Exploiting SMILEs and the CMIP5 Archive to Understand Arctic Climate Change Seasonality and Uncertainty

• Published in: Geophysical Research Letters
• Main Authors: You‐Ting Wu, Yu‐Chiao Liang, Yan‐Ning Kuo, Flavio Lehner, Michael Previdi, Lorenzo M. Polvani, Min‐Hui Lo, Chia‐Wei Lan
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: Geophysics. Cosmic physics; QC801-809; Climate change; Sea ice
• Online Access: https://doi.org/10.1029/2022GL100745; https://doaj.org/article/5155281f068b4fbc8bbc38924fd46a8e

Abstract Arctic Amplification (AA) exhibits a distinct seasonal dependence; it is weakest in boreal summer and strongest in winter. Here, we analyze simulations from single‐model initial‐condition large ensembles and Coupled Model Intercomparison Project Phase 5 to decipher the seasonal evolution of Arctic climate change. Models agree that the annual maximum AA shifts from autumn into winter over the 21st century, accompanied by similar shifts in sea‐ice loss and surface turbulent heat fluxes, whereas the maximum precipitation shifts only into late autumn. However, the exact seasonal timing and magnitude of these shifts are highly uncertain. Decomposing the uncertainty into model structural differences, emission scenarios, and internal variability reveals that model differences dominate the total uncertainty, which also undergo autumn‐to‐winter shifts. We also find that the scenario uncertainty is unimportant for projections of AA. These results highlight that understanding model differences is critical to reducing uncertainty in projected Arctic climate change.