Beyond Forcing Scenarios: Predicting Climate Change through Response Operators in a Coupled General Circulation Model

TL;DR

This paper demonstrates how response operators derived from statistical mechanics can accurately predict diverse climate responses to CO2 increase across multiple timescales using a coupled climate model.

Contribution

It introduces a novel application of response theory to predict climate change effects in a coupled GCM across various variables and timescales, including transient and equilibrium responses.

Findings

Successful prediction of ocean heat uptake dynamics.

Accurate forecasting of AMOC and ACC changes.

Precise temperature change predictions in the Northern Atlantic.

Abstract

Global Climate Models are key tools for predicting the future response of the climate system to a variety of natural and anthropogenic forcings. Here we show how to use statistical mechanics to construct operators able to flexibly predict climate change for a variety of climatic variables of interest. We perform our study on a fully coupled model - MPI-ESM v.1.2 - and for the first time we prove the effectiveness of response theory in predicting future climate response to CO$_2$ increase on a vast range of temporal scales, from inter-annual to centennial, and for very diverse climatic quantities. We investigate within a unified perspective the transient climate response and the equilibrium climate sensitivity and assess the role of fast and slow processes. The prediction of the ocean heat uptake highlights the very slow relaxation to a newly established steady state. The change in the Atlantic Meridional Overturning Circulation (AMOC) and of the Antarctic Circumpolar Current (ACC) is accurately predicted. The AMOC strength is initially reduced and then undergoes a slow and only partial recovery. The ACC strength initially increases as a result of changes in the wind stress, then undergoes a slowdown, followed by a recovery leading to a overshoot with respect to the initial value. Finally, we are able to predict accurately the temperature change in the Northern Atlantic.