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As the leading mode of Pacific variability, El Niño–Southern Oscillation (ENSO) causes vast and widespread climatic impacts, including in the stratosphere. Following discovery of a stratospheric pathway of ENSO to the Northern Hemisphere surface, here we aim to investigate if there is a substantial Southern Hemisphere (SH) stratospheric pathway in relation to austral winter ENSO events. Large stratospheric anomalies connected to ENSO occur on average at high SH latitudes as early as August, peaking at around 10 hPa. An overall colder austral spring Antarctic stratosphere is generally associated with the warm phase of the ENSO cycle, and vice versa. This behavior is robust among reanalysis and six separate model ensembles encompassing two different model frameworks. A stratospheric pathway is identified by separating ENSO events that exhibit a stratospheric anomaly from those that do not and comparing to stratospheric extremes that occur during neutral ENSO years. The tropospheric eddy-driven jet response to the stratospheric ENSO pathway is the most robust in the spring following a La Niña, but extends into summer, and is more zonally symmetric compared to the tropospheric ENSO teleconnection. The magnitude of the stratospheric pathway is weaker compared to the tropospheric pathway and therefore, when it is present, has a secondary role. For context, the magnitude is approximately half that of the eddy-driven jet modulation due to austral spring ozone depletion in the model simulations. This work establishes that the stratospheric circulation acts as an intermediary in coupling ENSO variability to variations in the austral spring and summer tropospheric circulation.

The forecast potential of springtime ozone on April surface temperatures at particular locations in the Northern Hemisphere has been previously reported. Evidence suggests that early springtime Arctic stratospheric ozone acts as a proxy for extreme events in the winter polar vortex. Here, using a state‐of‐the‐art chemistry‐climate model, reanalysis and observations, we extend the forecast potential of ozone on surface temperatures to aspects of the Northern Hemisphere cryosphere. Sea ice fraction and sea ice extent differences between years of March high and low Arctic stratospheric ozone extremes show excellent agreement between an ensemble of chemistry‐climate model simulations and observations, with differences occurring not just in April but extending through to the following winter season in some locations. Large snow depth differences are also obtained in regional locations in Russia and along the southeast coast of Alaska. These differences remain elevated until early summer, when snow cover diminishes. Using a conditional empirical model in a leave‐three‐out cross validation method, March total column ozone is able to accurately predict the sign of the observed sea ice extent and snow depth anomalies over 70% of the time during an ozone extreme year, especially in the region of the Bering strait and the Greenland Sea, which could be useful for shipping routes and for testing climate models.

Correlations between springtime stratospheric ozone extremes and subsequent surface temperatures have been previously reported for both models and observations at particular locations in the Northern Hemisphere. Here we quantify for the first time the potential use of ozone information for Northern Hemisphere seasonal forecasts, using observations and a nine‐member chemistry climate model ensemble. The ensemble composite correlations between March total column ozone (TCO) and April surface temperatures display a similar structure to observations, but with slightly lower correlation magnitudes. This is likely due to the larger number of cases smoothing out sampling error in the pattern, which is visible in the difference between correlations calculated from individual ensemble members. Using a linear regression model with March TCO as the predictor, predictions of the following April surface temperatures in regions that show large correlations are possible up to 4 years following the regression model end date in individual ensemble members, and up to 6 years in observations. We create an empirical forecast model to predict the sign of the observed as well as the modeled surface temperature anomalies using March TCO. Through a leave‐three‐years‐out cross‐validation method, we show that March TCO can forecast the sign of the April surface temperature anomalies well in parts of Eurasia that show the lowest model internal variability.