Evaluation of IPCC Soil Moisture Simulations for the latter half of the 20th Century

Haibin Li1, Alan Robock1, Martin Wild 2

1Department of Environmental Sciences, Rutgers University,

New Brunswick, NJ, USA

2Institute for Atmospheric and Climate Science ETH, Swiss Federal Institute of Technology,

Zurich, Switzerland

Outline

• Why??Model intended for projections should provide reliable reproduction

of past/current climate • Data sets

Observations & model outputs

• AnalysisSeasonal cycle, interannual variability, spatial pattern.

Long term soil moisture evolution in warm season – trend analysis.Sensitivity experiment – radiation vs. soil moisture

• Conclusions

Observations

Region DomainYear

(0-1 m)Correlation

(0-1 m)Year

(0-10 cm)Correlation(0-10 cm)

No. of stations

Ukraine 46-52N, 22-40E 1958-1999 0.55 (0.74) 1976-1999 0.52 (0.73) 26*

Russia 51-59N, 32-57E 1958-1999 0.56 (0.77) 1970-1999 0.56 (0.78) 29*

Mongolia 46.5-50.5N, 101-107E 1970-1999 0.31 (0.67) 1970-1999 0.16 (0.58) 5

Northern China

43-48N, 123.5-128.5E 1981-1999 0.33 (0.59) 1981-1999 0.26 (0.54) 8

Central China

34-37N, 104.5-108.5E 1981-1999 0.45 (0.73) 1981-1999 0.58 (0.79) 5

Illinois 37.5-42N, 88-91W 1984-1999 0.72 (0.84) 1984-1999 0.64 (0.81) 18

Model outputs:Model Name Organization Land Surface Reference

MIROC3.2 (medres)

Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC), JAPAN

MATSIRO (no tiling), ~2.8º

Takata, et a.l 2003

GISS - EHNASA / Goddard Institute for

Space Studies, USALand Surface Model,4 by 5

Rosenzweig and Abramopoulos, 1997;

Friend and Kiang, 2005GISS - ER

MRI-CGCM2.3.2Meteorological Research

Institute, JapanSiB L3 (no vegetation

scheme), ~2.8ºSellers et al. 1986; Sato et

al. 1989

FGLOALS-g1.0LASG / Institute of Atmospheric

Physics, ChinaCLM2.0, ~2.8º Dai et al. 2003

CGCM3.1 (T47)Canadian Centre for Climate

Modeling & Analysis, Canada

CLASS ~3.75 º Verseghy et al. 1991

CCSM3, USANational Center for Atmospheric

Research, USACLM3.0, ~1.4 º Oleson et al. 2004

UKMO-HADCM3 Hadley Centre for Climate Prediction and Research / Met Office, Met Office, UK

MOSES-I (2.5 by 3.75) Cox et al. 1999

UKMO-HADGEM1 MOSES-II (1.25 by 1.875) Essey et al. 2001

Seasonal cycleT

op 1

0 cm

(un

it: c

m)

Tot

al s

oil c

olum

n (S

WI)

Better agreement over FSU regions.

Less discrepancy in warm seasons.

Now single model performs superior than others at all regions.

Better simulations in Ukraine, Russia and Illinois boxes. Poor results at Mongolia and China.

CGCM3.1 (t47) has stronger interannual variations than obs at all regions.Two UK models have interannual variations comparable to obs.

Taylor Diagram for top 10 cm soil moisture

Taylor Diagram for precipitation

Better simulation of precipitation not necessarilymeans more realistic soil moisture. Other factors also contribute.

Interannual variability (Coefficient of variance)

Generally, the drier the region is, the stronger interannual variation.

Large inter-model differences and model-to-obs difference.

Seasonal trends

a. Soil moisture b. precipitation

c. temperature d. Radiation (SW)

Soil moisture evolution in summer (JJA), 1958-1999.

Upward trend for both regions. Model ensembles show little change. The observed trend is far above that of model’s range.

Evidence from pan evaporation (Peterson et al. 1995 ) and solar radiation at the earth surface (Wild et al. 2005).

Pre

cipi

tatio

nT

empe

ratu

re

Observed trend for temperature is well constrained in models.

Precipitation & temperature all increase slightly but are not statistically significant.

What likely contributes to the observed trend in soil moisture?

Radiation: from dimming to brightening.Soil moisture: from upward trend to level-off.

Sensitivity experiments from ECHAM5 model (linear trend: units in mm/decade)

Coincidence?

Linear trend for top 10 cm (1981-1999, mm/decade)

Conclusions1. Less discrepancy in warm season simulations.

2. Better simulations over Ukraine and Russia and Illinois boxes but not necessarily a result of good precipitation.

3. Large differences between obs and models in terms of interannual variability. Some model systematically exhibits stronger interannual variations.

4. Significant trends in warm season (JJAS) for soil moisture. Prep & temp don’t show much change. Model simulated trend for temperature is generally reliable.

5. Sensitivity analysis from ECHAM5 model did show model with indirect aerosol scheme may have a better change to capture observed soil moisture pattern.

Acknowledgements: