Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere ...

ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 4, 273−279

Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere Land System (FROALS) Model HAN Zhen-Yu1, 2, ZHOU Tian-Jun1, and ZOU Li-Wei1 1

State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Graduate University of the Chinese Academy of Sciences, Beijing 100049, China Received 23 March 2012; revised 19 April 2012; accepted 20 April 2012; published 16 July 2012

Abstract The authors examine the Indian Ocean sea surface temperature (SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System (FROALS) model. The regional coupled model exhibits pronounced cold SST biases in a large portion of the Indian Ocean warm pool. Negative biases in the net surface heat fluxes are evident in the model, leading to the cold biases of the SST. Further analysis indicates that the negative biases in the net surface heat fluxes are mainly contributed by the biases of sensible heat and latent heat flux. Near-surface meteorological variables that could contribute to the SST biases are also examined. It is found that the biases of sensible heat and latent heat flux are caused by the colder and dryer near-surface air in the model. Keywords: Indian Ocean, SST biases, FROALS, evaluation Citation: Han, Z.-Y., T.-J. Zhou, and L.-W. Zou, 2012: Indian Ocean SST biases in a Flexible Regional Ocean Atmosphere Land System (FROALS) model, Atmos. Oceanic Sci. Lett., 5, 273–279.

1

Introduction

The Indian Ocean (IO) plays a crucial role in the Asian-Australian (A-A) monsoon variability due to the importance of both the land-sea thermal contrast in controlling the strength of A-A monsoon and the sea surface temperature (SST)-convection relationship. It is believed that the interannual variability of Indian monsoon activity depends heavily on air-sea interactions that take place during the travel of the monsoon current across the ocean (Shukla, 1975; Webster et al., 1999; Meehl and Arblaster, 2002). The significant impacts of the IO on East Asian and western North Pacific monsoon variability have also been identified in recent years (Li et al., 2008; Xie et al., 2009; Wu et al., 2010). Partly due to their increased resolution and better orographic representation, regional climate models (RCMs) have played active roles in regional climate studies. RCMs generally show better performance than AGCMs over the IO (e.g., Dash et al., 2006; Dobler and Ahrens, 2010; Mukhopadhyay et al., 2010; Polanski et al., 2010; Lucas-Picher et al., 2011). However, many RCMs still show low skill in simulating precipitation over the oceanic regions, partly due to the absence of two-way air-sea Corresponding author: ZHOU Tian-Jun, [email protected]

interaction. Recent studies have shown that coupled regional climate models (CRCMs) generally improve the simulation (Seo et al., 2008; Ratnam et al., 2009). A Flexible Regional Ocean Atmosphere Land System (FROALS) model was established at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics (LASG, IAP) (Zou and Zhou, 2011, 2012). An evaluation of the model performance over the western North Pacific found that the SST shows cold biases, which partly stem from the overestimation of convection frequency by the atmospheric model (Zou and Zhou, 2011). However, the performance of this model over the IO is unknown. The main motivation of the current study is to assess the performance of the FROALS model over the IO. We attempt to address the following questions: (1) How well does the FROALS model capture the climatological features of the IO SST? (2) What are the major sources of the SST biases? The remainder of the paper is organized as follows. A brief description of the FROALS model, its experimental design, and the datasets employed in this study are described in section 2. Section 3 compares the simulation results from FROALS with the observational data. Finally, conclusions are given in the last section.

2 2.1

Model, experiment, and data description Model description

The atmospheric component of FROALS is Regional Climate Model version 3 (RegCM3), which was developed at the Abdus Salam International Centre for Theoretical Physics (ICTP) (Pal et al., 2007). The oceanic component is the Princeton Ocean Model version 2000 (POM2K), which was developed at Princeton University (see http://www.aos.princeton.edu/WWWPUBLIC/htdocs. pom/ for details). The RegCM3 and POM2K models are coupled through the Ocean Atmosphere Sea Ice Soil 3.0 (OASIS3.0) coupler (Valcke, 2006). Further details about this model can be found in Zou and Zhou (2011, 2012). Different from the original version (Zou and Zhou, 2011, 2012), a radiation method is used along the open boundaries of the ocean to allow for stable, long-term integrations. We require that the normal barotropic velocities satisfy the following radiation condition, according to Flather (1976): Ub = Uobs + (c/H)(ηb−1 − ηobs), (1)

