The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.

Description:Seasonal temperature climatology of the Northeast Pacific Ocean was computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period. Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal temperature climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatology of the Northeast Pacific Ocean, in high spatial resolution of 1/300 degree.References:Foreman, M. G. G., W. R. Crawford, J. Y. Cherniawsky, and J. Galbraith (2008). Dynamic ocean topography for the northeast Pacific and its continental margins, Geophys. Res. Lett., 35, L22606, doi: 10.1029/2008GL035152.Data Sources:NOAA, MEDS and IOS observational dataUncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

Description:Seasonal sigma-t climatology of the Northeast Pacific Ocean was computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period.Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal sigma-t climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatology of the Northeast Pacific Ocean, in high spatial resolution of 1/300 degree.References:Foreman, M. G. G., W. R. Crawford, J. Y. Cherniawsky, and J. Galbraith (2008). Dynamic ocean topography for the northeast Pacific and its continental margins, Geophys. Res. Lett., 35, L22606, doi: 10.1029/2008GL035152Data Sources:NOAA, MEDS and IOS observational dataUncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

Bivalve aquaculture has direct and indirect effects on plankton communities, which are highly sensitive to short-term (seasonal, interannual) and long-term climate changes, although how these dynamics alter aquaculture ecosystem interactions is poorly understood. Here, we investigate seasonal patterns in plankton abundance and community structure spanning several size fractions from 0.2 µm up to 5 mm, in a deep aquaculture embayment in northeast Newfoundland, Canada. Using flow cytometry and FlowCam imaging, we observed a clear seasonal relationship between fraction sizes driven by water column stratification (freshwater input, nutrient availability, light availability, water temperature). Plankton abundance decreased proportionally with increasing size fraction, aligning with size spectra theory. Within the bay, greater mesozooplankton abundance, and a greater relative abundance of copepods, was observed closest to the aquaculture lease. No significant spatial effect was observed for phytoplankton composition. While the months of August to October showed statistically similar plankton composition and size spectra slopes (i.e., food chain efficiency) and could be used for interannual variability comparisons of plankton composition, sampling for longer periods could capture long-term phenological shifts in plankton abundance and composition related to various processes, including climate change. Conclusions provide guidance on optimal sampling to monitor and assess aquaculture pathways of effects.Cite this data as: Sharpe H, Lacoursière-Roussel A, Gallardi D (2024). Ecological insight of seasonal plankton succession to monitor shellfish aquaculture ecosystem interactions. Version 3.2. Fisheries and Oceans Canada. Sampling event dataset. https://doi.org/10.25607/2ujdvh

Description:Seasonal climatologies for temperature of the Northeast Pacific Ocean were computed to cover the period 2001 to 2020. Historical observations included all available conductivity-temperature-depth (CTD), bottle and profiling floats in the NODC World Ocean Database, Marine Environmental Data Services (MEDS), Institute of Ocean Sciences Water Properties website and the Canadian Integrated Ocean Observing System (CIOOS Pacific).Methods:Interpolation was carried out in up to fifty-two vertical levels from surface to 5000m. Data-Interpolating Variational Analysis (DIVA) was used for spatial interpolation for all years within each season and estimates projected onto a consistent grid. The average of the grid nodes was calculated to obtain the seasonal climatology. DIVA was used again on the final climatology followed by a median filter and a 5-point smoother. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal temperature climatologies for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatologies of the Northeast Pacific Ocean, in high spatial resolution of 1/300 degree.Data Sources:NODC, MEDS, IOS and CIOOS Pacific Data.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

Description:Seasonal climatologies for salinity of the Northeast Pacific Ocean were computed to cover the period 2001 to 2020. Historibal observations included all available conductivity-temperature_depth (CTD), bottle and profiling floats in the NODC World Ocean Database, Marine Environmental Data Services (MEDS), Institute of Ocean Sciences Water Properties website and the Canadian Integrated Ocean Observing System (CIOOS Pacific).Methods:Interpolation was carried out in up to fifty-two vertical levels from surface to 5000m. Data-Interpolating Variational Analysis (DIVA) was used for spatial interpolation for all years within each season and estimates projected onto a consistent grid. The average of the grid nodes was calculated to obtain the seasonal climatology. DIVA was used again on the final climatology followed by a median filter and a 5-point smoother. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal salinity climatology for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatology of the Northeast Pacific Ocean, with high spatial resolution of 1/300 degree.Data Sources:NODC, MEDS, IOS and CIOOS Pacific Data.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

Description:Seasonal climatologies (temperature, salinity, and sigma-t) of the Northeast Pacific Ocean were computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period in spatial resolution ranging from approximately 100m to 70km.Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

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.

Description:Seasonal climatologies (temperature, salinity, sigma-t, and elevation) of the Northeast Pacific Ocean were computed from historical observations including all available conductivity-temperature-depth (CTD), bottle, expendable bathy-thermograph (XBT), and Argo data in NOAA (http://www.argo.ucsd.edu/), Marine Environmental Data Service (MEDS), and Institute of Ocean Sciences archives over 1980 to 2010 period. Methods:Calculations, including smooth and interpolation, were carried out in sixty-five subregions and up to fifty-two vertical levels from surface to 5000m. Seasonal averages were computed as the median of yearly seasonal values. Spring months were defined as April to June, summer months were defined as July to September, fall months were defined as October to December, and winter months were defined as January to March. The data available here contain raster layers of seasonal climatologies (temperature, salinity, sigma-t, and elevation) for the Canadian Pacific Exclusive Economic Zone (EEZ), a subset of seasonal climatologies of the Northeast Pacific Ocean, in high spatial resolution of 1/300 degree.Uncertainties:Uncertainties are introduced when quality controlled observational data are spatially interpolated to varying distances from the observation point. Climatological averages are calculated from these interpolated values.

The Canadian Seasonal to Inter-annual Prediction System (CanSIPS) carries out physics calculations to arrive at probabilistic predictions of atmospheric elements from the beginning of a month out to up to 12 months into the future, resulting in seasonal forecasts. Atmospheric elements include temperature, precipitation, wind speed and direction and others. This product contains raw numerical results of these calculations. Geographical coverage is global. Data is available on a grid at a horizontal resolution of 2.5 degrees and 1 degree and for a few selected vertical levels. In addition, forecast probabilities for below, near, and above normal temperature and precipitation are available at both resolutions. Predictions and corresponding hindcast are made available monthly.