The UK has led the polar remote sensing field for over four decades. The Centre for Polar Observation and Modelling at UCL has been at the forefront of this research (i.e. polar mission CryoSat-2) over the last two decades. The unparalleled success of the SAR CryoSat-2 radar altimeter mission in delivering the most accurate to date picture of the sea ice thickness and polar ocean dynamic topography over the last eight years has revolutionised the way we understand the polar regions. In the Arctic this means that we now understand by how much the total volume of ice has diminished and what impact this has had on the underlying ‘dormant’ Arctic Ocean.
In this study you will use machine learning tools to perform data fusion in time and space of the wide range of satellite, airborne and in-situ data collected in the polar regions over the past two decades to build a statistically robust picture of the trends in the key climate variables: 1) the total sea ice volume and 2) the high latitudes ocean dynamic topography (and currents).
Your role will be to cross-calibrate and refine our current processing of past and present radar and laser altimeter satellite data (with other remote sensing and in-situ data proxies) from the early 1990s (ERS), through the 2000s (ENVISAT), and up to the 2020s (CryoSat-2, AltiKa, Sentinel-3, ICESat-2. These multi-decadal time series will inform our understanding of the long-term changes that are emerging in the polar regions and their implications for our mid-latitudes.