Assessing uncertainty for decision‐making in climate adaptation and risk mitigation
Assessing uncertainty for decision‐making in climate adaptation and risk mitigation
复制标题
评估气候适应和风险缓解决策的不确定性
DOI：
10.1002/joc.6996
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
R. Buizza
中科院分区：
文献类型：
--
作者：
Reggiani;E. Todini;O. Boyko;R. Buizza
未来的水资源可用性或作物产量研究，与中长期范围内的河流流量，降水，温度或蒸发量的统计数据联系在一起，在气候影响和风险分析中变得越来越频繁。在过去的二十年中，气候模式的多系统集成带来了使用模式集合发布概率气候预测的概念。这些概率预测尚未在决策支持中得到充分利用，仍然主要用于量化某些气候变量和指标的不确定性带。这种有限使用的原因之一是多系统系综色散是次优的，并且不能提供预测概率密度的准确和可靠的表示，这对于不确定条件下的理性决策支持至关重要。本文件的目的有两个。首先，它旨在强调将气候预测与贝叶斯范式结合使用对明智决策的潜在好处。其次，它讨论了如何适当地制定概率预报的气候预测集合中包含的信息与观测相结合，以改善未来气候状态的概率密度函数的估计。结果表明，所提出的贝叶斯方法产生无偏和更尖锐的预测分布的温度相对于使用未经处理的合奏分布。它还产生相对于可靠性增强平均（REA）方法改进的预测密度。
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.
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气候和水。
影响因子：
56.9
作者：
P. Abelson
通讯作者：
P. Abelson
DOI：
10.2307/2987329
发表时间：
1970-06
期刊：
--
影响因子：
--
作者：
M. Degroot
通讯作者：
M. Degroot
后处理集合气候预报的最佳实践。
影响因子：
4.9
作者：
Philip G. Sansom;C. Ferro;D. Stephenson;L. Goddard;S. Mason
通讯作者：
S. Mason
DOI：
10.1111/j.2517-6161.1977.tb01600.x
发表时间：
1977-01-01
期刊：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL
影响因子：
--
作者：
DEMPSTER, AP;LAIRD, NM;RUBIN, DB
通讯作者：
RUBIN, DB
影响因子：
5.1
作者：
Eyring, Veronika;Bony, Sandrine;Taylor, Karl E.
通讯作者：
Taylor, Karl E.