• Albertson, J. D., , and Kiely G. , 2001: On the structure of soil moisture time series in the context of land surface models. J. Hydrol., 243 , 101119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., , Pegion P. J. , , Schubert S. D. , , and Suarez M. J. , 2000: Atlas of seasonal means simulated by the NSIPP1 atmospheric GCM. NASA Tech. Memo. 2000-104606, Vol. 17, 194 pp.

  • Boer, G. J., , McFarlane N. A. , , and Lazare M. , 1992: Greenhouse gas–induced climate change simulated with the CCC second-generation general circulation model. J. Climate, 5 , 10451077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boer, G. J., , McFarlane N. A. , , and Lazare M. , 1992: Greenhouse gas–induced climate change simulated with the CCC second-generation general circulation model. J. Climate, 5 , 10451077.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonan, G. B., , Oleson K. W. , , Vertenstein M. , , Levis S. , , Zeng Z. , , Dai Y. , , Dickinson R. E. , , and Yang Z-L. , 2002: The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model. J. Climate, 15 , 31233149.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 214 pp. [Available online at http://www.ccsm.ucar.edu/models/atm-cam/docs/description/.].

  • Conaty, A. L., , Jusem J. C. , , Takacs L. , , Keyser D. , , and Atlas R. , 2001: The structure and evolution of extratropical cyclones, fronts, jet streams, and the tropopause in the GEOS general circulation model. Bull. Amer. Meteor. Soc., 82 , 18531867.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cox, P. M., , Betts R. A. , , Bunton C. B. , , Essery R. L. H. , , Rowntree P. R. , , and Smith J. , 1999: The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Climate Dyn., 15 , 183203.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., , and Manabe S. , 1988: The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Climate, 1 , 523547.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., , and Zeng F. J. , 1999: An update to the distribution and treatment of vegetation and soil properties in SSiB. COLA Tech. Rep. 78, 25 pp. [Available from the Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.].

  • Entin, J. K., , Robock A. , , Vinnikov K. Ya , , Hollinger S. E. , , Liu S. , , and Namkhai A. , 2000: Temporal and spatial scales of observed soil moisture variations in the extratropics. J. Geophys. Res., 105 , D9. 1186511877.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Essery, R. L. H., , Best M. J. , , Betts R. A. , , Cox P. M. , , and Taylor C. M. , 2003: Explicit representation of subgrid heterogeneity in a GCM land surface scheme. J. Hydrometeor., 4 , 530543.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Z-C., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part II: Analysis. J. Hydrometeor., 7 , 611625.

  • Hirschi, M., , Seneviratne S. I. , , and Schär C. , 2006: Seasonal variations in terrestrial water storage for major midlatitude river basins. J. Hydrometeor., 7 , 3960.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hollinger, S. E., , and Isard S. A. , 1994: A soil moisture climatology of Illinois. J. Climate, 4 , 822833.

  • Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set. Bull. Amer. Meteor. Soc., 78 , 520.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Kinter, J. L., and Coauthors, 1997: Formulation. Vol. 1. The COLA atmosphere–biosphere general circulation model. COLA Tech. Rep. 51, 46 pp. [Available from the Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.].

  • Koster, R. D., , and Suarez M. J. , 1992: Modeling the land surface boundary in climate models as a composite of independent vegetation stands. J. Geophys. Res., 97 , 26972716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , and Suarez M. J. , 1996: Energy and water balance calculations in the Mosaic LSM. NASA Tech. Memo. 104606, Vol. 9, 59 pp. [Available from NASA Center for Aerospace Information, 800 Elkridge Landing Rd., Linthicum Heights, MD 21090-2934.].

  • 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 , 15781591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., , and Suarez M. J. , 2001: Soil moisture memory in climate models. J. Hydrometeor., 2 , 558570.

  • Koster, R. D., , Suarez M. J. , , and Heiser M. , 2000: Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeor., 1 , 2646.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004a: Regions of strong coupling between soil moisture and precipitation. Science, 305 , 11381140.

  • Koster, R. D., and Coauthors, 2004b: Realistic initialization of land surface states: Impacts on subseasonal forecast skill. J. Hydrometeor., 5 , 10491063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7 , 590610.

  • Kowalczyk, E. A., , Garratt J. R. , , and Krummel P. B. , 1994: Implementation of a soil-canopy scheme into the CSIRO GCM—Regional aspects of the model response. CSIRO Atmospheric Research Tech. Paper 32, Aspendale, Australia, 59 pp.

  • Laio, F., , Porporato A. , , Ridolfi L. , , and Rodriguez-Iturbe I. , 2001: Plants in water-controlled ecosystems: Active role in hydrological processes and response to water stress. II. Probabilistic soil moisture dynamics. Adv. Water Res., 24 , 707723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., , and Avissar R. , 1999a: A study of persistence in the land–atmosphere system using a general circulation model and observations. J. Climate, 12 , 21392153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., , and Avissar R. , 1999b: A study of persistence in the land–atmosphere system with a fourth-order analytical model. J. Climate, 12 , 21542167.

    • Crossref
    • 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 , 11341146.

    • Crossref
    • 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 , 656669.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McFarlane, N. A., , Boer G. J. , , Blanchet J-P. , , and Lazare M. , 1992: The Canadian Climate Centre second-generation general circulation model and its equilibrium climate. J. Climate, 5 , 10131044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGregor, J. L., 1996: Semi-Lagrangian advection on conformal-cubic grids. Mon. Wea. Rev., 124 , 13111322.

