The impact of land cover generated by a dynamic vegetation model on climate over east Asia in present and possible future climate
- 1School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea
- 2National Institute of Meteorological Research, Korea Meteorological Administration, Jeju, Republic of Korea
- 3Met Office Hadley Centre, FitzRoy Road, Exeter, UK
Abstract. This study investigates the impacts of land cover change, as simulated by a dynamic vegetation model, on the summertime climatology over Asia. The climate model used in this study has systematic biases of underestimated rainfall around Korea and overestimation over the South China Sea. When coupled to a dynamic vegetation model, the resulting change in land cover is accompanied by an additional direct radiative effect over dust-producing regions. Both the change in land surface conditions directly and the effect of increased bare-soil fraction on dust loading affect the climate in the region and are examined separately in this study. The direct radiative effect of the additional dust contributes to increasing the rainfall biases, while the land surface physical processes are related to local temperature biases such as warm biases over North China. In time slice runs for future climate, as the dust loading changes, anomalous anticyclonic flows are simulated over South China Sea, resulting in reduced rainfall over the South China Sea and more rainfall near Korea and south China. In contrast with the rainfall changes, the influence of land cover change and the associated dust radiative effects are very small for a future projection of temperature, which is dominated by atmospheric CO2 increase. The results in this study suggest that the land cover simulated by a dynamic vegetation model can affect, and be affected by, model systematic biases on regional scales over dust emission source regions such as Asia. In particular, the analysis of the radiative effects of dust changes associated with land cover change is important in order to understand future changes in regional precipitation in global warming.