DSCIM: The Data-driven Spatial Climate Impact Model

This repository is an implementation of DSCIM, referred to as DSCIM-FACTS-EPA, which updates the DSCIM-EPA implementation of the SC-GHG specification for the U.S. Environmental Protection Agency’s (EPA) 2023 technical report, "Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances". It includes the option to input exogenous global mean surface temperature (GMST) and global mean sea level (GMSL) trajectories. DSCIM-FACTS-EPA currently provides instructions for installing and running the Framework for Assessing Changes To Sea-level (FACTS) to obtain GMSL from GMST and ocean heat content (OHC).

This Python application enables the calculation of sector-specific partial social cost of greenhouse gases (SC-GHG) and SC-GHGs that are combined across sectors. The main purpose is to parse the monetized spatial damages from different sectors and integrate them into SC-GHGs for different discount levels, pulse years, and greenhouse gases.

Outline

This README is organized as follows:

Types of run cases
Installation and setup of dscim-facts-epa
Running dscim-facts-epa SC-GHG command line tool out of the box
Running dscim-facts-epa SC-GHGs in batch mode
DSCIM + FACTS run process overview
Format of GMST, OHC, GMSL input files
FACTS-specific setup
- Installation of facts
  - Docker (Windows, Mac OS)
  - Not Docker (Linux)
- Running facts with bash run script
Modifying a generated config
Further Information
- Creating a dscim-facts-epa run config
- Input files

Types of run cases

By default, DSCIM-FACTS-EPA can run SC-GHGs for carbon dioxide, methane, and nitrous oxide for pulse years 2020-2080 in 10 year increments for the Resources for the Future socioeconomic pathways (RFF-SPs). For alternative gases or pulse years, the user can provide new GMST and GMSL trajectories. The user can provide these trajectories directly as input files, or can use the DSCIM-FACTS-EPA FACTS runner to generate GMSL from ocean heat content (OHC) and GMST. The potential use cases of this repository are thus:

The user wants to generate the default Climate Impact Lab (CIL) RFF SC-GHGs. This is the same functionality as dscim-epa.
The user has alternative GMST and OHC files following the guidelines below (usually directly from a simple climate model, such as FaIR) and wants to generate GMSL files from FACTS and use the CIL damage functions to generate SC-GHGs from those files. This is the primary use-case of this repository.
The user has alternative GMST and GMSL files following the guidelines below and wants to use the CIL damage functions to generate SC-GHGs based on those files. This is expected to be an unlikely use-case but we list it for completeness.

flowchart LR

A[1. Default] --> D(Setup)
D --> |1.| E{Running SC-GHGs}

B[2. GMST/GMSL] --> D(Setup)
D --> |2.| F(Formatting GMST/GMSL files)
F --> |2.| H(Creating a run config)

C[3. GMST/OHC] --> D(Setup)
D --> |3.| F(Formatting GMST/GMSL files)
F --> |3.| G(Running FACTS)
G --> |3.| H(Creating a run config)
H --> E{Running SC-GHGs}

Installation and setup of `dscim-facts-epa`

To begin, we assume you have a system with conda available from the command line, and some familiarity with it. A conda distribution is available from miniconda or Anaconda. This helps to ensure that required software packages are correctly compiled and installed.

Begin in the dscim-facts-epa project directory, which can be downloaded and unzipped, or cloned with git in a terminal. If the repository is downloaded rather than cloned, you may see a message like “fatal: not a git repository (or any of the parent directories): .git” when running the SC-GHG command line tool, which can be safely ignored. To clone the repository:

git clone https://github.com/ClimateImpactLab/dscim-facts-epa.git

Next, from within the root directory of dscim-facts-epa, set up a conda environment for this analysis. This replicates the software environment used for analysis. With conda from the command line this is

conda env create -f environment.yml

and then activate the environment with

conda activate dscim-facts-epa

Be sure that all commands and analyses are run from this conda environment.

With the environment set up and active, the next step is downloading the required DSCIM-FACTS-EPA input data into the local directory. Assuming you are in the dscim-facts-epa/scripts directory, from the command line run:

python directory_setup.py

Note that this will download several gigabytes of data and may take several minutes, depending on your connection speed.

Running SC-GHGs

Default SC-GHGs (CO2, CH4, N2O for pulse years 2020, 2030, 2040, 2050, 2060, 2070, 2080) can be run once installation is complete. Alternatively, these steps can be followed using exogenous climate inputs, including GMSL produced by FACTS, to produce SC-GHGs of the users choice. Instructions for installing and running FACTS follow here.

