Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change
Combining CMIP data with a regional convection-permitting model and observations to project extreme rainfall under climate change
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将 CMIP 数据与区域对流允许模型和观测相结合，预测气候变化下的极端降雨
DOI：
10.1088/1748-9326/ac26f1
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发表时间：
2021
影响因子：
6.7
通讯作者：
Klein C
中科院分区：
文献类型：
--
作者：
Klein C
由于相关的水文风险，迫切需要提供未来极端降雨率的合理量化变化。对流允许（CP）气候模拟是在捕捉极端降雨及其对全球变暖下大气变化的敏感性方面取得的重大进展。然而，它们是计算成本高，限制了合奏和覆盖的时间段的不确定性评估。这与气候模式相互比较项目（CMIP）5和6的集合相反，后者不能捕获相关的对流过程，但提供了一系列可信的预测降雨变化的大气驱动因素。在这里，我们量化的极端降雨在西非风暴的大气降雨驱动程序的变化的敏感性，使用观测和CP预测代表十年的代表性浓度路径8.5在2100年左右。我们说明了如何这些物理关系，然后可以用来重建更好地了解极端降雨量的变化，从CMIP，包括不包括CP模型的时间段。我们发现，在所有CMIP模型中，萨赫勒地区重建的每小时极端降雨量增加，2070-2100年的合理范围为37%-75%（平均55%，2030-2060年为18%-30%）。这是相当高的+ 0-60%（平均+ 30%），我们从一个传统的极端降雨量度量的基础上原始每日CMIP降雨量，这表明这样的分析可能低估极端降雨强度。我们的结论是，基于过程的降雨尺度是一个有用的方法，创建随时间变化的降雨预测符合CP模式的行为，重建重要的信息，中期决策。这种方法也更好地使极端降雨预测的不确定性的沟通，反映了我们目前的知识状态，其对全球变暖的反应，远离粗尺度气候模型的局限性。
Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37%–75% for 2070–2100 (mean 55%, and 18%–30% for 2030–2060). This is considerably higher than the+ 0–60%(mean+ 30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.
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