基于BP-CCA统计降尺度的中亚春季降水的多模式集合模拟与预估

利用中亚地区30个观测台站逐月降水资料及同期ERA-40再分析资料，结合8个CMIP5全球气候模式模拟与未来预估大尺度环流场，使用基于变形典型相关分析的统计降尺度方法（BP-CCA）建立降尺度模型，评估多个气候模式对当前气候下中亚地区春季降水的降尺度模拟能力，并对春季降水进行降尺度集合未来预估。结果表明，建立的降尺度模型能够很好地模拟出交叉检验期内春季降水的时间变化和空间结构：降尺度春季降水与相应观测序列的平均时间相关系数为0.35，最高为0.62，平均空间相关系数为0.87。气候模式对中亚春季降水的模拟能力通过降尺度方法得到了显著提高：8个模式降尺度后模拟的降水气候平均态相对误差绝对值降至0.2%—8%，相比降尺度前减小了10%—60%，模拟的降水量场与相应观测场的空间相关均超过0.77；对比降尺度前多模式集合结果，多模式降尺度集合模拟的相对误差绝对值由64%减小至4%，空间相关系数由0.47增大至0.81，标准化均方根误差降至0.59，且多模式降尺度集合结果优于大部分单个模式降尺度结果。多模式降尺度集合预估结果表明，在RCP4.5排放情景下，21世纪前期（2016—2035年）、中期（2046—2065年）和末期（2081—2100年）的全区平均降水变化率分别为-5.3%、3.0%和17.4%。21世纪前期中亚大部分地区降水呈减少趋势，降水呈增多趋势的站点主要分布在南部。21世纪中期整体降水变化率由减少变为增多趋势，21世纪末期中亚大部分台站降水增多较为明显。21世纪初期和末期可信度高的台站均主要位于中亚西部地区。

Multi-model statistical downscaling of spring precipitation simulation and projection in central Asia based on canonical correlation analysis

Using precipitation observations at 30 meteorological stations in central Asia, the European Centre for Medium-Range Weather Forecasts 40-year reanalysis dataset (ERA-40), and eight CMIP5 (Coupled Model Inter-comparison Project Phase 5) climate models, statistical downscaling models were constructed based on BP-CCA (the combination of empirical orthogonal function and canonical correlation analysis). The downscaling ability of multiple-model simulations of spring precipitation was evaluated and future changes in precipitation were projected. The results show that the average correlation coefficient between time coefficients of downscaled spring precipitation and corresponding observations is 0.35, and the highest correlation coefficient is 0.62. Spatial correlations were also improved with an average value of 0.87. The absolute values of domain-averaged relative precipitation errors for most models were reduced by 0.2%-8% after statistical downscaling. As a result of statistical downscaling of multi-model ensemble (SDMME) simulation, the relative error reduced from 64% to 4%, the spatial correlation increased from 0.47 to 0.81, and the RMSE reduced to 0.59. These results demonstrate that the simulation of SDMME is better than that of multi-model ensemble (MME) and the downscaling results of most individual models. The projections of SDMME reveal that under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the projected domain-averaged precipitation changes for the early (2016-2035), middle (2046-2065) and end (2081-2100) of the 21 century are -5.3%, 3.0% and 17.4%, respectively. In the early 21 century, precipitation shows a decreasing trend in most areas and an increasing trend in southern part of central Asia. Significant increasing trend is predicted to occur mainly in the middle and end periods of the 21 century, with a larger magnitude in the latter. The credibility of SDMME forecast gradually enhances with longer forecast time.