资源环境科技发展态势分析平台

Using the Regional Climate Model version 4.3 (RegCM4.3), this paper investigates the potential effects of reforestation on the regional climate over the Loess Plateau in China with a focus on land-atmosphere interactions. Two land surface schemes are used here: the default Biosphere Atmosphere Transfer Scheme (BATS) and the Community Land Model version 3.5 (CLM3.5). Five simulations using a series of hypothetical reforestation scenarios from 1990 to 2009 have been performed. Results show that in the default BATS simulations, the surface air temperature increases significantly during both summer (June-July-August) and winter (December-January-February) seasons. These patterns are particularly evident over the south-eastern plateau where extensive areas of irrigated crop are converted to forest. In experiments with CLM3.5 and BATS with non-irrigation, in which irrigated crops are specified as regular crops, reforestation generally produces a more pronounced cooling effect in summer, and a slight cooling winter in CLM3.5 but a slight warming in BATS with non-irrigation. The land surface energy balance equation involving latent heat flux (LHF), absorbed solar radiation and downward atmospheric longwave radiation is used to explain the reforestation-induced seasonal responses. In the default BATS simulations, increased temperature induced by reforestation is predominantly driven by the reduced year round LHF. In contrast, the reforestation-induced summer cooling in CLM3.5 and BATS with non-irrigation is caused by the enhanced LHF. This study suggests that the default parameterization of irrigated crop in BATS overestimates soil water content and leading to excessive evapotranspiration (ET) in the Loess Plateau. Hence, non-irrigated cropland would be a more plausible land cover representation for the Loess Plateau, as demonstrated from the reforestation simulations using CLM3.5 and BATS with non-irrigation. An improved description of land characteristics in climate models is highly needed for a more reliable prediction of climate responses to land surface change.