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Z.-C. Guo P. Dirmeyer X. Gao M. Zhao

The sensitivity of soil moisture to external forcing in SSiB land surface scheme. Z.-C. Guo P. Dirmeyer X. Gao M. Zhao. __________________________________ The 85th AMS Annual Meeting, San Diego, CA, Jan. 11, 2005. Introduction.

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Z.-C. Guo P. Dirmeyer X. Gao M. Zhao

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  1. The sensitivity of soil moisture to external forcing in SSiB land surface scheme Z.-C. Guo P. Dirmeyer X. Gao M. Zhao __________________________________ The 85th AMS Annual Meeting, San Diego, CA, Jan. 11, 2005

  2. Introduction • Soil moisture is one of the most important state variables for both GCM/LSS initialization and evaluating the performance of GCM and LSS • Sensitivity of soil moisture to the choice of external forcing data sets was examined with SSiB land surface scheme through a suite of experiments within the GSWP framework • Observation datasets: • Global Soil Moisture Data Bank • Observed monthly precipitation over 160 stations in China

  3. Several types of sensitivity experiments a: precipitation b: radiation c: vegetation d: with or without observations e: mixes Exp Description N1 Native Parameters (if applicable) P1 Hybrid ERA-40 precipitation (instead of NCEP/DOE) P2 NCEP/DOE hybrid with GPCC corrected for gauge undercatch (no satellite data) P3 NCEP/DOE hybrid with GPCC (no undercatch correction) P4 NCEP/DOE precipitation (no observational data) P5 NCEP/DOE hybrid with Xie daily gauge precipitation R1 NCEP/DOE radiation RS NCEP/DOE shortwave only RL NCEP/DOE longwave only R2 ERA-40 radiation M1 All NCEP meteorological data (no hybridization with observational data) M2 All ECMWF meteorological data (no hybridization with observational data) V1 U.Maryland vegetation class data I1 Climatological vegetation Sensitivity Experiments A B ERA-40 precipitation (no observational data) B B C PE Hybrid ERA-40 precip. A R3 ISCCP radiation C C C A

  4. Impact of forcing data on quality of simulated soil moisture a. The hybridization of observations with the reanalyses significantly improves the quality of simulated soil moisture radiation M1 + P2 precipitation vegetation B0 b. precipitation, radiation fluxes, and vegetation parameters have a large impact on the quality of simulated soil moisture. no observation c. Precipitation’s impact on the quality of simulated soil moisture.

  5. Different forcing data vs. different LSSs Correlations Different forcing Different LSSs

  6. Different forcing data vs. different LSSs RMSE Different forcing Different LSSs

  7. Impacts of forcing data on soil moisture simulations vary from region to region Median Correlation China Illinois I1 PE P3 P2 V1 PE India Mongolia PE P5 P2 V1 P3 PE Russia(S) Russia(W) R3 P2 R2 V1 P2 R2

  8. Measure skills Correlation I1 PE P3 PE I1 P3 Significant Correlations B0 I1 P3 P3 V1 R1 RMSE R3 P5 P2 R2 M2 V1

  9. Good precipitation produces better soil moisture simulations China SW (40 stations) Precipitation (160 stations)

  10. Impacts on annual mean of soil moisture

  11. Summary • The hybridization of observations with the reanalyses significantly improves the quality of simulated soil moisture. • Precipitation, radiation fluxes, and vegetation parameters have a large impact on the quality of simulated soil moisture. • Differences of model performance in simulating soil moisture resulted from the choice of external forcing data are as large as those resulting from different LSSs • Impacts of forcing data on soil moisture simulations vary from region to region. • Good precipitation produces better soil moisture simulations.

  12. Thank You!

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