained using equatorial beta plane model with zero basic flow and linearized equations (Gill, 1980). The studies by Matsuno (1966) and Gill (1980) paved the baseline for the equatorial wave dynamics.
The interrelationship between the pressure variations over different stations across the world (Walker, 1925) is identified to have a potential to predict the monsoon conditions in a seasonal scale. Most of these variations are attributed to so called Southern Oscillation. Walker Circulation is defined as a meridional average zonal-vertical circulation over the equator driven by differential heating pattern (Matsuno, 1966; Webster, 1972; Gill, 1980; Geisler, 1981). Quasi geostrophic response to the equatorial heating is demonstrated by Matsuno (1966) using single layer divergent barotropic atmospheric model. The response of orographic forcing and latent heat release on a simple two layer baroclinic atmosphere model (Webster, 1972) represents the mean circulation features and its seasonal variations
4
Chapter 1. Introduction over the tropics. The response of equatorial and off-equatorial heating on a non-dispersive β-plane two layer incompressible atmosphere with rigid lid (Gill, 1980) indicates easterlies to the eastern side of the heat source (Kelvin response) along the equator and westerlies to the west of the heat source (Rossby response) on either side of the equator. Geisler (1981) simulated the walker circulation in a linearized primitive equation model on a sphere without topography.
The symmetric component of Walker circulation maximizes in the boreal winter, while the asymmetric component related to the monsoon circulation grows rapidly in the boreal spring season, attains it maximum strength in the summer and further decays slowly in the fall season (Webster and Yang, 1992). The zonal wind shear anomaly between 850 hPa and 200 hPa averaged over 5◦ N to 20◦ N and 40◦ E to 110◦ E is defined as the index for asymmetric walker circulation (Webster and Yang, 1992) which also has a strong correlation with the southern oscillation.
The Hadley circulation is known to have convection over the tropics and subsidence away from the equator. But over the monsoon domains, strengthening and penetration the winter hemisphere Hadley cell to the summer hemisphere, results in an apparent reversal of meridional circulation (Schulman, 1973). A meridional cell of Monsoon Hadley Circulation is identified to better represent the variability of the south Asian monsoon (Goswami et al., 1999) and overcome the limitations of the asymmetric Walker circulation index (Webster and Yang, 1992). The intensity of Monsoon-Hadley circulation can be estimated as the meridional wind shear anomaly between 850 hPa and 200 hPa over the region 70◦ E to 110◦ E and 10◦ N to 30◦ N. This index is defined based on the seasonal migration of the ITCZ from its mean winter position of 5◦ S to the mean summer location of 20◦ N. The summer location of ITCZ is confined to 5◦ N latitude, outside the monsoon domain. The northward displacement of ITCZ is associated with an asymmetric offequatorial heat source which drives a regional Hadley circulation with convection over south Asia and subsidence over the southern Indian Ocean.
5
Chapter 1. Introduction In general, along with the monsoon Hadley cell of south Asia, the equatorial walker cell has ascending motion over Indonesia and western Pacific force subsidence along the equatorial Indian Ocean. Hence, there is a strong coupling between the regional Monsoon Hadley Circulation and the Planetary Walker Circulation (Goswami et al., 1999; Webster et al., 1998; Peixoto and Oort, 1992). In summary, solar radiation, Coriolis force, surface topography, moist convection, the equatorial wave dynamics and global teleconnection are the various factors driving the monsoon system in general.
1.3
Unique features of south Asian monsoon
The monsoon represents the nearly opposite directions of the prevailing wind during summer and winter. The Monsoon is stronger over the Afro-Asian domain than the American domain (Keshavamurty and Rao, 1992). This is because of the existence of Monsoon-Hadley circulation (reverse Hadley cell) extending up to 30◦ N latitude, while the planetary Hadley circulation exists over about two-third of the tropics elsewhere during the same season (Schulman, 1973). The seasonal seesaw between planetary Hadley circulation and the Monsoon-Hadley circulation results in the seasonal reversal of prevailing wind direction (figure 1.2). Monsoon is the most dominant and important feature of south Asian climate(Wang, 2006). The Mascarene high over southwestern Indian Ocean, Heat low over the arid regions of middle east, Monsoon trough over India, Cross equatorial monsoon low level jet, etc. are the most dominant surface features related to south Asian monsoon (figure 1.3). On the other hand, the upper troposphere has a Tibetan anticyclone located between Northern Subtropical Westerly Jet-stream (NSWJ) and Tropical Easterly Jet-stream (TEJ).
The existence of Monsoon Hadley circulation in opposite direction to the planetary Hadley circulation (Schulman, 1973) is one of the unique features of the south Asian monsoon. The cross equatorial Monsoon Low Level Jet-stream (MLLJ) is also unique for the south Asian monsoon system. The surface pressure associated with south Asian
6
Chapter 1. Introduction monsoon exhibits a trough extending from the northwest India to the head Bay of Bengal. Most of the monsoon depressions generated in the bay are steered along the monsoon trough. The Continental Tropical Convergence Zone (CTCZ) along with the monsoon trough is located over the land and about 20◦ latitude away from the equator (figure 1.2), which are also not evident elsewhere in the world. The Oceanic Tropical Convergence Zone (OTCZ) still exists in the eastern equatorial Indian Ocean, where there is strong convection during the intraseasonal break phase of the monsoon. Thus, south Asian monsoon is also unique due to the simultaneous existence of two convergence zones along the same longitude.
The abrupt onset is another unique feature of South Asian Monsoon, while over other regions the reversal of wind system is a gradual process. The subsidence induced by the pre-monsoon moist convection over the Bay of Bengal forms a lid of subsiding dry air over the Arabian Sea, the Indian land and the Tibetan Plateau, which suppresses the onset of the Monsoon (Park, 2010). During this period, the moist static energy is accumulated in the lower troposphere (Boos and Kuang, 2010; Cane, 2010) and the adiabatic compression of subsiding dry air heats up the mid troposphere (Tamura et al., 2010). The explosive abrupt onset of south Asian monsoon happens once the lid of subsidence is broken by the accumulation of adiabatic and moist-static energy from the either side.
The tropospheric temperature variations have a strong relationship (He et al., 2003) with the onset, withdrawal, and length of rainy season of south Asian Monsoon. The meridional gradient of the tropospheric temperature (Goswami and Xavier, 2005) is defined as the temperature difference between two boxes, one extends from 15◦ S to 10◦ N and the other 10◦ N to 35◦ N along the longitude range from 30◦ E to 110◦ E averaged over the upper troposphere (200 hPa to 600 hPa; Xavier et al., 2007; Goswami and Xavier, 2005; Sabeerali et al., 2012)). The lower levels (below 600 hPa level) are excluded to eliminate the influence of ground temperature. The length of rainy season is defined as the period during which the value of tropospheric temperature gradient remains positive.
7
Chapter 1. Introduction
Figure 1.3: Schematic view of the semi-permanent features during Asian summer monsoon c season (Krishnamurti and Bhalme, 1976, American Meteorological Society. Used with permission.) The transition from negative to positive value of tropospheric temperature gradient is defined as the onset and the transition from positive to negative value of tropospheric temperature gradient is defined as the withdrawal of south Asian monsoon. It also has interannual variations associated with various climate indices (Goswami and Xavier, 2005; Xavier et al., 2007; Tamura et al., 2010).
The Mascarene high is the high pressure area at the sea level located in the southwestern Indian Ocean centered near 30◦ S and 50◦ E (Tyagi et al., 2012). The name Mascarene high is originated because of its location near the Mascarene Island. Mascarene high is one of the important features of the tropical general circulation which has profound influence on the south Asian climate and weather. The mean monthly value of Sea level pressure over Mascarene high is about 1025 hPa throughout the south Asian monsoon season (Ananthakrishnan et al., 1968). The fluctuations in the Mascarene high due to passage of southern hemisphere extra-tropical westerly waves can impose variability in south Asian monsoon rainfall with a 9 day lag period (Krishnamurti and Bhalme, 1976).
8
Chapter 1. Introduction The tropical easterlies flowing from the Mascarene high region cross the equator along the western Indian Ocean and the eastern Africa, turn southwesterly direction head towards the monsoon trough crossing the Arabian sea. This cross equatorial system of low level wind is commonly known as MLLJ. MLLJ is the carrier of moisture to the monsoon domain. The core of MLLJ is at a level of 850 hPa and generally has a speed of 20-30 m/s and occasionally strengthen up to 50 m/s over the western Indian Ocean.
The monsoon trough is a major semi-permanent feature of the summer monsoon circulation in the lower troposphere and exerts considerable influence on the summer monsoon activity in South Asia. The trough line runs at surface level from Ganganagar to Kolkata through Allahabad, with west to southwest winds to south and easterlies to the north of the trough line. The air mass to the south of the trough line is driven from the Arabian Sea while the air to the north is driven from the Bay of Bengal. The orographic steering of MLLJ based on the potential vorticity conservation is the dynamics beyond the existence of Monsoon Trough.
The eastern equatorial Indian Ocean, the Western Ghats region of India and monsoon trough region extend northwestward from the northern Bay of Bengal are the major convective zones of south Asian monsoon system. The latent heat release due to the convection gives a positive feedback to the tropospheric heating and the monsoon system. The east-west asymmetry of the huge south Asian monsoon system results in large subsidence over southwest Asia (Chou et al., 1989) and the radiative heating over this region causes the formation of heat low.
1.4
Unique circulation features of the Indian Ocean
Indian Ocean is very unique due to strong seasonality of the ocean circulation, which is mostly associated with the monsoon winds. During boreal summer, southeasterly and southwesterly trade winds on either side of the equator drive a net southward Ekman transport in the tropical Indian Ocean (Levitus, 1988). The southward Ekman flow 9
Chapter 1. Introduction balanced with south equatorial current, east African coastal current and Somali current form the shallow meridional overturning cell of the Indian Ocean (Schott and McCreary, 2001). While the meridional overturning drives cooling in the Arabian Sea, the sea surface temperature over the Bay of Bengal is strongly influenced by surface fluxes (Shenoi et al., 2002). The strength of the wind stress, stratification of the ocean, etc. are the factors which differentiate the two regions. The fresh water flux due to local precipitation as well as river run-off discharge results in strong stratification of the Bay of Bengal. Saline water flux from the Red Sea and the Caspian Sea along with wind stress causes strong vertical mixing which cools the Arabian Sea.
The Seychelles dome region in the tropical western Indian Ocean is a location of strong seasonal variations in the thermocline depth (Rao et al., 2002; Xie et al., 2002; Rao and Behera, 2005; Yokoi et al., 2008; Jayakumar and Gnanaseelan, 2012). The wind stress drives open-ocean upwelling and the thermocline uplifts in the tropical southern Indian Ocean (Seychelles) region (Xie et al., 2002). This region has a strong coupling between the thermocline and SST. The wind stress curl due to meridional gradient of zonal wind as well as the planetary beta effect on the zonal wind stress together result in a semiannual cycle of Ekman pumping and the thermocline depth variations at Seychelles Dome (Yokoi et al., 2008). The negative wind stress curl between south easterly trade winds and equatorial westerlies can cause unique open ocean upwelling throughout boreal summer and fall, but the planetary beta effect on zonal wind stress force downwelling during the Boreal summer (Yokoi et al., 2008) and upwelling during boreal winter. The combination of these two forces results in the semiannual cycle of the thermocline depth in the Seychelles dome region. In addition to Seychelles region thermocline ridge, upwelling zones of Sumatra/Java, southeast Sri Lanka and Lakshadweep Seas provide thermocline SST feedback (Rao and Behera, 2005). Either El-Ni˜ no or Sumatra cooling can force equatorial easterlies and westward propagation of downwelling Rossby waves in the southern Indian Ocean, which in turn modify the SST and precipitation pattern in the tropical western Indian Ocean (Xie et al., 2002).
10
Chapter 1. Introduction There is about 16 cm sea level height difference (Wyrtki, 1987) between the western Pacific and the eastern Indian Ocean, which also has a seasonal variation from 8 cm during boreal spring to 24 cm during autumn season. This creates a pressure gradient force from the western pacific to the eastern Indian Ocean for the upper oceanic layer up to about 200m depth. The only openings between the two basins are a few narrow channels between the islands of maritime continent region namely, Lombok strait, Ombai strait and Timor strait opening to eastern Indian Ocean (Valsala and Ikeda, 2007). The western Pacific water mass can pass through Makkasar strait, Banda Sea, Java Sea and reaches the above three openings (Valsala and Ikeda, 2007). This through flow is popularly known as Indonesian Trough Flow (ITF). Torres strait between Australia and Papua New Guinea is not a pathway of the through flow between Pacific and Indian Ocean (Valsala and Ikeda, 2007), because it is very narrow and shallow.
1.5
Monsoon variability and prediction
The south Asian monsoon is not uniform in space and time. The time scale of monsoon variability ranges from diurnal to multi-decadal and beyond. The multidecadal variations are mainly driven by ocean-atmosphere coupled interaction outside the tropics, while shorter timescale interannual variations are mostly driven by coupled dynamics in the tropical ocean basins. Subseasonal and smaller timescales are associated with various factors like internal dynamics of atmosphere, land surface hydrology, as well as oceanland-atmosphere coupled interactions. There are various techniques used for the timely forecast of the monsoon system based on its time scales and meteorological basis of each time scale. The seasonal forecast of monsoon focus on the seasonal mean performance of monsoon and its year to year variations. Similarly, extended range prediction focuses on active break cycles, medium range prediction focuses on formation and propagation of synoptic systems, short range prediction deals with day to day variations and locations of monsoon weather systems. This thesis focus on the seasonal prediction of the interannual variation of monsoon and potential sources of the seasonal predictions skill of monsoon
11
Chapter 1. Introduction in a dynamical model.
1.6
Tropical teleconnection
The seasonal mean strength of South Asian Monsoon is not same for all the years. There are some years with severe drought and some with heavy flood. Slowly varying factors like ocean, land, sea ice, snow cover etc. are the sources for the long range (seasonal) prediction of year to year variations in the seasonal mean strength of monsoon. Most of the year to year variations of monsoon system are attributed to the conditions of other climate variability indices. But each monsoon is unique and its relationship among other climate indices are not linear. There are multiple factors such as El-Ni˜ no Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), Eurasian Snow Cover (ESC), Meridional Gradient of Tropospheric Temperature (TT), etc. having their influence on South Asian Monsoon and there is year to year variation in the dominance of each of these factors (Kumar et al., 1999; Ashok et al., 2001; Krishnan et al., 2006; Goswami et al., 2006; Vernekar et al., 1995; Xavier et al., 2007). One of the important challenges of seasonal prediction is to quantify the impact of each of these components well in advance at least one or two seasons earlier.
1.6.1
El-Ni˜ no southern oscillation
Sir Gilbert Walker has identified coherent and reproducible patterns of low frequency variations of global atmosphere (Webster and Yang, 1992) among which Southern Oscillation (SO), an east west oscillation of atmospheric pressure pattern in Indo-Pacific region is very much important to understand the interannual variations of south Asian Monsoon (Shukla and Paolino, 1983). Southern Oscillation has two dominant time scales (biennial and low frequency; Rasmusson et al., 1990) out of which, the most dominant one with four to five years periodicity (Philander, 1990) represents ocean-atmosphere coupled dynamics with basin-scale (Bjerknes, 1969). Southeastern Pacific Ocean (Peru coast) region is generally a cool pool region associated with thermocline uplift and entrainment of cold
12
Chapter 1. Introduction subsurface water due to prevailing easterly wind. Eventually, thermocline waves due to westerly(easterly) wind burst cause downwelling(upwelling) and reduce(enhance) the cold water entrainment, which results in the warm(cold) event commonly known as El-Ni˜ no (La-Ni˜ na ) in the Pacific Ocean. Variations in the SST and the surface pressure are identified to be due to coupled ocean atmosphere interaction known as ENSO. Several studies address the ENSO-Monsoon relationship and its weakening in the recent time (Lau and Nath, 2000; Kumar et al., 1999, 2006; Ashok et al., 2007). Lau and Nath (2000) using a set of sensitivity experiment based on different SST forcing scenarios concluded that the ENSO related perturbations in the Indian Ocean lead to atmospheric changes which reduce the conventional ENSO-Monsoon relationship. La-Ni˜ na (El-Ni˜ no ) condition is likely to have stronger(weaker) positive tropospheric temperature gradient to persist over the south Asian monsoon domain and hence, early (delayed) onset and delayed (early) withdrawal of monsoon results in the increase (decrease) of the length of rainy season (Xavier et al., 2007; Sabeerali et al., 2012). Compared to canonical El-Ni˜ no condition with equatorial eastern Pacific warming, those El-Ni˜ no events with equatorial central pacific warming (Ashok et al., 2007) are more effective in focusing drought producing subsidence over South Asian Monsoon regime (Kumar et al., 2006). Kumar et al. (1999) reported that there is a weakening in the historical ENSO-Monsoon relationship due to shift in the walker circulation anomalies and the changes in land ocean contrast during the warming scenario. However, the future climate projections using coupled climate model do not exhibits any weakening of ENSO-monsoon relationship (Annamalai et al., 2007; Rajeevan and Nanjundiah, 2009).
1.6.2
Indian ocean dipole
In general, drought years are associated with El-Ni˜ no condition and flood years are associated with La-Ni˜ na condition. Some years like 1974, 1985 were droughts not associated with El-Ni˜ no condition and some other drought years (1966, 1968, 1979) had very weak signal in the Pacific. Further, the flood conditions in some years (1983, 1994, 1997)
13
Chapter 1. Introduction are not associated with La-Ni˜ na condition, in the Pacific Ocean. Among these 1997 was one of the strongest El-Ni˜ no year (Ashok et al., 2001; Kumar et al., 1999) and 1994 is one of the largest flood (Krishnan et al., 2011) condition. Asian monsoon circulation is significantly correlated with two dominant SST modes in Indian Ocean (Yang et al., 2010) namely Indian Ocean basin mode and Indian Ocean Dipole Mode.
A second mode of interannual climate variability known as Indian Ocean Dipole (IOD; Saji et al., 1999) or Indian Ocean Zonal Mode (IOZM; Webster et al., 1999) or Equatorial Indian Ocean Oscillation (EQUINOO; Gadgil et al., 2004) was identified to have strong relationship with the monsoon variability. Positive IOD condition enhances the monsoon meridional circulation with surface convergence and upper level divergence in the monsoon regime (Ashok et al., 2001). Ashok et al. (2001) have analyzed a period from 1958 to 1997 and identified that the IOD-Monsoon relationship is high (low) during those periods when ENSO-Monsoon relationship is low (high).
1.7
Extratropical teleconnections
The interannual variability of monsoon is associated not only with the tropical SST variations but also with the extratropical SST variations (Chattopadhyay et al., 2015; Krishnamurthy and Goswami, 2000; Krishnamurthy and Krishnamurthy, 2014; Prabhu et al., 2015). The intensity and the variability of the South Asian Monsoon is not uniform in the historical data, because monsoon also has a variability of decadal to multidecadal timescale (Krishnamurthy and Goswami, 2000).Krishnamurthy and Krishnamurthy (2014) have identified that most of the decadal variability of the monsoon are related to Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and Atlantic Tripole Oscillation (ATO). Extratropical SST variations like AMO, PDO as well as the low frequency variations in the land surface factors like ESC drive significant teleconnections through location and intensity of NSWJ. Since the Tibetan anticyclone located between the NSWJ and TEJ is one of the integral part of the monsoon system,
14
Chapter 1. Introduction any changes in location or intensity of the jet-streams significantly influence the monsoon system.
The Southern Annular Mode (SAM) condition during February-March is evident to have delayed relationship with south Asian monsoon by modulating the Hadley circulation over the central Pacific Ocean (Prabhu et al., 2015) and further through tropical teleconnection pathway. The positive phase of SAM, which has convective anomaly over 70◦ S and subsidence anomaly over 40◦ S results in anomalous subsidence over equatorial central Pacific during boreal summer and favorable for the south Asian monsoon. The SAM is evident to have strong relationship with the SST and MLD variations in the Southern Ocean (Screen et al., 2010; Sall´ee et al., 2010).
The Northern Annular Mode (NAM) has one pole of the sea level pressure anomaly over the Arctic Ocean and the opposite anomalies over sub-polar region are centered over North Atlantic and North Pacific Oceans (Ambaum et al., 2001; Ding and Wang, 2005). The sea level pressure anomalies over sub-polar centers are are not coherent to each other due to the existence of NAO and Pacific North American pattern (PNA). The ESC of winter/spring season is strongly correlated with the winter time Arctic Oscillation Index (Bamzai, 2003).
The winter time ESC is inversely related to summer time monsoon rainfall (Hahn and Shukla, 1976). This type of relationship is suggested earlier between Indian summer monsoon and Himalayan snowfall (Blanford, 1884; Walker, 1910). Recent analysis with the advantage of satellite observation and modeling studies confirm that the relationship is also valid for snow cover over the high latitude regions. Saha et al. (2012) have reported that increased snow cover over western Eurasia trigger a barotropic stationary wave which causes the weakening of upper level monsoon circulation and thereby reduce the intensity of monsoon rainfall.
15
Chapter 1. Introduction The NAO is originally defined as the sea level pressure seesaw between Icelandic low and Azores high (Hurrell et al., 2003; Visbeck et al., 2001). The strength of westerly wind between 40◦ N and 60◦ N latitudes provides another index for the NAO. More than a regional variability, NAO has its footprints throughout the hemisphere. There is a positive relationship between NAO and monsoon rainfall (Goswami et al., 2006). The SST variations at decadal timescale have significant role in NAO evolution (Kucharski et al., 2006).
The AMO is an oscillation in the north Atlantic SST which is closely related to the thermohaline circulation and convective renewal of intermediate and deep water-mass. The intensity of winter time convective renewal of intermediate and deep water-mass of the Labrador Sea and the Greenland-Iceland-Norwegian Sea are modulated by the NAO and there by this influences the Atlantic thermohaline circulation, results in SST variations of longer timescale (Hurrell et al., 2003). There is a positive relationship between Indian Monsoon and the AMO (Goswami et al., 2006; Krishnamurthy and Krishnamurthy, 2014). Positive (negative) phase of AMO causes strengthening (weakening) of Eurasian tropospheric temperature and thereby delayed (early) withdrawal of the Monsoon and hence, increases (decreases) the seasonal mean rainfall. The phase of AMO has strong coherence with the decadal variations of interannual NAO mode (Goswami et al., 2006). Warm phase of AMO extends the positive tropospheric temperature gradient beyond the climatological withdrawal date causing delayed withdrawal (Goswami et al., 2006) of south Asian monsoon while the cold phase of AMO forces early withdrawal.
The ATO is a kind of oscillation having three poles. One pole is near to the east of Newfoundland (40◦ N to 55◦ N and 60◦ W to 40◦ W). The second pole is located near the southeastern coast of the United States (25◦ N to 35◦ N and 80◦ W to 60◦ W) exhibit the opposite sign to the first (Schneider and Fan, 2012) and the third one over the tropical eastern Atlantic have like sign to the first one. The difference in SST between northern pole (first) and the opposite pole (second) is defined as the SST index for the
16
Chapter 1. Introduction ATO (Czaja and Marshall, 2001; Marshall et al., 2001). The 10 to 20 years periodicity of Atlantic tripole oscillation is also evident in the spectrum of Sea Level Pressure (SLP) variations in Greenland-Icelandic low. But the absence of coherent oscillations in subtropical high makes this mode independent of the NAO. The ATO (Schneider and Fan, 2012; Fan and Schneider, 2012) SST mode has negative correlation with the monsoon rainfall (Krishnamurthy and Krishnamurthy, 2014).
The PDO (Mantua and Hare, 2002; Schneider and Cornuelle, 2005) is driven by coupled mechanism of multi-year persistence of North Pacific upper ocean temperature. The positive phase of PDO prominently favors troughs in the atmospheric circulation over the region, while negative phase of PDO favors ridges (Bond and Harrison, 2000). The dynamic response of thermocline in the Kuroshio/Oyashio Extension region to wind anomalies associated with variabilities in Aleutian low (Schneider et al., 2002; Seager et al., 2001) is one of the mechanism proposed for the PDO. Thermocline Rossby waves are generated at latitude of zero wind stress curl, which is located according to the strength of wind anomaly (Mantua and Hare, 2002). Using an Ocean General Circulation Model (OGCM) forced with surface wind (climatology with and without interannual monthly anomalies) Sun et al. (2014) demonstrated the role of interannual fluctuations in the surface wind in simulating the PDO like SST pattern. The anomalies persist for several years because the deep winter mixing is the only mechanism for the dissipation of mid-latitude thermocline anomalies (unlike the tropics due to the absence of equatorial kelvin waves). The surface wind forcing is peaking the spring months of February/March and the peak of SST response to the wind forcing is during late spring or early summer with a lag of three months. Persistent anomalies in the atmospheric flow are associated with northern Pacific SST anomalies in the decadal time scale.
The Pacific Decadal mode SST has inverse relationship with the monsoon rainfall over India (Krishnan and Sugi, 2003; Krishnamurthy and Krishnamurthy, 2014). El-Ni˜ no condition during warm phase of PDO are more likely to force a prevailing dry condition in
17
Chapter 1. Introduction
Figure 1.4: Global SST pattern (blue: cool and red: warm) during June through September (JJAS) favorable for the south Asian monsoon. Various climate modes are labeled at their respective locations in the map India. Similarly, La-Ni˜ na condition during the cold phase of PDO usually has a prevailing wet Monsoon condition. Hence, the simulation of correct phase of PDO is also important in the seasonal prediction of interannual variability of monsoon. The phases of PDO significantly influence the skewness of the ENSO-Monsoon relationship while there is no significant variations in the strength ENSO-Monsoon correlation Krishnamurthy and Goswami (2000).
A simple schematic representation of different SST patterns favorable for the good monsoon condition is described in figure 1.4, where blue shadings represent the cool SSTs and red shadings represent warm SST. The section 3.2 provides more detailed discussion on the global SST correlated with AISMR. The cool pool in central Pacific indicates ENSO, while cool pool in eastern tropical Indian Ocean represents IOD. The north Atlantic Ocean is marked with NAO, AMO, and ATO, while the north Pacific ocean is marked with PDO. Even if, NAM and SAM are defined based on the location of polar jet-streams, these can modulate the SST and the Mixed Layer Depth (MLD) variations of the high latitude region. ESC and TT are the other features which are not directly related to ocean dynamics.
18
Chapter 1. Introduction
1.8
Internal interannual variability
Even if most of the interannual variations in the monsoon are associated with slowly varying factors outside the atmosphere, the interannual variations in the subseasonal oscillations also contribute to the seasonal strength of the monsoon (Goswami and Ajaya Mohan, 2001). There are two major periodicities of subseasonal variations namely Monsoon Intra-Seasonal Oscillation (MISO) with 30 to 60 days periodicity and Rossby mode with 10 to 20 day periodicity. MISO which propagates northward (Goswami, 2005; Sikka and Gadgil, 1980; Yasunari, 1980) explains more than 65% of total intraseasonal variance. The Rossby mode (10 to 20 day) oscillation which propagates westward (Chatterjee and Goswami, 2004; Chen and Chen, 1993; Krishnamurti and Bhalme, 1976) explains 25% of the total intraseasonal variance. Synoptic systems like monsoon lows, depressions, etc. with 5 to 7 day life-cycle also have significant role in day to day rainfall variations within the monsoon season (Goswami et al., 2003).
