The last decade has seen the success of stochastic parameterizations in short-term, medium-range, and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to represent model inadequacy better and to improve the quantification of forecast uncertainty. Developed initially for numerical weather prediction, the inclusion of stochastic parameterizations not only provides better estimates of uncertainty, but it is also extremely promising for reducing long-standing climate biases and is relevant for determining the climate response to external forcing. This article highlights recent developments from different research groups that show that the stochastic representation of unresolved processes in the atmosphere, oceans, land surface, and cryosphere of comprehensive weather and climate models 1) gives rise to more reliable probabilistic forecasts of weather and climate and 2) reduces systematic model bias. We make a case that the use of mathematically stringent methods for the derivation of stochastic dynamic equations will lead to substantial improvements in our ability to accurately simulate weather and climate at all scales. Recent work in mathematics, statistical mechanics, and turbulence is reviewed; its relevance for the climate problem is demonstrated; and future research directions are outlined.

The current standard version of the Whole Atmosphere Community Climate Model (WACCM) simulates Southern Hemisphere winter and spring temperatures that are too cold compared with observations. This “cold-pole bias” leads to unrealistically low ozone column amounts in Antarctic spring. Here, the cold-pole problem is addressed by introducing additional mechanical forcing of the circulation via parameterized gravity waves. Insofar as observational guidance is ambiguous regarding the gravity waves that might be important in the Southern Hemisphere stratosphere, the impact of increasing the forcing by orographic gravity waves was investigated. This reduces the strength of the Antarctic polar vortex in WACCM, bringing it into closer agreement with observations, and accelerates the Brewer–Dobson circulation in the polar stratosphere, which warms the polar cap and improves substantially the simulation of Antarctic temperature. These improvements are achieved without degrading the performance of the model in the Northern Hemisphere stratosphere or in the mesosphere and lower thermosphere of either hemisphere. It is shown, finally, that other approaches that enhance gravity wave forcing can also reduce the cold-pole bias such that careful examination of observational evidence and model performance will be required to establish which gravity wave sources are dominant in the real atmosphere. This is especially important because a “downward control” analysis of these results suggests that the improvement of the cold-pole bias itself is not very sensitive to the details of how gravity wave drag is altered.

The increase of spatial resolution allows the ECMWF operational model to explicitly resolve a significant portion of the atmospheric gravity wave (GW) field, but the realism of the simulated GW field in the ECMWF analyses still needs to be precisely evaluated. Here the authors use data collected during the Concordiasi stratospheric balloon campaign to assess the representation of GWs in the ECMWF analyses over Antarctica and the Southern Ocean in spring 2010. The authors first compare the balloonborne GW momentum fluxes with those in ECMWF analyses throughout the campaign and find a correct agreement of the geographical and seasonal patterns. However, the authors also note that ECMWF analyses generally underestimate the balloon fluxes by a factor of 5, which may be essentially due to the spatial truncation of the ECMWF model. Intermittency of wave activity in the analyses and observations are found comparable. These results are confirmed on two case studies dealing with orographic and nonorographic waves, which thus supports that the ECMWF analyses can be used to study the geographical and seasonal distribution of GW momentum fluxes. The authors then used both datasets to provide insights on the missing GW drag at 60°S in general circulation models in the Southern Hemisphere spring. These datasets suggest that a significant part of the missing drag may be associated with nonorographic GWs generated by weather systems above the Southern Ocean.

International audienceSatellite-derived sea ice concentration (SIC) and reanalyzed atmospheric data are used to explore the predictability of the winter Euro-Atlantic climate resulting from autumn SIC variability over the Barents– Kara Seas region (SIC/BK). The period of study is 1979/80–2012/13. Maximum covariance analyses show that the leading predictand is indistinguishable from the North Atlantic Oscillation (NAO). The leading covari-ability mode between September SIC/BK and winter North Atlantic–European sea level pressure (SLP) is not significant, indicating that no empirical prediction skill can be achieved. The leading covariability mode with either October or November SIC/BK is moderately significant (significance levels ,10%), and both predictor fields yield a cross-validated NAO correlation of 0.3, suggesting some empirical prediction skill of the winter NAO index, with sea ice reduction in the Barents–Kara Seas being accompanied by a negative NAO phase in winter. However, only November SIC/BK provides significant cross-validated skill of winter SLP, surface air temperature, and precipitation anomalies over the Euro-Atlantic sector, namely in southwestern Europe. Statistical analysis suggests that November SIC/BK anomalies are associated with a Rossby wave train–like anomaly across Eurasia that affects vertical wave activity modulating the stratospheric vortex strength, which is then followed by downward propagation of anomalies that impact transient-eddy activity in the upper troposphere, helping to settle and maintain the NAO-like pattern at surface. This stratospheric pathway is not detected when using October SIC/BK anomalies. Hence, only November SIC/BK, with a one-month lead time, could be considered as a potential source of regional predictability