GCRI · Climate and Hazards
Climate baselines
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.
Climate baselines on the record.
Climate baselines: how the risk develops.
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.
Reference periods move
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.
Stations are unevenly spread
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.
Reanalysis blends model and observation
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.
Records need homogenising
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.
Climate signals worldwide.
Daytime surface heat, rain and marine heat across the world, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Climate signals, for this page
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- GHRSST MUR25 (NASA JPL PO.DAAC)
- Provider
- NASA JPL PO.DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite analysis
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite analysis of how far sea temperature departs from its usual value for the date shown; a marine heat indicator, not a coral bleaching alert.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- GHRSST MUR (NASA JPL PO.DAAC)
- Provider
- NASA JPL PO.DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite analysis
- Newest at build
- 2026-10-12
- Resolution
- about 1 km grid
- Uncertainty
- Satellite analysis. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Climate signals, framed on the world.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERT · NASA AIRSSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Observed (EO): NASA AIRS. We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (22 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Sea ice concentration: GHRSST MUR, NASA JPL PO.DAAC · 12 Oct 2026
A baseline is the reference every domain measures change against.
- WaterCouplingRainfall and evaporation normals set expected water availability.What to watchNormals period stated with each anomaly
- FoodCouplingCrop calendars and suitability rest on temperature and rainfall baselines.What to watchGrowing season shifts
- EnergyCouplingDegree-day baselines set heating and cooling demand.What to watchDegree days against the reference period
Baseline datasets are designed for the Observatory Signal series. WMO recommends the 1991–2020 normals for operational use and keeps 1961–1990 for long-term change; every anomaly states its reference period and would carry NS-3b metadata under the proposed Nexus Standards.
- Digitised station records that lengthen local baselines.
- Homogenisation notes for station series.
Multi-hazard outlook.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
What is measured, and how well.
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 | Published statistics |
| Homogenisation adjustments applied | count of breakpoints adjusted | per release | Dataset documentation | REPORTED |
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Climate and Hazards in Foundry
- Tested at Climate and Hazards in Universe
- Codified in Climate and Earth Systems in Standards
- Published in Climate and Hazards in Reports
- Taught in Faculty of Earth and Environmental Sciences in Academy
Where the evidence comes from.
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 |
Model climate baselines as a system. Run the cascade.
Assembled from the Nexus Standards area profile Climate risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Sea-level rise
- Coastal flood (set off downstream)
- Storm surge (set off downstream)
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Climate-driven migration and relocation (set off downstream)
- Mass displacement (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-3bDataset and product metadata profile (STAC, DCAT, PROV-O)NS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H65Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H71Hazard family: Deep-time and cosmic catastrophes (NHS-1 planet.deep-time)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H75Hazard family: Climate-sensitive illness (NHS-1 health.climate-sensitive)NRS-H7Hazard family: Large-scale circulation anomalies (NHS-1 atmo.climate-mode)
Observation
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- CMIP6 climate projections (Copernicus Climate Data Store) (listed for reference)
- NGFS Scenario Explorer (listed for reference)
- INFORM Risk Index (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Total greenhouse gas emissions per year
- Direct economic loss attributed to disasters in relation to global GDP
Example geography
- Pacific Island states: 150 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
Who decides, and what the evidence serves.
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 |
Outputs this topic produces
- Baseline climatologies with reference period and dataset stated
- Comparisons of observational products for a region, with spread shown
- Station network gap maps for national services
- Anomaly series labelled by evidence class
How climate baselines is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for climate baselines could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
What experts ask.
Baseline questions concern the record and the reference.
- How long and how reliable is the observational record at a given place?
- Which reference period is being used, and does it already include warming?
- How have the mean and the extremes of temperature, rainfall and other variables changed?
- How much of an observed change can be attributed to human influence rather than natural variability?
- Is the past still a sound guide to design and planning?
Mechanisms and drivers.
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.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Station records | Long local histories of temperature, rainfall and wind | Gaps, relocations and instrument changes require homogenisation |
| 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 |
How it is studied, and what the methods cannot say.
- 01Climate and atmospheric scienceDetection and attribution of change · Extreme event attribution · Process-based model evaluationApplied here
- 02Spatial statistics and geostatisticsInterpolating station data · Merging satellite and gauge data · Spatial clustering of eventsApplied here
- 03Earth observation and remote sensingFlood extent mapping · Vegetation and drought monitoring · Active fire and burned areaApplied here
- 04High-performance, cloud and edge computingRunning and storing ensembles · Analysis near the data · Edge processing for observationsApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is a climate normal?
What is a reanalysis?
Why do temperature datasets disagree?
Why do different reports use different baselines?
Is the historical record still useful?
Does the platform publish climate normals?
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