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Major sudden stratospheric warmings (SSWs), vortex formation, and final breakdown dates are key highlight points of the stratospheric polar vortex. These phenomena are relevant for stratosphere‐troposphere coupling, which explains the interest in understanding their future changes. However, up to now, there is not a clear consensus on which projected changes to the polar vortex are robust, particularly in the Northern Hemisphere, possibly due to short data record or relatively moderate CO2 forcing. The new simulations performed under the Coupled Model Intercomparison Project, Phase 6, together with the long daily data requirements of the DynVarMIP project in preindustrial and quadrupled CO2 (4xCO2) forcing simulations provide a new opportunity to revisit this topic by overcoming the limitations mentioned above. In this study, we analyze this new model output to document the change, if any, in the frequency of SSWs under 4xCO2 forcing. Our analysis reveals a large disagreement across the models as to the sign of this change, even though most models show a statistically significant change. As for the near‐surface response to SSWs, the models, however, are in good agreement as to this signal over the North Atlantic: There is no indication of a change under 4xCO2 forcing. Over the Pacific, however, the change is more uncertain, with some indication that there will be a larger mean response. Finally, the models show robust changes to the seasonal cycle in the stratosphere. Specifically, we find a longer duration of the stratospheric polar vortex and thus a longer season of stratosphere‐troposphere coupling., Ministerio de Ciencia, Innovación y Universidades, European Council, National Science Foundation (United States), NASA High-End Computing (HEC) Program, NASA Modeling, Analysis and Prediction program, Met Office Hadley Centre Climate Programme, MEXT| Integrated Research Program for Advancing Climate Models, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models.

A tighter integration of modeling frameworks for climate and air quality is urgently needed to assess the impacts of clean air policies on future Arctic and global climate. We combined a new model emulator and comprehensive emissions scenarios for air pollutants and greenhouse gases to assess climate and human health co-benefits of emissions reductions. Fossil fuel use is projected to rapidly decline in an increasingly sustainable world, resulting in far-reaching air quality benefits. Despite human health benefits, reductions in sulfur emissions in a more sustainable world could enhance Arctic warming by 0.8 °C in 2050 relative to the 1995–2014, thereby offsetting climate benefits of greenhouse gas reductions. Targeted and technically feasible emissions reduction opportunities exist for achieving simultaneous climate and human health co-benefits. It would be particularly beneficial to unlock a newly identified mitigation potential for carbon particulate matter, yielding Arctic climate benefits equivalent to those from carbon dioxide reductions by 2050.

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their

Recent observational and theoretical studies of the global properties of small-scale atmospheric gravity waves have highlighted the global effects of these waves on the circulation from the surface to the middle atmosphere. The effects of gravity waves on the large-scale circulation have long been treated via parametrizations in both climate and weather forecasting applications. In these parametrizations, key parameters describe the global distributions of gravity wave momentum flux, wavelengths, and frequencies of the waves. Until recently, global observations could not define the needed parameters because the waves are small in scale and intermittent in occurrence. Recent satellite and other global data sets with improved resolution along with innovative analysis methods are now providing constraints for the parametrizations that can improve the treatment of these waves in climate prediction models. Research using very high resolution global models has also recently demonstrated the capability of resolving gravity waves and their circulation effects, and when tested against observations, these models are showing some very realistic properties. Here we review recent studies on gravity wave effects in stratosphere-resolving climate models, recent observations and analysis methods that reveal global patterns in gravity wave momentum fluxes, and results of the very high resolution model studies, and we outline some future research needs to improve the treatment of these waves in climate simulations., In Press, Quarterly Journal of the Royal Meterological Society. Prepared in collaboration with NWRA/Colorado Research Assoc., Boulder, CO; State University of New York at Sunnybrook, NY; University of Toronto, Toronto, Canada; Environment Canada; Forschungzentrum Juelich, Germany; University of Tokyo; UPMC Univ Paris 06, Laboratoire de Meteorologie Dynamique, France; Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan; New York University; and University of Adelaide, Australia. Sponsored in part by National Science Foundation's Physical and Dynamic Meteorology Program Grant no.0632378.

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