Climate Change Canada Research Articles

Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

Soil moisture is a key variable in Earth systems, controlling the exchange of water and energy between land and atmosphere. Thus, understanding its spatiotemporal distribution and variability is important. Environment and Climate Change Canada (ECCC) has developed a new land surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS land surface scheme features sophisticated parameterizations of hydrological processes, including water transport through the soil. It has been shown to provide more accurate simulations of the temporal and spatial distribution of soil moisture compared to the current operational land surface scheme. Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, we simulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme over an experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps were produced between May and November 2015. Simulated soil moisture values were compared with estimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture and Agri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic Aperture Radar (SAR) imagery. Statistical analysis of the results showed an overall promising performance of the SVS land surface scheme in simulating soil moisture values at high resolution scale. Investigation of the SVS output was conducted both independently of the soil texture, and as a function of the soil texture. The SVS model tends to perform slightly better over coarser textured soils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulated SVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. The Root Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113 over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. The unbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore, results show an Index of Agreement (IA) between the simulated and the derived soil moisture always higher than 0.90.

Satellite-derived emissions of carbon monoxide, ammonia, and nitrogen dioxide from the 2016 Horse River wildfire in the Fort McMurray area

Abstract. In May 2016, the Horse River wildfire led to the evacuation of ∼ 88 000 people from Fort McMurray and surrounding areas and consumed ∼ 590 000 ha of land in Northern Alberta and Saskatchewan. Within the plume, satellite instruments measured elevated values of CO, NH3, and NO2. CO was measured by two Infrared Atmospheric Sounding Interferometers (IASI-A and IASI-B), NH3 by IASI-A, IASI-B, and the Cross-track Infrared Sounder (CrIS), and NO2 by the Ozone Monitoring Instrument (OMI). Daily emission rates were calculated from the satellite measurements using fire hotspot information from the Moderate Resolution Imaging Spectroradiometer (MODIS) and wind information from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, combined with assumptions on lifetimes and the altitude range of the plume. Sensitivity tests were performed and it was found that uncertainties of emission estimates are more sensitive to the plume shape for CO and to the lifetime for NH3 and NOx. The satellite-derived emission rates were ∼ 50–300 kt d−1 for CO, ∼ 1–7 kt d−1 for NH3, and ∼ 0.5–2 kt d−1 for NOx (expressed as NO) during the most active fire periods. The daily satellite-derived emission estimates were found to correlate fairly well (R∼0.4–0.7) with daily output from the ECMWF Global Fire Assimilation System (GFAS) and the Environment and Climate Change Canada (ECCC) FireWork models, with agreement within a factor of 2 for most comparisons. Emission ratios of NH3∕CO, NOx∕CO, and NOx∕NH3 were calculated and compared against enhancement ratios of surface concentrations measured at permanent surface air monitoring stations and by the Alberta Environment and Parks Mobile Air Monitoring Laboratory (MAML). For NH3∕CO, the satellite emission ratios of ∼ 0.02 are within a factor of 2 of the model emission ratios and surface enhancement ratios. For NOx∕CO satellite-measured emission ratios of ∼0.01 are lower than the modelled emission ratios of 0.033 for GFAS and 0.014 for FireWork, but are larger than the surface enhancement ratios of ∼0.003, which may have been affected by the short lifetime of NOx. Total emissions from the Horse River fire for May 2016 were calculated and compared against total annual anthropogenic emissions for the province of Alberta in 2016 from the ECCC Air Pollutant Emissions Inventory (APEI). Satellite-measured emissions of CO are ∼1500 kt for the Horse River fire and exceed the total annual Alberta anthropogenic CO emissions of 992.6 kt for 2016. The satellite-measured emissions during the Horse River fire of ∼30 kt of NH3 and ∼7 kt of NOx (expressed as NO) are approximately 20 % and 1 % of the magnitude of total annual Alberta anthropogenic emissions, respectively.