After setting up the dscim-facts-epa environment and input data, if not already active, activate the environment by typing conda activate dscim-facts-epa. You can run SC-GHG calculations under different conditions with or without a config file.

Assuming you are in the dscim-facts-epa/scripts folder, if you want to run the CIL spec (default) SC-GHGs, you can run:

python command_line_scghg.py

Alternatively, if you have run FACTS, or are using a GMSL file of your own, you can run:

python command_line_scghg.py name_of_config.yml

and follow the on-screen prompts. When the selector is a carrot, you may only select one option. Use the arrow keys on your keyboard to highlight your desired option and click enter to submit. When you are presented with X and o selectors, you may use the spacebar to select (X) or deselect (o) then click enter to submit once you have chosen your desired number of parameters. Once you have completed all of the options, the DSCIM run will begin.

Command line options

Command line options

Below is a short summary of what each command line option does. To view a more detailed description of what the run parameters do, see the Documentation for Data-driven Spatial Climate Impact Model (DSCIM).

Sector

The user may only select one sector per run. Sectors represent the combined SC-GHG or partial SC-GHGs of the chosen sector.

Discount rate

These runs use endogenous Ramsey discounting that are targeted to begin at the chosen near-term discount rate(s).

Pulse years

Pulse year represents the SC-GHG for a pulse of greenhouse gas (GHG) emitted in the chosen pulse year(s).

Domain of damages

The default is a global SC-GHG accounting for global damages in response to a pulse of GHG. The user has the option to instead limit damages to those occurring directly within the territorial United States. This is only a partial accounting of the cost of climate change to U.S. citizens and residents because it excludes international transmission mechanisms, like trade, cross-border investment and migration, damage to the assets of U.S. citizens and residents outside the United States, or consideration of how GHG emission reduction activity within the United States impacts emissions in other countries.

Optional files

By default, the script will produce the expected SC-GHGs as a .csv. The user also has the option to save the full distribution of 10,000 SC-GHGs -- across emissions, socioeconomics, and climate uncertainty -- as a .csv, and the option to save global consumption net of baseline climate damages ("global_consumption_no_pulse") as a netcdf .nc4 file.

Running SC-GHGs in batch mode

DSCIM-FACTS-EPA can be run non-interactively, without using the command line tool, using scripts/batch_scghg.py.

Modify scripts/batch_scghg.py to include the outputs desired from your run:

    ####################
    # EDIT these parameters:
    
    # Which sectors to save out from: 
    # "combined", "coastal", "agriculture", "mortality", "energy", "labor",
    sectors = [
        "combined",
        "coastal",
        "agriculture",
        "mortality",
        "energy",
        "labor",
    ]

    # Pulse years to save out
    # pulse years should be present in climate files
    pulse_years = [
        2020,
        2030,
        2040,
        2050,
    ]

    # Target discount rates from:
    # "1.5% Ramsey", "2.0% Ramsey", "2.5% Ramsey",
    target_disc = [
        "1.5% Ramsey",
        "2.0% Ramsey",
        "2.5% Ramsey",
    ]

    # List of whether to run Global/Territory US SCGHGs
    # Can be "global", "terr_us", or both
    terr_us = ["global",]
    
    # Whether to save out global consumption no pulse
    gcnp = True

    # Whether to save out uncollapsed SCGHGs
    uncollapsed = True
    ####################

After setting up the dscim-facts-epa environment and input data, if not already active, activate the environment by typing conda activate dscim-facts-epa. You can run SC-GHG calculations under different conditions with or without a config file.
Run the SC-GHG computation. Assuming you are in the dscim-facts-epa/scripts folder, if you want to run the CIL spec (default) SC-GHGs, you can run:

python batch_scghg.py

If you have run FACTS, or are using a GMSL file of your own, make sure to edit the script to point to the correct config file before running the above command:

    ####################
    # EDIT path and filename to config if necessary.
    # Path to the config for this run. Default is "current working directory", cwd
    conf_name = "generated_conf.yml"
    fullpath = Path(os.getcwd()) / conf_name
    conf = read_replace_conf(fullpath)

DSCIM + FACTS Run process overview

Ignoring environment setup for a moment, the general run process for use-case 2 -- running DSCIM-FACTS-EPA with exogenous climate inputs and using FACTS to generate GMSL from GMST and OHC files -- is summarized here. Detailed instructions for each step are provided later in the README.

