Pseudo Global Warming

As the climate in Western Canada continues to warm throughout the 21st century, many communities and industries will face increasing water threats. In order to make informed decisions on future water security, it is necessary to have a detailed understanding of future climate changes. Dr. Yanping Li and colleagues at the Global Institute for Water Security (University of Saskatchewan) are using a new modelling approach, called the Pseudo Global Warming (PGW) method, to provide high resolution (4 km grid spacing) future climate conditions for the Mackenzie and Saskatchewan River Basins. 

Information Brochure

A summary of the future changes in temperature and precipitation (2001-2015 vs. 2086-2100) including a brief description of the PGW method can be found in our information brochure.

Additional Information

About the PGW Method

The PGW method is a new modelling approach that provides high resolution (up to 4 km grid) future climate conditions without the need for an ensemble of simulations. This information can give insight into future hydrological, meteorological and atmospheric conditions at a local scale where the precipitation is influenced by topography and convection. 

The PGW method adds a climate perturbation signal to a present-day conditions during the future period of interest. The initial and boundary conditions are generated by coupling 6-hourly reanalysis data (ERA-Interim reanalysis) and climatological monthly mean perturbations of global warming  that are extracted from Atmosphere-Ocean coupled General Circulation Models (AOGCM). The result is the forcing data for the future climate, called the PGW condition. It can then be dynamically downscaled by using the Weather Research and Forecasting (WRF) regional weather and climate model. The downscaled PGW condition is then compared to the historical condition (also downscaled) developed from ERA-Interim reanalysis data to determine the projected change in future climate.

This process can be repeated using various future greenhouse gas concentrations trajectories (RCP2.6, RCP4.5, RCP6.0, RCP8.5) to come up with a range of possible future climatic scenarios. 

Advantages of using the PGW Method?

Future climate predictions are commonly estimated by the General Circulation Model (GCM) which has a low horizontal resolution of 0.75 to 1 degree (>100 km). Due to the low resolution in current GCM runs, the impact of the complex terrain are poorly represented and can’t be used to estimate climate change in a small region. In order to downscale GCM runs, a common method includes applying a bias correction followed by use of statistical downscaling methods to distribute the precipitation according to present-day observations. This method introduces potentially large biases and lacks the representation of topographic and convective precipitation.

The PGW approach uses a dynamical downscaling technique to obtain future climate conditions at higher resolutions (up to 4 km) and is considered more sophisticated than other approaches to represent the atmosphere and land surface change, both dynamically and physically. Large scale model biases are reduced by using the PGW approach and can be used to compare the current and future climate without an ensemble of simulations. 


How CCRN is using PGW data

Researchers are currently using the PGW method to explore atmospheric, meteorological and hydrological aspects of our future climate. Some examples include:

  • Investigate the future impacts of global warming on the hydrology and atmosphere in the Saskatchewan and Mackenzie River Basins.
  • Improve the understanding of hydrological processes and snowpack modelling across Western Canada.
  • Modelling various atmospheric processes responsible for severe weather, convection, and freezing rain under future climate scenarios.
  • Investigate historical extreme events (i.e. 2013 June flood in Alberta) under future climate scenarios.

Possible Output Variables

There are numerous output variables that are produced by the WRF model simulations in the PGW method. The following table summarizes the possible variables categorized into three classes:

  • Class A: 1 hour timescale, 2-dimensional
  • Class B: 3 hour timescale, 2-dimensional
  • Class C: 3 hour timescale, 3-dimensional


Class A Variables Class B Variables Class C Variables
  • Surface Temperature (2 m)
  • Wind Speed - East-West Direction (10 m)
  • Wind Speed - North-South Direction (10 m)
  • Surface Pressure
  • Specific Humidity
  • Snow Water Equivalent
  • Surface Skin Temperature
  • Upward Heat Flux
  • Surface Moisture Flux
  • Latent Heat Flux
  • Albedo
  • Accumulated Precipitation (grid scale)
  • Incoming/Outgoing Longwave/Shortwave radiation fluxes
  • Surface Temperature
  • Wind Speed - East-West Direction (10 m)
  • Wind Speed - North-South Direction (10 m)
  • Soil Moisture
  • Soil Temperature
  • Surface Skin Temperature
  • Potential Temperature
  • Base State Lapse Rate
  • Incoming/Outgoing Longwave/Shortwave radiation fluxes
  • Accumulated Precipitation (grid scale)
  • Snow Water Equivalent
  • Fraction of Frozen Precipitation
  • Vegetation Fraction 
  • Water Vapour (all phases - snow, rain, ice, grauple)
  • Friction Velocity
  • Surface Runoff
  • Subsurface Runoff
  • Temperature
  • Wind Speed (U,V,W)
  • Cloud Fraction
  • Total Pressure
  • Cloud Water Mixing Ratio
  • Water Vapour (all phases - snow, rain, ice, grauple)
  • Geopotential Height


YouTube Video

Yanping Li and her research team work with atmospheric scientists in locations like the National Centre for Atmospheric Research centre in Boulder, Colorado to understand how climate change is impacting weather systems.