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 Homogenized Surface Pressure data consist of monthly, seasonal and annual means of hourly sea level and station pressure (hectopascals) for 626 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).

CANGRD is a set of Canadian gridded annual, seasonal, and monthly temperature and precipitation anomalies, which were interpolated from stations in the Adjusted and Homogenized Canadian Climate Data (AHCCD); it is used to produce the Climate Trends and Variations Bulletin (CTVB).

Monthly, seasonal and annual trends of mean hourly sea level and station pressure change (hectopascals) 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 pressure 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.

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.

Monthly, seasonal and annual trends of daily minimum, mean and maximum surface air temperature change (degrees Celsius) 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 temperature 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 trends of mean surface air temperature change (degrees Celsius) for 1948-2016 based on Canadian gridded data (CANGRD) are available at a 50km resolution across Canada. Temperature trends represent the departure from a mean reference period (1961-1990). CANGRD data are interpolated from adjusted and homogenized climate station data (i.e., AHCCD datasets). Homogenized climate data incorporate adjustments to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation.

Monthly, seasonal and annual trends of total precipitation change (millimetres) based on adjusted 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 precipitation 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.

The AHCCD collection includes two types of climate datasets: homogenized and adjusted. The homogenized datasets include monthly or daily surface air temperature, precipitation, and wind speed data for hundreds of climatological locations across Canada. These datasets incorporate a homogenization process, which typically uses statistical methods to detect and remove discontinuities in observed climate records caused by non-climatic influences, such as changes in measurement instruments or monitoring station relocations. In some cases, non-statistical adjustment methods are also applied to correct measured values for known biases caused by instruments or observation techniques (for example, under-catch by certain rain gauges or wind effects). Sometimes, data from multiple nearest stations are combined to extend the length of the record. Together, these methods produce consistent and more reliable Canadian homogenized station data series suitable for detecting and analyzing climate trends and variability. The adjusted datasets are available for precipitation variables only (i.e., rainfall, snowfall, and total precipitation). These datasets do not undergo a homogenization process; instead, they apply corrections for known measurement biases. AHCCD datasets are updated as new adjustment or homogenization methods are developed and/or to include the most recent observations. The homogenized datasets are also used to develop Canadian gridded homogenized datasets (https://catalogue.ec.gc.ca/geonetwork/srv/eng/catalog.search#/metadata/10ff2865-ff61-4f35-af71-3902da51f23a) to provide more continuous spatial representations of climate variables, including in areas where direct observations are unavailable.While homogenized data are the most suitable for studying long-term climate trends, both homogenization and adjustment procedures can introduce their own uncertainties or biases, and the results may change as methods are updated based on new research. It is important for users to carefully read the dataset documentation and understand how the data were processed to ensure their suitability for their research needs and other applications.

Electoral division of the 2017 election.**Collection context** Creation of districts in collaboration with the legal services and the electoral data of the Chief Electoral Officer (DGE). Balancing of districts according to anthropogenic constraints and number of voters.**Collection method** Computer-aided mapping.**Attributes*** `ID_SEC_DIS` (`long`): Identifier* `NAME_DISTRI` (`varchar`): District name* `NO` (`long`): District number* `AREA` (`varchar`): Area* `ADVISORY_NAME` (`varchar`): Name of the advisor* `SOURCE` (`varchar`): Source* `DATE_CREAT` (`date`): Creation date* `DATE_MODIF` (`date`): Date of modification* `USER_MODIF` (`varchar`): Modified byFor more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**