The interannual variations in the MISO activity (Lawrence and Webster, 2001) have significant impact on the seasonal mean performance of monsoon. These are also known as internal interannual variations since these are independent of ENSO-Monsoon relationship and other climate indices. One of the greatest challenges in the seasonal prediction of AISMR is the internal interannual variation (Sperber and Palmer, 1996), because this component is unpredictable as per the current understanding of the seasonal predictability of monsoon.
Even if, monsoon variability involves droughts and floods at different interannual time scales, the probability of two consecutive droughts or floods is very rare (Meehl et al., 1994; Meehl, 1997; Chang and Li, 2000), and hence there is a phase change of Monsoon anomaly at biennial time scale, which is attributed to the so-called Tropical Tropospheric Biennial Oscillation (TBO). In fact, we cannot consider Indian Ocean and Pacific Ocean to be separate entities, because these are bridged together with atmospheric circulation system known as Walker Circulation (Chang and Li, 2000) which interacts with ENSO,
19
Chapter 1. Introduction IOD as well as monsoon-Hadley Circulation. The interannual variations in the IndoPacific ocean-atmosphere coupled system drive a negative feedback between SST and the walker circulation resulting in the biennial scale selection.
1.9
Seasonal prediction of monsoon
The accurate prediction of monsoon is important for the socio-economic well-being (Gadgil and Gadgil, 2006) of India and other south Asian countries. The Gross Domestic Production (GDP) of south Asian countries is very much related to rain-fed agriculture. Seasonal precipitation plays an important role in the recharge of ground water table. Monsoon rainfall is the major source for most of the rivers in the region, except those originating from the Himalayan glaciers. The drought condition due to under performance of the monsoon rainfall significantly reduces the yield of agricultural products and thereby affects the economy due to shortage of resources. The good monsoon condition is favorable for agricultural productivity and thereby economy. On the other hand, severe flood conditions harmfully affect not only agricultural productivity, but also cause damages to the life and property of common population. Due to the above reasons, it is very much important to understand the science of Monsoon and to instrument all the possible ways to provide early and accurate prediction of seasonal performance of the monsoon.
Lorenz (1969) has identified the inherent limitation of a standalone atmosphere model and reported that the weather predictability is limited to about 14 days. Many pioneer works starting from Blanford (1884) suggested use of surface boundary conditions to predict the summer monsoon rainfall over India. Charney (1977); Shukla and Misra (1977); Shukla (1975); Hahn and Shukla (1976); Charney and Shukla (1981), etc. point out the importance of boundary conditions at Earth’s surface for the prediction of Monsoon Rainfall. Shukla (1975) pointed out the importance of the Arabian Sea SST in the seasonal prediction of AISMR. Further, Shukla and Misra (1977) identified that one month lag correlation between the Arabian Sea SST and Monsoon rainfall is as strong as the si-
20
Chapter 1. Introduction multaneous correlation, while the lag correlation with the wind speed is not significant. Changes in land surface conditions (Charney, 1977, 1975) can influence albedo and the thermal stability of the atmosphere, which can further modify the atmospheric circulation. An inverse relationship between the wintertime snow cover over Eurasia (Hahn and Shukla, 1976) and summertime rainfall over India is also identified by the earlier studies. Thus, it can be concluded that the long time memory of the Ocean, Land and Cryosphere are the sources of the seasonal predictability of Monsoon.
Sperber and Palmer (1996) have concluded that, stand-alone atmospheric models forced with SST boundary condition (Atmospheric Model Intercomparison Project (AMIP) simulation) failed to capture most of the interannual variability of AISMR during 10 year (1979-1988) period of the study. A set of twelve revised versions of atmospheric models has performed better in predicting the AISMR during the same period (Sperber, 1999) compared to the earlier version of the same models (Sperber and Palmer, 1996) participated in the AMIP project. A couple of studies (Preethi et al., 2010; Rajeevan et al., 2012) have investigated the interannual prediction skill of the AISMR using ocean-atmosphere coupled general circulation models. Preethi et al. (2010) have shown that the prediction skill of the AISMR in DEMETER coupled models (Palmer et al., 2004) varies from -0.3 to 0.43. Further, Rajeevan et al. (2012) have shown that compared to the prediction skill (0.28) of DEMETER multi-model ensemble (Palmer et al., 2004), there is significant improvement (0.45) in multi-model ensemble of the newer version of coupled models participated in the ENSEMBLE project (Hewitt, 2004). Recent studies (Chattopadhyay et al., 2016; Ramu et al., 2016) have reported that the NCEP Climate Forecast System Version 2 (CFSv2) coupled model initialized with February initial condition have a better seasonal prediction skill (Anomaly Correlation Coefficient (ACC) ≈ 0.5) for AISMR. In light of this, it has now become important to understand how to improve the prediction skill of the AISMR further. To address this, we have carried out couple of sensitivity experiments to quantify the role of ocean dynamics in the Indo-Pacific basins (George et al., 2015).
21
Chapter 1. Introduction
1.10
Dynamical simulations of monsoon
During the recent past lot of sensitivity studies have tried to address the monsoon and its relationship among the the other climate indices (Godbole, 1973; Lau and Nath, 2000; Wu and Kirtman, 2004; Krishnan et al., 2011). Some of them are based on stand alone Atmospheric General Circulation Model (AGCM), while some others are based on 50m uniform mixed layer slab ocean models (SLAB) and another set used Coupled OceanAtmosphere General Circulation Model (CGCM) with dynamical ocean and atmosphere.
The symmetric primitive equation models (Godbole, 1973) without considering zonal asymmetry were used in a set of sensitivity experiments to analyze the importance of orography and the moisture on the monsoon system. The first successful simulation of atmospheric flow (Charney and Eliassen, 1949) using single layer quasi-geostrophic and equivalent-barotropic model at 500 hPa reveals that the zonal asymmetry of geopotential height can be simulated as a response to orographic steering on the zonal mean flow. In addition to inertial-gravity waves (Bretherton, 1964), equatorial Rossby waves (Matsuno, 1966) are also simulated in a quasi-geostrophic model on equatorial β plane with divergent-barotropic and hydrostatic approximations. The response to equatoriallysymmetric linear heating profile on an equatorial β plane with zero basic flow (Gill, 1980) results in steady state solutions with zonal asymmetry. Equatorial Rossby wave and Hadley circulation response are obtained to the western side of the center of heating. On the other hand towards the eastern side of the center of heating, there is equatorial kelvin wave response and the Walker Circulation. In the case of off-equatorial heating (asymmetric to equator) cross equatorial flow is observed, while the Kelvin wave response is absent. The combination of equatorial and off-equatorial heating profile simulates a circulation pattern corresponds to South Asian Summer Monsoon (Gill, 1980).
22
Chapter 1. Introduction
Citation Godbole (1973)
Experiment Dry-Oro
Gill (1980)
Moist-Oro Dry-Flat Moist-Flat Sym-Heat
Lau and Nath (2000)
Asym-Heat Combined-Heat TOGA
GOGA TOGA-ML
Ashok et al. (2004)
Clim ENSO
IOD
Wu and Kirtman (2004)
Yu and Lau (2005)
ENSO-IOD COLA-CGCM DecIOclim DecIOanom Indo-Pacific Run Indian Ocean Run
Kumar et al. (2005)
GOGA
MLM
TOML
DEMETER Krishnan et al. (2011)
E1 E2
Achuthavarier et al. (2012)
CFSv1
Sharmila et al. (2013)
IOCSST IOVSST GFS(m)-FR CFSv2-FR GFS(d)-FR
Zhu and Shukla (2013)
CFSv2-Cpl
CFSv2-Atm
Description Zonally symmetric primitive equation model having dry atmosphere with Himalayan orography Moist atmosphere with Himalayan orography Dry atmosphere without Himalayan orography Moist atmosphere without Himalayan orography Quasigeostrophic equatorial beeta plane with zero background flow with linear heating profile centered at equator Linear heating and cooling on either side of equator combination of equatorial and off equatorial heating Tropical Ocean Global Atmosphere: SST anomalies in the tropical Pacific between 25◦ S and 25◦ N only. Seasonally varying climatology outside. Global Ocean Global Atmosphere: SST anomalies between 40S and 60N. Tropical Ocean Global Atmosphere with Mixed Layer Ocean: SST anomalies in the tropical Pacific between 25S and 25N only and 50m uniform steady slab elsewhere. Seasonally varying climatological SST Seasonally developing ENSO forcing (monthly composit of 1965, 1969, 1976, 1986, 1987 1991, multiplied with correlated spatial pattern) Seasonally varying IOD forcing (monthly composite of 1961, 1967, 1976, 1994 multiplied with correlated spatial pattern) Combined ENSO IOD forcing COLA AGCM T42L18 coupled with GFDL-MOM3. SST Climatology in Indian Ocean SST anomaly in Indian Ocean UCLA AGCM coupled with MOM OGCM in Indo-Pacific domain while monthly varying climatological SST is used outside the MOM domain UCLA AGCM coupled to MOM OGCM only in Indian Ocean while monthly varying climatological SST is used outside the MOM domain Stand alone AGCMs in tire-two modeling strategy globally forced with observed SST have very poor seasonal prediction skill of monsoon (ACC: GFDL-R30=−0.02, NCAR-CCM3=−0.27). AGCMs coupled with mixed layer ocean model. Compared to GOGA, the seasonal prediction skill of monsoon is better (ACC: GFDL-R30=+0.43, NCAR-CCM3=+0.22). AGCM coupled with mixed layer model only in tropical eastern Pacific Ocean and forced with climatological SST elsewhere. Seasonal prediction skill of monsoon (ACC=+0.25) is better compared to GOGA. CGCM have good seasonal prediction skill of monsoon (ACC median value=+0.2). 50m Uniform Slab coupled with COLA AGCM Monthly MLD anomalies of 1994 superposed on 50m Slab depth CFSv1 coupled model: MOM3 Ocean coupled with GFS between 65◦ S and 50◦ N Daily Climatology of SST in Indian Ocean Daily Varying SST in Indian Ocean Stand alone atmospheric model (60 year freerun) forced with monthly observed SST interpolated to daily. Fully coupled model (100 year free run) initialized with NCEP GODAS. Stand alone atmosphere model (50 year free run) forced with daily SST simulated by the coupled model. Tier-1 hindcast for 28 years (1982-2009) using CFSv2 oceanatmosphere coupled climate model initialized in month of April and integrated for six months with coupling interval of 30 minutes Tier-2 hindcast for 28 years (1982-2009) using stand alone AGCM component of CFSv2 forced with daily SST output of corresponding CGCM prediction.
Table 1.1: Details of the earlier sensitivity experiments.
23
Chapter 1. Introduction
1.10.1
Atmospheric general circulation model experiments
A series of pacemaker experiments (Lau and Nath, 2000) carried out earlier to understand Atmospheric response to the SST anomalies over the focused region. Those experiments were done using AGCM forced with observed SST anomalies over some regions while climatological forcing is used elsewhere. Tropical Ocean Global Atmosphere (TOGA), Global Ocean Global Atmosphere (GOGA) and similar experiments focus on the atmospheric response of the ENSO in the presence and absence of SST anomalies over other regions. In some other experiments to understand atmospheric response to ENSO, IOD and the combined forcing, AGCMs were forced with monthly varying SST anomaly (without considering the interannual variation) representing peak phase of those conditions (Ashok et al., 2004).
In TOGA run the SST variations are prescribed only in the tropical Pacific Ocean and the remaining oceanic region is forced with climatological SST values (Lau and Nath, 2000). This is to switch-off the SST variability outside the tropical Pacific Ocean. Whatever variabilities observed in the atmosphere in a TOGA run is attributed to the tropical Pacific forcing and teleconnection. In this experiment, tropical Pacific acts as a pacemaker which drives climate variability associated with El-Ni˜ no Southern Oscillation.
GOGA experiment is an AGCM experiment in which SST variation are prescribed for all basins, unlike in TOGA run (Lau and Nath, 2000). The difference between TOGA and GOGA output is usually attributed to be the effect of SST variations outside the tropical Pacific Ocean. One of the important limitations of this type of experiment is that it could not consider role of atmospheric coupled interaction on the SST evolution (Wang et al., 2004) especially over Asia-Pacific monsoon domain, where the SST-rainfall relationship is negative.
Tropical Ocean Global Atmosphere with Mixed Layer ocean elsewhere (TOGA-ML) is designed to incorporate the ocean-atmosphere coupled thermodynamic feedback, outside
24
Chapter 1. Introduction the tropical Pacific, while ocean dynamics is absent over there. TOGA-ML experiment is almost identical to TOGA experiment, but the SSTs outside the tropical Pacific Ocean is evolved from a uniform 50m slab ocean model (Lau and Nath, 2000). TOGA run restricts the interannual variation of the ocean outside the tropical Pacific while TOGAML allows the flux driven variation and thermodynamically forced teleconnection from Tropical Pacific variations.
El-Ni˜ no forcing simulation uses a persisting El-Ni˜ no type of forcing (Ashok et al., 2004) to understand the mean features of the teleconnection pattern. In order to generate ENSO forcing SST, the monthly Ni˜ no 3 SST index is composited for pure ENSO years (1965, 1969, 1976, 1986, 1987, 1991). Further, a spatial map of SST correlated with Ni˜ no 3 SST index is generated. Finally, the monthly varying Ni˜ no 3 composite is multiplied with spatial map of correlation and imposed on a climatological SST to obtain the forcing SST throughout the year at each grid point. Unlike in TOGA-GOGA-series of experiments, El-Ni˜ no Forcing Simulation do not have the interannual variations in the forcing field.
Persisting positive IOD condition is used in IOD forcing experiment (Ashok et al., 2004), similar to El-Ni˜ no forcing simulation. In order to generate IOD forcing SST, monthly Indian Ocean Dipole Mode Index (Indian Ocean Dipole Mode Index (IODMI)) is composited for pure IOD years (1961, 1967, 1976, 1994). A spatial map of SST correlated with IODMI is generated and further, it is multiplied with monthly varying composite of IODMI and imposed on a climatological SST to generate IOD forcing SST at each grid point throughout the year.
Simulation which uses the combination of above two forcing fields represent the coexistence of both IOD and ENSO (Ashok et al., 2004). The combination of ENSO anomalies and IOD anomalies imposed on the climatological SST simultaneously to obtain the combined forcing for the AGCM experiment. This experiment helps to understand the teleconnection pattern during El-Ni˜ no co-existing with positive IOD condition.
25
Chapter 1. Introduction
1.10.2
Limitations of tier-2 modeling strategy
Recently, Gadgil and Srinivasan (2011) reported that the excessive teleconnection with ENSO results in poor prediction skill of AISMR in many of the AGCMs participated in the experiment for Seasonal Prediction of Indian Monsoon (SPIM) experiment. Tier-2 modeling strategy in which the atmosphere is forced by SST either observed or predicted by an ocean model is not good enough at some sensitive regions where strong ocean atmosphere coupling is involved (Wang et al., 2004). Hence, tier one modeling strategy in which SST is evolved through strong ocean atmosphere coupling becomes important. Above mentioned studies are not sufficient to distinguish the role of ocean dynamics apart from the flux interactions, since the sensitivity experiments were designed on SSTforcing strategy. Hence a carefully designed coupled sensitivity experiment is essential to distinguish the role of ocean dynamics at individual basin apart from the flux driven SST evolution.
1.10.3
Tier-1 modeling of monsoon
Unlike in the AGCM experiments forced with SST boundary condition, the SLAB ocean coupled experiments allow SST to evolve thermodynamically from the surface fluxes of the Atmosphere (Washington and Meehl, 1984; Meehl and Washington, 1985; Dommenget, 2010). A uniform slab depth of 50 m is usually taken as MLD in such experiments. But in reality MLD has spatial and temporal variation associated with underlying Ocean dynamics and the coupled dynamics. In order to identify the importance of MLD variations and the ocean atmospheric interactions associated with IOD on simulation of correct phase of south Asian monsoon, Krishnan et al. (2011) have done some sensitivity experiments using COLA AGCM coupled with Slab ocean model.
Now a days, most of the operational agencies are using ocean-atmosphere coupled general circulation model (tier-1 configuration) for seasonal scale prediction of monsoon. Sharmila et al. (2013) have studied the difference between tier-1 and tier-2 configura-
26
Chapter 1. Introduction tions on the mean simulation of northward propagating MISO characteristics. Zhu and Shukla (2013) have done comparison between tier-1 and tier-2 configurations on the seasonal prediction of Asia-Pacific monsoon using state-of-the-art coupled general circulation model.
1.10.4
Commonly used decoupling strategies
Some of the recent sensitivity experiments (Wu and Kirtman, 2004; Yu and Lau, 2005; Achuthavarier et al., 2012) used coupled models regionally decoupled with observed or climatological SST-forcing (tier-2 configuration). Wu and Kirtman (2004); Achuthavarier et al. (2012) intent to understand the role of Indian Ocean on the ENSO-Monsoon relationship, while Yu and Lau (2005) target on the role of ENSO on Indian Ocean SST variations. There is another strategy using prescribed climatological wind stress fluxes to the ocean in order to remove atmospheric coupling while keeping the ocean dynamics Sun et al. (2014). This strategy focus on the oceanic processes and the influence of atmospheric forcing on it. Both of the above experiment strategies use some kind of forcing field which resembles the tier two modeling strategy. Identical twin experiments without external forcing by switching-off certain components is better to estimate the role of that particular component in the model simulation.
1.11
Gaps in the current understanding
Even though the earlier studies have identified and reported various factors influencing the seasonal strength of the AISMR, the contribution of coupled ocean-atmosphere dynamics in individual basin for AISMR interannual variance and the seasonal prediction skill, are not clear from the literature. Most of the previous sensitivity studies were either based on mean simulation or a case study of individual (or a set of similar) years. Only a few (Kumar et al., 2005; Zhu and Shukla, 2013) of them address the sensitivity of seasonal prediction skill of south Asian monsoon. Even then none of them have both control and sensitivity experiments in tier-1 strategy (without using SST forcing).
27
Chapter 1. Introduction
1.12
Motivation
Most of the sensitivity studies in the past were done on simulation mode and hence were unable to address the sensitivity of seasonal prediction skill of some parameters. Unlike the freerun simulations used in previous studies, the experiments in the current study focus on the seasonal prediction skill of south Asian monsoon and hence, use hindcast runs initialized every year. The intension of this study is to understand the role of ocean dynamics (including coupled dynamics) on the seasonal prediction of South Asian Monsoon. For this purpose, an identical twin type of experiment strategy is used in which ocean dynamics (along with coupled dynamics) is switched on and off while keeping other factors unchanged. The decoupling strategy of the previous experiments used external SST-forcing which also disturbs the natural thermodynamics ocean atmosphere feedback along with dynamical component. Hence, this study introduces a new decoupling strategy by which ocean dynamics (coupled dynamics also) is switched-off without disturbing the natural thermodynamic feedback between atmosphere and ocean. Keeping these facts in mind, I have chosen my topic as “Role of Ocean-Atmosphere Coupling in the Seasonal Prediction of South Asian Monsoon ”. The detailed objectives of this thesis are: 1. To identify the key spots of strong air-sea coupling in relation to South Asian Monsoon (Chapter 3). 2. To investigate the role of Ocean dynamics of key spots in the seasonal prediction of South Asian Monsoon (Chapter 4). 3. To investigate the role of Ocean dynamics of key spots on the relation between South Asian Monsoon and ENSO (Chapter 5). The detailed description of the experiments is provided in chapter 2. The key regions where ocean dynamics drives the SST evolution are identified in chapter 3. Seasonal prediction skill of south Asian Monsoon is analyzed in the chapter 4. The relationship among ENSO, IOD and AISMR is analyzed in chapter 5. The final chapter summarizes 28
Chapter 1. Introduction the major findings of this study and the future scope. The outcome from this study would be helpful to improve seasonal prediction of South Asian Monsoon by identifying the limitations of the present system for seasonal prediction of South Asian monsoon.
29
Chapter 2 Experiment design and data This study is intended to understand the relative importance of the Indian Ocean and Pacific Ocean dynamics (including coupled dynamics) in the seasonal prediction of South Asian Monsoon. Studies using observed data alone can not delineate the role of ocean dynamics in the individual basin. Operational hindcast output can be used to estimate the seasonal prediction skill of South Asian Monsoon, but its sensitivity to the ocean dynamics in the individual basin is still unknown. Earlier sensitivity studies were done in simulation mode, using long and continuous free-run of the model. A sensitivity experiment in hindcast mode is essential to delineate the role of ocean dynamics in individual basin on the seasonal prediction of South Asian Monsoon. Further, as described in chapter 1 the decoupling strategy used in the earlier sensitivity experiments could not distinguish the surface thermodynamics from the ocean dynamics. Hence, a newly designed decoupling strategy is instrumented in this study in order to maintain the surface thermodynamics while switching-off the ocean dynamics (also coupled dynamics) of a particular basin.
A set of sensitivity experiments along with fully coupled CFSv2 (control) hindcast run (CTL) using a state-of-the-art coupled model, intended to understand the relative role of active ocean dynamics (and coupled dynamics) of different basins in forcing the interannual variability of the AISMR. The details of the model and the sensitivity experiment used in this study are discussed in this chapter.
30
Chapter 2. Experiment design and data
2.1
Observed and reanalysis datasets
Various observed as well as reanalysis products are used in this study in order to analyze the south Asian monsoon and the related atmospheric and oceanic features and also to evaluate the model simulations. Observed gridded interpolated or reconstructed datasets are available for surface parameters like SST, precipitation, etc. On the other hand, multi level parameters in the atmosphere are obtained from reanalysis products. Some of the ocean parameters are obtained from data assimilation products. More details about the observed, assimilated and reanalysis products are provided in the corresponding subsections.
Precipitation In order to evaluate the model simulated rainfall, the pentad precipitation from Global Precipitation Climatology Project (GPCP; Xie et al., 2003) is used. For additional verification, GPCP high resolution (1◦ x 1◦ ) daily global data (Huffman et al., 1997), Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) with horizontal resolution (2.5◦ x 2.5◦ ) as well as India Meteorological Department (IMD; 1◦ x 1◦ ) daily gridded rainfall dataset version 2 (Rajeevan et al., 2006) for Indian land region are also used. IMD rainfall dataset is prepared using 1803 rain gauges distributed throughout the Indian land region maintained continuously for the last few decades. Daily/Pendad dataset is initially converted to monthly and then seasonal mean of June through September is computed.
Sea surface temperature The monthly National Ocean and Atmosphere Administration (NOAA) Extended Reconstructed SST version 3 (ERSSTv3; Smith et al., 2008) with a 2◦ x 2◦ grid horizontal resolution is utilized to verify the model simulated SST. In addition to this, the high resolution (0.25◦ x 0.25◦ as well as 1◦ x 1◦ ) Optimum Interpolated daily SST (OISST Reynolds et al., 2007) data is also utilized for cross verification. The optimum interpolated
31
Chapter 2. Experiment design and data SST analysis uses the in-situobservations from ships and buoys. The satellite based SSTs are bias corrected using the in-situobservations before providing it to the analysis. The monthly HadISST dataset available from Hadley center (Rayner, 2003) from a period 1871 to present is also refered for additional verification. Reduced space optimum interpolation procedure is used on the SSTs from Marine Data Bank, International Comprehensive Ocean-Atmosphere Data Set (ICOADS), in-situobservations and bias corrected satellite products. HardISST dataset is reliable except for the polar region.
Surface fluxes WHOI Objectively Analyzed Air-sea Fluxes (OAFlux) data obtained from Woods Hole Oceanographic Institution (WHOI) is used for validating radiation and thermal flux over ocean surface (Yu et al., 2008). The fluxes obtained from ECMWF Re-Analysis (ERA)-40 and ERA-interim reanalysis products are also used for additional verification.
Atmospheric temperature and wind profile The National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis product is used for the evaluation of zonal, meridional and vertical velocity field (Kalnay et al., 1996). Air temperature is also obtained to derive vertically averaged tropospheric temperature. The horizontal resolution of the NCEP-NCAR reanalysis product is 2.5◦ x 2.5◦ . Best available observations and different numerical weather prediction model outputs are utilized for the generation of reanalysis product. A state-of-the-art data assimilation system is used for this purpose. The data assimilation system is kept frozen to avoid discontinuities that could arise from the changes in the assimilation system. Even if some studies (Bengtsson et al., 2004; Wu and Xie, 2003) have reported that the changes in the observation system after the satellite era causes some discontinuities in the reanalysis product, some other studies (Suhas and Goswami, 2010) have reported that the shift is also noticed in the dataset derived from conventional observation alone and hence, due to physical reality rather than the the art
32
Chapter 2. Experiment design and data effect of change in the observation technique. The ERA-Interim (Dee et al., 2011) is used for additional verification of atmospheric profile parameters. The ERA-Interim data is the latest reanalysis product of European Center for Medium Range Weather Forecast (ECMWF). It is available with the horizontal resolution of 1.5◦ x 1.5◦ for a period from 1979 to the present.
Ocean temperature and current profile Ocean temperature and current profiles are taken from Simple Ocean Data Assimilation (SODA) dataset (Carton et al., 2000). Depth of 20◦ C isotherm (D20) and MLD are computed using SODA output. Further, MLD is also verified using French Research Institute for Exploitation of the Sea (IFREMER) dataset (Mont´egut, 2004) and Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) dataset (Menemenlis et al., 2008).
2.2
Model description
NCEP, USA have developed CFSv2 using state-of-the-art components of atmosphere, ocean, land and sea ice coupled together (Saha et al., 2014). The model is build up on Earth System Modeling Framework (ESMF), which is a standard software platform for Earth system models (Hill et al., 2004). The standard, which defines a component architecture and a support infrastructure, is being developed under open-software practices. Target applications range from operational numerical weather prediction to climatesystem change and predictability studies. Some of the components are evolved from Flexible Modeling System (FMS; Balaji, 2004) which is used for the development of the Modular Ocean Model. The framework provide the basic modules like time management, input output management, parallelization, data override, etc. required for the development of models built on it.
