The Regional Air Quality Deterministic Prediction System FireWork (RAQDPS-FW) carries out physics and chemistry calculations, including emissions from active wildfires, to arrive at deterministic predictions of chemical species concentration of interest to air quality, such as fine particulate matter PM2.5 (2.5 micrometers in diameter or less). Geographical coverage is Canada and the United States. Data is available at a horizontal resolution of 10 km. While the system encompasses more than 80 vertical levels, data is available only for the surface level. The products are presented as historical, annual or monthly, averages which highlight long-term trends in cumulative effects on the environment.

This dataset consists of CLASS (Canadian Land Surface Scheme) simulations for ALMIP2 (AMMA [African Monsoon Multidisciplinary Analysis] Land Surface Model Intercomparison Project, Phase 2). The results of the intercomparison is published in the paper "Streamflows over a West African Basin from the ALMIP2 Model Ensemble" (http://journals.ametsoc.org/doi/full/10.1175/JHM-D-16-0233.1). Output files from the simulations contain variables for surface and sub-surface state, evaporation, water and energy balance and other hydrological processes for the ALMIP2 study area covering Benin, Niger and Mali in West Africa over the years 2005 to 2008. The AMMA project was organized with the main goal of obtaining a better understanding of the intraseasonal and interannual variability of the West-African monsoon. Land-atmosphere coupling is expected to be significant in this region, so a high priority goal of AMMA is to better understand and model the influence of the spatiotemporal variability of surface processes on the atmospheric circulation patterns and the regional water cycle. ALMIP2 deals with the local to mesoscales. Participating land surface, vegetation and hydrological models are evaluated and inter-compared in order to identify key processes which are not well modeled over this region, and to offer guidelines for future model development.

The CanESM2 large ensembles are 50-member perturbed initial condition ensembles from 1950 to 2020, with all historical forcings (historical), solar and volcanic forcings only (historicalNat), anthropogenic aerosols only (historicalMisc, p4), and ozone only (historicalMisc, p6). The model, forcings, variable names and file formats all follow those used in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Each of the five members of the existing CMIP5 historical ensemble had conditions perturbed in 1950 ten times to produce a new ensemble of fifty simulations starting in 1950, and a similar procedure was applied to the natural, aerosol and ozone-only ensembles. Simulations were run to 2005 using CMIP5 historical forcings and then to 2020 using RCP 8.5 forcings. The CanESM2 large ensembles were proposed by the Canadian Sea Ice and Snow Evolution Network (CanSISE) Climate Change and Atmospheric Research (CCAR) Network project, which also coordinated much of the initial analysis of the large ensembles. Relevant publications: Description of the large ensembles: Kushner, P. J., L. R. Mudryk, W. Merryfield, J. T. Ambadan, A. Berg, A. Bichet, R. Brown, C. Derksen, S. J. Déry, A. Dirkson, G. Flato, C. G. Fletcher, J. C. Fyfe, N. Gillett, C. Haas, S. Howell, F. Laliberté, K. McCusker, M. Sigmond, R. Sospedra-Alfonso, N. F. Tandon, C. Thackeray, B. Tremblay, and F. W. Zwiers, 2018: Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada’s Earth system model and climate-prediction system. The Cryosphere, 12, 1137–1156, doi:10.5194/tc-12-1137-2018. Kirchmeier-Young, M.C., F.W. Zwiers, and N.P. Gillett, 2017: Attribution of Extreme Events in Arctic Sea Ice Extent. J. Climate, 30, 553–571, https://doi.org/10.1175/JCLI-D-16-0412.1 Examples of applications of the large ensembles: Kirchmeier-Young, M.C., F.W. Zwiers, N.P. Gillett and A.J. Cannon, 2017: Attributing extreme fire risk in Western Canada to human emissions, Climatic Change, 1-15, https://doi.org/10.1007/s10584-017-2030-0 Gagné, M.-È., J. C. Fyfe, N. P. Gillett, I. V. Polyakov, and G. M. Flato, 2017: Aerosol-driven increase in Arctic sea ice over the middle of the twentieth century, Geophys. Res. Lett., 44, 7338–7346, https://doi.org/10.1002/2016GL071941 Gagné, M.-È., M. C. Kirchmeier-Young, N. P. Gillett, and J. C. Fyfe, 2017: Arctic sea ice response to the eruptions of Agung, El Chichón, and Pinatubo, J. Geophys. Res. Atmos., 122, https://doi.org/10.1002/2017JD027038 Fyfe, J.C. , C. Derksen, L. Mudryk, G.M. Flato, B.D. Santer, N.C. Swart, N.P. Molotch, X. Zhang, H. Wan, V.K. Arora, J. Scinocca, 2017: Large near-term projected snowpack loss over the western United States, Nature Comm., 8:14996, https://doi.org/10.1038/ncomms14996

