Abstract:
This dataset presents monthly gridded sea ice and ocean parameters for the Arctic derived from the European Space Agency's satellite CryoSat-2. Parameters include sea ice freeboard, sea ice thickness, sea ice surface roughness, mean sea surface height, sea level anomaly, and geostrophic circulation. Data are provided as monthly grids with a resolution of 25 km, mapped onto the NSIDC EASE2-Grid, covering the Arctic region north of 50 degrees latitude, for all winter months (Oct-Apr) between 2010 and 2018.
CryoSat-2 Level 1b Baseline C observed waveforms have been retracked using a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a sophisticated physically-based treatment of the effect of ice surface roughness on retracked ice and ocean elevations. Methods for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, deriving sea ice freeboard, and converting to thickness are detailed in Landy et al. (In Review). This dataset contains derived sea ice thicknesses from two processing chains, the first using the conventional snow depth and density climatology from Warren et al. (1999) and the second using reanalysis and model-based snow data from SnowModel (Stroeve et al., In Review). Sea surface height and ocean topography grids were derived from only those CryoSat-2 samples classified as leads. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter.
Funding was provided by ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS and NERC Project PRE-MELT grant NE/T000546/1.
Keywords:
Arctic, CryoSat-2, sea ice, sea ice freeboard, sea ice thickness, sea level
Landy, J., & Stroeve, J. (2020). Arctic sea ice and physical oceanography derived from CryoSat-2 Baseline-C Level 1b waveform observations, Oct-Apr 2010-2018 (Version 1.0) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/cbd2cf78-462a-4968-be20-05f9c125ad10
Use Constraints: | This data is supplied under Open Government Licence v3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. |
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Creation Date: | 2019-11-14 |
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Dataset Progress: | Complete |
Dataset Language: | English |
ISO Topic Categories: |
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Parameters: |
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Personnel: | |
Name | UK PDC |
Role(s) | Metadata Author |
Organisation | British Antarctic Survey |
Name | Jack C Landy |
Role(s) | Technical Contact, Investigator |
Organisation | University of Bristol |
Name | Julienne C Stroeve |
Role(s) | Investigator |
Organisation | University College London |
Parent Dataset: | N/A |
Reference: | Landy, J.C., Tsamados, M. and Scharien, R.K., 2019. A facet-based numerical model for simulating SAR altimeter echoes from heterogeneous sea ice surfaces. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4164 - 4180. DOI: 10.1109/TGRS.2018.2889763. Landy, J.C., Petty, A.A., Tsamados, M. and Stroeve, J.C., 2020. Sea ice roughness overlooked as a key source of uncertainty in CryoSat-2 ice freeboard retrievals. Journal of Geophysical Research: Oceans, 125(5). DOI: 10.1029/2019JC015820 Liston, G. E., Itkin, P., Stroeve, J., Tschudi, M., Stewart, J. S., Pedersen, S. H., et al. (2020). A Lagrangian Snow-Evolution System for Sea-Ice Applications (SnowModel-LG): Part I - Model Description. Journal of Geophysical Research: Oceans, 125, e2019JC015913. https://doi.org/10.1029/2019JC015913 Stroeve, J., Liston, G. E., Buzzard, S., Zhou, L., Mallett, R., Barrett, A., et al. (2020). A Lagrangian Snow-Evolution System for Sea Ice Applications (SnowModel-LG): Part II - Analyses. Journal of Geophysical Research: Oceans, 125, e2019JC015900. https://doi.org/10.1029/2019JC015900 Warren, S.G., Rigor, I.G., Untersteiner, N., Radionov, V.F., Bryazgin, N.N., Aleksandrov, Y.I. and Colony, R., 1999. Snow depth on Arctic sea ice. Journal of Climate, 12(6), pp.1814-1829. DOI: 10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2 |
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Quality: | NetCDF files were produced from the final gridded satellite observations and contain all associated metadata, or details about auxiliary datasets used in the processor. No smoothing or averaging was performed on the 25-km gridded sea ice and sea surface height parameters. A 300-km Gaussian convolution filter was used to smooth artefacts in the dynamic ocean topography grids, before deriving geostrophic currents. Both the random and systematic uncertainties relevant for each parameter have been carefully estimated and are provided in the data files. NetCDF files contain detailed descriptions of each derived parameter. |
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Lineage: | The data is derived from CryoSat-2 Baseline-C Level 1b waveform observations over the Arctic, north of 50 degrees altitude, for 2010 to 2018. |
Temporal Coverage: | |
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Start Date | 2010-10-01 |
End Date | 2018-04-30 |
Spatial Coverage: | |
Latitude | |
Southernmost | 50 |
Northernmost | 88.5 |
Longitude | |
Westernmost | -180 |
Easternmost | 180 |
Altitude | |
Min Altitude | N/A |
Max Altitude | N/A |
Depth | |
Min Depth | N/A |
Max Depth | N/A |
Data Resolution: | |
Latitude Resolution | 0.2 degree |
Longitude Resolution | 0.2 degree |
Horizontal Resolution Range | 10 km - < 50 km or approximately .09 degree - < .5 degree |
Vertical Resolution | N/A |
Vertical Resolution Range | N/A |
Temporal Resolution | 1 month |
Temporal Resolution Range | Monthly Climatology |
Location: | |
Location | Arctic |
Detailed Location | N/A |
Sensor(s): |
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Data Collection: | European Space Agency's CryoSat-2. |
Distribution: | |
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Distribution Media | Online Internet (HTTP) |
Distribution Size | 648 MB |
Distribution Format | TIFF |
Fees | N/A |
Distribution Media | Online Internet (HTTP) |
Distribution Size | 1.6 GB |
Distribution Format | netCDF |
Fees | N/A |
Data Storage: | The dataset contains 56 NetCDF (.nc) files, each including all gridded parameters for a single month, Oct-Apr 2010-2018. Quicklook TIFF (.tif) image files are also provided for each month, for the following parameters: sea ice freeboard using SnowModel, sea ice thickness using SnowModel, sea ice thickness uncertainty using SnowModel, sea ice thickness when using Warren 99 snow climatology, the ice thickness difference between SnowModel and Warren 99 processors, and the snow depth from SnowModel. |