Z.-C. Guo P. Dirmeyer X. Gao M. Zhao __________________________________ The 85th AMS Annual Meeting, San Diego, CA, Jan. 11, 2005 The sensitivity of soil.

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Presentation transcript:

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

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

Sensitivity Experiments Several types of sensitivity experiments a: precipitation b: radiation c: vegetation d: with or without observations e: mixes ExpDescription N1 Native Parameters (if applicable) P1Hybrid ERA-40 precipitation (instead of NCEP/DOE) P2NCEP/DOE hybrid with GPCC corrected for gauge undercatch (no satellite data) P3NCEP/DOE hybrid with GPCC (no undercatch correction) P4NCEP/DOE precipitation (no observational data) P5NCEP/DOE hybrid with Xie daily gauge precipitation R1NCEP/DOE radiation RSNCEP/DOE shortwave only RLNCEP/DOE longwave only R2ERA-40 radiation M1All NCEP meteorological data (no hybridization with observational data) M2All ECMWF meteorological data (no hybridization with observational data) V1U.Maryland vegetation class data I1Climatological vegetation A A B B B C C C A R3 ISCCP radiation C PE Hybrid ERA-40 precip. ERA-40 precipitation (no observational data)

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

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

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

Median Correlation ChinaIllinois IndiaMongolia Russia(S)Russia(W) I1 PE P3 P2 V1 PE PE P5 P2 V1 P3 PE R3 P2 R2 V1 P2 R2 Impacts of forcing data on soil moisture simulations vary from region to region

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

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

Impacts on annual mean of soil moisture

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.

Thank You!