This repository contains research-level code to reproduce the analysis and figures for "Hot extremes have become drier in the US Southwest" by Karen A. McKinnon, Andrew Poppick, and Isla R. Simpson. The manuscript is currently in review at Nature Climate Change, and the preprint is posted on Research Square.
Please the requirements.txt file for needed packages. In particular, you will need my packages humidity_variability and helpful_utilities.
There are two primary components of the analysis: (1) estimating the non-crossing quantile smoothing splines for each dataset, and (2) calculating and interpreting the amplification index. Part (1) can take a while, because the model is being fit many times (5 quantiles + a large number of stations or gridboxes).
(1) ISD
Download the ISD data from NOAA using:
python ./scripts/download_isd.py $tmpdir $savedir
Then fit the model using:
python ./scripts/est_q_quantiles.py $yr_start $yr_end $month_start $month_end $id_start $n_id $datadir $gjson_fname $predictor
Run:
python ./scripts/est_q_quantiles.py -h
for more information on the args. To reproduce the results in the published paper,:
yr_start=1973 yr_end=2019 month_start=7 month_end=9 gjson_fname=./shapefiles/interior_west.json predictor=GMT
(1) ERA5 and JRA-55
ERA5 hourly data for pressure, 2m temperature, and 2m dewpoint can be downloaded at the Copernicus Climate Data Store. An example script to download 2m temperature from ERA5 is at:
./scripts/download_ERA5_T.py
Note that you will need to install the Climate Data Store API, and set up authentication.
In order to run the quantile smoothing splines model, dewpoint and pressure are first used to calculate specific humidity, and then each gridbox must be formatted like a "station". An example of how to do so is at:
./scripts/preprocess_ERA.py
but note that this code will not run "out of the box".
JRA-55 six-hourly 2m air temperature and specific humidity are available via the NCAR Research Data Archive here. We rely on the analysis, not forecast, version, which incorporates screen-level measurements. Similar to ERA5, the JRA-55 data is formatted to look like a station before fitting the model. An exampleof how to do so is at:
./scripts/preprocess_JRA55.py
but note that this code will not run "out of the box".
Finally, the quantile smoothing splines model can be run using:
python ./est_q_quantiles_reanalysis.py $start_year $end_year $start_month $end_month $id_start $n_id $datadir $reanalysis_name
The two est_q*.py scripts are very similar, but the ISD-based on includes some additional QC not relevant for reanalysis output.
(2) Running the analysis and making the paper figures
There are two Jupyter Notebooks in the notebooks folder: one to reproduce the figures and calculations in the main text, and the other to reproduce the figures in the supplemental materials. Note that you will need to update the paths at the beginning of the notebook to where you have saved all of the relevant data.
Please contact Karen McKinnon (kmckinnon@ucla.edu) with any questions, or if you are using the code/methods in your work.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.