Emulating Land-Processes in Climate Models Using Generative Machine Learning

Recent advances in climate model emulation have been shown to accurately represent atmospheric variables from large general circulation models, but little investigation has been done into emulating land-related variables. The land-carbon sink absorbs around a third of the fossil fuel anthropogenic emissions every year, yet there is significant uncertainty around this prediction. We aim to reduce this uncertainty by first investigating the predictability of several land-related variables that drive land-atmospheric carbon exchange. We use data from the IPSL-CM6A-LR submission to the Decadal Climate Prediction Project (DCPP). The DCPP is initialized from observed data and explores decadal trends in relationships between various climatic variables. The land-component of the IPSL-CM6A-LR, ORCHIDEE, represents various land-carbon interactions and we target these processes for emulation. As a first step, we attempt to predict the target land variables from ORCHIDEE using a vision transformer. We then investigate the impacts of different feature selection on the target variables - by including atmospheric and oceanic variables, how does this improve the short and medium term predictions of land-related processes? In a second step, we apply generative modeling (with diffusion models) to emulate land processes. The diffusion model can be used to generate several unseen scenarios based on the DCPP and provides a tool to investigate a wider range of climatic scenarios that would be otherwise computationally expensive.