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Beena Balan Sarojini, Andreas Becker, Aiguo Dai, Paul J. Durack, David Easterling, Hayley J. ..... Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams,.
UPPLEMENT CHALLENGES IN QUANTIFYING CHANGES IN THE GLOBAL WATER CYCLE Gabriele C. Hegerl, Emily Black, Richard P. Allan, William J. Ingram, Debbie Polson, Kevin E. Trenberth, Robin S. Chadwick, Phillip A. Arkin, Beena Balan Sarojini, Andreas Becker, Aiguo Dai, Paul J. Durack, David Easterling, Hayley J. Fowler, Elizabeth J. Kendon, George J. Huffman, Chunlei Liu, Robert Marsh, Mark New, Timothy J. Osborn, Nikolaos Skliris, Peter A. Stott, Pier-Luigi Vidale, Susan E. Wijffels, L aura J. Wilcox, K ate M. Willett, and Xuebin Zhang by

This document is a supplement to “Challenges in Quantifying Changes in the Global Water Cycle,” by Gabriele C. Hegerl, Emily Black, Richard P. Allan, William J. Ingram, Debbie Polson, Kevin E. Trenberth, Robin S. Chadwick, Phillip A. Arkin, Beena Balan Sarojini, Andreas Becker, Aiguo Dai, Paul J. Durack, David Easterling, Hayley J. Fowler, Elizabeth J. Kendon, George J. Huffman, Chunlei Liu, Robert Marsh, Mark New, Timothy J. Osborn, Nikolaos Skliris, Peter A. Stott, Pier-Luigi Vidale, Susan E. Wijffels, Laura J. Wilcox, Kate M. Willett, and Xuebin Zhang (Bull. Amer. Meteor. Soc., 96, 1097–1115) • ©2015 American Meteorological Society • Corresponding author: Gabriele Hegerl, GeoSciences, Grant Institute, Kings Buildings, W. Mains Rd., Edinburgh EH9 3JW, United Kingdom • E-mail: [email protected] • DOI:10.1175/BAMS-D-13-00212.2

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R O C E S S I N G O F S AT E L L I T E PRECIPITATION DATA . A major challenge for space-based precipitation observations is the intermittency of precipitation and sampling in time. Retrievals are snapshots, and a substantial source of error can be how these are converted to hourly or daily values. In general, full accounting for intensity and duration of precipitation events has not been done. The Global Precipitation Climatology Project (GPCP) dataset (Adler et al. 2003) provides estimates of root-mean-square error for each gridbox value, primarily as a function of sensor type, location, and input data sampling. However, for climate applications the bias is also a dominant concern. This parameter has been highly resistant to estimation, with Smith et al. (2006) and Adler et al. (2012) providing experimental schemes for estimating the bias. Energy imbalance considerations suggest that the global average GPCP precipitation is low by 20% (Stephens AMERICAN METEOROLOGICAL SOCIETY

et al. 2012), but this conflicts with an upper bound estimate for the bias of around 9% in Adler et al. (2012), a suggested adjustment of 5% in Trenberth et al. (2009), and the close agreement between the current GPCP and the oceanic value reported in Behrangi et al. (2012). The premicrowave (MW) portion of the GPCP record (from 1979 to mid-1987) depends on an alternative dataset based on the outgoing longwave radiation (OLR) precipitation index (OPI; Xie and Arkin 1998). The OPI’s dynamic range is smaller than subsequent MW-driven estimates, which may affect the statistics of extremes and perhaps even long-term changes. Consequently, it is generally suggested that long-term statistics should be computed both from 1979 and mid-1987 to assess possible affects from the OPI. However, over land the GPCP is a hybrid product that incorporates a high-quality precipitation gauge analysis over the entire record (Schneider et al. 2015) and so the bias over land should be minimal throughout. JULY 2015

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Fig. ES1. Hydrographic (salinity and temperature) ocean profile data used in the analysis of Durack and Wijffels (2010). Global coverage for 5-yr temporal bins, from (top left) 1950–55 to (bottom right) 2005–10. Note the improved spatial and temporal observational coverage over the 2000–10 period compared to earlier periods because of the Argo float data.

