Abstract
Using the self-organizing maps (SOM) method, we ranked and compared the simulation results of atmospheric circulation and precipitation for 32 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) over China, and found that the ranking of the GCM’s ability to simulate the frequency of sea level pressure (SLP) weather patterns (WPs) was not correlated with the ranking of its ability to simulate annual precipitation WPs. Then, we attributed the precipitation simulation differences and identified three main components for the differences in the multi-model simulation results: internal variability, frequency differences, and the combined term of the two, with internal variability being the largest of the three components. These three deviations depend ultimately on two factors: the ability to simulate the frequency of WPs and the ability to simulate the corresponding average daily precipitation generated by these WPs, with the second factor playing a decisive role. Then, to address the drawback that the model ensemble results cannot be effectively improved when each single model that makes up the ensemble model is dry or wet, a solution was proposed to correct for the simulation differences: the nodal precipitation differences of each WP were corrected. After the correction of the simulation differences, the simulation capability of all the individual models was greatly improved, which increases our confidence in using the CMIP5 models for future weather patterns and precipitation simulation and forecasting.
Highlights
Introduction iationsThe study of future precipitation changes in the Chinese region is of great strategic importance to the socio-economic development and rational layout of water resources inChina
The aim was to analyze and predict the future precipitation of individual Chinese regions using the methods presented in this paper
By comparing the correlation between the frequency of occurrence of NCEP and each single model, it is concluded that the ability of the Global climate models (GCMs) to simulate the frequency of the weather patterns (WPs) is not correlated with their ability to simulate precipitation, i.e., the ranking of the GCMs’ ability to simulate precipitation differs significantly from the ranking of the
Summary
Introduction iationsThe study of future precipitation changes in the Chinese region is of great strategic importance to the socio-economic development and rational layout of water resources inChina. Global climate models (GCMs) are the most direct and important means to study future precipitation changes, but their prediction results are subject to large uncertainties. This uncertainty becomes an important issue to be addressed when assessing the regional impacts of climate change, especially at low resolution [1,2,3,4,5]. The credibility of model results for future climate change prediction depends on the ability of the model to simulate the contemporary climate. Before making future climate predictions, it is necessary to check how well the model can simulate the current climate [6,7,8]
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