---

name: climate-analysis description: >- Analyse labelled climate time series and gridded or station observations with checked calendars, aggregation, climatological baselines and spatial validation. Use for climate anomalies, regional summaries or station-to-grid analysis; distinguish descriptive analysis from future-time prediction. license: MIT metadata: version: "1.0.1" author: Geoscience Skills skill_type: workflow tags: '["Climate", "Time Series", "Spatial Analysis", "Validation"]' dependencies: '["xarray", "numpy", "pandas", "cftime", "verde", "pyproj", "scipy"]' complements: '["xarray", "verde", "pooch"]' workflow_role: analysis

Climate Analysis

Use xarray for labelled climate arrays and verde only when scattered spatial observations need interpolation. Existing regular grids do not require a new interpolation stage. Run the relevant stages in one agent; companion skills are helpful references rather than mandatory registrations.

Define the data contract

Identify the variable, physical units, spatial support, timestamp meaning, calendar, time bounds, missing-value encoding and source/version before combining files. Check coordinate uniqueness and order, longitude convention, latitude direction, projection, vertical level and ensemble/member dimensions. Preserve non-Gregorian calendars; do not silently coerce them into ordinary timestamps.

Distinguish an instantaneous value, interval mean, accumulated amount and rate. For precipitation or energy fluxes, use the time bounds and unit definition to decide integration or summation. Do not apply the same resampling operation to temperature means and accumulated rainfall. Record source URLs, licences and checksums; use the pooch skill only when downloading is actually requested.

Select the analysis

Deliverable	Main steps	Validation partition
Climatology/anomalies	Specify baseline years, calendar grouping, coverage and weights	Document baseline overlap; no forecasting claim from descriptive anomalies
Regional averages	Use cell areas or justified spatial weights and a fixed mask	Check missing coverage and area/weight normalisation for each period
Station-to-grid product	Project coordinates appropriately, fit a spatial model, mask unsupported regions	Withhold spatial blocks or stations before fitting any preprocessing
Forecast/downscaling evaluation	Define issue time, lead time and predictor availability, then fit	Withhold future time periods and unseen locations as required by deployment

Aggregate without losing meaning

Choose an explicit coverage rule for incomplete months/seasons and retain a coverage/count variable. Weight monthly means by represented duration, respecting the dataset's calendar; the xarray seasonal-mean example illustrates calendar-aware weighting. Renormalise over valid support only when that matches the stated estimand; never turn an entirely missing region or period into a zero observation.

Use supplied cell areas for irregular/projected grids. Cosine-latitude weights are an approximation for suitable regular longitude/latitude grids, not a general area calculation. Define whether December belongs to the following winter year before grouping DJF; a long-term seasonal climatology and a sequence of individual seasonal means are different outputs.

For anomalies, record baseline years and grouping. In prediction experiments, estimate climatology, trends, scaling, missing-value models and bias corrections using training data only. Do not allow centred windows, filled gaps or target aggregation windows to reach into future observations unavailable at issue time.

Spatial modelling and validation

For station observations, retain station identifiers and repeated-observation groups. Use a distance-appropriate CRS for a regional planar model; do not feed unqualified geographic degrees into metre-scale block sizes. Fit trends, normalisation, hyperparameters and interpolation within each training fold.

Use Verde spatial block validation when evaluating interpolation away from measured stations. Choose block size and any buffer from spatial support/dependence and the intended deployment; randomly splitting neighbouring samples can overstate performance. For future predictions, also hold out contiguous time periods. A station appearing in both sets may still be valid for a same-station forecast, but does not test an unseen location; state which question the split answers.

Outputs and uncertainty

Export a labelled dataset retaining units, calendar, CRS, baseline, masks and coverage, plus a record of processing choices and package versions. Reopen the output and check coordinates, dimensions and selected analytical summaries. Report errors by location/season and against a suitable climatology or persistence baseline. Account for temporal/spatial dependence when estimating uncertainty; model spread is not automatically a calibrated predictive interval, and trend significance is not causal attribution.

Executed station case

The NOAA station example executes the workflow from real daily maximum temperatures through QC, calendar-aware means, short-reference anomalies, spatial holdout and a labelled NetCDF export. Its offline script requires the recorded source and provenance files and preserves existing output directories.

The example separates each station's descriptive anomaly from a spatial model relative to a station-network reference. Every spatial fold learns that reference from training stations alone; the held-out station's past values are also excluded. The final grid contains model estimates, with a recorded support mask and spherical cell areas. A regional model mean is not a measured regional climate change.

Tests also cover synthetic 360_day/noleap calendars, missing coverage and all-NaN periods/regions. This verification does not establish forecasts, 30-year climate normals, homogenization, precipitation integration or arbitrary regridding accuracy. Use a data-specific validation for those branches.