Environment and Climate Change Canada provides Spills Technology Databases including Physicochemical Properties of Petroleum Products. This database contains information on the properties and composition of various types of oils and petroleum products.

The dataset contains large ensembles of bias adjusted daily climate model outputs of minimum temperature, maximum temperature, precipitation, relative humidity, surface pressure, wind speed, incoming shortwave radiation, and incoming longwave radiation on a 0.5-degree grid over North America. Intended uses include hydrological/land surface impact modelling and related event attribution studies. The CanLEADv1 dataset is based on archived climate model simulations in the Canadian Regional Climate Model Large Ensemble (CanRCM4 LE) https://open.canada.ca/data/en/dataset/83aa1b18-6616-405e-9bce-af7ef8c2031c and Canadian Earth System Model Large Ensembles (CanESM2 LE) https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c datasets. Specifically, CanLEADv1 provides bias adjusted daily climate variables over North America derived from 50 member initial condition ensembles of CanESM2 (ALL and NAT radiative forcings) and CanESM2-driven CanRCM4 (ALL radiative forcings) simulations (Scinocca et al., 2016; Fyfe et al., 2017). Raw CanESM2 LE and CanRCM4 LE outputs are bias adjusted (Cannon, 2018; Cannon et al., 2015) so that they are statistically consistent with two observationally-constrained historical meteorological forcing datasets (S14FD, Iizumi et al., 2017; EWEMBI, Lange, 2018). File names, formats, and metadata headers follow the recommended Data Reference Syntax for bias-adjusted Coordinated Regional Downscaling Experiment (CORDEX) simulations (Nikulin and Legutke, 2016). Multiple initial condition simulations can be used to investigate the externally forced response, internal variability, and the relative role of external forcing and internal variability on the climate system (e.g., Fyfe et al., 2017). Large ensembles of ALL and NAT simulations can be compared in event attribution studies (e.g., Kirchmeier-Young et al., 2017). Availability of bias adjusted outputs from the CanESM2-CanRCM4 modelling system can be used to investigate the added value of dynamical downscaling (Scinocca et al., 2016). Multiple observational datasets are used for bias adjustment to partly account for observational uncertainty (Iizumi et al., 2017). For CanESM2 LE, there are two sets of radiative forcing scenarios (ALL, which consists of historical and RCP8.5 forcings for the periods 1950-2005 and 2006-2100, respectively, and NAT, which consists of historicalNat forcings for the period 1950-2020), two observationally-constrained target datasets for bias adjustment (S14FD and EWEMBI), and 50 ensemble members, which gives a total of 2 × 2 × 50 = 200 sets of outputs. For CanRCM4 LE, historicalNat simulations were not run; hence, there are 2 × 50 = 100 sets of outputs. In both cases, CanLEADv1 provides variables on the CORDEX NAM-44i 0.5-degree grid. CanESM2 outputs (~2.8-degree grid) and CanRCM4 outputs (0.44-degree grid), are bilinearly interpolated onto the NAM-44i grid before bias adjustment. A multivariate version of quantile mapping (Cannon, 2018) is used to adjust the distribution of each simulated variable, as well as the statistical dependence between variables, so that these properties match those of the target observational dataset. Bias adjustment is performed on a grid cell by grid cell basis. Outside of the historical calibration period, the climate change signal simulated by the climate model is preserved (Cannon et al., 2015). References: Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2), 31-49. Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959. Fyfe, J. C., Derksen, C., Mudryk, L., Flato, G. M., Santer, B. D., Swart, N. C., Molotch, N. P., Zhang, X., Wan, H., Arora, V. K., Scinocca, J., & Jiao, Y. (2017). Large near-term projected snowpack loss over the western United States. Nature Communications, 8, 14996. Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N., & Nishimori, M. (2017). Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. Journal of Geophysical Research: Atmospheres, 122(15), 7800-7819. Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P., & Cannon, A. J. (2017). Attributing extreme fire risk in Western Canada to human emissions. Climatic Change, 144(2), 365-379. Lange, S. (2018). Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset. Earth System Dynamics, 9(2), 627-645. Nikulin, G., & Legutke, S. (2016). Data Reference Syntax (DRS) for bias-adjusted CORDEX simulations. http://is-enes-data.github.io/CORDEX_adjust_drs.pdf Scinocca, J. F., Kharin, V. V., Jiao, Y., Qian, M. W., Lazare, M., Solheim, L., Flato, G. M., Biner, S., Desgagne, & Dugas, B. (2016). Coordinated global and regional climate modeling. Journal of Climate, 29(1), 17-35.

