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126 results for “Arctic sea ice”
On the importance of representing snow over sea-ice for simulating the Arctic boundary layer
<p>Correctly representing the snow on sea-ice in coupled numerical weather prediction models has great potential to improve weather forecast and climate monitoring applications, such as climate reanalyses, which are usually produced using such systems. In this study two different methodologies to account for the effect of the snowpack accumulating over the sea-ice in 5-day global forecasts are compared. </p> <p>This dataset contains the high-resolution (dx=9km) model data for the different simulations, with and without the snow over sea-ice, co-located with observations at the locations of the SHEBA and N-ICE2015 field campaigns. The dataset also contains the daily averaged skin temperature, for the different simulations (interpolated on 0.25x0.25) performed over the extended time period, 2015-01-01 to 2015-02-28. </p>
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>
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>
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>
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>
A semantic segmentation dataset of Arctic sea ice from Operation IceBridge data
<p>This dataset is a semantic segmentation dataset of Arctic sea ice based on deep learning method from Operation IceBridge images. It contains 29,372 labeled images, each of which corresponds to an image of Operation IceBridge and are stored in TIFF format. The dataset can be accessed using ArcGIS, ENVI, and the GDAL library in Python easily. Where label 1 represents melt ponds, label 2 represents sea ice/snow, label 3 represents submerged ice, and label 4 represents open ocean water, respectively.</p>
Multi-frequency altimetry snow depth product over Arctic sea ice
<p>Satellite altimetry can be used to estimate sea ice thickness, an essential variable to better understand and forecast the dynamic ice cover. Nevertheless, some sources of uncertainty remain, and one of the most important concerns the snow depth, a key parameter to convert the measured ice freeboard into sea ice thickness.</p> <p>Snow depth can be estimated using different altimeter frequencies with different snow penetration capabilities. We have developed a monthly snow depth product based on the differences between CryoSat-2 SAR Ku and IceSat-2 laser altimeters covering the period 2018-2021 with a spatial resolution of 25 km. </p> <p> </p>
Monthly total freeboard and snow depth over Arctic sea ice from AMSR-E&2 and AVHRR measurements (2003-2020)
<p><strong>[Data description]</strong></p> <p>Monthly total freeboard (Ft), snow depth (hs), and snow depth uncertainty over Arctic sea ice data for January-February-March months of the 2003-2020 period produced by Lee and Shi et al. (2021, manuscript under review) are provided.</p> <p>Both variables are derived from satellite passive infrared and microwave measurements: Total freeboard was obtained from AMSR-E&2 measurements and snow depth was estimated from the AMSR and AVHRR measurements.</p> <p>The uploaded file titled "monthly averaged total freeboard and snow depth (JFM 2003-2020).zip" contains two directories: one for total freeboard and the other for snow depth. Naming convention is "variable_yyyymm.bin" and data format is 32-bit floating point array in shape of 304 x 448 (25 km polar stereographic grid).</p> <p>Here we provide an example Python code to read monthly snow depth of January 2003 using numpy.<br> import numpy as np<br> hs = np.fromfile('hs_200301.bin', dtype=np.float32).reshape(448,304)</p> <p>Geocoordinate tools for the 25 km polar stereographic grid are available at NSIDC website (https://nsidc.org/data/polar-stereo/tools_geo_pixel.html)<br> <br> <strong>[Abbreviations]</strong></p> <p>AMSR: Advanced Microwave Scanning Radiometer<br> AVHRR: Advanced Very High Resolution Radiometer<br> NSIDC: National Snow and Ice Data Center</p>
Spatial Damped Anomaly Persistence (SDAP) Forecasts of Sea Ice Presence in the Arctic between 1999 and 2020
<p>Spatial Damped Anomaly Persistence Forecasts of Sea Ice Presence in the Arctic between 1999 and 2020. Each netcdf file corresponds to a single initialisation, done at the start of the stated month, and the forecasts for the following 120 days, both probabilistic (SDAP) and deterministic (SAP) forecasts. The forecasts were derived using OSI SAF sea-ice concentration records (OSI SAF 450 and 430b) and follow the resolution of that dataset (25 km EASE-2 grid). Further details regarding the forecasting method and the results can be found in Niraula et Goessling, 2021 (in review).</p> <p> </p> <p>Please note that while the filenames say "DampedForecast", each file contains both Damped or Deterministic forecasts associated with the date.</p> <p> </p>
A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A climatology of thermodynamic vs. dynamic Arctic wintertime sea ice thickness effects during the CryoSat-2 era, submitted to: The Cryosphere. 2022</p> <p>Code can be found at:</p> <pre>https://doi.org/10.5281/zenodo.7987926</pre>
Bias corrected era5 skin temperature over the Arctic sea ice – 1981 to 2018 monthly means and climatology
<p>This dataset is generated in the context of the peer-reviewed study of Zampieri et al., 2023. The users can find a detailed description of the bias correction strategy and information on the scientific value of the dataset in the paper. Please, do not hesitate to contact me to obtain further information and suggestions on how to employ this dataset for your specific purpose. </p> <p><strong>References:</strong></p> <p>Zampieri, L.,<strong> </strong>Arduini, G., Holland, M., Keeley, S., Mogensen, K., Shupe, M., Tietsche, S. (2023) A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses. <em>Monthly Weather Review</em>. DOI:<a href="https://doi-org.cuucar.idm.oclc.org/10.1175/MWR-D-22-0130.1">10.1175/MWR-D-22-0130.1</a></p> <p><strong>Acknowledgments:</strong></p> <p>As part of the Virtual Earth System Research Institute (VESRI), funding for the Multiscale Machine Learning In coupled Earth System Modeling (M2LInES) project was provided to Lorenzo Zampieri by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. </p>
Brighter Ocean: Arctic sea ice solar partitioning
Open the record for dataset details and reuse information.
