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Christopher G. Fletcher, Steven C. Hardiman, Paul J. Kushner, and Judah Cohen

Abstract

Variability in the extent of fall season snow cover over the Eurasian sector has been linked in observations to a teleconnection with the winter northern annular mode pattern. Here, the dynamics of this teleconnection are investigated using a 100-member ensemble of transient integrations of the GFDL atmospheric general circulation model (AM2). The model is perturbed with a simple persisted snow anomaly over Siberia and is integrated from October through December. Strong surface cooling occurs above the anomalous Siberian snow cover, which produces a tropospheric form stress anomaly associated with the vertical propagation of wave activity. This wave activity response drives wave–mean flow interaction in the lower stratosphere and subsequent downward propagation of a negative-phase northern annular mode response back into the troposphere. A wintertime coupled stratosphere–troposphere response to fall season snow forcing is also found to occur even when the snow forcing itself does not persist into winter. Finally, the response to snow forcing is compared in versions of the same model with and without a well-resolved stratosphere. The version with the well-resolved stratosphere exhibits a faster and weaker response to snow forcing, and this difference is tied to the unrealistic representation of the unforced lower-stratospheric circulation in that model.

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Judah Cohen, Jason C. Furtado, Justin Jones, Mathew Barlow, David Whittleston, and Dara Entekhabi

Abstract

Previous research has linked wintertime Arctic Oscillation (AO) variability to indices of Siberian snow cover and upward wave activity flux in the preceding fall season. Here, daily data are used to examine the surface and tropospheric processes that occur as the link between snow cover and upward forcing into the stratosphere develops. October Eurasian mean snow cover is found to be significantly related to sea level pressure (SLP) and to lower-stratosphere (100 hPa) meridional heat flux. Analysis of daily SLP and 100-hPa heat flux shows that in years with high October snow, the SLP is significantly higher from approximately 1 November to 15 December, and the 100-hPa heat flux is significantly increased with a two-week lag, from approximately 15 November to 31 December. During November–December, there are periods with upward wave activity flux extending coherently from the surface to the stratosphere, and these events occur nearly twice as often in high snow years compared to low snow years. The vertical structure of these events is a westward-tilting pattern of high eddy heights, with the largest normalized anomalies near the surface in the same region as the snow and SLP changes. These results suggest that high SLP develops in response to the snow cover and this higher pressure, in turn, provides part of the structure of a surface-to-stratosphere wave activity flux event, thus making full events more likely. Implications for improved winter forecasts exist through recognition of these precursor signals.

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Ziyi Cai, Qinglong You, Fangying Wu, Hans W. Chen, Deliang Chen, and Judah Cohen

Abstract

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.

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James Foster, Glen Liston, Randy Koster, Richard Essery, Helga Behr, Lydia Dumenil, Diana Verseghy, Starly Thompson, David Pollard, and Judah Cohen

Abstract

Confirmation of the ability of general circulation models (GCMs) to accurately represent snow cover and snow mass distributions is vital for climate studies. There must be a high degree of confidence that what is being predicted by the models is reliable, since realistic results cannot be assured unless they are tested against results from observed data or other available datasets. In this study, snow output from seven GCMs and passive-microwave snow data derived from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) are intercompared. National Oceanic and Atmospheric Administration satellite data are used as the standard of reference for snow extent observations and the U.S. Air Force snow depth climatology is used as the standard for snow mass. The reliability of the SMMR snow data needs to be verified, as well, because currently this is the only available dataset that allows for yearly and monthly variations in snow depth. [The GCMs employed in this investigation are the United Kingdom Meteorological Office, Hadley Centre GCM, the Max Planck Institute for Meteorology/University of Hamburg (ECHAM) GCM, the Canadian Climate Centre GCM, the National Center for Atmospheric Research (GENESIS) GCM, the Goddard Institute for Space Studies GCM, the Goddard Laboratory for Atmospheres GCM and the Goddard Coupled Climate Dynamics Group (AIRES) GCM.] Data for both North America and Eurasia are examined in an effort to assess the magnitude of spatial and temporal variations that exist between the standards of reference, the models, and the passive microwave data. Results indicate that both the models and SMMR represent seasonal and year-to-year snow distributions fairly well. The passive microwave data and several of the models, however, consistently underestimate snow mass, but other models overestimate the mass of snow on the ground. The models do a better job simulating winter and summer snow conditions than in the transition months. In general, the underestimation by SMMR is caused by absorption of microwave energy by vegetation. For the GCMs, differences between observed snow conditions can be ascribed to inaccuracies in simulating surface air temperatures and precipitation fields, especially during the spring and fall.

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Hengchun Ye, Eric J. Fetzer, Ali Behrangi, Sun Wong, Bjorn H. Lambrigtsen, Crysti Y. Wang, Judah Cohen, and Brandi L. Gamelin

Abstract

This study uses 45 years of observational records from 517 historical surface weather stations over northern Eurasia to examine changing precipitation characteristics associated with increasing air temperatures. Results suggest that warming air temperatures over northern Eurasia have been accompanied by higher precipitation intensity but lower frequency and little change in annual precipitation total. An increase in daily precipitation intensity of around 1%–3% per each degree of air temperature increase is found for all seasons as long as a station’s seasonal mean air temperature is below about 15°–16°C. This threshold temperature may be location dependent. At temperatures above this threshold, precipitation intensity switches to decreasing with increasing air temperature, possibly related to decreasing water vapor associated with extreme high temperatures. Furthermore, the major atmospheric circulation of the Arctic Oscillation, Scandinavian pattern, east Atlantic–western Eurasian pattern, and polar–Eurasian pattern also have significant influences on precipitation intensity in winter, spring, and summer over certain areas of northern Eurasia.

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