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Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections

Nature Climate Change volume 13, pages 710–718 (2023)Cite this article

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An Author Correction to this article was published on 21 July 2023

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Abstract

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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Fig. 1: Underlying mechanism of using atmospheric circulation to constrain SIE and GrIS SMB.
Fig. 2: Divergent contribution from upper-level circulation to Arctic summer surface temperatures in ERA5 and climate models.
Fig. 3: Mismatch between observed and modelled sensitivity of the Arctic to changes in large-scale circulation.
Fig. 4: The response of Arctic surface temperature to atmospheric circulation in century-long reanalyses and large ensemble simulations.
Fig. 5: Constraining the date of the first sea-ice-free September and widespread GrIS melting.
Fig. 6: Possible causes of the model sensitivity issues rooted in tropical variability.

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Data availability

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).

Code availability

Python codes of the analysis and for creating the figures are available from Zenodo https://doi.org/10.5281/zenodo.7863452 (ref. 87).

Change history

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Acknowledgements

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.

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  1. Dániel Topál

    Present address: Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Authors and Affiliations

  1. Institute for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences (MTA-Centre for Excellence), ELKH, Budapest, Hungary

    Dániel Topál

  2. Department of Geography and Earth Research Institute, University of California–Santa Barbara, Santa Barbara, CA, USA

    Dániel Topál & Qinghua Ding

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Contributions

Q.D. conceived the original idea and conducted the nudging simulations. Q.D. and D.T. together refined the concept and worked out the methodology. D.T. analysed the data, created the figures and led writing of the paper assisted by Q.D.

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Correspondence to Dániel Topál or Qinghua Ding.

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Extended data

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.

Extended Data Fig. 2 The impact of winds on the temperature field in the Arctic.

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).

Extended Data Table 1 CMIP5 and CMIP6 models participating in our study
Full size table

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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

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