Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I
Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:0...
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Zenodo
2022
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Online Access: | https://dx.doi.org/10.5281/zenodo.6321322 https://zenodo.org/record/6321322 |
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ftdatacite:10.5281/zenodo.6321322 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
sea ice deformation sea ice modelling scaling analysis rheology |
spellingShingle |
sea ice deformation sea ice modelling scaling analysis rheology Bouchat, Amélie Hutter, Nils Chanut, Jérôme Dupont, Frédéric Dukhovskoy, Dmitry Garric, Gilles Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I |
topic_facet |
sea ice deformation sea ice modelling scaling analysis rheology |
description |
Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation. There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite. 1. File naming convention "< Model simulation label >" + _ + "deformation" + _ + "< year >" 2. Variables included (x1,y1), (x1,y2), (x3,y3), (x4,y4) : Position of the cells' corners (Lagrangian trajectories) - (meters); A : Cells' area - (meters squared); dudx, dudy, dvdx, dvdy : Cell's velocity derivatives (strain rates/deformation) - (1/seconds); d_dudx, d_dudy, d_dvdx, d_dvdy : Trajectory error on cells' velocity derivatives - (1/seconds); time : Day of year. *Note: the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets. 3. Variable structure All variables (except time ) are matrices with axes ( it, i, j ), where it is the time stamp/iteration and i,j are the cells identifiers. See below for how the cells are defined: |--------------------------------------------------------------> j-axis | | ( x1_ij,y1_ij ) o ------------------- o ( x2_ij,y2_ij ) | | | | | A_ij or dudx_ij | | | | | ( x4_ij,y4_ij ) o ------------------- o ( x3_ij,y3_ij ) | | V i-axis Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j) 4. Recommended citation usage If all simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022). If only selected simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers). |
format |
Dataset |
author |
Bouchat, Amélie Hutter, Nils Chanut, Jérôme Dupont, Frédéric Dukhovskoy, Dmitry Garric, Gilles Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang |
author_facet |
Bouchat, Amélie Hutter, Nils Chanut, Jérôme Dupont, Frédéric Dukhovskoy, Dmitry Garric, Gilles Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang |
author_sort |
Bouchat, Amélie |
title |
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I |
title_short |
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I |
title_full |
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I |
title_fullStr |
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I |
title_full_unstemmed |
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I |
title_sort |
model lagrangian trajectories and deformation data analyzed in the sea ice rheology experiment - part i |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://dx.doi.org/10.5281/zenodo.6321322 https://zenodo.org/record/6321322 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://zenodo.org/communities/sirex https://dx.doi.org/10.5281/zenodo.6321323 https://zenodo.org/communities/sirex |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.6321322 https://doi.org/10.5281/zenodo.6321323 |
_version_ |
1766191649879228416 |
spelling |
ftdatacite:10.5281/zenodo.6321322 2023-05-15T18:17:26+02:00 Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I Bouchat, Amélie Hutter, Nils Chanut, Jérôme Dupont, Frédéric Dukhovskoy, Dmitry Garric, Gilles Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang 2022 https://dx.doi.org/10.5281/zenodo.6321322 https://zenodo.org/record/6321322 unknown Zenodo https://zenodo.org/communities/sirex https://dx.doi.org/10.5281/zenodo.6321323 https://zenodo.org/communities/sirex Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY sea ice deformation sea ice modelling scaling analysis rheology Dataset dataset 2022 ftdatacite https://doi.org/10.5281/zenodo.6321322 https://doi.org/10.5281/zenodo.6321323 2022-04-01T13:41:39Z Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation. There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite. 1. File naming convention "< Model simulation label >" + _ + "deformation" + _ + "< year >" 2. Variables included (x1,y1), (x1,y2), (x3,y3), (x4,y4) : Position of the cells' corners (Lagrangian trajectories) - (meters); A : Cells' area - (meters squared); dudx, dudy, dvdx, dvdy : Cell's velocity derivatives (strain rates/deformation) - (1/seconds); d_dudx, d_dudy, d_dvdx, d_dvdy : Trajectory error on cells' velocity derivatives - (1/seconds); time : Day of year. *Note: the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets. 3. Variable structure All variables (except time ) are matrices with axes ( it, i, j ), where it is the time stamp/iteration and i,j are the cells identifiers. See below for how the cells are defined: |--------------------------------------------------------------> j-axis | | ( x1_ij,y1_ij ) o ------------------- o ( x2_ij,y2_ij ) | | | | | A_ij or dudx_ij | | | | | ( x4_ij,y4_ij ) o ------------------- o ( x3_ij,y3_ij ) | | V i-axis Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j) 4. Recommended citation usage If all simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022). If only selected simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers). Dataset Sea ice DataCite Metadata Store (German National Library of Science and Technology) |