Indigenous involvement in the Canadian species at risk recovery process

Many countries, including Canada, are beginning to recognize the important roles of Indigenous Peoples in threatened species recovery. To determine whether official recognition is translating into actual involvement of Indigenous Peoples in species recovery planning in Canada, we examined recovery documents for species under the Canadian Species at Risk Act (SARA). We scored each document for the level of involvement with Indigenous Peoples on a scale of 0–5. We analyzed the data using permutation-based ANOVAs and post hoc pairwise permutation tests to determine the impact of region, taxonomic group, and responsible agency on scores. Fifty two percent of documents suggested no Indigenous involvement, despite a legal requirement to consult Indigenous Peoples to the extent possible. Documents for species in central Canada and Quebec indicated significantly lower involvement than in other regions. Documents for taxonomic categories such as mosses indicated lower involvement than those containing iconic and economically valuable taxa, such as fish and mammals. Documents coordinated by Environment and Climate Change Canada indicated significantly lower involvement than those coordinated by Parks Canada or Fisheries and Oceans Canada. These regional and taxonomic discrepancies, along with differences among agencies suggest short-term priorities for improving Indigenous Peoples’ involvement in species at risk recovery planning in Canada.

Estimation of ground‐based GNSS Zenith Total Delay temporal observation error correlations using data from the NOAA and E‐GVAP networks

In preparation for possible higher frequency assimilation of ground‐based Global Navigation Satellite System (GB‐GNSS) Zenith Total Delay (ZTD) observations at Environment and Climate Change Canada (ECCC), two popular diagnostic methods are applied to observation‐minus‐background and observation‐minus‐analysis departures from the ECCC Global Deterministic Prediction System (GDPS) to estimate temporal ZTD observation error correlations within the 6 h assimilation window. The GDPS uses a four‐dimensional ensemble‐variational (4D‐EnVar) data assimilation system with a background error covariance matrix (B‐matrix) obtained from an equal blend of 3D static and 4D flow‐dependent (ensemble‐based) background error covariances. The two diagnostic methods, the Desroziers method and the Hollingsworth–Lönnberg method, are applied using ZTD observations from the North American NOAA network and the European EIG EUMETNET GNSS Water Vapour Programme (E‐GVAP) network with similar results obtained for both networks. Correlations estimated with the Desroziers method drop off rapidly with time difference becoming significantly negative for observations separated by more than 2–3 h. The results are unexpected as there is no obvious physical basis for negative correlations. It is shown how suboptimal specification of the background error covariances in the 4D‐EnVar assimilation system B‐matrix could explain the unexpected aspects of the results obtained with this method. In contrast, diagnosed observation error covariances without negative temporal correlations are obtained with the Hollingsworth–Lönnberg method, where a decorrelation time‐scale on the order of 4 h is found. Both methods capture some expected features of the error correlations, such as highly correlated errors between observations within the same 1 h data processing blocks. While considerable uncertainty is associated with the results due to the inherent assumptions and limitations of both methods, the results do suggest that ZTD observation error decorrelation time‐scales are on the order of a few hours rather than days as suggested by early pioneering work with different error correlation estimation methods.

Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment

AbstractBecause of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses.

A fully autonomous ozone, aerosol and nighttime water vapor lidar: a synergistic approach to profiling the atmosphere in the Canadian oil sands region