33
Chapter 2. Experiment design and data
2.2.1
Atmosphere model Main features Sigma-pressure hybrid coordinate system Goddard Space Flight Center (GSFC) shortwave radiation; Solar radiation transfer First-order vertical diffusion scheme for Planetary Boundary Layer
Citation Sela (2009) Chou et al. (1998); Hou et al. (2002) Troen and Mahrt (1986); Hong and Pan (1996) Simplified Arakawa-Schubert convection Pan and Wu (1995); with momentum mixing Hong and Pan (1998) Cloud condensate is a prognostic variable Moorthi et al. (2001) Cloud microphysics parameterization Zhao and Carr (1997); Sundqvist et al. (1989) Shallow convection parameterization Tiedtke (1983) Orographic gravity wave Kim and Arakawa (1995) Sub-grid scale mountain blocking Lott and Miller (1997); Alpert (2004) Convection induced gravity waves Saha et al. (2014) Diagnostically determined fractional cloud Xu and Randall cover (1996) Rapid radiative transfer model long wave pa- Clough et al. (2005); rameterization Sundqvist et al. (1989) Monte Carlo independent Column approxi- Barker et al. (2002); mation Pincus (2003) Table 2.1: Main features of the atmosphere model
The NCEP Global Forecast System (GFS) with a spectral triangular truncation of 126 waves in horizontal (T126) and a finite differencing in vertical with 64 sigma-pressure hybrid levels is used as the atmospheric component of the CFSv2 (Moorthi et al., 2001; Sun et al., 2010). The spectral resolution of T126 is equivalent to nearly 100 km (0.937◦ ) grid resolution. The equivalent grid resolution for triangular truncation spectral model is defined as average spacing between Gaussian latitudes in the transform grid which is same as the spacing between longitudes at the equator (Laprise, 1992). Consider ’a’ is the radius of the Earth and ’N’ is the spectral resolution, then the equivalent grid resolution is derived as follows.
34
Chapter 2. Experiment design and data
2πa 3N + 1 13.3 3 ≈ 10 km N
L≈
≈ 106kmf orT 126
(2.1) (2.2) (2.3)
The wavelength of the shortest resolving zonal wave at the equator is given by
πa N 20 3 ≈ 10 km N
Lze ≈
≈ 159kmf orT 126
(2.4) (2.5) (2.6)
Virtual temperature is used as prognostic variable in the CFSv2 (Saha et al., 2014), while the earlier version of the same model (CFSR; Saha et al., 2010) used enthalpy as the prognostic variable. Model uses simplified Arakawa–Schubert convection with momentum mixing (Pan and Wu, 1995; Hong and Pan, 1998). The shallow convection is based on Tiedtke (1983). Orographic gravity wave (Kim and Arakawa, 1995) and subgrid scale mountain blocking (Lott and Miller, 1997) are used in the model. Gravity wave drag induced by cumulus convection is parameterized (Saha et al., 2014) without horizontal propagation. A rapid radiative transfer model (RRTM; Clough et al., 2005) with advanced cloud-radiation interaction scheme and Monte Carlo independent Column approximation (McICA; Barker et al., 2002; Pincus, 2003) with random column cloud generator is used to address the cloud radiation relationship in the model. RRTM calculate fluxes and cooling rate for longwave spectra for an arbitary clear atmosphere; considering water vapour (H2 O), carbon-dioxide (CO2 ), ozone (O3 ), methane (CH4 ), nitrous-oxide (N2 O), halocarbons (CX4 ), etc. The CO2 mixing ratio is computed by superimposing climatological seasonal cycle on the initial estimate.
35
Chapter 2. Experiment design and data
2.2.2
Ocean model Main features Mass conservation (non-Boussinesq approach) is considered for kinematics, dynamics and physics instead of volume conservation (Boussinesq approximation) Shortwave penetration Explicit free surface with time dependent volume allow fresh water input. Time-independent depth profile
Citation Griffies et al. (2001)
Sweeney et al. (2005) Griffies et al. (2001)
Bryan and Lewis (1979) K-Profile vertical mixing parameterization Large et al. (1994) Richardson number dependent scheme, ver- Pacanowski and Phitical eddy viscosity and diffusivity lander (1981) Isoneutral Smagorinsky horizontal mixing Griffies and Hallberg scheme for lateral eddy viscosity and diffu- (2000); Smagorinsky sivity (1963) Mesoscale eddy parameterization scheme Griffies (1998); Gent and Mcwilliams (1990) Tidal mixing parameterization Simmons et al. (2004) Anisotropic friction scheme Large et al. (2001); Smith and McWilliams (2003) Biharmonic friction operators Reutskiy (2005) Table 2.2: Main features of the ocean model The GFDL Modular Ocean Model version 4p0d (MOM4) is used as ocean component (Griffies et al., 2004) in CFSv2. MOM4 has a horizontal resolution of 0.25◦ between 10◦ S to 10◦ N latitude band and 0.5◦ elsewhere. Model has 40 vertical levels, out of which 27 levels are at the upper 400 m depth. Model has a 10 m vertical resolution upto 240 m depth from the surface and decrease gradually while going down. The bottom layer have a thickness of 511 m extending upto 4.5 km. The model uses tripolar grid (north of 65◦ N) with two north poles located one over northern Russia and another over northern Canada (Wu and Grumbine, 2013). MOM4 employs the non-Boussinesq approach and hence, kinematics, dynamics and physics are based on mass conservation instead of volume conservation used in Boussinesq approximation. External mode solver for the needs of z-coordinate ocean climate modeling is based on explicit free surface (Griffies et al., 2001) 36
Chapter 2. Experiment design and data where top model grid cell has time dependent volume allowing conservative fresh water input.
The MOM4 uses nonlocal K-Profile vertical mixing parameterization (Large et al., 1994) and isoneutral (Griffies et al., 1998; Griffies, 1998; Gent and Mcwilliams, 1990) Smagorinsky horizontal mixing scheme (Griffies and Hallberg, 2000) consistent with those used in NCEP Climate Forecast System Reanalysis (CFSR) version of the model (Saha et al., 2010). The MOM4 ocean model is implemented with a wide range of vertical mixing schemes other than the K-Profile Parameterization scheme (Large et al., 1994); including the time-independent depth profile (Bryan and Lewis, 1979), the Richardson number dependent scheme (Pacanowski and Philander, 1981). The spatial and temporal evolution of tidal mixing depending on model state is parameterized (Simmons et al., 2004). The Horizontal friction schemes implemented in MOM4 include constant and grid dependent viscosity schemes, as well as the Smagorinsky viscosity scheme (Griffies and Hallberg, 2000). The anisotropic scheme (Large et al., 2001; Smith and McWilliams, 2003) has been implemented for both the Laplacian and biharmonic friction operators (Reutskiy, 2005). Griffies (2004) presents the details of each of these schemes and there rationale. Shortwave penetration in the ocean is parameterized based on Sweeney et al. (2005).
2.2.3
Land surface model Main features Total and liquid soil moisture are prognostic. Snow pack parameterization Freezing of active soil layer Soil heat flux in presence of thin snow layer Vegetation dependency on root zone depth, snow cover fraction, soil heat flux, thermal conductivity, albedo etc.
Citation Ek et al. (2003) Koren et al. (1999) Koren et al. (1999) Lunardini (1981) Ek et al. (2003)
Table 2.3: Main features of the land surface model In addition to ocean and atmosphere components, there is a four layer Noah Land
37
Chapter 2. Experiment design and data Surface Model (Noah LSM) in CFSv2, which is identical to that used in operational GFS as well as CFSR except a couple of modifications to address the biases in the soil temperature and moisture (Ek et al., 2003; Saha et al., 2010; Mitchell et al., 2005). Noah LSM is basically evolved from Oregon State University Land Surface Model (OSU LSM), which is also used in NCEP Climate Forecast System Version 1 (CFSv1). The four layers are 10 cm, 30 cm, 60 cm and 100 cm respectively. The root zone depth is spatially varying according to the vegetation type.
Unlike the earlier version of land model, Noah LSM considers the vegetation type (eg. forest/grassland) to determine the snow cover fraction, soil heat flux, thermal conductivity, albedo, etc. for a given snow water equivalent. Total and liquid soil moisture are prognostic variables in the model. Freezing of active soil layer (Koren et al., 1999) is considered. Snow pack parameterization is based on Koren et al. (1999). In the presence of thin snow layer, soil heat flux is considered based on Lunardini (1981).
Compared to the earlier version of Noah LSM used in CFSR, the new version of Noah LSM for CFSv2 has some refinement in vegetation parameters and the rooting depth in order to improve the evaporation, soil temperature as well as the surface air temperature (Saha et al., 2014). Further, the runoff parameters were refined to correct the soil moisture climatology (Saha et al., 2014).
2.2.4
Sea ice model Main features Dynamical motion of sea ice
Citation Hunke and Dukowicz (1997) Winton (2000) Winton (2000)
Ice thermodynamics Brine constant parameterization.
Table 2.4: Main features of sea-ice model A three layer dynamical sea ice model, GFDL Sea Ice Simulator (SIS) with two ice layer and one snow layer (Winton, 2000), is also coupled together with the other compo-
38
Chapter 2. Experiment design and data nents. SIS also use tripolar grid as in the case of ocean model (Wu and Grumbine, 2013). The sea ice model deals with dynamical motion of sea ice (Hunke and Dukowicz, 1997), transport of sea ice, ice thickness categories, surface albedo and vertical thermodynamics. Vertical resolution of ice and snow, as well as the representation of conductivities, light transmission, and heat capacity including latent heat are the various factors involved in vertical thermodynamics of the ice model. Even if the spectrum of ice models ranges from slab model with zero heat capacity to highly sophisticated multilayer models, an intermediate model with one snow layer and two ice layers with constant heat conductivity and a simple parameterization of brine constant is good enough to represent the seasonal cycle of sea ice. Sea ice thickness in the model (Wu and Grumbine, 2013) is classified into five categories such as 0.0 m to 0.1 m, 0.1 m to 0.3 m, 0.3 m to 0.7 m, 0.7 m to 1.1 m and greater than 1.1 m. Salinity dependent sea water freezing temperature is fixed at the ice bottom (Wu and Grumbine, 2013). Sea ice formation release salt and heat to the ocean (Griffies et al., 2004). The lower ice layer only have sensible heat capacity, but upper ice layer have both sensible and latent heat capacity, while snow layer do not have any heat capacity (Wu and Grumbine, 2013).
2.2.5
Coupler
The CFSv2 model runs in multiple processors and uses multiple datasets shared between individual component models. The Multiple Program Multiple Data (MPMD) parallel programing model handles the message passing and data sharing between the different components of CFSv2. There are three executables namely GFS atmosphere model, MOM4 ocean model and the Coupler, have separate data flow and runs on separate group of nodes, but exchange data at regular time steps.
GFS atmosphere runs in atmospheric time step independently until the next coupler time step. On every coupler time step, the GFS atmosphere pass the instantaneous variables to the coupler and receive surface boundary conditions from the coupler. SIS alone run in the fast time step (Sea Ice Time step) while the MOM4 ocean wait until the slow
39
Chapter 2. Experiment design and data time step. Instantaneous variables are received from the coupler and the surface boundary conditions for the atmosphere are passed to the coupler at every coupler time step. The MOM4 ocean along with sea ice model (slow ice) run at every ocean time step. Both instantaneous and accumulated variables are received from the coupler. Instantaneous variables are required for the sea ice model and the accumulated variables are required for the ocean model. Ocean model update the sea surface temperature at every ocean time step and sends it to the coupler.
At every coupler time step, sea ice model receives instantaneous variables such as downward short wave radiation at surface, long wave radiation at surface, atmospheric bottom temperature, wind, humidity, pressure and snowfall. MOM4 ocean and sea Ice model sent sea surface temperature (SST), sea ice fraction, sea ice thickness and snow depth to the atmosphere at every coupler time step. Ocean model receives the accumulated variables such as net downward shortwave radiation, net (downward) longwave radiation, sensible heat flux, latent heat flux, wind stress and precipitation from the coupler at every ocean time step. The surface temperature given to the atmosphere is updated at sea ice regions and land regions at fast time step while those over liquid ocean region remain unchanged until the slow time step.
2.3
Initial conditions
The CFSR output (Saha et al., 2010) are used as the initial conditions for the model run. The data assimilation and reanalysis are done in a homogeneous and continuous system of the same model.
2.3.1
Atmospheric initial conditions
The atmospheric data assimilation system named as Global Data Assimilation System (GDAS) used in the CFSR project (Kleist et al., 2009) is a continuation of the earlier versions (Wu et al., 2002) used in NCEP-National Center for Atmospheric Research (NCAR)
40
Chapter 2. Experiment design and data Reanalysis 1 (R1) project and the NCEP-Department of Energy (DOE) Global Reanalysis 2 (R2) project. A fixed set of conventional observations as well as temperature retrieval from TOVS/ATOVS (Derber and Wu, 1998) are given as inputs to the system. The initial system based on 3D-VAR Spectral Statistical Interpolation (SSI) is further modified to 3D-VAR Gridpoint Statistical Interpolation (GSI) (Kleist et al., 2009).
2.3.2
Ocean initial conditions
The NCEP Global Ocean Data Assimilation System (NCEP-GODAS) uses a three dimensional variational (3DVAR) scheme following its parent version known as GFDL Global Ocean Data Assimilation System (GFDL-GODAS) (Behringer, 2007). GFDL-GODAS is evolved from Ocean Data Assimilation System (ODAS) based on Modular Ocean Model version 1 configured for Pacific Ocean (Derber and Rosati, 1989). Some adaptations are made to assimilate systematic salinity profile (in addition to the temperature) in NCEPGODAS, which became operational in 2003 as a part of CFSv1 project (Behringer and Xue, 2004). Revised background error covariances (Behringer et al., 1998) and assimilation of satellite altimeter data (Vossepoel and Behringer, 2000; Ji et al., 2000) are incorporated in the NCEP-GODAS system.
2.3.3
Precipitation, snow depth and sea ice concentration
The Global Land Data Assimilation System (GLDAS) is based on Noah LSM which is run in semicoupled mode and forced with atmospheric data assimilation system output along with observed precipitation, while the first guess of land-atmosphere simulation is done using fully coupled land-atmosphere-ocean model (Rodell et al., 2004). The NESDIS IMS (Helfrich et al., 2007) and Air Force Weather Agency’s SNODEP (Hall, 1987; Kopp, 1996) data were used to estimate physical snow depth for the assimilation system. In order to attain the equilibrium in the land surface climatology of the model, the CFSR-GLDAS is initialized with two years average operational GFS land surface state of the calender day followed by a 12 month spin-up. Since the daily rain gauge data is used as direct forcing,
41
Chapter 2. Experiment design and data a 24 hour surface analysis cycle is used. The pentad CMAP analysis and daily gauge analysis are used as direct forcing to the land surface analysis of CFSR-GLDAS. The earlier versions (R1 and R2) use GDAS precipitation analysis alone to force the land. The R2 land use observed precipitation to nudge the soil moisture. In CFSR-GLADS, an optimal precipitation forcing is generated using a latitude dependent blending function. Higher weight is given for satellite based CMAP analysis in the tropics, while the gauge analysis get higher weight over midlatitudes and much higher weight is given for gauge analysis over North America, western Europe and Australia, where there is dense network of precipitation gauges. Over the high latitude regions the maximum weight is given for model precipitation. The IMS (Helfrich et al., 2007) data available in high resolution for the northern hemisphere starting from February 1997 is more accurate than the SNODEP (Hall, 1987; Kopp, 1996) data. If the IMD indicates snow cover over a region, the snow depth is assigned to be either SNODEP value or 2.5 cm whichever is greater. If the first guess snow depth is greater than twice (less than half) of the analyzed depth, then it is reassigned to twice (half) of the analyzed value. Otherwise the model value is retained. The sea ice component of CFSR assimilates only the sea ice concentration, due to lack of observation for the ice thickness and motion during the analysis period (Grumbine, 2010). Whenever the GODAS SST is warmer than 275.3 K, ice is not allowed to exist in the model. Sea ice in the guess field is removed also in the case when the observed sea ice concentration is less than 15 % of the guess. When the observed sea ice concentration is ≥ 15% of the guess field, then the observed value is assigned to the model field.
2.4
Hindcast initialization from February
The prediction skill of the interannual variability of the AISMR in the CFSv2 model initiated with February initial conditions is better compared to the model initiated with shorter lead time (Chattopadhyay et al., 2016). The Pacific Ocean exhibits larger systematic biases for hindcast initialized with March, April or May initial condition compared
42
Chapter 2. Experiment design and data to February initial condition. Hence, the CFSv2 hindcast initialized from the month of February is used for the control and sensitivity experiments in this study. In this study, both CTL run and sensitivity runs are made in hindcast mode (initialized every year) and integrated for 9 months lead time, for the period 1982 to 2009. Both CTL and sensitivity runs are an ensemble mean of five realizations of CFSv2 T126 model runs initialized with the February initial conditions (00z05Feb, 00z10Feb, 00z15Feb, 00z20Feb, 00z25Feb). Ramu et al. (2016) have carried out analysis using 10 member ensemble (including 12z initial conditions along with 00z for same initial dates) and Chattopadhyay et al. (2016) carried out analysis using 20 member ensemble (including four initial conditions per day such as 00z 06z 12z and 18z) and found that increasing the ensemble size (from 5 to 10 or 20) does not change the results.
2.5
Experiment design
The coupled sensitivity experiment designed for this study uses a strategy by which the thermodynamical flux interactions are maintained while completely removing ocean dynamics in a particular basin (George et al., 2015). This indirectly remove the dynamical component of ocean atmosphere coupling as well. Spatial and temporal variations in the MLD are not prescribed in the sensitivity experiment because it is a well known fact that MLD variations are also partly due to the coupled ocean-atmospheric dynamics. Since the model integration is for 9 months, we have not used any flux correction in any of the sensitivity experiments as model simulated SST do not drift significantly.
2.5.1
Sea surface temperature
The sea surface temperature is derived from various flux exchanges with the atmosphere as well as the dynamics of the ocean (Mendoza et al., 2005).
∆SST = [
1 Qnet − ][ ] − [→ v • ∇SST ] + [D] ρcp M LD
43
(2.7)
Chapter 2. Experiment design and data Where ∆SST is the time derivative of SST, ρ is the density of sea water (≈ 1000kg/m3 ), cp is the specific heat capacity of sea water (≈ 4 × 103 W kg −1 K −1 ), Qnet is the net heat − flux with downward (positive) sign convention, MLD is the depth of mixed layer, → v is the ocean surface current, ∇SST derivative of SST, D is the diffusion term. − − ), Advection term → v • ∇SST can be further decomposed into zonal advection (→ u ∂T ∂x − − meridional advection (→ v ∂T ) and vertical advection (→ w ∂T ) as follows ∂y ∂z
∂T → ∂T → ∂T → − − v • ∇SST = → u +− v +− w ∂x ∂y ∂z
(2.8)
In bulk formulation the temperature of the mixed layer is assumed to be uniform and hence, the vertical advection term is valid only for the bottom of the mixed layer. The time M LD variation in the mixed layer depth give rise to the entrainment term (∆M LD SSTM−T ). LD
∂SST → SST − TM LD SST − TM LD ∂SST → → − − +− v −− w − ∆M LD v • ∇SST = → u ∂x ∂y M LD M LD
(2.9)
− Further, the ocean current → v can be decomposed into Ekman current (due to wind stress) and geostropic current (due to pressure gradient).
→ − V e • ∇hori SST (SST − TM LD ) → − → − v • ∇SST = [ v g • ∇SST ] + [ ] + [H(we )we ] M LD M LD
(2.10)
→ − − Where → v g is the geostrophic current, V e is the vertically integrated Ekman current, H(we ) is the Heaviside function which indicates that the mixed layer temperature is affected by vertical transport only if its direction is upward.
The vertically integrated horizontal Ekman current is described as
44
Chapter 2. Experiment design and data
1 (f τy + rτx ) + r2 ) 1 Ve = [ (−f τx + rτy ) 2 ρ(f + r2 )
Ue = [
ρ(f 2
(2.11) (2.12)
The net heat flux is the sum of radiative fluxes (short wave and long wave), evaporation and sensible heat fluxes from the atmosphere.
Qnet = SWdown (1 − Alb) + LWdown − LWup − SH − LH
(2.13)
Where Qnet is the net heat flux (positive downward), SWdown is the downward short wave radiation received at the ocean surface, Alb is the Albedo (reflectivity) of the ocean surface, LWdown is the downward long wave radiation from the atmosphere received at the ocean surface, LWup is the upward long wave radiation emitted from the ocean surface (negative is to keep the sign conversion), SH is the sensible heat (conventionally upward positive and hence, minus sign to keep the sign convention), LH is the latent heat release from ocean due to the evaporation (negative sign is because the ocean is losing the energy).
2.5.2
Slab ocean model
Slab ocean model is a concept in which the ocean is assumed to be steady, stationary and uniform with a fixed thickness. The temperature is derived by assuming that the net heat flux at the surface is equally distributed to the entire slab depth (mixed layer depth) with a simple thermodynamical relationship.
∆SSTslab = [
1 Qnet ][ ] ρcp M LD
(2.14)
To ignore the ocean dynamics in evolution of SST, constant 50 meter uniform slab depth is assumed to be the MLD (Washington and Meehl, 1984; Meehl and Washington,
45
Chapter 2. Experiment design and data Region
Baundaries
Indian Ocean Pacific Ocean Rest of tropics Northern Extratropics Southern Extratropics
30o S to 30o N and 45o E to 120o E 30o S to 30o N and 120o E to 75o W 30o S to 30o N and 75o W to 45o E 30o N to 90o N 90o S to 30o S
SST for the Atmosphere CTL ISLAB PSLAB MOM4 SLAB MOM4 MOM4 MOM4 SLAB MOM4 MOM4 MOM4 MOM4 MOM4 MOM4 MOM4 MOM4 MOM4
Table 2.5: Experiments and model domain. 1985; Dommenget, 2010). The results from those sensitivity experiments which have a uniform 50 m slab MLD are compared with CTL in this study. The 50 m uniform regional Slab Ocean is used in several earlier studies like TOGA-ML experiment (Lau and Nath, 2000) in different context and different domains. The section 1.10 provides the details of such experiments. Flux correction is not used in the sensitivity experiment, in order to completely switch-off the ocean dynamics as well as coupled dynamics. Since the experiment is conducted on hindcast mode with maximum of nine months lead time, drift in the model climatology from the initial conditions is limited.
2.5.3
Sponge boundary
In order to remove the artificial SST gradient which could arise between the boundary of MOM4 coupling region and SLAB coupling region, a sponge boundary is implemented along the edges of the SLAB ocean model. The sponge boundary is the region where the atmosphere model is coupled with a weighted average of the MOM4 SST and SLAB SST. A sufficiently large width of 10◦ for the sponge boundary is good enough to suppress artificial SST gradient along the boundaries of two ocean models coupled to the atmospheric counterpart.
2.5.4
Control run
The CTL has ocean dynamics and ocean atmospheric coupling all over the globe. Detailed description of the model (section 2.2) is already given above.
46
Chapter 2. Experiment design and data
CTL Run: GFS T126 L64 AGCM
MOM 4
America
MOM 4
America
MOM4 OGCM in Pacific Ocean
America
MOM 4 OGCM in Indian Ocean
Indonesia
Africa
MOM 4
MOM 4
ISLAB Run: GFS T126 L64 AGCM
Africa
MOM 4
MOM4 is passive in Indian Ocean
Indonesia
50m SLAB in Indian Ocean
MOM4 OGCM in Pacific Ocean
PSLAB Run: GFS T126 L64 AGCM
MOM 4 OGCM in Indian Ocean
Indonesia
Africa
MOM 4
50m SLAB in Pacific Ocean
MOM4 is passive in Pacific Ocean
Figure 2.1: Zonal vertical cross section along the equator showing the role of the SLAB ocean in the sensitivity experiments.
2.5.5
Indian Ocean slab
Active ocean-atmospheric coupled dynamics (along with internal dynamics of ocean) are removed from the tropical Indian Ocean in CFSv2 Indian Ocean Slab experiment (ISLAB), instead a uniform slab (50 m) over the tropical Indian Ocean (30o S to 30o N; 45o E to 120o E) provides the flux driven SST (figure 2.1). In ISLAB, the atmosphere is coupled with fully dynamical Ocean elsewhere outside the tropical Indian Ocean. Hence, the difference between ISLAB and CTL run isolates the role of Indian Ocean coupled dynamics. A sponge boundary is instrumented along Agulhas region, Indonesian through flow region and southern boundary of Indian Ocean in order to avoid the artificial SST gradient between the two ocean models.
47
Chapter 2. Experiment design and data
2.5.6
Pacific Ocean slab
Similarly, the CFSv2 Pacific Ocean Slab experiment (PSLAB) is identical to the ISLAB (figure 2.1) except that coupled dynamics are removed only from the tropical Pacific Ocean (30o S to 30o N; 120o E to 75o W). Therefore, the comparison of the PSLAB and CTL run isolates the role of Pacific Ocean coupled dynamics. The sponge boundary around the slab region suppresses the possibility of artificial SST gradient between the two ocean models.
2.6
Work flow
The major objectives of this thesis are arranged into three working chapters. The chapter 3 addresses the identification of key regions and key factors of ocean-atmosphere coupled dynamics, which are important for the seasonal prediction of south Asian monsoon. The chapter 4 analyze the seasonal prediction skill of AISMR and its sensitivity to the ocean-atmosphere coupled dynamics over Indian Ocean and Pacific Ocean. Finally, chapter 5 focuses on the relationship among ENSO, IOD and monsoon.
48
Chapter 3 Key regions of south Asian monsoon predictors 3.1
Introduction
The pioneering studies identified that south Asian monsoon can be predicted using empirical models based on long time memory of Himalayan snow cover, Southern Oscillation, etc. (Blanford, 1884; Walker, 1925). Operational meteorological agencies still use those models modified with additional predictors. The seasonal prediction of south Asian monsoon using dynamical models are also based on the slowly varying factors like SST, soil moisture, sea ice, snow cover etc. (Charney and Shukla, 1981). As described in chapter 1, the seasonal mean strength of monsoon is modulated by various climate modes including tropical and extratropical SSTs. The ocean-atmosphere coupled dynamics in the entire area of the largest tropical basin (Pacific Ocean) has a dominant mode known as ENSO with periodicity of four to seven years (Rasmusson et al., 1990; Philander, 1990; Bjerknes, 1969). There is a weakening of ENSO-monsoon relationship in recent period (Kumar et al., 1999). Moreover, in the recent times, the monsoon variabilities are better related to central Pacific SST anomalies than the eastern Pacific SST anomalies (Kumar et al., 2006; Wang et al., 2015), which are identified as separate flavors of the ENSO (Ashok et al., 2007). However, most of the dynamical coupled models failed to capture the dif-
49
Chapter 3. Identify the key regions ferent flavors of ENSO as well as their relationship with monsoon (Wang et al., 2015). In the context of the weakening of ENSO-Monsoon relationship in the recent times (Kumar et al., 1999), another climate mode based on the Indian Ocean coupled dynamics, known as IOD, was identified (Saji et al., 1999; Webster et al., 1999; Gadgil et al., 2004) by the end of 20th century. Since the basin width of Indian Ocean is smaller than the Pacific Ocean, the periodicity of IOD (2-3 years) is about half of the ENSO.
