TARDIS: Thickness of Arctic sea ice Reconstructed by Data assimilation and artificial Intelligence Seamlessly

Extending Data Assimilation techniques using Machine Learning.


The primary objective of TARDIS is to extend a 10-years (2010-2020) multi-satellite time series of Arctic sea ice thickness further into the past and quantify the related reconstruction uncertainties.
The secondary objectives are twofold:
1) to propose a more efficient estimator for high-dimensional, dynamical and non-Gaussian variables such as the Arctic sea ice thickness, using Machine Learning as a tool to extend the capabilities of Data Assimilation.
2) to propose a new assimilation algorithm able to update the small scale variability of sea ice thickness, also called the Ice Thickness Distribution.

Project Summary

Satellites have been measuring the extent of the whole Arctic sea ice since the late 70’s. But only since 2010 are they also able to measure its thickness and thus monitor the decline of its total volume. This information would have been invaluable for climate studies had these new satellites been sent in orbit ten years earlier. In the absence of a time machine, can we learn enough from the last decade (since 2010) to reconstruct an earlier decade? What we have is a numerical model that can reproduce the known physics and assimilate satellite measurements of the ocean and sea ice during the well-observed decade; this is a well-established tool but it cannot use the scattered ice thickness measurements before the satellites: research cruises, older satellites, and proxy measurements. A more recent tool introduced by the TARDIS team now allows us to combine data assimilation and machine learning to build data-driven relationships between them. TARDIS will apply this method in order to finally reconstruct the past ice thickness for climate studies. If TARDIS succeeds at time travelling and spatial interpolation over the Arctic sea ice, the team will consider analogous applications of the same methodology.

Project Details
Funding Agency: 
Research Council of Norway
NERSC Principal Investigator: 
Laurent Bertino
Project Deputy Leader at NERSC: 
Jiping Xie
Coordinating Institute: 
Nansen Environmental and Remote Sensing Center
Project Status: