Research Projects

Theme A : Specific weather and climate phenomena permitted by high-resolution

Extreme precipitation events represent a significant economic and societal threat to the well being of Canadians. Specific examples include the 1998 freezing-rain storm in the St. Lawrence River Valley (SLRV) that imposed more than $3 billion in economic losses. Warm-season extreme rainfalls, particularly those associated with the transition of hurricanes to extratropical cyclones, can also inflict large impacts on the citizens of eastern Canada. For example, the extratropical transitions of Hurricanes Katrina (2005), Rita (2005), and Ike (2008), each contributed more than half of the...

The many types of precipitation that occur in Canada during winter storms may lead to major disruptions to the society by affecting power networks, the aviation industry and ground transportation. Winter precipitation commonly observed in Eastern Canada can be in the form of rain, freezing rain, ice pellets and wet snow. The prediction of the type of winter precipitation is challenging when it is formed at temperatures near freezing, as their formation and evolution imply phase changes that impact the dynamics and thermodynamics of storms.

Projected changes in weather storminess over Canada are expected to contribute to changes in weather extremes and hydro-meteorological hazards, through an interrelation of large-scale and regional-scale physical processes. The main objective of this project is to improve our understanding of the regional features of storm changes and the links between storm activities, including blocking events and weather extremes, and assessment of the effects of storminess changes on various timescales, from seasonal to decadal scales, for several regions across Canada.

The working assumption underlying regional climate modelling is that RCMs’ fine mesh permits the development of fine-scale structures that are required for an accurate description of local climates (e.g. Laprise et al., 2008). To this day however the quantification of the added value of RCMs remains a central issue. Frequency distribution analysis has confirmed the benefits of fine-mesh simulations in better reproducing the extreme values of key weather elements such as precipitation intensity (Mladjic et al., 2011).

Climate models are subject to structural uncertainty resulting from poorly constrained parameters of their parameterized physical processes. These parameters are usually tuned using expert judgment in a way that more often than not lacks planning, an established method and a clear aim. Recently, a promising objective optimization method has been developed and successfully tested on a European regional domain with the COSMO-CLM model (Bellprat et al., 2012a and b). The method has also been recently adapted at Ouranos within the same model for the North American continent.

Theme B : Statistical extremes allowed by fine mesh and land-atmosphere feedbacks

The ability of GCMs participating in CMIP5 (Coupled Model Intercomparison Project Phase 5) to simulate climate extremes has recently been evaluated by Sillmann et al. (2012a, b). The CMIP5 models were found to simulate temperature extremes about as well as previous CMIP3 models, but demonstrate improvement for precipitation extremes. Further, the study of Murdock et al. (2012), using reanalysis-driven RCMs participating in NARCCAP, has revealed smaller biases in temperature extremes in the Columbia River Basin.

Will there be more floods/droughts in future climate? What is the role of soil moisture-atmosphere coupling in modulating the extreme temperature/precipitation extremes over Canada? Climate change induced changes in the frequency and magnitude of floods and droughts could be detrimental to Canadians and therefore the study of climate change impacts on floods and droughts is important to ensure safety of Canadians, as well as for better management of freshwater resources and planning of appropriate adaptation strategies. Floods and droughts are multivariate events.

Output comparisons between different model versions are ubiquitous during climate model development, model tuning and while performing numerical experiments. As such, statistical tests of significance are fundamental tools to help us judge the merits of a given model modification. Despite that RCM studies are becoming a mature branch of climate modelling, surprisingly little has been done to define the most appropriate battery of these tests.

Theme C : Land-surface processes enhanced by improved representation of surface heterogeneity

A new atmospheric boundary-layer (ABL) scheme based on an equilibrium turbulent kinetic energy (TKE) formulation has been developed and tested in the single column model (SCM) employing the CCCma 4th-generation atmospheric physics package which is the basis for CCCma's regional (CanRCM4) and global (CanAM4) climate models. This scheme has been coupled to a stochastic parameterization of the effects of intermittent mixing in the stable ABL.

To better resolve the changing snowmelt and glacier hydrology of western North America, improved representations of surface heterogeneity, snowpack processes and snow-albedo feedback are needed in the current generation of RCMs. Multiple factors control mountain snowpack accumulation and ablation, including elevation, slope, aspect, wind, and vegetation. Snow melt is also affected by the evolution of the snowpack through the melt season, e.g.

Glacier retreat is ubiquitous in the world’s mountain and polar-regions, reshaping the landscape, impacting regional hydrology (e.g., Huss, 2011), and contributing to global sea level rise (Radic and Hock, 2011). Areal changes in glacier extent can be measured by satellite and are well documented (e.g., Paul et al., 2004; Bolch et al., 2010), but mass balance and volume changes need to be measured in the field and modelled through glaciological and regional climate models (RCMs).

When vegetation is modelled as a dynamic component of the climate system, then the structural attributes of vegetation (leaf area index, vegetation height and rooting depth) are able to respond to changes in climate and this in turn also affects the climate. Studies have shown that these bi-directional interactions, between the vegetation and climate, increase the variability of climate (Crucifix et al., 2005; Wang et al., 2011). In this regard, vegetation influences the extremes of climate. The Canadian Terrestrial Ecosystem Model (CTEM) has recently been implemented in CRCM5.

Lakes are important components of the climate system and can affect regional climate by modulating surface albedo, surface energy and moisture budgets. The earlier versions of CRCM did not have lakes, except for the Great Lakes that were modelled using a mixed-layer lake model with thermodynamic ice treatment. Based on the offline analysis of available lake models (Martynov, 2010), it was decided to retain two lake models, the Hostetler model (Hostetler, 1993) and the Flake model (Mirinov, 2007), which were implemented in CRCM5.

One of the most important features of the Canadian high-latitudes is permafrost. Till recently, climate models, both global and regional, were not capable of simulating near-surface permafrost. This was mostly due to the limitation that GCMs and RCMs employ land-surface schemes that vary in depth between 3 and 10 meters.

The increased recognition of the importance of land-climate interactions and feedbacks in modulating regional climate highlights the need for realistic representation of land-surface types and processes in climate models. The current versions of Canadian RCMs use adavanced state-of-the-art land-surface scheme CLASS. It was shown in some recent studies (e.g. Koster et al., 2004; Seneviratne et al., 2006) that in some areas and under some conditions, the state of the land surface systematically affects the atmospheric variability, particularly temperature and rainfall. Koster et al.

CORE Group

The CORE group is responsible for the following tasks:

  1. Liaison between co-investigators at the various participating institutions (UQAM, EC-CCCma, EC-CPS (Climate Processes Section), EC-RPN, EC-CDAS/AIRS (Climate Data Analysis Section/Adaptation and Impacts Research Section), Ouranos, PCIC);
  2. Integrating individual project results (knowledge and software) and transfer of technology (software and data) between government labs and UQAM;
  3. Performing selected long climate simulations, archival and distribution of results;
  4. Providing technical support to...