Relevant Degree Programs
Complete these steps before you reach out to a faculty member!
- Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
- Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Requirements" or on the program website.
- Identify specific faculty members who are conducting research in your specific area of interest.
- Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
- Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
- Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
- Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to peek someone’s interest.
- Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
- Be enthusiastic, but don’t overdo it.
G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.
Graduate Student Supervision
Doctoral Student Supervision (Jan 2008 - May 2019)
The exchange of energy between a glacier surface and its surroundings, known as its surface energy balance (SEB), is a primary control on surface ablation rates. In the modelling of glacier SEB, parameterisation rather than direct measurement is frequently used to estimate one or more of the contributing heat fluxes, with smaller fluxes often deemed negligible. The turbulent fluxes of sensible and latent heat are commonly parameterised using forms of the bulk aerodynamic method. These techniques were developed for flat, uniform surfaces, and substantial uncertainty remains in the validity of their application over sloped, inhomogeneous glacier terrain. A multi-year field campaign was performed on two glaciers in the Purcell Mountains of British Columbia, Canada, where season-long observations of the complete SEB were obtained at multiple locations. The obtained dataset was used to drive an ablation model which showed good agreement with observed rates at seasonal, daily, and sub-daily timescales, effectively closing the energy balance. Through eddy covariance measurements, the turbulent heat fluxes were observed to be important components of SEB at each location, providing 31% of seasonal melt energy, and up to 78% of melt energy on individual days, underlining the need for their accurate estimation. The rain heat flux, often assumed negligible, was a significant contributor to melt energy on daily and sub-daily timescales during heavy rainfall (up to 20% day⁻¹). An evaluation of common turbulent flux parameterisation methods found their performance to be highly sensitive to the choice of roughness length scheme and atmospheric stability function. Observed roughness length values differed from those commonly assumed for glacier surfaces, and varied substantially between locations, highlighting the need for site-specific values. Two techniques were developed for the remote estimation of roughness using digital elevation models, and performed well when compared with in situ observations. The occurrence of shallow, katabatic surface layers with low-level wind maximums was frequently observed over the sloped, glacial test sites. Existing stability parameters and functions used in turbulent flux parameterisation were found to be unreliable in these conditions, as was the commonly employed assumption of constant turbulent flux and friction velocity with height through this layer.
Master's Student Supervision (2010 - 2018)
All models of glacier melt, regardless of their complexity, must be forced by observed meteorologicalfields at or in the vicinity of the glacier in question. In the absence of these observations,the forcing is commonly derived from statistical or dynamical downscaling of low resolutionclimate reanalysis models. Here we focus on a dynamical downscaling via Weather Researchand Forecasting (WRF) model, which has previously showed promising results in simulating asurface energy balance (SEB) at several glacierized terrains. Our goal is to evaluate the WRFdownscaling approach at three mountain glaciers in the interior mountains of British Columbiawhere the automatic weather stations (AWSs) recorded data over several summer seasons. TheWRF model, nested within the ERA-Interim global reanalysis produced output fields at 7.5km and 2.5 km spatial resolution, as well as 1 km resolution for one of the sites. We analyzehow closely the WRF model output, at sub-daily and daily temporal resolution, resemblesthe observed meteorological fields and SEB fluxes needed to assess seasonal surface melt atthese glaciers. We find that the model at 2.5 km closely simulates the cumulative seasonalmelt (±10% difference) despite large biases in the individual components of the SEB model.Overestimation of the number of clear sky days explains the positive bias in the modeled netshortwave radiation. This positive bias, however, is compensated by a negative bias in the modelednet longwave radiation, and by an underestimation of sensible and latent heat fluxes. Theunderestimation in the latter two fluxes, calculated from the bulk aerodynamic method, is dueto underestimated near-surface wind speeds. Radiative fluxes, which are dominant drivers ofseasonal melting, are poorly downscaled with WRF, while successfully simulated by the ERAInterimat the course spatial resolution. Therefore, we advocate that SEB models be directlyforced with the output from global climate reanalysis. Finally, simulating turbulent heat fluxesat sloped glacier surfaces remains a major challenge, and the 1-km resolution state-of-the-artWRF model is not yet ready to tackle it.