This study is identifying risks, impacts, origins, and movement patterns of migratory bird pathogens in the Western Hemisphere. We are identifying determinants of disease to multiple pathogens in relation to demographic, spatiotemporal, and environmental factors using blue-winged teal sampled in North and South America. We are combining use of disease surveillance data, modeling of band recovery data, analysis of feather stable isotopes, use of satellite telemetry, and genotyping techniques to investigate origins and spread of diseases. This study will ultimately enable development of models predicting emergence and spread of diseases in migratory bird populations, and where they may enter into Canada.

Scientists publish the results of their work in scientific journals. New data that has contributed to climate research publications can be found in the related data records below.

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The International comparison of urban air quality indicators compare ambient levels (concentrations) of air pollutants in selected Canadian urban areas with levels in selected international urban areas. The indicators report the concentration of ground-level ozone (as the annual average of the daily maximum 8-hour average), fine particulate matter, sulphur dioxide and nitrogen dioxide (as the annual average of the daily 24-hour average) in ambient air. These indicators are intended to provide a general comparison of ambient levels of air pollutants in selected Canadian urban areas with those measured in other international urban areas. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The International comparison: air pollutant emissions in selected countries indicators provide emissions and emissions intensity for member countries of the Organisation for Economic Co-operation and Development. The emissions of 5 pollutants are reported: sulphur oxides, nitrogen oxides, carbon monoxide, volatile organic compounds and fine particulate matter. These indicators help to inform Canadians about how Canada's emissions compare to those of other countries. The indicators report on key air pollutants that contribute to smog and acid rain and help the government to identify priorities, track progress, and develop strategies and policies for reducing or controlling air pollution. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated.

Nallar R, Papp Z, Epp T, Leighton FA, Swafford SR, DeLiberto TJ, Dusek RJ, Ip HS, Hall J, Berhane J, Gibbs SEJ, Soos C (2015) Demographic and spatiotemporal patterns of avian influenza infection at the continental scale, and in relation to annual life cycle of a migratory host. PLOS ONE 10(6): e0130662. https://doi.org/10.1371/journal.pone.0130662 Since the spread of highly pathogenic avian influenza (HPAI) H5N1 in the eastern hemisphere, numerous surveillance programs and studies have been undertaken to detect the occurrence, distribution, or spread of avian influenza viruses (AIV) in wild bird populations worldwide. To identify demographic determinants and spatiotemporal patterns of AIV infection in long distance migratory waterfowl in North America, we fitted generalized linear models with binominal distribution to analyze results from 13,574 blue-winged teal (Anas discors, BWTE) sampled in 2007 to 2010 year round during AIV surveillance programs in Canada and the United States. Our analyses revealed that during late summer staging (July-August) and fall migration (September-October), hatch year (HY) birds were more likely to be infected than after hatch year (AHY) birds, however there was no difference between age categories for the remainder of the year (winter, spring migration, and breeding period), likely due to maturing immune systems and newly acquired immunity of HY birds. Probability of infection increased non-linearly with latitude, and was highest in late summer prior to fall migration when densities of birds and the proportion of susceptible HY birds in the population are highest. Birds in the Central and Mississippi flyways were more likely to be infected compared to those in the Atlantic flyway. Seasonal cycles and spatial variation of AIV infection were largely driven by the dynamics of AIV infection in HY birds, which had more prominent cycles and spatial variation in infection compared to AHY birds. Our results demonstrate demographic as well as seasonal, latitudinal and flyway trends across Canada and the US, while illustrating the importance of migratory host life cycle and age in driving cyclical patterns of prevalence.