Format user GMST and OHC files manually (See Formatting files)

Place formatted GMST/OHC files into dscim-facts-epa/scripts/input/climate
Set up Docker/Not Docker container/environment (see Installing and Running FACTS)
Edit dscim-facts-epa/scripts/facts.runs/facts_runs.sh script to specify pulse years, gases, and directory locations (See Running the bash script)
Run bash facts_runs.sh to generate a config file for running dscim-facts-epa command line tool (See Running the bash script)
Modify the generated config from step 5 to specify gas pulse conversions (See Modifying the auto-generated config)
Run dscim-facts-epa command line tool with newly generated config (Follow steps above in Running SC-GHGs)

Formatting files

To ensure that both FACTS and dscim-facts-epa can read new GMST, GMSL, and OHC files, a strict format must be adopted.

We require that there be a control and pulse version of the appropriate variable:
- For GMST, these are control_temperature and pulse_temperature
- For GMSL, these are control_gmsl and pulse_gmsl
- For OHC, these are control_ocean_heat_content and pulse_ocean_heat_content
Any combination of gases and pulse years can be supplied. SC-GHGs will then be runnable for those gases and pulse years.
We expect year to be at minimum covering 1850-2300 for FACTS input files, GMST and OHC. In facts, GMST and OHC are rebased to the appropriate reference period. The GMSL output produced by FACTS is 2010-2300 and is already relative to the appropriate base period for dscim-facts-epa GMSL damage functions (1990-2009). In dscim-facts-epa, GMST is automatically made relative to 2001-2010 to be consistent with the damage functions.
The runid dimension corresponds to the FaIR parameters and RFF-SPs crosswalk specified for EPA's September 2022 draft technical report, "Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances". Thus, each runid is associated with an RFF-SP index and a climate parameter index. We expect 10000 runids from 1 to 10000. The runid crosswalk can be obtained from here

Converting GMST and OHC .csv files into NetCDF4 (.nc4) files

Here we provide a code example for formatting and creating DSCIM-FACTS-EPA input climate .nc4 files from .csv files. It assumes the csv files (showing OHC here) have the following format (leftmost column that is unlabeled is a pandas index):

	runid	pulse_year	gas	year	control_ocean_heat_content	pulse_ocean_heat_content
281	  1	    2030	    co2	2031	75.956955	                75.956956
282	  1	    2030	    co2	2032	77.271190	                77.271191
283	  1	    2030	    co2	2033	78.616196	                78.616197
284	  1	    2030	    co2	2034	79.999884	                79.999886
285	  1	    2030	    co2	2035	81.406942	                81.406944

The following code should work if there is one pulse_year and one gas in the input .csv but may not generalize. It is meant to give a sense for how to work with the xarray package in Python to produce netcdf files of the correct format for DSCIM-FACTS-EPA.

import pandas as pd
import xarray as xr

# dictionary mapping var name in the filename to var name in the data
vardt = {"gmst":"temperature",
         "ohc": "ocean_heat_content"
        }

# loop through input variables
for var in vardt.keys():
    df = pd.read_csv(f"{var}_pulse.csv")
 
    # control variables only vary by runid and year
    ctl = df.set_index(["runid","year"])[f"control_{vardt[var]}"].to_xarray()

    # pulse variables vary by runid, year, pulse_year, and gas
    pls = df.set_index(["runid","year","pulse_year","gas"])[f"pulse_{vardt[var]}"].to_xarray()

    # merge together into one xr.Dataset before saving
    xr.merge([ctl,pls]).to_netcdf(f"{var}_pulse.nc4")

Below are examples of the structure of the default climate files when read in using xarray.

GMST

GMSL

OHC

Installing and Running FACTS

If you will be running FACTS to generate GMSL, ensure you have followed the Formatting GMST/GMSL files section above.

We recommend installing or cloning FACTS v1.1.2 found here. We copy the relevant steps from the FACTS quick start instructions here and adapt for use with dscim-facts-epa. FACTS can be set up to run in a Docker container (recommended) or to run on a Linux workstation. After cloning the repository, expand the option for your run environment below (Docker or Not Docker) and follow the steps.

clone the FACTS repository:

git clone https://github.com/radical-collaboration/facts.git --branch v1.1.2

FACTS will be run in "global only" mode to produce GMSL outputs from GMST and OHC inputs. This does not require setting up the facts emulandice module.

