Sponsor: Natural Sciences and Engineering Research Council of Canada
Lake Ice Cover (LIC) and Lake ice thickness (LIT) have been recognized as two thematic products of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Both are sensitive indicators of the state of climate through their dependency on changes in air temperature and, particularly in the case of LIT, on-ice snow mass (depth and density). The monitoring of LIC, from which dates associated with ice phenology (i.e. freeze-up, break-up and ice duration) can be derived, and LIT is not only relevant for documenting climate change or to improve weather forecasts in lake-rich regions of the Northern Hemisphere (NH), but it is also critical for the operation of winter ice roads that serve northern communities for several months of the year as well as for cultural and leisure activities. Although ground-based observational networks have provided much of the evidence of long-term trends, variability and regime shifts in lake ice phenology to date, there has been a drastic erosion in their coverage in both space and time over the last three decades for many countries of the NH. There is, therefore, a requirement to advance the development of retrieval algorithms of LIC and LIT from multi-frequency (optical to microwave) satellite remote sensing observations to provide broad-area coverage needed for climate reporting and operational use (e.g. weather forecasting, monitoring the status of winter ice roads). There is also a critical need to develop algorithms for the retrieval of on-ice snow depth and snow mass which are two important variables for improving estimates of ice thickness from satellite remote sensing and lake models used in offline mode or as lake parameterization schemes in climate and numerical weather prediction models.
The project aims to (1) develop new algorithms for the retrieval of lake ice (cover, phenology, and thickness) and on-ice snow (snow depth and snow mass) properties from Earth Observation (EO) data (optical and microwave); (2) assess and improve the lake ice and overlaying snow properties from physically-based lake models used as lake parameterization schemes in numerical weather prediction and climate models as well as machine learning/deep learning and hybrid approaches; and (3) document and explain lake-wide hemispheric-scale response of ice cover to contemporary climate conditions using satellite-derived ice products.
