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).

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.

Canadian hourly climate data are available for public access from the ECCC/MSC's National Climate Archive. These are surface weather stations that produce hourly meteorological observations, taken each hour of the day. Only a subset of the total stations found on Environment and Climate Change Canada’s Historical Climate Data Page is shown due to size limitations.The priorities for inclusion are as follows: stations in cities with populations of 10000+, stations that are Regional Basic Climatological Network status and stations with 30+ years of data.

Access historical weather, climate data, and related information for numerous locations across Canada. Temperature, precipitation, degree days, relative humidity, wind speed and direction, monthly summaries, averages, extremes and Climate Normals, are some of the information you will find on this site.

This dataset contains 21 statistics of wind and waves calculated from hourly reanalysis data of historical surface winds and ocean surface waves for the Canadian Beaufort Sea for the period 1970-2015 (each statistic has an annual and 12 monthly values). These data can be used for characterization of marine surface wind and wave climate conditions, trends and variability for Canadian Arctic waters, for use of coastal and offshore operations/risk management (e.g., shipping). The hourly wind and waves data may be obtained upon request from the Meteorological Service of Canada's Climate Services.

Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of 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.

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).

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.

This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.

The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.

The historical and future global warming thresholds and their corresponding time periods are as follows:

The following variables are included in the files:

This data set was distributed by NSIDC until October, 2003, when it was withdrawn from distribution because it duplicates the NOAA National Climatic Data Center (NCDC) data set TD-9816 'Canadian Monthly Precipitation' (Groisman, P.Y. 1998. National Climatic Data Center Data Documentation for TD-9816, Canadian Monthly Precipitation. National Climatic Data Center 151 Patton Ave., Asheville, NC. 21 pp.). TD-9816 contains monthly rainfall, snowfall and precipitation (the sum of rainfall and snowfall) values from 6,692 stations in Canada. NCDC investigator Pavel Groisman obtained the original data from the Canadian Atmospheric Environment Service (AES) in the early 1990s and adjusted the measurements to account for inconsistencies and changes in instrumentation over the period of record. TD-9816 contains both the original and adjusted data. Related data are the Historical Adjusted Climate Database for Canada, Version December 2002, and Rehabilitated Precipitation and Homogenized Temperature Data Sets provided by the Climate Monitoring and Data Interpretation Division's Climate Research Branch, Meteorological Service of Canada. Monthly Rehabilitated Precipitation and Homogenized Temperature Data Sets (updated annually) includes an alternative version of this data set using different correction methods. It is distributed by the Meteorological Service of Canada, who also provides a Microsoft Word document that compares the two different data correction methods.

The U.S. Daily Climate Normals for 1981 to 2010 are 30-year averages of meteorological parameters for thousands of U.S. stations located across the 50 states, as well as U.S. territories, commonwealths, the Compact of Free Association nations, and one station in Canada. NOAA Climate Normals are a large suite of data products that provide users with many tools to understand typical climate conditions for thousands of locations across the United States. As many NWS stations as possible are used, including those from the NWS Cooperative Observer Program (COOP) Network as well as some additional stations that have a Weather Bureau Army-Navy (WBAN) station identification number, including stations from the Climate Reference Network (CRN). The comprehensive U.S. Climate Normals dataset includes various derived products including daily air temperature normals (including maximum and minimum temperature normal, heating and cooling degree day normal, and others), precipitation normals (including snowfall and snow depth, percentiles, frequencies and other), and hourly normals (all normal derived from hourly data including temperature, dew point, heat index, wind chill, wind, cloudiness, heating and cooling degree hours, pressure normals). Users can access the data either by product or by station. Included in the dataset is extensive documentation to describe station metadata, filename descriptions, and methodology of producing the data. All data utilized in the computation of the 1981-2010 Climate Normals were taken from the ISD Lite (a subset of derived Integrated Surface Data), the Global Historical Climatology Network-Daily dataset, and standardized monthly temperature data (COOP). These source datasets (including intermediate datasets used in the computation of products) are also archived at the NOAA NCDC.

