Assessing Radiative Feedbacks and Their Contribution to the Arctic Amplification Measured by Various Metrics

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 early twentieth 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 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 leading contributor for all AA metrics. The net cloud feedback, which has large uncertainties, depends strongly on the data sets and AA metrics used. By quantifying the influence of internal variability on AA and related feedbacks based on global climate model ensemble simulations, we find that water vapor and cloud feedbacks are most heavily affected by internal variability. Plain Language Summary The Arctic has been observed to warm at a faster pace than the rest of the world, especially over the recent few decades. This phenomenon is known as Arctic amplification (AA), which has connections with different components of the Earth's climate system. Various metrics are used in the science community to measure AA in different data sets and to assess the performance of climate models in reproducing the observed history of AA. In this study, we apply a collection of AA metrics from the literature to the same historical data sets, based on observations and climate model experiments, to identify differences/similarities and pros/cons of these metrics. We also study how radiatively active processes in the climate system, like changes in snow/ice surface reflectivity (albedo), air temperature changes with height (lapse rate), humidity increase, and cloud changes, can in turn amplify (positive feedback) or dampen (negative feedback) this overall Arctic warming. Our study shows that changes in the Arctic surface albedo and lapse rate are the main drivers of AA. We also find that natural climate variations can greatly influence Arctic warming in climate models and their ability to reproduce the observed AA by affecting r