Localized impacts and economic implications from high temperature disruption days under climate change. Issue 2 (12th April 2022)

• Record Type: Journal Article
• Authors: Summers, Tim; Mackie, Erik; Ueno, Risa; Simpson, Charles; Hosking, J. Scott; Suciu, Tudor; Coburn, Andrew; Shuckburgh, Emily
• Is Part Of: Climate resilience and sustainability. Volume 1:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-12
• Subjects: bias -- climate -- correction -- disruption -- economic -- exceedance -- temperature; Climatic changes -- Periodicals; Sustainability -- Periodicals; Climatic changes; Sustainability; Climate change mitigation$2fast$0(OCoLC)fst01749583; Periodicals; 338.927
• DOI: 10.1002/cli2.35
• Languages: English
• ISSNs: 2692-4587

Abstract

Abstract: Most studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments, however, are measures of the localized impact, and also of extremes rather than averages. We have addressed this by examining the change in frequency of exceeding a daily mean temperature threshold, defined as 'disruption days', as it is often this exceedance which has the most dramatic impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution of climate change both geographically and temporally, the latter specifically to address the 5‐ to 20‐year time horizon which can be recognized in business planning. We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias‐corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst‐case scenario planning. Taking the city of Chicago as an example, the expected frequency of years with 40 or more disruption days above the 25°C threshold rises by a factor of four for a time period centred on 2040, compared with a period centred on 2000. Alternately, looking at the change in the number of days at a given likelihood, an example is Shenzhen, where the number of disruption days in a once‐per‐decade event exceeding the 25°C or 30°C threshold is expected to rise by a factor of four. In a future stage, superimposing these results onto maps of, for instance, GDP sensitivity or production days lost, will provide more accurate and targeted conclusions for future impacts of climate change. This method of quantifying costs on business‐relevant timescales will enable businesses and governments properly include risks associated with facilities, plan mitigating actions and make accurate provisions. It can also, for example, inform their disclosure of physical risks under the framework of the Task Force on Climate‐related Financial Disclosures. This approach is equally applicable to other weather‐related, localized phenomena likely to be impacted by climate change. Abstract : We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias‐corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst‐case scenario planning.