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a core project of
2018WCRPspon col July2018 01 1

Machine learning methods for sea ice analysis, prediction and projection

Neven Fučkar (neven.fuckar[at]ouce.ox.ac.uk)

The scientific questions that will be studied:  

-    Detection and classification (supervised and unsupervised) of anomalies in observed, reconstructed and modelled sea ice variables.
-    Comparison of machine learning, statistical and dynamical predictions.
-    Attribution of sea ice trends and extreme events to anthropogenic forcing factors.  
-    How does sea ice variability and predictability evolve with global climate change?

The processes that will be investigated:

-    Sea-ice-atmosphere-ocean interaction
-    Influence of the atmosphere and oceans on formation of sea ice modes of variability and extreme events
-    Storage and release of climate signals in sea ice variables with climate memory effects (thickness, volume and enthalpy)

The type of analyses that will be conducted:

-    Analysis of CMIP6 simulations, and related large ensembles, observations and reanalysis products.
-    Multi-method detection and attribution of trends and extreme events.
-    Tools: K-means clustering, SOM analysis, mixture models, kernel methods, …

Examples of active projects
•    How the structure and occurrence of Arctic SIT modes of variability evolves in CMIP6 projections
•    Attribution of Arctic SIE extremes in today’s climate and at the end of 21st century.


References:

Reichstein, M., et al. 2019: Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204. https://doi.org/10.1038/s41586-019-0912-1

Kim, Y. J., Kim, H.-C., Han, D., Lee, S., and Im, J.: Prediction of monthly Arctic sea ice concentration using satellite and reanalysis data based on convolutional neural networks, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-159, in review, 2019.

Fučkar, N.S., Guemas, V., Johnson, N.C. et al. 2019: Dynamical prediction of Arctic sea ice modes of variability, Clim Dyn 52: 3157. https://doi.org/10.1007/s00382-018-4318-9