Abstract. Lidar technology has been rapidly advancing over the past several decades. It can be used to measure a variety of atmospheric constituents at very high temporal and spatial resolutions. While the number of lidars continues to increase worldwide, there is generally a dependency on an operator, particularly for high-powered lidar systems. Environment and Climate Change Canada (ECCC) has recently developed a fully autonomous, mobile lidar system called AMOLITE (Autonomous Mobile Ozone Lidar Instrument for Tropospheric Experiments) to simultaneously measure the vertical profile of tropospheric ozone, aerosol and water vapor (nighttime only) from near the ground to altitudes reaching 10 to 15 km. This current system uses a dual-laser, dual-lidar design housed in a single climate-controlled trailer. Ozone profiles are measured by the differential absorption lidar (DIAL) technique using a single 1 m Raman cell filled with CO2. The DIAL wavelengths of 287 and 299 nm are generated as the second and third Stokes lines resulting from stimulated Raman scattering of the cell pumped using the fourth harmonic of a Nd:YAG laser (266 nm). The aerosol lidar transmits three wavelengths simultaneously (355, 532 and 1064 nm) employing a detector designed to measure the three backscatter channels, two nitrogen Raman channels (387 and 607 nm) and one cross-polarization channel at 355 nm. In addition, we added a water vapor channel arising from the Raman-shifted 355 nm output (407 nm) to provide nighttime water vapor profiles. AMOLITE participated in a validation experiment alongside four other ozone DIAL systems before being deployed to the ECCC Oski-ôtin ground site in the Alberta oil sands region in November 2016. Ozone was found to increase throughout the troposphere by as much as a factor of 2 from stratospheric intrusions. The dry stratospheric air within the intrusion was measured to be less than 0.2 g kg−1. A biomass burning event that impacted the region over an 8-day period produced lidar ratios of 35 to 65 sr at 355 nm and 40 to 100 sr at 532. Over the same period the Ångström exponent decreased from 1.56±0.2 to 1.35±0.2 in the 2–4 km smoke region.

Assessing the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions: current and future modelled scenarios

Abstract. A first regional assessment of the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions was conducted in this study. Model simulations were carried out on a limited-area domain (at 15 km horizontal resolution) centred over the Canadian Arctic, using the Environment and Climate Change Canada's on-line air quality forecast model, GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and CHemistry), to investigate the contribution from the marine shipping emissions over the Canadian Arctic waters (at both present and projected future levels) to ambient concentrations of criteria pollutants (O3, PM2.5, NO2, and SO2), atmospheric deposition of sulfur (S) and nitrogen (N), and atmospheric loading and deposition of black carbon (BC) in the Arctic. Several model upgrades were introduced for this study, including the treatment of sea ice in the dry deposition parameterization, chemical lateral boundary conditions, and the inclusion of North American wildfire emissions. The model is shown to have similar skills in predicting ambient O3 and PM2.5 concentrations in the Canadian Arctic and northern regions, as the current operational air quality forecast models in North America and Europe. In particular, the model is able to simulate the observed O3 and PM components well at the Canadian high Arctic site, Alert. The model assessment shows that, at the current (2010) level, Arctic shipping emissions contribute to less than 1 % of ambient O3 concentration over the eastern Canadian Arctic and between 1 and 5 % of ambient PM2.5 concentration over the shipping channels. Arctic shipping emissions make a much greater contributions to the ambient NO2 and SO2 concentrations, at 10 %–50 % and 20 %–100 %, respectively. At the projected 2030 business-as-usual (BAU) level, the impact of Arctic shipping emissions is predicted to increase to up to 5 % in ambient O3 concentration over a broad region of the Canadian Arctic and to 5 %–20 % in ambient PM2.5 concentration over the shipping channels. In contrast, if emission controls such as the ones implemented in the current North American Emission Control Area (NA ECA) are to be put in place over the Canadian Arctic waters, the impact of shipping to ambient criteria pollutants would be significantly reduced. For example, with NA-ECA-like controls, the shipping contributions to the population-weighted concentrations of SO2 and PM2.5 would be brought down to below the current level. The contribution of Canadian Arctic shipping to the atmospheric deposition of sulfur and nitrogen is small at the current level, < 5 %, but is expected to increase to up to 20 % for sulfur and 50 % for nitrogen under the 2030 BAU scenario. At the current level, Canadian Arctic shipping also makes only small contributions to BC column loading and BC deposition, with < 0.1 % on average and up to 2 % locally over the eastern Canadian Arctic for the former, and between 0.1 % and 0.5 % over the shipping channels for the latter. The impacts are again predicted to increase at the projected 2030 BAU level, particularly over the Baffin Island and Baffin Bay area in response to the projected increase in ship traffic there, e.g., up to 15 % on BC column loading and locally exceeding 30 % on BC deposition. Overall, the study indicates that shipping-induced changes in atmospheric composition and deposition are at regional to local scales (particularly in the Arctic). Climate feedbacks are thus likely to act at these scales, so climate impact assessments will require modelling undertaken at much finer resolutions than those used in the existing radiative forcing and climate impact assessments.

