Alain Royer’s research while affiliated with Université de Sherbrooke and other places

Northern regions are warming faster than the rest of the globe. It is difficult to predict ecosystem responses to warming because the thermal sensitivity of their biophysical components varies. Here, we present an analysis of the authors’ expert judgment regarding the sensitivity of six ecosystem components – permafrost, peatlands, lakes, snowpack, vegetation, and endothermic vertebrates – across northern landscapes ranging from boreal to polar biomes. We identified 28 discontinuous component states across a 3700 km latitudinal gradient in northeastern North America and quantified sensitivity as the transition time from an initial to a contrasting state following a theoretical step change increase in mean annual air temperature of 5 °C. We infer that multiple interconnected state shifts are likely to occur within a narrow subarctic latitudinal band at timescales of 10 to more than 100 years, and response times decrease with latitude. Response times differ between components and across latitudes, which is likely to impair the integrity of ecosystems.

Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada, during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally dependent emission and attenuation of radiation by the atmosphere. This is a major developmental step needed for top-of-atmosphere simulations of microwave brightness temperature at atmosphere-sensitive frequencies with SMRT. Nadir-simulated brightness temperatures at 89, 118, 157, 183 and 243 GHz were compared with airborne measurements and with ground-based measurements at 89 GHz. Inclusion of anisotropic atmospheric radiance in SMRT had the greatest impact on brightness temperature simulations at 183 GHz and the least impact at 89 GHz. Medians of simulations compared well with medians of observations, with a root mean squared difference of 14 K across five frequencies and two flights (n=10). However, snow pit measurements did not capture the observed variability fully as simulations and airborne observations formed statistically different distributions. Topographical differences in simulated brightness temperature between sloped, valley and plateau areas diminished with increasing frequency as the penetration depth within the snow decreased and less emission from the underlying ground contributed to the airborne observations. Observed brightness temperature differences between flights were attributed to the deposition of a thin layer of very-low-density snow. This illustrates the need to account for both temporal and spatial variabilities in surface snow microstructure at these frequencies. Sensitivity to snow properties and the ability to reflect changes in observed brightness temperature across the frequency range for different landscapes, as demonstrated by SMRT, are necessary conditions for inclusion of atmospheric measurements at surface-sensitive frequencies in numerical weather prediction.

Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m$^3$3 m$^{-3}$−3) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the $\tau -\omega$τ−ω model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m$^3$3 m$^{-3}$−3 and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.

The Arctic snowpack, characterized mainly by a dense wind slab (WS) layer overlaying less dense and porous depth hoar (DH), generates large uncertainties in microwave radiative transfer models used to interpret satellite observations. In this work, we tested two improvements recently implemented in the Snow Microwave Radiative Transfer (SMRT) model. First, an improvement of the snow microstructure parametrizations introduces a polydispersity geometrical parameter (K) related to the grain shape and microstructural arrangement. Second, the new electromagnetic model based on the strong contrast expansion (SCE) allows a continuous formulation of the scattering coefficient as a function of the density between low-density snow and hard and icy snow. The SCE model was compared with in situ observations to the commonly used Improve Born Approximation (IBA) electromagnetic model. Results show improved brightness temperature simulations at 19, 37 and 89 GHz compared to surface-based and satellite microwave radiometric measurements using polydispersity values ( K_WS = 0.80 and K_DH = 1.33) for scaling the measured optical grain size of the different snow layers with IBA. The finding is that the polydispersity values found in this study are of general applicability for Polar layered snowpack. The SCE model yields results similar to IBA for snow densities up to 500 kg m ^-3 but no analysis was investigated for the range of 500 - 700 kg m-3 (firn). These improvements in snow microstructure and the new SCE radiative transfer model allow better simulations of Polar snow and therefore better Polar snowpack monitoring by satellite.

Northern ecosystems are among the most exposed to warming and their responses are difficult to anticipate due to the variable sensitivity of their biophysical components. Using an analysis based on expert assessment, we investigated heterogeneity in the sensitivity to climate-driven state shifts across the vast northern landscape, from the boreal to the polar biomes. Over a 3,700 km latitudinal gradient in northeastern North America, we identified 28 discontinuous states for six ecosystem components: permafrost, peatlands, lakes, snowpack, vegetation, and endothermic vertebrates. Sensitivities were quantified by the estimated time required to shift from an initial to a contrasting state in response to a 5°C step increase in mean annual air temperature. The inferred scenario reveals that multiple interconnected state shifts are likely to occur within a narrow subarctic latitudinal band at timescales of 10 to >100 years. However, response times decrease with latitude, with freshwater systems at high latitudes displaying heightened susceptibility to rapid state shifts (timescales of 1 to 10 years). The lack of coherence in response times between components and across latitudes will likely impair the integrity of northern ecosystems and generate heterogeneous range shifts, resulting in the reconfiguration of landscapes and ecosystems.

Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) Model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally-dependent emission and attenuation of radiation by the atmosphere. This is a major developmental step needed for top-of-atmosphere simulations of microwave brightness temperature at atmosphere-sensitive frequencies with SMRT. Nadir simulated brightness temperatures at 89, 118, 157, 183 and 243 GHz were compared with airborne measurements and with ground-based measurements at 89 GHz. Inclusion of an anisotropic atmosphere in SMRT had the greatest impact on brightness temperature simulations at 183 GHz and the least at 89 GHz. Simulations compared well with observations, with a root mean squared error of 14 K, although snowpit measurements did not capture the observed variability fully as simulations and airborne observations formed statistically different distributions. Topographical differences in simulated brightness temperature between sloped, valley and plateau areas diminished with increasing frequency as the penetration depth within the snow decreased and less emission from the underlying ground contributed to the airborne observations. Observed brightness temperature differences between flights were attributed to the deposition of a thin layer of very low density snow. This illustrates the need to account for both temporal and spatial variability in surface snow microstructure at these frequencies. Sensitivity to snow properties and the ability to reflect changes in observed brightness temperature across the frequency range for different landscapes, as demonstrated by SMRT, is a necessary condition for inclusion of atmospheric measurements at surface-sensitive frequencies in numerical weather prediction.

There is significant spatial variability in snow cover characteristics across the Arctic. Current physical or empirical approaches to simulate or measure snow state variables suffer from poor spatial and/or temporal resolutions. Our current understanding of the spatio-temporal variability in Arctic snow cover leads to uncertainties in existing snow property retrievals from space or in models, thus leading to a poor representation of the snow cover in various climate models and reanalysis products. In this paper, we developed a method to derive total snow depth from a ground-based radar as well as distinguishing the two main layers generally observed in Arctic snowpacks: Depth Hoar and Wind Slab. This algorithm was developed for a 24 GHz Frequency Modulated Continuous Wave radar. The novelty of our approach resides in the fact that no validation snow pits are required if previous data on snow conditions (snow depth and density) are known. The impact of the underlying ecotypes on the radar-derived snow stratigraphy was also investigated. The RMSE of the snow depth ranges from 3.5 to 25 cm, but mostly varies between 10 and 15 cm (< 25%). The RMSE is based on snow pit data that has a corresponding radar measurement.

Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability is one of the primary constraints in model development. In terms of spatial resolution, active microwaves (synthetic aperture radar – SAR) can address the issue and outperform methods based on passive microwaves. Thus, high-spatial-resolution monitoring of snow depth (SD) would allow for better parameterization of local processes that drive the spatial variability of snow. The overall objective of this study is to evaluate the potential of the TerraSAR-X (TSX) SAR sensor and the wave co-polar phase difference (CPD) method for characterizing snow cover at high spatial resolution. Consequently, we first (1) investigate SD and depth hoar fraction (DHF) variability between different vegetation classes in the Ice Creek catchment (Qikiqtaruk/Herschel Island, Yukon, Canada) using in situ measurements collected over the course of a field campaign in 2019; (2) evaluate linkages between snow characteristics and CPD distribution over the 2019 dataset; and (3) determine CPD seasonality considering meteorological data over the 2015–2019 period. SD could be extracted using the CPD when certain conditions are met. A high incidence angle (>30∘) with a high topographic wetness index (TWI) (>7.0) showed correlation between SD and CPD (R2 up to 0.72). Further, future work should address a threshold of sensitivity to TWI and incidence angle to map snow depth in such environments and assess the potential of using interpolation tools to fill in gaps in SD information on drier vegetation types.

Satellite observations of snow-covered regions in the microwave range have the potential to retrieve essential climate variables such as snow height. This requires a precise understanding of how microwave scattering is linked to snow microstructural properties (density, grain size, grain shape and arrangement). This link has so far relied on empirical adjustments of the theories, precluding the development of robust retrieval algorithms. Here we solve this problem by introducing a new microstructural parameter able to consistently predict scattering. This “microwave grain size” is demonstrated to be proportional to the measurable optical grain size and to a new factor describing the chord length dispersion in the microstructure, a geometrical property known as polydispersity. By assuming that the polydispersity depends on the snow grain type only, we retrieve its value for rounded and faceted grains by optimization of microwave satellite observations in 18 Antarctic sites, and for depth hoar in 86 Canadian sites using ground-based observations. The value for the convex grains (0.6) compares favorably to the polydispersity calculated from 3D micro-computed tomography images for alpine grains, while values for depth hoar show wider variations (1.2–1.9) and are larger in Canada than in the Alps. Nevertheless, using one value for each grain type, the microwave observations in Antarctica and in Canada can be simulated from in-situ measurements with good accuracy with a fully physical model. These findings improve snow scattering modeling, enabling future more accurate uses of satellite observations in snow hydrological and meteorological applications.