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Notebook","category":"Climate Change","sub_category":"Climate Data Processing and Analysis","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Greenhouse gas Diagnostics for Earth System Simulations\n\n\u003c!-- Information badges --\u003e\n[![test status](https://github.com/E3SM-Project/gdess/actions/workflows/lint_and_test.yml/badge.svg)](https://github.com/E3SM-Project/gdess/actions/workflows/lint_and_test.yml)\n[![codecov](https://codecov.io/gh/E3SM-Project/gdess/branch/main/graph/badge.svg?token=UFKDMQ9KCM)](https://codecov.io/gh/E3SM-Project/gdess)\n[![Python 3.8|3.9](https://img.shields.io/badge/python-3.8|3.9-blue.svg)](https://www.python.org/downloads/release/python-380/)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.04326/status.svg)](https://doi.org/10.21105/joss.04326)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6981643.svg)](https://doi.org/10.5281/zenodo.6981643)\n\n[comment]: \u003c\u003e ([![latest version]\u0026#40;https://img.shields.io/badge/Current%20version-0.01a1-brightgreen.svg\u0026#41;]\u0026#40;https://github.com/dkauf42/gdess/releases/tag/0.01b1\u0026#41;)\n\n## Overview\n\nGDESS facilitates comparison of simulated atmospheric CO\u003csub\u003e2\u003c/sub\u003e against observational data. \nSpecifically, it selects, loads, and parses the observational datasets and model simulations to be in a consistent structure \nand generates data visualizations for several diagnostic use cases. \nThree diagnostic recipes currently implemented examine multidecadal trends, seasonal cycles, and meridional gradients.\n\n\n#### Motivation\n\nModel intercomparisons and benchmarking is critical for Earth System Models.\nBy comparing the values of variables, such as CO\u003csub\u003e2\u003c/sub\u003e,\nin these simulations against observed values, one can discover the needs and means for \nrefining model components, and learn much about Earth system dynamics in the process.\n\nDevelopment of `gdess` was initiated by a need to evaluate atmospheric CO\u003csub\u003e2\u003c/sub\u003e \nfor the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM)\nand its campaign focused on simulations of global biogeochemistry.\n\n#### Problem to solve\n\nAlthough there are existing diagnostic tools for Earth System Models\n(e.g. ESMValTool, ILAMB, BGC-val), they do not provide functionality specific\nfor atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentrations.\n\n[Documentation]: https://e3sm-project.github.io/gdess/\n\nSee `./gdess/bin/gdess_cli.py --help` for more command-line switches and the [documentation]\nfor more usage examples.\n\n## Contents\n\n* [Data requirements and availability](#data-requirements-and-availability)\n* [Getting started](#getting-started)\n* [Example](#example)\n* [Usage](#usage-description)\n* [Updating](#updating)\n* [Uninstalling](#uninstalling)\n* [Contributing](#contributing)\n* [Running built-in tests](#running-the-code-tests)\n* [Project structure](#project-structure)\n* [Credits](#credits)\n* [Disclaimer](#disclaimer)\n\n## Data requirements and availability\n\n**Observations - Globalview+ data** \n  - For parsing and running demo notebooks, one must first download the Obspack, \nwhich is available from the NOAA Global Monitoring Laboratory (GML) here:\n\u003chttps://www.esrl.noaa.gov/gmd/ccgg/obspack/data.php\u003e\n  - There is a sample file from NOAA GML included in this Git repository to enable the running of test cases.\n\n**Model Output - CMIP6** \n  - CMIP6 data do not need to be downloaded before running recipes, \nas GDESS can use the intake-esm dependency to retrieve CMIP6 data programmatically while running.\n  - It seems that recipes involving CMIP data won't work when on the PNNL VPN\n(presumably because the VPN blocks retrieval of the CMIP data catalog using a web URL, \nwhich is initiated by the [intake](https://github.com/intake/intake-esm) package).\n  \n**Model Output - E3SM**\n  - E3SM outputs do not need to be available to run recipes that compare Globalview+ and CMIP6 data.\n  - For parsing and running recipes from E3SM Collection, \n    one must have access to model output history as NetCDF file(s), \nand a script for post-processing E3SM output is provided in `gdess/bin/`.\n\n## Getting started\n\n### Installation\n\n👥 Clone this repository to the location of your choice.