The CanSISE Network has three Research Areas focussed on A) prediction,
B) climate change detection and attribution, and C) process studies related to snow and sea ice and their roles in climate. Each of these research areas has an associated series of collaborative projects whose outcomes are tied to the project's four Deliverables, which represent concrete targets for the Network to complete over its 5 year duration.

Please read on to learn more about our Research Areas and Deliverables.

• Area A: Snow and sea ice seasonal and climate prediction and projection 
  The CanSISE Network is working to advance Canada's capacity to forecast cold season climate and its evolution -- over a given season, over a given few years, and over a period of multiple decades. To support this effort, the Network is focussing on improving dynamical prediction of snow and sea ice coming from Canada's climate prediction services. Dynamical predictions are forecasts that start from the current conditions and move into the future. Dynamical predictions use models (numerical simulations) that encapsulate our best physical understanding of weather, snow, sea ice, and other processes. We focus on Canada's world leading CanSIPS dynamical prediction system, which was developed by Environment Canada scientists in Victoria and Dorval and is in operation at the Canadian Meteorological Centre. Although we have found that CanSIPS does a great job overall, we recognize that the system's start-up procedure and snow and sea ice models require updating. CanSISE Network scientists are improving CanSIPS by bringing in new observations of snow and sea ice to give the models better starting conditions for forecasts, and to test and optimize model representations of snow and sea ice processes. 
  
  Research Area A is especially relevant to Deliverables 1, 2, and 4 described below.
  
  
• Area B: Attributing change in snow and sea ice and modelling its impacts
  CanSISE scientists want to figure out how emissions of greenhouse gases and other human drivers have influenced Arctic and Canadian snow, sea ice, and temperature. While we often hear that global warming is causing the Arctic to change dramatically, there is still a lot of uncertainty about the size and fingerprint of human influence. This uncertainty comes from the large amount of spontaneous variability present in high latitude climates, from Arctic observations that have been relatively poorly sampled until recently, from limited understanding of how to best represent snow and sea ice processes in models, and from knowledge gaps on how greenhouse gases and other sources of pollution drive Arctic change. This uncertainty is compounded when we look for signs of human impacts on climate-influenced systems like the hydrological systems controlling Canadian water resources.
  
  The Network is taking advantage of recent progress in precisely quantifying these sources of uncertainty, using statistical methods rigorously developed for application to climate and climate impacts. Some of these techniques allow us to tease out the fingerprint of human influence on climate variables like temperature, sea ice and snow, and even figure out the odds that given extreme climate events have been caused by a given human influence. Other techniques allow us to bring our knowledge of global climate trends to the fine scale of river basins and catchments relevant for water resources and flood forecasting. The CanSISE Network is capitalizing on Canadian University, Environment Canada, and Pacific Climate Impacts Consortium expertise in these areas, with projects focusing on attribution of climate change in high latitude temperature and precipitation, snow cover and sea ice extent, and associated changes to hydrological systems (especially in the Western Cordillera).
  
  Research Area B is especially relevant to Deliverable 3 described below.
  
  
• Area C: Snow, sea ice, and related climate processes in observations and in Canadian GCMs
  Ultimately, the scientists of CanSISE are passionately curious about what drives high latitude variability and change --- this need to know motivates us all as climate and cryospheric scientists, whatever our specialization. But beyond our own scientific interests, we believe that a better scientific understanding will put Canada in a better position to be able to anticipate and adapt to future challenges brought about by Arctic and high latitude change. Without fundamental research our efforts to improve our prediction systems (Area A) and assess with increasing certainty whether humans have influenced Arctic climate (Area B) will suffer. With this in mind, Research Area C involves a set of connected projects on deepening our understanding of
  • Feedback processes that control the rate and seasonality of climatic responses to human influence,
  • Controls of sea ice drift and formation through wind variations,
  • Sea ice physics and sea-ice ocean connections, and how they might be captured in numerical models,
  • Connections between sea ice and snow evolution and global climate, and
  • Changes in sea ice in the Canadian Arctic Archipelago and in the distribution of snow on sea ice covered areas.

  Each of these projects feed directly into Area A (prediction) and B (attribution), and our improved understanding will give us more confidence in our assessments and predictions carried out in those research areas.

   

  Area C is particularly relevant for Deliverables 1 and 4.

• Deliverable 1: Assessment of S/SI Biases, Projections, and Predictions in the Canadian GCMs
  Slated for completion in Year 2 of the project, Deliverable 1 will provide Environment Canada and other CanSISE stakeholders such as the Pacific Climate Impacts Consortium and the Canadian Ice Service an assessment of how Canadian prediction systems (including CanSIPS and CanESM) fare overall in the area of snow and sea ice processes and predictions. Early results of Research Areas A and C will feed into a report that will be coauthored by several scientists in the Network. Some of the results for this report were provided at the Network's first workshop in Victoria.
  
  
• Deliverable 2: Assessment of Canadian snow and sea ice for the next decade
  Deliverable 2 is focussed on an assessment of the near-term future of Canadian and Northern Hemisphere wide snow and sea ice in state-of-the art climate predictions evaluated in the context of the best available observations. The assessment aims to discuss a wide range of climate simulations available from the Coupled Model Intercomparison Project, but will focus on the behaviour of the Canadian prediction systems. This Deliverable will involve the efforts of scientists working in Areas A and C.
  
  
• Deliverable 3: ACRE – Attribution of cryospheric events. 
  This Deliverable focusses on the urgent need to better understand extreme climate events in snow and sea ice that have happened in recent decades in the broader context of cryospheric change. Research related to attribution of cryospheric change and events taking place in Area B will be highlighted in focussed workshops, and the results and specialized statistical methods developed from this research will be shared with several groups at Environment Canada, the Pacific Climate Impacts Consortium, the Canadian Ice Service, and other stakeholders.
  
  
• Deliverable 4: Observations to improve snow and sea ice prediction. 
  This Deliverable is an outcome of Areas A and C and looks ahead to the future of snow and sea ice observational work in light of the scientific research of CanSISE. It will aim to identify key observed parameters that are required to improve our ability to simulate and predict snow, sea ice, and related climate variables. This Deliverable is motivated by the large cost of field and satellite observations, and the understanding that a rigorous cost-benefit analysis based on our best scientific understanding is required before new observational networks in the field and remote sensing instruments in space can be designed and deployed. This deliverable will contribute to recommendations for an optimal and cost-effective future Northern Hemipshere observation system to support climate monitoring and prediction.