Download data from dry-run output

Once you have successfully executed a GEOS-Chem dry-run, you can use the output from the dry-run (contained in the log.dryrun file) to download the data files that GEOS-Chem will need to perform the corresponding “production” simulation. You may download from one of several GEOS-Chem mirror sites, which are described in the following sections.


Before you use the script, make sure to initialize a Conda environment by typing conda activate ENV-NAME (where ENV-NAME is the name of your environment).

Also make sure that you have installed the PyYAML module to your conda environment. PyYAML will allow the script to read certain configurable settings from a YAML file in your run directory.

Choose a data portal

You can download input data data from one of the following mirror sites:

The mirror (aka WashU)

If you are using GEOS-Chem on your institutional computer cluster, we recommend that you download data from the WashU (Washington University in St. Louis) mirror site ( This mirror, which is maintained by Randall Martin’s group at WashU, is the main data mirror mirror for GEOS-Chem.


We have also set up a Globus endpoint named GEOS-Chem data (WashU) on the WashU mirror site. If you need to download many years of data, it may be faster to use Globus (particularly if your home institution supports it).

The s3://gcgrid mirror (aka Amazon)

If you are running GEOS-Chem Classic on the Amazon Web Services cloud, you can quickly download the necessary data for your GEOS-Chem simulation from the :file:`s3://gcgrid` bucket to the Elastic Block Storage (EBS) volume attached to your cloud instance.

Navigate to your GEOS-Chem Classic run directory and type:

$ ./download log.dryrun amazon

This will start the data download process using the aws s3 cp commands, which should execute much more quickly than if you were to download the data from Compute Canada. It will also produce the log of unique data files.


Copying data from s3://gcgrid to the EBS volume of an Amazon EC2 cloud instance is always free. But if you download data from s3://gcgrid to your own computer system, you will incur an egress fee. PROCEED WITH CAUTION!

The mirror (aka Rochester)

The U. Rochester site (which is maintained by Lee Murray’s research there) contains the GCAP 2.0 met field data. This met field data is useful if you wish to perform simulations stretching back into the preindustrial period, or running into the future.

To download data from the Rochester mirror, type:

$ ./download log.dryrun rochester

Run the script on the dryrun log file

Navigate to your GEOS-Chem run directory where you executed the dry-run and type:

$ ./ log.dryrun washu

The Python program is included in the GEOS-Chem run directory that you created. This Python program creates and executes a temporary bash script containing the appropriate wget commands to download the data files. (We have found that this is the fastest method.)

The program will also generate a log of unique data files (i.e. with all duplicate listings removed), which looks similar to this:

!!! Start Date       : 20160701 000000
!!! End Date         : 20160701 010000
!!! Simulation       : standard
!!! Meteorology      : GEOSFP
!!! Grid Resolution  : 4.0x5.0
./GEOSChem.Restart.20160701_0000z.nc4 --> /n/holylfs/EXTERNAL_REPOS/GEOS-CHEM/gcgrid/data/ExtData/GEOSCHEM_RESTARTS/v2018-11/
... etc ...

This name of this “unique” log file will be the same as the log file with dryrun ouptut, with .unique appended. In our above example, we passed log.dryrun to, so the “unique” log file will be named log.dryrun.unique. This “unique” log file can be very useful for documentation purposes.

Skip download, but create log of unique files

If you wish to only produce the *log of unique data files without downloading any data, then type the following command from within your GEOS-Chem run directory:

$ ./ log.dryrun --skip-download

or for short:

$ ./ log.dryrun --skip

This can be useful if you already have the necessary data downloaded to your system but wish to create the log of unique files for documentation purposes (such as for benchmark simulations, etc.)