Comparison of Metrics for Probabilistic Climate Change Projections of Mediterranean Precipitation (COMEPRO)

气候缓解和适应战略需要对未来气候变化的可靠估计，而大气-海洋耦合环流模式(AOGCM)仍然是最先进的工具。然而，不同气候模型的气候预测在地球的一些区域和一些气候变量方面有很大差异，例如降水，这意味着气候影响研究方面的重大不确定性。气候模式模拟之间的差异源于未知的初始条件、不同的分辨率和强迫因子、不同的物理参数和假设的排放情景。由于无法预测哪个模型预测最真实，因此最全面的气候变化信息由所有可用的气候变化模拟组成。通过将这些多模型信息转换成概率密度函数(PDF)，可以直接评估高于或低于给定气候变化水平的概率。这种概率预测确实与规划进程有关，因为它们提供了代表预期的气候变化的多模型平均估计，以及对模型不确定性的量化衡量。拟议的项目旨在通过CMIP3、CMIP5、EMSEMBLES和CORDEX多模式集合以及统计缩小尺度方法，评估对未来降水变化的这种概率预报。重点是地中海地区作为所谓的气候变化热点，以及对季节性均值和极端值之间的PDF进行比较。主要的新颖方面是调查通过参与气候变化预测的适当加权因子可以在多大程度上减少多模型PDF的宽度。这意味着必须接受一定数量的模型不确定性，因为它是由未知的初始条件或不确定的经验参数引起的内在问题。加权因子通过各种比较度量来评估，例如贝叶斯统计、回归分析、时空过滤、最优指纹和模型性能，涉及统计降尺度技术的动力模式、环流类型和重要预测因子的表示。此外，还对多模式集合上的PDF进行了评估，以了解降水产生过程链中的各种现象，包括辐射、蒸发、平流和云量。这将有助于确定不同气候模型预测之间的差异开始的水平。

Climate mitigation and adaptation strategies require reliable estimates of future climate change for which coupled atmosphere-ocean general circulation models (AOGCMs) still represent the state-of-the-art tool. However, climate projections from different climate models vary considerably in some regions of the Earth and in terms of some climatic variables like for instance precipitation, implying important uncertainties on the side of climate impact research. The differences between climate model simulations arise from the unknown initial conditions, different resolutions and forcing agents, different physical parameterizations and the assumed emission scenarios. As it cannot be anticipated which model projection is most realistic, the most comprehensive climate change information is composed of all available climate change simulations. By transforming this multi-model information into a probability density function (PDF) the probability above or below a given level of climate change can directly be assessed. Such probabilistic predictions are indeed relevant for planning processes because they provide a multi-model mean estimate of climate change which represents expectation, and a quantitative measure of model uncertainty. The proposed project aims at evaluating such probabilistic predictions of future precipitation changes from the CMIP3, CMIP5, EMSEMBLES and CORDEX multi-model ensembles and from statistical downscaling approaches. The focus is on the Mediterranean region as a so-called hot spot of climate change and on the comparison of PDFs between seasonal means and extremes. The major novel aspect is to investigate to what extent the width of the multi-model PDFs can be reduced by appropriate weighting factors of the participating climate change projections. This implies that some amount of model uncertainty has to be accepted because it is an intrinsic problem arising from the unknown initial conditions or from uncertain empirical parameters. The weighting factors are assessed by various comparative metrics like Bayesian statistics, regression analysis, spatio-temporal filtering, optimal fingerprinting, and model performance concerning the representation of dynamical modes, circulation types and important predictors for statistical downscaling techniques. In addition, the PDFs over the multi-model ensembles are also assessed for various phenomena in the process chain towards the generation of precipitation, including radiation, evaporation, advection and cloudiness. This will help to identify the level at which the dispersion between different climate model projections begins.