Summer snow on Arctic sea ice modulated by the Arctic Oscillation.

Since the 1970s, Arctic sea ice has undergone unprecedented change, becoming thinner, less extensive and less resilient to summer melt. Snow's high albedo greatly reduces solar absorption in sea ice and the upper ocean, which mitigates sea-ice melt and ocean warming. However, the drivers of summertime snow depth variability are unknown. The Arctic Oscillation is a mode of natural climate variability, influencing Arctic snowfall and air temperatures. Thus, it may affect summertime snow conditions on Arctic sea ice. Here we examine the role of the Arctic Oscillation in summer snow depth variability on Arctic sea ice in 1980-2020 using atmospheric reanalysis, snow modelling and satellite data. The positive phase leads to greater snow accumulation, ranging up to ~4.5 cm near the North Pole, and higher surface albedo in summer. There are more intense, frequent Arctic cyclones, cooler temperatures aloft and greater snowfall relative to negative and neutral phases; these conditions facilitate a more persistent summer snow cover, which may lessen sea-ice melt and ocean warming. The Arctic Oscillation influence on summertime snow weakens after 2007, which suggests that future warming and Arctic sea-ice loss might modify the relationship between the Arctic Oscillation and snow on Arctic sea ice.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity.

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.