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    The Global Deterministic Prediction System (GDPS) is a coupled atmosphere (GEM), ocean and sea ice (NEMO-CICE) deterministic numerical weather prediction model. Forecasts are carried out twice a day for 10 days lead time. The geographical coverage is global on a native Yin-Yang grid at 15 km horizontal resolution. Data is available for 33 vertical levels and interpolated on a global latitude-longitude uniform grid with 0.2 degree horizontal resolution. Variables availability in number and time frequency is a function of forecast lead time.

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    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.

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    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

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    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.

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    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.

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    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.

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    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.

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    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.

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    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.

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    The integrated pan-Arctic snow melt onset dataset was generated by combining active and passive microwave satellite derived estimates for northern high latitude land surface, ice caps, large lakes, and sea ice (Wang et al., 2011). This dataset allows snow melt dynamics to be examined in a full pan-Arctic context and enables exploration of interactions between the terrestrial and marine components of the cryosphere. It provides a unique dataset for validating snow cover simulations from regional or global climate models during the spring transition period when snow cover exerts the strongest feedback to the climate system.