Arctic sea ice is diminishing with climate warming1 at a rate unmatched for at least 1,000 years2. As the receding ice pack raises commercial interest in the Arctic3, it has become more variable and mobile4, which increases safety risks to maritime users5. Satellite observations of sea-ice thickness are currently unavailable during the crucial melt period from May to September, when they would be most valuable for applications such as seasonal forecasting6, owing to major challenges in the processing of altimetry data7. Here we use deep learning and numerical simulations of the CryoSat-2 radar altimeter response to overcome these challenges and generate a pan-Arctic sea-ice thickness dataset for the Arctic melt period. CryoSat-2 observations capture the spatial and the temporal patterns of ice melting rates recorded by independent sensors and match the time series of sea-ice volume modelled by the Pan-Arctic Ice Ocean Modelling and Assimilation System reanalysis8. Between 2011 and 2020, Arctic sea-ice thickness was 1.87 ± 0.10 m at the start of the melting season in May and 0.82 ± 0.11 m by the end of the melting season in August. Our year-round sea-ice thickness record unlocks opportunities for understanding Arctic climate feedbacks on different timescales. For instance, sea-ice volume observations from the early summer may extend the lead time of skilful August–October sea-ice forecasts by several months, at the peak of the Arctic shipping season.
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ESA Level-2 Baseline-D CryoSat-2 observations for May–September 2011–2020 from the ESA GPOD SARvatore and SARInvatore services were publicly available online for the initial manuscript submission but have since been removed. Please contact the corresponding author directly for access to these data. The dataset of samples for training and testing the CNN classification algorithm for CryoSat-2 is available from https://doi.org/10.1016/j.rse.2021.1127447. Daily observations of sea-ice drift are available from the NSIDC Polar Pathfinder dataset at https://nsidc.org/data/nsidc-0116/versions/443. Remotely sensed observations of melt-pond fraction are available from the Sentinel-3 OLCI sensor through the University of Bremen at https://seaice.uni-bremen.de/melt-ponds/54. Snow depth and density estimates from SnowModel-LG are available from NSIDC at https://doi.org/10.5067/27A0P5M6LZBI25. Weekly 12.5-km estimates of the sea-ice age are available from the Version 4 product at NSIDC at https://nsidc.org/data/nsidc-061143. The Airborne EM dataset includes observations from the AWI RV Polarstern ARK-XXVI/3 TransArc campaign in 201164, available from https://doi.org/10.1594/PANGAEA.937197, and the IceBird campaigns from 2016 to 201822. Daily ULS sea-ice draft observations from BGEP moorings A, B and D are available from https://www.whoi.edu/beaufortgyre for the period between 2011 and 2018. Daily ULS and ADCP ice draft observations from five moorings in the Laptev Sea for 2010 to 2016 are publicly available from https://doi.org/10.1594/PANGAEA.899275 and https://doi.org/10.1594/PANGAEA.912927. Monthly ULS ice draft observations from four moorings in Fram Strait between 2010 and 2018 are publicly available from https://doi.org/10.21334/npolar.2021.5b717274. SIC is available from the OSISAF ‘OSI-450’ climate data record at https://osi-saf.eumetsat.int/products/osi-45070. Reanalysed model estimates of SIV are available from the Applied Physics Laboratory Version 2.1 reprocessed PIOMAS8,30 at http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid. The final pan-Arctic CryoSat-2 SIT data spanning October 2010 to July 2020 are available from the British Antarctic Survey Polar Data Centre at https://doi.org/10.5285/D8C66670-57AD-44FC-8FEF-942A46734ECB.
The MATLAB FBEM for simulating the backscattered SAR altimeter waveform from snow-covered sea ice, including an option for simulating waveforms from melt-pond-covered sea ice, is publicly available at https://doi.org/10.5281/zenodo.6554740. The look-up table for the EM bias correction is available at https://doi.org/10.5281/zenodo.6558485. The code for converting CryoSat-2 radar freeboards to thickness is available at https://doi.org/10.5281/zenodo.6558483.
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M.T., J.C.L., Y.A., G.J.D., J.C.S. and H.D.B.S.H. acknowledge financial support from the UKRI Natural Environment Research Council Project (NERC) ‘PRE-MELT’ under the split grant awards NE/T001399/1, NE/T000546/1 and NE/T000260/1. J.C.L. was also supported by the European Space Agency Living Planet Fellowship ‘Arctic-SummIT’ under grant ESA/4000125582/18/I-NS and the CIRFA project through the Research Council of Norway (RCN) under grant number 237906. D.G.B. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC). M.T. and Y.A. acknowledge support from ‘Towards a marginal Arctic sea ice cover’ (NE/R000085/1). We thank the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation) service available through the ESA GPOD for providing Level-2 CryoSat-2 observations; and the WHOI Beaufort Gyre Exploration Programme and AWI IceBird Programme for providing essential observations for ground truthing new satellite datasets. ADCP and ULS moorings were deployed, recovered and processed within the framework of the Russian–German project CATS/Transdrift (grant 63A0028B) and QUARCCS (grant 03F0777A).
The authors declare no competing interests.
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Extended data figures and tables
CryoSat-2 data and auxiliary data products are shown in blue. Key processing steps are shown in orange.
Extended Data Fig. 2 CryoSat-2 sea ice thickness validation against airborne observations from the 2011 TransArc campaign of the AWI Polarstern Icebreaker.
