By Reinel Sospedra-Alfonso
Snow is the component of the cryosphere having the largest seasonal variation. A fundamental quantity describing the snow cover is Snow Water Equivalent (SWE = depth of water that results from the melting of a column of snow). To produce useful seasonal forecasts of SWE, a forecasting system must initialize this variable somewhat realistically, SWE itself must have potential predictability, and the forecasting system must be able to capitalize on this potential predictability to yield predictive skill.
We first assess the ability of CanSIPS (see description of CanSIPS below) to initialize SWE realistically, using gridded observational data products for comparison. We examine whether CanSIPS accurately reproduces the observed seasonal cycle of SWE and its interannual variations. We then study the temporal and geographical dependence of the potential predictability of SWE in the forecast runs.
CanSIPS is Environment Canada's operational seasonal prediction system, based on two versions of CCCma's coupled climate model, CanCM3 and CanCM4. Each model version produces 10 ensemble members, which are initialized by separate assimilation runs. Each such run assimilates the same observational data but originates from different initial conditions giving rise to ensemble spread. These assimilation run states provide forecast initial conditions for the atmosphere, land surface and sea ice.
Potential predictability provides predictability in a world where the statistical characteristics of the observations equal the statistical characteristics of the model forecast runs. In the figure above, potential predictability of SWE from the Canadian Coupled Climate Model version 4 (CanCM4) forecast runs at days (from top left to right) 1, 8, 15 and 31. Forecast initial time is February 1st.
Reinel is a CanSISE supported Post-Doctoral Fellow who works with William Merryfield at CCCma in Victoria, BC.