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Creators/Authors contains: "Bitz, Cecilia M."

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  1. Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR. 
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    Free, publicly-accessible full text available October 1, 2024
  2. Abstract

    A hierarchy of general circulation models (GCMs) is used to investigate the linearity of the response of the climate system to changes in Antarctic topography. Experiments were conducted with a GCM with either a slab ocean or fixed SSTs and sea ice, in which the West Antarctic ice sheet (WAIS) and coastal Antarctic topography were either lowered or raised in an idealized way. Additional experiments were conducted with a fully coupled GCM with topographic perturbations based on an ice-sheet model in which the WAIS collapses. The response over the continent is the same in all model configurations and is mostly linear. In contrast, the response has substantial nonlinear elements over the Southern Ocean that depend on the model configuration and are due to feedbacks with sea ice, ocean, and clouds. The atmosphere warms near the surface over much of the Southern Ocean and cools in the stratosphere over Antarctica, whether topography is raised or lowered. When topography is lowered, the Southern Ocean surface warming is due to strengthened southward atmospheric heat transport and associated enhanced storminess over the WAIS and the high latitudes of the Southern Ocean. When topography is raised, Southern Ocean warming is more limited and is associated with circulation anomalies. The response in the fully coupled experiments is generally consistent with the more idealized experiments, but the full-depth ocean warms throughout the water column whether topography is raised or lowered. These results indicate that ice sheet–climate system feedbacks differ depending on whether the Antarctic ice sheet is gaining or losing mass.

    Significance Statement

    Throughout Earth’s history, the Antarctic ice sheet was at times taller or shorter than it is today. The purpose of this study is to investigate how the atmosphere, sea ice, and ocean around Antarctica respond to changes in ice sheet height. We find that the response to lowering the ice sheet is not the opposite of the response to raising it, and that in either case the ocean surface near the continent warms. When the ice sheet is raised, the ocean warming is related to circulation changes; when the ice sheet is lowered, the ocean warming is from an increase in southward atmospheric heat transport. These results are important for understanding how the ice sheet height and local climate evolve together through time.

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  3. Abstract

    The climate response to the Mt. Pinatubo volcanic eruption is analyzed using large ensembles of Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. In contrast to previous work, we find that standard measures of the global temperature response to volcanic forcing are not significantly correlated with climate sensitivity across models. Isolating the shortwave response due to non‐cloud effects does not improve the correlation with climate sensitivity. Earlier constraints on climate sensitivity based on the response to Mt. Pinatubo are consistent with having arisen by chance because of the small size of the ensembles used. Our results suggest that the response to Mt. Pinatubo cannot be used to constrain the climate sensitivity to increased greenhouse gas concentrations, as has been proposed, because the radiative feedbacks in response to volcanic eruptions are not well correlated with the feedbacks governing the long‐term response to greenhouse gas forcing.

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  4. Abstract The predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) time scales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability; in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on nonextreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and nonextreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events and also that, while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization. Significance Statement We studied weather model forecasts of changes in Arctic sea ice extent on day-to-day time scales in different regions and seasons. We were especially interested in extreme sea ice loss days, or days in which sea ice melts very quickly or is reduced due to diverging forces such as winds, ocean currents, and waves. We find that forecast models generally capture the observed timing of extreme sea ice loss days. We also find that forecasts of sea ice extent are worse on extreme sea ice loss days compared to typical days, and that forecast errors remain elevated following extreme sea ice loss events. 
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  5. Abstract

    The projected decline in Arctic sea ice extent as the Earth warms in response to increased greenhouse gas concentrations will occur in conjunction with increased precipitation in the Arctic, and more of that precipitation is projected to fall as rain, especially in autumn and early winter. A recently proposed method of offsetting the decline in Arctic sea ice extent would pump seawater on the sea ice surface. Either way, we envision the liquid water first infiltrating the overlying snow layer creating slush. Winter conditions would then freeze the slush to directly thicken the ice. The net reduction in insulation would increase basal growth, adding an indirect thickening effect. Simulating the response to augmented snow layer flooding gives insights that are relevant in the future Arctic with or without the implementation of geoengineering. We use a hierarchy of models to show that flooding snow on sea ice is most effective at thickening Arctic sea ice when flooding begins early in the sea ice growth season. For the geoengineering scheme to be most effective, the pumps must be deployed almost immediately, while there is still a sufficient area of sea ice over which to flood, and must continue for decades. Sea ice loss would be best mitigated if flooding is combined with reducing greenhouse gas emissions. Furthermore, the increase in rainfall over the Arctic in the 21st century is unlikely to offset a substantial portion of the loss due to warming.

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  6. Abstract

    Here we investigate the role of the atmospheric circulation in the Atlantic Meridional Overturning Circulation (AMOC) by comparing a fully‐coupled large ensemble, a forced‐ocean simulation, and new experiments using a fully‐coupled global climate model where winds above the boundary layer are nudged toward reanalysis. When winds are nudged north of 45°N, agreement with RAPID array observations of AMOC at 26.5°N improves across several metrics. The phasing of interannual variability is well‐captured due to the response of the local Ekman component in both wind‐nudging and forced‐ocean simulations, however the variance remains underestimated. The mean AMOC strength is substantially reduced relative to the fully‐coupled model large ensemble, which is biased high, due to the impact of winds on surface buoyancy fluxes over the subpolar gyre. Nudging winds toward observations also reduces the 1979–2016 trend in AMOC, suggesting that improvement in the representation of the high‐latitude atmosphere is important for projecting long‐term AMOC changes.

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  7. 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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