Future water availability or crop yield studies, tied to statistics of river flow, precipitation, temperature or evaporation over medium to long‐term horizons, are becoming frequent in climate impact and risk analysis. During the last two decades, access to multi‐system integration of climate models has given rise to the concept of using model ensembles to issue probabilistic climatological projections. These probabilistic projections have not yet been exploited to the full extent in decision support, and are still used to mainly quantify uncertainty bands only for selected climate variables and indicators. One of the reasons of this limited use is the fact that the multi‐system ensemble dispersion is sub‐optimal and does not provide an accurate and reliable representation of the predictive probability density, which is essential for rational decision support under uncertain conditions. The aims of this paper are twofold. First, it seeks to highlight the potential benefits of using climate projections in conjunction with Bayesian paradigms towards educated decision‐making. Second, it discusses how to appropriately formulate probabilistic forecasts by coherently integrating information contained in climate projection ensembles with observations to improve the estimation of the probability density function of future climate states. The results show that the proposed Bayesian approach yields unbiased and sharper predictive distributions for temperature with respect to using the unprocessed ensemble distribution. It also yields improved predictive densities with respect to the Reliability Ensemble Averaging (REA) method.

中文翻译：

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评估气候适应和风险缓解决策的不确定性

与气候变化和风险分析中的中长期水流，降水量，温度或蒸发量相关的未来水供应或作物产量研究正变得越来越频繁。在过去的二十年中，使用气候系统的多系统集成引发了使用模型集合发布概率性气候预测的概念。这些概率预测尚未在决策支持中得到充分利用，仍仅主要用于选择气候变量和指标来量化不确定性带。这种有限使用的原因之一是，多系统集成散度不是最优的，并且不能提供准确可靠的预测概率密度表示，这对于不确定条件下的理性决策支持至关重要。本文的目的是双重的。首先，它试图强调将气候预测与贝叶斯范式结合使用对教育决策的潜在好处。其次，它讨论了如何通过将气候预测集合中包含的信息与观测值相结合地整合来适当地制定概率预测，以改进对未来气候状态的概率密度函数的估计。结果表明，相对于使用未处理的集合分布，所提出的贝叶斯方法产生了温度的无偏和更清晰的预测分布。相对于可靠性集成平均（REA）方法，它还产生了改进的预测密度。