Elisa Manzini

We analyze the sources for spread in the response of the Northern Hemisphere wintertime stratospheric polar vortex (SPV) to global warming in Climate Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) model projections. About half of the intermodel spread in SPV projections by CMIP6 models, but less than a third in CMIP5 models, can be attributed to the intermodel spread in stationary planetary wave driving. In CMIP6, SPV weakening is mostly driven by increased upward wave flux from the troposphere, while SPV strengthening is associated with increased equatorward wave propagation away from the polar stratosphere. We test hypothesized factors contributing to changes in the upward and equatorward planetary wave fluxes and show that an across‐model regression using projected global warming rates, strengthening of the subtropical jet and basic state lower stratospheric wind biases as predictors can explain nearly the same fraction in the CMIP6 SPV spread as the planetary wave driving (r = 0.67). The dependence of the SPV spread on the model biases in the basic state winds offers a possible emergent constraint; however, a large uncertainty prevents a substantial reduction of the projected SPV spread. The lack of this dependence in CMIP5 further calls for better understanding of underlying causes. Our results improve understanding of projected SPV uncertainty; however, further narrowing of the uncertainty remains challenging.

The observed winter Barents-Kara Sea (BKS) sea ice concentration (SIC) has shown a close association with the second empirical orthogonal function (EOF) mode of Eurasian winter surface air temperature (SAT) variability, known as Warm Arctic Cold Eurasia (WACE) pattern. However, the potential role of BKS SIC on this WACE pattern of variability and on its long-term trend remains elusive. Here, we show that from 1979 to 2022, the winter BKS SIC and WACE association is most prominent and statistically significant for the variability at the sub-decadal time scale for 5–6 years. We also show the critical role of the multi-decadal trend in the principal component of the WACE mode of variability for explaining the overall Eurasian winter temperature trend over the same period. Furthermore, a large multi-model ensemble of atmosphere-only experiments from 1979 to 2014, with and without the observed Arctic SIC forcing, suggests that the BKS SIC variations induce this observed sub-decadal variability and the multi-decadal trend in the WACE. Additionally, we analyse the model simulated first or the leading EOF mode of Eurasian winter SAT variability, which in observations, closely relates to the Arctic Oscillation (AO). We find a weaker association of this mode to AO and a statistically significant positive trend in our ensemble simulation, opposite to that found in observation. This contrasting nature reflects excessive hemispheric warming in the models, partly contributed by the modelled Arctic Sea ice loss.

Nonlinearities and asymmetries of El Niño Southern Oscillation (ENSO) stratospheric pathway to the North Atlantic and Europe are examined in large ensembles conducted with fully coupled climate models during wintertime. The analysis is centered on historical experiments of the Max Planck Institute Grand Ensemble (MPI‐GE, 95 members) and expanded to six other ensembles of more limited size. In MPI‐GE, significant responses are obtained for each ENSO phase and three different intensities (weak, moderate and strong). Overall, linear relationships are found for either El Niño or La Niña key diagnostics that characterize the pathway. These relationships are generally weaker for the cold La Niña than for the warm El Niño so that asymmetries between them develop as the events intensify. Specifically for strong events, the extra‐tropical North Pacific and stratospheric responses are asymmetric, with larger responses for El Niño. In addition, the stratospheric asymmetry in strong events seems to contribute to the asymmetry in strong events in the North Atlantic—Europe response in the troposphere in late winter. The extra‐tropical North Pacific response shows general agreement between MPI‐GE and the other large ensembles. However, this agreement is not as large when other parts of the pathway are compared. Relatively high inter‐model response spread confirms the typical model uncertainty found when examining atmospheric circulation responses which include the stratosphere in state‐of‐the‐art climate models.

Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt‐4xCO₂ runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near‐surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift.

