Deep Learning for Climate Model Output Statistics
This work provides an improved method for reducing errors in climate model outputs, which is crucial for more accurate climate change assessments for researchers and policymakers.
The paper addresses systematic and representation errors in climate model outputs, particularly for precipitation. It proposes ConvMOS, a convolutional neural network architecture, which significantly reduces errors in the REMO climate model and outperforms three common MOS approaches.
Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.