Estimating heat stress from climate-based indicators: present-day biases and future spreads in the CMIP5 global climate model ensemble
Estimating heat stress from climate-based indicators: present-day biases and future spreads in the CMIP5 global climate model ensemble
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DOI：
10.1088/1748-9326/10/8/084013
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发表时间：
2015-08
影响因子：
6.7
通讯作者：
Y. Zhao;A. Ducharne;B. Sultan;P. Braconnot;R. Vautard
中科院分区：
文献类型：
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作者：
Y. Zhao;A. Ducharne;B. Sultan;P. Braconnot;R. Vautard
人类暴露于热应激的增加是全球变暖的可能后果之一，它对健康和劳动能力有不利影响。在这里，我们考虑气候变化下的热应力的演变使用21个大气环流模式（GCM）。三个热应力指标，温度和湿度条件的基础上，被用来研究当今的模式偏差和传播在未来的气候预测。目前根据观测数据对热应力指标的估计表明，潮湿的热带地区往往比其他地区经历更频繁的热应力，热应力的总频率为250-300 d/年。最严重的热应力出现在萨赫勒和印度南部。由于干冷模式的偏差，目前的GCM模拟往往低估了热带地区的热应力。在中高纬度地区，基于模型的估计与观测结果更吻合，但这是由于湿度和温度的补偿误差。在气候变化情景RCP 8.5下，预计到世纪末，热应力的严重程度将增加，与观测相比，在某些地区达到前所未有的水平。对影响预计热应力总扩散的不同因素的分析表明，扩散主要是由选择大气环流模型而不是选择指标驱动的，即使模拟指标经过偏差校正。这支持多模式集成方法的效用，以评估气候变化对热应力的影响。
The increased exposure of human populations to heat stress is one of the likely consequences of global warming, and it has detrimental effects on health and labor capacity. Here, we consider the evolution of heat stress under climate change using 21 general circulation models (GCMs). Three heat stress indicators, based on both temperature and humidity conditions, are used to investigate present-day model biases and spreads in future climate projections. Present day estimates of heat stress indicators from observational data shows that humid tropical areas tend to experience more frequent heat stress than other regions do, with a total frequency of heat stress 250–300 d yr−1. The most severe heat stress is found in the Sahel and south India. Present-day GCM simulations tend to underestimate heat stress over the tropics due to dry and cold model biases. The model based estimates are in better agreement with observation in mid to high latitudes, but this is due to compensating errors in humidity and temperature. The severity of heat stress is projected to increase by the end of the century under climate change scenario RCP8.5, reaching unprecedented levels in some regions compared with observations. An analysis of the different factors contributing to the total spread of projected heat stress shows that spread is primarily driven by the choice of GCMs rather than the choice of indicators, even when the simulated indicators are bias-corrected. This supports the utility of the multi-model ensemble approach to assess the impacts of climate change on heat stress.