A verification framework for interannual-to-decadal predictions experiments
A verification framework for interannual-to-decadal predictions experiments
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DOI：
10.1007/s00382-012-1481-2
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发表时间：
2013-01-01
期刊：
影响因子：
4.6
通讯作者：
Delworth, T.
中科院分区：
文献类型：
--
作者：
Goddard, L.;Kumar, A.;Delworth, T.
十年预测在气候科学界和其他领域都很受欢迎，但对他们的技能知之甚少。也没有任何商定的协议来评估他们的技能。本文提出了一个健全的和协调的框架，验证十年后报实验。该框架说明了十年后报量身定制，以满足CMIP 5（耦合模式相互比较项目第5阶段）的要求和规格。所选的指标解决的关键问题，在初始化的十年后报的信息内容。这些问题是：（1）与未初始化的气候变化预测相比，后报中的初始条件是否会导致更准确的气候预测？以及（2）预测模型的集合传播平均而言是否是预测不确定性的适当表示？第一个问题是通过比较初始化和未初始化的后报的确定性指标来解决的。第二个问题是解决通过一个概率度量应用于初始化后报和比较不同的方式来归因于预测的不确定性。主张在平滑的区域尺度和网格尺度上进行验证，因为平滑的区域尺度可以说明广泛的可预测性领域，因为将气候数据输入应用程序或决策模型的十年预测实验的许多用户将使用网格尺度的数据，或将其缩小到更高的分辨率。对CMIP 5年代际后报技术的全面阐述不是本文的目的。提出的结果只是说明框架，这将使这些研究。然而，从CMIP 5的结果中开始出现的广泛结论包括：（1）相对于气候平均，年际到年代际尺度的大部分可预测性来自外部强迫，特别是温度;（2）虽然中等，但初始条件比单独的外部强迫增加了额外的技能;然而，初始化的影响可能导致某些区域的预测总体上比未初始化的气候变化预测差;（3）有限的后报记录和缺乏气候质量的观测数据阻碍了我们量化预期技能和模型偏差的能力;和（4）与季节到年际模式预测一样，集合成员的扩散不一定是预测不确定性的良好表示。作者建议采用此框架作为比较预测系统之间预测质量的起点。该框架可以提供一个基线，据此可以量化未来的改进。该框架还为使用这些模型预测提供了指导，这些预测在根本上不同于社区中许多人已经熟悉的气候变化预测，包括调整均值和条件偏差，以及考虑如何最好地处理预测的不确定性。
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model's ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.