Major sudden stratospheric warmings (SSWs) are the most important phenomena of the wintertime boreal stratospheric variability. During SSWs, the polar temperature increases abruptly, and easterlies prevail in the stratosphere. Their effects extend farther from the polar stratosphere, affecting near‐surface circulation. According to observations, SSWs are not equally distributed in time, with decades experiencing very few events, while others experiencing SSWs almost every winter. Some sources of this SSW multidecadal variability can be traced back to sea surface temperature changes. Here, we investigate the effects of Pacific decadal variability (PDV) and Atlantic multidecadal variability (AMV) on SSWs. We use for the first time a large ensemble of historical experiments to examine the modulation of the frequency, tropospheric precursors, and impact of SSWs by the PDV and AMV. We find a strong impact of the PDV on the occurrence of SSWs, with a higher SSW frequency for the positive phase of the PDV. This PDV influence is mediated by constructive interference of PDV anomalies with tropospheric stationary waves. The main effect of AMV is, instead, a modulation of the tropospheric response to SSWs, a finding that can be useful for predicting the tropospheric fingerprint of SSWs.

The atmospheric circulation response to the anthropogenic increase of CO2 is known to generally involve a number of pathways of change, often leading to contrasting responses, the so called “tug of wars”. Multiple pathways of change in atmospheric dynamics are due to the development of direct and indirect dynamical responses to global warming, the latter being the thermodynamical response to the anthropogenic increase of CO2. The most known direct dynamical response to global warming is the strengthening and poleward shift of the tropospheric eddy-driven jets, a direct consequence of the tropical upper troposphere warming. During Northern Hemisphere winter, where and when the two-way dynamical stratosphere-troposphere coupling is active, stratosphere-troposphere coupling has been shown to provide for indirect dynamical responses, which are relevant to regional circulation changes, over the North Atlantic and Eurasia, for instance affecting the European precipitation changes. However, it has been found that the dynamical response of the stratospheric vortex is highly uncertain. Changes from ensemble of models (such as the CMIP intercomparisons) show a spread in the stratospheric responses, although the stratospheric response can be significant by model. This spread in the stratospheric responses in turn leads to uncertainty in the impact on surface climate of the stratospheric change. For instance, a stratospheric vortex weakening with global warming will counteract to some extend in some regions, the strengthening and poleward shift of the tropospheric eddy-driven jets. However, a polar vortex strengthening will feedback positively on the strengthening and poleward shift of the tropospheric eddy-driven jets. Here we review our knowledge and include new selected results, based on CMIP6, on the role of dynamical stratosphere-troposphere coupling in providing for pathway of atmospheric circulation changes in response to global warming.

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.

This study investigates the dynamics that led to the repeated cold surges over midlatitude Eurasia, exceptionally warm conditions and sea ice loss over the Arctic, and the unseasonable weakening of the stratospheric polar vortex in autumn and early winter 2016–2017. We use ERA‐Interim reanalysis data and COBE sea ice and sea surface temperature observational data to trace the dynamical pathways that caused these extreme phenomena. Following abnormally low sea ice conditions in early autumn over the Pacific sector of the Arctic basin, blocking anticyclones became dominant over Eurasia throughout autumn. Ural blocking (UB) activity was four times above climatological levels and organized in several successive events. UB episodes played a key role in the unprecedented sea ice loss observed in late autumn 2016 over the Barents‐Kara Seas and the weakening of the stratospheric vortex. Each blocking induced circulation anomalies that resulted in cold air advection to its south and warm advection to its north. The near‐surface warming anomalies over the Arctic and cooling anomalies over midlatitude Eurasia varied in phase with the life cycles of UB episodes. The sea ice cover minimum over the Barents‐Kara Seas in 2016 was not observed in late summer but rather in mid‐November and December shortly after the two strongest UB episodes. Each UB episode drove intense upward flux of wave activity that resulted in unseasonable weakening of the stratospheric vortex in November. The surface impact of this weakening can be linked to the migration of blocking activity and cold spells toward Europe in early winter 2017.

The Max Planck Institute Grand Ensemble (MPI‐GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations (1850–2005) and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO₂ scenario. These advantages make MPI‐GE a powerful tool. We present an overview of MPI‐GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI‐GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI‐GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects.