Z.-C. Guo P. Dirmeyer X. Gao M. Zhao __________________________________ The 85th AMS Annual Meeting,...
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Transcript of Z.-C. Guo P. Dirmeyer X. Gao M. Zhao __________________________________ The 85th AMS Annual Meeting,...
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
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 vegetationA
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
China Illinois
India Mongolia
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!