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- Author or Editor: François Massonnet x
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Abstract
In this study the impact of extreme cyclones on Arctic sea ice in summer is investigated. Examined in particular are relative thermodynamic and dynamic contributions to sea ice volume budgets in the vicinity of Arctic summer cyclones in 2012 and 2016. Results from this investigation illustrate that sea ice loss in the vicinity of the cyclone trajectories during each year was associated with different dominant processes: thermodynamic processes (melting) in the Pacific sector of the Arctic in 2012, and both thermodynamic and dynamic processes in the Pacific sector of the Arctic in 2016. Comparison of both years further suggests that the Arctic minimum sea ice extent is influenced by not only the strength of the cyclone, but also by the timing and location relative to the sea ice edge. Located near the sea ice edge in early August in 2012, and over the central Arctic later in August in 2016, extreme cyclones contributed to comparable sea ice area (SIA) loss, yet enhanced sea ice volume loss in 2012 relative to 2016. Central to a characterization of extreme cyclone impacts on Arctic sea ice from the perspective of thermodynamic and dynamic processes, we present an index describing relative thermodynamic and dynamic contributions to sea ice volume changes. This index helps to quantify and improve our understanding of initial sea ice state and dynamical responses to cyclones in a rapidly warming Arctic, with implications for seasonal ice forecasting, marine navigation, coastal community infrastructure, and designation of protected and ecologically sensitive marine zones.
Abstract
In this study the impact of extreme cyclones on Arctic sea ice in summer is investigated. Examined in particular are relative thermodynamic and dynamic contributions to sea ice volume budgets in the vicinity of Arctic summer cyclones in 2012 and 2016. Results from this investigation illustrate that sea ice loss in the vicinity of the cyclone trajectories during each year was associated with different dominant processes: thermodynamic processes (melting) in the Pacific sector of the Arctic in 2012, and both thermodynamic and dynamic processes in the Pacific sector of the Arctic in 2016. Comparison of both years further suggests that the Arctic minimum sea ice extent is influenced by not only the strength of the cyclone, but also by the timing and location relative to the sea ice edge. Located near the sea ice edge in early August in 2012, and over the central Arctic later in August in 2016, extreme cyclones contributed to comparable sea ice area (SIA) loss, yet enhanced sea ice volume loss in 2012 relative to 2016. Central to a characterization of extreme cyclone impacts on Arctic sea ice from the perspective of thermodynamic and dynamic processes, we present an index describing relative thermodynamic and dynamic contributions to sea ice volume changes. This index helps to quantify and improve our understanding of initial sea ice state and dynamical responses to cyclones in a rapidly warming Arctic, with implications for seasonal ice forecasting, marine navigation, coastal community infrastructure, and designation of protected and ecologically sensitive marine zones.
Abstract
Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.
Abstract
Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.
