Differentiable Programming for Earth System Modeling

TL;DR

This paper discusses how making Earth System Models differentiable can significantly improve their calibration, accuracy, and integration with machine learning, leading to more reliable climate predictions and better handling of complex Earth system behaviors.

Contribution

It introduces the concept of differentiable Earth System Models and demonstrates their potential to enhance calibration, incorporate observational data, and integrate machine learning techniques.

Findings

Differentiable ESMs enable objective calibration.

Differentiable ESMs facilitate hybrid models with ML components.

Recent work shows improved climate modeling with differentiability.

Abstract

Earth System Models (ESMs) are the primary tools for investigating future Earth system states at time scales from decades to centuries, especially in response to anthropogenic greenhouse gas release. State-of-the-art ESMs can reproduce the observational global mean temperature anomalies of the last 150 years. Nevertheless, ESMs need further improvements, most importantly regarding (i) the large spread in their estimates of climate sensitivity, i.e., the temperature response to increases in atmospheric greenhouse gases, (ii) the modeled spatial patterns of key variables such as temperature and precipitation, (iii) their representation of extreme weather events, and (iv) their representation of multistable Earth system components and their ability to predict associated abrupt transitions. Here, we argue that making ESMs automatically differentiable has huge potential to advance ESMs, especially with respect to these key shortcomings. First, automatic differentiability would allow objective calibration of ESMs, i.e., the selection of optimal values with respect to a cost function for a large number of free parameters, which are currently tuned mostly manually. Second, recent advances in Machine Learning (ML) and in the amount, accuracy, and resolution of observational data promise to be helpful with at least some of the above aspects because ML may be used to incorporate additional information from observations into ESMs. Automatic differentiability is an essential ingredient in the construction of such hybrid models, combining process-based ESMs with ML components. We document recent work showcasing the potential of automatic differentiation for a new generation of substantially improved, data-informed ESMs.