The wind power density layer shows the modeled wind power density [W/m2] 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 W/m2 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.

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.

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 such as evapotranspiration, 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 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.

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).

Climate observations are derived from two sources of data. The first are Daily Climate Stations producing one or two observations per day of temperature, precipitation. The second are hourly stations that typically produce more weather elements e.g. wind or snow on ground.

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 Coastal Infrastructure Vulnerability Index (CIVI) was jointly developed by DFO Science Branch, Small Craft Harbours (SCH) Program and the Economic Analysis and Statistics Directorate. The CIVI was designed with the intent of developing a climate change adaptation tool that would support management decisions regarding the long-term infrastructure planning for SCH sites.The CIVI provides a numerical indication of the relative vulnerability of small craft harbour sites to the effects of climate change and was designed with three component sub-indices: Environmental Exposure (natural forces), Infrastructure, and Socio-economic. The spatial component for the coastline was derived from the CanVec 1:50,000 hydrographic layer (https://open.canada.ca/data/en/dataset/9d96e8c9-22fe-4ad2-b5e8-94a6991b744b).This layer combines the 1:50,000 CanVec coastline of Canada with the following CIVI environmental exposure variables:- projected sea level rise (for the decades 2030, 2040,...2100) in meters- wave height (metres) and wind speed (metres/second)- change in sea ice coverage in Atlantic Canada from the 1970s to the 2000sSea level change:Data for relative sea level change (SLC) were derived from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC 2014, AR5). The projected relative sea level change under the high emission scenario (RCP8.5) was calculated for all years between 2006 and 2100. Sea level change for the years 2030, 2040, 2050, 2060, 2070, 2080, 2090, and 2100 were used.Wind Speed and Wave HeightModelled hindcasts of yearly maximum wind speed (1990 - 2012) and wave height (1990- 2014) were used. This dataset was generated from IFREMER wave hindcasts using the WAVEWATCH III model with wind data from NCEP Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010). Two high resolution (10 minute) grids of Atlantic and Pacific maximum modeled wind speeds and maximum significant wave height were used for southern Canadian coastal areas while a coarser (30 minute) worldwide grid was used for the Arctic areas. From these datasets the mean annual maximum wind speed over 23 years and the mean maximum significant wave height over 25 years were calculated.Change in sea ice coverage:Sea ice data from the Canadian Ice Service were acquired for Atlantic and Arctic Canada, representing percent ice coverage for each week over four decades (1970s, 1980s, 1990, 2000s). For each decade a single dataset was calculated to represent the sum of all weeks with ice coverage in excess of 50%, with a maximum possible score of 52 weeks for each decade. To measure change in ice duration, the summary mapsheet from the 2000s was subtracted from the 1970s summary mapsheet. The final dataset represents the change between the 1970s and 2000s in the number of weeks with ice concentrations greater than 50%. A positive number indicates a reduction in weeks of ice coverage, a negative number an increase in ice coverage.The data for individual small craft harbours included here contains predicted sea level change for the decades between 2030 and 2100, wave height, windspeed, change in sea ice coverage, population, and the final environmental exposure sub-index value (ESI). The population for each harbour is derived from the 2016 Census of Canada data for the Census subdivision (CSD) geographic unit.Reference:Relative sea-level projections for Canada based on the IPCC Fifth Assessment Report and the NAD83v70VG national crustal velocity modelhttps://doi.org/10.4095/327878 IPCC, 2014. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.Cite this data as: Greenan B. and Greyson P. Coastal Environmental Exposure Layer. Published March 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.