A framework for uncertainty quantification in climate risk assessments (POSTER)

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 tool. Such tools are increasingly being used in combination with ensembles of climate model projections to quantify risk on climate timescales, presenting the ensemble spread as a measure of climate 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 tool, demonstrating an approach for providing a richer quantification of uncertainty. We show how a statistical 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. We present an application of the framework to an idealised example related to heat-stress and the associated risk of reduced outdoor physical working capacity in the UK.