Arctic sea-ice change tied to its mean state through thermodynamic processes

• Published in: Nature Climate Change
• Main Authors: Massonnet, François, Vancoppenolle, Martin, Goosse, Hugues, Docquier, David, Fichefet, Thierry, Blanchard-Wrigglesworth, Edward
• Other Authors: Barcelona Supercomputing Center
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Publishing Group 2018
• Subjects: Àrees temàtiques de la UPC::Energies; Sea ice; Seasonal prediction (Meteorology); Global climate change; Sea-ice variability; Previsió del temps; Arctic; Climate change
• Online Access: http://hdl.handle.net/2117/119884; https://doi.org/10.1038/s41558-018-0204-z

One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover. While all state-of-the-art climate models consistently reproduce the sign of these changes, they largely disagree on their magnitude the reasons for which remain contentious. As such, consensual methods to reduce uncertainty in projections are lacking. Here, using the CMIP ensemble, we propose a process-oriented approach to revisit this issue. We show that intermodel differences in sea-ice loss and, more generally, in simulated sea-ice variability, can be traced to differences in the simulation of seasonal growth and melt. The way these processes are simulated is relatively independent of the complexity of the sea-ice model used, but rather a strong function of the background thickness. The larger role played by thermodynamic processes as sea ice thins further suggests that the recent and projected reductions in sea-ice thickness induce a transition of the Arctic towards a state with enhanced volume seasonality but reduced interannual volume variability and persistence, before summer ice-free conditions eventually occur. These results prompt modelling groups to focus their priorities on the reduction of sea-ice thickness biases. The research leading to these results has received funding from the Belgian Fonds National de la Recherche Scientifique (F.R.S.-FNRS), and the European Commission’s Horizon 2020 projects APPLICATE (GA 727862) and PRIMAVERA (GA 641727). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in the Supplementary Information) for producing and making available their model output. We acknowledge the CESM Large Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone for access to the CESM-LE data. The authors thank C. M. Bitz and D. Notz for useful discussions, and F. Kauker for providing the ITRP data. The authors thank M. M. Holland and E. C. Hunke for the review of this manuscript. Peer Reviewed Postprint (author's final draft)