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!