There are other climate modes, which also have considerable relationship with monsoon in addition to ENSO and IOD, as discussion on sections 1.6 and 1.7 earlier. North Atlantic Oscillation (NAO; Hurrell et al., 2003; Visbeck et al., 2001), Atlantic Multidecadal Oscillation (AMO; Hurrell et al., 2003; Goswami et al., 2006; Krishnamurthy and Krishnamurthy, 2014), Atlantic Tripole Oscillation (ATO; Schneider and Fan, 2012; Fan and Schneider, 2012), Pacific Decadal Oscillation (PDO; Mantua and Hare, 2002; Schneider and Cornuelle, 2005), Northern Annular Mode (NAM; Ambaum et al., 2001; Ding and Wang, 2005; Ding et al., 2011), Southern Annular Mode (SAM; Screen et al., 2010; Sall´ee et al., 2010), etc. are some of those modes, which have their influence on the monsoon. However, those interactions are at longer (decadal) timescale (Kucharski et al., 2006).
This chapter focuses on the identification of those key spots where ocean-atmosphere coupled dynamics are important in the evolution of the SST anomalies, which have its strong teleconnections to the south Asian monsoon at seasonal and interannual timescale. In order to identify the involvement of ocean-atmosphere coupled dynamics in evolution of SST anomalies, correlation analysis is carried out for MLD and D20 with SSTs at each grid point. The correlation analysis of SST with dynamical parameters like MLD and D20 highlights the regions where strong ocean-atmosphere coupled dynamics occur.
Further, this chapter addresses the simulation of the mean monsoon features in CFSv2 coupled model. Most of the coupled models including those participated in the Coupled Model Intercomparison Project (CMIP5) have some common tropical biases like excessive equatorial Pacific cold tongue (Li and Xie, 2014; Zheng et al., 2012; Wang et al., 2013; 50
Chapter 3. Identify the key regions Megann et al., 2014; Rajeevan and Nanjundiah, 2009), double-ITCZ (Li and Xie, 2014; Bellucci et al., 2010; Hirota et al., 2011; Lin, 2007; Hwang and Frierson, 2013), dry bias over Indian land (Sabeerali et al., 2013) etc. Similar biases are also reported in CFSv2 (Saha et al., 2012; George et al., 2015; Chattopadhyay et al., 2016). Both the surface heat flux and the upper ocean heat advection contribute to the cold tongue bias in coupled model (Zheng et al., 2012) in association with weak Bjerknes feedback (Bjerknes, 1969). The ITCZ migration is minimum over the eastern Pacific, where it is located in the northern hemisphere irrespective of the season (Philander et al., 1996). Excessive latent heat feedback and insufficient shortwave feedback are attributed to cause excessive tropical precipitation and double-ITCZ (Lin, 2007) in coupled models. The double-ITCZ is reported to be strongly associated with the SST threshold leading to the onset of deep convection in the model (Bellucci et al., 2010) and the precipitation scheme (Hirota et al., 2011). The entrainment of environmental dry air over subsidence region is to be improved in the deep convection scheme of the coupled models for the dynamical suppression of deep convection and simulation of proper precipitation distribution and elimination of double-ITCZ. Hwang and Frierson (2013) reported that the double-ITCZ can also be associated with the extratropical cloud bias, since the ITCZ location is very much sensitive to the meridional heating profile outside the tropics. This chapter also gives a special attention on the Indian Ocean due to its unique seasonal features (section 1.4) associated with monsoon.
3.2
Global SST pattern favorable for monsoon
Earlier studies of (Kumar et al., 2006; Ashok et al., 2001) have shown that south Asian monsoon rainfall is favored by cool SSTs over central equatorial Pacific Ocean and eastern tropical Indian Ocean. The figure 3.1 shows correlation between SST and AISMR, which confirms the earlier results of Kumar et al. (2006); Ashok et al. (2001). Negative correlations are also shown over Southern Ocean sector south of New Zealand, subtropical (about 30◦ latitude) northern and southern Atlantic Ocean. The regions with positive
51
Chapter 3. Identify the key regions
Figure 3.1: Observed global SST correlated with All India Summer Monsoon Rainfall Index correlations are tropical western Pacific, western Indian Ocean, Arctic Ocean, Sea of Okhotsk (of northwestern Pacific Ocean), central north Atlantic (east of Newfoundland), Scotia Sea region (between south Pacific and south Atlantic Oceans) etc. (figure 3.1). A detailed discussion on the previous studies of monsoon teleconnections with various climate modes are provided in chapter 1 and further in section 3.1. The negative correlation observed over equatorial central Pacific along with positive correlation over eastern and western Pacific Ocean indicate the La-Ni˜ na Modoki pattern favorable for the south Asian monsoon (Kumar et al., 2006; Ashok et al., 2007). The negative correlation over southeastern tropical Indian Ocean and positive correlation over western tropical Indian Ocean indicates the positive phase of IOD favorable for the south Asian monsoon (Ashok et al., 2001; Saji et al., 1999). The positive correlation over Arctic Ocean and negative correlation over both northern Pacific and northern Atlantic Ocean indicates the relation with NAM (Ambaum et al., 2001; Ding and Wang, 2005; Ding et al., 2011). Significant correlations are also observed over the southern ocean along with southern Pacific and southern Atlantic Ocean. The negative correlation over Southern Ocean sector south of New Zealand and the positive correlation over Scotia Sea region (between south Pacific and south Atlantic Oceans) are associated with SAM (Prabhu et al., 2015; Screen et al., 2010; Sall´ee et al., 2010), which also have a non-annular component of meridional wind
52
Chapter 3. Identify the key regions pattern advect warm air to Weddel Sea and cold air to Ross Sea region (Lefebvre, 2004). The positive correlation over central north Atlantic (east of Newfoundland) sandwiched between negative correlation on either side is associated with ATO (Schneider and Fan, 2012; Fan and Schneider, 2012).
The SST variations outside the tropical region are driven by long period oscillations associated with high latitude dynamics (Schneider et al., 2002; Seager et al., 2001), while those over the tropical region are dominantly driven by equatorial wave dynamics (Rasmusson et al., 1990; Philander, 1990; Bjerknes, 1969). This thesis mainly focuses on the tropical ocean-atmosphere coupled dynamics at seasonal/interannual timescale and their teleconnections with AISMR. The existence of fast moving equatorial wave plays an important role in determining the time scale of tropical ocean-atmosphere coupled dynamics. Tropical Atlantic (30◦ N to 30◦ S) SSTs do not exhibit significant relationship with south Asian monsoon variabilities at interannual timescale (figure 3.1). Recently, Surendran et al. (2015) have shown that most of the AISMR variance can be explained by considering the climate modes over Indo-Pacific basins. Therefore, the following chapters in this thesis mainly focus on the ocean-atmosphere coupled dynamics in the Indo-Pacific region. The La-Ni˜ na pattern in Pacific Ocean (negative correlation over the central tropical Pacific and positive correlation over the western Pacific warm pool region) and a positive dipole pattern in the Indian Ocean (positive correlation over western Indian Ocean and negative correlation over the eastern Indian Ocean) are favorable for good monsoon conditions (figure 3.1). The following section analyzes the role of ocean-atmosphere coupled dynamics in the SST evolution of the tropical Ocean.
3.3
Coupled dynamics of the tropical oceans
The figure 3.2 shows the map of (a) SST-rainfall correlation, (b) SST-MLD correlation and (c) SST-D20 correlation at each grid point. During June through September (boreal summer) season (JJAS), most of the tropical regions have positive local correlation
53
Chapter 3. Identify the key regions between SST and rainfall (figure 3.2a). The eastern equatorial Pacific Ocean, eastern tropical Indian Ocean and western equatorial Indian Ocean exhibit the positive correlation between the SST and precipitation. This indicates that the convective radiative feedback which cools the SST through cloud cover is not prominent in the the seasonal scale over these regions. The warm pool regions of western Pacific, South China Sea and Bay of Bengal have negative correlation between SST and Rainfall (Wang et al., 2004). Being the warm pool region, SST is not a limiting factor for convection; therefore, the atmospheric conditions determine the triggering of convection (Graham and Barnett, 1987). The moisture transport and the SST gradient are the major factors driving convection over this region. The atmospheric stratification due to previous convection, subsidence and divergence of surface wind due to teleconnections from other convective regions etc. are the possible limiting factors suppressing convection over warm pool regions. The SST over these regions are dominantly forced through wind evaporation SST feedback and convective radiative feedback. The latent heat flux through evaporation is a dominant factor driving the SST variation through wind-evaporation-SST feedback (Xie et al., 1994). The cloud cover reduces the shortwave radiation reaching the surface and thereby, cool the SST in convective-radiative feedback. Whenever there is absence of convection, more solar influx results in SST warmer than mean.
The top most layer of the ocean is well mixed and hence, exhibits uniformity in the temperature and the salinity within this layer. The regions with negative correlation between SST and MLD (figure 3.2b) indicate either (1) the shallow MLD warms the SST because energy received through surface flux is distributed in small volume of water or (2) deepening MLD cools the SST by the entrainment of subsurface cold water, which implies that mixing plays an important role in the SST evolution in these regions. First case indicate that subsurface dynamics does not play any significant role in the SST evolution of these regions with negative correlation between SST and MLD. Detailed discussion of the mixed layer budget is provided in the next section. Surface mixing have dominent role in the SST evolution over the off equatorial regions of Pacific, Atlantic
54
Chapter 3. Identify the key regions
Figure 3.2: Observed correlation between SST and (a) rainfall, (b) MLD, (c) D20 at each grid point for the summer monsoon season (JJAS) and Indian Ocean. The regions with positive correlation between SST and MLD indicate that deepening (shouling) of MLD results in warm (cold) SST, suggesting that advective processes by Ekman dynamics (upwelling and downwelling) are dominant in these regions. The eastern equatorial Pacific Ocean and the western tropical Pacific Ocean are the regions where dynamical processes control the SST evolution, as indicated by positive correlation between SST and MLD. In the Indian Ocean, both heat fluxes and Ocean dynamics equally contribute to the SST evolution as evident from weak positive/negative SST-MLD correlation coefficient.
The regions with positive correlation between SST and D20 indicate that the thermocline variations primarily determine the SST variations in these regions (Xie et al., 2002; Rao et al., 2002). Upwelling regions (eg. eastern equatorial Pacific) have shallow thermocline and cold SSTs while downwelling regions (eg. western tropical Pacific) have deep thermocline and warm SSTs. The regions of strong Thermocline-SST coupling, as indicated by positive correlation between SST and D20 (figure 3.2c), are (1) eastern equatorial Pacific Ocean, (2) western tropical Pacific Ocean, (3) southeastern tropical Indian
55
Chapter 3. Identify the key regions Ocean and (4) Seychelles Dome region. These analyses confirm the earlier studies on the tropical ocean-atmosphere coupled dynamics (Xie et al., 2002; Rao et al., 2002; Philander, 1990; Rasmusson et al., 1990).
In summary, the SSTs over equatorial eastern Pacific Ocean are strongly driven by thermocline dynamics. Both thermocline dynamics and the atmospheric conditions drive the SST variations in the western tropical Pacific Ocean. The surface mixing does not force the SST variations over this region, which may be because of deep thermocline. Both thermocline dynamics and the surface mixing plays an important role in the SST evolutions in eastern tropical Indian Ocean and western equatorial Indian Ocean, while convective radiative feedback is not significant in the seasonal scale over there. The major focus of this chapter is to evaluate the representation of these features in the CFSv2 model, while the next chapter quantifies their contributions to the seasonal prediction skill of monsoon.
3.4
Simulation of mean features in CFSv2
The northern Bay of Bengal, the Western Ghats of India, the tropical eastern Indian Ocean, the equatorial Western Pacific and the tropical eastern Pacific Ocean are the regions of strong precipitation (figure 3.3a). The model simulates these features reasonably well except the underestimation of land rainfall and the overestimation of ocean rainfall (figure 3.3b). The model overestimates precipitation over the eastern Arabian Sea, the eastern Bay of Bengal, the foot hills of Himalayas, the eastern equatorial Indian Ocean and the equatorial western Pacific (figure 3.3b), meanwhile, underestimates the precipitation over land region especially northwest India, Pakistan, Sri-Lanka and South America. There is strong land-ocean contrast in the precipitation bias (figure 3.3c). These biases are also noticed in many of the CMIP5 models (Sabeerali et al., 2013).
56
Chapter 3. Identify the key regions
Figure 3.3: (a) Seasonal (JJAS) climatology of precipitation from GPCP dataset (Xie et al., 2003), (b) CFSv2 T126 simulation of seasonal (JJAS) precipitation climatology and (c) seasonal precipitation bias of CFSv2 T126
57
Chapter 3. Identify the key regions
Sea surface temperature During JJAS, the sea surface temperature is warm over eastern Arabian Sea, entire Bay of Bengal, tropical eastern Indian Ocean, South China Sea, Philippine Sea and western Pacific including southern Pacific convergence zone (figure 3.4a). CFSv2 T126 model with
Figure 3.4: (a) Seasonal (JJAS) climatological SST in ERSSTv3 dataset (Smith et al., 2008), (b) CFSv2 T126 simulation of seasonal (JJAS) SST climatology and (c) seasonal SST bias of CFSv2 model. February initial condition (FebIC) is able to capture these features (figure 3.4b), but with a warm bias in the eastern Pacific and cold bias over northwest and southwest Pacific and southern and western Indian Ocean (figure 3.4c). There is a narrow band of cold bias over the equatorial central Pacific. A warm bias is observed (figure 3.4c) over South China Sea and Maritime Continent Region (MCR). Pokhrel et al. (2012) point out the role of overestimated latent heat of evaporation in causing these cold SST biases. Zheng et al. (2011) point out the role of underestimated stratus cloud decks over eastern Pacific 58
Chapter 3. Identify the key regions causing penetration of more short wave radiation and warm SST bias.
3.5
Mixed layer heat budget
The SST evolution is mainly driven by mixed layer heat budget and hence, biases in the individual budget-terms can provide a clear idea about the SST biases. The mixed layer temperature at every grid point is forced by surface heat fluxes, horizontal and vertical heat transport as well as the heat diffusion (Equations 2.7 and 3.1 Mendoza et al., 2005). ∂T 1 Qnet − =[ ][ ] − [→ v • ∇SST ] + [D] ∂t ρcp M LD
(3.1)
The diffusion terms are small compared to the other terms and hence, neglected for the simplicity of analysis. All the terms of the equation are multiplied with ρcp to get the W standard unit of energy flux (power) per volume ( m 3 ; Equation 3.2).
ρcp
Qnet ∂T − =[ ] − ρcp [→ v • ∇SST ] ∂t M LD
(3.2)
Mixed layer depth Most of the northern tropical regions have about 50 m MLD, whereas it is about 100m in the south hemisphere, especially in the southern Pacific Ocean (figure 3.5a). But the model simulated deep MLD region extends westward up to the Indian Ocean and near to the Madagascar Island (figure 3.5b), where the observed MLD is less than 120 m. Subduction zones of southern tropical Indian Ocean exhibiting deep MLD bias (figure 3.5c). MLD is overestimated in central Arabian Sea, where the model simulates the MLD deeper than 90 m. On the other hand, MLD is underestimated in northwestern tropical Pacific Ocean (figure 3.5c), where the observed depth is more than 60 m (figure 3.5a) and model could simulate only half of it (figure 3.5b). This causes an overestimation of warming tendency over these regions (figure 3.6), but still the SST have the cold bias (figure 3.4c). Shallow MLD biases observed in northeastern tropical Pacific Ocean, eastern
59
Chapter 3. Identify the key regions
Figure 3.5: (a) Seasonal (JJAS) climatology of MLD in IFREMER dataset (Mont´egut, 2004), (b) CFSv2 T126 simulation of seasonal (JJAS) MLD climatology and (c) seasonal MLD bias of CFSv2 T126
60
Chapter 3. Identify the key regions tropical Indian Ocean (figure 3.5c) also force warming tendencies (figure 3.6b and d). But due to enhanced latent heat lose (figure 3.9c) and thereby reduction of net surface flux (figure 3.11c), the warming has not occurred over eastern Arabian Sea (figure 3.4c).
Temperature tendency The temperature tendency is computed as centered difference using monthly temperature data and made unit conversion as discussed above. The tendency of mixed layer temperature during JJAS is positive over the northern hemisphere and negative over the southern hemisphere (figure 3.6). But all over the Indian Ocean the mixed layer temperature during JJAS has a cooling tendency. Very strong cooling tendency is observed in the
Figure 3.6: The temperature tendency of mixed layer ( W/m3 ) is represented as (a) observed seasonal (JJAS) climatology, (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 eastern Pacific Ocean, which is slightly underestimated in the CFSv2 model. Model also simulates excessive cooling in the equatorial central Pacific ocean. The warm (cold) SST
61
Chapter 3. Identify the key regions bias at tropical southeastern (equatorial central) Pacific Ocean (figure 3.4c) is associated with underestimation (overestimation) of cooling tendency of the region. The model also has a cooling bias in the SST tendency in tropical Indian Ocean (figure 3.4c).
Surface fluxes In general, shortwave radiation is underestimated in tropical Pacific and Indian Ocean, while there is overestimation over northern Indian Ocean and eastern Pacific Ocean. Observed net short wave radiation flux is above 270 W over the northern central pacific (figure 3.7a), which is underestimated in the model (figure 3.7b). Observed net shortwave
Figure 3.7: (a) Observed seasonal (JJAS) climatology of Shortwave radiation (positive towards ocean), (b) CFSv2 T126 simulation and (c) the seasonal bias of CFSv2 T126 radiation is below 200 W over the northeastern tropical Pacific Ocean (figure 3.7a) is overestimated in the model (figure 3.7b and c), due to the absence of cirrus cloud deck simulation (Zheng et al., 2011) and resulted warm SST bias (figure 3.4c). The net short
62
Chapter 3. Identify the key regions wave radiation observed over the southeastern tropical Pacific Ocean is less than 120 W (figure 3.7a) and the model simulates above 220 W net short wave radiation (figure 3.7b). Most of this extra energy received is remitted as net long wave radiation (figure 3.8c) and hence, the flux driven SST tendency (figure 3.11d) is not enough to explain the warm bias (figure 3.4c) over this region. The net shortwave radiation is strongly overestimated over the northern Indian Ocean (figure 3.7c). Net short wave radiation is underestimated in equatorial eastern Pacific, north central Pacific and southwestern Indian Ocean (figure 3.7c). Net long wave radiation flux is almost zero over southeastern tropical Pacific Ocean, northeastern tropical Pacific Ocean, eastern Bay of Bengal and eastern Arabian Sea (figure 3.8a). The model lost about 100 W from southeastern and northeastern tropical Pacific
Figure 3.8: (a) Observed seasonal (JJAS) climatology of longwave radiation (positive towards ocean), (b) CFSv2 T126 simulation of seasonal (JJAS) LW climatology and (c) seasonal LW bias of CFSv2 T126 Ocean in the form of net long wave radiation flux (figure 3.8b). Model also overestimates 63
Chapter 3. Identify the key regions (sign is negative) the long wave radiation flux over Arabian Sea and Bay of Bengal (figure 3.8b) and compensates the overestimated short wave radiation flux (figure 3.7c). The long wave radiation flux over the southwestern tropical Pacific Ocean is faithfully simulated in the model (figure 3.8c). Even if the model simulates the long wave radiation flux over the southeastern tropical Indian Ocean (figure 3.8a and b), the magnitude and the spacial extend of longwave radiation are overestimated over the southern Indian Ocean (figure 3.8b and c). About 200 W of latent heat lose is observed in southern Indian Ocean (figure 3.9a) and the same is overestimated to about 240 W (figure 3.9b) in the model. Similarly,
Figure 3.9: (a) Observed seasonal (JJAS) climatology of latent heat flux (positive away from ocean), (b) CFSv2 T126 simulation of seasonal (JJAS) latent heat flux climatology and (c) seasonal latent heat flux bias of CFSv2 T126 about 120 W (figure 3.9a) is observed over the Arabian Sea and the same over the Bay of Bengal; but they are overestimated to about 200 W by the model (figure 3.9b). About 50 W of latent heat lose observed over northeastern tropical Pacific Ocean is overestimated 64
Chapter 3. Identify the key regions to about 100 W in the model (figure 3.9 a and b). About 150 W latent heat lose over the southern central Pacific is overestimated to about 200 W and extended further eastward up to southeastern tropical Pacific Ocean (figure 3.9a,b and c). Overall, the latent heat of evapouration is overestimated in the model (figure 3.9c). This is the major reason for the cold SST bias in the model (Pokhrel et al., 2012). Observed sensible heat flux is above 20 W over the southeastern Indian Ocean and south western Pacific Ocean on either side of the Australian continent (figure 3.10a). Model simulates this faithfully (figure 3.10b), but the spatial extend is more than what is
Figure 3.10: (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) sensible heat flux (positive away from ocean) climatology and (c) seasonal sensible heat flux bias of CFSv2 T126 observed. Ocean gains the sensible heat over the upwelling regions of western Indian Ocean (figure 3.10a), which is overestimated in the model (figure 3.10b). But the model lose more sensible heat in the central Arabian Sea (figure 3.10c) compared to the observations. Model underestimates the sensible heat lose over the northeastern Pacific and eastern 65
Chapter 3. Identify the key regions equatorial Pacific Ocean (figure 3.10c) compared to the observations. Overestimation of sensible heat lose can be noticed over southern tropical Indian Ocean (figure 3.10c).
Entire northern Indian Ocean is observed to have a net gain of heat flux (figure 3.11a). Overestimated sensible and latent heat lose from Arabian Sea and eastern Bay of Bengal is evident in the model (figure 3.11b and c). Southern Indian Ocean and southwestern Pacific Ocean lose net heat flux from the surface (figure 3.11a), which is overestimated in the model (figure 3.11b). The model also underestimates the heat gain over the northern and equatorial Pacific (figure 3.11c) due to underestimated shortwave radiation flux (figure 3.7c). Net heat flux in the model is having an overall negative bias (figure 3.11c), which lead to a cold bias in the SST.
The seasonal variation of the temperature tendency is dominantly driven by the variations in surface fluxes (figure 3.11). CFSv2 model captures the surface flux forcing reasonably. Overall, Indo-Pacific region has negative bias in the surface fluxes during JJAS (figure 3.11), which is partially responsible for the cold SST biases over Indian Ocean and western Pacific Ocean. The bias in surface fluxes of eastern Pacific force significant cooling tendency, but the model has warm SST biases over this region (figure 3.4c). The mixed layer temperature tendency of southeastern and northeastern tropical Pacific Ocean also exhibits warming biases (figure 3.6c).
Further, the equation 3.2 assumes that the energy received in the form of surface flux are fully utilized within the mixed layer, neglecting the deep penetration of solar radiation below the mixed layer. This assumption holds good in the regions with deep mixed layer while highly stratified regions with shallow mixed layer allow some radiations to penetrate below the MLD (Sengupta and Ravichandran, 2001). The fraction of shortwave radiation (e−
mld λ
; Emden, 1913), which penetrates deeper below the MLD (figure 3.12) is to be
considered in the case of shallow MLD condition to obtain the budget closing. The length of attenuation, λ depends on various factors including the turbulence and chlorophyll content in the water. Considering that the attenuation length is not same for all the 66
Chapter 3. Identify the key regions
Qnet Figure 3.11: Surface heat flux forcing on unit volume of the mixed layer ( M ; W/m3 ) LD (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
67
Chapter 3. Identify the key regions
M LD Figure 3.12: Short wave radiation peneterate below the mixed layer ( Qsw ; W/m3 ) M LD (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
68
Chapter 3. Identify the key regions bands of solar spectrum, a parameterization with a pair of attenuation length (One for the red spectrum and other for the blue-green band) and a coefficient ’R’ (irradiance constant) are used (Paulson and Simpson, 1977; Dickey and Simpson, 1983). In the case of clear fresh water, the irradiance constant is 0.4 and the attenuation lengths are 5.0 m and 40.0 m respectively (Kraus, 1972). Open ocean water has the irradiance constant of 0.58 and the attenuation lengths of 0.35 m and 23 m, while the muddy water of the coastal region has the values 0.78, 1.4 m and 7.9 m respectively (Jerlov, 1968).
I(z) = I(0)[Re
− mld λ
Q(z) = Q(0)[Re
1
− mld λ 1
+ (1 − R)e
− mld λ 2
(3.3)
− mld λ2 2
+ (1 − R)e
∂T Qnet Qsw [Re ρcp =[ ]−[ ∂t M LD
]
− mld λ 1
]
+ (1 − R)e M LD
(3.4) − mld λ2 2
]
− ] − ρcp [→ v • ∇SST ]
(3.5)
The equation 3.2 can be modified to equation 3.5 by considering the deep penetration of solar radiation below the MLD. The global distribution of the budget term for deep penetration (figure 3.12) indicates that it is large enough to remove more than 1 W/m3 radiant energy over some regions of tropical Pacific Ocean, while the CFSv2 model underestimates this term as in the case of net surface fluxes (figure 3.11).
The thermodynamical forcing on MLD represented by the sum of first two terms on Right-Hand Side (RHS) of the equation 3.5 is shown in figure 3.13 which indicates a slightly reduced forcing on the northern hemisphere due to deep penetration of shortwave radiation below the shallow mixed layer. Significant thermodynamical cooling biases are observed over eastern Pacific Ocean, northwestern Pacific Ocean, Arabian Sea and Seychelles Dome regions (figure 3.13c). Even after considering the deep penetration of shortwave radiation below the MLD, the thermodynamical cooling tendency (forcing) persists over the northeastern tropical Pacific Ocean, where the model has warm SST (figure 3.4c) and warming tendency biases (figure 3.6c) for JJAS season.
69
Chapter 3. Identify the key regions
M LD Figure 3.13: Thermodynamical forcing on the mixed layer ( Qnet−Qsw ; W/m3 ) (a) M LD observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
70
Chapter 3. Identify the key regions
Advection The horizontal heat advection in the mixed layer is computed as the dot product of − velocity vector and the temperature gradient ([→ v • ∇SST ]) at individual levels and then averaged along the MLD, while the vertical heat advection is computed at the bottom of −Tmld ). Considering these, equation 3.5 can be modified as the mixed layer (ρcp wmld SST M LD
equation 3.6.
− mld
− mld
Qnet Qsw [Re λ1 + (1 − R)e λ2 ]2 ∂T =[ ]−[ ] ρcp ∂t M LD M LD ∂T ∂T SST − Tmld − ρcp [u ] − ρcp [v ] − ρcp wmld ∂x ∂y M LD
(3.6)
The cooling tendency of equatorial central Pacific Ocean are partially explained by the zonal heat advection (3.14). The zonal heat advection in observations warms the
Figure 3.14: Zonal heat transport forcing on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
71
Chapter 3. Identify the key regions equatorial central Pacific, while cooling on either side of it, but in CFSv2 model, the warming due to zonal heat advection is not present.
There is a positive tendency due to meridional heat advection over the equatorial and southern tropical Indian Ocean (3.15), and the model reasonably simulate this. On the
Figure 3.15: Meridional heat transport forcing on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 other hand, the meridional divergence of heat and associated cooling over equatorial eastern Pacific Ocean is significantly underestimated in the model. This could be one of the key reason for the warm SST bias over the eastern Pacific.
