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High-resolution modelling identifies the Bering Strait’s role in amplified Arctic warming

Nature Climate Change volume 14, pages 615–622 (2024)Cite this article

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Abstract

The Arctic region has warmed nearly four times faster than the global average since 1979, with far-reaching global implications. However, model projections of Arctic warming rates are uncertain and one key component is the ocean heat transport (OHT) into the Arctic Ocean. Here we use high-resolution historical and future climate simulations to show that the OHT through the Bering Strait exerts a more substantial influence on Arctic warming than previously recognized. The high-resolution ensemble exhibits a 20% larger warming rate for 2006–2100 compared with standard low-resolution model simulations. The enhanced Arctic warming in the high-resolution simulations is primarily attributable to an increased OHT through the narrow and shallow Bering Strait that is nearly four times larger than in the low-resolution simulations. Consequently, the projected rate of Arctic warming by low-resolution climate simulations is likely to be underestimated due to the model resolution being insufficient to capture future changes in Bering Strait OHT.

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Fig. 1: Model–observation comparison near the Bering Strait.
Fig. 2: Projected Arctic warming.
Fig. 3: Relationship between projected changes in Bering Strait OHT and Arctic warming.
Fig. 4: Future changes in sea-ice concentration and surface heat fluxes.

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Data availability

The CMIP5 data used in this study can be downloaded from https://esgf-node.llnl.gov/search/cmip5/. The CESM data used in this work are available from https://ihesp.github.io/archive/products/ihesp-products/data-release/DataRelease_Phase2.html. The MODIS data can be download from http://apdrc.soest.hawaii.edu/data/data.php. The mooring data can be downloaded from https://psc.apl.washington.edu/HLD/Bstrait/Data/BeringStraitMooringDataArchive.html.

Code availability

The CESM codes are available via GitHub at https://github.com/ihesp/CESM_SW (ref. 67).

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Acknowledgements

The high-resolution CESM simulations were initiated by the International Laboratory for High-Resolution Earth System Prediction—a collaboration between the Qingdao Pilot National Laboratory for Marine Science and Technology, Texas A&M University and the US National Science Foundation (NSF) National Center for Atmospheric Research (NCAR). A major portion of the high-resolution CESM simulations were completed on Frontera at the Texas Advanced Computing Center (TACC) of the University of Texas at Austin, TX, United States, under project number ATM20005. We thank J. Edwards for his assistance in porting and optimizing the CESM code on Frontera at TACC. P.C., G.D., S.G.Y., F.C., G.X. and Q.Z. are supported by the National Academies of Science and Engineering Gulf Research Program grant number 2000013283 and the US NSF grant number AGS-2231237. S.G.Y., P.C. and Q.Z. are supported by the Department of Commerce grant number NA20OAR4310408. M.S. is supported by NASA grant number 80NSSC20K0768, NSF grant number OPP-1751363 and ONR grant number N00014-21-1-2868. W.W. and Y.L. were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling programme of the US Department of Energy’s Office of Science, as a contribution to the HiLAT-RASM project. Y.L. was also supported by the Center for Nonlinear Studies at Los Alamos National Laboratory. N.R. is supported by the US Department of Energy, Office of Science, Office of Biological & Environmental Research RGMA component of the Earth and Environmental System Modeling Program under award number DE-SC0022070. NSF NCAR is a major facility sponsored by the US NSF under cooperative agreement number 1852977.

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Authors and Affiliations

  1. Department of Oceanography, Texas A&M University, College Station, TX, USA

    Gaopeng Xu, M. Cameron Rencurrel, Ping Chang, Xiaoqing Liu & Qiuying Zhang

  2. Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA

    Ping Chang

  3. Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN, USA

    Xiaoqing Liu

  4. National Science Foundation National Center for Atmospheric Research, Boulder, CO, USA

    Gokhan Danabasoglu, Stephen G. Yeager, Nan Rosenbloom & Frederic Castruccio

  5. Polar Science Center/Applied Physics Laboratory, University of Washington, Seattle, WA, USA

    Michael Steele

  6. Los Alamos National Laboratory, Los Alamos, NM, USA

    Wilbert Weijer

  7. International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA

    Wilbert Weijer

  8. Stanford University, Stanford, CA, USA

    Yuchen Li

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Contributions

P.C. conceptualized the study. P.C., G.X., M.C.R. and X.L. conducted the investigation. Q.Z., N.R., F.C. and S.G.Y. ran the simulations. G.X., M.C.R. and X.L. performed the analysis. G.X. visualized the data. P.C. and G.D. acquired funding. G.X. and M.C.R. wrote the original draft, P.C., G.D., M.S., W.W., S.G.Y., Y.L. and G.X. reviewed and edited the manuscript.

