Login / Signup

A year-round satellite sea-ice thickness record from CryoSat-2.

Jack C LandyGeoffrey J DawsonMichel TsamadosMitchell BushukJulienne C StroeveStephen E L HowellThomas KrumpenDavid G BabbAlexander S KomarovHarry D B S HeortonH Jakob BelterYevgeny Aksenov
Published in: Nature (2022)
Arctic sea ice is diminishing with climate warming 1 at a rate unmatched for at least 1,000 years 2 . As the receding ice pack raises commercial interest in the Arctic 3 , it has become more variable and mobile 4 , which increases safety risks to maritime users 5 . 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 forecasting 6 , owing to major challenges in the processing of altimetry data 7 . 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 reanalysis 8 . 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.
Keyphrases
  • climate change
  • optical coherence tomography
  • high resolution
  • deep learning
  • machine learning
  • risk assessment
  • human health