Member Research Profile - Chad Thackeray, PhD Student

Please briefly describe your position within the CanSISE Network.

I am a PhD student at the University of Waterloo, working under the supervision of Chris Fletcher (Professor, University of Waterloo) and Chris Derksen (MRD, Environment Canada) who are both funded CanSISE investigators.

What are your primary research goals?

The primary goals of my research are to investigate and quantify how well current global climate models represent snow cover, albedo, and snow albedo feedback (SAF). 

Can you tell us a bit more about SAF and the role it plays in climate models? 

SAF is a climate feedback process that amplifies warming through the melting of snow cover, which reveals a much darker (less reflective) surface. This increases the solar radiation that is absorbed by the ground surface. There is a large spread in how well various climate models represent this process, contributing to inter-model variation in global climate sensitivity (how much warming will occur in response to a certain amount of radiative forcing) with consequences for regional climate change.

Please explain in lay terms how you investigate this?  

We can calculate SAF “strength” by looking at how surface temperature, snow cover and surface reflectivity (albedo) change from one month to the next during the snow melt period (March-April-May-June). We use a number of observational estimates of these properties derived from satellites to evaluate the accuracy of the models.

Tell us about something new and exciting that your research has led to.

We recently published results showing that model performance related to simulating the seasonality of snow and albedo is poorest over the boreal forest (, where the way in which a model represents snow in forest canopies (the vegetation layer) can have a large impact on SAF accuracy ( This is a particularly complex environment because these forests retain their canopy vegetation year-round meaning they mask the reflective underlying surface. 

Old Jack Pine forest site, January 1997 (Source:
Old Jack Pine forest site, January 1997 (Source:

What kinds of implications does this have for understanding climate change?

The importance of SAF is best demonstrated by prior research showing that variability in SAF in climate models accounts for 40-50% of the spread in projections of 21st century warming over Northern Hemisphere land. Our work attempts to diagnose the physical processes that are leading to a large amount of model uncertainty so that improvements can be made. Better representation of snow and albedo in the models could help with improving projections of climate warming over this region.


How does your research fit into the broader scope of the CanSISE Network and how will your research contribute to improving the Canadian global climate model?

My research on snow albedo feedback (SAF) is a part of Research Area C (Snow and sea ice processes and climate interactions). This research will help determine how the Canadian climate model (CanESM2) performs in comparison with other global climate models when it comes to the simulation of snow and albedo, highlighting areas where development may be needed.


What are your next steps? 

My next steps include a number of novel climate model simulations where we hope to attribute the impact of current model biases related to snow and albedo on climate (i.e., temperature, atmospheric circulation).


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Recent Publications

Curry, C. L., and F. W. Zwiers, 2018: Examining controls on peak annual streamflow and floods in the Fraser River Basin of British Columbia. Hydrol. Earth Syst. Sci., 22, 2285–2309, doi:10.5194/hess-22-2285-2018.

Hay, S., P. J. Kushner, R. Blackport, and K. E. McCusker, 2018: On the Relative Robustness of the Climate Response to High-Latitude and Low-Latitude Warming. Geophysical Research Letters, 45, 6232–6241, doi:10.1029/2018GL077294.

Kushner, P. J., and Coauthors, 2018: Canadian snow and sea ice: assessment of snow, sea ice, and  related climate processes in Canada’s Earth system model and climate-prediction system. The Cryosphere, 12, 1137–1156, doi:10.5194/tc-12-1137-2018.

Mudryk, L. R., and Coauthors, 2018: Canadian snow and sea ice: historical trends and projections. The Cryosphere, 12, 1157–1176, doi:10.5194/tc-12-1157-2018.

Oudar, T., P. Kushner, J. C. Fyfe, and M. Sigmond, 2018: No impact of anthropogenic aerosols on early 21st century global temperature trends in a large initial-condition ensemble. Accepted. Geophysical Research Letters.

Tandon Neil F., Kushner Paul J., Docquier David, Wettstein Justin J., and Li Camille, 2018: Reassessing Sea Ice Drift and Its Relationship to Long-Term Arctic Sea Ice Loss in Coupled Climate Models. Journal of Geophysical Research: Oceans, 0, doi:10.1029/2017JC013697.