CLIMoGrid is a gridded thermodynamic lake ice model used to simulate ice phenology and thickness on lakes. This model was developed by reworking the original one-dimensional CLIMo model to accept inputs from spatial data. The original model utilized data from weather stations as inputs hence providing one result over an entire lake. Weather station data are point source and hence records do not fully explore individual lakes, their respective local climatic conditions and the different factors that influence them. To account for these conditions, the current one-dimensional model is rebuilt to a gridded model which utilizes satellite data to provide lake ice phenology and thickness.
To achieve the multi-grid simulation, climate data from satellite sources including European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and Terra/Aqua-MODIS served as the major inputs for the model. Data including wind speed (m s-1), mean air temperature(°C), relative humidity (%), snow depth (m) and cloud cover, compiled since 2003 was extracted from these sources which served as model inputs. With this data, ice phenology and thickness is generated for each satellite pixel representing 1-km2 over the surface of the lake.
By allowing this input of remote sensing and modelling spatial data, the dimensionality over lakes is greatly increased from that of a single dimension, which provides a higher resolution and spatial variation over the lake.
CLIMoGrid_v.01 has been tested for two of the largest Northern Canadian Lakes, the Great Slave Lake and Great Bear Lake. Simulations for these lakes have been created for the period of 2003 to 2020 showing ice thickness, freeze-up and break-up dates. The spatial and temporal variability of thickness on these lakes is clearly highlighted with this model. Our scope from this model is to produce a spatial grid of lake ice thickness allowing us to establish scaling relationships in the data that are important for ice thickness.