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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 positive 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

    We characterize high‐frequency variability of sea ice extent (HFVSIE) in observations and climate models. We find that HFVSIE in models is biased low with respect to observations, especially at synoptic timescales (<20 days) in the Arctic year‐round and at monthly timescales (30–60 days) in Antarctica in winter. Models show large spread in HFVSIE, especially in Antarctica. This spread is partly explained by sea ice mean‐state while model biases in sea level pressure (SLP) and wind variability do not appear to play a major role in HFVSIE spread. Extreme sea ice extent (SIE) changes are associated with SLP anomaly dipoles aligned with the sea ice edge and winds directed on‐ice (off‐ice) during SIE loss (gain) events. In observations, these events are also associated with distinct ocean wave states during the cold season, when waves are greater (smaller) and travel toward (away from) the sea ice edge during SIE loss (gain) events.

     
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  3. 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.

     
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  4. Abstract

    Recent field programs have highlighted the importance of the composite nature of the sea ice mosaic to the climate system. Accordingly, we previously developed a process‐based prognostic model that captures key characteristics of the sea ice floe size distribution and its evolution subject to melting, freezing, new ice formation, welding, and fracture by ocean surface waves. Here we build upon this earlier work, demonstrating a new coupling between the sea ice model and ocean surface waves and a new physically based parameterization for new ice formation in open water. The experiments presented here are the first to include two‐way interactions between prognostically evolving waves and sea ice on a global domain. The simulated area‐average floe perimeter has a similar magnitude to existing observations in the Arctic and exhibits plausible spatial variability. During the melt season, wave fracture is the dominant FSD process driving changes in floe perimeter per unit sea ice area—the quantity that determines the concentration change due to lateral melt—highlighting the importance of wave‐ice interactions for marginal ice zone thermodynamics. We additionally interpret the results to target spatial scales and processes for which floe size observations can most effectively improve model fidelity.

     
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  5. Abstract. Ocean surface waves play an important role in maintaining the marginal ice zone, a heterogenous region occupied by sea ice floes with variable horizontal sizes. The location, width, and evolution of the marginal ice zone are determined by the mutual interaction of ocean waves and floes, as waves propagate into the ice, bend it, and fracture it. In previous work, we developed a one-dimensional “superparameterized” scheme to simulate the interaction between the stochastic ocean surface wave field and sea ice. As this method is computationally expensive and not bitwise reproducible, here we use a pair of neural networks to accelerate this parameterization, delivering an adaptable, computationally inexpensive, reproducible approach for simulating stochastic wave–ice interactions. Implemented in the sea ice model CICE, this accelerated code reproduces global statistics resulting from the full wave fracture code without increasing computational overheads. The combined model, Wave-Induced Floe Fracture (WIFF v1.0), is publicly available and may be incorporated into climate models that seek to represent the effect of waves fracturing sea ice. 
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  6. Model output from experiments described in Cooper et al 2022.

    Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.

    Includes WW3 1-D wave spectra (hourly) for 2018-07.

    Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.

    Note: uncompressed file size is 2x the tar.gz file size.

     
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  7. Model output from experiments described in Cooper et al 2022.

    Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.

    Includes WW3 1-D wave energy spectra (hourly) for 2018-07.

    Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.

    Note: uncompressed file size is 2x the tar.gz file size.

     
    more » « less
  8. Model output from experiments described in Cooper et al 2022.

    Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.

    Includes WW3 1-D wave spectra (hourly) for 2018-07.

    Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.

    Note: uncompressed file size is 2x the tar.gz file size.

     
    more » « less
  9. Model output from experiments described in Cooper et al 2022.

    Includes WW3 output of ice concentration and significant wave height (hourly) for 2018-01-01 through 2018-12-31.

    Includes WW3 1-D wave spectra (hourly) for 2018-07.

    Includes CICE output of representative radius (daily) for 2018-01-01 through 2018-12-31.

    Note: uncompressed file size is 2x the tar.gz file size.

     
    more » « less