Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance?
Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance?
复制标题
能否使用当代表现指标来限制非洲气候预测的不确定性？
DOI：
10.1007/s10584-015-1554-4
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发表时间：
2016
期刊：
影响因子：
4.8
通讯作者：
R. Graham
中科院分区：
文献类型：
--
作者：
D. Rowell;C. Senior;M. Vellinga;R. Graham
对非洲气候变化的预测是高度不确定的，模型之间对当地降雨量和温度变化的大小存在巨大差异，在一些地区，甚至在降雨量变化迹象方面存在差异。在弱势群体和适应资源稀少的背景下，这对决策者具有重大影响。解决这种不确定性的一种方法是根据模型的历史气候表现对模型进行排名，而忽略那些最不熟练的模型。通过定义23项模型技能指标并重点关注非洲的两个脆弱地区--萨赫勒地区和大非洲之角，对这一方法进行了系统评估。在39个CMIP5模型的性能上实现了一些区分，尽管它们在气候模型排名中的指标之间的分歧意味着在使用这些指标来稳健地判断模型的相对性能时存在一些不确定性。重要的是，当通过总体业绩衡量选择能力更强的模型时，预测的不确定性不会减少，因为这些模型通常分布在所有预测范围内(也许萨赫勒中部至东部的降雨除外)。这表明该方法的基本假设是错误的，这个假设是，最强烈地推动预测变化中的错误和不确定性的建模过程是其错误被历史气候的标准指标观察到的过程的子集。现在，进一步的研究必须制定一种专家判断方法，利用对驱动非洲预测变化中的错误和不确定性的机制的深入了解来区分模型。
Projections of climate change over Africa are highly uncertain, with wide disparity amongst models in their magnitude of local rainfall and temperature change, and in some regions even disparity in the sign of rainfall change. This has significant implications for decision-makers within the context of a vulnerable population and few resources for adaptation. One approach towards addressing this uncertainty is to rank models according to their historical climate performance and disregard those with least skill. This approach is systematically evaluated by defining 23 metrics of model skill and focussing on two vulnerable regions of Africa, the Sahel and the Greater Horn of Africa. Some discrimination in the performance of 39 CMIP5 models is achieved, although divergence amongst metrics in their ranking of climate models implies some uncertainty in using these metrics to robustly judge the models' relative performance. Importantly, when the more capable models are selected by an overall performance measure, projection uncertainty is not reduced because these models are typically spread across the full range of projections (except perhaps for Central to East Sahel rainfall). This suggests that the method’s underlying assumption is false, this assumption being that the modelled processes that most strongly drive errors and uncertainty in projected change are a subset of the processes whose errors are observed by standard metrics of historical climate. Further research must now develop an expert judgement approach that will discriminate models using an in-depth understanding of the mechanisms that drive the errors and uncertainty in projected changes over Africa.