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ATMOSPHERIC AND OCEANIC SCIENCE LETTERS

where subscripts b and b−1 denote the boundary point and the interior point closest to b, respectively; obs denotes the observation; U denotes the normal barotropic velocity; c = (gH)1/2, the local shallow water wave speed; H denotes the water depth; and η denotes the surface elevation. Because the fresh water flux is not included in the coupling process, to reduce unrealistic salinity changes in the mixed layer, a weak relaxation of salinity to the monthly climatology in the upper five layers of the model is applied. 2.2

Experimental design and observational data

In this study, the FROALS model was set up over a broad IO domain from 20°S to 40°N, 40°E to 120°E. The Tibet Plateau is included to avoid steep orography along the boundary regions. The horizontal resolution of the RegCM3 is 60 km, and there are 18 vertical levels. RegCM3 is initialized on 1 January 1995 using the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset (Kalnay et al., 1996). The boundary conditions of the RegCM3 are derived from NCEP-NCAR reanalysis and updated every six hours. The horizontal resolution of the POM2K is 0.5°, and there are 30 vertical levels (see Table 1). The level thickness varies non-uniformly, with a higher resolution in the upper ocean and a lower resolution in the deep ocean. The minimum and maximum depths for the model are 5 m and 5000 m, respectively. The initial conditions of the POM2K are derived from Simple Ocean Data Assimilation (SODA) 2.1.4 monthly data (Carton and Giese, 2008). The climatological ocean current and surface elevation from SODA are also employed for the oceanic boundary conditions. The air-sea coupling frequency is three hours. After a spin-up of three years, a consecutive 10-year simulation from 1998 to 2007 is performed. In the following analysis, the model’s monthly climatology is constructed based on the 10-year model outputs. To assess the model performance, the following observational products are used: 1) In ocean model outputs, the SST is defined as the mean temperature over the first layer of several meters thick, which may not be consistent with some observational products (Donlon et al., 2002). Table 1 Layer

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Therefore, we use the mean temperature of the upper 5 m (T5m) to represent the SST for both model outputs and observations. The observed SST used in the evaluation is the monthly mean T5m from SODA. 2) Because there are significant uncertainties in the surface heat flux and radiation estimates, two products of the monthly mean surface turbulent fluxes, near-surface meteorological variables (Objectively Analyzed air-sea Fluxes (OAFlux) (Yu and Weller, 2007) and Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF) 2c (Chou et al., 2003)) and surface radiation (International Satellite Cloud Climatology Project-Flux Data (ISCCP-FD) (Zhang et al., 2004) and Surface Radiation Budget (SRB) 3.0 (Gupta et al., 2006)) are used to evaluate the model outputs. Here, we deduct the shortwave penetration into the subsurface (deeper than 5 m) for both model outputs and observations. The deduction factor is 0.296, according to a simple subsurface heating parameterization (Paulson and Simpson, 1977), SWpen =SW0 ( R ⋅ e −5 / a1 + (1 − R )e −5 / a2 ) , (2)

where R (=0.62) is a separation constant, and a1 (=0.6 m) and a2 (=20.0 m) are the attenuation length scales; SWpen denotes the shortwave penetration into the subsurface; and SW0 denotes the shortwave radiation at the surface. Then, the ensemble observation of surface turbulent fluxes (surface radiations) is the arithmetic average value of OAFlux and GSSTF (ISCCP and SRB). Finally, these observed fields and model outputs are all remapped onto a uniform 0.5×0.5 horizontal grid.