Physical properties of summer sea ice in the Pacific sector of the Arctic in 2008-2018
<p>This dataset includes the data of physical properties (ice temperature, salinity, density, and crystal structure) of summer sea ice in the Pacific sector of the Arctic collected by the Chinese National Arctic Research Expedition (CHINARE) programs in 2008/10/12/14/16/18.</p> <p>The files contain the following data fields:</p> <p><strong>readme</strong> – a detailed description of all data</p> <p><strong>Ice station information</strong> – specific information (location, date, air temperature, etc.) of ice stations</p> <p><strong>Ice temperature</strong> – ice temperature along ice depth for individual ice cores</p> <p><strong>Ice salinity</strong> – ice salinity along ice depth for individual ice cores</p> <p><strong>Ice density</strong> – ice density along ice depth for individual ice cores</p> <p><strong>Ice crystal structure</strong> – Picture sequences of ice crystal structure along ice depth for individual ice cores</p>
Wintertime daily Arctic sea ice surface form drag from ASCAT during 2006-2021
<p>This dataset contains wintertime (Nov-Apr) daily Arctic sea ice surface form drag derived from ASCAT (2006-2021). The spatial resolution is 12.5*12.5 km projected onto a polar stereographic grid. The daily file (in netCDF format) contains three variables: Cdnfr, Cdn, and SIC_90_label. Cdnfr is the surface form drag (at 10m and in neutral stability), which is only valid for pixels with sea ice concentration (SIC) over 90%. Cdn is the total drag coefficient over sea ice as a sum of skin drag, surface form drag (Cdnfr), and floe form drag. SIC_90_label is a binary map labeling pixels with SIC>90% as 1. The geolocation information is provided in "PS_12500_grid_lat_lon.nc", which contains longitude and latitude for each grid cell and projection information. For the use of the dataset and details of the retrieval algorithm, please cite and read the following paper:<br>Zhang Z, Hui F, Shokr M, et al. Winter Arctic Sea Ice Surface Form Drag During 1999–2021: Satellite Retrieval and Spatiotemporal Variability[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, doi: 10.1109/TGRS.2023.3347694.</p>
Wintertime daily Arctic sea ice surface form drag from QuikSCAT during 1999-2009
<p>This dataset contains wintertime (Nov-Apr) daily Arctic sea ice surface form drag derived from QuikSCAT (1999-2009). The spatial resolution is 12.5*12.5 km projected onto a polar stereographic grid. The daily file (in netCDF format) contains three variables: Cdnfr, Cdn, and SIC_90_label. Cdnfr is the surface form drag (at 10m and in neutral stability), which is only valid for pixels with sea ice concentration (SIC) over 90%. Cdn is the total drag coefficient over sea ice as a sum of skin drag, surface form drag (Cdnfr), and floe form drag. SIC_90_label is a binary map labeling pixels with SIC>90% as 1. The geolocation information is provided in "PS_12500_grid_lat_lon.nc", which contains longitude and latitude for each grid cell and projection information. For the use of the dataset and details of the retrieval algorithm, please cite and read the following paper:</p> <p>Zhang Z, Hui F, Shokr M, et al. Winter Arctic Sea Ice Surface Form Drag During 1999–2021: Satellite Retrieval and Spatiotemporal Variability[J]. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2023, doi: 10.1109/TGRS.2023.3347694.</p>
The prediction data analyzed in the article: "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018"
<p>The outputs of seasonal predictions with the Coupled Arctic Prediction System version 1 analyzed in the article, "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018", including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Ice mass budget diagnostics</p> <p>Accumulated downward shortwave radiation at the surface (ASWDN)</p> <p>Accumulated downward longwave radiation at the surface (ALWDN)</p> <p>Near surface air temperature (T2) </p> <p>Temperature and salinity profile of the upper ocean under sea ice </p>
Data in "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study"
<p>The files contain the data of results in the paper "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study".</p>
Processed model output and observational products used in `Observed winds crucial for September Arctic sea ice loss'
<p>Processed model output from wind-nudging experiments used to investigate Arctic sea ice loss. Also includes processed observational data shown in the manuscript.</p> <p> </p> <p>For further details, see </p> <p>Roach, L. A and Blanchard-Wrigglesworth E. (2022). Observed winds crucial for September Arctic sea ice loss. Accepted at Geophysical Research Letters</p>
Interaction between Arctic sea ice and the AMOC
<p>Code and data for</p> <p>Liu, W., Fedorov, A. Interaction between Arctic sea ice and the Atlantic meridional overturning circulation in a warming climate. <em>Clim Dyn</em> <strong>58, </strong>1811–1827 (2022). https://doi.org/10.1007/s00382-021-05993-5</p>
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