Climatological monthly-mean temperature and salinity data were computed for each of the 27 Line P stations (https://www.dfo-mpo.gc.ca/science/data-donnees/line-p/index-eng.html). For any particular station, data were accepted as belonging to that station if the location was within 10 km of the intended station (or 24km at Ocean Station Papa, P26). Data were binned by month/year over all available data for each station up to and including 2012. Hence the time interval that the mean state was computed from starts between 1956 and 1960 and ends at the end of 2012. Standard deviations were computed for each month independently and at each 5-m depth bin and were estimated as the variability between different years for the month in question.

For nearly three decades, the SCRIBE system has been used to assist meteorologists in preparing weather reports. The philosophy behind SCRIBE is that a set of weather element matrices are generated for selected stations or sample points and then transmitted to regional weather centers. The matrices are then decoded by SCRIBE and can be modified via the graphical interface by the users. The resulting data is then provided to a text generator, which produces bilingual public forecasts in plain language.The various rules related to the Scribe matrices hinder scientific innovation, do not exploit the richness of the Numerical Weather Prediction (NWP), reduce the comprehension of meteorological forecasts, and and may require frequent interventions from forecasters.As part of a larger modernization plan for the Meteorological Service of Canada (MSC), in which the role of the forecaster is evolving, the goal is to replace the Scribe matrices, available on the MSC Datamart, and their limited number of points across Canada with Weather Elements on the Grid ("WEonG").Weather Elements on Grid (WEonG) based on the Regional Deterministic Prediction System (RDPS) is a post-processing system designed to compute the weather elements required by different forecast programs (public, marine, aviation, air quality, etc.). This system amalgamates numerical and post-processed data using various diagnostic approaches. Hourly concepts are produced from different algorithms using outputs from the Regional Deterministic Prediction System (RDPS).

The Standardized Precipitation Index (SPI) has been recognized as the most accessible index for quantifying and reporting meteorological drought. On short timescales, the SPI is closely related to soil moisture, while at longer timescales, the SPI can be related to groundwater and reservoir storage. The model uses observed historical precipitation amounts to compute probability distributions which are then normalized using an incomplete gamma function over a range of timescales. The values can be interpreted as the number of standard deviations by which the observed anomaly deviates from the long-term mean. where positive values (greater than zero) result from above average conditions.

For nearly three decades, the SCRIBE system has been used to assist meteorologists in preparing weather reports. The philosophy behind SCRIBE is that a set of weather element matrices are generated for selected stations or sample points and then transmitted to regional weather centers. The matrices are then decoded by SCRIBE and can be modified via the graphical interface by the users. The resulting data is then provided to a text generator, which produces bilingual public forecasts in plain language.The various rules related to the Scribe matrices hinder scientific innovation, do not exploit the richness of the Numerical Weather Prediction (NWP), reduce the understanding of weather forecasts, and and may require frequent interventions from forecasters.As part of a larger modernization plan for the Meteorological Service of Canada (MSC), in which the role of the forecaster is evolving, the goal is to replace the Scribe matrices, available on the MSC Datamart, and their limited number of points across Canada with Weather Elements on the Grid ("WEonG").Weather Elements on Grid (WEonG) based on the Global Deterministic Prediction System (GDPS) is a post-processing system designed to compute the weather elements required by different forecast programs (public, marine, aviation, air quality, etc.). This system amalgamates numerical and post-processed data using various diagnostic approaches. Hourly concepts are produced from different algorithms using outputs from the Global Deterministic Prediction System (GDPS).

