This dataset includes fifteen GeoTIFFs of Sea Surface Temperature (SST) organized into five distinct marine regions along Canada's Pacific coast, derived from Sentinel 3 satellite data for the period of April-September 2017. For each region, three GeoTIFFs are provided which represent the mean, maximum, and standard deviation values of SST (degrees Celcius). Jupyter notebooks with Python code used for data downloading and processing are also included for reference.The primary objective of this dataset is to provide detailed, regional spatial information on SST for key areas of Canada's Pacific coast, including the nearshore environment. The data can be used for various applications including species distribution modelling.This dataset is intended to fill the knowledge gap by providing high-resolution, spatially explicit regional SST data for Canada's Pacific coast. Existing datasets may not provide sufficient spatial resolution or may not include comprehensive statistical measures (mean, max, standard deviation) of SST for these specific areas.The dataset is structured by region. For each of the five regions, a set of three related GeoTIFFs is provided, representing the mean, max, and standard deviation of SST. Within each regional set, the three layers share the same spatial reference system, resolution, and extent, making them suitable for direct use in analytical stacks (e.g., for species distribution models).The Sentinel-3 satellites, part of the European Union's Copernicus Programme, are equipped with the Sea and Land Surface Temperature Radiometer (SLSTR) which measures SST among other parameters. The SST data products in this dataset are derived from Sentinel-3 satellite data.The intent of the data is to represent the marine environment and so a mask that excludes land was applied during data download and extraction. The SST data products have been resampled using a bilinear interpolation from their native resolution to a 20 m resolution to provide more detailed spatial information.

Daily sea surface temperature and salinity observations have been carried out at several locations on the coast of British Columbia since the early part of the 20th century. Observations started at the Pacific Biological Station (Departure Bay) in 1914; 11 stations were added in the mid-1930s and several more in the 1960s. The number of stations reporting at any given time has varied as sampling has been discontinued at some stations and started or resumed at others.Presently termed the British Columbia Shore Station Oceanographic Program (BCSOP), there are 12 active participating stations. Most of the stations are at lighthouses staffed by Fisheries and Oceans Canada, but three (Race Rocks, Amphitrite Point, and Active Pass) are sampled by contracted observers.Observations are made daily using seawater collected in a bucket lowered into the surface water at or near the daytime high tide. This sampling method was designed long ago by Dr. John P. Tully and has not been changed in the interests of a homogeneous data set. This means, for example, that if an observer starts sampling one day at 6 a.m., and continues to sample at the daytime high tide on the second day the sample will be taken at about 06:50 the next day, 07:40 the day after etc. When the daytime high-tide gets close to 6 p.m. the observer will then begin again to sample early in the morning, and the cycle continues. Since there is a day/night variation in the sea surface temperatures the daily time series will show a signal that varies with the14-day tidal cycle. This artifact does not affect the monthly sea surface temperature data.

Wang, Z., Greenan, B.J.W., Hannah, C.G., and Layton, C. 2025. Past and future sea surface temperature changes in the oceans surrounding Canada. Can. Tech. Rep. Hydrogr. Ocean. Sci. 404: v + 44 pThis study presents changes in the sea surface temperature (SST) in the oceans surrounding Canada using past observations and model projections of future scenarios. The past changes are derived using an SST product, HadISST, in which a recent period (2012-2022) was referenced to a 26-year climatology (1955-1980). The future changes in SST are estimated using a 22-member ensemble of CMIP6 models. The SST changes for overlapping periods from the CMIP6 ensemble and the HadISST in the 10 regions of the Canadianshelf waters are in general agreement, although the CMIP6 results tend to overestimate the observed changes by about 0.1 oC. One exception to this is the Scotian Shelf where the CMIP6 models underestimate the observed SST change. The Gulf of Maine, Scotian Shelf, Gulf of St. Lawrence and southern Newfoundland shelf are the regions with the largest observed SST increases around Canada. The Gulf of St. Lawrence has the highest correlation (r=0.65) with the Atlantic Multi-decadal Oscillation (AMO) among the subregions in the North Atlantic Ocean, and the British Columbia Shelf is correlated with the Pacific Decadal Oscillation (r=0.58). Under the four climate scenarios (SSP1-2.6 to SSP5-8.5), among the mid-century (2040-2059) annual mean SST changes (reference period of 1990-2014) in the 10 regions, the Gulf of St. Lawrence is projected to have the largest increases in temperature (1.8 – 2.5oC), and Baffin Bay has the smallest increases (0.5 – 0.9oC), However, for the summer means, the southern Beaufort Sea has the largest SST increase (2.4 -3.1oC) with Baffin Bay having the smallest changes (1.3-2.1oC).Cite this data as: Wang, Z., Greenan, B.J.W., Hannah, C.G., and Layton, C. (2025) Data of:Past and Future Sea Surface Temperature Changes in the Oceans Surrounding Canada.Published: October 2025. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.https://open.canada.ca/data/en/dataset/3c336e55-4266-406a-922d-bbf8e717558c

Data from the analysis of sea surface temperature, sea surface salinity, bottom temperature, and bottom salinity, over the Gulf of Maine and Scotian Shelf, for 23 CMIP6 models. The analysis includes an evaluation of CMIP6 model performance for the CMIP6 historical (1950-2014) experiment. Future projections are summarized for CMIP6 scenarios SSP245 and SSP370 with the calculation of relative annual and seasonal changes between the historical period (1950-2014) and three future periods (2030-2039, 2040-2049, 2030-2049).Wang, Z., DeTracey, B., Maniar, A., Greenan, B., Gilbert, D. and Brickman, D., Future hydrographic state of the Scotian Shelf and Gulf of Maine from 23 CMIP6 models. Can. Tech. Rep. Hydrogr. Ocean. Sci. XXX: vii + XXXp.Cite this data as: Wang, Z., DeTracey, B., Maniar, A., Greenan, B., Gilbert, D. and Brickman, D. Future hydrographic state of the Scotian Shelf and Gulf of Maine from 23 CMIP6 Models. Published July 2022. Ocean Ecosystem Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/6247bb5a-14b3-461d-9ed3-b42553107bbc

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.