Docker (Recommended)

Docker (Recommended)

The RADICAL toolkit does not support MacOS or Windows. Therefore, to run on a Mac or Windows (the latter with Windows Subsystem for Linux; WSL2), you need to run within a Linux virtual machine or container. On Windows, once you have installed WSL2, type "ubuntu" in the Command Prompt to open a linux terminal for the remaining commands. For both Windows and Mac, we recommend installing Docker Desktop, which should be opened prior to the following steps.

FACTS provides a Docker container in the docker/ directory. This container provides the Linux, Python, R, and RADICAL toolkit environment needed for FACTS to run. FACTS itself does not reside within the container because of needs related to storage space for module data, persistence of changes, and writability. The instructions below assume FACTS resides outside the container in $HOME/facts and mounts it within the container as /opt/facts. At the moment, the docker environment appears to work fairly reliably when using localhost as the resource, but working with remote resources will require additional configuration.

The sandbox directory resides within the container's HOME directory at /home/jovyan/radical.pilot.sandbox. You will likely wish to keep an eye on the size of this directory if you are doing runs that involve large files.

To install FACTS through Docker please follow the steps below after downloading or cloning facts:

Download modules-data:

wget -P facts/modules-data -i facts/modules-data/modules-data.global_only.urls.txt

If you don’t have wget, another option for file downloads is curl: First, make sure you are in $HOME/facts/modules-data and then run:

xargs -n 1 curl -L -O < $HOME/facts/modules-data/modules-data.global_only.urls.txt

Replace $HOME/facts with the path to your cloned or downloaded facts repository. This may take a few minutes depending on your connection speed. As of December 2022, the data for stable FACTS modules are available on Zenodo at https://doi.org/10.5281/zenodo.7478191 and https://doi.org/10.5281/zenodo.7478447 (note, split between two Zenodo entries because of size limitations). Because we are only doing global projections with the modules used in the Kopp et al. (2023) manuscript, this downloads only a subset of the total FACTS data.

Build the docker container. Make sure the Docker application is already running first by opening the Docker application. It is also recommended to have the dscim-facts-epa conda environment activated when building the docker container, with conda activate dscim-facts-epa. Then, run the following:

cd facts/docker
sh develop.sh

This will build the Docker image from the instructions in develop.sh. When you run facts, you will first start the facts image, which creates a container (usually with a randomly-generated name) for running in. Outputs saved within the container do not persist once the container is deleted. Therefore, when starting the docker image, a few directories are mounted so that inputs, outputs, and logs will persist beyond the session.

Start the facts docker image:

docker run -it --volume=$HOME/facts:/opt/facts --volume=$HOME/dscim-facts-epa:/opt/dscim-facts-epa -w /opt/dscim-facts-epa/scripts/facts.runs facts

Replace $HOME/dscim-facts-epa and $HOME/facts with the path to your cloned or downloaded dscim-facts-epa repository and facts repository, respectively. This command will start the container, mounting the dscim-facts-epa directory and the facts directory. Once the container is running, your working directory will be /opt/dscim-facts-epa/scripts/facts.runs. If you additionally want to mount the radical.sandbox (recommended), the command is:

docker run -it --volume=$HOME/facts:/opt/facts --volume=$HOME/dscim-facts-epa:/opt/dscim-facts-epa --volume=$HOME/tmp/radical.pilot.sandbox:/home/jovyan/radical.pilot.sandbox -w /opt/dscim-facts-epa/scripts/facts.runs facts

Mounting the sandbox will persist the facts run session output logs even after the container is shutdown and deleted.

You are ready to run FACTS. Proceed to Running the bash script

Not Docker

Not Docker

To run outside of Docker, FACTS must be run on a Linux machine.

Download global-only modules-data from Zenodo:

wget -P facts/modules-data -i facts/modules-data/modules-data.global-only.urls.txt

As of December 2022, the data for stable FACTS modules are available on Zenodo at https://doi.org/10.5281/zenodo.7478191 and https://doi.org/10.5281/zenodo.7478447 (note, split between two Zenodo entries because of size limitations). Because we are only doing global projections with the modules used in the Kopp et al. (2023) manuscript, this downloads only a subset of the total FACTS data.

