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More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice

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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Fig. 1: Relationship between ARs and Arctic sea ice.
Fig. 2: Arctic AR frequency trends in NDJ for 1979–2021.
Fig. 3: Trends in the physical processes related to the AR melting effect in recent decades.
Fig. 4: SIA growth in early winter.
Fig. 5: AR frequency trends in NDJ in model ensembles.
Fig. 6: Mechanisms of 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. 16 are available via figshare at https://doi.org/10.6084/m9.figshare.21405051.v2.

References

  1. Stroeve, J. C. et al. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys. Res. Lett. 39, L16502 (2012).

    Article  Google Scholar 

  2. Bailey, H. et al. Arctic sea-ice loss fuels extreme European snowfall. Nat. Geosci. 14, 283–288 (2021).

  3. Cohen, J., Agel, L., Barlow, M., Garfinkel, C. I. & White, I. Linking Arctic variability and change with extreme winter weather in the United States. Science 373, 1116–1121 (2021).

  4. Zhang, P. et al. A stratospheric pathway linking a colder Siberia to Barents-Kara Sea sea ice loss. Sci. Adv. 4, eaat6025 (2018).

    Article  CAS  Google Scholar 

  5. Dalpadado, P. et al. Productivity in the Barents Sea—response to recent climate variability. PLoS ONE 9, e95273 (2014).

  6. Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677 (2015).

    Article  Google Scholar 

  7. Park, D.-S. R., Lee, S. & Feldstein, S. B. Attribution of the recent winter sea ice decline over the Atlantic sector of the Arctic Ocean. J. Clim. 28, 4027–4033 (2015).

    Article  Google Scholar 

  8. Woods, C. & Caballero, R. The role of moist intrusions in winter Arctic warming and sea ice decline. J. Clim. 29, 4473–4485 (2016).

    Article  Google Scholar 

  9. Hofsteenge, M. G., Graversen, R. G., Rydsaa, J. H. & Rey, Z. The impact of atmospheric Rossby waves and cyclones on the Arctic sea ice variability. Clim. Dynam. 59, 579–594 (2022).

  10. Petty, A. A., Holland, M. M., Bailey, D. A. & Kurtz, N. T. Warm Arctic, increased winter sea ice growth? Geophys. Res. Lett. 45, 12922–12930 (2018).

    Article  Google Scholar 

  11. Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).

    Article  Google Scholar 

  12. Barton, B. I., Lenn, Y.-D. & Lique, C. Observed Atlantification of the Barents Sea causes the polar front to limit the expansion of winter sea ice. J. Phys. Oceanogr. 48, 1849–1866 (2018).

    Article  Google Scholar 

  13. Polyakov, I. V. et al. Borealization of the Arctic Ocean in response to anomalous advection from sub-Arctic seas. Front. Mar. Sci. 7, 491 (2020).

  14. Skagseth, Ø. et al. Reduced efficiency of the Barents Sea cooling machine. Nat. Clim. Change 10, 661–666 (2020).

    Article  CAS  Google Scholar 

  15. Tsubouchi, T. et al. Increased ocean heat transport into the Nordic Seas and Arctic Ocean over the period 1993–2016. Nat. Clim. Change 11, 21–26 (2021).

    Article  Google Scholar 

  16. Nash, D., Waliser, D., Guan, B., Ye, H. & Ralph, F. M. The role of atmospheric rivers in extratropical and polar hydroclimate. J. Geophys. Res. Atmos. 123, 6804–6821 (2018).

    Article  Google Scholar 

  17. Ralph, F. M. et al. Atmospheric rivers emerge as a global science and applications focus. Bull. Am. Meteorol. Soc. 98, 1969–1973 (2017).

    Article  Google Scholar 

  18. Zhu, Y. & Newell, R. E. A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Weather Rev. 126, 725–735 (1998).

    Article  Google Scholar 

  19. Newman, M., Kiladis, G. N., Weickmann, K. M., Ralph, F. M. & Sardeshmukh, P. D. Relative contributions of synoptic and low-frequency eddies to time-mean atmospheric moisture transport, including the role of atmospheric rivers. J. Clim. 25, 7341–7361 (2012).

