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Focusing on monitoring and modelling all the components of the polar regions, their climate, ocean circulation and teleconnections with the lower latitudes.
Research highlights include:
- First publication from the 2019/2020 MOSAiC Arctic expedition in the Arctic onboard the German icebreaker vessel Polarstern showing discussing the history of the chosen sea ice floe from its inception in the Siberian shelf to the high Arctic where the first camp was set up (Tsamados) Link
- Study published in Nature exploring the linkages between Arctic Amplification (AA) and midlatitude weather variability. The study contrast findings on the strength of this teleconnection between models and observations and between different models with observations showing a stronger link between AA and export of excess heating from the Arctic to lower latitudes than seen in most models (Stroeve). Link
- Unique methods for the satellite altimeter measurement of Arctic ocean dynamic topography and sea ice thickness and mass. These are essential precursors to the investigation of how global warming is affecting basin-wide Arctic sea ice mass and its transport to sub-polar seas, and to understanding how wind-stress will ‘spin-up’ the Arctic surface circulation as the sea ice declines (Tsamados) Link
- Study in Science showing that Arctic sea ice disappearing rapidly, leading to prediction of an ice-free summer in the near future. Simulations of the timing of summer sea-ice loss differ substantially, making it difficult to evaluate the pace of the loss. We observe a linear relationship between monthly-mean September sea-ice area and cumulative CO2 emissions. This allowed us to predict Arctic summer sea ice directly from the observational record and interestingly most models underestimate this loss (Stroeve). Link
- We combine several AI techniques to produce a state of the art statistical sea ice forecast of September sea ice extent with a lead time of up to 3 months. The complex climate network approach exploits relationships within climate time series data to generate nodes (regions) and links (teleconnections) in the climate system and sea ice. This network information is then utilized within a linear Gaussian process regression forecast model (a Bayesian inference technique) to generate pan-Arctic and regional predictions of the sea-ice cover. This method has application for informing risks in shipping in the ice infested regions of the Arctic Ocean (Tsamados, Stroeve, Gregory) Link
- A new snow on sea ice product was developed based on the simple idea that radar altimeters operating at different frequencies penetrate differencially into the snow pack. This lead to the application of a new methodology to the AltiKa (Ka-bang radar altimeter) and CryoSat-2 (Ku-band radar altimeter) to generate a winter snow on sea ice thickness product. This type of methodology paves the way to other dual frequency approaches including with the recently launched NASA’s ICESat-2 (Tsamados, Stroeve, Lawrence) Link