Adjustment of ocean carbon sink predictions with an emission-driven Earth system model using a deep neural network (Proposals Track)
Reinel Sospedra-Alfonso (Environment and Climate Change Canada); Parsa Gooya (Environment and Climate Change Canada); Johannes Exenberger (Graz University of Technology)
Abstract
Near-term predictions of the Global Carbon Budget (GCB) with Earth system models (ESMs) driven by specified CO2 emissions were used to inform the GCB annual update for the first time in 2023. These predictions are biased because they are initialized indirectly from the ESMs response to physical observational constraints, and because the ESMs themselves are imperfect representations of the climate system. We propose a deep learning-based post-processing method to adjust GCB predictions using an autoencoder, which outperforms standard bias and trend correction methods.