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Changes in Arctic moisture transport over the North Pacific associated with sea ice loss

  • Marie C. McGrawEmail author
  • Cory F. Baggett
  • Chengji Liu
  • Bryan D. Mundhenk
Article
  • 49 Downloads

Abstract

Recent work has emphasized the important role of midlatitude moisture fluxes in enhancing Arctic warming and sea ice loss. Conversely, less attention has been paid to the impact of Arctic warming and sea ice loss on midlatitude moisture fluxes. Analysis of an atmosphere-only general circulation model indicates that sea ice loss promotes changes in the large-scale midlatitude atmospheric circulation that have a substantial impact on moisture transport into and out of the Arctic. While poleward moisture transport into the Arctic does increase in a reduced sea ice climate, the increase in equatorward moisture transport out of the Arctic is larger, particularly in boreal winter over the North Pacific. A decomposition of the meridional moisture transport reveals that this increase in equatorward moisture transport is driven, at least in part, by changes in the background circulation. Specifically, sea ice loss drives a series of large-scale tropospheric circulation changes, including an increase in cyclonic Rossby wave breaking over the North Pacific that results in a preferential enhancement of equatorward moisture transport out of the Arctic in the days following the peak of the Rossby wave breaking event.

Keywords

Moisture transport Arctic amplification Sea ice loss Rossby wave breaking 

Notes

Acknowledgements

Many thanks to Lantao Sun of the NOAA/Earth System Research Laboratory in Boulder, CO, for providing us with the model simulations, and to Elizabeth Barnes for support and feedback. The model data used in this paper are available from the corresponding author upon request. This research was supported by the Climate and Large-Scale Dynamics Program of the National Science Foundation under Grant AGS-1419818. This research has also been conducted as part of the NOAA MAPP S2S Prediction Task Force and supported by NOAA Grant NA16OAR4310064. Analysis was performed in Python V2.7.8, MATLAB Release 2016b, and the National Center for Atmospheric Research Command Language (NCL) version V6.4.0.

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Atmospheric ScienceColorado State UniversityFort CollinsUSA
  2. 2.Department of Atmospheric ScienceUniversity of WashingtonSeattleUSA
  3. 3.Climate Prediction Center/NCEP/NWS/Innovim, LLCCollege ParkUSA
  4. 4.14th Weather Squadron, United States Air ForceAshevilleUSA

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