Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) Climate Projections_RCP 8.5 (2046-2065)

Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) Climate Projections_RCP 8.5 (2046-2065) 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 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 Data Sources: Model output Uncertainties: 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. 2024-11-08 Fisheries and Oceans Canada amber.holdsworth@dfo-mpo.gc.ca Nature and EnvironmentScience and TechnologyClimateModelling Data DictionaryPDF https://api-proxy.edh.azure.cloud.dfo-mpo.gc.ca/catalogue/records/d35d85c1-ae76-4f83-96f4-1b6d1c7dee60/attachments/Data_Dictionary_NEP36.pdf ReferencesPDF https://api-proxy.edh.azure.cloud.dfo-mpo.gc.ca/catalogue/records/d35d85c1-ae76-4f83-96f4-1b6d1c7dee60/attachments/References_NEP36.pdf NEP36-CanOE RCP 8.5 2046-2065 Monthly AlkaliniNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_Alkalini_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly DICNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_DIC_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly NO3NetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_NO3_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly O2NetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_O2_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly PHNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_PH_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly TCHLNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_TCHL_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly omega_aNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_omega_a_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly saltNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_salt_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly sigmaNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_sigma_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly tempNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_temp_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly maxO2NetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_maxO2_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly minO2NetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_minO2_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly maxPHNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_maxPH_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly minPHNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_minPH_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly maxtempNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_maxtemp_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly mintempNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_mintemp_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly CflxNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_Cflx_RCP85_2046-2065_monthly.nc NEP36-CanOE RCP 8.5 2046-2065 Monthly TPPNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/RCP85/NEP36-CanOE_TPP_RCP85_2046-2065_monthly.nc NEP36-CanOE MaskNetCDF https://dfogis.azureedge.net/FGP/NEP36-CanOE/NEP36-CanOE-MASK.nc NEP36-CanOE RCP 8.5 2046-2065 mapESRI REST https://gisp.dfo-mpo.gc.ca/arcgis/rest/services/FGP/NEP36_CanOE_Monthly_Temperature_RCP85_2046_2065/MapServer NEP36-CanOE RCP 8.5 2046-2065 mapESRI REST https://gisp.dfo-mpo.gc.ca/arcgis/rest/services/FGP/NEP36_CanOE_Monthly_Temperature_RCP85_2046_2065/MapServer

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 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

Data Sources:

Model output

Uncertainties:

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.

Data and Resources
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Contact Information

Delivery Point: Institute of Ocean Sciences 9860 West Saanich Road P.O. Box 6000

City: Sidney

Administrative Area: British Columbia

Postal Code: V8L 4B2

Country: Canada

Electronic Mail Address: amber.holdsworth@dfo-mpo.gc.ca

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