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6 results for “sea ice leads”
Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: </p> <p>McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. <em>Geophysical Research Letters</em>, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a></p> <p> </p> <p>Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'.</p> <p>All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are:</p> <ul> <li>ua: zonal winds</li> <li>zg: geopotential height</li> <li>ts: surface temperature</li> <li>hfls, hfss, rlds, rlus: surface heatfluxes</li> <li>Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> <p>The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. </p> <p>Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. </p>
MODIS Sea ice leads detections using a U-Net
<p>Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2002 for the season between November through April.</p>
VIIRS Sea ice leads detections using a U-Net
<p>Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, leads play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2011 for the season between November through April.</p>
VIIRS Sea ice leads detections using a U-Net
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MODIS sea ice leads detections using a U-Net
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Recurring Spring Leads and Landfast Ice in the Beaufort and Chukchi Seas, 1993-2004, Version 1
This data set contains information on recurring spring leads and landfast ice extent in the Beaufort and Chukchi Seas. This data set maps and documents the spatial and temporal distribution of recurring leads and landfast ice in this area spanning 1993 through 2004 and are based on visible and infrared Advanced Very High Resolution Radiometer (AVHRR) data.
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