Algal bloom transport in Lake Erie using remote sensing and hydrodynamic modelling: Sensitivity to buoyancy velocity and initial vertical distribution

In this study, we simulate three-dimensional transport of algal blooms in Lake Erie using a combination of remote sensing and hydrodynamic modelling. The remote sensing algorithms use data from the Sentinel-3 OLCI satellite sensor to derive chlorophyll-a concentration from cyanobacteria blooms in Lake Erie. The derived chlorophyll-a concentration initializes an algal bloom transport model driven by the lake component of the Water Cycle Prediction System for the Great Lakes, a system of coupled atmosphere-lake-hydrological models operated out of Environment and Climate Change Canada. The bloom is modelled as Microcystis aeruginosa, a buoyant species that is often dominant in harmful algal blooms in western Lake Erie. Short-term (a few days) predictions of algal bloom transport from July 27 to October 8, 2017 are modelled in both Eulerian and Lagrangian frameworks. The Eulerian framework is used to evaluate the sensitivity of model results to the initial vertical distribution of the bloom. In this work, the Lagrangian framework is limited to two-dimensional surface confined particles. We use several error metrics to evaluate model predictions. We find that results are sensitive to the buoyancy velocity for cases where the bloom was initially distributed over a large portion of the water column. An initial vertical distribution selected from modelled chlorophyll-a half depth shows the highest accuracy for the entire range of buoyancy velocities tested. We also find that the Pierce skill score is difficult to interpret, particularly in cases where bloom intensity is greatly overpredicted by the model.

Using Geospatial Methods to Quantify the Co-Dispersion of Mercury Sources and Exposures in River Otter (Lontra Canadensis) for Risk Prediction

Mercury is an environmental contaminant of concern due to the established effects of methylated mercury (MeHg) on neurological functioning. Sentinel species such as the river otter (Lontra canadensis) are commonly used to monitor environmental mercury concentrations. Fur mercury has proven to be a reliable and cost-effective measure of exposure to MeHg in river otters and is a good proxy for assessing environmental mercury concentrations. A total of 311 geolocated fur samples were obtained from wildlife biomonitoring programs at Environment and Climate Change Canada and from the North American Fur Auction. These samples represent 131 unique locations throughout Canada collected between 2013 and 2016. Total mercury (THg) was measured on a MA-3000. Clustering of high THg was assessed using Getis and Ord’s Gi*. The association between fur THg and mercury sources was assessed using linear regression modelling alongside spatial regression methods such as geographically weighted regression. A cluster of low fur THg concentrations was observed in Alberta, and clusters of high fur THg concentrations were observed in northeastern Ontario, New Brunswick, and Nova Scotia. There is a statistically significant (p<0.05) negative relationship between fur THg and proximity to dam reservoir water, and pH of soil. There was also a statistically significant interaction between proximity to dam reservoir water and the pH of the soil. Otters that live closer to the reservoir water of dams have higher fur THg concentrations. This effect is enhanced if the soil pH surrounding the reservoir is also low. Using a geographically weighted regression these variables explain up to 35% of the variance. Results from this research are important for assessing the impact of future hydroelectric dam development on aquatic ecosystems. This research also further supports the use of river otter fur as a biomonitoring tool for environmental mercury exposure.

Establishing Appropriate Hot Weather Alerting Criteria for British Columbia, Canada