\n```shell script\ngit clone https://github.com/E3SM-Project/gdess.git ~/gdess\ncd ~/gdess\n```\n\n🌍 Using [conda](https://docs.conda.io/projects/conda/en/latest/index.html#), \ncreate a runtime or development environment and install dependencies. \n*Note: Replace “myenv” with the preferred name of your environment, e.g. \"gdess_env\". \nFrom here on we’ll use “myenv” to refer to our environment.*\n\n```shell script\nconda create -n myenv python=3.8\nconda activate myenv\nconda config --add channels conda-forge\nconda install --file requirements.txt\n```\n\n💾 Install the package:\n```shell script\npip install .\n```\n\n(Optional) Retrieve the example data files used for running tests:\n```shell\ngit lfs install\ngit lfs pull\n```\n\n### Configuration\n\nInstead of passing data filepaths to the recipe functions each time, \nyou can define environment variables for the directory containing Globalview+ Obspack\nand CMIP model output.\n\n##### On a Mac/Linux OS:\n- Specify the path to the repo on the first line of `gdess/bin/set_path_vars.sh`. \nFor instance, by executing: `export GDESS_REPO=${HOME}/gdess`\n- Set paths to the test data by running the script at the command line:\n```shell\nsource ./gdess/bin/set_path_vars.sh\n```\n- To use stored data other than the test data, modify the other data paths in the scripts. \nFor example, to set the paths for every future terminal instance (and if you are running in a bash environment), \nset these paths in your `~/.bash_profile`:\n```bash\nexport GDESS_CMIP_DATA=Path/to/Model/Output/NetCDF/Files\nexport GDESS_GLOBALVIEW_DATA=Path/to/Obspack/NetCDF/Files\n```\n\n##### On a Windows OS:\n- Follow the instructions provided in `./gdess/bin/set_path_vars.bat`\n\n\n[comment]: \u003c\u003e (These variables are retrieved in the `gdess/config/defaults.ini` file.)\n\n## Example\n\n- The following steps generate this figure:\n\u003cimg src=\"./.images/seasonal_example_20210903.png\" alt=\"components\" width=\"712\" height=\"280\"/\u003e\n\n  1. Install the `gdess` package according to the [installation](#installation) instructions\n  2. Follow the [configuration](#configuration) instructions to use the included example data files\n  3. \n```./gdess/bin/gdess_cli.py --verbose seasonal --start_yr 1980 --end_yr 2015 --model_name BCC.esm-hist --cmip_load_method local --station_list smo```\n\n- Other examples are given in the provided jupyter notebooks, \nwhich show recipe usage (`trends_recipe.ipynb`, `seasonal_cycle_recipe.ipynb`, and `meridional_recipe.ipynb`). To run them:\n  1. Enable your conda environment via: `python -m ipykernel install --user --name=myenv`\n  2. Start jupyter by entering: `jupyter notebook`\n  3. Navigate within jupyter to `gdess/notebooks/demo/`\n\n## Usage description\n\nDiagnostic recipes can be run from the command line or from within a Python kernel:\n\n**Command line**\n  - Use `gdess/bin/gdess_cli.py` followed by the recipe name and then recipe options. To see available commands, run, e.g. `gdess/bin/gdess_cli.py --help` or `gdess/bin/gdess_cli.py seasonal --help`  \n  - To specify options from a file [recommended for repeated tests], use the `@` symbol prefix for the filename. \n    For example, using the provided file: `gdess/bin/gdess_cli.py @recipe_options_example.txt` \n\n\n**In a Python kernel** \n  - The diagnostic recipes can be called directly, with options passed as a Python `dict` object.\n  - Check out the demonstration notebooks in `gdess/notebooks/demo/`. The notebooks show recipe usage.\n\n## Updating\n\nTo use the latest version of this repository:\n- Enter the `gdess/` directory\n- Activate your desired environment\n- Run the commands:\n\n   ```\n   git pull\n   git checkout main\n   pip install . --upgrade\n   ```\n\n## Uninstalling\n\n🚮 To remove this package from your environment:\n\n```\npip uninstall gdess\n```\n\n## Contributing\n\nIf you're interested in participating in the development of `gdess`, \nwant to suggest features or report bugs, \nplease leave us a message through the [issue tracker](https://github.com/E3SM-Project/gdess/issues).\n\nNote that most work is done in the 'develop' branch, \nso please `git checkout develop` \n(or you can create a new branch) before modifying code.