Abstract
Connections across seasons in atmospheric circulation and sea ice have long been sought to advance seasonal prediction. This study presents a link between the springtime stratosphere and Arctic sea ice in summer through autumn. The polar stratospheric vortex dominates the winter stratosphere before breaking down each spring, which is called the stratospheric final warming, as solar radiation returns to the pole. Interannual variability of this breakdown is dynamically driven, leading to different springtime tropospheric and surface circulation patterns. To examine the different impacts of delayed and early final warmings, a multimodel composite was generated from selected CMIP5 models. Additionally, regressions were performed on JRA-55 against an index of springtime polar vortex strength. In both the multimodel composites and reanalysis regressions, significant anomalies in sea ice thickness persist several months following an anomalous timing of the final warming. A later final warming or stronger springtime polar stratospheric vortex leads to negative sea ice thickness anomalies in the East Siberian Sea and positive anomalies in the Beaufort Sea in comparison with an earlier final warming or weaker polar vortex. The spring polar stratospheric vortex is related to spring polar surface circulation patterns. The winds associated with this pattern induce anomalous sea ice motion, moving ice from the East Siberian Sea toward the Beaufort Sea. Reduced sea ice in the East Siberian Sea is linked to anomalous warmth over this region in autumn. Our results suggest that the timing of the stratospheric final warming exerts an influence on the tropospheric circulation and sea ice through autumn, which has implications for seasonal climate prediction.
| Original language | English |
|---|---|
| Pages (from-to) | 3079-3092 |
| Number of pages | 14 |
| Journal | Journal of Climate |
| Volume | 33 |
| Issue number | 8 |
| DOIs | |
| State | Published - Apr 15 2020 |
Funding
Acknowledgments. The authors would like to acknowledge the insightful and helpful comments of three anonymous reviewers. This work was supported in part by the Natural Environment Research Council (Grant NE/M006123/1). BA was funded by the Programme ‘‘Ayudas para la contratación de personal postoctoral de formación en docencia e investigación en los dptos de la UCM’’ of the Universidad Complutense de Madrid. This research is part of POLARCSIC activities. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 2 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors would also like to acknowledge the xarray, pandas, and seaborn Python libraries (Hoyer and Hamman 2017; McKinney 2010; Waskom et al. 2018, respectively) used in processing, analyzing, and plotting data for this paper. The authors would like to acknowledge the insightful and helpful comments of three anonymous reviewers. This work was supported in part by the Natural Environment Research Council (Grant NE/M006123/1). BA was funded by the Programme ''Ayudas para la contrataci?n de personal postoctoral de formaci?n en docencia e investigaci?n en los dptos de la UCM'' of the Universidad Complutense de Madrid. This research is part of POLARCSIC activities. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 2 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The authors would also like to acknowledge the xarray, pandas, and seaborn Python libraries (Hoyer and Hamman 2017; McKinney 2010; Waskom et al. 2018, respectively) used in processing, analyzing, and plotting data for this paper.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | |
| Natural Environment Research Council | NE/M006123/1 |
| Universidad Complutense de Madrid |