In general, incorporating precipitation gauge data, even at the monthly scale, is highly effective at controlling bias in the final combination for total or mean amount where the gauges exist. Reviews by Kidd and Huffman (2011) and Tapiador et al. (2012), among others, provide summaries of the algorithms, and the International Precipitation Working Group maintains lists of publicly available, long-term, quasi-global datasets (www.isac.cnr.it/~ipwg/data /datasets.html). The above discussion applies to estimating total precipitation. The challenges are larger for estimating precipitation characteristics, such as intensity. QUALITY CONTROL AND HOMOGENEITY OF IN SITU AND GRIDDED DATA. It is beyond the scope of the present paper to discuss homogeneity problems in station data and satellite data in detail. We refer to the literature (e.g., Easterling et al. 1999; Sevruk 1982; Doesken 2003; Kunkel et al. 2007) for discussion of the difficulty of measuring precipitation in the form of snow. A large body of work exists on identifying inhomogeneities (e.g., Schneider et al. 2014), but the extent of homogeneity checks applied varies between datasets.

1901−10

1911−20

1921−30

1931−40

Precipitation observations from rain gauges are particularly susceptible to measurement problems that can impact efforts to quantify the hydrological cycle. The two main problems with gauge data are the use of gauges with different measurement mechanisms and the undercatch problem associated with windy conditions. This depends on whether the gauge has a wind shield or not and is particularly problematic for snow, light rain, or any precipitation in high wind conditions (Easterling et al. 1999; see also Sevruk 1982), along with other problems associated with frozen precipitation (Doesken 2003; Kunkel et al. 2007). Depending on the characteristics of the undercatch correction applied for a particular dataset, this can lead to either a long-term increase or decrease in precipitation, especially in a warming climate as rain partly replaces snow. Ensuring temporal homogeneity and quality of rain gauge data collected worldwide from a variety of sources poses a particular challenge (e.g., Schneider et al. 2014). Annual and monthly precipitation totals cover a wide range, with high variability for shorter sampling durations. This variability makes it impossible to apply fully automated schemes that would simply screen out data exceeding

1941−50

1951−60

1961−70

1971−80

1981−90

1991−00

2001−10

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60

40

20 latitude 0

−20

−40

−60 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 0 10 20 averaged number of stations per 2.5 degree grid cell

Fig. ES2. Station number over time from in situ stations per 2.5° grid box from Global Precipitation Climatology Centre (GPCC)-full data version6 (Schneider et al. 2011). Note that more data are used compared to other land precipitation datasets (see, e.g., Fig. 2 for CRU TS3.21) because of the different inclusion criteria and the larger data archive of GPCC. AMERICAN METEOROLOGICAL SOCIETY

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Table ES1. Key datasets for observing long-term changes discussed in the body of the paper. For more information see Schneider et al. (2014) and the website referred to there, and for precipitation data see the website of the International Precipitation Working Group (www.isac.cnr.it/~ipwg/data/datasets.html). Dataset

Publication

Temporal Spatial coverage, coverage, grid size; most most robust robust region period

Comments, including on longterm reliability

In situ precipitation CRU TS

Harris et al. (2014), updated to CRU TS3.22

1901–2013, best coverage for 1951–95

Land precipitation Interpolation from time-varying coverage 0.5° × 0.5° of stations may affect long-term trends, unless data are masked to use only grid boxes in the vicinity of available station observations. No corrections for longterm inhomogeneities, though in some cases corrections have been applied by data sources (e.g., National Meteorological Services).

Zhang

Zhang et al. (2007), updated

1900–2009

Land precipitation Only grid boxes with station data, no 5° × 5° interpolation. Only long-term observed stations; hence, sparse coverage, no further homogeneity check prior to gridding.