A heat health warning system (HHWS) was developed for the greater Vancouver area of British Columbia (BC), Canada following an unprecedented extreme hot weather event that resulted in a 40% increase in weekly mortality. While the greater Vancouver HHWS has been operational since 2012, there are currently no HHWSs covering the remainder of the province. In collaboration with Environment and Climate Change Canada we established hot weather alerting criteria for four regions with broadly similar climates in BC.We used daily forecasted and observed minimum and maximum air temperatures and daily mortality counts for May through September during 2004 to 2016. For each date (dayt), we selected daytime forecast high temperatures for dayt and dayt+1 and the overnight forecast low temperature falling between dayt and dayt+1 from forecasts made in the afternoon of dayt-2. We tried a range of combinations of minimum and maximum temperature thresholds for each of four climatic areas. For each date, we assigned a hot weather warning category equal to the sum of the total degrees by which the daytime forecast highs fell under the maximum threshold and the overnight forecast low fell under the minimum threshold. We assessed associations between the categories and daily mortality using time-series models.We established the following minimum/maximum hot weather alerting criteria, to be adopted in the summer of 2018: 16°C/29°C in the Southwest; 18°C/35°C in the Southeast; 13°C/28°C in the Northwest; and 14°C/29°C in the Northeast. These values were established by balancing a number of considerations regarding the appropriateness of each threshold, including evidence-based associations with daily mortality and minimization of warning fatigue. These criteria identified the two province-wide extreme hot weather events that affected BC in 2009 and 2015.Hot weather alerting criteria covering all of BC can help public health authorities and citizens prepare for extreme hot weather events.

Outdoor Thermal Comfort during Anomalous Heat at the 2015 Pan American Games in Toronto, Canada

Mass sporting events in the summertime are influenced by underlying weather patterns, with high temperatures posing a risk for spectators and athletes alike. To better understand weather variations in the Greater Toronto Area (GTA) during the Pan American Games in 2015 (PA15 Games), Environment and Climate Change Canada deployed a mesoscale monitoring network system of 53 weather stations. Spatial maps across the GTA demonstrate large variations by heat metric (e.g., maximum temperature, humidex, and wet bulb globe temperature), identifying Hamilton, Ontario as an area of elevated heat and humidity, and hence risk for heat-related illness. A case study of the Hamilton Soccer Center examined on-site thermal comfort during a heat event and PA15 Soccer Games, demonstrating that athletes and spectators were faced with thermal discomfort and a heightened risk of heat-related illness. Results are corroborated by First Aid and emergency response data during the events, as well as insight from personal experiences and Twitter feed. Integrating these results provides new information on potential benefits to society from utilizing mesonet systems during large-scale sporting events. Results further improve our understanding of intra-urban heat variability and heat-health burden. The benefits of utilizing more comprehensive modeling approaches for human heat stress that coincide with fine-scale weather information are discussed.

GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene

Abstract. Environment and Climate Change Canada's online air quality forecasting model, GEM-MACH, was extended to simulate atmospheric concentrations of benzene and seven polycyclic aromatic hydrocarbons (PAHs): phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, and benzo(a)pyrene. In the expanded model, benzene and PAHs are emitted from major point, area, and mobile sources, with emissions based on recent emission factors. Modelled PAHs undergo gas–particle partitioning (whereas benzene is only in the gas phase), atmospheric transport, oxidation, cloud processing, and dry and wet deposition. To represent PAH gas–particle partitioning, the Dachs–Eisenreich scheme was used, and we have improved gas–particle partitioning parameters based on an empirical analysis to get significantly better gas–particle partitioning results than the previous North American PAH model, AURAMS-PAH. Added process parametrizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapour sorption to the snow surface. The resulting GEM-MACH-PAH model was used to generate the first online model simulations of PAH emissions, transport, chemical transformation, and deposition for a high-resolution domain (2.5 km grid cell spacing) in North America, centred on the PAH data-rich region of southern Ontario, Canada and the northeastern US. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the US Average spring–summertime model results were found to be statistically unbiased from measurements of benzene and all seven PAHs. The same was true for the fall–winter seasonal mean, except for benzo(a)pyrene, which had a statistically significant positive bias. We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to particulate matter and oxidant processes and transport. Our analysis focused on four key components to the prediction of atmospheric PAH levels: spatial variability, sensitivity to mobile emissions, gas–particle partitioning, and wet deposition. Spatial variability of PAHs ∕ PM2.5 at a 2.5 km resolution was found to be comparable to measurements. Predicted ambient surface concentrations of benzene and the PAHs were found to be critically dependent on mobile emission factors, indicating the mobile emissions sector has a significant influence on ambient PAH levels in the study region. PAH wet deposition was overestimated due to additive precipitation biases in the model and the measurements. Our overall performance evaluation suggests that GEM-MACH-PAH can provide seasonal estimates for benzene and PAHs and is suitable for emissions scenario simulations.

