For producing temperature, salinity and oxygen climatology layers for the Northeast Pacific ocean, averaged over 1991-2020. The output is intended as an update from the 1981-2010 climatologies from Christian and Foreman (2013).
Author: Hana Hourston (@hhourston)
- Python >= 3.7
- Julia >= 1.6.3 (older versions weren't tested but may be ok)
- R >= 4.1.1 (older versions weren't tested but may be ok)
- Unix-like environment or Windows environment
To install the project requirements in a conda virtual environment (my_venv):
conda activate my_venv
conda install pandas numpy gsw tqdm matplotlib r-oce conda-build dask
conda install -c conda-forge rpy2
Install basemap: follow the instructions at https://matplotlib.org/basemap/users/index.html (or install easily with Pycharm if using)
Install Rtools: https://cran.r-project.org/bin/windows/Rtools/rtools40.html
Install Julia: https://julialang.org/downloads/
Install DIVAnd in Julia:
using Pkg
Pkg.add("DIVAnd")
- Create value-vs-depth (vvd) csv tables
- Add duplicate check flags to the vvd tables
- Apply the duplicate check flags to the vvd tables
- Do a latitude-longitude check to screen out data outside of the predetermined area of 30$^\circ$ N
$<=$ 60$^\circ$ N latitude and -160$^\circ$ E$<=$ -115$^\circ$ E longitude. - Apply the source quality depth and data flags to the vvd data
- Remove data with NaN depths or values
- WOA18 depth checks
- WOA18 range checks
- WOA18 gradient checks
- Vertical interpolation using modified Reiniger-Ross (1968) method to standard levels (a combination of WOA18 standard levels and standard levels from Christian and Foreman, 2013)
- Replicate value check
- To flag replicated values produced from vertical interpolation
- Standard deviation checks on 5-degree squares
- Separate data by standard level and season (needed for ODV DIVA interpolation method)
- Separate data by standard level, season and year (for Julia DIVAnd method)
- Deprec
(Option B for variational analysis; option A uses ODV DIVA) 16. DIVAnd in Julia to create fields for NEP on regular GEBCO 6-minute grid 17. Linear interpolation to the unstructured triangle grid from Christian and Foreman (2013) 18. Average unstructured triangle grid data over 1991-2020 for each season/depth combination 19. DIVAnd the data from step 18 to the full NEP on regular GEBCO 6-minute grid 20. Linear interpolation to the unstructured triangle grid from Christian and Foreman (2013)
- CIOOS Pacific search by organization: https://catalogue.cioospacific.ca/organization
- Institute of Ocean Sciences Water Properties: https://www.waterproperties.ca/
- NODC WODSelect: https://www.ncei.noaa.gov/access/world-ocean-database-select/bin/dbsearch.pl
oceoceApprox() function documentation: https://dankelley.github.io/oce/reference/oceApprox.html- DIVAnd documentation: https://gher-ulg.github.io/DIVAnd.jl/latest/#DIVAnd.jl-documentation
- DIVA GitHub repository: https://github.com/gher-ulg/DIVA
- DIVA User Guide: https://github.com/gher-ulg/Diva-User-Guide/raw/master/DivaUserGuide.pdf
- GEBCO elevation data access: https://www.gebco.net/data_and_products/gridded_bathymetry_data/
Christian, J. R. and Foreman, M.G.G. 2013. Climate Trends and Projections for the Pacific Large Aquatic Basin.Can. Tech. Rep. Fish. Aquat. Sci. 3032: xi + 112 p.