Intercomparison of prediction skills of ensemble methods using monthly mean temperature simulated by CMIP5 models

This study focuses on an objective comparison of eight ensemble methods using the same data, training period, training method, and validation period. The eight ensemble methods are: BMA (Bayesian Model Averaging), HMR (Homogeneous Multiple Regression), EMOS (Ensemble Model Output Statistics), HMR+ with positive coefficients, EMOS+ with positive coefficients, PEA_ROC (Performance-based Ensemble Averaging using ROot mean square error and temporal Correlation coefficient), WEA_Tay (Weighted Ensemble Averaging based on Taylor’s skill score), and MME (Multi-Model Ensemble). Forty-five years (1961-2005) of data from 14 CMIP5 models and APHRODITE (Asian Precipitation- Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data were used to compare the performance of the eight ensemble methods. Although some models underestimated the variability of monthly mean temperature (MMT), most of the models effectively simulated the spatial distribution of MMT. Regardless of training periods and the number of ensemble members, the prediction skills of BMA and the four multiple linear regressions (MLR) were superior to the other ensemble methods (PEA_ROC, WEA_Tay, MME) in terms of deterministic prediction. In terms of probabilistic prediction, the four MLRs showed better prediction skills than BMA. However, the differences among the four MLRs and BMA were not significant. This resulted from the similarity of BMA weights and regression coefficients. Furthermore, prediction skills of the four MLRs were very similar. Overall, the four MLRs showed the best prediction skills among the eight ensemble methods. However, more comprehensive work is needed to select the best ensemble method among the numerous ensemble methods.     

海洋层积云中的云滴谱宽度及其影响因子

云滴谱宽度对模式中云的光学厚度的参数化、气溶胶间接效应的评估以及降水形成过程的研究至关重要。本文利用美国POST（Physics of Stratocumulus Top）项目2008年7月19日的飞机观测资料，分析了微物理量和云滴谱的垂直分布及微物理过程。结果表明，该云系云滴谱宽度在云底附近较大，这是由低层核化过程导致的；中层凝结增长过程使得云滴谱宽度随高度增加逐渐减小；云顶附近夹卷混合过程导致云滴谱宽度增大。绝热云中垂直速度的增大会促进云凝结核的活化使云滴数浓度增大，促进凝结增长使云滴尺度增大、云滴谱宽度减小，云滴谱宽度与云滴数浓度、云滴尺度呈现负相关关系；云洞中受夹卷混合过程影响，垂直速度减小，云滴蒸发，云滴数浓度和云滴尺度减小、云滴谱宽度增大，且该效应随绝热程度减小而增强。建议云滴谱宽度的参数化将垂直速度、云滴数浓度、云滴尺度和绝热程度等考虑在内。