(a) Comparison of CryoSat-2 sea ice thickness observations with airborne EM thickness measurements. The AEM data were averaged to 80-km scale before comparing with CryoSat-2. The technique for deriving CryoSat-2 ice thickness uncertainties is described within the Methods section. (b) Map and dates of AEM sea ice thickness data collection overlaid on the CryoSat-2-derived sea ice thickness field for Aug 15th–Sept 15th 2011. Information on the SIT data available from TransArc can be found here https://doi.org/10.1594/PANGAEA.937197. Map in panel b produced using MATLAB code from ref. 42.
Extended Data Fig. 3 CryoSat-2 sea ice thickness validation against airborne observations from the AWI IceBird Program 2016–2018.
(a) Map of the airborne EM observations used for sea ice thickness validation. Three annotations in the Beaufort Sea mark the locations of the BGEP Moorings (see Extended Data Fig. 4). (b) Comparison of CryoSat-2 sea ice thickness observations with coinciding AEM measurements. The airborne data were averaged to 80-km scale before comparing with CryoSat-2. (c) Mean ice thickness difference between CryoSat-2 and the AEM as a function of the distance of observations from the coast. Map in panel a produced using MATLAB code from ref. 42.
Extended Data Fig. 4 Comparison of sea ice draft measured by the Beaufort Gyre Exploration Program (BGEP) Mooring Upward-Looking Sonar (ULS) sensors with ice draft estimates by CryoSat-2 in a 150 km radius surrounding each mooring.
CryoSat-2 draft observations in winter (green points) use the LARM sea ice product (Landy, et al., 2020) and in summer (blue points) use the processing algorithm presented here. BGEP Mooring A is located at approximately 75N 150W, Mooring B at 78N 150W, and Mooring D at 74N 140W and are shown in Extended Data Fig. 3a. Information on the BGEP mooring ULS data can be found here https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/.
Extended Data Fig. 5 Comparison of sea ice draft measured by moored Upward Looking Sonar (ULS) and Acoustic Doppler Current Profiler (ADCP) sensors in the Laptev Sea with ice draft estimates by CryoSat-2 in a 150 km radius surrounding each mooring.
CryoSat-2 draft observations in winter (green points) use the LARM sea ice product (Landy, et al., 2020) and in summer (blue points) use the processing algorithm presented here. Locations and information for the ULS and ADCP sensors on Laptev Sea moorings can be found here https://doi.org/10.1594/PANGAEA.899275 and https://doi.org/10.1594/PANGAEA.912927 respectively.
Extended Data Fig. 6 Arctic sea ice thickness anomalies [m] measured over the entire year at biweekly intervals by CryoSat-2 in 2016, compared to the 2011–2020 average.
Observations for October–April are obtained from the LARM algorithm (Landy, et al., 2020). Observations for May–September are obtained from the new method presented here. Black contours represent the sea ice extent (15% ice concentration edge). Maps produced using MATLAB code from ref. 42.
In black, the SIV anomaly after removing the climatological seasonal cycle of SIV obtained from the 2010–2020 time series of SIV from CryoSat-2 SIT and OSISAF SIC. In red, blue, and purple are the contributions of SIC anomalies, SIT anomalies, and their correlated component, respectively, to the time series of SIV anomalies. The correlations between the anomalies of SIV with respective anomalies of SIT, SIC, and their correlated component, are 0.97, 0.27, and 0.21. SIT anomalies provide the dominant contribution to SIV interannual variability compared to SIC anomalies.
Extended Data Fig. 8 Reproduction of the lag correlation plots in Fig. 3 of the main paper but with SIE and SIV time series linearly detrended before calculating the correlations.
(a) Correlations between SIV and later SIE and (b) correlations between SIE and later SIE. Black lines mark correlations with a statistical significance of p = 0.1 and stippling marks where SIE->SIV correlations are higher than SIE->SIE for (a) or vice versa for (b). The grey lines mark lead times for each month as contours. (c) Mean (with standard deviation envelope) correlation for September SIE including two regions of predictability where SIV offers improvements over SIE.
Extended Data Fig. 9 Lag correlation plots between SIE and earlier SIV for different regions of the Arctic.
The black lines mark correlations with a statistical significance of p = 0.1 and regions are defined by the NSIDC MASIE system, as displayed on the map. The Central Arctic Ocean region, referred to in the main text, is shown in white between the marginal Arctic seas. Map in the bottom-right panel produced using MATLAB code from ref. 42.
Extended Data Fig. 10 Standard deviations on bootstrapped correlation coefficients between SIV leading SIE and SIE leading SIE.
Correlations are recalculated 100 times but each time randomly sampling N minus 1 of the 9–11-year time series available from, CryoSat-2, with replacement, so that one pair of observations is excluded from the calculation. The variability of the 100 recalculated correlation coefficients provides a measure of the robustness of the patterns identified in Fig. 3 of the main text (top row) and Extended Data Fig. 7 (bottom row), i.e., with and without detrending time series, respectively. Black lines show contours of p = 0.1 from the correlation plots in Fig. 3 in the main text (top row) and Extended Data Fig. 7 (bottom row).
This Supplementary Information file contains two sections (A, Radar altimeter echo model simulations; B, Sea-ice thickness sensitivity to radar penetration depth) and includes Supplementary Figs. 1–4, Table 1 and references
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Landy, J.C., Dawson, G.J., Tsamados, M. et al. A year-round satellite sea-ice thickness record from CryoSat-2. Nature 609, 517–522 (2022). https://doi.org/10.1038/s41586-022-05058-5