Editorial Type:
Article
Article Type:
Research Article

A Revised Framework for Analyzing Soil Moisture Memory in Climate Data: Derivation and Interpretation

Sonia I. Seneviratne Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

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Randal D. Koster Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 Feb 2012
DOI:
https://doi.org/10.1175/JHM-D-11-044.1
Page(s):
404–412
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Abstract

A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).

Corresponding author address: Sonia I. Seneviratne, Institute for Atmospheric and Climate Science, Universitätsstrasse 16, ETH Zurich, CH-8092 Zürich, Switzerland. E-mail: sonia.seneviratne@env.ethz.ch

Abstract

A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land–Atmosphere Coupling Experiment (GLACE).

Corresponding author address: Sonia I. Seneviratne, Institute for Atmospheric and Climate Science, Universitätsstrasse 16, ETH Zurich, CH-8092 Zürich, Switzerland. E-mail: sonia.seneviratne@env.ethz.ch
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Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Betts, A. K., 2004: Understanding hydrometeorology using global models. Bull. Amer. Meteor. Soc., 85, 1673–1688.

  • Delworth, T. L., and Manabe S. , 1988: The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Climate, 1, 523–547.

    • Search Google Scholar
    • Export Citation

  • Guo, Z.-C., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7, 590–610.

    • Search Google Scholar
    • Export Citation

  • Hirschi, M., and Coauthors, 2011: Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat. Geosci., 4, 17–21, doi:10.1038/ngeo1032.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Milly P. C. D. , 1997: The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. J. Climate, 10, 1578–1591.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Suarez M. J. , 2001: Soil moisture memory in climate models. J. Hydrometeor., 2, 558–570.

  • Koster, R. D., and Coauthors, 2004a: Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeor., 5, 1049–1063.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2004b: Regions of strong coupling between soil moisture and precipitation. Science, 305, 1138–1140.

  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7, 590–610.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., Mahanama S. P. P. , Livneh B. , Lettenmaier D. P. , and Reichle R. H. , 2010a: Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nat. Geosci., 3, 613–616.

    • Search Google Scholar
    • Export Citation

  • Koster, R. D., and Coauthors, 2010b: Contribution of land initialization to subseasonal forecast skill: First results from a multi-model experiment. Geophys. Res. Lett., 37, L02402, doi:10.1029/2009GL041677.

    • Search Google Scholar
    • Export Citation

  • Lorenz, R., Jaeger E. B. , and Seneviratne S. I. , 2010: Persistence of heat waves and its link to soil moisture memory. Geophys. Res. Lett., 37, L09703, doi:10.1029/2010GL042764.

    • Search Google Scholar
    • Export Citation

  • Mahanama, S. P. P., and Koster R. D. , 2003: Intercomparison of soil moisture memory in two land surface models. J. Hydrometeor., 4, 1134–1146.

    • Search Google Scholar
    • Export Citation

  • Mahanama, S. P. P., and Koster R. D. , 2005: AGCM biases in evaporation regime: Impacts on soil moisture memory and land–atmosphere feedback. J. Hydrometeor., 6, 656–669.

    • Search Google Scholar
    • Export Citation

  • Orlowsky, B., and Seneviratne S. I. , 2010: Statistical analyses of land–atmosphere feedbacks and their possible pitfalls. J. Climate, 23, 3918–3932.

    • Search Google Scholar
    • Export Citation

  • Schlosser, C. A., and Milly P. C. D. , 2002: A model-based investigation of soil moisture predictability and associated climate predictability. J. Hydrometeor., 3, 483–501.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., Lüthi D. , Litschi M. , and Schär C. , 2006a: Land–atmosphere coupling and climate change in Europe. Nature, 443, 205–209.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., and Coauthors, 2006b: Soil moisture memory in AGCM simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) data. J. Hydrometeor., 7, 1090–1112.

    • Search Google Scholar
    • Export Citation

  • Seneviratne, S. I., Corti T. , Davin E. L. , Hirschi M. , Jaeger E. B. , Lehner I. , Orlowsky B. , and Teuling A. J. , 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev., 99 (3–4), 125–161, doi:10.1016/j.earscirev.2010.02.004.

    • Search Google Scholar
    • Export Citation

  • Taylor, C. M., and Ellis R. J. , 2006: Satellite detection of soil moisture impacts on convection at the mesoscale. Geophys. Res. Lett., 33, L03404, doi:10.1029/2005GL025252.

    • Search Google Scholar
    • Export Citation
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