To run FACTS outside of a docker, the user can use the dscim-facts-epa environment installed above. Activate the environment by typing conda activate dscim-facts-epa and install additional python packages:

conda install radical.entk==1.42.0 radical.saga==1.47.0 radical.utils==1.47.0 radical.pilot==1.47.0 radical.gtod==1.47.0 -c conda-forge

Test your install by running the dummy experiment:

cd facts
python3 runFACTS.py experiments/dummy

There are known issues with the software underlying FACTS that may cause this experiment to hang or crash. For some solutions, see the page here

Note that all the input files for the experiment (which can be tens of GB if you are doing local sea-level projections that rely upon CMIP output) will get copied to a sandbox created for each run. If you are running FACTS using localhost as a resource, this sandbox directory is ~/radical.pilot.sandbox. If you have space limits on your home directory, you may want to make this a symlink to a directory with fewer space limits prior to running FACTS. The task-level .out and .err files in the sandbox are key to debugging module-level code failures; thus, this sandbox is not deleted by default. However, if you wish to save space and do not need these files for debugging, you may wish to save space by deleting the subdirectories of the sandbox folder after each run.

Note that the data files for a FACTS experiment are transferred to the compute resource with each experiment run. Thus, while it might in principle be possible to run FACTS on your desktop and use a remote HPC resource, you probably don’t want to do this. Most likely, you want to install and run FACTS directly on the remote resource. At a minimum, you will want to have a fast, high-capacity network connection to the resource. If you need to run on a HPC resource not previously configured for RADICAL-Pilot (see the RADICAL-Pilot documentation) , the resource will need to be configured. To get assistance with this, create an issue on the RADICAL-Pilot repo.

You are ready to run FACTS. Proceed to Running the bash script

Running the bash script

The user must now make modifications to the dscim-facts-epa/scripts/facts.runs/facts_runs.sh script to ensure all files are found and run specifications are set. Those changes are:

if you want to rerun previous successful experiments (not recommended), change overwrite to 1
on line 12 of the script, change pulse_years to the desired pulse years to be run by FACTS
on line 13, change gas to the desired gases to be run by FACTS
on line 15, change the path to the path of your GMST file
on line 16, change the path to the path of your OHC file
on line 18, change the path to the path of your GMSL file (where you want the GMSL file to be saved)

If not running in the Docker Container:

on line 20, change facts_dir to where you have cloned your FACTS repository
on line 21, change dscim_facts_epa_dir to where you have cloned this repository

Assuming you are in the dscim-facts-epa/scripts/facts.runs folder run:

bash facts_runs.sh

Running FACTS is a relatively memory-intensive and disk-space-intensive process. To successfully run FACTS, you will need a moderately powerful workstation (or server) with at least 32 gigabytes of computer RAM. By default, FACTS uses two CPU cores and is not particularly sensitive to clock speed or number of CPU cores. In addition, FACTS currently requires around 30 gigabytes of disk space per pulse year-gas, which means that 3 gases and 7 pulse years (a total of 22 runs including the control) will require approximately 660 gigabytes of disk space. Alternatively, one can run subsets of runs at a time and clear memory in between. To clear memory after a run has been completed, remove the subdirectories in the ~/radical.pilot.sandbox folder.

Note that the more pulse year and gas dimensions your input climate files have, the longer this run will take as pulse year-gas combinations are run in sequence. On a fast machine, each combination can take approximately 10 minutes, meaning that for a run of 3 gases for 7 pulse years, the run will take 220 minutes.

The run script will create the appropriate number of FACTS "experiments" (22 in the example case), run through them, and concatenate the outputs into the format expected by dscim-facts-epa. The output GMSL file is automatically placed in the directory specified in the facts_runs.sh file.

If a docker was used, exit it once the run is complete using the exit command. The facts_runs.sh script will automatically generate a config .yml file and print the filename to the terminal. This is the config you will use when running the dscim-facts-epa SC-GHG command line tool. You will first need to specify the pulse conversion factor of the gas. To do so, proceed to the next section (Modifying the auto-generated config).

