Matei, Daniela

The main drivers of the continental Northern Hemisphere snow cover are investigated in the 1979–2014 period. Four observational datasets are used as are two large multi-model ensembles of atmosphere-only simulations with prescribed sea surface temperature (SST) and sea ice concentration (SIC). A first ensemble uses observed interannually varying SST and SIC conditions for 1979–2014, while a second ensemble is identical except for SIC with a repeated climatological cycle used. SST and external forcing typically explain 10 % to 25 % of the snow cover variance in model simulations, with a dominant forcing from the tropical and North Pacific SST during this period. In terms of the climate influence of the snow cover anomalies, both observations and models show no robust links between the November and April snow cover variability and the atmospheric circulation 1 month later. On the other hand, the first mode of Eurasian snow cover variability in January, with more extended snow over western Eurasia, is found to precede an atmospheric circulation pattern by 1 month, similar to a negative Arctic oscillation (AO). A decomposition of the variability in the model simulations shows that this relationship is mainly due to internal climate variability. Detailed outputs from one of the models indicate that the western Eurasia snow cover anomalies are preceded by a negative AO phase accompanied by a Ural blocking pattern and a stratospheric polar vortex weakening. The link between the AO and the snow cover variability is strongly related to the concomitant role of the stratospheric polar vortex, with the Eurasian snow cover acting as a positive feedback for the AO variability in winter. No robust influence of the SIC variability is found, as the sea ice loss in these simulations only drives an insignificant fraction of the snow cover anomalies, with few agreements among models.

To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.

A coordinated set of large ensemble atmosphere‐only simulations is used to investigatethe impacts of observed Arctic sea ice‐driven variability (SIDV) on the atmospheric circulation during1979–2014. The experimental protocol permits separating Arctic SIDV from internal variability andvariability driven by other forcings including sea surface temperature and greenhouse gases. The geographicpattern of SIDV is consistent across seven participating models, but its magnitude strongly depends onensemble size. Based on 130 members, winter SIDV is ~0.18 hPa2for Arctic‐averaged sea level pressure(~1.5% of the total variance), and ~0.35 K2for surface air temperature (~21%) at interannual and longertimescales. The results suggest that more than 100 (40) members are needed to separate Arctic SIDV fromother components for dynamical (thermodynamical) variables, and insufficient ensemble size always leadsto overestimation of SIDV. Nevertheless, SIDV is 0.75–1.5 times as large as the variability driven by otherforcings over northern Eurasia and Arctic.

Assessing the skill of the Atlantic meridional overturning circulation (AMOC) in decadal hindcasts (i.e. retrospective predictions) is hampered by a lack of obser- vations for verification. Models are therefore needed to reconstruct the historical AMOC variability. Here we show that ten recent oceanic syntheses provide a common signal of AMOC variability at 45°N, with an increase from the 1960s to the mid-1990s and a decrease thereafter although they disagree on the exact magnitude. This signal corre- lates with observed key processes such as the North Atlantic Oscillation, sub-polar gyre strength, Atlantic sea surface temperature dipole, and Labrador Sea convection that are thought to be related to the AMOC. Furthermore, we find potential predictability of the mid-latitude AMOC for the first 3–6 year means when we validate decadal hindcasts for the past 50 years against the multi-model signal. However, this predictability is not found in models driven only by external radiative changes, demonstrating the need for initialization of decadal climate predictions.