Climate Risk Management (Jan 2023)
Quantifying uncertainty and sensitivity in climate risk assessments: Varying hazard, exposure and vulnerability modelling choices
Abstract
Open-source climate risk assessment platforms allow for accessible and efficient estimation of current and future climate risk by combining information about hazard, exposure and vulnerability. Such assessments require making a number of choices, such as which hazard data source to use, and the data and approach taken to represent the exposure and vulnerability. As these choices are, to some extent, subjective, when assessing risk and informing adaptation decisions, alternative options should be considered to understand the uncertainty and sensitivity of risk to uncertain input data and assumptions. We present a novel approach to quantify the uncertainty and sensitivity of risk estimates, using the CLIMADA open-source climate risk assessment platform. This work builds upon a recently developed extension of CLIMADA, which uses statistical modelling techniques to model and stochastically simulate alternative realisations of climate risk, allowing for a richer quantification of climate model ensemble uncertainty. Here, we further analyse the propagation of hazard, exposure and vulnerability uncertainties by varying a number of input factors based on a discrete, scientifically justified set of options. We explore the uncertainty and sensitivity of risk to these variations, using the PAWN (PiAnosi and WagNer) method for global sensitivity analysis, allowing for an attribution of uncertainty to different drivers of the total uncertainty budget. We demonstrate the approach through an application to assess heat-stress risk to outdoor physical working capacity in the UK. In this application, we demonstrate how the risk estimated across plausible input settings better captures uncertainty and extreme outcomes (important for decision making); that all uncalibrated/non-bias-adjusted climate data sources underestimate risk in the recent past (highlighting the need for data calibration); and that when a global warming level framing is used it is the choice of global warming level that this risk is most sensitive to (2 °C or 4 °C warmer than pre-industrial), particularly in the south of the UK. This highlights the importance of mitigating climate change to reduce heat-stress risk.
