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CMIP6 Equatorial-Wave Diagnostics in Python

DOI

A Python workflow for Wheeler–Kiladis precipitation spectra, Kelvin and equatorial-Rossby filtered precipitation, skill-score NetCDF files, individual figures, and multi-model comparison panels. Precipitation-based equatorial-wave skill metrics are converted from Qiang's NCL scripts, which were used in TaiESM model evaluation paper.

Data are not distributed with this repository.

Example output

Five-model equatorial-wave comparison

The upper panels compare the observed and ACCESS-CM2 symmetric wavenumber–frequency spectra. The lower panel compares ten precipitation-based equatorial-wave skill metrics for ACCESS-CM2, ACCESS-ESM1-5, HadGEM3-GC31-LL, KACE-1-0-G, and UKESM1-0-LL.

Bottom-panel metrics

Number Label Diagnostic
01 Eastward Agreement with the observed eastward-propagating symmetric precipitation spectrum (positive zonal wavenumbers).
02 Westward Agreement with the observed westward-propagating symmetric precipitation spectrum (negative zonal wavenumbers).
03 EK [MAM] Equatorial Kelvin-wave precipitation-variance skill for March–May.
04 EK [JJA] Equatorial Kelvin-wave precipitation-variance skill for June–August.
05 EK [SON] Equatorial Kelvin-wave precipitation-variance skill for September–November.
06 EK [DJF] Equatorial Kelvin-wave precipitation-variance skill for December–February.
07 ER [MAM] Equatorial Rossby-wave precipitation-variance skill for March–May.
08 ER [JJA] Equatorial Rossby-wave precipitation-variance skill for June–August.
09 ER [SON] Equatorial Rossby-wave precipitation-variance skill for September–November.
10 ER [DJF] Equatorial Rossby-wave precipitation-variance skill for December–February.

The Eastward and Westward metrics compare selected regions of the normalized wavenumber–frequency spectrum. The EK and ER metrics compare the spatial patterns and amplitudes of seasonally grouped, wave-filtered precipitation variance between each model and the observational reference.

Each metric uses the skill score:

score = (1 + pattern_correlation)^4 / [4 (SDR + 1/SDR)^2]

Here, SDR is the model spatial standard deviation divided by the observed spatial standard deviation. A score approaches 1 when both spatial pattern and amplitude agree with observations; values approaching 0 indicate poor agreement. The horizontal 0.5 line is a visual reference, not a universal pass/fail threshold.

Required data

Each model needs global daily precipitation with:

  • variable pr (also recognizes PREC, precip, and PRECT);
  • dimensions/coordinates time, lat, and lon;
  • coverage of 1980–2014;
  • units kg m-2 s-1 or mm/day;
  • one or more consecutive files from the same ensemble and grid.

Two observational reference files are required:

DATA_ROOT/obs/SpaceTime.obs.PRECT.nc
DATA_ROOT/obs/kf_filter_GPCP.nc

Installation

python -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt

On NCI Gadi, conda/analysis3 provides the dependencies:

module use /g/data/xp65/public/modules
module load conda/analysis3
export PYTHONNOUSERSITE=1

Run one model from raw precipitation

python eqwave_pipeline.py all \
  --model ACCESS-CM2 \
  --data-root /path/to/work-data \
  --raw-files /path/to/pr_1980_1999.nc /path/to/pr_2000_2014.nc \
  --output Panel_EQ-Wave_OBS-ACCESS-CM2.png

The data-root must contain the obs directory shown above. Model results are written beneath that root.

Available stages:

python eqwave_pipeline.py spectrum ...
python eqwave_pipeline.py filter ...
python eqwave_pipeline.py scores ...
python eqwave_pipeline.py plot ...

To reuse existing expensive spectrum/filter intermediates:

python eqwave_pipeline.py all ... --reuse-existing

Discover CMIP6 model files

First make a lightweight filename index:

find /path/to/CMIP6/CMIP -type f -name 'pr_day_*_historical_*.nc' \
  > cmip6_pr_files.txt

Then build the catalogue:

python build_cmip6_catalog.py \
  --file-list cmip6_pr_files.txt \
  --output cmip6_daily_pr_1980_2014.csv

The selector prefers r1i1p1f1, the native gn grid, the latest version, and complete 1980–2014 coverage.

Make a multi-model panel

The top panels contain OBS and one selected model. Repeated --result arguments add models to the bottom skill-score panel:

python plot_multi_model.py \
  --obs-spectrum /path/to/obs/SpaceTime.obs.PRECT.nc \
  --top-model ACCESS-CM2 \
  --result ACCESS-CM2 /results/ACCESS-CM2 \
  --result ACCESS-ESM1-5 /results/ACCESS-ESM1-5 \
  --result HadGEM3-GC31-LL /results/HadGEM3-GC31-LL \
  --result KACE-1-0-G /results/KACE-1-0-G \
  --result UKESM1-0-LL /results/UKESM1-0-LL \
  --output multi_model_panel.png

Each result directory must contain the CMIP6/SpaceTime, CMIP6/STScore, and CMIP6/EQ-WaveScore subdirectories produced by eqwave_pipeline.py.

PBS on Gadi

Copy and edit the included template:

cp submit_model.pbs.example submit_model.pbs

Replace PROJECT, prepare a text file containing one raw precipitation path per line, then submit:

qsub -v MODEL=ACCESS-CM2,RAW_FILES_FILE=/path/raw_files.txt,OBS_ROOT=/path/data,OUTPUT_ROOT=/scratch/PROJECT/user/ACCESS-CM2 submit_model.pbs

Outputs

CMIP6/SpaceTime/SpaceTime.MODEL.PRECT.nc
CMIP6/Kr-filter/kf_filter_Pre_MODEL.nc
CMIP6/STScore/Skill-Score_MODEL_Pre_ST_1980-2014.nc
CMIP6/EQ-WaveScore/Skill-Score_EQ-Wave_Pre_MODEL.nc
Panel_EQ-Wave_OBS-MODEL.png

Scientific scope

These diagnostics measure tropical space–time spectral power and Kelvin/ER wave skill. They are relevant to MJO evaluation but do not alone measure every aspect of MJO simulation. See NOTICE.md for provenance and validation notes.