Projections Of Future Climate Change Over China Based On Multi-model Ensemble And Downscaling Methods

Based on the University of East Anglia's Climate Research Unit time series (CRU TS2.1) temperature and precipitation data sets as well as the results of 28 AOGCMs, performance of the 28 models over China is evaluated in terms of mean squared error (MSE) and interannual variability. Different weights were then given to the models according to their performances in present-day climate, with the purpose to obtain the probabilistic projection of climate change over China. Furthermore, two downscaling techniques, dynamical downscaling named LMDZ and empirical statistical one named SDSM, have been used over Southeast China as to produce high-resolution climate change information under gobal warming. The main results are as follows:(1) Results of the evaluation for the current climate show that five models that have relatively higher resolutions, namely INGV_ECHAM4, UKMO_HADCM3, CSIRO_MK3.5, NCAR_CCSM3.0 and MIROC3.2 (hires), perform better than others over China. Rank-based weighting of model results can improve probabilistic projections of climate change. Under the A1B scenario, surface air temperature is projected to increase significantly for both middle and end of the 21st century, with larger magnitude over the north and in winter. There are also significant increases in rainfall in 21st century under the A1B scenario, especially for the period 2070-2099.(2) The coupled models which have the assimilation of observational data into decadal prediction outperform the CMIP3/IPCC AR4 GCMs with no initialization. Both of the four decadal prediction models and the CMIP3 MME can simulate warming signal in the late 20th century over China, especially in the northern part. The CMIP3 MME can not reproduce the pattern of "wet South and dry North" in the eastern part of China in recent 20 years. In contrast, the four decadal prediction models show better agreement with observations in simulating the pattern of "wet South" in China in recent 20 years, although they still can not reproduce the pattern of "dry North"(3) LMDZ, which is forced by ERA-40 reanalysis data, can realistically simulate the climate distribution of the surface air temperature and precipitaion, as well as climate extremes that are expressed in terms of extreme indices, although the model tends to overestimate the extreme precipitation. The inter-annual variability can also be well simulated for most of the climate indices. All of those indicate that LMDZ can be used to simulate the projected climate change over this region under global warming.(4) Results with greenhouse gas forcing from the SRES-A2 emission scenario show that there is a significant increase for mean, daily-maximum and minimum temperature in the entire region, associated with a decrease in the number of frost days and an increase in the heat wave duration. The annual frost days are projected to significantly decrease by 12-19 days while the heat wave duration to increase by about 7 days. A warming environment gives rise to changes in extreme precipitation events.(5) The evaluation of simulated extreme indices of temperature and precipitation for the current climate shows that the downscaled temperature-related indices match the observations well for all seasons, and SDSM can modify the systematic cold biases of the AOGCMs. For indices of precipitation extremes most AOGCMs intend to underestimate the intensity, but SDSM improves this significantly. Scenario results using A2 emissions show that in all seasons there is a significantly increase for mean daily-maximum and minimum temperature in the 29 meteorological stations, associated with a decrease in the number of frost days and with an increase in the heat wave duration. Precipitation extremes are projected to increase over most of the 29 meteorological stations.