Abstract. The Canadian Sea Ice and Snow
Evolution (CanSISE) Network is a climate research network focused on
developing and applying state-of-the-art observational data to advance
dynamical prediction, projections, and understanding of seasonal snow cover
and sea ice in Canada and the circumpolar Arctic. This study presents an
assessment from the CanSISE Network of the ability of the second-generation Canadian
Earth System Model (CanESM2) and the Canadian Seasonal to Interannual
Prediction System (CanSIPS) to simulate and predict snow and sea ice from
seasonal to multi-decadal timescales, with a focus on the Canadian sector. To
account for observational uncertainty, model structural uncertainty, and
internal climate variability, the analysis uses multi-source observations,
multiple Earth system models (ESMs) in Phase 5 of the Coupled Model
Intercomparison Project (CMIP5), and large initial-condition ensembles of
CanESM2 and other models. It is found that the ability of the CanESM2
simulation to capture snow-related climate parameters, such as cold-region
surface temperature and precipitation, lies within the range of currently
available international models. Accounting for the considerable disagreement
among satellite-era observational datasets on the distribution of snow water
equivalent, CanESM2 has too much springtime snow mass over Canada,
reflecting a broader northern hemispheric positive bias. Biases in seasonal
snow cover extent are generally less pronounced. CanESM2 also exhibits
retreat of springtime snow generally greater than observational estimates,
after accounting for observational uncertainty and internal variability. Sea
ice is biased low in the Canadian Arctic, which makes it difficult to assess
the realism of long-term sea ice trends there. The strengths and weaknesses
of the modelling system need to be understood as a practical tradeoff: the
Canadian models are relatively inexpensive computationally because of their
moderate resolution, thus enabling their use in operational seasonal
prediction and for generating large ensembles of multidecadal simulations.
Improvements in climate-prediction systems like CanSIPS rely not just on
simulation quality but also on using novel observational constraints and the
ready transfer of research to an operational setting. Improvements in
seasonal forecasting practice arising from recent research include accurate
initialization of snow and frozen soil, accounting for observational
uncertainty in forecast verification, and sea ice thickness initialization
using statistical predictors available in real time.
Review of “Characterizing spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: The clutha Catchment, New Zealand”