Abstract
In recent decades, Arctic sea-ice coverage underwent a drastic decline in winter, when sea ice is expected to recover following the melting season. It is unclear to what extent atmospheric processes such as atmospheric rivers (ARs), intense corridors of moisture transport, contribute to this reduced recovery of sea ice. Here, using observations and climate model simulations, we find a robust frequency increase in ARs in early winter over the Barents–Kara Seas and the central Arctic for 1979–2021. The moisture carried by more frequent ARs has intensified surface downward longwave radiation and rainfall, caused stronger melting of thin, fragile ice cover and slowed the seasonal recovery of sea ice, accounting for 34% of the sea-ice cover decline in the Barents–Kara Seas and central Arctic. A series of model ensemble experiments suggests that, in addition to a uniform AR increase in response to anthropogenic warming, tropical Pacific variability also contributes to the observed Arctic AR changes.
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Data availability
ERA5, MERRA2 and JRA55 reanalysis data are available at https://cds.climate.copernicus.eu/#!/home, https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/ and https://jra.kishou.go.jp/JRA-55/index_en.html. NSIDC SIC data are available from https://nsidc.org/data/G02202. The CESM2 simulations used in this study are available at: CESM2 Large Ensemble Community Project (https://www.cesm.ucar.edu/community-projects/lens2/data-sets), CESM2 Pacific Pacemaker Ensemble72 (https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.pacific.pacemaker.html) and CAM6 Prescribed SST AMIP ensembles (https://www.cesm.ucar.edu/working-groups/climate/simulations/cam6-prescribed-sst). CESM2 pre-industrial outputs are available from the Coupled Model Intercomparison Project Phase 6 archive at https://pcmdi.llnl.gov/CMIP6/. See the Supplementary Information for the data information of the datasets only used in supplementary.
Code availability
The code73 for the AR detection method used in this study is available via the UCLA Dataverse at https://doi.org/10.25346/S6/SJGRKY. The results, data and codes74 used to produce Figs. 1–6 are available via figshare at https://doi.org/10.6084/m9.figshare.21405051.v2.
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Acknowledgements
We thank J. Lu at PNNL and S. Lee and S. B. Feldstein at PSU for helpful discussions. We would like to acknowledge the NCAR’s CESM project which is supported by NSF and CESM’s CVCWG and the Computational Information Systems Laboratory NCAR Community Computing resources (doi: 10.5065/D6RX99HX) for providing the CESM simulations used in this study and thank A. Phillips and I. R. Simpson at NCAR for helpful information on these model outputs. P.Z. was supported by PSU. NSF grant number AGS-1832842 and NASA grant number 80NSSC21K1522 were awarded to G.C. NASA award number 80NSSC20K1254 and NSF award number OPP-1825858 were awarded to M.T. L.R.L was supported by the Office of Science, US Department of Energy Biological and Environmental Research as part of the Regional and Global Model Analysis programme area. B.G. was supported by NASA and the California Department of Water Resources. NASA grant number 21-OSST21-0006 was awarded to L.L. Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract no. DE-AC05-76RL01830.
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P.Z. conceived the study, analysed the data and wrote the initial draft of the paper. G.C., M.T. and L.R.L. provided feedback on analysis and contributed to constructive revisions. All authors contributed to editing and revisions.
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Extended data
Extended Data Fig. 1 ABK SIA anomalies when ARs make landfalling on the ice cover in CESM2 pre-industrial simulation.
Same as the composite SIA anomalies in Fig. 1c but for a 40-year segment (1160-1199) from CESM2 pre-industrial simulation. There is no significant background trend in the Arctic in these 40 years. The same AR detection procedure is conducted for these 40 years using daily data. The color shadings denote the 2.5-97.5% intervals of the anomalies, and the solid segments denote the significant anomalies based on 1000 bootstrap samples. The SIA anomalies show a significant retreat following ARs reaching the ice edge, supporting the results in observations (Fig. 1c).
Extended Data Fig. 2 AR-induced trends in cloud radiative effect in cumulated DLW (left) and snowfall (right) in NDJ in ERA5 (a,b) and the model ensemble from PAC2 (c).
See Method for the calculation details of the total amounts of the flux variables associated with ARs in NDJ. The cloud radiative effect of DLW is expressed as the difference between DLW and clear sky DLW. The cloud radiative effect of longwave radiation in PAC2 is missing due to no clear sky DLW output in PAC2. Dots denote trends that are statistically significant at the 0.05 level according to the t-test for ERA5 and the 1000 bootstrap samples for PAC2.
Extended Data Fig. 3 Proportional contribution of cloud radiative effect to the cumulated surface DLW related to ARs in NDJ for 1979-2021 in ERA5.
The linear fit is shown as the black line and the equation.
Extended Data Fig. 4 AR frequency trend in selected individual members in LENS2.
The left column shows the mean AR frequency trend in 5 LENS2 members who are most (least) similar to GOGA2 in the area of (0°-110°E, 45°-90°N). Here, we regard the AR trend pattern in GOGA2 as the reference pattern considering the system consistency. The results are similar for using PAC2 as the reference pattern. The middle and right columns are the contributions of dynamic and thermodynamic effects, similar to that in Fig. 6. The dots indicate the AR changes are significantly different from the other 45 members in LENS2 at the 0.05 level based on 1000 bootstrap samples. The results are similar in the composites of the LENS2 sub-ensembles with the largest (smallest) trends in ABK, which we have confirmed.
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Zhang, P., Chen, G., Ting, M. et al. More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice. Nat. Clim. Chang. 13, 266–273 (2023). https://doi.org/10.1038/s41558-023-01599-3
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DOI: https://doi.org/10.1038/s41558-023-01599-3
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