Title: Pattern-aware feedback framework for regional climate response
Lecturer: Prof. Jian Lv (Ocean University of China，OUC)
Time: Friday December 6, 2024 at 10:00 AM
Venue: Lecture Hall D103, School of Atmospheric Sciences
Abstract: The so-called pattern-effect for explaining the discrepancy in the climate feedback between models and between model and observation has gained considerable traction in climate change research community recently. However, the conceptual underpinning behind the pattern-effect literature is flawed. We devise a pattern-aware feedback framework for the forced climate response using a suite of Green’s function-based solar radiation perturbation experiments to overcome the caveat of the existing climate feedback literature that disregards the co-variation between circulation and the radiative processes. By considering the energy balance for the atmospheric column, a linear response function (LRF) for important climate variables and feedback quantities such as moist static energy, sea surface temperature, albedo, cloud optical depth, lapse rate etc., is learned from the experiment data. The resultant LRF decodes the efficiency of the energy diffusion in both the ocean and atmosphere, and the pattern-aware feedbacks from the radiatively active processes. The LRF can then be decomposed into forcing-response mode pairs which are in turn used to construct a reduced-order model (ROM) describing the dominant dynamics of climate response. These mode pairs capture the nonlocal effects in the climate response and feedback. An intriguing outcome of our approach is that the most excitable mode of the LRF captures the polar amplified response of the climate system and this mode is explainable in the data-learned, pattern-aware feedback framework. As it turns out, learning the climate feedbacks of a climate system is mathematically equivalent to solving an inverse problem of the system.