Localised economic impacts from high temperature disruption days under climate change-猫眼课题宝

DOI：

10.31223/x5502q

发表时间：

2021-04

期刊：

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中科院分区：

其他

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作者：

T. Summers;Erik Mackie;Risa Ueno;Charles H. Simpson;S. Hosking;Tudor Suciu;Andrew S. Coburn;E. Shuckburgh

42大多数关于气候变化影响的研究都以全球平均气温变化的形式得出主要结果。然而，对企业和政府更有用的是44项直接或间接的经济影响指标。我们通过研究超过日平均温度阈值的频率如何变化来解决这个问题，46定义为“中断日”，因为通常这种中断对个人或经济行为产生最大的影响。我们的责任分析解决了地理和时间上的气候变化问题，后者专门解决了业务规划中可以识别的20年时间范围。[50]我们对51个ECMWF ERA 5的气象再分析数据和CMIP 5气候模式模拟的输出进行了偏置校正和分位数映射。通过确定该偏差校正数据集中超过平均温度阈值的每日频率，我们可以比较预测频率和历史频率，以估计中断天数的变化。此外，通过结合18个不同气候模型的结果，我们可以估计更多极端事件的可能性，同时考虑到模型的变化。这对于最坏情况的规划是有用的。57以芝加哥市为例，与以2000年为中心的时期相比，以2040年为中心的时期，在高于25 ℃阈值的58个中断日中有40个或更多的预期频率增加了4倍。另一方面，在给定的可能性下，观察60天的变化，一个例子是深圳，在每十年发生一次的超过25 ℃或30 ℃阈值的事件中，61天的中断天数预计将增加四倍。63将这些结果叠加到GDP敏感性或生产天数损失等地图上，64将为气候变化的未来影响提供更准确和有针对性的结论。这种在与企业相关的时间尺度上量化成本的方法将帮助企业和政府适当地包括与设施相关的风险，计划减轻风险的行动，并做出准确的准备。例如，它还可以为它们在气候相关财务披露工作队的框架下披露68有形风险提供信息。这种方法同样适用于其他可能受气候变化影响的与天气有关的局部现象。71

42 Most studies into the effects of climate change have headline results in the form of a global 43 change in mean temperature. More useful for businesses and governments however are 44 measures of the economic impact, either direct or indirect. We have addressed this by 45 examining how the frequency of exceeding a daily mean temperature threshold changes, 46 defined as “disruption days”, as it is often this exceedance which has the most dramatic 47 impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution 48 of climate change both geographically and temporally, the latter specifically to address the 549 20 year time horizon which can be recognised in business planning. 50 We apply bias correction with quantile mapping to meteorological reanalysis data from 51 ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily 52 frequency at which a mean temperature threshold is exceeded in this bias-corrected dataset, 53 we can compare predicted and historic frequencies to estimate the change in the number of 54 disruption days. Furthermore, by combining results from 18 different climate models, we can 55 estimate the likelihood of more extreme events, taking into account model variations. This is 56 useful for worst case scenario planning. 57 Taking the city of Chicago as an example, the expected frequency of years with 40 or more 58 disruption days above the 25oC threshold rises by a factor of four for a time period centred 59 on 2040, compared with a period centred on 2000. Alternately, looking at the change in the 60 number of days at a given likelihood, an example is Shenzhen, where the number of 61 disruption days in a once-per-decade event exceeding the 25oC or 30oC threshold is 62 expected to rise by a factor of four. 63 Superimposing these results onto maps of, for instance, GDP sensitivity or production days 64 lost, will provide more accurate and targeted conclusions for future impacts of climate 65 change. This method of quantifying costs on business-relevant timescales will help 66 businesses and governments properly include risks associated with facilities, plan mitigating 67 actions and make accurate provisions. It can also, for example, inform their disclosure of 68 physical risks under the framework of the Task Force on Climate-related Financial 69 Disclosures. This approach is equally applicable to other weather-related, localised 70 phenomena likely to be impacted by climate change. 71

Localised economic impacts from high temperature disruption days under climate change

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