Environment Canada Data License Agreement:

http://climate.weather.gc.ca/prods_servs/attachment1_e.html

Environment Canada Terms and Conditions:

http://www.ec.gc.ca/default.asp?lang=En&xml=5830C36B-1773-4E3E-AF8C-B21F54633E0A

Environment Canada Quality of Historic Weather Data Statement:

http://climate.weather.gc.ca/climate_data/data_quality_e.html

Please direct any inquiries directly to Environment Canada:

http://climate.weather.gc.ca/contactus/contact_us_e.html

Meteorological Observations describe datasets that contain information about weather and climate conditions as available on the City-Pages of the Environment Canada WeatherOffice.gc.ca web site. These pages contain information about current weather conditions and past climate including temperature, wind, and humidity measurements, written descriptions of current conditions, rain and snow amounts, average and extreme temperatures, etc. The current conditions are acquired from a variety of observing system operators and are provided in near-real time with limited quality assurance. Current condition information should not be considered as quality-controlled official values. The availability of values for every observation period is not guaranteed as they may be affected by observing system operations.

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.

This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.

The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.

The historical and future global warming thresholds and their corresponding time periods are as follows:

Global Warming Scenario

Time Period

Historical

1991-2021

Global Warming 0.5ºC

2003-2033

Global Warming 1.0ºC

2014-2044

Global Warming 1.5ºC

2024-2054

Global Warming 2.0ºC

2034-2064

Global Warming 2.5ºC

2042-2072

Global Warming 3.0ºC

2051-2081

Global Warming 3.5ºC

2064-2094

The following variables are included in the files:

Variable Description

RUN Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data

YEAR Year associated with the record

MONTH Month associated with the record

DAY Day of the month associated with the record

HOUR Hour associated with the record

YDAY Day of the year associated with the record

DRI_kJPerM2 Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

DHI_kJperM2 Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

DNI_kJperM2 Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

GHI_kJperM2 Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

TCC_Percent Instantaneous total cloud cover at the HOUR in % (range: 0-100)

RAIN_Mm Total rainfall in mm (total from previous HOUR to the HOUR indicated)

WDIR_ClockwiseDegFromNorth Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)

WSP_MPerSec Instantaneous wind speed at the HOUR in meters/sec

RHUM_Percent Instantaneous relative humidity at the HOUR in %

TEMP_K Instantaneous temperature at the HOUR in Kelvin

ATMPR_Pa Instantaneous atmospheric pressure at the HOUR in Pascal

SnowC_Yes1No0 Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)

