The wind speed layer shows the modeled wind speed [m/s] at a height of 100 m above ground level, at each grid point, averaged over the three year period from January 1, 2008 to December 31, 2010. Values are presented in bins with ranges of 0.5 m/s each. Further details including data at different heights, and for individual years, can be obtained by clicking on the dot representing the grid point location.

Monthly, seasonal and annual trends of mean wind speed change (kilometres per hour) based on homogenized station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of surface wind speed trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in surface wind speed based on an ensemble of twenty-nine Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Projected change in wind speed 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 ensemble of wind speed 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 wind speed (%) 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.

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.

The Homogenized Surface Wind Speed data consist of monthly, seasonal and annual means of hourly wind speed (kilometres per hour) at standard 10 metre level for 156 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

The maximum wind speed during the forecast period km/hr (mdws).Week 1 and week 2 forecasted index is available daily from September 1 to August 31.Week 3 and week 4 forecasted index is available weekly (Thursday) from September 1 to August 31.Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively.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 and weekly basis.

This data includes the average wind speed measured at various sites throughout the Yukon over discrete time periods from as early as 1944 to as recent as 2004. The specific time periods are included in the dataset, as is a brief description of each site. Other sites will be added as the data becomes available.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

The dataset represents known concentration areas of harvested or unharvested Atlantic razor clam (Ensis leei) in the Estuary and the Gulf of St. Lawrence, Quebec Region. It was created for the National Environmental Emergencies Centre (NEEC) for preparation and response purposes in case of an oil spill. Concentration areas were defined using data from Fisheries and Oceans Canada (DFO) inventories, various DFO research projects and commercial fisheries data. This layer is dependent on the inventories carried out and thus only represents the known areas of the Atlantic razor clam. This dataset does not represent the general distribution of the species nor the extent to which fishing is allowed. Most of the information comes from inventories that did not necessarily target this species, therefore its distribution is undoubtedly wider than what is recorded in the layer. In addition, the extent of shellfish beds can change over time in response to, among others, harvesting and recruitment rates. Some beds were mapped based on DFO research project data which were compiled in a benthic biodiversity Access database. Polygons drawn around these data are not precise and may be reviewed. However, the precision is sufficient for the resource protection and management needs in case of an environmental incident. Data sources and references:Anonym. 1991. Analyse de l'échantillonnage en mer des mactres de Stimpson. Programme d'adaptation des pêches de l'Atlantique. Pesca tec International. Pêches et Océans Canada. 134 p.Bernier, L. and L. Poirier. 1979. Évaluation sommaire du stock de mactres de l'Atlantique, Spisula solidissima Dillwyn, des Îles-de-la-Madeleine (golfe du Saint-Laurent). Cahier d'information, ministère de l'Industrie et du Commerce. 42 p.Bourdages, H., P. Goudreau, J. Lambert, L. Landry and C. Nozères. 2012. Distribution des bivalves et gastéropodes benthiques dans les zones infralittorale et circalittorale des côtes de l’estuaire et du nord du golfe du Saint-Laurent. Rapp. tech. can. sci. halieut. aquat. 3004 : iv + 103 p.Bourget, E. and D. Messier. 1983. Macrobenthic density, biomass, and fauna of intertidal and subtidal sand in a Magdalen Islands lagoon, Gulf of St. Lawrence. Can. J. Zool. 61(11):2509-2518.Brulotte, S. Unpublished data. Fisheries and Oceans Canada.Brulotte, S. 2013. Évaluation des stocks de la mactre de l’Atlantique, Spisula solidissima, des eaux côtières des Îles-de-la-Madeleine – méthodologies et résultats. Secr. can. de consult. sci. du MPO. Doc. de rech. 2013/082: x + 58 p.Brulotte, S. 2016. Évaluation des stocks de mactre de l’Atlantique, Spisula solidissima, des Îles-de-la-Madeleine, Québec en 2015 – méthodologie et résultats. Secr. can. de consult. sci. du MPO. Doc. de rech. 2016/074. x + 51 p.Brulotte, S., M. Giguère, S. Brillon and F. Bourque. 2006. Évaluation de cinq gisements de mye commune (Mya arenaria) aux Îles-de-la-Madeleine, Québec, de 2000 à 2003. Rapp. Tech. can. Sci. halieut. Aquat. 2640 : xii + 92 p.DFO. 2013. Assessment of Razor Clam stock in Québec’s Inshore Waters in 2012. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2013/052.Elouard, B, G. Desrosiers, J.C. Brêthes and Y. Vigneault. 1983. Étude de l'habitat du poisson autour des ilots créés par des déblais de dragage; lagune de Grande-Entrée, Îles-de-la-Madeleine. Can. Tech. Rep. Fish. Aquat. Sci. 1209:viii + 69 p.Gendreau, Y. 2018. MS Access database on benthic biodiversity. Fisheries and Oceans Canada.Provencher, L. and C. Nozères. 2011. Protocole de suivi des communautés benthiques de la zone de protection marine Manicouagan. Secr. can. de consult. sci. du MPO. Doc. de rech. 2011/051:iv +25 p.Thompson, M., D. Drolet and J.H. Himmelman. 2005. Localization of infaunal prey by the sea star Leptasterias polaris. Mar. Biol. 146(5):887-894.

The number of days during the forecast period with an average wind speed greater than 30 km/h (nswd_prob).Week 1 and week 2 forecasted probability is available daily from September 1 to August 31.Week 3 and week 4 forecasted probability is available weekly (Thursday) from September 1 to August 31.Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively.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 and weekly basis.

The probability of maximum wind above 70km/h (mdws70_prob).Week 1 and week 2 forecasted probability is available daily from September 1 to August 31.Week 3 and week 4 forecasted probability is available weekly (Thursday) from September 1 to August 31.Winds can significantly influence crop growth and yield mainly due to mechanical damage of plant vegetative and reproductive organs, an imbalance of plant-soil-atmosphere water relationships, and pest and disease distributions in agricultural fields. The maximum wind speed and the number of strong wind days over the forecast period represent short term and extended strong wind events respectively.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 and weekly basis.