    Article  Google Scholar 

  20. Lavers, D. A. & Villarini, G. The contribution of atmospheric rivers to precipitation in Europe and the United States. J. Hydrol. 522, 382–390 (2015).

    Article  Google Scholar 

  21. Chen, X., Leung, L. R., Wigmosta, M. & Richmond, M. Impact of atmospheric rivers on surface hydrological processes in western U.S. watersheds. J. Geophys. Res. Atmos. 124, 8896–8916 (2019).

    Article  Google Scholar 

  22. Hegyi, B. M. & Taylor, P. C. The unprecedented 2016–2017 Arctic sea ice growth season: the crucial role of atmospheric rivers and longwave fluxes. Geophys. Res. Lett. 45, 5204–5212 (2018).

    Article  Google Scholar 

  23. Gorodetskaya, I. V. et al. The role of atmospheric rivers in anomalous snow accumulation in East Antarctica. Geophys. Res. Lett. 41, 6199–6206 (2014).

    Article  Google Scholar 

  24. Mattingly, K. S., Mote, T. L. & Fettweis, X. Atmospheric river impacts on Greenland ice sheet surface mass balance. J. Geophys. Res. Atmos. 123, 8538–8560 (2018).

    Article  Google Scholar 

  25. Wille, J. D. et al. West Antarctic surface melt triggered by atmospheric rivers. Nat. Geosci. 12, 911–916 (2019).

    Article  CAS  Google Scholar 

  26. Francis, D., Mattingly, K. S., Temimi, M., Massom, R. & Heil, P. On the crucial role of atmospheric rivers in the two major Weddell Polynya events in 1973 and 2017 in Antarctica. Sci. Adv. 6, eabc2695 (2020).

    Article  Google Scholar 

  27. Persson, P. O. G., Shupe, M. D., Perovich, D. & Solomon, A. Linking atmospheric synoptic transport, cloud phase, surface energy fluxes, and sea-ice growth: observations of midwinter SHEBA conditions. Clim. Dynam. 49, 1341–1364 (2017).

    Article  Google Scholar 

  28. Doyle, S. H. et al. Amplified melt and flow of the Greenland ice sheet driven by late-summer cyclonic rainfall. Nat. Geosci. 8, 647–653 (2015).

    Article  CAS  Google Scholar 

  29. Ledley, T. S. Snow on sea ice: competing effects in shaping climate. J. Geophys. Res. Atmos. 96, 17195–17208 (1991).

    Article  Google Scholar 

  30. Merkouriadi, I., Cheng, B., Hudson, S. R. & Granskog, M. A. Effect of frequent winter warming events (storms) and snow on sea-ice growth—a case from the Atlantic sector of the Arctic Ocean during the N-ICE2015 campaign. Ann. Glaciol. 61, 164–170 (2020).

  31. Wang, Z., Walsh, J., Szymborski, S. & Peng, M. Rapid Arctic sea ice loss on the synoptic time scale and related atmospheric circulation anomalies. J. Clim. 33, 1597–1617 (2020).

    Article  Google Scholar 

  32. Gao, Y., Lu, J. & Leung, L. R. Uncertainties in projecting future changes in atmospheric rivers and their impacts on heavy precipitation over Europe. J. Clim. 29, 6711–6726 (2016).

    Article  Google Scholar 

  33. Ma, W., Chen, G. & Guan, B. Poleward shift of atmospheric rivers in the Southern Hemisphere in recent decades. Geophys. Res. Lett. 47, e2020GL089934 (2020).

    Article  Google Scholar 

  34. Payne, A. E. et al. Responses and impacts of atmospheric rivers to climate change. Nat. Rev. Earth Environ. 1, 143–157 (2020).

    Article  Google Scholar 

  35. Yang, W. & Magnusdottir, G. Springtime extreme moisture transport into the Arctic and its impact on sea ice concentration. J. Geophys. Res. Atmos. 122, 5316–5329 (2017).

