The Global Centre for Risk and Innovation (GCRI)
Every statement about a changing climate or an unusual event is a comparison with a baseline: a reference period of observed weather, averaged and described. Baselines come from station records, satellites and reanalyses, each with gaps and biases. This page sets out how baselines are built, why the choice of period and dataset matters, and what evidence they serve for the institutions that plan against today's climate and tomorrow's.
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A climate baseline is the reference description of past climate against which change and unusual events are measured, usually a thirty-year normal. Baselines come from stations, satellites and reanalyses, each with gaps and biases. The reference period and dataset must always be stated, because both change the size of an anomaly.
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Historical trends and reference periods. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Climate normals are averages over a fixed thirty-year period. As the climate warms, a recent normal makes today's extremes look less unusual than an earlier one would, so the period must always be stated.
Long station records cluster in wealthier, populated and low-lying places. Mountains, oceans, deserts and much of Africa are sparsely observed, so gridded products interpolate or model across large gaps.
Reanalyses run a weather model through the past, constrained by every available observation. They give complete, consistent fields, but where observations are few the model's own biases dominate.
Station moves, new instruments and urban growth create jumps in records that are not climate. Homogenisation detects and adjusts them, and the adjustments must be documented to be trusted.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Climate normal | An average of a climate variable over a standard thirty-year period, computed under WMO rules. |
| Anomaly | The difference between an observed value and its average over a stated reference period. |
| Homogenisation | Detecting and adjusting non-climatic jumps in records caused by station moves or instrument changes. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Temperature anomaly | degrees Celsius against a stated reference period | monthly | Station and gridded observations | OBSERVED |
| Precipitation anomaly | percent of reference-period mean | monthly | Gauge and satellite precipitation products | OBSERVED |
| Station record completeness | percent of days with valid observations | annual | National station networks | OBSERVED |
| Reanalysis temperature | degrees Celsius | hourly | Global reanalysis | MODELLED |
| Observing network density | stations per ten thousand square kilometres | annual | WMO observing system records | OFFICIAL |
| Homogenisation adjustments applied | count of breakpoints adjusted | per release | Dataset documentation | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| ERA5 reanalysis (Copernicus Climate Change Service, ECMWF) | Hourly global atmospheric, land and ocean fields from the mid-twentieth century | Free and open with attribution, Copernicus licence (to check current terms) |
| NOAA Global Historical Climatology Network daily (GHCN-Daily) | Daily station temperature and precipitation worldwide | Public domain, US government data |
| HadCRUT5 (Met Office Hadley Centre and University of East Anglia) | Global gridded surface temperature anomalies with uncertainty ensemble | Open Government Licence |
| Berkeley Earth surface temperature | Gridded land and ocean temperature series | Open, CC BY-NC 4.0 |
| GPCC precipitation (Deutscher Wetterdienst) | Gridded gauge-based precipitation over land | Free with attribution, DWD terms (to check) |
| CHIRPS (Climate Hazards Center, UC Santa Barbara) | Blended satellite and station rainfall for the tropics and subtropics | Open, licence to check |
Climate and Hazards prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| National meteorological and hydrological services | Which reference period and products to publish as the national baseline | Product comparisons; station completeness; homogenisation records |
| Infrastructure and building standards bodies | Which climate values to use as design inputs | Baselines with periods and uncertainty stated |
| Ministries of environment and planning | Where observing networks need strengthening | Station density and gap maps |
Baseline questions concern the record and the reference.
Climate varies naturally from year to year and decade to decade, through phenomena such as El Niño and the Southern Oscillation. A baseline must be long enough to average over that variability, which is why multi-decade reference periods are used. When the climate is warming, however, a recent baseline already includes part of the change, and an older baseline may understate the conditions people now experience.
Extremes can change faster than averages: a modest shift in the mean temperature can make formerly rare hot extremes much more common, because the tail of the distribution moves. Changes in measurement, such as relocated stations, new instruments or urban growth around a site, can introduce artificial trends that must be identified and corrected.
| Method or data | What it shows | Limits |
|---|---|---|
| Station records | Long local histories of temperature, rainfall and wind | Gaps, relocations and instrument changes require homogenization |
| Gridded observational datasets | Consistent coverage across regions | Sparse stations in many regions mean values are interpolated |
| Reanalysis | Physically consistent reconstructions of past weather | Model-dependent, especially where observations are sparse |
| Satellite records | Global coverage of many variables | Records are relatively short and need calibration across missions |
| Detection and attribution studies | Whether change exceeds natural variability and why | Depend on models and on the framing of the question |
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.