Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment |
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Authors: | Guiling Wang |
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Institution: | (1) Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road, U-2037, Storrs, CT 06269, USA |
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Abstract: | This study examines the impact of greenhouse gas warming on soil moisture based on predictions of 15 global climate models
by comparing the after-stabilization climate in the SRESA1b experiment with the pre-industrial control climate. The models
are consistent in predicting summer dryness and winter wetness in only part of the northern middle and high latitudes. Slightly
over half of the models predict year-round wetness in central Eurasia and/or year-round dryness in Siberia and mid-latitude
Northeast Asia. One explanation is offered that relates such lack of seasonality to the carryover effect of soil moisture
storage from season to season. In the tropics and subtropics, a decrease of soil moisture is the dominant response. The models
are especially consistent in predicting drier soil over the southwest North America, Central America, the Mediterranean, Australia,
and the South Africa in all seasons, and over much of the Amazon and West Africa in the June–July–August (JJA) season and
the Asian monsoon region in the December–January–February (DJF) season. Since the only major areas of future wetness predicted
with a high level of model consistency are part of the northern middle and high latitudes during the non-growing season, it
is suggested that greenhouse gas warming will cause a worldwide agricultural drought. Over regions where there is considerable
consistency among the analyzed models in predicting the sign of soil moisture changes, there is a wide range of magnitudes
of the soil moisture response, indicating a high degree of model dependency in terrestrial hydrological sensitivity. A major
part of the inter-model differences in the sensitivity of soil moisture response are attributable to differences in land surface
parameterization. |
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