    Article  Google Scholar 

  36. Ding, Q. et al. Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland. Nature 509, 209–212 (2014).

    Article  CAS  Google Scholar 

  37. Meehl, G. A., Chung, C. T. Y., Arblaster, J. M., Holland, M. M. & Bitz, C. M. Tropical decadal variability and the rate of Arctic sea ice decrease. Geophys. Res. Lett. 45, 11,326–11,333 (2018).

    Article  Google Scholar 

  38. Wu, Y., Lu, J., Ding, Q. & Liu, F. Linear response function reveals the most effective remote forcing in causing September Arctic sea ice melting in CESM. Geophys. Res. Lett. 48, e2021GL094189 (2021).

    Article  Google Scholar 

  39. Onarheim, I. H. & Årthun, M. Toward an ice-free Barents Sea. Geophys. Res. Lett. 44, 8387–8395 (2017).

    Article  Google Scholar 

  40. Box, J. E. et al. Greenland ice sheet rainfall, heat and albedo feedback impacts from the mid-August 2021 atmospheric river. Geophys. Res. Lett. 49, e2021GL097356 (2022).

    Article  Google Scholar 

  41. Fausto, R. S., van As, D., Box, J. E., Colgan, W. & Langen, P. L. Quantifying the surface energy fluxes in South Greenland during the 2012 high melt episodes using in-situ observations. Front. Earth Sci. 4, 82 (2016).

  42. Gettelman, A. et al. High climate sensitivity in the Community Earth System Model Version 2 (CESM2). Geophys. Res. Lett. 46, 8329–8337 (2019).

    Article  Google Scholar 

  43. Luo, D. et al. Impact of Ural blocking on winter warm Arctic–cold Eurasian anomalies. Part I: blocking-induced amplification. J. Clim. 29, 3925–3947 (2016).

    Article  Google Scholar 

  44. Clark, J. P. & Lee, S. The role of the tropically excited Arctic warming mechanism on the warm Arctic cold continent surface air temperature trend pattern. Geophys. Res. Lett. 46, 8490–8499 (2019).

    Article  Google Scholar 

  45. Vihma, T. et al. The atmospheric role in the Arctic water cycle: a review on processes, past and future changes, and their impacts. J. Geophys. Res. Biogeosci. 121, 586–620 (2016).

    Article  Google Scholar 

  46. Gimeno, L. et al. Atmospheric moisture transport and the decline in Arctic sea ice. WIREs Clim. Change 10, e588 (2019).

    Article  Google Scholar 

  47. Bintanja, R. et al. Strong future increases in Arctic precipitation variability linked to poleward moisture transport. Sci. Adv. 6, eaax6869 (2020).

    Article  CAS  Google Scholar 

  48. Zahn, M., Akperov, M., Rinke, A., Feser, F. & Mokhov, I. I. Trends of cyclone characteristics in the Arctic and their patterns from different reanalysis data. J. Geophys. Res. Atmos. 123, 2737–2751 (2018).

    Article  Google Scholar 

  49. Valkonen, E., Cassano, J. & Cassano, E. Arctic cyclones and their interactions with the declining sea ice: a recent climatology. J. Geophys. Res. Atmos. 126, e2020JD034366 (2021).

    Article  Google Scholar 

  50. Webster, M. A., Parker, C., Boisvert, L. & Kwok, R. The role of cyclone activity in snow accumulation on Arctic sea ice. Nat. Commun. 10, 5285 (2019).

    Article  CAS  Google Scholar 

  51. McCrystall, M. R., Stroeve, J., Serreze, M., Forbes, B. C. & Screen, J. A. New climate models reveal faster and larger increases in Arctic precipitation than previously projected. Nat. Commun. 12, 6765 (2021).

    Article  CAS  Google Scholar 

  52. Swart, N. C., Fyfe, J. C., Gillett, N. & Marshall, G. J. Comparing trends in the southern annular mode and surface westerly jet. J. Clim. 28, 8840–8859 (2015).

    Article  Google Scholar 

  53. CESM2 Pacific Pacemaker Ensemble (NCAR, 2022); https://doi.org/10.26024/gtrs-tf57

  54. Guan, B. Tracking atmospheric rivers globally as elongated targets (tARget), version 1. UCLA Dataverse https://doi.org/10.25346/S6/SJGRKY (2021).

