PNNL

Abstract

We devise a pattern-aware feedback framework for representing the forced climate response using a suite of Green’s function experiments with solar radiation perturbations. By considering the column energy balance, a comprehensive linear response function (CLRF) for important climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, and lapse rate is learned from Green’s function data. The learned CLRF delineates the effects of the energy diffusion in both the ocean and atmosphere and the pattern-aware feedbacks from the aforementioned radiatively active processes. The CLRF can then be decomposed into forcing–response mode pairs, which are in turn used to construct a reduced-order model describing the dominant dynamics of climate responses. These mode pairs capture nonlocal effects and teleconnections in the climate and thus make the reduced-order model apt for capturing regional features of climate response. A key observation is that the CLRF captures the polar-amplified response as the most excitable mode of the climate system, and this mode is explainable in the data-learned pattern-aware feedback framework. The reduced-order model can be used for predicting the response for a given forcing and for reconstructing the forcing from a given response; we demonstrate these capabilities for multiple independent forcing scenarios. Significance Statement Climate sensitivity and feedbacks have traditionally been examined as a zero-dimensional problem, neglecting the crucial patterns of response and forcing. This limitation has long hindered the acquisition of robust, patterned climate information essential for informed decision-making. In this study, we strive to develop an innovative pattern-aware feedback framework by solving an inverse problem in a finite-dimensional space using a data-driven optimization approach. The resultant reduced-order representation of the original system not only affords a feedback framework to quantify the patterns of the climate response but also reveals the most excitable—and thus most robust—modes of the climate system. The predictive power exhibited by the reduced-order model shows promise for optimizing climate forcing for certain research applications.