The vertical advection term has a very small magnitude compared to other terms in the heat budget. The vertical advection component brings cold water along the equatorial belt. The vertical advection contributes significantly to the heat budget of the upwelling regions of eastern Pacific Ocean.
72
Chapter 3. Identify the key regions
Figure 3.16: Vertical heat transport forcing on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
73
Chapter 3. Identify the key regions
Entrainment The magnitude of the instantaneous value of vertical advection is quite small, while the entrainment of cold subsurface water due to MLD variations is significant to be considered in the budget. The tendency of mixed layer depth is considered as entrainment velocity (we =
∂M LD ), ∂t
which could be multiplied with temperature gradient between surface
and subsurface (just below MLD) to get the cooling due to entrainment. The Heaviside function Λ (Λ =1 if we > 0 and Λ =0 if we < 0 ) is used to filter out detainment from the entrainment velocity term (Mendoza et al., 2005). Thus, the equation 3.6 can be modified as equation 3.7.
− mld
− mld
Qnet Qsw [Re λ1 + (1 − R)e λ2 ]2 ∂T =[ ]−[ ] ρcp ∂t M LD M LD ∂T SST − Tmld ∂T ] − ρcp wmld − ρcp [u ] − ρcp [v ∂x ∂y M LD ∂M LD SST − Tmld − ρcp [Λ ][ ] ∂t M LD
(3.7)
The entrainment of subsurface water during JJAS season imposes cooling tendency over northwestern Pacific Ocean, Arabian Sea, Seychelles Dome region and the southeastern tropical Pacific Ocean (figure 3.17). The model slightly underestimates the entrainment especially over northwestern Pacific Ocean.
The dynamical terms altogether result in cooling in the equatorial (especially eastern and central) Pacific Ocean, while slight warming is evident elsewhere in the tropical Pacific Ocean (figure 3.18). The mixed layer dynamics in the Indian Ocean results in the south heat transport as evident from the cooling of northern Indian Ocean and warming of southern tropical Indian Ocean (figure 3.18). The dynamical forcing terms result in some cooling bias over equatorial central Pacific and the southeastern tropical Indian Ocean (figure 3.18c). The analysis also indicates that the warming tendency (figure 3.6c) and warm SST bias (figure 3.4c) of southeastern tropical Pacific Ocean may be due to the dynamical terms of mixed layer budget, especially the zonal and meridional advection.
74
Chapter 3. Identify the key regions
Figure 3.17: Entrainment due to MLD variations on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
75
Chapter 3. Identify the key regions
Figure 3.18: Seasonal mean tendency of SST resulting from all the dynamical terms in the MLD budget (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126
76
Chapter 3. Identify the key regions The total budget (right hand side of equation 3.7) shows a warming tendency in the northern Pacific Ocean while there is cooling tendency in the southern Pacific and southern Indian Ocean. There is a warm bias in the total budget, which represents the temperature tendency. The tropical Indo-Pacific SST has an overall cooling tendency except in the northern Pacific (figure 3.6a). Model faithfully captures this feature (figure 3.6b). The
Figure 3.19: The budget total forcing (right hand side of equation 3.7) on unit volume of the mixed layer (a) observations and (b) CFSv2 T126 simulation of seasonal (JJAS) climatology and (c) seasonal bias of CFSv2 T126 flux driven SST warming tendency in northern Indian Ocean and the tropical eastern Pacific (figure 3.11a) is underestimated by the model (figure 3.11b), especially over the central Arabian Sea and eastern Bay of Bengal. Model overestimates the flux driven SST cooling in the southern Indian Ocean and southwestern Pacific (figure 3.11). Flux driven SST warming tendency observed over thermocline dome region (figure 3.11a) is not simulated in the model (figure 3.11b) due to reduced short wave radiation flux (figure 3.7c) as well as enhanced latent and sensible heat fluxes (figure. 3.9c and 3.10c). The dynamical terms (figure 3.18) try to compensate these discrepancies in simulating the flux 77
Chapter 3. Identify the key regions driven SST tendencies. Hence, model simulates enhanced warming tendency driven by coupled ocean-atmosphere dynamics over southern Indian Ocean and southwest Pacific Ocean (figure 3.18b).
3.6
Time evolution of regional heat budget
The snap shot of the mixed layer budget for JJAS season is not sufficient to get clear picture about the sources of the SST biases in the model, because the model is initialized in the month of February and hence, the temperature tendencies of the premonsoon season also can contribute to the SST bias in the model. Regional average budget analysis is carried out for specific regions which are having strong biases in the SST, in order to get further informations about the sources of SST biases in the model. For example, the budget total of all the terms in RHS of equation 3.7 represented in figure 3.19 is not sufficient to explain strong warm bias over the eastern tropical Pacific Ocean (figure 3.4c).
In an earlier study, Murtugudde and Busalacchi (1999) have reported that the regions in northern Indian Ocean (5◦ N to 15◦ N in Arabian Sea and Bay of Bengal) have semiannual cycle in the SST and net heat fluxes, while regions in southern tropical Indian Ocean (10◦ S to 15◦ S) have dominant annual cycle with positive peak in March-April and negative peak in August-September. Even if the northern Indian ocean has semiannual cycle with two positive peaks (one during March-April and another during September-October), the boreal autumn peak (September-October) is lower than the boreal spring peak (MarchApril). This could be due to lower surface shortwave radiation and deep penetration of shortwave radiation below the mixed layer. The transition from winter to summer causes strong entrainment cooling of northern Indian Ocean during early summer (May-June) and the horizontal advection from the upwelling region brings cold water to the Arabian Sea during summer monsoon peak months (July-August). The regions selected in this section are different from those used in Murtugudde and Busalacchi (1999), however, most of the findings of that study are valid irrespective of the choice of the box dimension.
78
Chapter 3. Identify the key regions The heat budget of Arabian Sea (10◦ N to 25◦ N and 50◦ E to 80◦ E) is dominantly driven by surface fluxes along with entrainment, vertical and zonal heat advection (figure 3.20). The surface flux is maximum during spring and autumn, out of which a major portion penetrates below the shallow mixed layer and the resultant causes a heating of about 2 W/m3 . Surface fluxes are less than 1 W/m3 during June-July and become negative during the winter season (from November onwards). Entrainment accounts for a cooling beyond 0.5 W/m3 during June is also accompanied by vertical advection component of almost same magnitude and zonal advection cooling which peeks (about 1 W/m3 ) in July. Over the Arabian Sea region, the budget total at RHS as well as the temperature tendency at Left-Hand Side (LHS) of the equation 3.7 has a semiannual cycle with negative tendency during June-July and the positive peaks during March-April and September (figure 3.20). The surface fluxes have a maximum bias of about -2.5 W/m3 during April-May, while there is about +1 W/m3 bias in the deep penetration of solar radiation. A bias of about 2 W/m3 in cooling tendency based on mixed layer budget is expected during April and reduces gradually to the summer season, but in reality, the temperature tendency have a cooling bias of about 0.5 W/m3 persistant from April to September. This is because of ±0.5 W/m3 uncertainty in the observed and simulated mixed layer budget. The heat budget over the Bay of Bengal (10◦ N to 25◦ N and 80◦ E to 100◦ E) is dominantly driven by surface fluxes, while the entrainment cooling is more or less balanced with warm water advection (figure 3.21). The semiannual cycle of temperature tendency has positive peaks during March-April and September-October with a dip during JuneJuly months. However, the second peak in the observed temperature tendency is almost zero, because the deep penetration of solar radiation below the MLD is almost equal to the net surface flux (Sengupta and Ravichandran, 2001) due to fresh water stratification during late summer and autumn season. The shortwave penetration term (figure 3.21) of the budget during summer (May-October) is computed using the parameterization suggested by Kraus (1972) considering the transparency of fresh water. The vertical advection term is positive during premonsoon and negative during peak monsoon months,
79
Chapter 3. Identify the key regions
Figure 3.20: Monthly time evolution of heating terms in Arabian Sea for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126
80
Chapter 3. Identify the key regions
Figure 3.21: Monthly time evolution of heating terms in Bay of Bengal for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126
81
Chapter 3. Identify the key regions both having magnitude less than 0.5 W/m3 . The surface fluxes have a bias of about -2.5 W/m3 during the month of April which slowly reduces during the summer season, which is expected to cause almost same magnitude of temperature tendency bias. But there is 3 W/m3 uncertainty in the observed budget for April which reduced to less than 1 W/m3 on the following months. The actual bias in the temperature tendency remains within a limit of ±1 W/m3 , which is equivalent to 0.65◦ C through out the period of simulation. Over the western pole of IOD (10◦ S to 10◦ N and 50◦ E to 70◦ E), surface fluxes are reduced (but still positive) during May to September (figure 3.22), where as the model overestimates this variation with negative surface flux during May to July. The entrainment due to MLD variation contributes to a cooling of 0.5 W/m3 during the MayJune months, which is perfectly simulated in the model. The zonal transport tries to cool the ocean and meridional transport converges warm water over the region. The model underestimates both zonal and meridional transport, especially during the peak monsoon month of July with a bias of 0.5 W/m3 . An overall bias of −1.0 W/m3 which accounts for a cooling of about 0.65◦ C/month (especially for June). The surface fluxes have persisting negative bias ranging from 0.5 W/m3 to 1.5 W/m3 which can cause the same magnitude of temperature tendency bias. The maximum expected bias of -2 W/m3 during June, with 1 W/m3 uncertainty in observed budget result in -1 W/m3 bias of temperature tendency in reality. The surface fluxes over east pole of IOD (10◦ S to 0◦ N and 90◦ E to 110◦ E) is almost zero during June through August (figure 3.23), while the model simulates negative values. The entrainment of about 0.4 W/m3 during early summer is very well simulated in the model. The meridional heat convergence of about 0.7 W/m3 , which accounts for about 0.5◦ C warming is also perfectly simulated in the model. The surface fluxes have a persisting bias of about 0.5 W/m3 , while the actual temperature tendency has maximum bias of 0.5 W/m3 during June, which reduces to zero during August.
82
Chapter 3. Identify the key regions
Figure 3.22: Monthly time evolution of heating terms in west pole of IOD for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126
83
Chapter 3. Identify the key regions
Figure 3.23: Monthly time evolution of heating terms in east pole of IOD for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126
84
Chapter 3. Identify the key regions The surface fluxes over Ni˜ no 3.4 (5◦ S to 5◦ N and 210◦ E to 270◦ E) is about 7 W/m3 during March and reduces to 2 W/m3 during June (figure 3.24). The model simulated
Figure 3.24: Monthly time evolution of heating terms in Ni˜ no 3.4 region for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126 surface flux is 5 W/m3 during March and 1 W/m3 during June with a bias of -2 W/m3 . The meridional heat divergence varies between 0.5 W/m3 and 1 W/m3 which accounts for a cooling of about 0.5◦ C/month is a major ingredient of the mixed layer budget of
85
Chapter 3. Identify the key regions Ni˜ no 3.4 region. Zonal transport (0.2 W/m3 ) and entrainment (-0.4 W/m3 ) have there peak magnitude during the month of May. The vertical advection component is about 1 W/m3 during the month of March is reasonably simulated in the model.
Figure 3.25: Monthly time evolution of heating terms in northeastern tropical Pacific (region with warm SST bias in CFSv2) for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126
86
Chapter 3. Identify the key regions
Figure 3.26: Monthly time evolution of heating terms in southeastern tropical Pacific (region with warm SST bias in CFSv2) for (a) observed climatology, (b) CFSv2 T126 simulation and (c) bias of CFSv2 T126
87
Chapter 3. Identify the key regions There is a warm SST bias over northeastern tropical Pacific Ocean (10◦ N to 30◦ N and 210◦ E to 260◦ E) (figure 3.4c), which can be attributed to a bias of 0.4 W/m3 in the temperature tendency (about 0.25◦ C/month) during boreal spring and premonsoon months (figure 3.25). However, this bias could not be explained by mixed layer budget, because there is a spurious gap between the temperature tendency and the budget total due to uncertainties in the surface fluxes. However, the surface flux as well as the temperature tendency has annual cycle with positive peak during June-July. But the deep penetration of shortwave radiation below the mixed layer removes about 1 W/m3 during July-August. Entrainment (0.5 W/m3 during November) results in a cooling tendency during autumn and winter. The model simulated temperature tendency follows the budget total and the net surface flux very well. About -2 W/m3 bias in surface fluxes is expected to cause the same magnitude of bias in the temperature tendency during July-October. But with an uncertainty of 1 W/m3 in the observed budget, the actual magnitude of cold bias during July to October is less than 0.5 W/m3 . The temperature tendency has positive bias of same magnitude during March to June which can accumulate 2 W/m3 results in 1.3◦ warm bias in SST (figure 3.4c). Southeastern tropical Pacific Ocean (20◦ S to 0◦ N and 250◦ E to 290◦ E) including Ni˜ no 1, Ni˜ no 2 and eastern portion of Ni˜ no 3 exhibits warm SST bias in the model (figure 3.4c), which is associated with a bias of 0.5 W/m3 in the temperature tendency (about 0.3◦ C/month) from June to October (figure 3.26). The surface fluxes and temperature tendency have annual cycle with a minimum during boreal summer. The entrainment cooling of 0.5 W/m3 during boreal spring and early summer is reasonably simulated in the model. Due to shallow mixed layer during spring, a part of surface shortwave radiation is penetrated below the mixed layer. However, surface flux and the budget total indicate cooling tendency over the region. This is because of large uncertainty in the surface fluxes. During the spring and early summer season, the surface flux exhibits about -2 W/m3 bias over southeastern tropical Pacific Ocean, which could results in large cooling tendency, but this is due to the uncertainty in the observed budget and hence, the actual bias in
88
Chapter 3. Identify the key regions tendency is not beyond 0.5 W/m3 . The warming bias of 0.5 W/m3 from June to October results in warm SST bias (figure 3.4c).
3.7
Subsurface thermal structure
The above analysis considers only the mixed layer budget without considering the subsurface dynamics. However, the second term in the equation 3.5 deposits some energy to the subsurface ocean. Murtugudde et al. (2002) have reported that the enhancement of subsurface heating can increase mixing by reducing the stratification. Further, the deep mixing reduces surface divergence, upwelling and entrainment. The figure 3.27 describes the simulation of subsurface thermal structure of equatorial Indo-Pacific region. The vertical extend of the Indo-Pacific warm pool is upto 100-150 m in eastern Indian Ocean and western Pacific Ocean, while the thermocline shoals to about 75 m at western equatorial Indian Ocean and to about 50 m at eastern equatorial Pacific Ocean (figure 3.27a). The model simulates overall thermocline depth of equatorial Indo-Pacific reasonably (figure 3.27b), while there are thermocline level warm biases of about 1◦ at Indian Ocean and eastern Pacific Ocean (figure 3.27c) which can cause deep thermocline biases over the region. Therefore, Indian Ocean has cool bias at surface and warm bias at thermocline level. The warm SST bias of eastern Pacific Ocean is due to the extension of thermocline level warm bias of the region.
Thermocline depth The thermocline depth is defined as the depth at which there is strong vertical gradient in the ocean temperature. Usually, it is referred as the depth of 20◦ C isotherm. Subtropical gyre western boundaries of northwest and southwest Pacific Ocean have D20 deeper than 200 m (figure 3.28a), which is well simulated in the model (figure 3.28b). But there is a narrow band of shallow thermocline in the northern tropical Pacific (figure 3.28a), which is overestimated in the model (figure 3.28b). Schott and McCreary (2001) reported that Indian Ocean dynamics are very unique due to strong ocean atmosphere
89
Chapter 3. Identify the key regions
Figure 3.27: (a) Observed seasonal (JJAS) climatology of temperature, (b) CFSv2 T126 simulation of seasonal (JJAS) temperature climatology and (c) seasonal temperature bias of CFSv2 T126
90
Chapter 3. Identify the key regions
Figure 3.28: (a) Observed seasonal (JJAS) climatology of D20, (b) CFSv2 T126 simulation of seasonal (JJAS) D20 climatology and (c) seasonal D20 bias of CFSv2 T126
91
Chapter 3. Identify the key regions coupling and seasonality. Thermocline dome region (figure 3.28a) located to the north of Madagascar Island is simulated in the model (figure 3.28b). This is one of the key spot of strong ocean atmosphere interaction (Rao et al., 2002; Xie et al., 2002; Saji et al., 2006). Observed thermocline is deeper than 150 m to the south of the dome region (figure 3.28a), which is overestimated with a bias of 25 m (figure 3.28c) in the model. About 150 m thermocline depth (figure 3.28a) is captured in the central Arabian Sea (figure 3.28b), while there is a north-south gradient of thermocline bias in the Arabian Sea (figure 3.28c) which could be related to overestimated southward transport of surface water. Shallow thermocline bias is simulated in south central Pacific (figure 3.28c), associated with a diverging surface wind bias (figure 4.2c; discussed in chapter 4).
Heat transport in the tropical Indian Ocean Realizing the importance of the dynamics in the Indian Ocean, 500 m rigid column is considered for the Indian Ocean heat transport in this section, instead of considering the MLD budget discussed in the previous section. Figure 3.27 has shown that there is warm bias of subsurface water near the thermocline of Indian Ocean, in contrast to surface cold bias. The top 500 m depth is considered for the Lateral Boundary Heat Transport (LBHT) in Table 3.1. The dynamics and thermodynamics (using bulk formula for a rigid (Unit: Peta Watt) Arabian Sea Bay of Bengal Southern Tropical Indian Ocean
Observations CFSv2 Observations CFSv2 Observations CFSv2
Q net
LBHT
Net Heat
0.35 0.19 0.15 0.04 -0.51 -1.49
-0.42 -0.67 -0.83 -0.55 1.86 1.19
0.07 -0.47 -0.68 -0.51 1.35 -0.30
Mean SST U nit :o C 28.01 27.26 28.80 28.56 25.36 24.74
Table 3.1: SST forcing terms of Tropical Indian Ocean (top 500 m) averaged for June through September (JJAS). box) of top 500 m depth of Indian Ocean, separately for Arabian Sea, Bay of Bengal and southern tropical Indian Ocean are analyzed on the basis of the importance of subsurface processes. The observed sensible and latent heat fluxes are reported (Yu et al. 2008) to
92
Chapter 3. Identify the key regions have an error of about 3 W/m2 for the boreal summer season and for radiative fluxes the error is 10-15 W/m2 . Seasonal mean SST in the Arabian Sea is underestimated by 0.75◦ C and bias in the Bay of Bengal is only - 0.24◦ C. Model removes more heat through LBHT from Arabian Sea while the surface flux is slightly underestimated. The bias in the net value is negative ( − 0.54 ± 0.35P W ) leading to strong cold bias in the Arabian Sea. On the other hand, both surface flux and LBHT from Bay of Bengal are underestimated and the bias in the net value is positive and it is within the error limits ( 0.17P W ± 0.20P W ) for the Bay of Bengal in the model. In comparison to observations, model removes more (equal or slightly less) heat from the Arabian Sea (Bay of Bengal) hence, cold (neutral) SST bias is observed in the Arabian Sea (Bay of Bengal). All the above findings highlight the fact that the Indian Ocean dynamics are not properly captured in the model leading to strong biases in the model SST in addition to underestimating the net heat fluxes in the atmosphere. In the Arabian Sea, in spite of underestimation of net heat fluxes into the Sea, the meridional heat transport removes more heat from the Arabian Sea and therefore, results in strong cold bias. On the other hand, Qnet is underestimated and less heat is removed from the Bay of Bengal, hence, the SST cold bias is reduced. As far as the equatorial and southern tropical Indian Ocean (30o E:100o E; 30o S:5o N ) is considered, the surface fluxes are computed along with the heat transport through the northern, eastern and southern boundary. The heat lose through the surface fluxes (Table 3.1) is overestimated ( − 0.51P W observed and
− 1.49P W simulated) and heat gain
through eastern boundary is underestimated ( 1.23P W observed and 0.68P W simulated) in the model, while heat transport through northern and southern boundaries are more or less the same (bias of the order 0.01PW) as that of observations. Overestimated heat transport from Arabian Sea ( 0.42P W observed and 0.67P W simulated) is balanced by the underestimated heat transport from the Bay of Bengal ( 0.83P W observed and 0.55P W simulated). Hence, the cold SST bias ( − 0.62o C) in the equatorial and southern tropical Indian Ocean is due to enhanced surface heat lose and reduced eastern boundary heat transport.
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3.8
Ocean dynamics and SST evolution in CFSv2
The SST-precipitation relationship is positive throughout the tropical Indo-Pacific region except some parts of the northwestern Pacific, south China Sea and Bay of Bengal (figure 3.2a). The positive correlation is overestimated in the model (figure 3.29a) compared to observations. This may be due to cold SST bias over the warm pool regions. In observations, the SST is not a limiting factor for convection over the warm pool region. But in the model, the SST-precipitation positive relationship is spread to more area because SST is a limiting factor due to cold bias. However, it is a good sign that the model is still able to represent the negative correlation over the sensitive regions.
Figure 3.29: CFSv2 simulated correlation between SST and (a) rainfall, (b) MLD, (c) D20 at each grid point for the summer monsoon season (JJAS)
The CFSv2 model reasonably simulates the regions with negative correlation between SST and MLD (figure 3.29b). The Arabian Sea, the Bay of Bengal, the South China Sea and the north central Pacific have strong negative correlation which indicates that mixing with deep (shallow) MLD cools (warms) the SST over these regions. However, the model overestimate the relationship as indicated by significant negative correlation over more area. Especially, over the Indian Ocean, the model simulates strong negative correlation 94
Chapter 3. Identify the key regions between SST and MLD in contrast to observations (figure 3.29b). This indicates strong mixing to deep MLD cools SSTs in this region, which could be one of the reason for the cold bias in northern Indian Ocean.
The CFSv2 coupled model is very good in simulating the regions with strong thermoclineSST coupling as indicated by positive correlation between SST and D20 (figure 3.29c). As in the observations (figure 3.2c), the model also has strong thermocline SST coupling over the equatorial eastern Pacific, the tropical western Pacific, eastern tropical Indian Ocean and western equatorial Indian Ocean (figure 3.29c). However, the relationship in the Phillipine Sea in the tropical western Pacific is a little weaker compared to the observations.
Consistent with observations, the CFSv2 model is able to capture ocean-atmosphere coupled dynamics over the eastern Pacific. The model overestimates the MLD-SST negative correlation and SST-precipitation positive correlation over the tropical ocean, especially the Indian Ocean. However, the sensitive key spots are properly represented in the model. The eastern equatorial Pacific, the western tropical Pacific, eastern tropical Indian Ocean and western equatorial Indian Ocean are the identified key regions of ocean-atmosphere coupled dynamics sensitivity to the SST evolution. The role of SST anomalies in the seasonal mean monsoon simulation is discussed in the next section.
3.9
Monsoon related Indo-Pacific SSTs in CFSv2
The CFSv2 model is able to capture this large scale spatial pattern (figure 3.30b) of observed correlation over the Pacific Ocean. However, compared with observations the Pacific ENSO is strongly coupled to AISMR in CFSv2 model. Several studies in recent times have highlighted that the central Pacific warming (El-Ni˜ no Modoki) is more conducive to force drought condition over India (Kumar et al., 2006) compared with eastern Pacific warming (canonical El-Ni˜ no). However, many coupled models failed to capture this relationship (Wang et al., 2015). CFSv2 also suffers from the same limitation by 95
Chapter 3. Identify the key regions
Figure 3.30: CFSv2 simulated correlation of SST with All India Summer Monsoon Rainfall Index which strong negative correlation between AISMR and SST is concentrated over the eastern Pacific. In contrast to observations, the CFSv2 model shows positive correlations all over central/eastern Indian Ocean and a negative correlation along the strong cross equatorial wind path. The positive correlation over the western Pacific warm pool region is very strong compared with observations. This suggests that in CFSv2, a negative dipole like structure in the Indian Ocean and La-Ni˜ na like condition in the tropical Pacific will enhance precipitation over India. This indicates that in CFSv2, the monsoon teleconnections over the tropical Indian Ocean are exactly opposite to the observed teleconnections.
3.10
Conclusion
The dynamical ocean atmosphere coupling in Pacific Ocean is very much important for the SST evolution. Atmospheric fluxes and Ocean dynamics play equal role in determining SST evolutions in the Indian Ocean. CFSv2 coupled model exhibit dry bias over the land region and wet bias over the oceanic region. Overall cold SST bias is noticed over IndoPacific region, while eastern Pacific Ocean exhibit warm bias. Thermocline level warm bias is also noticed over the eastern Pacific Ocean and it is is strongly associated with SST bias over there. In contrast to the surface cold bias over the Indian Ocean, subsurface temperature profile of the Indian Ocean displays strong warm bias at the thermocline level. Tropical SSTs in the Indian Ocean and Pacific Ocean are important in forcing the AISMR while tropical Atlantic ocean does not display any significant association with the interannual variations of AISMR. The Indian Ocean SST teleconnections with AISMR are 96
Chapter 3. Identify the key regions wrongly captured in CFSv2. The following chapters describe how the biases in the coupled system impact the simulation and prediction skill of AISMR in CFSv2 model. Chapter 4 mainly addresses the seasonal prediction skill of AISMR, while chapter 5 addresses the relationship among ENSO, IOD and monsoon. Chapter 5 also focuses on the probable reason for misrepresentation of the IOD-monsoon relationship.
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Chapter 4 Seasonal prediction and simulation of all Indian summer monsoon rainfall 4.1
Introduction
Recent studies (Kumar et al., 2005; Preethi et al., 2010; Rajeevan et al., 2012) have reported that the potential predictability of AISMR is yet to be reached, even if there is a progress in the multi model ensemble anomaly correlation coefficient from 0.28 (DEMETER; Palmer et al., 2004) to 0.45 (ENSEMBLE; Hewitt, 2004) in ocean atmosphere coupled general circulation models, initialized with May initial condition for a period 1960-2005. It is important to study how to further improve the AISMR prediction skill in coupled models.
The CFSv2 coupled model have very good seasonal prediction skill of monsoon (≈ 0.5) and the representation of ENSO-monsoon relationship (George et al., 2015; Chattopadhyay et al., 2016; Ramu et al., 2016). However, cold biases in the warm pool regions of the northern Indian Ocean, which leads to the overall weakening of monsoon system and cause dry bias over the Indian Land region. The precipitation annual cycle over the Indian land region exhibits a sharp increase in rainfall during May and gradual decrease from September. Even if the model simulates the phases of the annual cycle, its am-
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Chapter 4. Seasonal Prediction and Simulation of AISMR plitude is underestimated (Ramu et al., 2016). Sharmila et al. (2013) reported that the northward propagating MISO is well captured in fully coupled configuration of CFSv2 model, while the stand alone AGCM part of CFSv2 does not simulate it due to lack of active ocean-atmosphere coupled interactions. This along with similar earlier experiments (Achuthavarier et al., 2012; Wu and Kirtman, 2004; Kumar et al., 2005) reveal the limitation of tier-2 modeling strategy against tier-1 modeling strategy in which the SST is evolved within the model. Zhu and Shukla (2013) has conducted hindcast experiment to compare the performance of tier-1 and tier-2 strategies in the seasonal prediction of Asia-Pacific summer monsoon.
