Fisheries and Oceans Canada (DFO) has been conducting surface water trawl surveys since 1992 in the coastal waters of British Columbia, Washington, Oregon and Alaska and in the high seas of the Gulf of Alaska. These surveys initially focused on determining the migratory patterns (1992-2002) and on the growth and physiology (2003-2016) of juvenile Pacific Salmon. Since 2016, the focus has been expanded to include all components of the pelagic ecosystem while retaining a strong focus on juvenile Pacific Salmon. Given the change in research priorities, there are differences between years in location and timing. The survey series are provided based on large marine ecosystems, so data will vary in availability. These survey data contain fishing and catch information along with biological information recorded. Surveys available here have published reports that outline overall operations and any oceanographic data, zooplankton and additional samples collected.

Multi-model ensembles of surface wind speed based on projections from twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of surface wind speed (m/s) are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better-projected climate change information.

Precipitation (moisture availability) establishes the economic yield potential and product quality of field crops. Both dry and wet precipitation extremes have the ability to inhibit proper crop growth. The maximum daily precipitation index covers the risk of excessive precipitation in the short term, while the other indices pertain to longer term moisture availability. Agriculture is an important primary production sector in Canada. Agricultural production, profitability, sustainability and food security depend on many agrometeorological factors. Extreme weather events in Canada, such as drought, floods, heat waves, frosts and high intensity storms, have the ability to significantly impact field crop production. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily basis.

The’ Qu'Appelle Valley Lakes system – Topography and Imagery’ series consists of topographic and imagery data for lakes within the Qu'Appelle River Valley in central Saskatchewan. This data was collected in the fall of 2008 and consists of contour lines, shorelines, spot heights, tile index, and imagery

The “Soils of Canada, Derived” national scale thematic datasets display the distribution and areal extent of soil attributes such as drainage, texture of parent material, kind of material, and classification of soils in terms of provincial Detailed Soil Surveys (DDS) polygons, Soil Landscape Polygons (SLCs), Soil Order and Great Group. The relief and associated slopes of the Canadian landscape are depicted on the local surface form thematic dataset. The purpose of the “Soils of Canada, Derived” series is to facilitate the cartographic display and basic queries of the Soil Landscapes of Canada at a national scale. For more detailed or sophisticated analysis, users should investigate the full “Soil Landscapes of Canada” product.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice concentration based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Sea ice concentration is represented as the percentage (%) of grid cell area. Therefore, projected change in sea ice concentration is with respect to the reference period of 1986-2005 and expressed as a percentage (%). The 5th, 25th, 50th, 75th and 95th percentiles of the ensembles of sea ice concentration change are available for the historical time period, 1900-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in sea ice concentration (%) for four time periods (2021-2040; 2041-2060; 2061-2080; 2081-2100), with respect to the reference period of 1986-2005, for RCP2.6, RCP4.5 and RCP8.5 are also available in a range of formats. The median projected change across the ensemble of CMIP5 climate models is provided. Note: Projections among climate models can vary because of differences in their underlying representation of earth system processes. Thus, the use of a multi-model ensemble approach has been demonstrated in recent scientific literature to likely provide better projected climate change information.

Assess the importance of atmospheric deposition of contaminants as a contributor to ecological impacts of oil sands development and identify sources. • Use snowpack measurements sampled across a gridwork to develop maps of winter-time atmospheric contaminant loadings for the region ~100 km from the major upgrading facilities • Assess long-term trends in winter-time atmospheric deposition • Determine the potential impact of wintertime snowpack mercury loads on tributary river water mercury concentrations (Spring Freshet) using Geographic Information System and hydrological modelling approaches • Compare snowpack loadings to those obtained from precipitation monitoring and compare spatial patterns to PAC air measurements obtained from passive sampling network All data are subjects of a publication containing method details, full QA/QC, interpretations and conclusions. Citations: A. Dastoor, A. Ryjkov, G. Kos, J. Zhang, J.L. Kirk, M. Parsons, A. Steffen. 2021. Impact of Athabasca oil sands operations on mercury levels in air and deposition. Atmospheric Chemistry and Physics 21, 12783-12807. L. Chibwe, D.C.G. Muir, Y. Gopalapillai, D. Shang, F. Yang, J.L. Kirk, C. Manzano, B. Atkinson, X. Wang, C. Teixeira. 2021. Long-term spatial and temporal trends, and source apportionment of polycyclic aromatic compounds in the Athabasca Oil Sands Region. Environmental Pollution 268A, 115351. J. Culp, I. Droppo, P. di Cenzo, A. Alexander-Trusiak, D. Baird, S. Beltaos, G. Bickerton, B. Bonsal, R. Brua, P. Chambers, Y. Dibike, N. Glozier, J.L. Kirk, L. Levesque, M. McMaster, D.C.G. Muir, J. Parrott, D. Peters, K. Pippy, J. Roy. 2021. Ecological effects and causal synthesis of oil sands activity impacts on river ecosystems: water synthesis review. Environmental Reviews 29. Doi: https://doi.org/10.1139/er-2020-0082. Y. Gopalapillai, J. L. Kirk, M.S. Landis, D.C.G. Muir, C.A. Cooke, C.A., A. Gleason, A. Ho, E. Kelly, D. Schindler, X. Wang, G. Lawson. 2019. Source analysis of pollutant elements in winter air deposition in the Athabasca oil sands region: A Temporal and Spatial Study. ACS Earth and Space Chemistry 38, 1656-1668. W. Wasiuta, J.L. Kirk, P.A. Chambers, A.C. Alexander, F.R. Wyatt, R.C. Rooney, C.A. Cooke. 2019. Accumulating mercury and methylmercury burdens in watersheds impacted by oil sands pollution. Environmental Science & Technology 53, 12856-12864. C. Manzano, D. Muir, J. L. Kirk, C. Teixeira, M. Siu, X. Wang, J.P. Charland, D. Schindler, E. Kelly. 2016. Temporal variation in the deposition of polycyclic aromatic compounds in snow in the Athabasca Oil Sands area of Alberta. Environmental Monitoring and Assessment 188, 542. and J.L. Kirk, D. Muir, A. Gleason, X, Wang, R. Frank, I. Lehnherr, F. Wrona. 2014. Atmospheric deposition of mercury and methyl mercury to landscapes and waterbodies of the Athabasca oil sands region. Environmental Science & Technology 48, 73747383.

Difference from Normal Temperature values are computed by subtracting the normal monthly average temperature from the average monthly temperature of the month. The average monthly temperature is computed by obtaining the mean value of average daily temperatures for a month. If the month was colder than normal the value computed will be negative and if it was warmer the value will be positive. Long-term average is 1991-2020. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.

Accumulated Precipitation represents the amount of total precipitation in mm (solid and/or liquid) which has been recorded over a given month or ISO week. Accumulation of precipitation values serve to inform users about the total amount of precipitation that has fallen in a specific region during a set time period. The National Agroclimate Series of Derived Indicators (NASDI) products provide a collection of comprehensive and regularly updated datasets on key agroclimatic variables, including accumulated precipitation, standardized precipitation index, and difference from normal temperature, among others. These datasets incorporate both real-time and historical climate information, offering enhanced insight into conditions and trends across Canada’s diverse agricultural regions.