The Arctic has been suggested to see seasonally ice-free conditions within two-to-three decades under high-emissions scenarios. However, the time of emergence of the first ice-free month remains uncertain due to a wide range of estimates for Arctic climate sensitivity to anthropogenic forcing. Here, we propose a recalibration of the sea ice and Greenland ice sheet response to climate change, based on the finding that the sensitivity of the Arctic cryosphere to atmospheric circulation in climate models substantially differs from the observed one. Assuming that Arctic climate sensitivity of models recalibrated by observations remains unchanged in coming decades, this approach yields a delay in the projected timing of the first September sea-ice-free Arctic and widespread Greenland melting of roughly a decade compared to the uncalibrated ensemble. This indicates the importance of accounting for the role of large-scale atmospheric forcing and circulation changes in Arctic climate change.
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The MAR model simulations (ftp://ftp.climato.be/fettweis/MARv3.11), the reanalyses (ERA5 https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview; NCEP2 https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html; JRA-55 http://rda.ucar.edu/datasets/ds628.1/; MERRA2 https://disc.gsfc.nasa.gov/datasets?project=MERRA-2), the SMILE data (https://www.cesm.ucar.edu/projects/community-projects/MMLEA/) and the CESM2-LE data (https://www.cesm.ucar.edu/projects/community-projects/LENS2/) are freely available using the access links. The CMIP5 and CMIP6 data are freely available at ESGF (https://esgf-node.llnl.gov/projects/cmip6/; https://esgf-node.llnl.gov/projects/cmip5/). CESM2 prescribed SST AMIP simulations (TOGA) are available at https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.cam6.prescribed_sst_amip.html, the CESM2 Pacemaker simulations are available at https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.pacific.pacemaker.html. Sea ice and Greenland SMB from the CESM2 wind-nudging simulations are available from Zenodo https://doi.org/10.5281/zenodo.7863452 (ref. 87).
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This project is jointly supported by Climate Variability & Predictability (NA18OAR4310424, Q.D.) and Modeling, Analysis, Predictions and Projections (NA19OAR4310281, Q.D.) programmes as part of NOAA’s Climate Program Office, NSF’s Polar Programmes (OPP-1744598, Q.D.) and the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation (D.T.) in addition to the 2019-2.1.11-TÉT-2020-00114 (D.T.) and FK135115 financed from the source of the National Research, Development and Innovation fund (D.T.). We thank I. Baxter and T. Ballinger for inspiring discussions and for insightful comments on the text in addition to M. Pintér for proofreading. We acknowledge the Climate Variability & Change Working Group for providing the CESM2 Pacemaker experiments. We acknowledge the CESM Large Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone (https://doi.org/10.5065/D6RX99HX) and the CESM Polar Climate Working Group.
The authors declare no competing interests.
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Extended Data Fig. 1 Comparison of monthly sea ice extent (SIE) trends and the climatologies in our wind-nudging experiment to observations.
(a) Comparison of monthly sea ice extent (SIE) trends over 1979–2020 in the CESM2-nudged (black for ensemble mean and grey for 4 individual members) and in two observational products (pink). Climatological sea ice concentrations (SIC) in (b) NASA Team September sea ice and in (c) CESM2-nudged over 1979–2020. Panels (d) and (e) show the surface mass balance (SMB) climatologies over 1979–2020 in (d) MAR and (e) CISM2-nudged.
Comparison between (a) time series of summer (JJA) Arctic zonal-mean ( > 70°N) temperature anomalies (relative to 1979–2020) averaged between the surface and 500hPa pressure level in ERA5 (pink) and the CESM2-nudged simulation (4-member mean; black). Also shown: 1979–2020 summer (June–July-August: JJA) linear trend of zonal-mean temperature (shading: T) and vertical velocity (contours; ω) over the Arctic in (b) ERA5 and (c) in the CESM2-nudged simulation. Note the corresponding slopes of the linear trends over 1979–2020 in the legend of (a).
Extended Data Fig. 3 Divergent contribution from upper-level circulation to Arctic summer surface temperatures in three other reanalysis and in the CESM2 Large Ensemble.
Arctic ( > 70°N) surface temperature (SAT; thick black, upper panel) and Arctic streamfunction index (ASI; thick grey, lower panel) obtained from (a) NCEP2, (b) MERRA2 (c) JRA-55 reanalysis and (d) the CESM2-LE. The pink lines in (a)-(d) show the residual SAT time series after removing the effect of ASI via linear regression. The thin transparent lines in (d) indicate randomly selected 5 members from each of the ensembles. The differences between the pink (residual) and black (raw) lines in the upper panels of (a)-(d) illustrate the role of atmospheric forcing in contributing to Arctic warming. The 1979–2020 linear trend is removed from both SAT and ASI before creating the regression model.
Extended Data Fig. 4 Comparison of the constrained & unconstrained September sea ice extent time series in three model ensembles with observations.
September sea ice extent (SIE) between 1979–2100 in two single-model initial-condition large ensembles, in 21 CMIP6 models along with the NASA Team (solid red line) and NSIDC (dashed red lines) SIE observations. Unconstrained (raw) projections are indicated with solid blue lines and the constrained projections (Methods) are highlighted with dashed orange lines (see legend and panel titles). The dashed black line indicates the virtually ice-free conditions in the Arctic occurring below 1 million km2 ice extent.
Extended Data Fig. 5 Possible causes of the model sensitivity issues rooted in tropical variability using annual means.
Ratios of 8- to 40-year band-pass filtered annual mean sea surface temperature (SST) variance to total SST variance in (a) ERSSTv5 (b) in 31 CMIP5 models and (c) in 29 CMIP6 models. (The calculations are done for each CMIP model separately then averaged.) Also shown: ratios of 2- to 8-year band-pass filtered sea surface temperature (SST) variance to total SST variance in (d) ERSSTv5 (e) in 31 CMIP5 models and (f) in 29 CMIP6 models.
Extended Data Fig. 6 Possible causes of the model sensitivity issues rooted in tropical variability based on century-long reanalysis of sea surface temperatures.
Ratios of 8- to 40-year band-pass filtered summer mean (June–July-August, JJA) sea surface temperature (SST) variance to total SST variance over 1854-2021. (b) is the same but for 2–8-yr band-pass filter. (c) and (d) are the same as (a) and (b), respectively, but for annual means.
Extended Data Fig. 7 Improved modelled sensitivity of the Arctic to changes in large-scale circulation in Pacific Pacemaker experiments.
Scatterplot of the raw and residual summer surface air temperature (SAT) trends over (a) the Arctic and (b) over the Greenland ice sheet (GrIS) in the mean of three reanalyses (ERA5; NCEP2; JRA-55: referred to as “Obs” in the legend), in the CESM2 Large Ensemble, 10-member CESM2 TOGA and 10-member CESM2 Pacemaker simulations (see legend for time period differences between the runs and the Methods for details on the experiments). The small, transparent markers refer to the individual ensemble members, while the larger markers are the ensemble means. The CESM2-LE is separated into 50-members forced by CMIP6 standards and 50-members with prescribed smoothed biomass burning emissions (BB). Note the upper x axes in each of the panels, which refer to the R values (Methods).
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Topál, D., Ding, Q. Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections. Nat. Clim. Chang. 13, 710–718 (2023). https://doi.org/10.1038/s41558-023-01698-1