In 2018, Fisheries and Oceans Canada initiated the Multidisciplinary Arctic Program (MAP) – Last Ice, the first ecosystem study of the poorly characterized region of the Lincoln Sea in the Marine Protected Area of Tuvaijuittuq, where multiyear ice still resides in the Arctic Ocean. MAP-Last Ice takes a coordinated approach to integrate the physical, biochemical, and ecological components of the sea ice-ocean connected ecosystem and its response to climate and ocean forcings. The cross-disciplinary program establishes baseline ecological knowledge for Tuvaijuittuq and, in particular, for its unique multiyear ice ecosystem. The database provides baseline data on the abundance of bacteria and viruses in multi- and first-year ice and in surface waters of the Lincoln Sea in Tuvaijuittuq, and their relation to bio-physical conditions. The data were collected during the 2018 spring field campaign of the MAP-Last Ice Program, at an ice camp offshore of Canadian Forces Station (CFS) Alert.

This dataset contains the surface temperature and salinity data of the enlarged coastal thermograph network of the St. Lawrence river, estuary and gulf system. It includes data from the Canadian Hydrographic Service water level network (SINECO), the Department of Fisheries and Oceans (DFO)-Quebec long-termed thermograph monitoring program network and the oceanographic buoy network.Each station is linked with a .png file showing the temperature and salinity time series and with a .csv file containing the surface temperature and salinity data themselves (columns : Station,Latitude,Longitude,Date(UTC),Depth/Profondeur(m),Temperature/Température(ºC),Salinity/Salinité(psu)).Supplemental InformationA detailed description of the networks (SINECO, oceanographic buoys and the DFO-Quebec thermograph monitoring program) is available at the St. Lawrence Global Observatory (SLGO) portal :SINECO : https://ogsl.ca/en/tide-gauges-dfo-chs/Oceanographic buoys : https://ogsl.ca/en/marine-conditions-buoys-dfo/Thermographs: https://ogsl.ca/en/marine-conditions-thermographs-dfo/Technical Reports related to the Thermograph Network (the last one is also available at the same hypertext link mentionned above) :Pettigrew, B., Gilbert, D. and Desmarais R. 2016. Thermograph network in the Gulf of St. Lawrence. Can. Tech. Rep. Hydrogr. Ocean Sci. 311: vi + 77 p.Pettigrew, B., Gilbert, D. and Desmarais R. 2017. Thermograph network in the Gulf of St. Lawrence: 2014-2016 update. Can. Tech. Rep. Hydrogr. Ocean Sci. 317: vii + 54 p.

This dataset was collected in support of a Competitive Science and Research Fund project (21-CC-05-06 Impacts of coastal acidification and climate change stressors on the Atlantic sea scallop: larval supply, recruitment and adaptive capacity to multiple global change drivers) lead by Fisheries and Oceans Canada (DFO). The objective of this research is to characterize coastal environmental conditions associated with scallop spawning and larval drift in Passamaquoddy Bay, New Brunswick. This dataset includes temperature, conductivity, salinity, sigma-theta, sea pressure, and depth information taken at weekly intervals at the sampling stations. In total, this dataset represents a total of 62 CTD profiles collected across 3 sampling stations over 22 sampling days from June to October 2022. Sampling stations were selected to compare scallop recruitment signals from Chamcook Harbour, a decommissioned scallop aquaculture site in Big Bay (MS-1077) and in the middle of Passamaquoddy Bay. Data were processed in accordance with instrumentation manufacturer guidelines and DFO Ocean Data and Information Section QAQC procedures. Cite this data as: Miller, E., Quinn, B., Azetsu-Scott, K., Childs, D., Gabriel, C-E., Newhook, M. 2025. Impacts of coastal acidification and climate change stressors on the Atlantic sea scallop. Published October 2025. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B

This record contains two-weekly minimum sea ice concentration images of the Canadian Beaufort Sea at 1.1 km resolution. The dataset originated from the Canadian Ice Service (CIS) Digital Archive weekly regional charts for the western Arctic (See “additional credit” for a link to these data), created by synthesis of remotely-sensed, ship and airborne observations (Fequet, 2005). These vector ice charts were gridded at 1.1 km resolution and aggregated into two-week composites by calculating the minimum sea-ice concentration at each grid cell over each two-week interval in each year. Week numbers were defined using the ISO 8601 convention, and sea-ice concentration isrepresented in tenths (with 0/10 corresponding to an ice-free pixel, ranging to 10/10 corresponding to 100% pixel coverage with sea-ice). The result is 12 composite images per year in 1998 through 2020 (23 years), corresponding to https://open.canada.ca/data/en/dataset/ee27e86f-7b18-4e3f-8444-0c5efb6110a4. For further details, see Galley et al., 2022.

Seasonal and annual multi-model ensembles of projected change (also known as anomalies) in sea ice concentration based on an ensemble of twenty-eight Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models are available for 1900-2100. Sea ice concentration is represented as the percentage (%) of grid cell area. Therefore, projected change in sea ice concentration 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 ensembles of sea ice concentration 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 sea ice concentration (%) 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.