Regions of strong coupling between soil moisture and precipitation

Science

Volumn 305, Issue 5687, 2004, Pages 1138-1140

  Koster, Randal D ^a   Dirmeyer, Paul A ^b   Guo, Zhichang ^b   Bonan, Gordon ^c   Chan, Edmond ^d   Cox, Peter ^e   Gordon, C T ^f   Kanae, Shinjiro ^g   Kowalczyk, Eva ^h   Lawrence, David ⁱ   Liu, Ping ^j   Lu, Cheng Hsuan ^k   Malyshev, Sergey ^l   McAvaney, Bryant ^m   Mitchell, Ken ^k   Mocko, David ^j   Oki, Taikan ⁿ   Oleson, Keith ^c   Pitman, Andrew ^o   Sud, Y C ^a   Taylor, Christopher M ^p   Verseghy, Diana ^d   Vasic, Ratko ^q   Xue, Yongkang ^q   Yamada, Tomohito ⁿ   more..

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b Ctr Ocean Land Atmosphere Studs   (United States)

^c NATIONAL CENTER FOR ATMOSPHERIC RESEARCH   (United States)

^d ENVIRONMENT CANADA   (Canada)

^e Hadley Centre for Climate Prediction and Research   (United Kingdom)

^f NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

^g RESEARCH INSTITUTE FOR HUMANITY AND NATURE   (Japan)

^h CSIRO   (Australia)

ⁱ UNIVERSITY OF READING   (United Kingdom)

^j SAIC FREDERICK INC   (United States)

^k CLIMATE PREDICTION CENTER   (United States)

^l PRINCETON UNIVERSITY   (United States)

^m BUREAU OF METEOROLOGY RESEARCH CENTRE   (Australia)

ⁿ UNIVERSITY OF TOKYO   (Japan)

^o MACQUARIE UNIVERSITY   (Australia)

^p CENTRE FOR ECOLOGY AND HYDROLOGY   (United Kingdom)

^q UNIVERSITY OF CALIFORNIA   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; MATHEMATICAL MODELS; MOISTURE; PRECIPITATION (CHEMICAL);

CLIMATE MODELING; SOIL MOISTURE;

SOILS;

RAIN;

ANOMALY; CLIMATE MODELING; FORECASTING METHOD; PRECIPITATION (CLIMATOLOGY); SEASONAL VARIATION; SOIL MOISTURE;

ARTICLE; ATMOSPHERE; CLIMATE; CLINICAL OBSERVATION; DATA ANALYSIS; LAND BIOME; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRECIPITATION; PRIORITY JOURNAL; SEASONAL VARIATION; SOIL MOISTURE; SUMMER;

EID: 4143112257     PISSN: 00368075     EISSN: None     Source Type: Journal    
DOI: 10.1126/science.1100217     Document Type: Article

References (32)

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5. S. Historical soil moisture measurements are mostly confined to Asia (27). Even if global soil moisture fields did exist, using observations to establish that soil moisture affects precipitation is difficult because the other direction of causality is much stronger-precipitation has a first-order impact on soil moisture.
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14. The participating AGCMs are from the following groups. (The order presented here is alphabetical and does not match the order of the histogram bars in Fig. 1.) (i) Bureau of Meteorology Research Centre (BMRC), Australia; (ii) The Canadian Center for Climate Modeling and Analysis (CCCma), Canada; (iii) Center for Climate System Research (CCSR), University of Tokyo, and National Institute for Environmental Studies (NIES), Japan; (iv) Center for Ocean-Land-Atmosphere Studies (COLA), United States; (v) Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia; (vi) NASA/Goddard Space Flight Center Laboratory for Atmospheres, Climate and Radiation Branch, United States (GEOS); (vii) Geophysical Fluid Dynamics Laboratory (GFDL), United States; (viii) Hadley Center (HadAM3), UK; (ix) National Center for Atmospheric Research (NCAR), United States; (x) National Center for Environmental Prediction (NCEP), United States; (xi) NASA Seasonal-to-Interannual Prediction Project [now part of the Global Modeling and Assimilation Office (GMAO)], United States; and (xiii) University of California, Los Angeles (UCLA).
15. GLACE is a joint project of the Global Energy and Water Cycle Experiment (GEWEX) Global Land Atmosphere System Study (GLASS) and the Climate Variability Experiment (CLIVAR) Working Group on Seasonal-to-Interannual Prediction (WGSIP), all under the aegis of the World Climate Research Programme (WCRP).
16. Coupling strength in this report refers to the general ability of land surface moisture anomalies-either local or remote-to affect precipitation in a given region. Inferences regarding soil moisture measurement in the indicated hot spots require an assumption of local influence.
17. The prescribed soil moistures necessarily differed from model to model, because each time series had to be fully consistent with the individual model using it. The prescribed moistures for a given model came, in fact, from one of the model's simulations in the first, "variable soil moisture" ensemble. In GLACE, subsurface moisture refers to a soil moisture prognostic variable having an assigned effective depth of more than 5 cm from the surface. Soil moistures corresponding to shallower depths are not reset in the experiment. For details, see the experiment plan posted on the GLACE Web site: http://glace.gsfc.nasa.gov/.
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21. Indirect (and thus limited in its own right) observational estimates (28) of the North American hot spot roughly agree with that shown in the figure.
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23. Neither equatorial Africa nor the northernmost reaches of Canada are traditionally considered transition zones between wet and arid climates, and the appearance of hot spots in these regions is not so easily explained. On average, the models do show a pronounced sensitivity of evaporation to soil moisture in equatorial Africa, belying its intuitive status as an atmosphere-controlled evaporation regime.
24. For a single model at a single grid cell, an Ω difference of 0.06 is significant at the 95% confidence level. If the grid cells within a region are completely independent, then a regional (averaged over, say, 10 grid cells) Ω difference of 0.002 is significant at the 95% confidence level. The actual 95% confidence value for the bars in the histograms lies somewhere between these two values, because although multiple grid cells contribute to the regional average, the grid cells are not fully independent. A single exact value cannot be computed, because each AGCM uses its own horizontal grid resolution and simulates unique spatial correlation structures.
25. This study has focused on soil moisture effects alone. Vegetation properties and processes also have substantial impacts on climate in a number of regions. For example, Charney et al. (29) describe the importance of surface albedo on North American climate; Dickinson and Henderson-Sellers (30) show the impact of deforestation on Amazonian climate; and Xue et al. (31) demonstrate the importance of vegetation processes on east Asian climate. This issue, like the soil moisture issue, is a subject of continuing investigation.
26. The relative importance of sea surface temperature (SST) forcing in the indicated hot spots cannot be determined in this experiment, for two reasons. (i) The SST signal in either of the two ensembles cannot be separated from the strong seasonality signal associated with the Sun's transit across the sky over the 3-month simulation period. (ii) Only a single year of SSTs is prescribed, so the effect of interannually varying SSTs is not captured. As noted in the text, some studies (8) show a strong SST impact in the tropics and subtropics (encompassing the indicated hot spots in Africa and southern Asia) and a relatively weak impact in midlatitudes (encompassing the North American hot spot).
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31. Y. K. Xue et al., J. Geophys. Res. 109, D03105, 10.1029/2003JD003556 (2004).
32. We thank the listed institutions for the computational support needed to perform the simulations. We also thank M. Kistler for help with the GLACE project Web page and T. Bell for statistical advice.