Heat Waves

Dataset and region

Dataset
Region

Variables on this page

VariableFinding

Detection

A change is called detected when the Mann–Kendall test finds the trend unlikely to have arisen by chance against a background of internal variability:

Likelihood

Mann–Kendall two-sided p < 0.10. The reported rate is the Theil–Sen slope.

Recomputed per window

The test runs on whatever window you select, so narrowing the window changes both the slope and the verdict.

Per dataset and region

Each combination is tested on its own and the verdict describes only the series on screen. GHCN and Berkeley Earth can reach different verdicts for the same variable; switch between them to see it.

Significance is not the same as importance. Each chart also draws the 66% and 90% historical range bands so the fitted change can be compared with ordinary year-to-year spread by eye — a trend can clear p < 0.10 while remaining small against that spread. Bear in mind too that a linear trend is a poor summary of a series whose largest feature is a single decade; for CONUS the 1930s dominate.

Methodology

Every metric runs through one implementation (scripts/metrics.py) for all four dataset and region combinations, so a difference between two panels is a difference in the data rather than in the arithmetic. A site is a station for GHCN and a land grid point for Berkeley Earth.

Data and reproducibility

This is a static page. Every value is precomputed and loaded from dashboard-series.js; there is no server, no database, and no network request at view time. The statistics, charts and downloads all run in your browser.

Rebuild with python scripts/build_dashboard_data.py. The CONUS GHCN series come from the parent project's own checkpoints and reproduce data/heat_wave_index_raw_adj_wtd.csv and data/annual_hottest_daily_high_raw_adj_wtd.csv exactly; the Berkeley CONUS Heat Wave Index reproduces data/heat_wave_index_berkeley.csv exactly. Tests assert all three.