However, these studies are not sufficient to distinguish the role of individual basin on the seasonal prediction skill of monsoon. Further, it is important to understand the role of dynamical ocean-atmosphere coupling apart from the thermodynamical part of oceanatmosphere coupling on the seasonal prediction of monsoon. The relative importance of the Indian Ocean and Pacific Ocean dynamics (along with ocean-atmosphere coupled dynamics) on the interannual variability and seasonal prediction of the AISMR, are analysed in this chapter, using a set of sensitivity experiments with CFSv2 coupled model. The model description and the experiment strategies are discussed in chapter 2, while the chapter 3 provides a detailed discussion of the SST and rainfall biases in CFSv2 coupled model. The seasonal mean SST in tropics clearly displays warm SSTs (>28◦ C) over the well known Indo-Pacific warm pool region (figure 4.1a) and CFSv2 coupled model simulates the SST distribution reasonably with a cold bias of 0.5◦ over the warm pool (figure 4.1c). In spite of cold SST bias (figure 4.1c), the model overestimates rainfall over oceanic regions (figure 4.1d), probably due to the fact that the SSTs over these regions are still above the critical SST (27.5◦ C) for convection to occur. Contrary to this, the rainfall over land regions is underestimated (figure 4.1d).
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Figure 4.1: Seasonal (JJAS) Mean, Observed Climatology (top row), CTL bias (second row), difference between ISLAB and CTL (third row) and difference between PSLAB and CTL (bottom row) for SST (left panel) and Precipitation (right panel)
4.2
Observed convection, circulation and tropospheric temperature
Wind at 850 hPa Southeasterly trade winds in the southern tropical Indian Ocean cross the equator over the western Indian Ocean and form the southwesterly MLLJ in the northern hemisphere (Joseph and Raman, 1966; Findlater, 1966), which converges over the south Asian and the east Asian monsoon domains (figure 4.2a). The cross equatorial flow and the MLLJ are integral part of the unique monsoon circulation. The tropical easterlies observed over the Pacific Ocean are supported by strong anticyclonic circulations observed over northeastern and southeastern Pacific Ocean (figure 4.2a).
However, the model underestimates the monsoon circulation, as indicated by an anticyclonic wind bias over the south Asian monsoon domain (figure 4.2b). Further, the
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Figure 4.2: (a) Observed seasonal (JJAS) climatology of 850 hPa wind, (b) seasonal 850 hPa wind bias of CFSv2 T126 (c) ISLAB and (d) PSLAB runs.
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Chapter 4. Seasonal Prediction and Simulation of AISMR southwesterly bias in the southern tropical Indian Ocean results in weakening of the zonal component of the southeasterly mean flow over the region. This results in a convergence bias of surface wind over the southeastern Indian Ocean.
Further, as discussed in section 3.5 of chapter 3, the equator-ward bias in the meridional component of surface wind over the southern Indian Ocean and the southern central Pacific region (figure 4.2c) drive sensible heat loss from the ocean (figure 3.10c).
Walker and Hadley circulation features Indo-Pacific Walker circulation has a convective branch over the Indian Ocean and western Pacific Ocean, which extends up to the dateline and a subsidence branch there after, which extends up to the Peru coast (figure 4.3a). Observed monsoon-Hadley circulation (zonal averaged over the monsoon domain: 70◦ E to 90◦ E) has two convective arms located between 15 degree North and 5 degree South (figure 4.3e) representing the symmetric and the asymmetric modes of monsoon-Hadley circulation. The asymmetric mode with convection over the northern hemisphere and subsidence over the southern hemisphere (figure 4.3e) is stronger than the symmetric mode having convection over the equator and subsidence away from the equator.
Tropospheric temperature The observed temperature of the troposphere is warmer over the northern latitudes compared to the equatorial region and the southern latitudes (figure 4.4a) in feedback to the asymmetric mode of monsoon-Hadley circulation (figure 4.3e) with convection over the northern latitudes. The structure of TT and associated circulation resemble a Rossby wave pattern in response to tropical heat source asymmetric to the equator (Gill, 1980; Matsuno, 1966). This plays an important role in driving the large scale monsoon circulation (Goswami et al., 2006; Xavier et al., 2007; Sabeerali et al., 2012).
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Chapter 4. Seasonal Prediction and Simulation of AISMR
Figure 4.3: Seasonal (JJAS) mean, observed climatology (top row), CTL bias (second row), difference between ISLAB and CTL (third row) and difference between PSLAB and CTL (bottom row) for Walker (zonal-vertical) circulation averaged between 5◦ S to 5◦ N (left) and Hadley (meridional-vertical) circulation averaged between 70◦ E to 90◦ E (right)
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Chapter 4. Seasonal Prediction and Simulation of AISMR
Figure 4.4: Tropospheric temperature averaged between 600 hPa and 200 hPa (shaded) overlaid with wind vector at 850 hPa for (a) observations, (b) CTL run, (c) ISLAB run and (d) PSLAB run
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4.3
Model simulation of convection, circulation and tropospheric temperature
Northeasterly wind bias in east Asian monsoon domains and the southwesterly bias over southern tropical Indian Ocean as well as enhanced equatorial westerlies in the Indian Ocean (figure 4.2b) drive convective bias in the eastern equatorial Indian Ocean (figure 4.3c). The model overestimates the symmetric mode of monsoon-Hadley circulation (figure 4.3f) due to strong convection of equatorial Indian Ocean (figure 3.3c), and underestimates asymmetric mode (figure 4.3f), thereby results in dry bias over the Indian land region (figure 3.3c). The overestimation of symmetric mode of monsoon-Hadley circulation is also associated with overestimation of convective limb of Walker circulation (figure 4.3c) over the Indian Ocean. The convection over the Indian landmass has reduced in the model (figure 4.3f) compared to observations (figure 4.3e), through the Rossby response to the Walker circulation pattern, thereby provides positive feedback to the symmetric Hadley circulation. The anticyclonic wind bias located over the south Asian monsoon domain in the model (figure 4.2b), results in the weakening of MLLJ (figure 4.2b), as a feedback to weakening of asymmetric monsoon-Hadley circulation.
The large-scale thermal structure of the troposphere is represented in the model (figure 4.4b), but there is an underestimation of TT (figure 4.4 b) in feedback to weak simulation of the asymmetric mode of monsoon-Hadley circulation. Saha et al. (2013) has shown that the CFSv2 model exhibits a strong dry and a cold bias in the upper troposphere over the Indian subcontinent and therefore, weakens the TT. The representation of realistic precipitation (stratiform/convective) over the Asian monsoon region is important for the realistic representation of tropospheric temperature (Sabeerali et al., 2015; Saha et al., 2013). However, this is a common limitation for most of the coupled models participated in CMIP5 (Sabeerali et al., 2015).
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Chapter 4. Seasonal Prediction and Simulation of AISMR The westerly wind bias in the equatorial western Pacific (figure 4.2b) drives divergence and subsidence over the Maritime Continent region (figure 4.3c). The model underestimates convection over the maritime continent region (figure 4.3b), while overestimates the convection over either side (figure 4.3c). The model has land-ocean contrast in the precipitation bias (figure 4.1d), which is also clearly evident in the Walker circulation. The upward limb of the Walker circulation over the central/eastern equatorial Indian Ocean is stronger in the model(figure 4.3b), compared to observations (figure 4.3a) whereas reduced convection is noticed over the Maritime continent (figure 4.3b). Similarly, land region of the south America also has a convective arm (figure 4.3a) in observations, but weaker in the model simulation (figure 4.3b).
4.4
Sensitivity experiments
Two sensitivity experiments were carried out in order to understand how coupled dynamics in each ocean basin (Indian Ocean and Pacific Ocean) contribute to the seasonal prediction skill of AISMR. The ocean dynamics (along with ocean-atmosphere coupled dynamics) is switched off at a particular basin using 50 m uniform slab ocean, provides SST purely driven by thermodynamical processes alone. Section 3.5 of chapter 3 describes the role of various factors on the evolution of mixed layer temperature. The thermodynamical forcing on the mixed layer shown in figure 3.13 indicates warming tendency at the northern hemisphere and cooling tendency at the southern hemisphere during boreal summer season, while the model has an overall negative bias in thermodynamical forcing over the Indo-Pacific region. The sum of all dynamical terms shown in figure 3.18 indicate warming tendency at the southern tropical Indian Ocean and cooling tendency at the northern tropical Indian Ocean as well as the eastern/central equatorial Pacific Ocean. The dynamical terms have overall warming tendency bias at the tropical Indo-Pacific region except at the equatorial central Pacific.
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M LD Figure 4.5: Thermodynamical forcing ( Qnet−Qsw ; W/m3 ) on an off line 50m uniform M LD slab ocean forced with seasonal (JJAS) climatology fluxes from (a) observations and (b) CFSv2 T126 simulation
During boreal summer season, warm sea surface temperature is observed over the Bay of Bengal, which is strongly influenced by surface fluxes (Shenoi et al., 2002), while the vertical mixing and the shallow meridional overturning circulation in response to the monsoon wind cools the Arabian Sea (Levitus, 1988). In ISLAB, warming at the northern Indian Ocean and cooling at the southern tropical Indian Ocean (figure 4.1e) are expected, due to absence of meridional heat transport, which is an integral part of the Indian Ocean dynamics during boreal summer season. In PSLAB experiment, a perennial El-Ni˜ no type bias (figure 4.1g) is expected, due to absence of dynamical cooling of the equatorial eastern Pacific Ocean. The figure 4.5 shows an off line (uncoupled) slab ocean model, which considers 50 m uniform MLD instead of spatial and temporal variations of MLD used in figure 3.13. Even if this gives a rough estimate of the thermodynamical budget in the observations and model simulation, the coupled thermodynamical feedback (when slab ocean is coupled with the atmosphere component) further modifies the budget. However, most of the SST features over the slab region, in contrast to the control run (figure 4.1c,e,g), are evident in figure 4.5 as well. This is a supporting evidence to analyze and evaluate the sensitivity of model SST bias. The coupled sensitivity experiment is
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Chapter 4. Seasonal Prediction and Simulation of AISMR conducted to distinguish the role of ocean-atmosphere coupled dynamics, apart from thermodynamical feedback, on the simulation and prediction of AISMR. The details of the sensitivity experiments are already described in chapter 2 and results from these sensitivity experiments are discussed in the following sections. It is important to address model biases in sensitivity experiments, before discussing the details of interannual variation of AISMR and its seasonal prediction skill.
4.4.1
Indian Ocean slab experiment
ISLAB, compared with CTL run, shows colder (warmer) SST in the southern (northern)tropical Indian Ocean (figure 4.1e), due to lack of southward heat transport in absence of Indian Ocean dynamics. The cold SST bias over the equatorial central Pacific has increased in the ISLAB run (figure 4.1e) and cold biases over the northwest and southwest Pacific remain unchanged (figure 4.1e).These results indicate that the Indian Ocean coupled dynamics has a crucial role in determining the SSTs over the southern Indian Ocean and the equatorial central Pacific, whereas the Indian Ocean coupled dynamics have no significant role in deciding the SSTs over northwest and southwest Pacific (as cold biases remain same in these regions). In response to the reduced SST bias over the northern Indian Ocean, the dry bias over the Indian landmass has decreased in the ISLAB run (figure 4.1f). By prescribing the slab in the Indian Ocean and calculating the SST simply from the net heat flux, it is clear that in absence of active coupled dynamics, SST in the northern tropical Indian Ocean exhibits slight warm bias (due to absence of southward heat transport) in contrast to the cold bias in the CTL run. This study confirms that SSTs in the northern Indian Ocean are primarily determined by surface heat fluxes, as suggested by Shenoi et al. (2002), whereas, realistic active coupled dynamics in the Indian Ocean are important in determining the correct SSTs over the southern Indian Ocean. The SSTs over southern tropical Indian Ocean are dominantly determined by the meridional (and zonal) advection of warm surface water southward (westward), through the northern (eastern) lateral boundary of the region. Chapter 3, especially section 3.7,
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Chapter 4. Seasonal Prediction and Simulation of AISMR provides detailed discussion about the heat budget of Indian Ocean.
4.4.2
Convection, circulation and tropospheric temperature in ISLAB
In absence of the Indian Ocean dynamics, ISLAB exhibits warm SSTs over the warm pool region of northern Indian Ocean. As a result of the improved SST and increased meridional gradient of SST, the asymmetric-monsoon-Hadley circulation is improved in ISLAB (figure 4.3g) and also results in the warming of troposphere (figure 4.4c) due to improved convection and latent heat release to the atmosphere. The improvement of TT provides positive feedback to the asymmetric monsoon-Hadley circulation.
As a result of the improved asymmetric monsoon-Hadley circulation and better simulation of TT, convective bias over the equatorial Indian Ocean reduces in ISLAB which results in improvement of walker circulation. Improvement of atmospheric circulation completely removes westerly wind bias over the western Pacific Ocean (figure 4.2c), restores the convection over the maritime continent region (figure 4.3c) and reduces overestimation of convection over the western Pacific Ocean.
4.4.3
SST precipitation lead lag relationship
The SST–precipitation lead–lag relationship (figure 4.6) in the warm pool regions (the Bay of Bengal, the eastern equatorial Indian Ocean and the northwest Pacific) is negative (Wang et al., 2004) and the model failed to capture this relationship due to strong cold bias (up to −1◦ C). Wang et al. (2004) reported this as a common limitation for most of the state-of-the-art coupled climate models. However, due to better simulation of SSTs in the warm pool regions in ISLAB, the lead–lag relationship of air–sea interaction is reasonably simulated. This further highlights the importance of active thermodynamical flux interaction of the Asia-Pacific monsoon domain, which is not possible in a tier-2 modeling strategy of sensitivity experiment.
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Figure 4.6: Lead lag (-20 day to +20 day) correlation between SST and precipitation at each grid point for Observations (left panel), CTL run (middle panel) and ISLAB (right panel)
4.4.4
Pacific Ocean slab experiment
In absence of the Pacific Ocean coupled dynamics (Dommenget, 2010), enhancement of magnitude and spatial extent of warm bias over the eastern equatorial Pacific Ocean (figure 4.1g), resembles a perennial El-Ni˜ no condition. This is due to absence of upwelling along the coast of Peru and the eastern equatorial Pacific in PSLAB run. During an ElNi˜ no event, the associated teleconnections force a warming over the western Indian Ocean and the Arabian Sea (Murtugudde and Busalacchi, 1999; Venzke et al., 2000). Similar warming associated with perennial El-Ni˜ no type bias is noticed in PSLAB run. Similarly, strengthening of cold bias over northwest and southwest Pacific Ocean (figure 4.1g) is due to absence of the dynamics associated with subtropical gyre and western boundary current. The perennial El-Ni˜ no type bias in PSLAB run further enhances dry bias over the Indian land region and there exists a wet bias over the oceanic region all along the equatorial belt (figure 4.1h). This response is due to the well-known ENSO-monsoon teleconnections, wherein El-Ni˜ no condition over the Pacific forces subsidence over the Indian land region (Rajeevan and Pai, 2007, and the references therein).
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4.4.5
Convection, circulation and tropospheric temperature in PSLAB
PSLAB exhibits westerly wind bias all along the equatorial Pacific Ocean (figure 4.2d), due to perennial El-Ni˜ no condition (figure 4.1g). Strong easterly bias at about 10◦ N to 20◦ N over the Arabian Sea and associated anticyclonic circulation to the north of it, indicates subsidence over the monsoon domain.
Walker circulation (figure 4.3d) indicates strong convection over the equatorial central and the eastern Pacific and subsidence over the equatorial Indian Ocean. Strong subsidence bias over 5◦ S (figure 4.3h) indicates the suppression of symmetric mode of the monsoon-Hadley circulation due to strong influence of Walker circulation. Suppression of convection affect both symmetric and asymmetric modes of monsoon-Hadley circulation (figure 4.3h). All these impacts result in weakening of monsoon circulation and enhanced dry bias over the Indian land region (figure 4.1h).
4.5
Interannual variation of all India summer monsoon rainfall Experiment OBS CTL ISLAB PSLAB
Mean 6.9 4.5 6.5 2.8
SD 0.62 0.5 0.36 0.25
ACC Ref 0.53 0.51 0.14
Table 4.1: Simulation of all India summer monsoon rainfall in CTL, ISLAB and PSLAB experiment.
The evolution of the AISMR over years (figure 4.7) shows that the magnitude of rainfall is underestimated in CFSv2 compared to observations. The magnitude of AISMR in ISLAB run is comparable with observations (figure 4.7). In PSLAB run, magnitude of AISMR is exceptionally weak (figure 4.7).
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Figure 4.7: Interannual variation of All India Summer Monsoon Rainfall in observations, CTL, ISLAB and PSLAB
Figure 4.8: Taylor diagram shows the all India summer monsoon rainfall prediction skill in CTL, ISLAB and PSLAB run
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Chapter 4. Seasonal Prediction and Simulation of AISMR ISLAB run predicts the correct sign of the anomaly (even if magnitude varies) in most of the flood years (anomaly > 0.2mm/day; eg. 1983, 1988, 1990, 1994, 1995, 1996, 1998, 2007). Most of strong monsoon years forced by La Nina condition are simulated better in ISLAB run compared to CTL. This indicates that the teleconnections of Pacific are better captured with a slab in the Indian Ocean, compared to CTL. ISLAB run predicted correct phase of AISMR anomaly in half of the drought years (anomaly < -0.2mm/day; 1982, 1987, 2001, 2002, 2004, 2009).
On the other hand, the drop in the AISMR prediction skill in ISLAB run is mainly due to its failure in predicting the phase of the AISMR anomaly in a few of the drought years (1986, 1991, 1992, 1999, 2000). Among these, 1986 and 1999 (negative IOD years) are correctly predicted in CTL run, while 1991, 1992 and 2000 are common failure for all the runs. There are two flood years (1984 and 2008) which ISLAB run failed to predict the correct phase. Out of them, 2008 is positive dipole years, predicted correctly in CTL run, while 1984 is common failure for both CTL and ISLAB. The AISMR phase of 1988, 1994, 1995, 1998, 2007 is successfully predicted in both CTL and ISLAB runs. The correct phase of drought years 1987 and 2002 is captured in both CTL and ISLAB, even if the magnitude of AISMR anomaly is not captured.
Ramu et al. (2016) have reported that the common failure years are due to excessive ENSO-monsoon teleconnections in the model. The observed ENSO-monsoon relationship after removing the failed years, are stronger than the relationship computed for the total period, which indicates that the model fails to predict AISMR anomalies of those years which are limiting the ENSO-monsoon relationship in observations (Ramu et al., 2016).
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4.6
Seasonal prediction skill of all India summer monsoon rainfall
Even though the seasonal mean rainfall over the Indian land region has improved in absence of Indian Ocean dynamics, the Anomaly Correlation Coefficient (ACC) of AISMR is not improved in ISLAB run (0.51) compared to the CTL run (0.53) (figure 4.8). This indicates that proper Indian Ocean dynamics is essential for further improvement of AISMR seasonal prediction skill in the model. On the other hand, ACC of AISMR in PSLAB run (0.14) is dropped significantly compared to other runs. This further indicates that, the major portion of the AISMR prediction skill in CFSv2 model results from ENSOmonsoon relationship. The observed interannual variance of AISMR anomaly is about 0.36 mm2 /day 2 while the same is 0.25 mm2 /day 2 in CTL run (70% of OBS). In ISLAB run, the interannual variance drops to 0.125 mm2 /day 2 (50% of CTL and 35% of OBS) which suggests that Indian Ocean coupled dynamics is essential for remaining 35% to 65% of AISMR variance. Similarly, the interannual variance drops to 0.06 mm2 /day 2 (20% of CTL and 17% of OBS) in PSLAB run, indicating that Pacific Ocean coupled dynamics is essential for the remaining 50% to 80% of AISMR variance in CFSv2 model. In order to make better predictions, the ENSO–monsoon teleconnections and Indian Ocean SST–monsoon teleconnections should be better represented in the model.
4.7
Conclusion
This result could be interpreted in two different ways: (1) in the CTL run, Indian Ocean coupled dynamics is not simulated reasonably; hence, the convection over the equatorial Indian Ocean overestimates and results in subsidence over the Indian landmass through modulation of local Hadley cell (figure 4.3); (2) in ISLAB run, the northern (southern) Indian Ocean exhibits warm (cold) bias due to absence of meridional heat transport and results in enhanced (suppressed) convection over the northern (southern) Indian Ocean (figure 4.3). Both of the above interpretations suggest that proper representation of
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Chapter 4. Seasonal Prediction and Simulation of AISMR air–sea heat fluxes and ocean dynamics in the tropical Indian Ocean can improve AISMR simulation.
The Pacific Ocean teleconnections are the major contributor for the seasonal prediction skill of AISMR in CFSv2 as indicated by significant drop of anomaly correlation coefficient of AISMR in PSLAB run compared to CTL run and ISLAB run. On the other hand, due to the reduction of cold bias in the warm pool regions in absence of dynamics in the Indian Ocean, ISLAB run simulates the climatological mean monsoon rainfall of the season better than any other run. The dynamics in both of the basins are contributing to the interannual variance of AISMR. Further, it is essential to understand the probable reasons for the misrepresentation of Indian Ocean SST-monsoon relationship in CFSv2 and hence, it is addressed in chapter 5.
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Chapter 5 Relationship among ENSO, IOD and AISMR 5.1
Introduction
Earlier studies starting from Walker (1925) have identified southern oscillation as one of the potential predictor of the Indian monsoon. Further, Bjerknes (1969) reported that El-Ni˜ no and southern oscillation are oceanic and atmospheric signatures of the oceanatmosphere coupled climate mode known as ENSO. The ENSO-Monsoon relationship (Kumar et al., 1999) is one of the major factors driving the interannual variations of the monsoon system. The dynamics in the Indian Ocean (Saji et al., 1999; Webster et al., 1999) also has significant role in determining the AISMR anomaly (Ashok et al., 2001; Rao et al., 2009). There are many cases (eg. 1994, 1999, etc) in which the active coupled dynamics in the Indian Ocean suppresses the ENSO-monsoon relationship. Several previous studies (Izumo et al., 2010; Webster and Hoyos, 2010; Yoo et al., 2010; Luo et al., 2010, and references there in) describe the interrelationship between the Indian Ocean and the Pacific Ocean through the Atmospheric bridge. The seasonal prediction of AISMR in CFSv2 model mainly depends on the Pacific Ocean coupled dynamics as described in the chapter 4 while the dynamics in the Indian Ocean is not contributing much to the AISMR prediction skill in the model. The probable reasons for this limitation are discussed in
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Chapter 5. Relationship among ENSO, IOD and AISMR the this chapter, on the basis of relationship among ENSO, IOD and monsoon. During boreal summer season, the IOD signal is more prominent over the eastern pole (Ajayamohan and Rao, 2008; Rao et al., 2009) and hence, the SST in IOD East Pole region (IOD-EP) during JJAS is used to represent the phase of IOD in this study. The successful prediction of IOD events on 2006 and 2007 using SINTEX-F coupled model (Luo et al., 2007, 2008) gives strong confidence to understand the ocean-atmosphere coupled dynamics involved in the genesis, maintenance and termination of IOD (Rao et al., 2009). The upwelling Rossby waves approaching the east African coast are reflected in the form of equatorial upwelling Kelvin waves propagate eastward and shoals the thermocline of the southeastern tropical Indian Ocean. Variations in strength of ITCZ as well as its meridional asymmetry during boreal spring and early boreal summer modulate alongshore wind of Sumatra/Java region, which drives thermocline variations as well as the surface fluxes to initiate the genesis of IOD event. Ocean-atmosphere coupled mode of eastward propagating intraseasonal oscillation, known an Madden-Julian oscillation (MJO), can trigger thermocline variations and equatorial waves at the southeastern Indian Ocean, during early boreal summer and this also results in the genesis of a dipole event (Rao et al., 2009).
5.2
Indo-Pacific Climate Indices
The mean simulation, interannual variance and seasonal prediction skill of Indo-Pacific climate indices in CFSv2 during the boreal summer season are analyzed (Table 5.1) before going into the details of their interactions and teleconnections. The observed mean SST over the Ni˜ no 3.4 region is about 27◦ C and the model simulates almost the same temperature. The observed mean SST over IOD-EP is about 28.5◦ C, while the model has a mean cold bias of about 0.5◦ C in this region. The seasonal prediction skill of early developing phase of ENSO during JJAS is represented by the ACC of 0.61 while the prediction skill of SST over IOD-EP is represented by the ACC of 0.2 for the boreal summer season. These values suggest that the model has a good skill in predicting the
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Mean SD ACC
Ni˜ no OBS 27.13 0.77 0.61
3.4 CTL 26.79 0.68
IOD-EP OBS CTL 28.59 27.93 0.41 0.3 0.2
Table 5.1: Model simulation of ENSO and IOD early phase of ENSO development while it have some limitation in the seasonal prediction of the early phase of IOD development.
5.3
Teleconnections
The observed correlation coefficient of -0.53, between Ni˜ no 3.4 and AISMR indicates that the El-Ni˜ no condition suppresses the monsoon rainfall over India (Table 5.2; Walker, 1925; Bjerknes, 1969). The positive phase of IOD is reported to be favourable for monsoon rainfall over India (Saji et al., 1999; Webster et al., 1999; Ashok et al., 2001). Since positive anomaly of IOD-EP SST during JJAS indicates the devloping negative phase of IOD (Rao et al., 2009), it has a negative correlation (-0.16; Table 5.2) with AISMR and this relationship is crutial during recent years (eg. 1994, 1997) in view of weakening of the ENSO-monsoon relationship (Kumar et al., 1999). In general El-Ni˜ no is associated with basin wide warming of Indian Ocean, while it shows cooling in the IOD-EP in some cases of ENSO-IOD co-occurence. Negative correlation (-0.28; Table 5.2) between Ni˜ no 3.4 SST and IOD-EP SST indicates the co-occurance of ENSO and IOD. The CFSv2 coupled model control run is over confident to ENSO-monsoon relationship, as indicated by overestimation of correlation coefficient (CC=-0.70; Table 5.2) between Ni˜ no 3.4 SST and AISMR. Recently, Ramu et al. (2016) reported that this strong relationship persists irrespective of atmospheric spectral resolution of CFSv2. On the other hand, the relationship between IOD and Monsoon is opposite (CC=0.43; Table 5.2), since the negative phase of IOD is favorable for monsoon in the model (George et al., 2015). The analysis of (Ramu et al., 2016) highlights that the magnitude of positive correlation is reduced in higher atmospheric resolution configuration of CFSv2 model.
