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The significance and robustness of the link between Arctic sea ice loss and changes in midlatitude weather patterns is investigated through a series of model simulations from the Community Atmosphere Model, version 5.3, with systematically perturbed sea ice cover in the Arctic. Using a large ensemble of 10 sea ice scenarios and 550 simulations, it is found that prescribed Arctic sea ice anomalies produce statistically significant changes for certain metrics of the midlatitude circulation but not for others. Furthermore, the significant midlatitude circulation changes do not scale linearly with the sea ice anomalies and are not present in all scenarios, indicating that the remote atmospheric response to reduced Arctic sea ice can be statistically significant under certain conditions but is generally nonrobust. Shifts in the Northern Hemisphere polar jet stream and changes in the meridional extent of upper-level large-scale waves due to the sea ice perturbations are generally small and not clearly distinguished from intrinsic variability. Reduced Arctic sea ice may favor a circulation pattern that resembles the negative phase of the Arctic Oscillation and may increase the risk of cold outbreaks in eastern Asia by almost 50%, but this response is found in only half of the scenarios with negative sea ice anomalies. In eastern North America the frequency of extreme cold events decreases almost linearly with decreasing sea ice cover. This study’s finding of frequent significant anomalies without a robust linear response suggests interactions between variability and persistence in the coupled system, which may contribute to the lack of convergence among studies of Arctic influences on midlatitude circulation.

Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport. Here we characterize regional‐scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow‐dependent transport errors. During a 1‐month summer period over North America, transport uncertainties yield an ensemble spread in instantaneous CO2 at 100 m above ground level comparable to the CO2 uncertainties resulting from 48% relative uncertainty in 3‐hourly natural CO2 fluxes. Temporal averaging reduces transport uncertainties but increases the influence of CO2 uncertainties from the lateral boundaries. The influence of CO2 background uncertainties is especially large for column‐averaged CO2. These results suggest that transport errors and CO2 background errors limit regional atmospheric inversions at two distinct timescales and that the error characteristics of transport and background errors should guide the design of regional inversion systems.

The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Norwegian Sea, the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the multi-model ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice higher than rates in the global/Northern Hemisphere. Model uncertainty is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the 21st century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015-2095. It is found that the largest model uncertainties are consistent with the oceanic regions with cold biases in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that the CMIP6 models’ simulation and projection of the Arctic near-surface temperature still exist large inter-model spread and uncertainties, and there are different behaviors over the ocean and land in the Arctic. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.