Localised economic impacts from high temperature disruption days under climate change

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 economic impact, either direct or indirect. We have addressed this by examining how the frequency of exceeding a daily mean temperature threshold changes, 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 5-20 year time horizon which can be recognised 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 25ºC 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 25ºC or 30ºC threshold is expected to rise by a factor of four.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 help 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, localised phenomena likely to be impacted by climate change.

Decision making for net zero policy design and climate action: considerations for improving translation at the research-policy interface: a UK Carbon Dioxide Removal case study

“…• Non-linear: nearly all modeling of future climate risks assumes that climate impacts are proportional to their drivers and behave in a linear fashion. Yet, there are non-linear changes in weather and climate variables, such as weather extremes (Summers et al, 2022), the potential for crossing climate tipping points (Mackie, 2021), and responses of human and natural systems which should also be captured in climate risk assessments and adaptation planning (Ebi et al, 2016). • Context-dependent: the impacts of climate change are context dependent as some societies have the capacity to adapt to significant levels of climate shocks and stresses, while others suffer severe impacts from lower levels of pressures (IPCC, 2022).…”

“…This contribution examined the nature of the research-policy translational interface through a combination of a literature review, interviews and input from UK Policy Workshops with stakeholders over the period Jan 2021 to May 2022 (Mackie et al, 2022). Issues explored included: why the plethora of climate risk assessments and decision support tools available to decisionmakers are not translating into effective policy action on climate risk; what the challenges, complexities and deep uncertainties associated with the translational process-particularly with regards to the CDR sector in dealing with developing a new sector as large as the Oil and Gas Industry where the evidence is nascent-within 27 years; and how the research translation pipeline could be improved to achieve more effective decision-making.…”