3

Results

The spatial distribution of annual mean SST biases in the IO from FROALS along with the SST from SODA are shown in Figs. 1a−b. The observed SST is characterized by a warm pool (>28°C) to the north of 10°S, except for the northwestern Arabian Sea, and shows a strong negative meridional gradient to the south of 10°S. Cold biases in the SST are evident in the board regions of the warm pool, with the greatest values in the Bay of Bengal (BoB), where the biases are larger than 1.5°C. Warm biases are found over the western Arabian Sea, the western

Values of sigma coordinates used in the ocean model. Sigma coordinate

Layer

Sigma coordinate

Layer

Sigma coordinate

1

0.0000

11

–0.1739

21

–0.6087

2

–0.0007

12

–0.2174

22

–0.6522

3

–0.0014

13

–0.2609

23

–0.6957

4

–0.0027

14

–0.3043

24

–0.7391

5

–0.0054

15

–0.3478

25

–0.7826

6

–0.0109

16

–0.3913

26

–0.8261

7

–0.0217

17

–0.4348

27

–0.8696

8

–0.0435

18

–0.4783

28

–0.9130

9

–0.0870

19

–0.5217

29

–0.9565

10

–0.1304

20

–0.5652

30

–1.0000

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HAN ET AL.: INDIAN OCEAN SST BIASES IN FROALS

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Figure 1 (a) Annual mean SST from SODA, (b) difference between FROALS and SODA (units: °C), (c) annual mean net surface heat flux from ensemble observation (see text for details), and difference between FROALS and ensemble observation (units: W m−2) for (d) net surface heat flux, (e) shortwave radiation, (f) latent heat flux, (g) longwave radiation, and (h) sensible heat flux. Rectangles: NIO (the northern Indian Ocean) and EIO (the equatorial Indian Ocean) denote 8°N–15°N, 65°E–97°E and 5°S–3°N, 60°E–95°E.

boundary near the equator, and the southern Tropical Indian Ocean. The SST biases are possibly related to the errors in the surface heat fluxes. The annual mean surface net heat flux (Qnet) observed and the biases in the FROALS are shown

in Figs. 1c−d. Based on the ensemble observation, which is defined as the sum of ensemble surface turbulence heat fluxes and radiation, the Qnet is positive (warming the ocean, positive downward) over most of the ocean basin, with a magnitude of several tens of watts per square meter.

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There are strong maxima at the western boundary near the equator, with lesser maxima off Somalia and Sumatra, while heat losses are found in the northern Arabian Sea. The FROALS has negative Qnet biases (i.e., insufficiently warming the ocean) in almost the entire region. Positive biases are only found in small regions near the coastline in the model. The nearly universal negative biases suggest that the underestimation of Qnet is a major cause of the cold SST biases. The biases in each component of Qnet are further examined to identify which component contributes the most to the negative biases. Figures 1e−h display the biases of surface shortwave radiation (SW), latent heat flux (LH), longwave radiation (LW), and sensible heat flux (SH), respectively. The biases of LH and SH are relative to the ensemble mean of OAFlux and GSSTF, while the biases of SW and LW are relative to the ensemble mean of ISCCP and SRB. Positive biases of LW are found in most of the IO warm pool, except part of the eastern equatorial Indian Ocean, indicating that the cold SST biases there are not caused by the LW biases. The major components contributing to the negative net surface heat flux biases are SW, LH, and SH. The seasonal variations of SST simulated by the FROALS are also evaluated. For brevity, we choose two regions with significant cold biases: the northern Indian Ocean (NIO: 8°N−15°N, 65°E−97°E) and the equatorial Indian Ocean (EIO: 5°S−3°N, 60°E−95°E), shown in Fig. 1b. In the observation, the NIO shows a bimodal SST distribution. This seasonality of the SST in the NIO is reasonably simulated by the FROALS, although the

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simulated values are systematically approximately 2°C colder throughout the year (Fig. 2a). In the EIO, the picture is quite different: the seasonal cycle of SST is dominated by the annual signal, but the FROALS features a strong semiannual pattern. The SST in the EIO also suffers significant cold biases over all months, with an average of 0.5°C to 1°C (Fig. 3a). A similar analysis of Qnet shows that the cold SST biases are associated with the model’s tendency to produce less Qnet year-round (not shown). To determine the main bias sources for the seasonal cycle of SST, the seasonal evolutions of the climatological SW, LH, and SH are displayed in Figs. 2−3. In the NIO, cooling by the LH is strongest in winter (Fig. 2c), mostly due to the cold and dry northeasterly winds (Figs. 4a−c). The spring is marked by peak SW (Fig. 2b), resulting in the strongest net surface heating of the year (not shown). The enhanced latent heat loss in summer by southwesterly winds (Fig. 4a) again favors cooling. These features are all well simulated, while the model has approximately 20 W m–2 LH cooling biases from April to September (Fig. 2c) and less than 10 W m–2 SW cooling biases from July to September (Fig. 2b). The SH mostly varies between 0 and −10 W m–2, with minimum loss during July to August. The simulated SH indicates a clear semiannual signal with maximum loss in both June and December; it contributes 10−15 W m–2 negative biases throughout the year on the same order of magnitude as the absolute value of SH (Fig. 2d). In the EIO, the SW exhibits a bimodality with one additional significant peak in the autumn, and the SST is cooled via LH by strong winds (Fig. 4d) in the summer.