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Correspondence to Ping Chang.

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Extended data

Extended Data Fig. 1 Inferred calculation method computed OHT.

(a-b) Similar to Fig. 1e & g, respectively, but includes both inferred calculation method-computed (dashed) and model-derived (solid) OHT in HR (red) and LR (blue). Error bars in (a) are the 95% limit of a 1-side Student’s t-test with the degree of freedom as 13.

Extended Data Fig. 2 Standard deviation of OHT and correlation coefficient between detrended sea ice area and OHT.

(a) The standard deviation of OHT from 2000 to 2021 in observations, HR, and LR. (b) The correlation coefficient between detrended sea ice area and OHT anomaly from 2000 to 2021 in observations, HR, and LR. Different colors in each group represent different ensemble members. The correlation coefficients are significant at 95% confidence level but not from LR ensemble #3 (−0.22). Different colors in each group represent different ensemble members.

Extended Data Fig. 3 Definition of the region near the Bering Strait.

Area with green shading is used to construct sea ice timeseries near the Bering Strait in Fig. 1h.

Extended Data Fig. 4 Projected Arctic warming.

The rate of surface air temperature changes in LR over the period 2006–2100.

Extended Data Fig. 5 Decomposition of changes in the Bering Strait OHT in HR.

The change is defined as the difference between the mean over 2081–2100 and that over 2006–2025. MOHT: monthly-mean OHT changes computed using monthly-mean temperature and velocity output; MOHT∆T: change in MOHT due to temperature change ∆T; MOHT∆v: change in MOHT due to velocity change ∆v; MOHT∆T∆v: change in MOHT due to the nonlinear product of temperature and velocity change ∆T∆v.

Extended Data Fig. 6 Scatterplot of changes in annual-mean Bering Strait OHT and Arctic surface air temperature over the Pacific and Atlantic sectors.

(a) Arctic surface air temperature over the Pacific sector and (b) the Atlantic sector of the Arctic from HR (triangle), LR (circle), and CMIP5 models (stars). Changes are defined as the difference between the mean over 2081–2100 and that over 2006–2015. The linear regression in (a) has a slope of 0.22 °C TW-1 and is significant at a 95% confidence level, while the linear regression in (b) has a slope of 0.06 °C TW-1 and is not statistically significant at a 95% confidence level.

Extended Data Fig. 7 Monthly Bering Strait OHT anomaly relative to the mean over 2006–2015.

Results are from HR (red), LR (blue), and CMIP5 models with RCP8.5 forcing (gray) as a function of CO2 concentration increase (bottom x-axis) and time (top x-axis). Black for the CMIP5 multi-model ensemble mean.

Extended Data Fig. 8 Sea ice concentration during different periods.

(a-d) Sea ice concentration in MAM in HR. (e-h) Similar to a-d, but in LR. (i-l) Sea ice concentration in DJF in HR. (m-p) Similar to i-l, but in LR. Orange contour is sea ice edge defined as 15% sea ice concentration.

Extended Data Fig. 9 Future changes in basal sea ice growth, upper-50-m-ocean temperature, sea ice concentration, and surface heat fluxes.

(a-d) Changes in basal growth of sea ice (ocean temperature in the upper 50 m) during boreal winter (DJF) in HR depicted by color shades (contours). (e-h) Same as a-d but for LR. (i-l) Changes in sea ice concentration (turbulent heat flux) during boreal winter (DJF) in HR depicted by color shades (contours). (m-p) Same as i-l but for LR. The first to fourth column represents changes averaged over 2030–2039, 2050–2059, 2070–2079, and 2090–2099 relative to the mean over 2006–2015, respectively. Contour interval in a-h is 0.5 oC and in i-p is 10 Wm−2.

Extended Data Fig. 10 Observed surface air temperature anomalies relative to the 1950–1980 mean.

GISTEMPv4 annual-mean surface air temperature anomalies from 1950 to 2020, relative to the 1950–1980 mean, averaged over the Arctic region (blue), Pacific sector of the Arctic (red), and Atlantic sector of the Arctic (orange). The Pacific sector covers the area from 66°N to 90°N and from 120°E to 240°E. The Atlantic sector covers the area from 66°N to 90°N and from 60°W to 60°E.

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Xu, G., Rencurrel, M.C., Chang, P. et al. High-resolution modelling identifies the Bering Strait’s role in amplified Arctic warming. Nat. Clim. Chang. 14, 615–622 (2024). https://doi.org/10.1038/s41558-024-02008-z

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