Evaluation of a high‐resolution numerical weather prediction model's simulated clouds using observations from CloudSat, GOES‐13 and in situ aircraft

This study aimed to assess tropical cloud properties predicted by Environment and Climate Change Canada's Global Environmental Multiscale (GEM) model when run with the Milbrandt–Yau double‐moment cloud microphysical scheme and one‐way nesting that culminated at a (∼300 km)2 inner domain with 0.25 km horizontal grid spacing. The assessment utilized satellite and in situ data collected during the High Ice Water Content (HIWC) and High Altitude Ice Crystals (HAIC) projects for a mesoscale convective system on 16 May 2015 over French Guiana. Data from CloudSat's cloud‐profiling radar and GOES‐13's imager were compared to data either simulated directly by GEM or produced by operating on GEM's cloud data with both the CFMIP (Cloud Feedback Model Intercomparison Project) Observation Simulator Package (COSP) instrument simulator and a three‐dimensional Monte Carlo solar radiative transfer model. In situ observations were made from research aircraft – Canada's National Research Council Convair‐580 and the French SAFIRE Falcon‐20 – whose flight paths were aligned with CloudSat's ground‐track. Spatial and temporal shifts of clouds simulated by GEM compared well to GOES‐13 imagery. There are, however, differences between simulated and observed amounts of high and low cloud. While GEM did well at predicting ranges of ice‐water content (IWC) near 11 km altitude (Falcon‐20), it produces too much graupel and snow near 7 km (Convair‐580). This produced large differences between CloudSat's and COSP‐generated radar reflectivities and two‐way attenuations. On the other hand, CloudSat's inferred values of IWC agree well with in situ samples at both altitudes. Generally, GEM's visible reflectances exceeded GOES‐13's on account of having produced too much low‐level liquid cloud. It is expected that GEM's disproportioning of cloud hydrometeors will improve once it includes a better representation of secondary ice production.

Lines on a map: conservation units, meta‐population dynamics, and recovery of woodland caribou in Canada

AbstractDelineating conservation units is a fundamental step in recovery planning for endangered species. Yet, challenges remain in the application and validation of scientifically evaluated conservation units in management practice. The Canadian government makes use of Designatable Units (DUs) as the primary conservation unit under their Species‐at‐Risk Act. DUs must be ecologically discrete and have demonstrated evolutionary significance, which, in the case of woodland caribou (Rangifer tarandus caribou), has led to the definition of multiple DUs across Canada. Simultaneously, Environment and Climate Change Canada has released two recovery strategies affecting four DUs, wherein DUs are subdivided into smaller conservation units. However, the two recovery strategies adopt different definitions for the conservation unit. For the Boreal DU, the Local Population is considered the conservation unit for recovery management, whereas for Southern Mountain DU, the conservation unit for recovery is the subpopulation, which may or may not be comprised of several Local Populations. The scientific rationale for the difference between recovery strategies is unclear, not necessarily supported by genetic or demographic evidence, and highlights a policy challenge facing caribou conservation. We argue that the current emphasis on protecting subpopulations within a DU might be inconsistent and unviable for recovery planning. Instead, the recognition and emphasis on maintaining meta‐population dynamics within DUs is essential and currently underutilized in the long‐term recovery of woodland caribou in Canada.

Initialization and Potential Predictability of Soil Moisture in the Canadian Seasonal to Interannual Prediction System

The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.

How important is biomass burning in Canada to mercury contamination?