GPCC-full data version6 product

Becker et al. (2013); Schneider et al. (2011)

1901–2010 (>30,000 stations), most robust from 1951–2005

L a n d p r e c i p i t a - Very large set of stations (>65,000), but t i o n 0 . 5° × 0 . 5°, changing data coverage may affect long1. 0 ° × 1. 0 °, a n d term trends. 2.5° × 2.5°

GPCC-Variability Analysis of Surface Climate Observations (VASClimO)

Beck et al. (2005)

1951–2000, 9,343 stations for all months

Land precipitat i o n 0 . 5° × 0 . 5°, 1. 0 ° × 1. 0 °, a n d 2.5° × 2.5°

Global Historical Climate Network (GHCN) monthly

Lawrimore et al. (2011); Vose et al. (1992)

1900–present

Land precipitation >20,000 stations 5° × 5°

Based on long-term most stable stations, follower dataset Homogenized Precipitation Analysis (HOMPRA) based on 17,000 stations under development.

Gridded satellite precipitation Precipitation: GPCP

Adler et al. (2003) (ftp:// precip.gsfc.nasa.gov /pub/gpcp-v2.2/doc /V2.2_doc.pdf)

1979–present, G lob al , mos t ro - Consistent satellite approach since midbest since b u s t 50 ° N – 50 ° S , 1987; earlier is a reasonable alternative mid-1987 2.5° × 2.5° calibrated to the later period; entire record over land is affected by quality of GPCC (above).

Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)

Funk et al. (2014)

1981–present

50°S – 50°N, 0.05° Thermal infrared (TIR) imagery and resolution Tropical Rainfall Measuring Mission (TRMM), adjusted with station data. Changing station data coverage may affect long-term trends.

Specific humidity (and other humidity variables): HadISDH

Willett et al. (2013) (www.metoffice.gov.uk /hadobs/hadisdh)

1973–present

L a n d o n l y, g l o b - Based on homogenized and quality al, 5° × 5° controlled Integrated Surface Database (ISD) station data, uncertainty estimates available.

Specific humidity: NOCSv2.0

Berry and Kent (2009, 2011) (www.noc.soton .ac.uk/ooc /CLIMATOLOGY/noc2 .php)

1971–present

Best in Nor thern Hemisphere, substantial uncertainty elsewhere, 1° × 1°

In situ humidity

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Based on International Comprehensive Ocean–Atmosphere Data Set (ICOADS) ship data, bias adjusted for ship height and instrument change, uncertainty estimates available.

Table ES1. Continued. Dataset

Publication

Temporal Spatial coverage, coverage, grid size; most most robust robust region period

Comments, including on longterm reliability

In situ humidity Specific and relative humidity

Specific humidity and relative humidity: HadCRUH

Dai (2006)

1976–2003,

L a n d a n d o c e a n , Based on ISD station data and ICOADS global, 5° × 5° ship and buoy data.

Dai et al. (2011)

1973–2008

Over land, station Radiosonde based, homogenized. data (2.5° × 2.5°)

Willett et al. (2008) (www.metoffice.gov.uk /hadobs/hadcruh/)

1973–2003

L a n d a n d o c e a n , Based on homogenized and quality global, 5° × 5° controlled ISD station data and ICOADS ship and buoy data.

Wentz (1997); Wentz et al. (2007)

1987–present

Over oceans 1° × 1° Combined satellite products using SSM/I and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) instruments. Global, upper 2000 Globally gridded combines ship obserdbar, 2° × 1° vations; Argo data to generate salinity change estimates; changes presented as 50-yr trends calculated from the 1950–2008 analysis period (see Fig. ES1).

Satellite humidity Tropospheric total column water vapor

Salinity (in situ and satellite) In situ salinity change estimates

Durack and Wijffels (2010); Commonwealth Scientific and Industrial Research Organisation (CSIRO; www.cmar .csiro.au/oceanchange/)

1950–2008

Satellite

Soil Moisture Ocean Salinity (SMOS); Aquarius (www.nasa.gov/mission_ pages/aquarius/overview/ index.html).

Dec 2009 on (SMOS) Jun 2011 on (Aquarius)

Instrument collects data in 386-km-wide swaths

Evaporation and freshwater flux (E – P) over ocean

HOAPS (Fairall et al. 1996, 2003; Fennig et al. 2012) (www.hoaps.zmaw .de/)

1987–2008

G l o b a l i c e - f r e e Satellites, using SSM/I; also available from ocean, 0.5° × 0.5° passive microwave water cycle dataset [Remote Sensing Systems (RSS)].

