Land Surface Initialization Using an Offline CLM3 Simulation with the GSWP-2 Forcing Dataset and Its Impact on CAM3 Simulations of the Boreal Summer Climate

Jee-Hoon Jeong School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea, and Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

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Chang-Hoi Ho School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea

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Deliang Chen Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden

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Tae-Won Park School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea

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Abstract

The impacts of initialized land surface conditions on the monthly prediction were investigated using ensemble simulations from the Community Atmosphere Model version 3 (CAM3). The land surface initialization was based on an offline calculation of Community Land Model version 3 driven by observation-based meteorological forcings from the Global Soil Wetness Project 2 (GSWP2). A simple but effective correction method was applied to the GSWP2 forcings prior to the offline calculation to reduce the discrepancies between the observation-forced land surface conditions and the modeling system, which can cause climate drift and initial shock problems. The climatological mean of GSWP2 forcings was adjusted to that of the target model (CAM3), while the monthly anomalies were scaled to the model statistics and high-frequency synoptic variabilities were included.

Ensemble hindcast experiments with and without land surface initialization were conducted for the boreal summer (May–September), for 1983–95. The initialization process is shown to prevent climate drift and to transfer the atmospheric anomalies to the land surface memory. Statistical analyses of the simulation results reveal that the land surface initialization increased the externally forced variance over most continental regions, which is translated to enhanced potential predictability, particularly for regions with strong land–atmosphere coupling.

Corresponding author address: Chang-Hoi Ho, Climate Physics Laboratory, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, Korea. Email: hoch@cpl.snu.ac.kr

Abstract

The impacts of initialized land surface conditions on the monthly prediction were investigated using ensemble simulations from the Community Atmosphere Model version 3 (CAM3). The land surface initialization was based on an offline calculation of Community Land Model version 3 driven by observation-based meteorological forcings from the Global Soil Wetness Project 2 (GSWP2). A simple but effective correction method was applied to the GSWP2 forcings prior to the offline calculation to reduce the discrepancies between the observation-forced land surface conditions and the modeling system, which can cause climate drift and initial shock problems. The climatological mean of GSWP2 forcings was adjusted to that of the target model (CAM3), while the monthly anomalies were scaled to the model statistics and high-frequency synoptic variabilities were included.

Ensemble hindcast experiments with and without land surface initialization were conducted for the boreal summer (May–September), for 1983–95. The initialization process is shown to prevent climate drift and to transfer the atmospheric anomalies to the land surface memory. Statistical analyses of the simulation results reveal that the land surface initialization increased the externally forced variance over most continental regions, which is translated to enhanced potential predictability, particularly for regions with strong land–atmosphere coupling.

Corresponding author address: Chang-Hoi Ho, Climate Physics Laboratory, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, Korea. Email: hoch@cpl.snu.ac.kr

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  • Bonan, G. B., Oleson K. W. , Vertenstein M. , Levis S. , Zeng X. , 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
  • Calvet, J-C., Noilhan J. , Roujean J-L. , Bessemoulin P. , Cabelguenne M. , Olioso A. , and Wigneron J-P. , 1998: An interactive vegetation SVAT model tested against data from six contrasting sites. Agric. For. Meteor., 92 , 7395.

    • 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, Boulder, CO, 214 pp.

  • Collins, W. D., and Coauthors, 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19 , 21222143.

  • Conil, S., Douville H. , and Tyteca S. , 2007: The relative influence of soil moisture and SST in climate predictability explored within ensembles of AMIP type experiments. Climate Dyn., 28 , 125145.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., 2000: Using a global soil wetness dataset to improve seasonal climate simulation. J. Climate, 13 , 29002922.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., 2001: Climate drift in a coupled land–atmosphere model. J. Hydrometeor., 2 , 89100.

  • Dirmeyer, P. A., 2006: The hydrologic feedback pathway for land–climate coupling. J. Hydrometeor., 7 , 857867.

  • Dirmeyer, P. A., Guo Z. , and Gao X. , 2004: Comparison, validation, and transferability of eight multiyear global soil wetness products. J. Hydrometeor., 5 , 10111033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., Gao X. , Zhao M. , Guo Z. , Oki T. , and Hanasaki N. , 2006: The Second Global Soil Wetness Project (GSWP-2): Multimodel analysis and implications for our perception of the land surface. Bull. Amer. Meteor. Soc., 87 , 13811397.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Entin, J. K., Robock A. , Vinnikov K. Y. , 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 , 1186511877.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gates, W. L., and Coauthors, 1999: An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull. Amer. Meteor. Soc., 80 , 2955.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirabayashi, Y., Oki T. , Kanae S. , and Musiake K. , 2003: Application of satellite-based surface soil moisture data to simulating seasonal precipitation. J. Hydrometeor., 4 , 929943.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • International GEWEX Project Office, 2002: GSWP-2: The second global soil wetness project science and implementation plan. IGPO Publication Series, No. 37, 65 pp.

  • Kanae, S., Hirabayashi Y. , Yamada T. , and Oki T. , 2006: Influence of “realistic” land surface wetness on predictability of seasonal precipitation in boreal summer. J. Climate, 19 , 14501460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kang, I-S., and Shukla J. , 2006: Dynamic seasonal prediction and predictability of the monsoon. The Asian Monsoon, B. Wang, Ed., Springer, 585–612.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Suarez M. J. , 2003: Impact of land surface initialization on seasonal precipitation and temperature prediction. J. Hydrometeor., 4 , 408423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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: 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, 2004b: Regions of strong coupling between soil moisture and precipitation. Science, 305 , 11381140.

  • Koster, R. D., and Coauthors, 2006: GLACE: The Global Land–Atmosphere Coupling Experiment. Part I: Overview. J. Hydrometeor., 7 , 590612.

  • Liu, Y., and Avissar R. , 1999: A study of persistence in the land–atmosphere system with a fourth-order analytical model. J. Climate, 12 , 21542168.

    • 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
  • Oki, T., Seto S. , and Musiake K. , 2000: Land surface monitoring by backscattering from TRMM/PR 2A21. Proc. Int. Geoscience and Remote Sensing Symp., Honolulu, HI, IEEE, 2032–2034.

    • Search Google Scholar
    • Export Citation
  • Oleson, K. W., and Coauthors, 2004: Technical description of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-461+STR, Boulder, CO, 174 pp.

  • Reynolds, R. W., and Smith T. M. , 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7 , 929948.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robock, A., Vinnikov K. Y. , Sirinivasan 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
  • Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations. J. Climate, 11 , 109120.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, J., 1981: Dynamical predictability of monthly means. J. Atmos. Sci., 38 , 25472572.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wagner, W., Lemoine G. , and Rott H. , 1999: A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sens. Environ., 70 , 191207.

    • 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
  • Zhao, M., and Dirmeyer P. A. , 2003: Production and analysis of GSWP-2 near-surface meteorology data sets. COLA Tech. Rep. 159, Center for Land–Ocean–Atmosphere Studies, Calverton, MD, 38 pp.

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