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Ni˜ no 3.4 vs AISMR IOD-EP vs AISMR Ni˜ no 3.4 vs IOD-EP
OBS -0.53 -0.16 -0.28
CTL -0.70 0.43 -0.21
Table 5.2: Relationship among ENSO, IOD and Monsoon The ENSO-IOD co-occurrence cases are reasonably captured in the model at least in the correlation (CC=-0.21; Table 5.2).
5.3.1
Sea Surface Temperature favorable for monsoon
The observed global SST correlated with AISMR shows a negative correlation over the central tropical Pacific and major portions of the eastern Indian Ocean (figure 5.1a), while positive correlation is observed over the western Pacific warm pool region and along the western Indian Ocean (figure 5.1a). The positive phase of IOD and La-Ni˜ na are associated with good monsoon condition (figure 5.1a). Recent studies have highlighted that the central Pacific warming (El-Ni˜ no Modoki Ashok et al., 2007) is more conducive to force drought condition over India (Figure 5.1a; Kumar et al., 2006) compared with eastern Pacific warming (canonical El-Ni˜ no). However, many coupled models failed to capture the recent changes in ENSO-monsoon relationship (Wang et al., 2015). Recently, (Pillai et al., 2016) has shown that higher atmospheric resolution configuration of CFSv2 model captures the flavors of ENSO better compared to lower atmospheric resolution configuration.
The model captures the large-scale spatial pattern of observed correlation over the Pacific Ocean (La-Ni˜ na condition; Figure 5.1b), but the ENSO-AISMR relationship overestimates in CTL, compared to observations (Table 5.2; Figure 5.1b). The CFSv2 also has some limitation by which strong negative correlation of SST (with AISMR) is concentrated over the eastern Pacific, as in the case of many other coupled models (Wang et al., 2015). The positive correlation over the western Pacific warm pool region is also very strong (figure 5.1b) compared to observations. The CFSv2 model shows positive correlations all over the eastern Indian Ocean and a negative correlation along the monsoon 119
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.1: Seasonal (JJAS) mean SST correlated with All India Summer Monsoon Rainfall Index for (a) observations, (b) CTL, (c) ISLAB and (d) PSLAB
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Chapter 5. Relationship among ENSO, IOD and AISMR wind track of the western Indian Ocean (figure 5.1b). This indicates that a negative IODlike structure in CFSv2, enhances precipitation over India, which shows exactly opposite monsoon teleconnections over the tropical Indian Ocean, compared to observations. This suggests that further improvement in AISMR skill is possible by improving the simulation of AISMR teleconnections with Indian Ocean SST.
In ISLAB run, the teleconnections over the Pacific Ocean are almost identical to the CTL run, although the influence of the eastern/central Pacific SST on the AISMR has increased and the negative correlation has further extended to the western Pacific (figure 5.1c). The extension of negative correlation to western Pacific is due to extension of easterlies into the western Pacific in ISLAB run (figure 5.2c). Strong negative correlations are noticed along the path of monsoon cross-equatorial flow (figure 5.1c), which suggests that the strong monsoon strengthens the cross-equatorial flow, and thereby, enhancing the latent heat lose from the ocean and cooling SST along its path (Shukla and Misra, 1977). But this cooling is partially compensated in presence of ocean-atmosphere coupled dynamics in the Indian Ocean.
In PSLAB run, the teleconnections between the Indian Ocean SST and AISMR are marginally better, compared with observations in the western tropical Indian Ocean wherein positive correlation between the western Indian Ocean SST and AISMR is faithfully captured (figure 5.1d). But almost all the correlation values are insignificant for PSLAB experiment, since the mean and variance of the AISMR are significantly reduced in absence of Pacific Ocean dynamics. However, the negative correlation pattern over the central and eastern equatorial Indian Ocean is not captured (figure 5.1d). This demonstrates that central and eastern equatorial Indian Ocean coupled dynamics are not represented properly also in PSLAB run.
In the absence of Pacific Ocean coupled dynamics in PSLAB run, the ENSO-Modoki pattern is reasonably captured in the monsoon teleconnections, with an underestimation of the correlation coefficient (figure 5.1d). This indicates that the Indian Ocean coupled 121
Chapter 5. Relationship among ENSO, IOD and AISMR dynamics plays a significant role in simulating the ENSO-Modoki pattern of monsoon teleconnections and the ocean-atmosphere coupled dynamics in Pacific Ocean amplify the magnitude of the relationship.
The CTL run reasonably captures the spatial patterns of SST in the tropical Pacific Ocean correlated with AISMR, while the model simulates opposite relationship in the Indian Ocean(figure 5.1). This indicates that, the ENSO-monsoon teleconnections are reasonably captured in the model, while the IOD-monsoon teleconnections are misrepresented in the model. But the discrepancy is removed when either the Indian Ocean or the Pacific Ocean dynamics is switched-off. The role of teleconnected wind on the relationship is to be analyzed for better understanding.
Wind at 850 hPa related to Monsoon Strong monsoon in observations is associated with anomalous strong cyclonic circulation over the northern Indian Ocean, which extends to the Indian landmass (figure. 5.2a). Strong anticyclonic circulation in the southern tropical Indian Ocean and anomalous easterlies to the south of the equator are also observed to be associated with strong monsoon (figure. 5.2a). Anomalous strengthening of sub tropical westerlies over the southern tropical Indian Ocean is also evident (figure. 5.2a).
In CFSv2 model, even if anomalous cyclonic vortex is present over the northern Indian Ocean, strong westerlies are noticed over the equator, which converges to another cyclonic vortex over the southern hemisphere (figure. 5.2b). This resembles a negative IOD condition, which is also indicated by the SST pattern correlated with AISMR (figure. 5.1b). This discrepancy has replicated also in the absence of Indian Ocean coupled dynamics as shown by ISLAB, even if the positive SST correlation is not evident (figures 5.1c and 5.2c,). Zonal wind bias (figure. 4.2c) in the southern tropical Indian Ocean is responsible for this misrepresentation of the relationship between the Indian Ocean surface wind and the monsoon rainfall.
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Figure 5.2: Seasonal (JJAS) mean wind at 850 hPa regressed with All India Summer Monsoon Rainfall Index for (a) observations, (b) CTL, (c) ISLAB and (d) PSLAB
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Chapter 5. Relationship among ENSO, IOD and AISMR The southern hemisphere cyclonic vortex is removed in absence of the Pacific Ocean coupled dynamics in PSLAB, and equatorial westerlies are converged to cyclonic vortex over the northern Indian Ocean centered at the Bay of Bengal (figure. 5.2d). Anticyclonic vortex over the north western Pacific Ocean is also well simulated in absence of coupled dynamics over the Pacific Ocean. Only PSLAB experiment simulates anomalous strengthening of subtropical westerlies over the southern tropical Indian Ocean associated with strong monsoon condition. Westerly pattern is noticed over the eastern equatorial Pacific and easterly pattern is noticed over the western equatorial Pacific Ocean as in case of observations, which indicates La-Ni˜ na Modoki condition with subsidence over the equatorial central Pacific Ocean.
5.3.2
Precipitation relationship with ENSO
The precipitation pattern associated with ENSO indicates that El-Ni˜ no condition suppresses precipitation over the south Asian region(figure 5.3a). CFSv2 model simulates
Figure 5.3: (a) Observed and (b) simulated correlation of precipitation with Ni˜ no 3.4 SST index this relationship very well with slight overestimation of negative correlation (figure 5.3b). Moreover, there is a significant positive correlation over the equatorial western Indian ocean and a negative correlation extends from the southeastern tropical Indian Ocean to the southwestern tropical Indian Ocean. 124
Chapter 5. Relationship among ENSO, IOD and AISMR
Sea Surface Temperature related to ENSO Observed Ni˜ no 3.4 SST is associated with warm eastern tropical Pacific, cool western tropical Pacific and a basin wide warm pattern in the Indian Ocean except for the southeastern Indian Ocean (figure 5.4a). Warm SST is distributed almost uniformly all over the western and the central Indian Ocean. Observed ENSO related SST pattern over the
Figure 5.4: (a) Observed and (b) simulated correlation of SST with Ni˜ no 3.4 SST index Pacific Ocean is very well represented in the model (Ramu et al., 2016). But instead of the basin wide warming pattern observed in the Indian ocean, model simulates a moderate dipole structure with strong warming in the western tropical Indian ocean and the cool pool is extended towards the southern central Indian ocean (figure 5.4b). This suggests that El-Ni˜ no in the model dominantly forces IOD in the tropical Indian Ocean. The enhanced precipitation over the western equatorial Indian ocean during El-Ni˜ no condition simulated by CFSv2 in contrast to observations (figure 5.3a,b), may be due to warm SST in the western tropical Indian ocean (figure 5.4b).
Wind at 850 hPa related to ENSO The regressed wind associated with El-Ni˜ no condition has westerlies over the equatorial Pacific Ocean(figure 5.5a). Westerlies are observed over the western equatorial Indian Ocean, which is associated with a cyclonic vortex over the southern Arabian Sea. East-
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Chapter 5. Relationship among ENSO, IOD and AISMR erlies are observed over the eastern equatorial Indian Ocean, associated with anticyclonic vortex over the southeastern tropical Indian Ocean (figure 5.5a). Strong westerlies diverge from the Bay of Bengal and the Indian land region and feed the westerly core over the equatorial western Pacific Ocean. The model represents the ENSO associated wind over
Figure 5.5: (a) Observed and (b) simulated regression of 850 hPa wind with Ni˜ no 3.4 SST index the equatorial Pacific Ocean (figure 5.5b), where as the model simulates easterlies over throughout the equatorial Indian Ocean (figure 5.5b) associated with twin anticyclone on either side of the equator. Anticyclonic wind pattern observed in the northwestern tropical Pacific Ocean (figure 5.5a) is not clear in the model simulation (figure 5.5b).
5.3.3
Precipitation relationship with IOD-EP
The early phase of negative dipole, which is indicated by boreal summer warming over IOD-EP, is associated with suppressed precipitation over the south Asian region (figure 5.6a). Observed positive correlation is concentrated over the eastern Indian Ocean (figure 5.6a). The model simulates enhanced precipitation over the Indian land during warm IOD-EP (negative phase of IOD; Fig 5.6b). The positive correlation over the southeastern tropical Indian Ocean also extends southwestward in the model simulation (figure 5.6b). 126
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.6: (a) Observed and (b) simulated correlation of precipitation with IOD-EP SST index
Sea Surface Temperature related to IOD-EP Observed negative dipole condition exhibits positive correlation in the eastern Indian Ocean, the Bay of Bengal and the western Pacific (figure 5.7a). It is also associated with some cooling over the equatorial central Pacific Ocean and slight warming over the eastern Pacific region. As reported by Ramu et al. (2016), CFSv2 simulates the observed
Figure 5.7: (a) Observed and (b) simulated correlation of SST with IOD-EP SST index correlation pattern except that there is a westward extension of the warm pool towards the south central Indian Ocean (figure 5.7b). The southwestward extension of precipitation (figure 5.6b) associated with negative dipole condition in the model simulation is due 127
Chapter 5. Relationship among ENSO, IOD and AISMR to the extended warm pool (figure 5.7b) simulation. The negative correlation over the central Pacific is shifted northward in the model, while positive correlation is observed over the southern central Pacific.
Wind at 850 hPa related to IOD-EP Negative IOD condition is associated with easterlies over the equatorial Pacific Ocean and westerlies over the equatorial Indian Ocean (figure 5.8a). Westerly wind over the Indian Ocean converges to a cyclonic circulation over the southeastern Indian Ocean (figure 5.7a). Model simulates regressed wind reasonably except that the cyclonic circulation
Figure 5.8: (a) Observed and (b) simulated regression of 850 hPa wind with IOD-EP SST index is elongated westward and westerlies are strengthened in the Indian Ocean (figure 5.8b). This strong cyclonic circulation may be the reason for simulation of strong convection over the southern tropical Indian Ocean (figure 5.6b). The regressed meridional wind over the central Pacific Ocean shows a southward component (figure 5.8b), which is also associated with cool (warm) SST (figure 5.7b) and suppressed (enhanced) convection (figure 5.6b) over northern (southern) central Pacific Ocean.
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5.4
Thermocline variations
The analysis indicates that strong monsoon condition in CFSv2 model is associated with strong westerlies over the equatorial Indian Ocean (figures. 5.2b-d) and SST warming (figure 5.1b) over the eastern equatorial Indian Ocean. The possible impact of this wind pattern on the thermocline is analyzed in this section. The similarities in model simulation of wind pattern associated with Ni˜ no 3.4 and IOD-EP SST variations indicates the atmospheric bridge is strongly connecting the two ocean basins. Finally, correlation analysis of mixed layer budget terms with the climate indices was carried out to understand possible mechanism driving improper teleconnections between the Indian Ocean SST and the monsoon rainfall over the Indian land region.
Thermocline Pattern Favorable for Monsoon The D20 variations correlated with monsoon index is negative in the equatorial central Pacific (figure. 5.9a) indicating that the La-Ni˜ na conditions (shallow thermocline) favors strong monsoon. Deep thermocline in the southwestern Indian Ocean and shallow thermocline in the eastern Indian ocean are observed (figure. 5.9a). These are good indicators for early developing phase of positive dipole condition favorable for monsoon rainfall. The
Figure 5.9: (a) Observed and (b) simulated correlation of D20 with All India Summer Monsoon Rainfall Index
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Chapter 5. Relationship among ENSO, IOD and AISMR model simulates shallow thermocline along the equatorial eastern Pacific and the adjoint coastal line (figure. 5.9b). This may be responsible for the canonical El-Ni˜ no pattern (Wang et al., 2015) simulated (figure. 3.30b) in contrast with the Modoki pattern observed (figure. 3.30a). In contrast to observed thermocline pattern (figure. 5.9a), model simulates deepening of thermocline (figure. 5.9b) in the eastern equatorial Indian Ocean, which resembles fully developed negative IOD condition.
Thermocline variations associated with ENSO Figure 5.10 (a) shows observed depth of 20◦ C isotherm associated with El-Ni˜ no condition. El-Ni˜ no is associated with deep thermocline in the equatorial eastern pacific and shallow thermocline in the western tropical pacific. The Indian Ocean does not exhibit any significant thermocline variation associated with ENSO. CFSv2 coupled model simu-
Figure 5.10: (a) Observed and (b) simulated correlation of D20 with Ni˜ no 3.4 SST index lates the ENSO related thermocline variations in the Pacific Ocean (figure 5.10 b), while the thermocline is shallow over the eastern equatorial Indian Ocean and the shallowness extends much westward along the equator. This is a well expected response to the strong easterly equatorial wind regressed with Ni˜ no 3.4 SST index (figure 5.5b).
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Chapter 5. Relationship among ENSO, IOD and AISMR
Thermocline variations associated with IOD-EP The negative phase of IOD (warm IOD-EP condition) is associated with deep thermocline at the eastern equatorial Indian Ocean and shallow thermocline at the western and the central tropical Indian Ocean (figure 5.11a). Shallow thermocline is also observed over the equatorial central Pacific Ocean, while the northwestern tropical Pacific Ocean has deep thermocline. The ENSO related thermocline variation simulated by CFSv2 in
Figure 5.11: (a) Observed and (b) simulated correlation of D20 with IOD-EP SST index the Indian Ocean indicates that there is strong ENSO-IOD coupling in the model. But the IOD related thermocline variations (figure 5.11b) in the indo-pacific region is reasonable compared to observations. Hence, it is concluded that even if El-Ni˜ no force shallow thermocline in the eastern equatorial Indian Ocean, it does not create significant cooling over there. Therefore, SST variations in the IOD-EP are mostly due to coupled dynamics in the Indian Ocean.
5.5
Variabilities in Mixed Layer Budget
Variabilities of mixed layer depth Shallow mixed layer depth in the equatorial eastern Pacific (figure. 5.12a) and the deep mixed layer in the northwestern tropical Pacific indicate that the La-Ni˜ na condition 131
Chapter 5. Relationship among ENSO, IOD and AISMR is favorable for the south Asian Monsoon. MLD associated with AISMR does not have any significant pattern observed in the Indian Ocean (figure. 5.12a). The model control
Figure 5.12: (a) Observed and (b) simulated correlation of MLD with All India Summer Monsoon Rainfall Index run captures MLD relationship in the Pacific Ocean (figure. 5.12b). But in contrast to observations, model generates some artificial MLD patterns in the Indian Ocean (figure. 5.12b) associated with strong monsoon condition. This is the key indicating that some mixed layer processes are also involved in the misrepresentation of the Indian Ocean SST Monsoon relationship.
MLD correlated with Ni˜ no 3.4 SST has deep MLD in the eastern equatorial Pacific Ocean and shallow MLD in the western tropical Pacific Ocean. Observed MLD variations in the Indian Ocean associated with ENSO are very small in magnitude (figure 5.13a). Shallow MLD in the eastern equatorial Indian Ocean, the southern Arabian Sea and the southwestern Indian Ocean are observed. Very small deepening of MLD is observed in the southern tropical Indian Ocean. ENSO related MLD variations in the pacific ocean (figure 5.13a) is reasonably simulated in model (figure 5.13b). Deep MLD simulation (figure 5.13b) in model is responsible for cool SST over the southern central Indian ocean during El-Ni˜ no condition(figure 5.4b). The model simulation has deep MLD in the southern central Indian ocean and shallow MLD in the northern Indian ocean(figure 5.13b).
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Figure 5.13: (a) Observed and (b) simulated correlation of MLD with Ni˜ no 3.4 SST index Deep MLD(figure 5.13b) simulation in the southern central Indian Ocean during El-Ni˜ no condition may be due to anticyclonic wind simulation (figure 5.5b) in the model. Negative IOD is observed to be associated with deep MLD in the eastern equatorial Indian Ocean and shallow MLD towards the west(figure 5.14a). The southeastern Pacific Ocean has shallow MLD and the northwestern Pacific Ocean has deep MLD (figure 5.14a). Model reasonably simulates MLD pattern correlated with IOD-EP SST, except an overes-
Figure 5.14: (a) Observed and (b) simulated correlation of MLD with IOD-EP SST index timation of negative correlation in south central Indian Ocean (figure 5.14b). The shallow MLD (figure 5.14b) simulation may be due to the elongated cyclonic circulation over the 133
Chapter 5. Relationship among ENSO, IOD and AISMR southern Indian Ocean (figure 5.8b). Westward extension of warm pool associated with negative IOD may be due to shallow MLD simulations (figure 5.14b).
5.5.1
Variabilities of shortwave radiation at surface
During years of strong monsoon, shortwave radiation (5.15a) received at the surface is modulated by cloudiness. Cloudy condition associated with strong monsoon extends from the western Indian Ocean, the entire Arabian Sea, the southern Bay of Bengal, the southern part of Maritime continent region up to the southwest Pacific Ocean. Clear sky conditions are observed over the equatorial central Pacific, which extend up to the northeast and the southeast Pacific. Model simulates (figure 5.15b) cloudiness over the
Figure 5.15: (a) Observed and (b) Simulated Seasonal (JJAS) short wave radiation flux correlated with AISMR Arabian Sea, the southern Bay of Bengal and the southern part of Maritime continent, while the simulated clear sky condition extends throughout the equatorial regions of the Pacific and the Indian Ocean as indicated by the enhanced shortwave radiation received at the surface.
The surface shortwave radiation correlated with Ni˜ no 3.4 is positive over the entire Indian Ocean, the southwestern, the northwestern and the eastern Pacific Ocean (figure
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Chapter 5. Relationship among ENSO, IOD and AISMR 5.16a). Negative correlation is observed over the tropical central Pacific Ocean and the southern Pacific Ocean. The model simulates negative correlations over the tropical cen-
Figure 5.16: (a) observed and (b) Simulated short wave radiation flux correlated with Nino3.4 tral Pacific Ocean and the southern Pacific Ocean (figure 5.16b). Negative correlations are simulated in the northwestern Pacific Ocean in contrast to observed positive correlation. Positive correlation simulated in the Indian Ocean is concentrated away from the equator leaving negative correlation along the equatorial Indian Ocean.
Clear dipole structure can be observed in the surface shortwave radiation flux to the Indian Ocean (5.17a) correlated with IOD-EP SST index. Positive correlations are observed almost allover the tropical Pacific Ocean. The model simulates positive correlations over the western equatorial Indian Ocean and negative correlation over the eastern equatorial Indian Ocean with IOD-EP SST index. But positive correlation over the western Indian Ocean is constrained to the equatorial region in model simulation, leaving negative correlations over the Arabian Sea and the southwestern Indian Ocean (5.17b). The model simulates a meridional tripole structure resembling a Rossby wave pattern. Strong negative correlation is also observed in the southern tropical Pacific Ocean (5.17b).
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Figure 5.17: (a) observed and (b) Simulated short wave radiation flux correlated with IOD-EP SST index
5.5.2
Variabilities of longwave radiation at surface
Longwave radiation emitted from the ocean surface results in cooling of SST and hence net longwave radiation has a negative sign convention. Positive correlation with long wave radiation indicates reduction of radiative cooling and negative correlation indicates enhanced cooling of SST. Cloudiness over a region is indicated by negative correlation with shortwave radiation flux and positive correlation with longwave radiation flux. Positive correlation of longwave radiation over the Indian Ocean indicates less longwave radiations are emitted (reduced radiative cooling) (figure 5.18a) due to blanket effect of cloudiness during strong monsoon years. As in the case of simulated shortwave radiation, simulation of longwave radiation in the model also indicates that clear sky condition (enhanced radiative cooling) persisting over the entire equatorial belt of the Indo-Pacific region (figure 5.18b). This indicates that the simulation of cloud and convection in the model is to be improved to capture the monsoon teleconnections. Positive correlation is also evident over the tropical central Pacific Ocean.
Observed longwave radiation exhibits negative correlation all over the Indo-Pacific region except the equatorial western, the central Pacific and the southwestern Indian Ocean
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Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.18: (a) Observed and (b) Simulated Seasonal (JJAS) long wave radiation flux correlated with AISMR (figure 5.19a). This indicates strong radiative cooling over the Indo-Pacific region during El-Ni˜ no condition. The long wave radiation flux over the central equatorial Pacific Ocean
Figure 5.19: (a) observed and (b) Simulated long wave radiation flux correlated with Nino3.4 is positively correlated (5.19a) with Ni˜ no 3.4 SST index, which is simulated very well in the model (5.19b). Positive correlation of longwave simulated in CFSv2 model, along the equatorial belt (figure 5.19b), indicates reduced radiative cooling due to cloudiness. This response of longwave radiation partially compensates the effect of reduced shortwave radiation (figure 5.16b). This indicates that cloud cover in the model is overestimated 137
Chapter 5. Relationship among ENSO, IOD and AISMR over the northeastern Pacific Ocean region during the ENSO condition (figures 5.16b and 5.19b).
Observed cloudiness over southeastern Indian Ocean is also evident from the positive correlation of longwave radiation (figure 5.20a). Slight positive correlation extending all over the Indo-Pacific region indicates overall suppression of radiative cooling during negative IOD (figure 5.20a). Even if the model simulates positive correlation over southeast-
Figure 5.20: (a) observed and (b) Simulated long wave radiation flux correlated with IOD-EP SST index ern Indian Ocean (figure 5.20b), the overall spatial pattern is different from observations. Model simulates negative correlation over the equatorial belt and strong positive correlation over the Arabian Sea, the southwestern Indian Ocean and the southern tropical Pacific Ocean.
5.5.3
Variabilities of latent heat flux
Observed latent heat flux (figure 5.21a) associated with strong monsoon condition indicates that the southeastern Pacific is the major moisture source for the monsoon. Suppressed evaporation (negative correlation) is observed in the Arabian Sea and the Bay of Bengal during strong monsoon condition. Model simulates suppressed evaporation (neg-
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Figure 5.21: (a) Observed and (b) Simulated Seasonal (JJAS) latent heat flux correlated with AISMR ative correlations) along the equatorial belt and more evaporation (positive correlation) over the northern and the southern Indian Ocean (5.21b) in contrast to the observed relationship.
Observed latent heat flux correlated with Ni˜ no 3.4 SST index is positive over the eastern equatorial Pacific, the southwestern tropical Pacific and the south central Pacific Ocean(figure 5.22a). Southeastern tropical Indian Ocean also shows positive correlation(figure 5.22a). The model simulates the same sign of the correlation pattern, but overestimated the strength of association (figure 5.22b). Moreover, positive correlation in the southeastern tropical Indian Ocean is extended up to the equatorial western Indian Ocean indicates local evaporation (figure 5.22b) is associated with the enhanced convection over there (figure 5.3b).
Observed latent heat pattern correlated with IOD-EP SST has positive correlation over the Arabian Sea, the southern Indian Ocean, the southeastern Pacific Ocean, etc. (figure 5.23a). The model perfectly simulates observed pattern of correlation with an exceptional negative correlation over the western equatorial Indian Ocean (figure 5.23b).
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Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.22: (a) observed and (b) Simulated latent heat flux correlated with Nino3.4
Figure 5.23: (a) observed and (b) Simulated latent heat flux correlated with IOD-EP SST index
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Chapter 5. Relationship among ENSO, IOD and AISMR
5.5.4
Variabilities of sensible heat flux
The observed sensible heat flux(5.24a) is negatively correlated with AISMR over the western Indian Ocean, the southeastern Indian Ocean, the southwestern and the equatorial Pacific Ocean. Moderate positive correlations are observed over the northwestern and the southeastern Pacific Ocean. Model simulates (5.24b) negative correlations reasonably
Figure 5.24: (a) Observed and (b) Simulated Seasonal (JJAS) sensible heat flux correlated with AISMR with slight overestimation. The positive correlations over the southwestern Indian Ocean, the northern Indian Ocean, the northwestern Pacific Ocean, the northeastern and the southeastern Pacific Ocean are strongly overestimated.
The observed positive correlation associated with El-Ni˜ no condition noticed over the southwestern and the equatorial Pacific Ocean (5.25a) indicates sensible heat lose from the ocean to the atmosphere. The model simulates the positive correlation over the southwestern and the equatorial Pacific Ocean (5.25b). On the other hand, model overestimates positive correlation over the western tropical Indian Ocean.
Sensible heat is positively correlated with the IOD-EP SST index all over the Indian Ocean (figure 5.26a) with strong positive correlation over the eastern equatorial Indian Ocean. Even if the positive correlation over the eastern equatorial Indian Ocean is simu-
141
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.25: (a) observed and (b) Simulated sensible heat flux correlated with Nino3.4
Figure 5.26: (a) observed and (b) Simulated sensible heat flux correlated with IOD-EP SST index
142
Chapter 5. Relationship among ENSO, IOD and AISMR lated in the model, it overestimates the relationship in the Bay of Bengal and the southwest Indian Ocean (figure 5.26b). Negative correlation is simulated over the western equatorial Indian Ocean, unlike observations.
