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552 results for “Sea ice”
North China Record-Breaking Rainfall of July 2021 tied to the Arctic Sea-ice change
<p><strong>1、General Introduction:</strong></p> <p>This dataset includes the model simulation data used in the article going to be submitted to Geophysical Research Letters, named “North China Record-Breaking Rainfall of July 2021 tied to the Arctic Sea-ice change”. The Community Atmosphere Model version 5 (CAM5; Neale et al., 2012) in the Community Earth System Model, version 1.2.1 (CESM1.2.1) with a 1.9<sup>◦</sup> ×2.5<sup>◦ </sup>finite volume grid and 30 hybrid sigma pressure levels is utilized to conduct atmospheric model experiments. To validate the impact of the Arctic sea-ice, the Community Atmosphere Model version 5 (CAM5; Neale et al., 2012) in the Community Earth System Model, version 1.2.1 (CESM1.2.1) with a 1.9◦ ×2.5◦ finite volume grid and 30 hybrid sigma pressure levels is adopted. Two experiments are designed. One is called the control experiment (CTL). In this experiment, the model is forced by the seasonal-varying climatology of SST and SIC as the lower boundary forcing. Another is called the sensitivity experiment (SEN). This experiment is the same as the CTL except that SIC and SST over the Barents-East Siberian Sea region (50°–165°E, 70°–80°N) in June and July are set to be the observational values in 2021. Both the CTL and SEN experiments are integrated for 35 years including a 10-year spin-up, whilst the last 25-year ensemble mean is analyzed in the article. The differences between the SEN and CTL experiments (SEN-CTL) reflect the atmospheric responses to the reduced SIC and increased SST over the Barents-East Siberian Sea region in June-July 2021.</p> <p><strong>2、Description of this dataset:</strong></p> <p>CTL represents the control experiment while SEN represents the sensitive experiment. OMEGA, PRECC, PRECL, T, U, V, Z3, and TS denote the vertical velocity, convective precipitation rate, large-scale (stable) precipitation rate, temperature, zonal wind, meridional wind, geopotential height, and surface temperature respectively. All files contain a time coverage from 1 to 35 model-year with a monthly time resolution.</p> <p>The post-processing from 30 hybrid sigma pressure levels to 17 pressure levels has been conducted on the raw data of the model simulations for the variables of OMEGA, T, U, V, and Z3.</p>
Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"
<p>Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript. </p>
CMIP6 model sea-ice concentration data
<p>Sea-ice concentration data from Climate System Models that participated in the sixth Coupled Model Intercomparison Project (CMIP6). <br> <br> Historical (1850-2014) and future projections (2015-2100) of sea-ice concentration were downloaded on the native model grid of 29 models, after which this data was regridded to a regular 1x1 degree latitude by longitude grid for multi-model comparison and calculation of statistics (mean, median, etc.).</p> <p>Future projections include the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios.</p> <p>Models included in the scenarios are shown in the excel spreadsheet.</p> <p> </p>
Daily 1 km seamless Antarctic sea ice albedo product from VIIRS data
<p><strong>Summary</strong>:<strong> </strong>In the context of climate change, the sea ice albedo feedback mechanism makes the albedo of Antarctic sea ice a crucial element in polar environmental evolution and global climate models. Existing albedo products for Antarctic sea ice are limited, with a coarse spatiotemporal resolution, and numerous data gaps due to persistent cloud cover. This study uses the Multiband Reflectance Iteration (MBRI) algorithm, which retrieves the Antarctic sea ice albedo using reflectance data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Moreover, the study reconstructs the albedo of cloudy-sky pixels by integrating spatiotemporal information and physical models. A new 1 km daily seamless albedo product for Antarctic sea ice has been developed, covering the period from 2012 to 2021. The results, validated against the automatic weather stations data from the Baseline Surface Radiation Network (BSRN), reveal that the proposed product has higher accuracy, finer spatiotemporal resolution, and superior spatial continuity compared to existing products. The algorithms used fully account for the anisotropy of the sea ice surface, and the high spatiotemporal resolution enables this dataset to enable quantitative analysis of both overall and localized Antarctic sea ice changes. This dataset is valuable for studying the radiation balance of Antarctic sea ice and conducting research in climate modeling. Uncertainty of the dataset is available at https://doi.org/10.5281/zenodo.15067607.