Figure 2 The seasonal cycle (upper panel) and the biases (lower panel) of the (a) SST (units: °C), (b) shortwave radiation (units: W m−2), (c) latent heat flux (units: W m−2), and (d) sensible heat flux (units: W m−2) averaged over the northern Indian Ocean.

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Figure 3 Same as Fig. 2 but for the equatorial Indian Ocean.

FROALS simulation exhibits SW warming biases from December to April with a magnitude of 10–20 W m–2 (Fig. 3b). The LH values are close to those from the observations, but the phase of the seasonal cycle in the FROALS is less satisfactory (Fig. 3c). The seasonal variations of SH are well simulated, showing maximum loss during summer, but there are still negative biases of approximately 6 W m−2 throughout the year (Fig. 3d). The cooling biases in LH and SH are systematic and can be found in different regions and months. Therefore, the errors in LH and SH appear to be the primary sources of cold SST biases in most regions. These negative biases of LH and SH may further stem from the errors of near-surface meteorological variables and SST. The equations for LH and SH are LH~ws(qa−qs(SST)), SH~ws(ta−SST), (3) where qa is surface air humidity; qs is air saturated humidity; ta is air temperature; and ws is wind speed. The cold errors in SST favor a lower release of both LH and SH. The biases of near-surface meteorological variables averaged over the two regions are shown in Fig. 4. The wind speed in the model is less than that from either OAFlux or GSSTF (Figs. 4a and 4d), which tends to induce smaller absolute values of both LH and SH. The colder and dryer sea surface air in the model (Figs. 4b and 4e; Figs. 4c and 4f) may result in higher absolute values of the surface turbulence fluxes.

4

Conclusions and discussion We assessed the performance of the FROALS model

over the IO by performing a consecutive 10-year simulation from 1998 to 2007. The results show that the cold biases of SST are evident in the simulation, covering most of the IO warm pool. The value of cold SST biases can exceed 1.5°C in the BoB. The seasonal variations of SST in the NIO show a semiannual cycle, whereas those in the EIO show an annual cycle. Only the former can be reasonably simulated by FROALS. The cold biases of SST persist throughout the year in both regions. The cold SST biases are primarily caused by errors in Qnet. Among the four components of Qnet, LH and SH together contribute more than 10 (30) W m–2 negative biases in the EIO (NIO) year round, and SW only contributes less than 10 W m–2 negative biases from July to September. In contrast, the biases of LW are positive, and thus, they do not cause the negative biases in Qnet. Therefore, the errors in LH and SH are the major sources of the cold SST biases. Analysis of near-surface meteorological variables shows that the negative biases of LH and SH are further due to the colder and drier near-surface air in the model. Warm bias errors are found in the SST over the western Arabian Sea, the western boundary near the equator, and southern Tropical Indian Ocean. The pattern of warm errors over the western Indian Ocean is similar to that of the state-of-the-art CGCMs (Bollasina and Nigam, 2009) and the CRCM (Seo et al., 2008) in summer. Their results show that a damped Somali jet in the models leads to a large warm bias in the upwelled water. In FROALS, the large negative biases in Qnet cannot lead to warm SST biases over these regions. Thus, the errors in the upper ocean processes may also contribute to the SST biases, which deserve further study.

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Figure 4 The seasonal cycle of the (a, d) wind speed (units: m s−1), (b, e) air temperature (units: °C), and (c, f) air humidity (units: g kg−1) averaged over (a−c) the northern Indian Ocean and (d−f) the equatorial Indian Ocean.

Acknowledgements. This study was supported by the National High Technology Research and Development Program of China (863 Program, Grant No. 2010AA012304).

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