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.

RCN Grant number: 325241