SNWD_Cm Instantaneous snow depth at the HOUR in cm

This catalogue includes model output data from two 11-year simulations performed using the version 6 of the Canadian Regional Climate Model (CRCM6-GEM5). CRCM6-GEM5 (Roberge et al., 2024) is a climate model developed at ESCER/UQAM that is based on the version 5 of the Global Environmental Multiscale (GEM5) model (McTaggart-Cowan et al., 2019), the CLASS land surface scheme (Verseghy 2000) and the FLake lake model (Martynov et al., 2012). GEM5 is developed by the group of Recherche en Prévision Numérique at Environment and Climate Change Canada (ECCC) and is used for numerical weather prediction in multiple weather prediction systems. CLASS is developed by the Canadian Centre for Climate Modelling and Analysis also at ECCC as part of the new CLASSIC scheme. The two simulations were performed using a 2.5-km horizontal grid spacing version of the CRCM6-GEM5 model (CRCM6-GEM5-2.5), following a similar configuration as described in Roberge et. al (2024). The main difference is that we used version 5.1.1 of the GEM5 model here, instead of version 5.0.2. The simulated domain has 1330 and 1060 grid points in the east-west and north-south directions respectively. The domain is centered over southern Quebec and covers a portion of northeastern North America. The two CRCM6-GEM5-2.5 simulations differ on the driving data used at the lateral boundaries. The historical climate simulation (denoted as hist) is driven by a 12-km version of the model that was in turn driven by the ERA5 reanalysis (Hersbach et al., 2020). The future climate simulation (denoted as pgw) is driven by a 12-km version of the CRCM6-GEM5 model (CRCM6-GEM5-12) that was also driven by the ERA5 reanalysis, but with an additional delta climate change perturbation, using the so-called Pseudo Global Warming (PGW) approach (Schär et al., 1996, Kröner et al., 2017) to simulate future changes. The climate change perturbation was taken from monthly mean fields averaged across an ensemble of 30 General Circulation Models (GCMs) from the sixth Coupled Model Intercomparison Project (CMIP6). The climate change signal was calculated between the periods 1990-2014 and 2076-2100 using the scenario SSP585. More details about the list of CMIP6 GCMs, the variables used and the interpolation into the ERA5 grid are provided in Argüeso (2023). The intermediate CRCM6-GEM5-12 simulations were run using spectral nudging in the interior of the domain as in Roberge et. al (2024), while the CRCM6-GEM5-2.5 simulations were only driven at the lateral boundaries by the CRCM6-GEM5-12 simulation.

This dataset contains 21 statistics of wind and waves calculated from hourly reanalysis data of historical surface winds and ocean surface waves for the Canadian East Coast for the period 1954-2015 (each statistic has an annual, 4 seasonal, and 12 monthly values). These data can be used for characterization of marine surface wind and wave climate conditions, trends and variability for Canadian waters (East Coast), and for use of coastal and offshore operations/risk management (e.g., oil platforms, shipping). The hourly wind and wave’s data may be obtained upon request from the Meteorological Service of Canada's Climate Services.

This data release contains historical SnowModel (Liston and Elder, 2006) output for the Crown of the Continent and surrounding areas in Montana, USA; and Alberta and British Columbia, Canada from September 1, 1981 through August 31, 2020. Fifteen daily variables were simulated or derived for this release: (1) snow water equivalent (swed), (2) liquid precipitation (rpre), (3) solid precipitation (spre), (4) albedo (albd), (5) glacial ice melt (glmt), (6) total precipitation (prec), (7) runoff (roff), (8) snow covered area (sca), (9) snow density (sden), (10) snowmelt (smlt), (11) snow depth (snod), (12) snow sublimation (ssub), (13) air temperature (tair), (14) wind speed (wspd), and (15) wind direction (wdir). The simulation used to produce these outputs was conducted on a 30 m geospatial grid and was forced using meteorology from a recently completed (2023) 4 kilometer reanalysis product using the Weather Research and Forecasting (WRF) model covering the conterminous United States (CONUS404, Rasmussen and others, 2023a; 2023b). Land cover information for the simulation was provided by the 2016 National Land Cover Database (Jin and others, 2019) and 30 m elevation information was provided by the National Elevation Dataset (Gesch and others, 2018).

This dataset contains 21 statistics (including extreme indices) of wind and waves calculated from hourly reanalysis data of historical surface winds and ocean surface waves for the Davis Strait Baffin Bay waters for the period 1979-2018 (each statistic has an annual and 12 monthly values). These data can be used for the characterization of marine surface wind and wave climate conditions, trends and variability for the Davis Strait Baffin Bay waters, for use of coastal and offshore operations/risk management (e.g., shipping). These data can be used for characterization of marine surface wind and wave climate conditions, trends and variability for the Davis Strait Baffin Bay waters, for use of coastal and offshore operations/risk management (e.g., shipping). The hourly wind and waves data may be obtained upon request from the Meteorological Service of Canada's Climate Services.