Modifying the auto-generated config

For any non-default gases, you will need to specify the pulse conversion factor of the gas. This conversion factor converts the final SC-GHG from $ / pulse size of FaIR gas species to $ / tonne of GHG. To do this, modify the gas_conversions portion of the config. By default, this is:

gas_conversions:
  CH4: 2.5e-08
  CO2_Fossil: 2.72916487e-10
  N2O: 6.36480131e-07

To add additional gases, create a new line and follow the formatting of the previous lines. New gases should match the coordinate values of your gas dimension in your gmst, gmsl, or ohc files. For example, the SCC default pulse size in DSCIM-FACTS-EPA is 1 GtC (1 gigatonne Carbon). To convert to $ / tonne CO2, molecular weights are used to convert C to CO2, and Gt is converted to tonnes: 1 / [((12+2*16)/12) * (1e9)] = 2.72916487e-10

Once this is done, proceed to the Running SC-GHGs step.

Further Information

Creating a `dscim-facts-epa` run config

If you already have alternative GMSL and GMST files, it is recommended to run them through the create_config.py. This script will generate a config that will allow you to directly begin running dscim-facts-epa using the user-specified GMST and GMSL inputs, gases, and pulse_years. To run this script, you will need to specify your correctly formatted gmst and gmsl files:

python create_config.py \
  --gmst_file /path/to/GMST_filename.nc4 \
  --gmsl_file /path/to/GMSL_filename.nc4 \
  --pulse_years pulseyear1 pulseyear2 ... \
  --gases gas1 gas2 ... \
  --input_dir /path/to/dscim-facts-epa/input \
  --output_dir /path/to/dscim-facts-epa/output \
  --config_dir /path/to/dscim-facts-epa/configs

Description of arguments:

--gmst_file: The path to your GMST file
--gmsl_file: The path to your GMSL file
--pulse_years (optional -- default: 2020): Space delimited pulse years. Pulse years must be included in the coordinates of your gmst/gmsl files
--gases (optional -- default: "CO2_Fossil"): Space delimited gases. Gases must be included in the coordinates of your gmst/gmsl files
--input_dir (optional -- default: the dscim-facts-epa/scripts/input that the config.py script belongs to) path to the inputs that were installed from the directory_setup.py script
--output_dir (optional -- default: the dscim-facts-epa/scripts/output that the config.py script belongs to) path to the output directory where the SC-GHGs will be saved
--config_dir (optional -- default: the same directory that the config.py script belongs to) path to the save directory for the generated config

Once this config is created, the final step is to specify the "pulse conversion" for each gas by modifying the config.

Input Files

These files are installed during the above Setup process and take up 4.65 GB of disk space.

Climate

Global mean surface temperature (GMST) trajectories output from FaIR: gmst_pulse.nc4
Ocean heat content (OHC) trajectories output from FaIR: ohc_pulse.nc4
Global mean sea level (GMSL) trajectories derived from FACTS run from the above GMST and OHC files: gmsl_pulse.nc4
Conversion factors to convert SC-GHGs to $/tonne of GHG: conversion_v5.03_Feb072022.nc4

Econ

RFF USA aggregated GDP and population trajectories: rff_USA_socioeconomics.nc4
RFF global aggregated GDP and population trajectories: rff_global_socioeconomics.nc4

Damage Functions

Files containing a set of damage function coefficients for each RFF draw for each economic sector and valuation choice.
RFF damage function emulator weights: damage_function_weights.nc4

Name		Name	Last commit message	Last commit date
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scripts		scripts
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LICENSE		LICENSE
README.md		README.md
environment.yml		environment.yml

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DSCIM: The Data-driven Spatial Climate Impact Model

Outline

Types of run cases

Installation and setup of `dscim-facts-epa`

Running SC-GHGs

Command line options

Sector

Discount rate

Pulse years

Domain of damages

Optional files

Running SC-GHGs in batch mode

DSCIM + FACTS Run process overview

Formatting files

Converting GMST and OHC .csv files into NetCDF4 (.nc4) files

GMST

GMSL

OHC

Installing and Running FACTS

Docker (Recommended)

Not Docker

Running the bash script

Modifying the auto-generated config

Further Information

Creating a `dscim-facts-epa` run config

Input Files

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DSCIM: The Data-driven Spatial Climate Impact Model

Outline

Types of run cases

Installation and setup of dscim-facts-epa

Running SC-GHGs

Command line options

Sector

Discount rate

Pulse years

Domain of damages

Optional files

Running SC-GHGs in batch mode

DSCIM + FACTS Run process overview

Formatting files

Converting GMST and OHC .csv files into NetCDF4 (.nc4) files

GMST

GMSL

OHC

Installing and Running FACTS

Docker (Recommended)

Not Docker

Running the bash script

Modifying the auto-generated config

Further Information

Creating a dscim-facts-epa run config

Input Files

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