Ocean Carbon Dioxide Removal Atlas: Global Ocean Alkalinity Enhancement Efficiency Map

Description

The files in this repository contain the model-based results from a large number of simulated Ocean Alkalinity Enhancement (OAE) experiments using the Community Earth System Model (CESM). These experiments are intended to explore the temporal and spatial impact of regionally confined alkalinity addition on the biogeochemical carbon system in the global ocean. To produce these model-based experiments, the CESM model was run in what is called a “forced ocean sea ice” configuration, wherein an independent dataset of historical atmospheric conditions (including the observed trend in atmospheric CO2) is used to drive the circulation and state of the CESM global ocean model. The details of these simulations are available in Zhou et al. (2024). This repository contains 1) a nearly 200 year “control” integration that can be used for historical and physical context for the experiment including the impact of the observed anthropogenic trend in atmospheric CO2 on the ocean; and 2) A series of “OAE perturbation experiments,” which span a 15 period beginning in the model simulated year 1999; and the “counterfactual” ocean state for these experiments, where there was no OAE perturbation to the biogeochemical system; 3) Globally-integrated OAE efficiencies for each point on the model’s horizontal grid, defined as the cumulative integral in time of CO2 uptake from the atmosphere divided by the amount of alkalinity added to the ocean; 4) region-masks that indicate the location of the 690 regions of the global ocean from which alkalinity releases were simulated.

Structure

This repository contains:

1. 496,800 netCDF files output from 2760 CESM simulations. Each simulated OAE perturbation experiment represents the time-evolving outcome of injecting alkalinity into a particular region of the ocean (the "polygon") over a particular time period (the "injection month"), and integrating an ocean physical-biogeochemical model forwards for 15 years. These results are stored in the data/experiments/ directory, following the pattern {polygon_id}/{injection_month}/alk-forcing.{polygon_id}-{injection_year}-{injection_month}.pop.h.{year}-{month}.nc. These files contain the globally complete set of biogeochemical variables that are impacted by the applied alkalinity perturbation. The data is saved as monthly averages, with one file per month, for a total of 180 files per 15 year simulation. The meaning of the identifiers are:
   • {polygon_id} - the unique integer ID of each polygon, ranging from 000 to 690.
   • {injection_year} - indicates the calendar year during which the alkalinity injection occurred. Presently all the data are from simulations beginning in 1999.
   • {injection_month} - the calendar month during which the alkalinity injection occurred. One of 01, 04, 07, 10.
   • {year} - the model year for which data is contained in this particular file. Model year 0347 corresponds to the calendar year 1999.
   • {month} - the simulation calendar month for which data is contained in this particular file. Ranges from 01 to 12. Note that the time variable in these files contains values that correspond to the timestamp at the end of each month. These netCDF files also contain variables representing the counterfactual condition for each of the OAE perturbation experiments. The counterfactual simulation is defined as the time-evolving biogeochemical state of the ocean in the absence of the alkalinity injection. These variables are stored using the same naming convention as above, but with a suffix _ALT_CO2 (e.g. ’ALK’ contains the experimental values and ’ALK_ALT_CO2’ contains the counterfactual values).
2. NetCDF files for the dataset from the control simulation. The control simulation is a so-called forced-ocean-sea-ice CESM integration from 1850 to 2021 and each of the OAE perturbation experiments was branched off this simulation in the year 1999. Since there is no feedback between alkalinity addition and the physical dynamics in the model, data from the control integration can be used to provide access to physical variables (e.g., ocean velocity, mixed layer depth, etc.) that were omitted from the experiments to save data volume. These are stored in various files under data/control/*.nc, with one file per data variable.
3. Integrated efficiency curves in the file data/OAE_efficiency_maps_alltime.nc. These contain the globally-integrated OAE efficiencies for each point on the model’s horizontal grid (rather than being defined for each spatial polygon). The efficiency curves are defined as the cumulative integral in time of CO2 uptake from the atmosphere divided by the amount of alkalinity added to the ocean. This file includes a variable OAE_efficiency defined over the dimensions season, N_month, nlat, and nlon. season is the month of the alkalinity release: ['January', 'April', 'July', 'October']; N_month is the number of months elapsed since the alkalinity release.
4. Masks for the spatial locations of each polygon_id, stored in the single netCDF4 file data/polygon_masks.nc. All 690 polygons together tile the entire global ocean.

Future Plans

We eventually aim to make this entire dataset accessible as a single Zarr Store via virtual chunk reference files.

How to access

BROWSE raw netCDF files

You can find any particular netCDF file of interest by navigating through this repository after clicking BROWSE on the left. You can then click DOWNLOAD to download the file directly (this does not require AWS credentials).

ACCESS DATA and open specific netCDF files with xarray

After signing in to Source Cooperative, you can click ACCESS DATA to generate temporary AWS credentials that will allow you to download the data via URL. You can download the entire dataset (Not recommended due to size) following the “Usage Examples” provided. You can also access specific files from a URL pointing to the bucket using xarray and s3fs, which you will need to have installed (e.g. from conda-forge). You will also need to have h5netcdf installed. You then need to run the following commands from an interactive python shell (e.g. in a jupyter notebook).

1import xarray as xr
2import s3fs
3
4
5!<copy/pasted temporary credentials>
6
7fs = s3fs.S3FileSystem(anon=True)
8data_dir = 's3://us-west-2.opendata.source.coop/cworthy/oae-efficiency-atlas/data/'
9data_file = experiments/{polygon_id}/{injection_month}/alk-forcing.{polygon_id}-{injection_year}-{injection_month}.pop.h.{year}-{month}.nc
10data_url = data_dir + data_file
11ds_data = xr.open_dataset(fs.open(data_url), engine='h5netcdf')
1import xarray as xr
2import s3fs
3
4
5!<copy/pasted temporary credentials>
6
7fs = s3fs.S3FileSystem(anon=True)
8data_dir = 's3://us-west-2.opendata.source.coop/cworthy/oae-efficiency-atlas/data/'
9data_file = experiments/{polygon_id}/{injection_month}/alk-forcing.{polygon_id}-{injection_year}-{injection_month}.pop.h.{year}-{month}.nc
10data_url = data_dir + data_file
11ds_data = xr.open_dataset(fs.open(data_url), engine='h5netcdf')

Open entire dataset as Zarr with xarray

Note: Access as Zarr is coming soon!

References

The generation of this data and analysis of the results is documented in the following publication; to cite this dataset please cite this paper: M. Zhou, M. Tyka, D. Ho, E. Yankovsky, S. Bachman, T. Nicholas, A. Karspeck, M. Long: Mapping the global variation in the efficiency of ocean alkalinity enhancement for carbon dioxide removal, 2024, Nature Climate Change, in press, doi: 10.1038/s41558-024-02179-9. see preprint

Contact Us

We are interested in how you are using this data! Please reach out to data@cworthy.org with any questions. We’d also appreciate information on your analysis goals and whether the data format and access patterns adequately support your use cases.

License

Open Data Commons Attribution License (ODC-By) v1.0 , packaged with the data in the LICENSE.txt file.