ClimateTools.jl

ClimateTools.jl is a workflow-oriented climate-analysis package built on top of YAXArrays.jl. It is designed for users who need to move from raw gridded observations and simulations to usable climate scenarios, bias-corrected products, indicators, and summary diagnostics.

What ClimateTools Covers

ClimateTools focuses on the common steps of applied climate analysis:

• Reading gridded CF-style datasets into YAXArray and Cube objects
• Selecting domains in time and space
• Regridding regular, curvilinear, and rotated-pole simulations
• Bias-correcting model simulations against observations or reanalyses
• Building climate scenarios from observational references and climate-model outputs
• Computing climate indicators, annual summaries, threshold counts, and spell statistics
• Performing selected diagnostics such as return-level estimation and time-series metrics

Data Model

The package operates on YAXArrays-native objects.

using YAXArrays

cube = Cube(open_dataset("myfile.nc"))

High-level functions generally take a single YAXArray or a small group of aligned cubes and return another YAXArray, preserving axes where possible.

Typical Workflow

A realistic ClimateTools workflow usually looks like this:

1. Open observational and simulation datasets.
2. Inspect dimensions, calendars, and coordinate conventions.
3. Subset the study period and spatial domain.
4. Interpolate or regrid datasets to a common grid.
5. Bias-correct model simulations.
6. Compute indicators and aggregations on the corrected data.
7. Validate and visualize the results.

using ClimateTools
using YAXArrays

obs = Cube(open_dataset("obs.nc"))
ref = Cube(open_dataset("ref.nc"))
fut = Cube(open_dataset("fut.nc"))

qq = qqmap(obs, ref, fut; method="additive", detrend=true)
txx = tx_max(qq)

Start Here

If you are new to the package, read the pages in this order:

1. Quick Start
2. Data and Subsetting
3. Interpolation and Regridding
4. Bias Correction
5. Building Climate Scenarios
6. Indices and Aggregations

Building Climate Scenarios

ClimateTools is especially useful for climate-scenario construction. In that context, a scenario usually means a climate-model projection that has been aligned to an observational reference through spatial interpolation and statistical post-processing.

The dedicated Building Climate Scenarios guide walks through:

• choosing observational references
• preparing historical and future simulations
• regridding observations and simulations onto a common grid
• selecting a bias-correction method
• validating the resulting corrected fields
• computing derived indices for impact-oriented analysis

Compute Model

Internally, ClimateTools prefers the YAXArrays xmap pattern for whole-dimension transforms and reductions such as time aggregations, period-wise indices, quantile summaries, and regridding over full grids.

Some workflows still use mapCube, mainly where multiple inputs share the same dimension name but not the same coordinate values. Bias-correction workflows based on observational, historical-model, and future-model series are the main example.

Documentation Structure

• Installation: package setup and optional tooling
• Quick Start: first end-to-end result
• Data and Subsetting: opening datasets, selecting ranges, polygon masking
• Interpolation and Regridding: regular and rotated-grid workflows
• Bias Correction: method selection and usage guidance
• Building Climate Scenarios: full scenario workflow
• Indices and Aggregations: grouped catalog of indices and summaries
• Examples: worked examples by use case
• Validation and Diagnostics: how to assess outputs
• Troubleshooting: common pitfalls
• API Overview: grouped exported functions

References and Attribution

Bias-correction and indicator workflows in ClimateTools are informed by the climate-services ecosystem, including xclim’s documentation style for workflow separation and the published literature behind each correction method. The references used in the docs are collected on the References page.