Evaporation over ocean

Woods Hole OceanoDaily, 1985 on; 1° × 1° grid graphic Institution monthly 1958 (WHOI) (Yu 2007; Yu and on Weller 2007; http://oaflux. whoi.edu/evap.html).

some threshold. Therefore, any reliable quality control (QC) procedure is a trade-off between a quick but rather conservative screening and preserving as much information as possible while still identifying and omitting erroneous data. Some or all of the following QC measures are actually taken when generating gridded datasets (see Table S1): checking data and station provenance, metadata identification and harmonization, checking for coding, conversion, and other obvious errors, checking of spatial consistency of data with neighboring stations, cross-checking the same data information received from different sources, building representative station-specific AMERICAN METEOROLOGICAL SOCIETY

Global, top 1–2 cm Three passive microwave radiometers of ocean detecting surface emission (0.2 PSS-78 precision).

Satellite and reanalysis based with error estimates; contains an imbalance with oceans gaining heat (Berry and Kent 2011).

background statistics to check new data against, flagging data as questionable in a first screening step for a later manual examination instead of immediate rejection, and comparison of gridded products against similar observational and reanalysis products. Harmonization and loading of the station data into a relational database system is a powerful tool, in particular for cross-checking data that should be redundant. When dealing with daily or subdaily totals, QC is additionally challenged by potential ambiguities with regard to the time referencing of the data and the method utilized to calculate the reported total (van den Besselaar et al. 2012). JULY 2015

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The assembly of data on a regular grid poses the next challenge, with methods ranging from spatial interpolation (see Fig. ES2; Becker et al. 2013; Harris et al. 2014) to averaging available long-term homogeneous stations over 5° grid boxes (Zhang et al. 2007). In contrast to temperature, estimates of multiyear precipitation anomalies are not much more reliable than estimates of annual or seasonal precipitation anomalies in data-sparse regions, as spatial covariance for longer time averages is only slightly stronger than for shorter time averages (Wan et al. 2013). BACKGROUND TO HUMIDITY VARIABLES. Specific humidity is the ratio of the mass of water vapor to the mass of air, while relative humidity gives the fractional saturation of the air. Traditionally, humidity is observed in situ by comparing a wet-bulb temperature with the simultaneous dry-bulb temperature. Errors arise if the reservoir is allowed to dry out or freeze from poor ventilation in light winds or icy conditions or from inaccurate ice-bulb versus wet-bulb assumptions made during calculation when the air temperature is close to zero. The dewpoint temperature and relative humidity can be directly observed either through chilling a surface (in the former) or, more commonly, through changes in voltage in a capacitance sensor. As conversions between humidity variables are nonlinear, applying them to time means introduces errors. Humidity data are prone to inhomogeneity because of changes in station location or instrument type/practice on land and ship heights and instrument type over ocean. More robust products have undergone homogenization [e.g., Hadley Centre integrated surface humidity dataset (HadISDH) and Hadley Centre and Climatic Research Unit (CRU) Global Surface Humidity dataset (HadCRUH) land data] and bias adjustment [e.g., the National Oceanography Centre Surface Flux dataset version 2.0 (NOCSv2.0) marine data], and have uncertainty estimates (NOCSv2.0 and HadISDH) available. Static in situ products, using stations over land and both ships and buoys over ocean, exist from Dai and HadCRUH for specific humidity and relative humidity (see Table ES1). NO C Sv 2 . 0 (w w w . n o c . s o t o n . a c . u k / o o c /CLIMATOLOGY/noc2.php) is an annually updated marine specific humidity product from ships only. HadISDH (www.metoffice.gov.uk/hadobs/hadisdh) is an annually updated land-specific humidity product (other humidity variables are planned). There is generally good agreement between all in situ products. Dai and HadCRUH show a sharp decline in marine relative humidity in 1982, and the origin of this change might be climatic (Berry and Kent 2009) or because of errors in the data (Willett et al. 2008). Satellite estimates of near-surface specific humidity from Special Sensor Microwave Imager (SSM/I) brightness temperatures also exist as part of the Hamburg ES108 |

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Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) dataset (Bentamy et al. 2002; www.hoaps .zmaw.de).

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