The sensitivity of soil moisture to external forcing
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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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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

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 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


Z c guo p dirmeyer x gao m zhao

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.


Z c guo p dirmeyer x gao m zhao

Different forcing data vs. different LSSs

Correlations

Different forcing

Different LSSs


Z c guo p dirmeyer x gao m zhao

Different forcing data vs. different LSSs

RMSE

Different forcing

Different LSSs


Z c guo p dirmeyer x gao m zhao

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


Z c guo p dirmeyer x gao m zhao

Measure skills

Correlation

I1 PE P3

PE I1 P3

Significant Correlations

B0 I1 P3

P3 V1 R1

RMSE

R3 P5 P2

R2 M2 V1


Z c guo p dirmeyer x gao m zhao

Good precipitation produces better soil moisture simulations

China

SW (40 stations)

Precipitation (160 stations)


Z c guo p dirmeyer x gao m zhao

Impacts on annual mean of soil moisture


Summary

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

Thank You!


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