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IPCC-WG1 Scientific Plotting Skill

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@GeoGeekLab GeoGeekLab released this 04 Aug 05:29
· 15 commits to main since this release
v1.00
82652c1

Release notes

Overview

This is the first public release of IPCC-WG1 Scientific Plotting Skill, a reusable and reproducible toolkit for publication-quality climate science visualization.

The project distills scientific plotting practices from representative IPCC Working Group I repositories and restructures them into a modern, configuration-driven Python workflow. It is designed for climate diagnostics, model ensembles, spatial statistics, uncertainty communication, and figure delivery for reports, papers, and technical assessments.

Highlights

  • Reproducible scientific plotting workflow
  • Configuration-driven figure recipes
  • Publication-ready Matplotlib defaults
  • Climate model ensemble summaries
  • Explicit uncertainty and model-agreement encoding
  • Multiple-testing control using Benjamini–Hochberg FDR
  • Area-weighted spatial statistics
  • IPCC-style low-agreement hatching
  • PDF and high-resolution PNG export
  • Machine-readable provenance and input-file hashing
  • Unit tests and an end-to-end quick-start example

Core capabilities

Ensemble statistics

The package provides utilities for calculating:

  • Ensemble median or mean
  • Configurable quantile intervals
  • Grid-cell model counts
  • Sign agreement across models
  • Low-agreement and insufficient-sample classifications

These functions are intended to make ensemble aggregation assumptions explicit and auditable.

Statistical significance

The included fdr_bh_mask() implementation applies the Benjamini–Hochberg procedure to control the false discovery rate when testing multiple grid cells or regions.

Scientific visual encoding

The plotting utilities support:

  • Single-column and double-column publication dimensions
  • Conservative typography and vector-font settings
  • Discrete and continuous scientific colour scales
  • Low-agreement hatching overlays
  • Separation of statistical results from visual styling
  • Local loading of licensed or externally distributed RGB colour maps

Spatial analysis

The package includes area-aware spatial aggregation using:

  • Cosine-latitude weighting for regular latitude–longitude grids
  • Explicit cell-area weighting when grid-cell areas are available

Provenance

Each figure workflow can record:

  • Input file SHA-256 hashes
  • Git commit identifier
  • Software and dependency versions
  • Analysis parameters
  • Random seeds
  • Data and method citations

Repository structure

The release includes:

  • SKILL.md — complete skill definition and methodology
  • README.md — installation and usage guidance
  • pyproject.toml — Python project configuration
  • Environment and dependency definitions
  • Reusable plotting and statistical modules
  • YAML figure recipe example
  • Unit tests
  • Quick-start workflow
  • Example PDF and PNG figures
  • Example NetCDF plotted-data output
  • Example provenance metadata

Recommended workflow

Scientific question
→ Figure contract
→ Data validation
→ Explicit statistical transformation
→ Compact plotted-data product
→ Declarative visual mapping
→ PDF and PNG export
→ Provenance, tests, and citations

Validation

This release has been checked using:

  • Unit tests for core statistical utilities
  • An end-to-end synthetic ensemble example
  • NetCDF plotted-data generation
  • PDF and 300 dpi PNG export
  • Provenance JSON generation
  • Rendered-figure inspection for clipping, overlap, and typography issues

Installation

Download and extract the release archive, then install the package from the repository root:

python -m pip install -e .

For development and testing:

python -m pip install -e ".[dev]"
pytest

Intended use

This project is suitable for:

  • Climate model evaluation
  • Multi-model ensemble analysis
  • Detection and attribution diagnostics
  • Regional climate assessment
  • Extreme-event analysis
  • Scientific reports and journal figures
  • Reproducible figure pipelines

Important note

This is an independent methodological toolkit inspired by publicly available IPCC-WG1 code and reproducibility practices. It is not an official IPCC software product and does not imply endorsement by the IPCC.

Initial release

As the first stable release, v1.0.0 establishes the core API, repository structure, reproducibility conventions, and reference plotting workflow.

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