The Nonlinear Radiative Feedback Effects in the Arctic Warming

Yi Huang,Han Huang,Aliia Shakirova

doi:10.3389/feart.2021.693779

Abstract

The analysis of radiative feedbacks requires the separation and quantification of the radiative contributions of different feedback variables, such as atmospheric temperature, water vapor, surface albedo, cloud, etc. It has been a challenge to include the nonlinear radiative effects of these variables in the feedback analysis. For instance, the kernel method that is widely used in the literature assumes linearity and completely neglects the nonlinear effects. Nonlinear effects may arise from the nonlinear dependency of radiation on each of the feedback variables, especially when the change in them is of large magnitude such as in the case of the Arctic climate change. Nonlinear effects may also arise from the coupling between different feedback variables, which often occurs as feedback variables including temperature, humidity and cloud tend to vary in a coherent manner. In this paper, we use brute-force radiation model calculations to quantify both univariate and multivariate nonlinear feedback effects and provide a qualitative explanation of their causes based on simple analytical models. We identify these prominent nonlinear effects in the CO2-driven Arctic climate change: 1) the univariate nonlinear effect in the surface albedo feedback, which results from a nonlinear dependency of planetary albedo on the surface albedo, which causes the linear kernel method to overestimate the univariate surface albedo feedback; 2) the coupling effect between surface albedo and cloud, which offsets the univariate surface albedo feedback; 3) the coupling effect between atmospheric temperature and cloud, which offsets the very strong univariate temperature feedback. These results illustrate the hidden biases in the linear feedback analysis methods and highlight the need for nonlinear methods in feedback quantification.

Highlights

Radiative forcing and feedbacks strongly influence the Arctic climate
Based on a heuristic climate change scenario of broad interest: the abrupt quadrupling of atmospheric CO2 (4xCO2), we investigate how the nonlinear radiative effects arise from the univariate and multivariate variations of the feedback variables, such as atmospheric and surface temperature (t), water vapor (q), surface albedo (a) and cloud (c), and measure how the nonlinear effects compare to the linear effects in terms of magnitude and pattern
We present an overview of the nonlinear effects in both longwave and shortwave radiative feedbacks in the CO2-driven Arctic warming

Summary

Introduction

The warming in the Arctic has occurred in a faster pace than the global average, due to greenhouse gas forcing and amplifying feedbacks (Stocker et al, 2013). It requires accurate quantification of the radiative effects of associated feedback variables (surface albedo, atmospheric temperature, water vapor, cloud, etc.,) in order to ascertain their contributions to the climate change of interest. Based on the energy budget balance with regard to the Top-of-Atmosphere (TOA), surface or atmospheric budget and assuming the warming induced thermal radiation (Planck effect) balances the radiation changes caused by feedbacks, one can infer how much global or regional warming, e.g., the Arctic warming amplification, can be attributed to individual feedbacks (Held and Soden 2000; Lu and Cai 2009; Pithan and Mauritsen 2014). Due to its simple concept and computational efficiency, a large number of studies have been conducted using this method (e.g., Soden and Held 2006; Zelinka et al, 2012; Vial et al, 2013; Zhang and Huang, 2014) and precomputed kernels based on different atmospheric datasets, including climate models, reanalyses and satellite data (e.g., Soden et al, 2008; Yue et al, 2016; Huang et al, 2017)

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