</p> <p><strong>Spatial resolution</strong>: 1 km</p> <p><strong>Temporal resolution</strong>: daily (2012-2021)</p> <p><strong>Format</strong>: GeoTIFF</p> <p><strong>Projection</strong>: This dataset adopts Sinusoidal projection and is gridded using the MODIS Sinusoidal Tile Grid</p> <p><strong>How to name and use data files</strong>: The dataset has a longitude range of 180°W to 180°E and a latitude range of 50°S to 80°S, covering 53 tiles (v14: h06~h29; v15: 09~h26; V16: h11~h17, h21~h24). The GeoTIFF file contains a band that represents the shortwave sea ice albedo under clear-sky or cloudy-sky conditions, with 16-bit integer values of 0-10000 and a scale factor of 0.0001. The ocean water and Antarctic continent are set to a filling value of -1. The file name is "Antarctic_Sea_Ice_Albedo_ {yyyyddd}_ {hv}.tif ”, where yyyy represents year, ddd represents day of the year, and hv represents the number of the tile. For example, "Antarctic_Sea_Ice_Albedo_2014270_h18v15.tif" represents the sea ice albedo data of the h18v15 area on the 270th day of 2014. </p> <p><strong>Contacts</strong>: Weifeng Hao (haowf@whu.edu.cn), Chao Ma (macwhu@whu.edu.cn)</p> <p> </p> <p> </p>
A Seasonally Delayed Sea Ice Response and Arctic Amplification during the Last Glacial Inception
<p>Presented here is the datasets and corresponding codes used in creating Figures of the article "<strong><span>A Seasonally Delayed Sea Ice Response and Arctic Amplification during the Last Glacial Inception</span></strong>".</p>
Gridded northern hemisphere NSIDC sea ice region codes (version 2021) at resolutions between 1 and 75 km
<p>Gridded Arctic Marine Regions Codes at resolutions of 1, 3.125, 6.25, 12.5, 25, 50 & 75 km. The source data are shapefiles from the National Snow and Ice Data Center (NSIDC):</p> <p><a href="https://insidecires.colorado.edu/rendezvous/uploads/Rendezvous_2021_1448_1619668996.pdf">A new regional mask for Arctic sea ice trends and climatologies (J. Scott Stewart and Walter N. Meier, NSIDC) </a>(last accessed June 2024)</p> <table> <tbody> <tr> <th>Region Code</th> <th>Region Name</th> </tr> </tbody> <tbody> <tr> <td>0</td> <td>Undefined Region</td> </tr> <tr> <td>1</td> <td>Central Arctic</td> </tr> <tr> <td>2</td> <td>Beaufort Sea</td> </tr> <tr> <td>3</td> <td>Chukchi Sea</td> </tr> <tr> <td>4</td> <td>East Siberian Sea</td> </tr> <tr> <td>5</td> <td>Laptev Sea</td> </tr> <tr> <td>6</td> <td>Kara Sea</td> </tr> <tr> <td>7</td> <td>Barents Sea</td> </tr> <tr> <td>8</td> <td>East Greenland Sea</td> </tr> <tr> <td>9</td> <td>Baffin Bay & Labrador Sea</td> </tr> <tr> <td>10</td> <td>Gulf of St. Lawrence</td> </tr> <tr> <td>11</td> <td>Hudson Bay</td> </tr> <tr> <td>12</td> <td>Canadian Archipelago</td> </tr> <tr> <td>13</td> <td>Bering Sea</td> </tr> <tr> <td>14</td> <td>Sea of Okhotsk</td> </tr> <tr> <td>15</td> <td>Sea of Japan</td> </tr> <tr> <td>16</td> <td>Bohai Sea</td> </tr> <tr> <td>17</td> <td>Baltic Sea</td> </tr> <tr> <td>18</td> <td>Gulf of Alaska</td> </tr> </tbody> </table> <p>Data files contain resolution in meters in the filename (nsidc_arctic_region_mask_2021_ease2_{resolution_m}.nc). The file format is netCDF-4 and the datafile contains one variable (region_id). The coordinate reference system of the variables is defined by EPSG:6931 (WGS 84 / NSIDC EASE-Grid 2.0 North) and the bounds of the data set are supplied as xc and yc variables in the data file. </p>
Figure 5 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 5. Location of 34 descending tracks of satellites ERS-1 (phases C and G), ERS-2 and ENVISAT, which were used to analyze the position of the sea ice edge in the Barents Sea. The red line is the average climatic position of the sea ice edge. The green line shows the position of track N444, the purple line shows the track N360. Dashed line is a reference line used for calculation the distance to the ice edge along the tracks.