Coastal managers and ocean engineers rely heavily on projected average and extreme wave conditions for planning and design purposes, but when working on a local or regional scale, are faced with much uncertainty as changes in the global climate impart spatially varying trends. Future storm conditions are likely to evolve in a fashion that is unlike past conditions and is ultimately dependent on the complicated interaction between the Earth’s atmosphere and ocean systems. Despite a lack of available data and tools to address future impacts, consideration of climate change is increasingly becoming a requirement for organizations considering future nearshore and coastal vulnerabilities. To address this need, the USGS used winds from four different atmosphere-ocean coupled general circulation models (AOGCMs) or Global Climate Models (GCMs) and the WaveWatchIII numerical wave model to compute historical and future wave conditions under the influence of two climate scenarios. The GCMs respond to specified, time-varying concentrations of various atmospheric constituents (such as greenhouse gases) and include an interactive representation of the atmosphere, ocean, land, and sea ice. The two climate scenarios are derived from the Coupled Model Inter-Comparison Project, Phase 5 (CMIP5; World Climate Research Programme, 2013) and represent one medium-emission mitigation scenario (RCP4.5; Representative Concentration Pathways) and one high-emissions scenario (RCP8.5). The historical time-period spans the years 1976 through 2005, whereas the two future time-periods encompass the mid (years 2026 through 2045) and end of the 21st century (years 2081 through 2099/2100). Continuous time-series of dynamically downscaled hourly wave parameters (significant wave heights, peak wave periods, and wave directions) and three-hourly winds (wind speed and wind direction) are available for download at discrete deep-water locations along four U.S. coastal regions: • Pacific Islands • West Coast • East Coast • Alaska Coasts The Alaskan region includes a total of 25 model output points. Six output points surround the Arctic coast, eight surround the Aleutian Islands, four are within the shallow region of the Bering Sea, and the remaining seven are within the Gulf of Alaska. The U.S. West Coast region stretches from the U.S.- Mexico border to the U.S.- Canada border and includes open coast areas of California, Oregon, and Washington. The West Coast region includes fifteen model output points. Eight model output points are co-located with observation buoys and are identified by National Oceanic and Atmospheric Administration National Data Buoy Center (NDBC, http://www.ndbc.noaa.gov/) station numbers (N46229, N46213, N46214, N46042, N46028, N46069, N46219, N46047). The U.S. East and Gulf Coasts encompass fifteen coastal states stretching from the Gulf Coast States and Florida in the south to the U.S.-Canada border north of Maine. The region includes seventeen model output points; seven are co-located with NDBC observation buoys (N44011, N44014, N41001, N41002, N41010, N42001, N42055). Data summaries for the U.S. East and Gulf Coast regions are provided from the 1.25° x 1.00° global (NWW3) wave model grid (described in Data and Methods section below). Data summaries for the U.S. West Coast region are available from the NWW3 grid and from the finer resolution 0.25° x 0.25° Eastern North Pacific (ENP) grid nested within the NWW3 grid. Data summaries for the southern coast of Alaska are also available from the ENP grid. In cases where model data exist for both the NWW3 and ENP grids, both sets of data are available for download (http://dx.doi.org/10.5066/F7D798GR). The data and cursory overviews of changing conditions along the coasts are summarized in Storlazzi and others (2015) and Erikson and others (2016). References Cited: Erikson, L.H., Hegermiller, C.A., Barnard, P.L., and Storlazzi, C.D., 2016, Wave projections for United States mainland coasts: U.S. Geological Survey pamphlet to accompany data release, https://doi.org/10.5066/F7D798GR. Erikson, L.H., Hegermiller, C.A., Barnard, P.L., Ruggiero, P., and van Ormondt, M., 2015b, Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios: Journal of Ocean Modelling, v. 96, p. 171–185, https://doi.org/10.1016/j.ocemod.2015.07.004. Erikson, L.H., Hemer, M.A., Lionello, P., Mendez, F.J., Mori, N., Semedo, A., Wang, X.L., and Wolf, J., 2015a, Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling: Proceedings Coastal Sediments 2015, 13 p., https://doi.org/10.1142/9789814689977_0243. Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J-F., Matsumoto, K., Montzka, S.A., Raper, S.C.B., Riahi, K., Thomson, A., Velders, G.J.M., and van Vuuren, D.P.P., 2011, The RCP greenhouse gas concentrations and their extensions from 1765 to 2300: Climate Change, v. 109, p. 213–241, https://doi.org/10.1007/s10584-011-0156-z. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., and Wilbanks, T.J., 2010, The next generation of scenarios for climate change research and assessment: Nature, v. 463, p. 747–756, https://doi.org/10.1038/nature08823. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafai, P., 2011, RCP 8.5: Exploring the consequence of high emission trajectories: Climatic Change, v. 109, p. 33–57, https://doi.org/10.1007/s10584-011-0149-y. Storlazzi, C.D., Shope, J.B., Erikson, L.H., Hegermiller, C.A., and Barnard, P.L., 2015, Future wave and wind projections for United States and United States-affiliated Pacific Islands: U.S. Geological Survey Open-File Report 2015–1001, 426 p., https://doi.org/10.3133/ofr20151001. Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012, An overview of CMIP5 and the experiment design: Bulletin of the American Meteorological Society, v. 93, p. 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1. Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M.A., Clarke, L.E., Edmonds, J.A., 2011, RCP4.5: A pathway for stabilization of radiative forcing by 2100: Climatic Change, v. 109, p. 77–94, https://doi.org/10.1007/s10584-011-0151-4. van Vuuren, D.P., Edmonds, J.A., Kainuma, M., Riahi, K., Thomson, A.M., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., and Rose, S., 2011, The representative concentration pathways: an overview: Climatic Change, v. 109, p. 5–31, https://doi.org/10.1007/s10584-011-0148-z.

