Grants and Contributions

Although these lakes are sensitive to climate change, Canada does not currently have a program to monitor these lakes, or the capacity to model future changes to these Arctic-Boreal lakes. This project will integrate a field monitoring program in Canada's Western Arctic, with AirSWOT and SWOT data, and a hydrologic lake model to improve our understanding of the current and future state of Arctic lakes.

Rapidly warming temperatures in the Arctic are bringing unprecedented changes to extreme events like snowstorms, melting polar ice, and wildfires, which profoundly impact the people and ecosystems of Canada's North. This project combines data from Canadian-supported satellite instruments and other observing networks, to improve computer models that are used to make forecasts of extreme weather and climate in the Arctic, and across the globe.

The overall objective of this project is to enhance our knowledge in the Earth system sciences, through improved analysis and modelling of combined SMAP and SMOS data along with RADARSAT Constellation Mission data.

Stratospheric aerosols—tiny particles suspended 10-30 km aloft—play an important role in atmospheric radiative transfer. Perturbations to the stratospheric aerosol layer, from volcanic eruptions or forest fires, can have a significant climate impact. While satellite observations provide invaluable information on stratospheric aerosols, their radiative forcing remains uncertain. We will use OSIRIS and MAESTRO measurements of aerosol extinction and size distribution to inform improvements to the Easy Volcanic Aerosol (EVA) simple aerosol model. EVA output will be used in the SASKTRAN radiative transfer model and the Canadian Earth System Model (CanESM) to improve understanding of stratospheric aerosol radiative forcing.

This project aims to develop a multi-variate data assimilation system to integrate the space-based soil moisture measurements from SMAP and SMOS and the terrestrial water storage observations from GRACE/GRACE-FO with hydrological modeling.

Developing and incorporating dynamic flood inundation parameterization in regional Earth system models, which are used in concert with global Earth system models to study climate and climate change across space and time. The proposed innovative development will be guided by space-based observations and cutting-edge machine learning approaches. The developed model will be applied to understand evolving inundation characteristics and their impacts on the local climate and region of influence, for different future greenhouse gas emission scenarios. The findings of this research will inform decision-making related to climate change adaptation and mitigation strategies across Canada

TREx Auroral Transport Model

Verify the model used to interpret TREx with readings from satellites. TREx is a network of auroral cameras and radio receivers spread across Canada designed to study space weather.

Auroral Prediction of Energetic Electron Flux

Using machine learning and ground-based observations, perform modeling of the “ring current” in our upper atmosphere.

Multi-instrument Studies of Ionospehric Structures at Extreme High Latitudes

Verify electron density measures taken by satellites, using an international network of radars called “SuperDARN”.

High Resolution ICEBEAR E-region Investigations

Calibrate the ICEBEAR radar, an innovative Canadian digital radar that can measure waves signals our atmosphere.