Assessing climate risk using ensembles: A novel framework for applying and extending open-source climate risk assessment platforms

Laura C. Dawkins*, Dan J. Bernie, Jason A. Lowe, Theodoros Economou

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)

Abstract

Climate change adaptation decisions often require the consideration of risk rather than the environmental hazard alone. One approach for quantifying risk is to use a risk assessment framework which combines information about hazard, exposure and vulnerability to estimate risk in a spatially consistent way. In recent years, publicly available, open-source risk assessment frameworks have been made available, including the CLIMADA platform. Such tools are increasingly being used in combination with ensembles of climate model projections to quantify risk on climate time-scales, presenting the ensemble spread as a measure of climate model uncertainty. As climate models are computationally expensive to run, this quantification of uncertainty derived from the ensemble of projections is often limited by the number of members available. We present a novel framework involving the application and extension of the CLIMADA open-source climate risk assessment platform, demonstrating an approach for overcoming this limitation. We first show how the CLIMADA platform can be applied to an ensemble of UKCP18 regional climate projections to assess climate risk coherently across space in an idealised example for the UK. We then show how a Generalised Additive Model, involving an ‘ensemble member’ random effect term, can be used to statistically represent the climate model ensemble summary of risk and be used to simulate many more realisations of risk, representative of a larger collection of plausible ensemble members. Specifically, we apply the framework to an idealised example related to heat-stress and the associated risk of reduced outdoor physical working capacity in the UK, based on three global warming levels (recent past, 2 ◦C and 4 ◦C warmer than pre-industrial). We show how, in this idealised example, in a 2 ◦C warmer world (relative to pre-industrial), the UK could lose on average 15 million (or 2.5% of) days of outdoor physical work in a working year (225 days) as a result of heat-stress, which could equate to more than £1.5 billion of economic loss (roughly 0.07% of UK annual GDP). The uncertainty quantification provided by the framework allows for an upper range estimate which better quantifies climate model uncertainty. In a 4 ◦C warmer world this indicates the plausibility of38 million (or 6.2% of) working days lost in a year, possibly equating to more than £3.8 billion of economic loss (roughly 0.17% of UK annual GDP). Finally, we discuss limitations of the approach and recommend a number of extensions and areas of future work.
Original languageEnglish
Article number100510
Number of pages18
JournalClimate Risk Management
Volume40
Early online date29 Apr 2023
DOIs
Publication statusPublished - 11 May 2023

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