This dataset contains 21 statistics of wind and waves calculated from hourly reanalysis data of historical surface winds and ocean surface waves for the Canadian East Coast for the period 1954-2018 (each statistic has an annual, 4 seasonal, and 12 monthly values). These data can be used for characterization of marine surface wind and wave climate conditions, trends and variability for Canadian waters (East Coast), and for use of coastal and offshore operations/risk management (e.g., oil platforms, shipping). The hourly wind and wave’s data may be obtained upon request from the Meteorological Service of Canada's Climate Services.

The AdjDlyRS dataset contains adjusted daily rainfall (R) and snowfall (S) data from all Canadian stations reporting rainfall and snowfall for which we have metadata to do the adjustments (Wang et al. 2017). The processing includes inspection and adjustments using quality control procedures customized for producing gridded datasets (Wang et al. 2017), including: (1) conversion of snowfall ruler measurements to their water equivalents; (2) corrections for gauge undercatch and evaporation due to wind effect, for gauge specific wetting loss, and for trace precipitation amount; and (3) treatment of flags (e.g. accumulation flags). Version 2020 or later versions of this dataset also includes identification and correction of random erroneous values, including false zeros, which usually arose from missing values being misrecorded as 0 precipitation in the climate Archive (Cheng et al. 2022). All the identified erroneous daily values are set to missing. A total of 3346 stations were processed, but the data series are not homogenized. Most of the stations are located in southern Canada and have short and/or seasonal data records. The number of stations changes over time: there are 512-958 stations in the period 1948-1964, 1012-2038 stations in the period 1965-2008, and only around 300 stations in the recent years. Note that the unadjusted/raw total precipitation data in Environment and Climate Change Canada's digital Archive underestimate more than 25% of the total precipitation in northern Canada, and about 10-15% in most of southern Canada (Wang et al. 2017). References: (1) Wang, X. L., Xu, B. Qian, Y. Feng, E. Mekis, 2017: Adjusted daily rainfall and snowfall data for Canada, Atmosphere-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. (2) Cheng, V. Y.S., X. L. Wang, Y. Feng, 2022: A quality control system for historical in situ precipitation data. Atmosphere-Ocean (submitted)