Statistically downscaled multi-model ensembles of minimum temperature 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 minimum temperature from GCM outputs were downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2). A historical gridded minimum temperature dataset of Canada (ANUSPLIN) was used as the downscaling target. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled minimum temperature (°C) are available for the historical time period, 1951-2005, 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.

Each pixel value corresponds to the quality control, cloud cover and snow fraction value for each pixel in the Best-Quality Max-NDVI product.

The national wetland layer contains wetland data compiled from the best available data from each region, classified by wetland type. Wetlands are mapped as polygons in geographic layers, which are integrated into a master geodatabase at the national scale.Information from each contributing dataset was classified based on the Canadian Wetland Classification System, which contains five main wetland classes (Bog, Fen, Marsh, Swamp, and Shallow Water) that represent the types of wetlands encountered in Canada. An additional category, “partially classified” was used to preserve boundary information for wetlands that could not be classified into the main categories with existing information.

Statistically downscaled multi-model ensembles of maximum temperature 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. The 5th, 25th, 50th, 75th and 95th percentiles of the monthly, seasonal and annual ensembles of downscaled maximum temperature (°C) are available for the historical time period, 1951-2005, 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 Grain Elevators in Canada dataset maps the list of grain elevators in Canada as provided by the Canadian Grain Commission (CGC). The elevators have been located as much as possible to an actual location rather than generalizing to the station name centroid. Additionally car spot information from CN, CP and the grain companies has been added where this has been published. This dataset attempts to provide a temporal and geographical extent of the grain elevators in Canada.

Description: This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021. Methods: This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans. The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone. Model Output Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes. While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016). Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditions Uncertainties: The historical climatologies were evaluated using observational climatologies generated from stations with a long time series of data over the time period including CTDs, nutrient profiles, lighthouse, satellite SST and buoy data. The model is able to represent the historical conditions with an acceptable bias. The resolution of this model is insufficient to represent the narrow straits and channels of this region so the dataset includes a cautionary mask to exclude these regions. These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability. We caution that our experimental design does not permit analysis of the effects of natural climate variability. We recommend using both of the scenarios (RCP4.5 and RCP8.4) to estimate the scenario uncertainty in these projections.

30-year Average precipitation represents the average amount (mm) of precipitation received in a month across a 30 year period (1961-1991, 1971-2000, 1981-2010, 1991-2020). These values are calculated across Canada in 10x10 km cells.

The Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

Aquatic invasive species (AIS) are non-native species that pose ecological and/or economic threats to Canada's coastal waters and resources. In response, Fisheries and Oceans Canada (DFO) established a program to detect and track the spread of aquatic invasive species in Canadian waters (2006). In the Newfoundland and Labrador (NL) Region, these species include biofouling organisms (tunicates, bryozoans, crustaceans, seaweeds), European Green Crab, and recently, several freshwater species (data not available at this time). DFO NL Science monitors for AIS in partnership with other DFO branches, the provincial government, Memorial University, including the Marine Institute, industry, first nations, and NGOs. Methods for detecting AIS used by DFO and their partners, include settlement plates, trapping, seining, eDNA (water samples), qPCR (species confirmation), SCUBA surveys along wharves, floating docks and vessel hulls, and video surveys at high-risk harbours. The data collected from DFO's monitoring program and their partners provides an overview of the distribution of AIS in the NL Region. This information can be used by the general public, scientists, and DFO managers. AIS NL Biofouling Species Fisheries and Oceans Canada's (DFO) National Marine Biofouling Monitoring Program conducts annual field surveys to monitor the introduction, establishment, spread, species richness, and relative abundance of native and some non-native species in Newfoundland and Labrador (NL) Region since 2006. Standardized monitoring protocols employed by DFO's NL, Maritimes, Gulf, and Quebec regions include biofouling collector plates deployed from May to October at georeferenced intertidal and shallow subtidal sites, including public docks, and public and private marinas and nautical clubs. Initially, (2006-2017), the collectors consisted of three 10 cm by 10 cm PVC plates deployed in a vertical array and spaced approximately 40 cm apart, with the shallowest plate suspended at least 1 m below the surface to sample subtidal and shallow intertidal species (McKenzie et al 2016a). Three replicate arrays were deployed at least 5 m apart per site. Since 2018, collector networks have been modified to improve statistical replication, including up to 10 individual collectors deployed per site at 1 m depth and at least 5 m apart (as above) from May to October. Since 2006, seven invasive biofouling organisms have been detected in Newfoundland and Labrador harbours, marinas and coastal areas. Should be cited as follows: DFO Newfoundland and Labrador Region Aquatic Invasive Species Marine Biofouling Monitoring Program. Published March 2024. Coastal and Freshwater Ecology, Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John’s, Newfoundland and Labrador. Reference: Tunicates Golden star tunicate (Botryllus schlosseri) 2006 The Golden star tunicate was the first invasive tunicate detected in NL waters. It was reported in Argentia by the US Navy around 1945. It was found in 2006 on wharf structures in Argentia, Placentia Bay during the first AIS survey (Callahan et al 2010). This colonial tunicate is recognized by it star shaped grouping of individuals within the colony. It is currently found in Placentia Bay, Fortune Bay, St. Mary’s Bay, Conception Bay and the west coast of NL. The data provided here indicates the detections of this AIS in coastal NL. Violet tunicate (Botrylloides violaceus) 2007 The violet tunicate was first detected in NL waters in 2007 in Belleoram, Fortune Bay on wharf structures and vessels (McKenzie et al. 2016b). This colonial tunicate forms irregular shaped colonies usually of a solid color (orange, purple, yellow or cream). It is currently found in relatively small colonies in four harbours in NL; Placentia Bay (1), Fortune Bay (1), Conception Bay (1) and the west coast of NL (2). The data provided here indicates the detections of this AIS in coastal NL. Vase tunicate (Ciona intestinalis) 2012 The Vase tunicate, is a high impact solitary invader and was first detected by DFO in 2012 on the Burin Peninsula at Ship Cove and Little Bay, Placentia Bay. Various mitigation measures (McKenzie et al. 2016b) contained this invasive tunicate to a small area for six years within Placentia Bay. First detected in Fortune Bay as an established population in 2019, increasing reports of the Vase tunicate have been made along the south coast of Newfoundland. The data provided here indicates the detections of this AIS in coastal NL. Bryozoans Coffin box bryozoan (Membranipora membranacea) 2002 The Coffin box is a bryozoan (filter feeding animal) that forms white colored encrusting colonies, particularly on seaweed, but also on vessels and other surfaces. The cells are rectangular or “coffin box” shaped. It was first detected on the west coast of NL in 2002 and has since spread throughout the island, including southern Labrador. The data provided here indicates the detections of this AIS in coastal NL. Orange ripple bryozoan (Schizoporella japonica Ortmann) 2022 The Orange ripple bryozoan is an orange heavily calcified encrusting species found mainly on man made structures, rocks, shellfish, and vessels. It was first identified in NL in 2022 in Arnold’s Cove, Placentia Bay, but has likely been in NL for at least two years and is now suspected in several locations in Placentia Bay and Fortune Bay. A 2023 survey of the south coast of NL found several harbours invaded by this species. The data provided here indicates the detections of this AIS in coastal NL. Crustaceans (Biofouling) Japanese skeleton shrimp (Caprella mutica) 2006 The Japanese skeleton shrimp is tiny (1.5 -3.5 cm) and was first found on settlement plates in Placentia Bay during the first AIS biofouling survey in 2006. This species is currently found in many places in Placentia Bay, Conception Bay, Trinity Bay, and on the south coast of NL. This species inhabits, sometimes in large numbers (100,000s) ropes, moorings, and docks. The data provided here indicates the detections of this AIS in coastal NL. Seaweed Oyster thief (Codium fragile) 2012 Oyster thief is a green seaweed with thick spongy Y-shaped branches that resemble fingers (another common name is dead man’s fingers). This invasive species was first found in Placentia Bay in 2012. It is now found in several locations in Placentia Bay and Fortune Bay. It has also been found in one location in Notre Dame Bay. The data provided here indicates the detections of this AIS in coastal NL. AIS NL European green crab Fisheries and Oceans Canada's (DFO) AIS Science Program conducts annual field surveys to monitor the introduction, establishment, spread, and relative abundance of the European green crab in the NL region since 2006. Standardized monitoring and trapping protocols (McKenzie et al. 2022) are used by DFO's NL, Maritimes, Gulf, Quebec, and Pacific regions. The Fukui trap is the most commonly used trap but other methods are also used including shoreline collection, seining, and SCUBA dive surveys. Fukui traps have been deployed annually at both new and long-term monitoring locations throughout coastal Newfoundland and Labrador, particularly within Placentia Bay where they were first detected in North Harbour in 2007 (Blakeslee et al. 2010). It should be noted that there are two different populations of European Green Crab, a hybridized population in Placentia, St. Mary’s, and Fortune Bays and a cold tolerant population on the west and southwestern coast of NL (Lehnert et al. 2018). After initial detection in 2007, a separate invasion occurred in 2009 on the west coast of NL, and this cold-tolerant population is spreading eastward along the south coast toward areas inhabited by the original hybrid strain. The data provided here indicates the detections of this AIS in coastal NL. Should be cited as follows: DFO Newfoundland and Labrador Region Aquatic Invasive Species Green Crab Monitoring Program. Published March 2024. Coastal and Freshwater Ecology, Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John’s, Newfoundland and Labrador. From 2018-2022, the Coastal Environmental Baseline Program provided additional support to enhance sampling efforts in Placentia Bay.