Abstract
Multimodel Arctic Ocean “climate response function” experiments are analyzed in order to explore the effects of anomalous wind forcing over the Greenland Sea (GS) on poleward ocean heat transport, Atlantic Water (AW) pathways, and the extent of Arctic sea ice. Particular emphasis is placed on the sensitivity of the AW circulation to anomalously strong or weak GS winds in relation to natural variability, the latter manifested as part of the North Atlantic Oscillation. We find that anomalously strong (weak) GS wind forcing, comparable in strength to a strong positive (negative) North Atlantic Oscillation index, results in an intensification (weakening) of the poleward AW flow, extending from south of the North Atlantic Subpolar Gyre, through the Nordic Seas, and all the way into the Canadian Basin. Reconstructions made utilizing the calculated climate response functions explain ∼50% of the simulated AW flow variance; this is the proportion of variability that can be explained by GS wind forcing. In the Barents and Kara Seas, there is a clear relationship between the wind-driven anomalous AW inflow and the sea ice extent. Most of the anomalous AW heat is lost to the atmosphere, and loss of sea ice in the Barents Sea results in even more heat loss to the atmosphere, and thus effective ocean cooling. Release of passive tracers in a subset of the suite of models reveals differences in circulation patterns and shows that the flow of AW in the Arctic Ocean is highly dependent on the wind stress in the Nordic Seas.
Original language | English |
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Pages (from-to) | 6286-6322 |
Number of pages | 37 |
Journal | Journal of Geophysical Research: Oceans |
Volume | 124 |
Issue number | 8 |
DOIs | |
Publication status | Published - 1 Aug 2019 |
Bibliographical note
Publisher Copyright:©2019. The Authors.
Funding
The first author is particularly grateful to Prof. John Marshall and Prof. Helge Drange for constructive discussions and comments that helped to improve the manuscript, Evangelia Efstathiou for good suggestions, and the consortium supporting the development of the NorESM model. M. M. and L. H. S. were supported by the Centre for Climate Dynamics at the Bjerknes Centre for Climate Research, funded by the Norwegian Research Council. M. I. was partially supported by the ITU (ITU‐TGA‐2017‐40657). Computing resources used in this work for ITU was provided by the National Center for High Performance Computing of Turkey (UHeM) under Grant 5004782017. R. Gelderloos and TWNH were financially supported by NOAA Grant NA15OAR4310172. C. L., C. T., and V. H. were supported through the projects ArcticMix, supported by the Copernicus Marine Environment Monitoring Service (CMEMS), and FREDY, supported by the French LEFE/INSU program. Q. W. was supported by the Helmholtz Climate Initiative REKLIM. T. K. and R. Gerdes are supported by the cooperative project 03F0729E (RACE II, Regional Atlantic Circulation and Global Climate), funded by the German Federal Ministry for Education and Research (BMBF). R. G. gratefully acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Projektnummer 268020496, TRR 172, within the Transregional Collaborative Research Center “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms” (AC). S. C., H. J., and Y. K. are grateful for funding from the U.K. Natural Environment Research Council, via the UK‐OSNAP project (NE/K010948/1) and a DTP studentship. The HiGEM coupled climate model data are available from NERC's Centre for Environmental Data Analysis (CEDA; http://badc.nerc.ac.uk ). This model was developed from the Met Office Hadley Centre Model by the U.K. High‐Resolution Modelling (HiGEM) Project and the U.K. Japan Climate Collaboration (UJCC). HiGEM was supported by a NERC High Resolution Climate Modelling Grant (R8/H12/123). UJCC was supported by the Foreign and Commonwealth Office Global Opportunities Fund, and jointly funded by NERC and the DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). The HiGEM model integrations were performed using the Japanese Earth Simulator supercomputer, supported by JAMSTEC. The work of Pier Luigi Vidale and Malcolm Roberts in leading the effort in Japan is particularly valued. The Alberta team gratefully acknowledge the financial and logistic support of grants from the Natural Sciences and Engineering Research Council (NSERC) of Canada (RGPIN 04357 and RGPCC 433898), as well as Polar Knowledge Canada (PKC‐NST‐1617‐0003). We are grateful to the NEMO development team and the Drakkar project for providing the model and continuous guidance and to Westgrid and Compute Canada for computational resources. For more details on the Alberta configuration, visit http://knossos.eas.ualberta.ca/anha/anhatable.php . An extracted set of time series from the various model simulations that have been used to make the figures are made available on the Bjerknes Climate Data Center ( https://www.bcdc.no/ ).
Funders | Funder number |
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Copernicus Marine Environment Monitoring Service | |
Defra Met Office Hadley Centre Climate Programme | GA01101 |
FREDY | |
Helmholtz Climate Initiative REKLIM | 03F0729E |
National Center for High Performance Computing of Turkey | |
National Oceanic and Atmospheric Administration | NA15OAR4310172 |
Department of Energy and Climate Change | |
Polar Knowledge Canada | PKC‐NST‐1617‐0003 |
Ulusal Yüksek Başarımlı Hesaplama Merkezi, Istanbul Teknik Üniversitesi | 5004782017 |
Natural Sciences and Engineering Research Council of Canada | RGPCC 433898, RGPIN 04357 |
Natural Environment Research Council | NE/K010948/1, R8/H12/123 |
Deutsche Forschungsgemeinschaft | TRR 172, 268020496 |
Bundesministerium für Bildung und Forschung | |
Norges Forskningsråd | ITU‐TGA‐2017‐40657 |
Bjerknessenteret for klimaforskning, Universitetet i Bergen |
Keywords
- Arctic Ocean
- Atlantic Water
- FAMOS
- model intercomparison
- sea ice
- wind forcing