PNNL

Abstract

Arctic amplification (AA), characterized by a more rapid surface air temperature (SAT) warming in the Arctic than the global average, is a major feature of global climate warming. Various metrics have been used to quantify AA based on SAT anomalies, trends, or variability, and they can yield quite different conclusions regarding the magnitude and temporal patterns of AA. This study examines and compares various AA metrics for their temporal consistency in the region north of 70°N from the mid-20th to the early 21st century using observational data and reanalysis products. We also quantify contributions of different radiative feedback mechanisms to AA based on short-term (monthly) climate variability in reanalysis and model data using the Kernel-Gregory approach. Albedo and lapse rate feedbacks are positive and comparable, with albedo feedback being the dominant contributor for all AA metrics. The net cloud feedback has large uncertainties. It depends strongly on the data and AA metrics used. Utilizing global climate model ensemble simulations, the influence of internal variability on AA and feedback analysis is quantified. Water vapor and cloud feedbacks are most heavily influenced by internal variability. A moist static energy balance model (EBM), incorporating regional feedbacks and diffusivity from reanalysis products, is further employed to establish a robust connection between the estimated regional feedbacks and their contribution to the polar amplified warming. Our analysis of feedback contributions to AA based on EBM provides a quantitative measure of the sensitivity of feedbacks in the Arctic climate system to the choice of different AA metrics.