The industry is now seeing an expansion in data-driven practices, with new climate datasets emerging and becoming mainstream. Climate change itself is a phenomenon comprised of many interlinked processes, each with their own risks and uncertainties. To cater to the growing demand for quantitative climate change risk metrics, vendors have begun to explicitly model climate change risk.
Climate change consists of many complicated sub-processes that interact with one another through flow-on effects, which increases modelling complexities. Many of the underlying processes that govern climatic variables are dynamically evolving over time,1 resulting in modelling difficulties when solely using historic climate data to forecast the future.2
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