Description:Night-time sea surface temperature (SST) was retrieved from the MODIS instrument on the Aqua satellite, with data distributed by the NASA Ocean Biology Processing Group, and averaged into monthly climatological composites. The data span the years 2003-2020; records were created at 1 km pixel resolution to be consistent with other satellite products.Methods:MODIS-Aqua night long-wave Sea Surface Temperature (SST) images were acquired from the NASA Ocean Biology Processing Group at processing Level-2 (version 2018), 1-km resolution, spanning the period 2003-01-01 to 2020-12-31. Image pixels were aligned and mapped to a regular grid using the SeaDAS program, retaining all pixels with a quality level of ‘1’ or lower, which is recommended for scientific analysis. The monthly mean value at all pixels was calculated for individual years, and used to produce maps of the monthly climatological mean and standard deviation of SST. Additionally, the number of occurrences of valid data at each pixel over the period of observation were calculated. Pixels with fewer than two occurrences over the entire period of observation were removed from these maps, and set to a NaN value in the tif files. A few small gaps between pixels (near the edges of individual images) were filled using the median value of surrounding pixels, provided there were greater than 4 values. Finally, all rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.01 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided.Data Sources:NASA Ocean Biology Processing Group. (2017). MODIS-Aqua Level 2 Ocean Color Data Version R2018.0. NASA Ocean Biology Distributed Active Archive Center. https://doi.org/10.5067/AQUA/MODIS/L2/OC/2018Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

Real-time water level and flow (discharge) data collected at over 2100 hydrometric stations across Canada (last 30 days).

The Canadian Weathership Program collected meteorological data at Station Papa (50N, 145W) in the North Pacific Ocean between 1949 and 1981. In 2014, researchers at the University of Washington (UW) Applied Physics Laboratory (APL) and the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory (PMEL) analyzed this historic data to determine its efficacy as a scientific tool. The data available here are the Government of Canada data files that were utilized for this analysis. The "OWSP Full Data (1949-1981)" file contains the entire Canadian Weathership Program record of data collected from Station Papa and the "OWSP Daily Averaged Wind Speed and Wave Height Data (1949-1981)" file contains daily averaged values of wind speed and wave height generated by the UW APL and NOAA PMEL researchers. The Data Dictionary for each data file contains notes on any quality controls that were applied to the data by the UW APL and NOAA PMEL researchers. The UW documents titled, "Data Documentation for Dataset 1170 (DSI-1170), Surface Marine Data, National Climatic Data Center" (https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/25570/td1170.pdf?sequence=6&isAllowed=y) and "Table detailing units of data values in each file" (https://digital.lib.washington.edu/researchworks/handle/1773/25570), provide further information on the key values, point scales, and other units that were used in these datasets.

This dataset contains area-based management measures that have been recognized as 'other effective area-based conservation measures' (OECMs). It also contains basic information about OECMs, specifically their names, size, objectives, associated prohibitions, and DFO region. Spatial data for OECMs will be evaluated regularly, taking the most recent available information into account. In addition new 'OECMs will be identified over time. Therefore, this dataset may change over time.

Weather Elements on Grid (WEonG) based on the High Resolution Deterministic Prediction System (HRDPS) is a post-processing system designed to compute the weather elements required by different forecast programs (public, marine, aviation, air quality, etc.). This system amalgamates numerical and post-processed data using various diagnostic approaches. Hourly concepts are produced from different algorithms using outputs from the pan-Canadian High Resolution Deterministic Prediction System (HRDPS-NAT).

Statistically downscaled multi-model ensembles of projected change (also known as anomalies) in maximum temperature (°C) are available at a 10km spatial resolution for 1951-2100. Statistically downscaled ensembles are based on output from twenty-four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCM). Daily maximum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded maximum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. Projected change in maximum temperature (°C) is with respect to the reference period of 1986-2005. Seasonal and annual averages of projected maximum temperature change to 1986-2005 are provided. Specifically, the 5th, 25th, 50th, 75th and 95th percentiles of the downscaled ensembles of maximum temperature change are available for the historical time period, 1901-2005, and for emission scenarios, RCP2.6, RCP4.5 and RCP8.5, for 2006-2100. Twenty-year average changes in statistically downscaled maximum temperature (°C) 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 downscaled 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.