DOI: 10.5281/zenodo.11049391
title: Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets

description: [Description]

This dataset is curated for a manuscript submitted to Earth and Space Science by Hoyeon Shi and his colleagues in April 2024. It consists of three directories, each described below. A more detailed description of this dataset is available in the preprint (doi: 10.22541/essoar.171500700.06739421/v1).

01_retrieval_results

This directory includes CryoSat-2-based monthly fields of Arctic sea ice thickness, snow depth, total freeboard, ice freeboards, ice draft, and bulk sea ice density for the winter months (January-March) of the 2011-2022 period. Those variables are obtained using six combinations of two retrieval methods and three radar freeboard correction methods.

Retrieval methods



alpha method: A simultaneous retrieval method based on Shi et al. (2020) and Shi et al. (2023), combining CryoSat-2, AVHRR, and AMSR data

CS2IS2 method: A simultaneous retrieval method based on Kwok and Marcus (2018) and Kwok et al. (2020), combining CryoSat-2 and ICESat-2 data


Radar freeboard correction methods



Wave speed correction method: Mallet et al. (2020)

Empirical correction method: An empirical correction derived from the CS2_OIB_matchup data, using snow depth as a predictor

Bias correction method: An empirical correction derived from the CS2_OIB_matchup data, doing bias correction


The datasets used for generating this dataset are as follows:



CryoSat-2 - AWI CryoSat-2 sea ice thickness v2p4 (ftp://ftp.awi.de/sea_ice/product/cryosat2/v2p4/)

ICESat-2- NSIDC ATL20 dataset (doi: 10.5067/ATLAS/ATL20.004)

AVHRR- Copernicus Marine Service's surface temperature datasets (doi: 10.48670/MOI-00130, doi: 10.48670/MOI-00123)

AMSR- JAXA AMSR-E 6.9 GHz brightness temperature (doi: 10.57746/EO.01gs73ayng11rpwk7n54aynyj1)- JAXA AMSR2 6.9 GHz brightness temperature (doi: 10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j)

Auxiliary data- Sea ice concentration: OSI SAF (doi: 10.15770/EUM_SAF_OSI_0013, doi: 10.15770/EUM_SAF_OSI_0014)- Sea ice type: OSI SAF (doi: 10.15770/EUM_SAF_OSI_NRT_2006)


The naming convention is 'RetrievalMethod_CorrectionMethod_yyyymm.bin'. The 'RetrievalMethod' is either 'alpha' or 'CS2IS2', and the 'CorrectionMethod' is either 'WaveSpeed,' 'Empirical,' or 'BiasCorrection.' The data format is a 32-bit floating point array in the shape of 6 x 448 x 304 (25 km polar stereographic grid). The first dimension indicates the variables (in the order of snow depth (0), sea ice thickness (1), ice freeboard (2), total freeboard (3), sea ice draft (4), and bulk sea ice density (5)). For example, to read the sea ice thickness of January 2020 based on the alpha method with an empirical correction, you may write this Python command:

import numpy as npdata = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)hi = data[1,:,:]

The unit of thickness-related variable is cm, and the unit of density is kg/m3. The 25 km polar stereographic grid information is available on the NSIDC website (doi: 10.5067/N6INPBT8Y104).

02_valdiation data 

This directory includes reference data used for quality assessment of retrievals. There are three sub-directories:

'Mooring_draft_psn25_monthly' includes sea ice draft measurements from the moorings in the Beaufort Sea (https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/) and the Laptev Sea (doi: 10.1594/PANGAEA.912927, doi: 10.1594/PANGAEA.899275).

'OIB_SD_psn25_monthly' and 'OIB_TFB_psn25_monthly' include airborne snow depth and total freeboard measurements from NASA's Operation IceBrdige campaign (doi: 10.5067/G519SHCKWQV6, doi: 10.5067/GRIXZ91DE0L9).

Original data were processed to become monthly gridded data to make a comparison with satellite retrievals. The naming convention of each file is 'Var_yyyymm.bin,' where 'Var' is the variable name (SD: snow depth, TFB: total freeboard, Di: ice draft). For example, you can use the following code to read the OIB snow depth in March 2014.

import numpy as nphs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)

03_CS2_OIB_matchup

This directory includes a match-up of AWI's CryoSat-2 L2P track data and OIB track data. The matching was done by resampling two high-resolution data on a coarser-resolution common grid (25 km polar stereographic grid) using a drop-in-a-bucket resampling method. The file format is CSV, and it is straightforward to understand when it is opened.

 

[Notes for the dataset submitted to the SIN'XS project - version 1.2]

Version 1.2 of this dataset was submitted to the SIN'XS project. The main changes compared to version 1 are:



Update of the coefficients for the radar freeboard correction equation - Eq. (13) in the preprint



The coefficients were derived from 333 data points of CS2_OIB_matchups, where the observation dates of CS2 and OIB are the same.

New coefficients for the empirical correction method: c_0 = 6.794842717012541 cm, c_1 = -0.13516016 cm/cm

New coefficients for the bias correction method: c_0 = 3.341401628723425 cm, c_1 = 0 cm/cm



Extension of the period for the CS2IS2 retrieval method: from January-March to November-April

Output re-gridded to the SIN'XS grid specification using the nearest neighbour method


 

[Abbreviations]

AMSR: Advanced Microwave Scanning RadiometerAVHRR: Advanced Very High Resolution RadiometerAWI: Alfred Wegener InstituteCS2: CryoSat-2JAXA: Japan Aerospace Exploration AgencyNASA: National Aeronautics and Space AdministrationNSIDC: National Snow and Ice Data CenterOIB: Operation IceBridgeOSI SAF: Ocean and Sea Ice Satellite Application FacilitySIN'XS: Sea Ice-thickness product iNter-comparison eXerciSe