README.md

Cloud Feedback Metrics

The clould feedback metrics implemented to PMP is originally from assessed-cloud-fbks, developed by @mzelinka at LLNL. This code performs the analysis of Zelinka et al. (2022). It computes GCM cloud feedback components and compares them to the expert-assessed values from Sherwood et al. (2020).

Instructions

To use, please consider follow these steps:

1. Install PMP

Install PMP via conda following these instructions. For example:

conda create -n [YOUR_CONDA_ENVIRONMENT] -c conda-forge pcmdi_metrics

2. Activate PMP installed conda environment

Activate your environment (if PMP installed env is differnt than your current one):

conda activate [YOUR_CONDA_ENVIRONMENT]

3. Clone PMP repo to your local

Clone PMP repo to your local for pre-calculated data:

git clone https://github.com/PCMDI/pcmdi_metrics

Once completed, go to pcmdi_metrics/pcmdi_metrics/cloud_feedback directory

cd [YOUR LOCAL CLONED PMP REPOSITORY]
cd pcmdi_metrics/pcmdi_metrics/cloud_feedback

4. Edit parameter files

In param/my_param.py, update the "User Input" section so it points to your model's amip and amip-p4K files.

# User Input:
# ================================================================================================
model = "GFDL-CM4"
variant = "r1i1p1f1"

input_files_json = "./param/input_files.json"

# Flag to compute ECS
# True: compute ECS using abrupt-4xCO2 run
# False: do not compute, instead rely on ECS value present in the json file (if it exists)
# get_ecs = True
get_ecs = False

# Output directory path (directory will be generated if it does not exist yet.)
xml_path = "./xmls/"
figure_path = "./figures/"
output_path = "./output"
output_json_filename = "_".join(["cloud_feedback", model, variant]) + ".json"
# ================================================================================================

You will need to update param/input_files.json file as well to provide data path for your input files.

5. Run the code and inspect the generated output files

Run calculation:

python cloud_feedback_driver.py -p param/my_param.py

Once code is completed, check output directory (output_path from the above parameter file) for JSON and figures directory (figure_path from the above parameter file) for figures and text tables.

References

• Zelinka et al. (2022): Evaluating climate models’ cloud feedbacks against expert judgement, J. Geophys. Res., 127, e2021JD035198, doi:10.1029/2021JD035198.

• Sherwood et al. (2020): A combined assessment of Earth’s climate sensitivity, Rev. Geophys., 58, e2019RG000678, doi:10.1029/2019RG000678.