Abstract. Wildfire frequency has increased in past four decades in Canada and is expected to increase in future as a result of climate change (Wotton et al., 2010). Mercury (Hg) emissions from biomass burning are known to be significant; however, the impact of biomass burning on air concentration and deposition fluxes in Canada has not been previously quantified. We use estimates of burned biomass from FINN (Fire INventory from NCAR) and vegetation-specific emission factors (EFs) of mercury to investigate the spatiotemporal variability of Hg emissions in Canada. We use Environment and Climate Change Canada's GEM-MACH-Hg (Global Environmental Multi-scale, Modelling Air quality and Chemistry model, mercury version) to quantify the impact of biomass burning in Canada on spatiotemporal variability of air concentrations and deposition fluxes of mercury in Canada. We use North American gaseous elemental mercury (GEM) observations (2010–2015), GEM-MACH-Hg, and an inversion technique to optimize the EFs for GEM for five vegetation types represented in North American fires to constrain the biomass burning impacts of mercury. The inversion results suggest that EFs representing more vegetation types – specifically peatland – are required. This is currently limited by the sparseness of measurements of Hg from biomass burning plumes. More measurements of Hg concentration in the air, specifically downwind of fires, would improve the inversions. We use three biomass burning Hg emissions scenarios in Canada to conduct three sets of model simulations for 2010–2015: two scenarios where Hg is emitted only as GEM using literature or optimized EFs and a third scenario where Hg is emitted as GEM using literature EFs and particle bound mercury (PBM) emitted using the average GEM∕PBM ratio from lab measurements. The three biomass burning emission scenarios represent a range of possible values for the impacts of Hg emissions from biomass burning in Canada on Hg concentration and deposition.We find total biomass burning Hg emissions to be highly variable from year to year and estimate average 2010–2015 total atmospheric biomass burning emissions of Hg in Canada to be between 6 and 14 t during the biomass burning season (i.e. from May to September), which is 3–7 times the mercury emission from anthropogenic sources in Canada for this period. On average, 65 % of the emissions occur in the provinces west of Ontario. We find that while emissions from biomass burning have a small impact on surface air concentrations of GEM averaged over individual provinces/territories, the impact at individual sites can be as high as 95 % during burning events. We estimate average annual mercury deposition from biomass burning in Canada to be between 0.3 and 2.8 t, compared to 0.14 t of mercury deposition from anthropogenic sources during the biomass burning season in Canada. Compared to the biomass burning emissions, the relative impact of fires on mercury deposition is shifted eastward, with on average 54 % percent of the deposition occurring in provinces west of Ontario. While the relative contribution of Canadian biomass burning to the total mercury deposition over each province/territory is no more than 9 % between 2010 and 2015, the local contribution in some locations (including areas downwind of biomass burning) can be as high as 80 % (e.g. northwest of Great Slave Lake in 2014) from May to September. We find that northern Alberta and Saskatchewan, central British Columbia, and the area around Great Slave Lake in the Northwest Territories are at greater risk of mercury contamination from biomass burning. GEM is considered to be the dominant mercury species emitted from biomass burning; however, there remains an uncertainty in the speciation of mercury released from biomass burning. We find that the impact of biomass burning emissions on mercury deposition is significantly affected by the uncertainty in speciation of emitted mercury because PBM is more readily deposited closer to the emission sources than GEM; an addition of ∼ 18 % percent of mercury emission from biomass burning in the form of PBM in the model increases the 6-year average deposition by ∼ 4 times.

The Canadian Ice Island Drift, Deterioration and Detection (CI2D3) Database

This project was undertaken with the financial support of the Government of Canada through Environment and Climate Change Canada and through the Polar Knowledge Canada project ‘Safe Passage’. Funding from the Canadian Foundation for Innovation and the Ontario Research Fund supported the purchase of computer equipment. RADARSAT-1 and -2 data were supplied by the Canadian Ice Service through a Joint Project Agreement.

Deriving a water quality guideline for protection of aquatic communities exposed to triclosan in the Canadian environment.

