Robustness of the stratospheric pathway in linking the Barents-Kara Sea sea ice variability to the mid-latitude circulation in CMIP5 models
This study investigates the robustness of the stratospheric pathway in linking the sea ice variability over the Barents-Kara Sea in late autumn and early winter to the mid-latitude circulation in the subsequent winter. Two groups of models from the Coupled Model Intercomparison Project phase 5 (CMIP5) archive, one with a well-resolved stratosphere (high-top models) and the other with a poorly-resolved stratosphere (low-top models) are explored to distinguish the role of the stratospheric pathway. The results show that, collectively, high-top models are able to capture the persistent mid-latitude circulation response in the subsequent winter. The response in low-top models is, however, weaker and not as long-lasting most likely due to lack of stratospheric variability. Analysis of eddy heat flux reveals that stronger vertical wave propagation leads to a stronger response in stratospheric polar vortex in high-top models. In particular, it shows that zonal wave-2 eddy heat flux is crucial in leading to a stronger linear constructive interference with the climatological waves in high-top models. The results find that multi-model ensemble of CMIP5 high-top models is able to capture the prolonged impact of sea ice variability on the mid-latitude circulation and out performs the low-top models in this regard. Our study suggests that the representation of the stratosphere in climate models plays an important role in amplifying and extending the mid-latitude circulation response.
KeywordsCMIP5 Barents-Kara Sea sea ice variability Troposphere–stratosphere coupling Planetary scale waves
We acknowledge the World Climate Research Programmes Working Group on Coupled Modeling for CMIP and we thank each climate modeling groups for producing and making available the model output. De and Wu were supported by National Science Foundation under grant NSF AGS-1406962 and Purdue University to conduct this research. We thank Lantao Sun for constructive discussion at early stage of this study. We are thankful to Paul Kushner and Chaim Garfinkel for important comments. We thank NCAR and participating scientists for a constructive workshop on CMIP analysis platform with a particular focus on multi-model ensemble analysis. We also thank Wen-wen Tung and Pengfei Zhang for useful discussion. We acknowledge the computing resources at Purdue University. Lastly, we are thankful to two anonymous reviewers for their insightful suggestions to improve the manuscript.
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