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. The data available here are the outputs of NEP36-CanOE_RCP 8.5; a projection of the 2046-2065 climate for the no mitigation scenario RCP 8.5. 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. 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

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. 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

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. 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

The Northeast Pacific (NEP) new regional climatology is derived from the NCEI World Ocean Database archive of temperature and salinity and covers a time period from 1955 to 2012, or roughly six decades. The NEP is an important region in the North Pacific Ocean. The NEP is home to the California Current System (CCS) and contains a large coastal upwelling zone along the west coast of North America. The CCS is one of the most productive ecosystems in the World Ocean, and its multidecadal variability is also important for long-term Earth and ocean climate change studies. Due to the economic significance and climatic importance of the CCS, intensive observational and research programs took place over many decades and yielded rich oceanographic data arrays of the CCS and adjacent NEP regions. To provide an improved oceanographic foundation and reference for multi-disciplinary studies of the CCS and NEP, the NCEI Regional Climatology Team developed a new set of high-resolution, quality-controlled, and long-term annual, seasonal and monthly mean temperature and salinity fields at standard depth levels.

The Northeast Pacific (NEP) Regional Climatology (RC) version 2 replaces the previous version of the NEP RC published in 2017. Version 2 based on the recently released World Ocean Database (WOD23), and covers a time period from 1955 to 2022. The updated high-resolution temperature and salinity climatologies allow researchers to assess decadal ocean climate change more precisely in the critically important NEP region. The updates substantially increase the value of the NEP RC for ocean climate studies and other applications. The NEP is one of the most productive ecosystems in the World Ocean, home to the California Current System (CCS) and a large coastal upwelling zone along the west coast of North America. The economic and climatic importance of the CCS has prompted intensive observation and research over the decades, yielding rich oceanographic data arrays of the area and its adjacent regions.

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 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.

ANN-NEP is a gridded surface ocean pCO2 data product for the Northeast Pacific open ocean. It was created using non-linear functional relationships between pCO2 observations from the Surface Ocean CO2 Atlas v2021 as well as additional data from Fisheries and Oceans Canada February 2019 Line P cruise, and a variety of independent predictor variables (see supplemental information) using an artificial neural network self-organizing-map-feed-forward-network approach described and evaluated in Duke et al. (2023). This file contains monthly pCO2 and air-sea CO2 flux fields from January 1998 to December 2019 at 1/12 degree by 1/12 degree (approximately 9 km by 5km; latitude by longitude) spatial resolution. The air-sea CO2 fluxes are computed from the air-sea CO2 partial pressure difference and a bulk gas transfer formulation following Duke et al. (2023).

Description: Seasonal climatologies of the Canadian Pacific Exclusive Economic Zone (CPEEZ) were computed from a numerical simulation of the British Columbia continental margin (BCCM) model for the 1993 to 2020 period, which can be considered as a representation of the climatological state of the region. Methods: The BCCM model is an ocean circulation-biogeochemical model implementation of the Regional Ocean Modelling System (ROMS version 3.5). The horizontal resolution is eddy-resolving at 3 km and the vertical discretization is based on a terrain-following coordinate system with 42 depth levels of increasing resolution near the surface. A detailed description of the BCCM model is given in Peña et al. (2019). 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 climatology of temperature, salinity, current speed, nitrate, oxygen, total alkalinity, dissolved inorganic carbon, pH, aragonite saturation state, phytoplankton, and primary production. The data include 47 vertical levels (surface, bottom, and 45 more selected depths), except for total phytoplankton (surface values only) and primary production (depth-integrated values). A layer giving the bottom depth in metres at the centre of each grid point is also provided. Model grids were set to NaN values in regions where the model output is highly uncertain, such as inlets, nearshore areas, and the Strait of Georgia. Uncertainties: Model results have been extensively evaluated against observations (e.g. altimetry, CTD and nutrient profiles, observed geostrophic currents), which showed the model can reproduce with reasonable accuracy the main oceanographic features of the region including salient features of the seasonal cycle and the vertical and cross-shore gradient of water properties. However, the model resolution is too coarse to allow for an adequate representation of inlets, nearshore areas, and the Strait of Georgia.

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.

ANN-NEPc is a gridded surface ocean pCO2 data product for the Northeast Pacific coastal ocean. It was created using non-linear functional relationships between pCO2 observations from the Surface Ocean CO2 Atlas v2021 as well as additional data from a Fisheries and Oceans Canada February 2019 Line P cruise, a West Coast Ocean Acidification cruise from July and August 2010, and La Perouse cruises from May 2007 and May 2010, and a variety of independent predictor variables (see supplemental information) using an artificial neural network self-organizing-map-feed-forward-network approach described and evaluated in Duke et al. (2024). This file contains monthly pCO2 and air-sea CO2 flux fields from January 1998 to December 2019 at 1/12 degree by 1/12 degree (approximately 9 km by 5km; latitude by longitude) spatial resolution within typically < 6 to 300 km of shore. The air-sea CO2 fluxes are computed from the air-sea CO2 partial pressure difference and a bulk gas transfer formulation following Duke et al. (2024).