author_facet Cheng, Sukun
Aydoğdu, Ali
Rampal, Pierre
Carrassi, Alberto
Bertino, Laurent
Cheng, Sukun
Aydoğdu, Ali
Rampal, Pierre
Carrassi, Alberto
Bertino, Laurent
author Cheng, Sukun
Aydoğdu, Ali
Rampal, Pierre
Carrassi, Alberto
Bertino, Laurent
spellingShingle Cheng, Sukun
Aydoğdu, Ali
Rampal, Pierre
Carrassi, Alberto
Bertino, Laurent
Oceans
Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
Marketing
Organizational Behavior and Human Resource Management
Strategy and Management
Drug Discovery
Pharmaceutical Science
Pharmacology
author_sort cheng, sukun
spelling Cheng, Sukun Aydoğdu, Ali Rampal, Pierre Carrassi, Alberto Bertino, Laurent 2673-1924 MDPI AG Marketing Organizational Behavior and Human Resource Management Strategy and Management Drug Discovery Pharmaceutical Science Pharmacology http://dx.doi.org/10.3390/oceans1040022 <jats:p>We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.</jats:p> Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion Oceans
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record_format ai
series Oceans
source_id 49
title Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_unstemmed Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_full Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_fullStr Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_full_unstemmed Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_short Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_sort probabilistic forecasts of sea ice trajectories in the arctic: impact of uncertainties in surface wind and ice cohesion
topic Marketing
Organizational Behavior and Human Resource Management
Strategy and Management
Drug Discovery
Pharmaceutical Science
Pharmacology
url http://dx.doi.org/10.3390/oceans1040022
publishDate 2020
physical 326-342
description <jats:p>We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.</jats:p>
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author Cheng, Sukun, Aydoğdu, Ali, Rampal, Pierre, Carrassi, Alberto, Bertino, Laurent
author_facet Cheng, Sukun, Aydoğdu, Ali, Rampal, Pierre, Carrassi, Alberto, Bertino, Laurent, Cheng, Sukun, Aydoğdu, Ali, Rampal, Pierre, Carrassi, Alberto, Bertino, Laurent
author_sort cheng, sukun
container_issue 4
container_start_page 326
container_title Oceans
container_volume 1
description <jats:p>We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.</jats:p>
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spelling Cheng, Sukun Aydoğdu, Ali Rampal, Pierre Carrassi, Alberto Bertino, Laurent 2673-1924 MDPI AG Marketing Organizational Behavior and Human Resource Management Strategy and Management Drug Discovery Pharmaceutical Science Pharmacology http://dx.doi.org/10.3390/oceans1040022 <jats:p>We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM are based on a brittle-like rheological framework. The study considers short-term ensemble forecasts of Arctic sea ice from January to April 2008. Ensembles are generated by perturbing the wind inputs and ice cohesion field both separately and jointly. The resulting uncertainty in the probabilistic forecasts is evaluated statistically based on the analysis of Lagrangian sea ice trajectories as sampled by virtual drifters seeded in the model to cover the Arctic Ocean and using metrics borrowed from the search-and-rescue literature. The comparison among the different ensembles indicates that wind perturbations dominate the forecast uncertainty (i.e., the absolute spread of the ensemble), while the inhomogeneities in the ice cohesion field significantly increase the degree of anisotropy in the spread—i.e., trajectories drift divergently in different directions. We suggest that in order to obtain enough uncertainties in a sea ice model with brittle-like rheologies, to predict sea ice drift and trajectories, one should consider using ensemble-based simulations where at least wind forcing and sea ice cohesion are perturbed.</jats:p> Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion Oceans
spellingShingle Cheng, Sukun, Aydoğdu, Ali, Rampal, Pierre, Carrassi, Alberto, Bertino, Laurent, Oceans, Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion, Marketing, Organizational Behavior and Human Resource Management, Strategy and Management, Drug Discovery, Pharmaceutical Science, Pharmacology
title Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_full Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_fullStr Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_full_unstemmed Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_short Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
title_sort probabilistic forecasts of sea ice trajectories in the arctic: impact of uncertainties in surface wind and ice cohesion
title_unstemmed Probabilistic Forecasts of Sea Ice Trajectories in the Arctic: Impact of Uncertainties in Surface Wind and Ice Cohesion
topic Marketing, Organizational Behavior and Human Resource Management, Strategy and Management, Drug Discovery, Pharmaceutical Science, Pharmacology
url http://dx.doi.org/10.3390/oceans1040022