The files in this repository contain the model-based results from a large number of simulated Direct Ocean Removal (DOR) experiments using the Community Earth System Model (CESM). These experiments are intended to explore the temporal and spatial impact of regionally confined carbon removal 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 identical to those of the Global Ocean Alkalinity Enhancement Efficiency Map — with the sole exception that DOR forcing was applied instead of alkalinity addition.
This repository contains:
A series of "DOR 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 DOR perturbation to the biogeochemical system
Globally-integrated DOR 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.
The following elements are not included in this repository because they are shared with the OAE dataset and are not duplicated here. Interested users can access them via the OAE dataset, where they were originally published:
A nearly 200-year “control” integration used for historical and physical context, including the response to the observed anthropogenic CO₂ trend. Data available at data/control/*.nc
Region masks identifying the 690 global ocean regions where carbon removal was applied. Available here
This repository contains:
496,800 netCDF files output from 2760 CESM simulations. Each simulated DOR perturbation experiment represents the time-evolving outcome of removing dissolved inorganic carbon (DIC) from a particular region of the ocean (the "polygon") over a particular time period (the "intervention 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}/{intervention_month}/smyle.cdr-atlas-v0.glb-dor.{polygon_id}-{intervention_year}-{intervention_month}.pop.h.{year}-{month}.nc. These files contain the globally complete set of biogeochemical variables that are impacted by the DOR 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.{intervention_year} - indicates the calendar year during which the DOR intervention occurred. Presently all the data are from simulations beginning in 1999.{intervention_month} - the calendar month during which the DIC removal 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 DOR perturbation experiments. The counterfactual simulation is defined as the time-evolving biogeochemical state of the ocean in the absence of the DIC removal. These variables are stored using the same naming convention as above, but with a suffix _ALT_CO2 (e.g. ’DIC’ contains the experimental values and ’DIC_ALT_CO2’ contains the counterfactual values).Integrated efficiency curves in the file data/DOR_efficiency_maps_alltime.nc. These contain the globally-integrated DOR 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 DIC removed from the ocean. This file includes a variable DOR_efficiency defined over the dimensions season, N_month, nlat, and nlon. season is the month of the DIC removal: ['January', 'April', 'July', 'October']; N_month is the number of months elapsed since the DIC removal.
We eventually aim to make this entire dataset accessible as a single Zarr Store via virtual chunk reference 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).
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).
A paper documenting the generation of these data and analysis of the results is in preparation.
The related OAE Efficiency Map data are documented in the following publication: 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
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.
Open Data Commons Attribution License (ODC-By) v1.0 , packaged with the data in the LICENSE.txt file.