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  1. Abstract

    Arctic surface warming under greenhouse gas forcing peaks in winter and reaches its minimum during summer in both observations and model projections. Many mechanisms have been proposed to explain this seasonal asymmetry, but disentangling these processes remains a challenge in the interpretation of general circulation model (GCM) experiments. To isolate these mechanisms, we use an idealized single-column sea ice model (SCM) that captures the seasonal pattern of Arctic warming. SCM experiments demonstrate that as sea ice melts and exposes open ocean, the accompanying increase in effective surface heat capacity alone can produce the observed pattern of peak warming in early winter (shifting to late winter under increased forcing) by slowing the seasonal heating rate, thus delaying the phase and reducing the amplitude of the seasonal cycle of surface temperature. To investigate warming seasonality in more complex models, we perform GCM experiments that individually isolate sea ice albedo and thermodynamic effects under CO2forcing. These also show a key role for the effective heat capacity of sea ice in promoting seasonal asymmetry through suppressing summer warming, in addition to precluding summer climatological inversions and a positive summer lapse-rate feedback. Peak winter warming in GCM experiments is further supported by a positivemore »winter lapse-rate feedback, due to cold initial surface temperatures and strong surface-trapped warming that are enabled by the albedo effects of sea ice alone. While many factors contribute to the seasonal pattern of Arctic warming, these results highlight changes in effective surface heat capacity as a central mechanism supporting this seasonality.

    Significance Statement

    Under increasing concentrations of atmospheric greenhouse gases, the strongest Arctic warming has occurred during early winter, but the reasons for this seasonal pattern of warming are not well understood. We use experiments in both simple and complex models with certain sea ice processes turned on and off to disentangle potential drivers of seasonality in Arctic warming. When sea ice melts and open ocean is exposed, surface temperatures are slower to reach the warm-season maximum and slower to cool back down below freezing in early winter. We find that this process alone can produce the observed pattern of maximum Arctic warming in early winter, highlighting a fundamental mechanism for the seasonality of Arctic warming.

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  2. Abstract

    Today’s global Earth system models began as simple regional models of tropospheric weather systems. Over the past century, the physical realism of the models has steadily increased, while the scope of the models has broadened to include the global troposphere and stratosphere, the ocean, the vegetated land surface, and terrestrial ice sheets. This chapter gives an approximately chronological account of the many and profound conceptual and technological advances that made today’s models possible. For brevity, we omit any discussion of the roles of chemistry and biogeochemistry, and terrestrial ice sheets.

  3. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>
  4. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>
  5. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave energy spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>
  6. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>
  7. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>
  8. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>
  9. Abstract
    <p>Model output from experiments described in Cooper et al 2022.</p> <p>Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.</p> <p>Includes WW3 1-D wave spectra (hourly) for 2018-07.</p> <p>Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.</p> <p>Note: uncompressed file size is 2x the tar.gz file size.</p>