  • McGregor, J. L., , and Dix M. R. , 2001: The CSIRO conformal-cubic atmospheric GCM. IUTAM Symposium on Advances in Mathematical Modeling of Atmosphere and Ocean Dynamics, P. F. Hodnett, Ed., Kluwer, 197–202.

    • Search Google Scholar
    • Export Citation
  • Milly, P. C. D., , and Dunne K. A. , 1994: Sensitivity of the global water cycle to the water-holding capacity of land. J. Climate, 7 , 506526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mocko, D. M., , and Sud Y. C. , 2001: Refinements to SSiB with an emphasis on snow-physics: Evaluation and validation with GSWP and Valdai data. Earth Interactions, 5 .[Available online at http://EarthInteractions.org.].

    • Search Google Scholar
    • Export Citation
  • Moorthi, S., , Pan H-L. , , and Caplan P. , 2001: Changes to the 2001 NCEP Operational MRF/AVN Global Analysis/Forecast System. NWS Technical Procedures Bulletin 484, 14 pp.

  • Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note, NCAR/TN-461+STR, 174 pp. [Available online at http://www.ccsm.ucar.edu/models/ccsm3.0/clm3/index.html.].

  • Pan, H. L., , and Mahrt L. , 1987: Interaction between soil hydrology and boundary-layer development. Bound.-Layer Meteor., 38 , 185202.

  • Pope, V. D., , Gallani M. L. , , Rowntree P. R. , , and Stratton R. A. , 2000: The impact of new physical parameterizations in the Hadley Centre climate model: HadAM3. Climate Dyn., 16 , 123146.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porporato, A., , and D’Odorico P. , 2004: Phase transitions driven by state-dependent Poisson noise. Phys. Rev. Lett., 92 .110601, doi:10.1103/PhysRevLett.92.110601.

    • Search Google Scholar
    • Export Citation
  • Robock, A., , Vinnikov K. Y. , , Srinivasan G. , , Entin J. K. , , Hollinger S. E. , , Speranskaya N. A. , , Liu S. , , and Namkhai A. , 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteor. Soc., 81 , 12811299.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudolf, B., , Hauschild H. , , Reuth W. , , and Schneider U. , 1994: Terrestrial precipitation analysis: Operational method and required density of point measurements. Global Precipitation and Climate Change, M. Desbois and P. Desalmond, Eds., NATO ASI Series I, Vol. 26, Springer-Verlag, 173–186.

    • 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 , 483501.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., , Viterbo P. , , Lüthi D. , , and Schär C. , 2004: Inferring changes in terrestrial water storage using ERA-40 reanalysis data: The Mississippi River basin. J. Climate, 17 , 20392057.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sud, Y. C., , and Walker G. K. , 1999a: Microphysics of clouds with the relaxed Arakawa–Schubert Cumulus Scheme (McRAS). Part I: Design and evaluation with GATE Phase III data. J. Atmos. Sci., 56 , 32213240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sud, Y. C., , and Walker G. K. , 1999b: Microphysics of clouds with the relaxed Arakawa–Schubert Cumulus Scheme (McRAS). Part II: Implementation and performance in GEOS II GCM. J. Atmos. Sci., 56 , 32213240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verseghy, D. L., 1991: CLASS—A Canadian land surface scheme for GCMs: I. Soil model. Int. J. Climatol., 11 , 111133.

  • Vinnikov, K. Ya, , and Yeserkepova I. B. , 1991: Empirical data and model results. J. Climate, 4 , 6679.

  • Vinnikov, K. Ya, , Robock A. , , Speranskaya N. A. , , and Schlosser A. , 1996: Scales of temporal and spatial variability of midlatitude soil moisture. J. Geophys. Res., 101 , D3. 71637174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., , and Dickinson R. E. , 2004: Time scales of layered soil moisture memory in the context of land–atmosphere interaction. J. Climate, 17 , 27522764.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., , Geller M. A. , , and Dickinson R. E. , 2002: The response of soil moisture to long-term variability of precipitation. J. Hydrometeor., 3 , 604613.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xue, Y., , Sellers P. J. , , Kinter J. L. , , and Shukla J. , 1991: A simplified biosphere model for global climate studies. J. Climate, 4 , 345364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, M., , and Dirmeyer P. , 2003: Production and analysis of GSWP-2 near-surface meteorology data sets. COLA Tech. Rep. 159, 22 pp. [Available from the Center for Ocean–Land–Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.].

  • Zobler, L., 1986: A World Soil File for Global Climate Modelling. NASA Tech. Memo. 87802, NASA Goddard Institute for Space Studies, New York, NY, 32 pp.

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Soil Moisture Memory in AGCM Simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) Data

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  • 1 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • 2 Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, Maryland
  • 3 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • 4 Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
  • 5 CSIRO Atmospheric Research, Aspendale, Victoria, Australia
  • 6 University of Reading, Reading, Berkshire, United Kingdom
  • 7 Science Applications International Corporation, Beltsville, Maryland
  • 8 National Centers for Environmental Prediction, Camps Springs, Maryland
  • 9 National Center for Atmospheric Research, Boulder, Colorado
  • 10 Meteorological Service of Canada, Toronto, Ontario, Canada
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Abstract

Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.

Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.

* Current affiliation: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

+ The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Dr. Sonia Seneviratne, Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland. Email: sonia.seneviratne@env.ethz.ch

Abstract

Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.

Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.

* Current affiliation: Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

+ The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Dr. Sonia Seneviratne, Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland. Email: sonia.seneviratne@env.ethz.ch

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