  55. Zhang, P. & Chen, G. Replication data for Zhang et al. 2022 Arctic ARs. figshare https://doi.org/10.6084/m9.figshare.21405051.v2 (2022).

  56. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  57. Gelaro, R. et al. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Article  Google Scholar 

  58. Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn Ser. II 93, 5–48 (2015).

    Article  Google Scholar 

  59. Meier, W. N., Fetterer, F., Windnagel, A. K. & Stewart, J. S. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4 (NSIDC, 2021); https://doi.org/10.7265/efmz-2t65

  60. Rodgers, K. B. et al. Ubiquity of human-induced changes in climate variability. Earth Syst. Dynam. 12, 1393–1411 (2021).

    Article  Google Scholar 

  61. Holland, P. R., Bracegirdle, T. J., Dutrieux, P., Jenkins, A. & Steig, E. J. West Antarctic ice loss influenced by internal climate variability and anthropogenic forcing. Nat. Geosci. 12, 718–724 (2019).

    Article  CAS  Google Scholar 

  62. Schneider, D. P. & Deser, C. Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling observed and modeled trends. Clim. Dynam. 50, 4599–4618 (2018).

    Article  Google Scholar 

  63. Yang, D. et al. Role of tropical variability in driving decadal shifts in the Southern Hemisphere summertime eddy-driven jet. J. Clim. 33, 5445–5463 (2020).

    Article  Google Scholar 

  64. Ting, M., Kushnir, Y., Seager, R. & Li, C. Forced and internal twentieth-century SST trends in the North Atlantic. J. Clim. 22, 1469–1481 (2009).

    Article  Google Scholar 

  65. DelSole, T., Tippett, M. K. & Shukla, J. A significant component of unforced multidecadal variability in the recent acceleration of global warming. J. Clim. 24, 909–926 (2011).

    Article  Google Scholar 

  66. Lu, J., Hu, A. & Zeng, Z. On the possible interaction between internal climate variability and forced climate change. Geophys. Res. Lett. 41, 2962–2970 (2014).

    Article  Google Scholar 

  67. DuVivier, A. K. et al. Arctic and Antarctic sea ice mean state in the Community Earth System Model Version 2 and the influence of atmospheric chemistry. J. Geophys. Res. Oceans 125, e2019JC015934 (2020).

    Article  Google Scholar 

  68. Kay, J. E. et al. Less surface sea ice melt in the CESM2 improves Arctic sea ice simulation with minimal non-polar climate impacts. J. Adv. Model. Earth Syst. 14, e2021MS002679 (2022).

    Article  Google Scholar 

  69. DeRepentigny, P., Jahn, A., Holland, M. M. & Smith, A. Arctic sea ice in two configurations of the CESM2 during the 20th and 21st centuries. J. Geophys. Res. Oceans 125, e2020JC016133 (2020).

    Article  Google Scholar 

  70. Yamagami, Y., Watanabe, M., Mori, M. & Ono, J. Barents-Kara sea-ice decline attributed to surface warming in the Gulf Stream. Nat. Commun. 13, 3767 (2022).

    Article  CAS  Google Scholar 

  71. Guan, B. & Waliser, D. E. Detection of atmospheric rivers: evaluation and application of an algorithm for global studies. J. Geophys. Res. 120, 12514–12535 (2015).

  72. Rutz, J. J. et al. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. Atmos. 124, 13777–13802 (2019).

  73. Lora, J. M., Shields, C. A. & Rutz, J. J. Consensus and disagreement in atmospheric river detection: ARTMIP global catalogues. Geophys. Res. Lett. 47, e2020GL089302 (2020).

    Article  Google Scholar 

  74. Zhang, P., Chen, G., Ma, W., Ming, Y. & Wu, Z. Robust atmospheric river response to global warming in idealized and comprehensive climate models. J. Clim. 34, 7717–7734 (2021).

    Google Scholar 

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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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Correspondence to Pengfei Zhang.

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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.

Supplementary information

Supplementary Information

Supplementary Text 1–5, Figs. 1–5 and Table 1.

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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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