Figure 6 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 6. Interannual variability of the position of the ice edge along track N118 according to altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKA satellites in 1992-2018. Dashed line shows a linear trend for the variability of the distance to the sea ice edge in the Barents Sea.
Figure 4 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 4. Interannual variability of the Barents Sea level anomalies according to satellite altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKa for the period 1992–2018 for June, July, August and September. Dashed lines show linear trends.
Figure 2 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 2. The position of the tracks of the ERS − 1/2, ENVISAT and SARAL/AltiKA (a) satellites with a repetition period of 35 days and the Sentinel – 3A / 3B satellites (b) with a repetition period of 27 days in the Barents Sea.
Figure 3 in Satellite Altimetry of Sea Level and Ice Cover in the Barents Sea
Figure 3. Interannual variability of the Barents Sea level anomalies according to satellite altimetry measurements of the ERS − 1/2, ENVISAT and SARAL/AltiKa for the period 1992 –2018. Dashed line shows a linear trend.
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (2 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes the files necessary for nudging the simulated temperature and salinity towards Copernicus GLORYS12V1 reanalysis values in a simulation from 1 September to 31 December 2013.</p> <p>The remaining input files for this period are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for phyiscs-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: input files (1 of 3)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes most of the input files necessary for a physics-only simulation from 1 September to 31 December 2013. The remaining input files for this period, which should be placed in the directory <code>sponge/</code> within the directory tree contained in this record, are available at <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a> and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample output files (from the physics and biogeochemistry modules) are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a> and <a href="https://doi.org/10.5281/zenodo.12746262" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12746262</a>.</p>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (2 of 2)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes daily-mean output of all (ocean circulation, sea ice, and biogeochemistry) modules for September 2015. Similar files for September 2013 are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample input files for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a>, <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>, and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p>
Simulation data for convection in radiatively heated melt ponds on sea ice
<p>This dataset includes data and processing code from simulations of radiatively heated convection in melt ponds on sea ice. </p> <p> </p> <p>Simulation results produced using code written by Andrew Wells and Tom Langton for use with and exploiting examples from the code Dedalus:</p> <p>http://dedalus-project.org/index.html</p> <p> </p> <p>Contact andrew.wells@physics.ox.ac.uk for further details. </p> <p>The data included in this version correspond to figures 3 and S5 , movies S2 and S3, and code described in supporting information in the study:</p> <p><strong>Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice </strong></p> <p>Joo-Hong Kim<sup>1</sup>, Woosok Moon<sup>2,3</sup>, Andrew J. Wells<sup>4</sup>, Jeremy P. Wilkinson<sup>5</sup>, Tom Langton<sup>4</sup>, Byongjun Hwang<sup>6,7</sup>, Mats A. Granskog<sup>8 </sup>and David Rees Jones<sup>9</sup></p> <p><sup>1</sup>Korea Polar Research Institute, Incheon, South Korea</p> <p><sup>2</sup>Nordic Institute for Theoretical Physics, Stockholm, Sweden</p> <p><sup>3</sup>Department of Mathematics, Stockholm University, Stockholm, Sweden</p> <p><sup>4</sup>Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK</p> <p><sup>5</sup>British Antarctic Survey, Cambridge, UK.</p> <p><sup>6</sup>Scottish Association for Marine Science, Oban, UK</p> <p><sup>7</sup>University of Huddersfield, Huddersfield, UK</p> <p><sup>8</sup>Norwegian Polar Institute, Fram Centre, Tromsø, Norway</p> <p><sup>9</sup>Dept. of Earth Sciences, University of Oxford, Oxford, UK</p> <p> </p> <p>Citation:</p> <p>Kim, J.-H., Moon, W., Wells, A. J., Wilkinson, J. P., Langton, T., Hwang, B., Granskog, M. A., & Rees Jones, D. W. (2018). Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL078077</p> <p>Alternative weblink:</p> <p>https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018GL078077</p> <p> </p> <p>v1: submitted during review of the manuscript.</p> <p>v2: updated with details of accepted publication.</p>