Triclosan is an antibacterial and antifungal chemical used in a variety of consumer products, including soaps, detergents, moisturizers, and cosmetics. Aquatic ecosystems may be exposed to triclosan following the release of remaining residues in wastewater effluents and biosolids. In December 2017, Environment and Climate Change Canada (ECCC) released a federal environmental quality guideline (FEQG) report that contained a federal water quality guideline (FWQG) for triclosan. This guideline will be used as an adjunct to the risk assessment and risk management of priority chemicals identified under the Government of Canada's Chemicals Management Plan (CMP). The FWQG value for triclosan (0.47 μg/L) was derived by ECCC using a hazardous concentration for 5% of species (HC5) from a species sensitivity distribution (SSD). We recalculated the FWQG after performing an independent analysis and evaluation of the available aquatic toxicity data for triclosan and compared our results with the ECCC FWQG value. Our independent analysis of the available aquatic toxicity data entailed conducting a literature search of all available and relevant studies, evaluating the quality and reliability of all studies considered using thorough and consistent study evaluation criteria, and thereby generating a data set of high-quality toxicity values. The selected data set includes 22 species spanning 5 taxonomic groups. An SSD was developed using this data set following the ECCC approaches. The HC5 from the SSD derived based on our validated data set is 0.76 μg/L. This HC5 value is slightly greater (i.e., less sensitive) than the value presented in ECCC's final FWQG. Integr Environ Assess Manag 2018;14:437-441. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Adaptive Probability Thresholding in Automated Ice and Open Water Detection From RADARSAT-2 Images

In this letter, we introduce adaptive probability thresholding in addition to our previously developed technique for automated detection of ice and open water from RADARSAT-2 ScanSAR dual-polarization HH–HV images. Situations where the probability threshold needs to be modified were identified based on the analysis of misclassified ice and water samples when the static probability threshold of 0.95 is applied. We found that with the use of the proposed approach, the fraction of misclassified ice samples decreased from 0.98% to 0.24% and the fraction of misclassified water samples decreased from 0.35% to 0.09% in the most clean verification scenario against Canadian Ice Service Image Analysis pure ice and water data, while the fraction of correctly classified ice and water samples did not decrease appreciably, from 72.2% to 65.4%. The developed approach will be implemented as a part of the data assimilation component of the operational Environment and Climate Change Canada Regional Ice-Ocean Prediction System.

Optimizing UV Index determination from broadband irradiances

Abstract. A study was undertaken to improve upon the prognosticative capability of Environment and Climate Change Canada's (ECCC) UV Index forecast model. An aspect of that work, and the topic of this communication, was to investigate the use of the four UV broadband surface irradiance fields generated by ECCC's Global Environmental Multiscale (GEM) numerical prediction model to determine the UV Index. The basis of the investigation involves the creation of a suite of routines which employ high-spectral-resolution radiative transfer code developed to calculate UV Index fields from GEM forecasts. These routines employ a modified version of the Cloud-J v7.4 radiative transfer model, which integrates GEM output to produce high-spectral-resolution surface irradiance fields. The output generated using the high-resolution radiative transfer code served to verify and calibrate GEM broadband surface irradiances under clear-sky conditions and their use in providing the UV Index. A subsequent comparison of irradiances and UV Index under cloudy conditions was also performed. Linear correlation agreement of surface irradiances from the two models for each of the two higher UV bands covering 310.70–330.0 and 330.03–400.00 nm is typically greater than 95 % for clear-sky conditions with associated root-mean-square relative errors of 6.4 and 4.0 %. However, underestimations of clear-sky GEM irradiances were found on the order of ∼ 30–50 % for the 294.12–310.70 nm band and by a factor of ∼ 30 for the 280.11–294.12 nm band. This underestimation can be significant for UV Index determination but would not impact weather forecasting. Corresponding empirical adjustments were applied to the broadband irradiances now giving a correlation coefficient of unity. From these, a least-squares fitting was derived for the calculation of the UV Index. The resultant differences in UV indices from the high-spectral-resolution irradiances and the resultant GEM broadband irradiances are typically within 0.2–0.3 with a root-mean-square relative error in the scatter of ∼ 6.6 % for clear-sky conditions. Similar results are reproduced under cloudy conditions with light to moderate clouds, with a relative error comparable to the clear-sky counterpart; under strong attenuation due to clouds, a substantial increase in the root-mean-square relative error of up to 35 % is observed due to differing cloud radiative transfer models.