Abstract

Regional climate models (RCMs) are useful tools used by many scientists and policymakers to represent possible climate futures. However, predictions from these models are far from perfect. RCMs suffer not only from the uncertainty of different emission scenarios and inter and intra-model variability, but also from regionally specific challenges such as complex topography, limited direct measurements for calibration, and unsuitable parametrisations schemes. Future predictions can therefore be contradictory and lack consensus. Furthermore, they are generally simply averaged with the model spread as a proxy for uncertainty. In this talk, I will present a probabilistic ensembling method to combine different RCM outputs based on Gaussian process regression. The aim is to quantify climate extremes in a more principled way using the probability distributions of past observations. More specifically, we’ll look at precipitation over High Mountain Asia, an area providing water resources to approximately 2 billion people.

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CAS UnionRoad is inviting you to a scheduled Zoom meeting.

Topic: CAS seminar: Kenza Tazi Time: Oct 8, 2024 12:00 PM London

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