\n\n## Running the code tests\n\nTo run the built-in tests:\n```shell\npytest\n```\n\n## Project structure\n\n#### Components\n\n\u003cimg src=\"./.images/structure_diagram_20210903.png\" alt=\"components\" width=\"607\" height=\"384\"/\u003e\n\n#### Directory Tree\n```\ngdess\n│\n├── README.md                            \u003c- Top-level README for users/developers of this project\n├── requirements.txt                     \u003c- Package dependencies\n├── recipe_options_example.txt\n│\n├── notebooks                            \u003c- Example jupyter notebooks to see diagnostic capabilities of gdess\n│   └──demo/\n│\n├── gdess                                \u003c- *Python package* for handling co2 diagnostics\n│   │\n│   ├── bin                  \n│   │   ├── gdess_cli.py                 \u003c- Run recipes from the command line\n│   │   ├── set_path_vars.sh             \u003c- Script to set up data file paths for running examples\n│   │   └── concat_and_remap_E3SM_co2.sh \u003c- Script template for post-processing of E3SM output\n│   │\n│   ├── recipes                          \u003c- Generate repeatable diagnostics that span multiple data sources available as recipes \n│   │   ├── surface_trends.py\n│   │   ├── seasonal_cycles.py\n│   │   ├── meridional_gradient.py\n│   │   ├── recipe_utils.py\n│   │   └── ...\n│   │\n│   ├── data_source                      \u003c- Load, parse, and manipulate data from a particular source\n│   │   ├── models/\n│   │   ├── observations/\n│   │   ├── multiset.py\n│   │   └── ...\n│   │\n│   ├── operations                       \u003c- Manipulate datasets (e.g. spatially or temporally) \n│   │   ├── Confrontation.py\n│   │   ├── geographic.py\n│   │   ├── time.py\n│   │   ├── convert.py\n│   │   ├── datasetdict.py\n│   │   └── ...\n│   │\n│   ├── formatters                       \u003c- Manipulate formatting in desired ways\n│   │   ├── args.py\n│   │   ├── nums.py\n│   │   ├── strings.py\n│   │   └── ...\n│   │\n│   ├── graphics                         \u003c- Make repeated graphic actions available \n│   │   ├── comparison_plots.py\n│   │   ├── single_source_plots.py\n│   │   ├── utils.py\n│   │   └── ...\n│   │\n│   ├── config                           \u003c- Configuration options\n│   │   ├── defaults.ini\n│   │   ├── stations_dict.json\n│   │   └── log_config.json\n│   │   \n│   └── recipe_parsers.py\n│\n├── tests                                \u003c- Unit and integration tests for development \n│   └── ...\n├── ccgcrv                               \u003c- Curve fitting code from NOAA GML (see credits in the README)  \n│   └── ...\n├── ci                                   \u003c- Environment specification for continuous integration  \n│   └── ...\n├── paper                                \u003c- Manuscript for the Journal of Open Source Software (JOSS)  \n│   └── ...\n│\n├── LICENSE\n├── MANIFEST.in\n├── setup.cfg\n└── setup.py\n```\n\n## Credits\n\n#### Dependencies\n\nThe full list of dependencies is in the `requirements.txt` file. \n\n#### Additional acknowledgements\n\n- Although not a dependency, ideas were also drawn from [xclim: Climate indices computations](https://github.com/Ouranosinc/xclim).\n- `ccgcrv`: The curve fitting code in the ccgcrv directory has been mildly modified \u0026mdash; \n  modified for improved code integration, i.e. the computational logic has not been changed \u0026mdash; \n  from the code made freely available by the NOAA Global Monitoring Laboratory. \nFrom their website, one can access the [source code](https://www.esrl.noaa.gov/gmd/aftp/user/thoning/ccgcrv/) \nand read a [description of its usage](https://www.esrl.noaa.gov/gmd/ccgg/mbl/crvfit/crvfit.html). \nAdditional details regarding the curve fitting approach can be found in the following publications:\n  - Thoning, K.W., P.P. Tans, and W.D. Komhyr, 1989, Atmospheric carbon dioxide at Mauna Loa Observatory, 2. Analysis of the NOAA/GMCC data, 1974 1985., J. Geophys. Res. ,94, 8549 8565. https://doi.org/10.1029/JD094iD06p08549\n  - Sweeney, C., Karion, A., Wolter, S., Newberger, T., Guenther, D., Higgs, J.A., Andrews, A.E., Lang, P.M., Neff, D., Dlugokencky, E., Miller, J.B., Montzka, S.A., Miller, B.R., Masarie, K.A., Biraud, S.C., Novelli, P.C., Crotwell, M., Crotwell, A.M., Thoning, K., Tans, P.P., 2015. Seasonal climatology of CO 2 across North America from aircraft measurements in the NOAA/ESRL Global Greenhouse Gas Reference Network. J. Geophys. Res. Atmos. 120, 5155–5190. https://doi.org/10.1002/2014JD022591\n\n- The Mauna Loa (MLO), American Samoa (SMO), South Pole (SPO), and Barrow Observatory (BRW) dataset files \nused in the tests directory was provided via the Obspack (GLOBALVIEWplus_v6.0_2020-09-11) by:\nC. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, \nT. P. Whorf, M. Heimann, and H. A. 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