Sea ice model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain the full climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format, with fields described within the file (and subsequently compressed).</p>
Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios"
<p>Supporting data for "Arctic sea ice response to flooding of the snow layer in future warming scenarios" submitted to Earth's Future in April 2021</p> <p>Contains model output from both the Icepack and CCSM4 experiments from the paper. File descriptions for the Icepack and CCSM4 data are contained in the files README_icepack and README_CCSM respectively.</p>
Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model
<p>Model output and observational data and scripts corresponding to the manuscript "Impact of the ice thickness distribution discretization on the sea ice concentration variability in the NEMO3.6-LIM3 global ocean–sea ice model"</p> <p><strong>Abstract. </strong></p> <p>This study assesses the impact of different sea ice thickness distribution (ITD) configurations on the sea ice concentration (SIC) variability in ocean-standalone NEMO3.6-LIM3 simulations. Three ITD configurations with different numbers of sea ice thickness categories and boundaries are evaluated against three different satellite products (hereafter referred to as “data”). Typical model and data interannual SIC variability is characterized by k-means clustering both in the Arctic and Antarctica between 1979 and 2014 in two seasons, January–March and August–October, which show the largest coherence across clusters in individual months. Analysis in the Arctic is done before and after detrending the series with a 2nd degree polynomial to separate interannual from longer-term variability.</p> <p>Before detrending, winter clusters capture SIC response to atmospheric variability at both poles and summer cluster a positive and negative trend in the Arctic and Antarctic SIC respectively. After detrending, Arctic clusters reflect SIC response to interannual atmospheric variability predominantly. Model–data cluster comparison suggests that no specific ITD configuration or category number increases realism of the simulated Arctic and Antarctic SIC variability in winter. In the Arctic summer, more thin-ice categories decrease model–data agreement without detrending but increase agreement after detrending. Overall, a single-category configuration agrees the worst with data.</p> <p>Direct model–data comparison of SIC anomaly fields shows that more thick-ice categories improve winter SIC variability realism in Central Arctic regions with very thick ice. By contrast, more thin-ice categories reduce model–data agreement in the Central Arctic in summer, due to an overly large simulated sea ice volume.</p> <p>In summary, whereas better resolving thin ice in NEMO3.6-LIM3 can hamper model realism in the Arctic but improve it in Antarctica, more thick-ice categories increase realism in the Arctic winter. And although the single-category configuration performs the worst overall, no optimal configuration is identified. Our results suggest that no clear benefit is obtained from increasing the number of sea ice thickness categories beyond the current usual standard of 5 categories in NEMO3.6-LIM3.</p>
Simulated Arctic sea ice characteristics from MIS-11c and MIS-5e time slice simulations
<p>Tarball containing netCDF files with single-variable fields from time-slice simulations during the MIS-11c and MIS-5e periods. Each field contains 100 years of monthly data (1200 months) and were used in creating the figures and analyses present in the corresponding paper that will be submitted in the coming weeks (4 Aug 2024 as of writing). Simulations were conducted with CESM v1.2.2 using only variable orbital and GHG parameters as documented in Crow et al. (2022; doi:10.5194/cp-18-775-2022). All fields except TREFHT (reference height or 2m surface air temperature) and MOC (Atlantic meridional overturning circulation) were produced by the CICE module within CESM and include: sea ice area fraction, sea ice thickness, and sea ice age. Metadata are contained within the headers of the netCDF files for further details.</p>
Phytoplankton blooms in the Southern Ocean in high vs. low sea ice years
<p>Processing files and relevant datasets for Schlosser & Strutton (2025), 'Phytoplankton blooms in the Southern Ocean in high vs. low sea ice years', published in Elementa. Available here: <span>https://doi.org/10.1525/elementa.2024.00055</span></p>
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