5.5.5
Variabilities of total thermodynamical forcing at mixed layer
M LD ; W/m3 ) correlated with The total thermodynamical forcing on MLD ( Qnet−Qsw M LD
AISMR, is positive over the eastern equatorial Pacific Ocean and almost everywhere in the Indian Ocean. Negative correlations are observed in the northern central Pacific and the southeastern Pacific Ocean (figure 5.28a). The warming tendencies (positive correlations) in the eastern equatorial Pacific Ocean and the tropical Indian Ocean are supported by reduced radiative heat lose (positive correlation in longwave radiation flux), reduced evaporation (negative correlation of latent heat flux) and reduced sensible heat lose (negative correlation of sensible heat flux).
Figure 5.27: (a) Observed and (b) simulated correlation of mixed layer thermodynamical M LD forcing ( Qnet−Qsw ; W/m3 ) with All India Summer Monsoon Rainfall Index M LD
The model captures the positive forcing over the eastern equatorial Pacific and the equatorial western Indian Ocean. Positive correlations are overestimated over the western
143
Chapter 5. Relationship among ENSO, IOD and AISMR Pacific Ocean (figure 5.27b). However, model simulates strong negative correlation with AISMR over the southwestern and the northern Indian Ocean. Enhanced sensible and latent heat fluxes (positive correlations; figures 5.24b 5.21b) as well as reduced shortwave radiation (negative correlation; 5.15b) drive thermodynamical cooling tendency (negative correlation) of the southwestern Indian Ocean associated with AISMR in the model. Model simulates enhanced shortwave radiation (positive correlation; figure 5.15b), which indicates reduction of the equatorial cloud cover and results in thermodynamical warming tendency over the equatorial region (figure 5.27b). The model simulates enhanced evaporation (positive correlation of latent heat flux; 5.21b) and suppressed shortwave radiation (negative correlation; 5.15b) over the Arabian Sea, which results in thermodynamical cooling tendency. Over the Bay of Bengal and the eastern Arabian Sea region, sensible heat lose (positive correlation 5.24b) is enhanced and results in thermodynamical cooling tendency (figure 5.27b).
The observed net heat flux associated with ENSO has thermodynamical cooling tendency over the eastern equatorial Pacific Ocean supported by enhanced radiative cooling (negative correlation of longwave; figure 5.19a) and evaporation (positive correlation of latent heat; figure 5.22a). Reduced sensible and latent heat fluxes (negative correlation; figures 5.25a and 5.22a) gives a positive forcing (warming tendency; figure 5.28a) over the southeastern tropical Pacific. Suppressed radiative cooling over the western equatorial Pacific Ocean (positive correlation of longwave; figure 5.19a) along with suppressed sensible heat lose (negative correlation; figure 5.25a) and evaporation (negative correlation of latent heat; figure 5.22a) contribute to the thermodynamical warming tendency (positive correlation; figure 5.28a) over the northwestern Pacific Ocean. Enhanced sensible heat flux (positive correlation; figures 5.25a) over the equatorial central Pacific Ocean and the southwestern Pacific Ocean impose cooling tendencies (negative correlation; figure 5.28a). There is an observed negative feed back to the basin wide warm SST condition (figure 5.28a) in the Indian Ocean. Enhanced evaporation (positive correlation of latent heat; figure 5.22a) over the southeastern tropical Indian Ocean and the Bay of Bengal and en-
144
Chapter 5. Relationship among ENSO, IOD and AISMR hanced sensible heat flux (positive correlation; figure 5.25a) over the Arabian Sea and the southeastern tropical Indian Ocean are the major contributors for the thermodynamical cooling tendency over the Indian Ocean (figure 5.28a).
Figure 5.28: (a) Observed and (b) simulated correlation of mixed layer thermodynamical M LD forcing ( Qnet−Qsw ; W/m3 ) with Nino3.4 Index M LD
The negative correlations over the equatorial Pacific and positive correlation over the southeastern Pacific Ocean are simulated in the model (figure 5.28b) and these are associated with same contributors as in observations. The model simulates enhanced shortwave radiation (positive correlation; figure 5.16b) over the Arabian Sea and the southwestern Indian Ocean drives warming tendency (positive correlation; figure 5.28b), which results in warm SST (positive correlation; figure 5.4b) over the western Indian Ocean associated with El-Ni˜ no . Enhanced evaporation (positive correlation of latent heat; figure 5.22b) unlike in observations over the northwestern Pacific Ocean results in slight cooling tendency (figure 5.28b).
The total thermodynamical forcing correlated with IOD-EP SST is negative over the southeastern Indian Ocean and positive over the western equatorial Indian Ocean, the Bay of Bengal and the equatorial Pacific Ocean (figure 5.29a). Enhanced sensible and latent heat fluxes (positive correlation; figures 5.26a and 5.23a) contribute to the thermody-
145
Chapter 5. Relationship among ENSO, IOD and AISMR namical cooling of the southeastern equatorial Indian Ocean. Thermodynamical warming tendency over the western equatorial Indian Ocean is mainly due to enhanced shortwave radiation (positive correlation; figure 5.17). Enhanced shortwave radiation (positive correlation; figure 5.17) and suppressed evaporation (negative correlation; figure 5.23) cause thermodynamical warming tendency over the Bay of Bengal (figure 5.29a). Enhanced shortwave radiation also causes warming tendency over the northwestern Pacific Ocean. The thermodynamical warming tendencies over the equatorial central Pacific and the southwestern tropical Pacific Ocean are due to suppressed sensible and latent heat fluxes (negative correlation; figures 5.26a and 5.23a).
Figure 5.29: (a) Observed and (b) simulated correlation of mixed layer thermodynamical M LD forcing ( Qnet−Qsw ; W/m3 ) with IOD-EP SST Index M LD
Model simulates net heat flux correlated with IOD-EP SST index reasonably, with an exception for the Bay of Bengal (figure 5.29a,b) and some overestimation and underestimation of the correlation values. This indicates that misrepresentation of SST patterns is not related to fluxes, but it is related to the misrepresentation of ocean dynamics and coupled dynamics. In contrast to observed warming tendency over the Bay of Bengal, the model simulates cooling tendency due to enhanced sensible heat flux (positive correlation; 5.26b). Suppressed shortwave radiation (negative correlation; figure 5.17) over the Arabian Sea and the southwestern Indian Ocean as well as enhanced sensible and latent heat 146
Chapter 5. Relationship among ENSO, IOD and AISMR flux (positive correlation; figures 5.26b and 5.23b) over the southern Indian Ocean drives thermodynamical cooling tendency (figure 5.29b). In addition to suppressed sensible and latent heat (negative correlation; figures 5.26b and 5.23b), enhanced shortwave radiation over the equatorial Indo-Pacific region, due to reduced equatorial cloud cover, results in the strong thermodynamical warming tendency (figure 5.29b).
5.5.6
Variabilities of zonal advection of heat
Zonal component of heat advection correlated with AISMR indicates slight cooling tendency over the equatorial central Pacific Ocean (figure 5.30a). CFSv2 model captures
Figure 5.30: (a) Observed and (b) simulated correlation of mixed layer zonal heat advection with All India Summer Monsoon Rainfall Index the cooling tendency driven by zonal advection over the equatorial central Pacific Ocean (figure 5.30b). The model also simulates cooling over the equatorial western Indian Ocean.
Zonal advection during El-Ni˜ no condition brings warming over the equatorial central Pacific Ocean (figure 5.31a). Warming tendency is also evident over the equatorial western Pacific. The model simulates warming tendency over the equatorial central Pacific, the eastern and the western equatorial Indian Ocean during El-Ni˜ no condition (figure 5.31b).
147
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.31: (a) Observed and (b) simulated correlation of mixed layer zonal heat advection with Nino3.4 Index During negative phase of IOD, heat convergence through zonal advection is observed over the southeastern Indian Ocean, whereas the zonal advection cools the equatorial western Indian Ocean region (figure 5.32a). A cooling tendency over the equatorial central Pacific Ocean is also observed associated with negative phase of IOD. The model
Figure 5.32: (a) Observed and (b) simulated correlation of mixed layer zonal heat advection with IOD-EP SST Index simulates the warming tendency over the southeastern equatorial Indian Ocean and cooling tendencies over the equatorial western Indian Ocean as well as the equatorial central Pacific Ocean (figure 5.32b). 148
Chapter 5. Relationship among ENSO, IOD and AISMR
5.5.7
Variabilities of meridional heat advection
Strong monsoon condition is associated with meridional heat divergence (cooling) over the equatorial central/eastern Pacific Ocean (figure 5.33a), which indicates the La-Ni˜ na condition in the Pacific Ocean. The meridional advection in CFSv2 coupled model as-
Figure 5.33: (a) Observed and (b) simulated correlation of mixed layer meridional heat advection with All India Summer Monsoon Rainfall Index sociated with AISMR simulates cooling of the equatorial central Pacific Ocean and a negative dipole pattern in the Indian Ocean (figure 5.33b). Meridional advection drives heat divergence from the tropical western Indian Ocean and heat convergence over the southeastern tropical Indian Ocean (figure 5.33b).
The meridional heat advection associated with Ni˜ no 3.4 SST has warming tendency along the equatorial central Pacific Ocean (figure 5.34b). In addition to observed warming tendency over the equatorial central Pacific Ocean, CFSv2 model simulates significant warming over the western tropical Indian Ocean and significant cooling over the southeastern Indian Ocean, which indicates the co-occurrence of El-Ni˜ no and positive IOD conditions (figure 5.34b).
The negative phase of IOD, indicated by IOD-EP warming, exhibits strong signal in the meridional heat advection (figure 5.35a) in that region. In addition to warming over the
149
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.34: (a) Observed and (b) simulated correlation of mixed layer meridional heat advection with Nino3.4 Index southeastern Indian Ocean, there is cooling over the equatorial central Pacific. In CFSv2
Figure 5.35: (a) Observed and (b) simulated correlation of mixed layer meridional heat advection with IOD-EP SST Index coupled model, negative phase of IOD exhibits strong meridional heat convergence over the southeastern equatorial Indian Ocean and divergence over the western tropical Indian Ocean (figure 5.35b). Advective cooling is also evident over the equatorial central Pacific Ocean and warming over the southern central Pacific Ocean.
150
Chapter 5. Relationship among ENSO, IOD and AISMR
5.5.8
Variabilities of vertical heat advection
The vertical advection of heat associated with AISMR exhibits cooling tendency over the equatorial eastern and the central Pacific Ocean, while there is warming over the northwestern Pacific Ocean (figure 5.36a). Southeastern tropical Indian Ocean exhibits cooling tendency associated with AISMR which indicates positive IOD condition. The
Figure 5.36: (a) Observed and (b) simulated correlation of mixed layer vertical heat advection with All India Summer Monsoon Rainfall Index cooling tendency over the equatorial eastern and the central Pacific Ocean as well as warming tendency of the northwestern Pacific Ocean is simulated in CFSv2 coupled model (figure 5.36b). However, the Indian Ocean exhibits exactly opposite relationship compared to observations. Model simulates warming tendency over the southeastern Indian Ocean and cooling tendency over the western and the central Indian Ocean (figure 5.36b). The vertical advection component correlated with Ni˜ no 3.4 SST indicates warming over the equatorial eastern and the central Pacific, cooling over the northwestern Pacific Ocean and cooling over the equatorial Indian Ocean (figure 5.37a). CFSv2 coupled model captures warming tendency over the equatorial eastern and the central Pacific Ocean, cooling tendencies over the northwestern Pacific Ocean and the equatorial Indian Ocean (figure 5.37b). In addition to that, model also simulates warming tendency (positive correlation) over the central Indian Ocean (figure 5.37b). 151
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.37: (a) Observed and (b) simulated correlation of mixed layer vertical heat advection with Nino3.4 Index During negative phase of IOD, the vertical advection component indicates warming tendencies over the eastern equatorial Indian Ocean and the northwestern tropical Pacific Ocean, while cooling tendencies are observed over the equatorial eastern and the central Pacific in addition to the northern and the southern central tropical Indian Ocean (figure 5.38a). CFSv2 model captures vertical advection component related to IOD-EP SST
Figure 5.38: (a) Observed and (b) simulated correlation of mixed layer vertical heat advection with IOD-EP SST Index variations (figure 5.38b). Cooling tendency over the southern central and the equatorial western Indian Ocean associated with vertical advection are overestimated in the model. 152
Chapter 5. Relationship among ENSO, IOD and AISMR
5.5.9
Variabilities of entrainment
Strong monsoon condition is associated with suppressed entrainment cooling (positive correlation) over the equatorial central Pacific Ocean, the northwestern Pacific Ocean and the southwestern Indian Ocean (figure 5.39a) CFSv2 coupled model captures suppression
Figure 5.39: (a) Observed and (b) simulated correlation of mixed layer entrainment with All India Summer Monsoon Rainfall Index of entrainment cooling over the equatorial central Pacific Ocean and the northwestern Pacific Ocean (figure 5.39b). In addition to this, there is suppression of entrainment over the southern central Indian Ocean and the southwestern Pacific Ocean.
El-Ni˜ no condition is associated with enhancement of entrainment cooling over the equatorial central Pacific Ocean (figure 5.40a) due to deepening of MLD. Strong advective heating over the equatorial central Pacific Ocean is partially balanced by the entrainment cooling due to deepening of MLD. CFSv2 coupled model simulates the entrainment cooling over the equatorial central Pacific Ocean due to deepening of MLD during El-Ni˜ no condition (figure 5.40b). The model also simulates strong entrainment cooling over the southern central tropical Indian Ocean and the southwestern Pacific Ocean.
Negative phase of IOD indicated by warm SST over IOD-EP is associated with deepening of MLD and entrainment cooling at the eastern Indian Ocean (figure 5.41a). Eastern
153
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.40: (a) Observed and (b) simulated correlation of mixed layer entrainment with Nino3.4 Index equatorial Pacific Ocean exhibits suppressed entrainment (positive correlation) due to shoaling of MLD. CFSv2 model captures the enhanced entrainment cooling of IOD-EP
Figure 5.41: (a) Observed and (b) simulated correlation of mixed layer entrainment with IOD-EP SST Index region due to deepening of MLD (figure 5.41b). The model simulates suppressed entrainment over the southern central and the equatorial western Indian Ocean due to shoaling of MLD. Southwestern tropical Pacific also exhibits suppression of entrainment cooling.
154
Chapter 5. Relationship among ENSO, IOD and AISMR
5.5.10
Variabilities of total dynamical forcing at mixed layer
Figure 5.42: (a) Observed and (b) simulated correlation of mixed layer dynamical forcing with All India Summer Monsoon Rainfall Index
Figure 5.43: (a) Observed and (b) simulated correlation of mixed layer dynamical forcing with Nino3.4 Index
Overall dynamical terms force cooling tendency over the equatorial eastern and the central Pacific Ocean during strong monsoon condition (figure 5.42a), which indicates that La-Ni˜ na is favorable for monsoon. Dynamical cooling tendency is observed over the northwestern Pacific Ocean. CFSv2 model captures the dynamical cooling tendency over the equatorial Pacific Ocean and dynamical warming tendency over the northwestern 155
Chapter 5. Relationship among ENSO, IOD and AISMR
Figure 5.44: (a) Observed and (b) simulated correlation of mixed layer dynamical forcing with IOD-EP SST Index Pacific Ocean (figure 5.42b). In addition to this, model also simulates a negative dipole pattern in the Indian Ocean. The meridional and vertical advection components (figure 5.33b and 5.36b) contribute to the dynamical warming tendency of the southeastern Indian Ocean and dynamical cooling tendency at the western tropical Indian Ocean (figure 5.42b).
The zonal, meridional and vertical advection during El-Ni˜ no condition imposes warming tendency over the equatorial central and the eastern Pacific Ocean (figure 5.43a). Cooling tendency over the northwestern Pacific Ocean and the southeastern Indian Ocean is also observed associated with El-Ni˜ no condition. The model reasonably captures warming tendency over the central equatorial Pacific Ocean and cooling tendency over the northwestern Pacific Ocean associated with El-Ni˜ no condition (figure 5.43b). In addition to this, the model also simulates dynamical warming at the equatorial western Indian Ocean and cooling at the southeastern Indian Ocean and strong cooling over the southern central Pacific Ocean.
The combination of advective terms associated with negative phase of IOD results in dynamical warming tendency at the southeastern Indian Ocean and dynamical cooling
156
Chapter 5. Relationship among ENSO, IOD and AISMR tendency at the western tropical Indian Ocean (figure 5.44a). Dynamical cooling tendency is also evident at the equatorial central Pacific Ocean. CFSv2 coupled model captures the dynamical warming at the southeastern Indian Ocean as well as the dynamical cooling at the western tropical Indian Ocean and the equatorial Pacific Ocean (figure 5.44b). Dynamical warming tendency is also evident at the tropical western Pacific Ocean in the model.
5.6
Conclusion
The model have resonable skill in seasonal prediction of early developing phase of ENSO. This along with strong ENSO-monsoon relationship results in good seasonal prediction skill of AISMR in the model. However, seasonal prediction skill of early developing phase of IOD is very poor in the model. Further, it represents opposite sign of Indian Ocean SST teleconnections with AISMR. In fact, AISMR simulation in the model is overconfident to the ENSO-monsoon relationship due to the absence of proper Indian Ocean SSTAISMR relationship. The discrepancies in the simulation of Indian Ocean MLD result in misrepresentation of Indian Ocean SST teleconnections with AISMR. This suggests that further improvement in AISMR skill is possible by improving the simulation of Indian Ocean teleconnections.
The limitations in the simulation of ocean-atmosphere coupled dynamics in the Indian Ocean are analysed in two different perspective such as: (1) thermocline response and (2) mixed layer response. Either La-Ni˜ na condition or strong monsoon condition is associated with enhanced equatorial westerlies over the Indian Ocean, which also results in cyclonic vorticity over the southern central Indian Ocean. The equatorial westerly wind drives warm SST and thermocline deepening at the eastern Indian Ocean. On the other hand, cyclonic vorticity drives shallow MLD and thereby warm SST over the southern Indian Ocean. Together this results in a negative dipole pattern co-occured with either La-Ni˜ na or strong monsoon condition. This may be the reason for poor seasonal prediction skill of
157
Chapter 5. Relationship among ENSO, IOD and AISMR early developing phase of IOD in the model. Since many other models also have similar limitations, some focused development activities are required to rectify the Indian Ocean dynamics in coupled models.
158
Chapter 6 Summary 6.1
Identification of Key Regions
As far as the tropical ocean is considered, the SST evolution is mainly controlled by two factors (1) the flux interactions with atmosphere, (2) mixing, entrainment and other dynamical processes. In the atmospheric perspective, the regions where the second factor has dominant role are important for long range prediction as it provides long memmory required for the prediction. The key regions of ocean-atmospheric coupled dynamics are identified to be (1) Eastern Equatorial Pacific (2) Western Tropical Pacific (3) Southeastern Indian Ocean (4) Western Tropical Indian Ocean.
On the other hand, at the warm pool regions of Asia-Pacific monsoon domain (Bay of Bengal, South China Sea, etc.) atmosphere has a strong influence on the SST evolution. This is because the SST in this region (which is already warm) is not a limiting factor for convection over there. Hence, other factors like large scale subsidence in the atmosphere, wind shear, moisture divergence etc. control the occurrence of convection. CFSv2 model is able to simulate these features reasonably well.
Strong monsoon years are usually associated with cool central pacific ocean with shallow thermocline and shallow Mixed layer over the eastern equatorial Pacific Ocean. CFSv2 model is able to represent large scale features associated with AISMR, with some overesti159
Chapter 6. Summary mation of ENSO-monsoon relationship and having some misrepresentation in the teleconnections of Indian Ocean SST and the representation of the flavors of ENSO (Canonical vs Modoki) associated with AISMR. Observed positive dipole is favorable for monsoon, while the model simulates the opposite relationship and therefore, the negative phase of IOD is favoring monsoon. Observed ENSO teleconnections of monsoon is more related to the ENSO-Modoki flavor while the model still represents the canonical-ENSO pattern. These misrepresentations are not unique for this particular model, but common for almost all the state-of-the-art coupled models.
6.2
Simulation and Prediction of Monsoon
CFSv2 coupled model simulates the mean features of the boreal summer season reasonably well. But there exists some cold (wet) bias in the SST (rainfall) over Indian Ocean and dry bias over the land region. The magnitude of the AISMR in the model has improved in the absence of cold bias in warm pool region, when the Indian Ocean dynamics are switched off (ISLAB). The SST-precipitation leadlag relationship over the Indo-Pacific warm pool region is better captured in ISLAB compared to the control run, due to the reduction of cold SST bias. Better simulation of ocean-atmosphere interaction and reduced dry bias in northern Indian Ocean in the ISLAB confirm that the dry bias over Indian landmass is primarily due to cold SST simulation in tropical Indian Ocean.
Reduced cold bias results in better representation of the atmospheric circulation and tropospheric temperature. There is a southwesterly bias in the southeasterly tropical wind over the southern Indian Ocean, which enhances the meridional flow and suppresses the zonal flow towards east African region. This also weakens the cross equatorial wind and associated circulation as indicated by the anticyclonic wind bias over the Arabian Sea. Enhanced convection over equatorial Indian Ocean indicates enhancement (suppression) of the equatorially symmetric Hadley circulation (asymmetric monsoon-Hadley circulation). The warming (reduction of cold bias) of northern Indian Ocean in the absence of ocean-
160
Chapter 6. Summary atmosphere coupled dynamics (ISLAB run) improves the asymmetry of monsoon-Hadley circulation. There is about 2◦ C cold bias in the tropospheric temperature over the Tibetan region, which is reduced to half in ISLAB experiment due to improvement in asymmetric monsoon-Hadley circulation associated with reduced SST biases of northern Indian Ocean. The reduction in tropospheric temperature bias further gives positive feedback for the monsoon circulation, through enhancement of vertical shear of zonal wind by thermal-wind relationship. This results in better representation of the convection and precipitation over Indian land region in ISLAB.
The results discussed above could be interpreted in two different ways as follows. Firstly, Indian Ocean coupled dynamics is not simulated reasonably in CFSv2 control run, hence, the convection over equatorial Indian Ocean is overestimated. This results in the enhancement of symmetric mode of monsoon-Hadley circulation, thereby drives suppression of the asymmetric mode of monsoon-Hadley circulation and causes subsidence over Indian land region. Secondly, absence of meridional heat transport in ISLAB causes warm (cold) bias over northern (southern) Indian Ocean and results in enhanced (suppressed) convection over northern (southern) Indian Ocean. Both the above interpretations suggest that the AISMR simulation can be improved by proper representation of air-sea fluxes and ocean-atmosphere coupled dynamics in the tropical Indian Ocean. The observed interannual variance (0.36 mm2 /day 2 ) of AISMR is reduced (0.25 mm2 /day 2 ) in control and further reduced to 0.12 mm2 /day 2 in ISLAB and 0.06 mm2 /day 2 in PSLAB run. This indicates that control, ISLAB and PSLAB simulate about 70%, 35% and 17% respectively of the observed variance of AISMR. Reduction in the interannual AISMR variance by 50% (compared to CTL) in absence of Indian Ocean coupled dynamics highlight the importance of ocean-atmosphere coupled dynamics in Indian Ocean in simulating the variance even if it is wrongly representing the phase relationship. Both Indian Ocean and Pacific Ocean Dynamics are important to represent the observed AISMR 161
Chapter 6. Summary variance.
The seasonal prediction skill of 0.53, 0.51 for control run and ISLAB run respectively indicates that most of the seasonal prediction skill in the model is resulted from ENSOmonsoon relationship, since Indian Ocean SST relationship with monsoon is misrepresented in the model. Significant drop of seasonal prediction skill (ACC=0.14) of monsoon in the PSLAB run compared to the control run, also indicates that prediction skill of the AISMR in CFSv2 mostly results from Pacific Ocean dynamics.
6.3
Relationship among ENSO, IOD and Monsoon
Since it is a well known fact that AISMR variability is strongly related to ENSO and IOD, the representation of these relationships is essential for the proper representation of monsoon variability. The misrepresentation in AISMR relationship with Indian Ocean SST in the CFSv2 model may be due to overestimation of ENSO-monsoon relationship and poor prediction skill of early developing phase of IOD.
The biases in the equatorial and southern tropical Indian Ocean wind pattern plays important role in the MLD and D20 variations in the Indian Ocean. The variations in zonal wind over equatorial Indian Ocean drives thermocline variations in equatorial eastern Indian Ocean while the vorticity induced in southern tropical Indian Ocean due to these variations drives the MLD of the region. The misrepresentation of thermocline variations results in generation of Pseudo-IOD condition associated with ENSO. The ENSO in the model is strongly related to the thermocline variations in the Indian Ocean unlike in observations which could be a reason for the poor prediction skill of early phase of IOD and the misrepresentation of Monsoon relationship with Indian Ocean SST.
On the other hand, cyclonic (anticyclonic) wind over souther Indian Ocean associated with negative (positive) dipole simulated in the model drives shallow (deep) MLD results in the westward extension of the warm(cold) SST over the east pole of IOD. Monsoon
162
Chapter 6. Summary relationship with Indian Ocean SST is to be corrected to further improve the seasonal prediction skill of Monsoon.
In summary, strong monsoon conditions (or La-Ni˜ na condition) cause equatorial westerly (which are not present in observations) due to enhancement of symmetrical mode of Hadley cell. Equatorial westerlies drive deep thermocline and deposit warm water to the thermocline level. The cyclonic vortex in the southern central Indian Ocean, associated with equatorial westerly, causes shoaling of MLD and results in the westward extension of warm pool over southeastern Indian Ocean.
6.4
Limitations
The model dependency is considered to be one of the most prominent limitation of this study. However, since most of the state-of-the-art coupled models share common systematic biases like underestimation of land precipitation, overestimation of oceanic precipitation, excessive cooling of tropical Pacific, double-ITCZ of tropical eastern Pacific, etc., most of the results of this experiment can be considered to be valid for other stateof-the-art coupled models as well.
Another limitation of this study is that the internal-ocean dynamics and ocean-atmospherecoupled dynamics are considered as single entity to the sensitivity experiment and hence, the analysis could not separate out the role of internal-ocean dynamics and the oceanatmosphere coupled dynamics. This can be achieved by switching off the atmospheric momentum fluxes to the ocean on another set of sensitivity experiments (together with the current experiments), which is not included in the current study due to practical limitation.
Yet another limitation of this study is that, due to misrepresentation of IOD-monsoon teleconnections in the model, it is unable to quantify the relative role of Indian Ocean and Pacific Ocean coupled dynamics in the real world. The ENSO-Modoki flavor of
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Chapter 6. Summary teleconnection with AISMR is not captured in the models and this also limits the realistic representation of monsoon teleconnections. Almost all the models have similar limitations, but they help to get better understanding of the processes involved in the real world through carefully designed sensitivity experiments.
6.5
Future Scope
This study points out the requirement for the improvement of Indian Ocean SST monsoon relationship in CFSv2 model. This could result in quantum improvement in the seasonal prediction skill of monsoon, since the entire skill of the model is currently based on the ENSO-monsoon relationship. This study also suggests that the zonal wind bias over equatorial and southern tropical Indian Ocean may be the reasons for the discrepancies.
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