New insight from CryoSat-2 sea ice thickness for sea ice modelling

• Published in: The Cryosphere
• Main Authors: Schröder, David, Feltham, Danny L., Tsamados, Michel, Ridout, Andy, Tilling, Rachel
• Format: Article in Journal/Newspaper
• Language: English
• Published: European Geosciences Union 2019
• Subjects: Arctic; albedo; Sea ice; The Cryosphere
• Online Access: https://centaur.reading.ac.uk/80975/; https://centaur.reading.ac.uk/80975/8/tc-13-125-2019.pdf; https://centaur.reading.ac.uk/80975/1/CICE_CS2_revised_9nov_notrack.pdf

Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.