Illustrative: zonal winds, the subtropical and polar-front jets and the Hadley, Ferrel and polar cells (heights exaggerated)

Risk Academy, led by the Nexus Consortium · Course catalogue · Earth and Environmental Sciences

Climate and Ocean Sciences

Climate science, variability and change

Climate science, variability and change, taught twice over: as the working methods of the field and as its frontier since 2024, with labs on the Academy's tools, open data and the Nexus Standards.

Core course Frontier course

The discipline

01Core course · L2 Intermediate

Climate science, variability and change: foundations and practice

This area covers the physical climate system: observed baselines, variability modes such as the El Niño-Southern Oscillation, projections from climate models under scenario families, and change in glaciers, permafrost and mountain regions.

Level
L2 Intermediate
Study time
about 24 hours (planning estimate)
Status
In authoring
Prerequisites
An L1 course in the area, or an entry review

Why now

It gives an annually updated, traceable estimate between IPCC cycles, useful for calibrating scenario baselines and the pace of warming in risk models. Earth System Science Data (Copernicus Publications), Indicators of Global Climate Change 2025, 11 June 2026

Learning outcomes

  1. Define climate variability and the other key terms of the discipline and use them correctly in a written case
  2. Explain climate baselines and observations and variability modes such as ENSO with a worked example and its source
  3. Apply the discipline's method to a documented case and explain each result with its source
  4. Analyse a named case against climate models and projections, stating evidence, date and limits

Modules

  1. Climate baselines and observations
    • Climate baselines and observations
    • Variability modes such as ENSO
    • Climate models and projections
  2. Scenario families (SSPs and RCPs)
    • Scenario families (SSPs and RCPs)
    • Downscaling and bias correction
    • Attribution of extreme events
  3. Glaciers, permafrost and mountain hazards
    • Glaciers, permafrost and mountain hazards
    • Climate services
    • Physical climate risk for organisations
  4. Lab: the discipline's method
    • Global warming shifts the distribution of local annual maximum temperature by an amplified amount; the heat level that once returned every 100 years then returns far more often; the probability ratio between the two climates is what attribution reports, and the new return period is what an adaptation standard must use.
    • Work the method through a documented case and explain each result
    • Read the method's sources and stated values

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-X1 Climate change risk and adaptation
  • NRS-H5 Hazard family: Thermal extremes
  • NRS-H7 Hazard family: Large-scale circulation anomalies
  • NRS-H28 Hazard family: Climate-state shifts
  • NRS-H15 Hazard family: Glacier, ice-sheet and permafrost change
  • NRS-H65 Hazard family: Planetary boundary transgressions
  • NRS-H66 Hazard family: Ice tipping elements
  • NRS-H69 Hazard family: Carbon-cycle feedbacks

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-C8 Evidence, uncertainty and provenance

Assessment

  • 40%Lab recordWork the discipline's method through a documented case and explain each result with its source and date
  • 40%Case memoApply the discipline's methods to a named place or organisation, stating evidence and limits
  • 20%Key terms checkShort answers on the key terms, marked against the definitions

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Intermediate, 4 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. Earth System Science Data (Copernicus Publications), Indicators of Global Climate Change 2025, 11 June 2026
  2. Philip et al., A protocol for probabilistic extreme event attribution analyses, 2020
  3. IPCC, Climate Change 2021: The Physical Science Basis, Working Group I contribution to the Sixth Assessment Report, 2021
  4. Shepherd, A common framework for approaches to extreme event attribution, 2016
02Live map

Climate signals in the Mediterranean

Surface heat, rain, snow, sea ice, marine heat and water storage, each a dated satellite or model product, over the Mediterranean.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Climate signals

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 Mediterranean.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 EONETSources 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 H3 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 H3 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 H3 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 H3 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 H3 cells where shown (modified). NASA open data
  • GRACE and GRACE-FO JPL mascon solution, 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 H3 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 H3 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 H3 cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 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.
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.
  • 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.
  • 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 (21 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
  • Snow cover: VIIRS NOAA-20 NDSI, NASA LANCE · 13 Oct 2026
  • Sea ice concentration: GHRSST MUR, NASA JPL PO.DAAC · 12 Oct 2026
  • Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
  • Water storage anomaly, monthly: GRACE-FO JPL mascons, NASA JPL PO.DAAC · July 2022

03How learning runs

How learning runs, as a process you can run

Select a scenario and press Play: course, pathway, practice, faculty, cells and record.

04What is changing

Frontier course · L3 advanced, programme courses and standards

Climate science, variability and change: frontier methods

The methods, data, tools and studies that changed climate science, variability and change between 2024 and 2026, taught by reproducing them on open data.

Level
L3 Advanced
Study time
about 28 hours (planning estimate)
Status
Planned
Prerequisites
Climate science, variability and change: foundations and practice; Working knowledge of a scripting language (Python or R) for the labs

Why now

It gives an annually updated, traceable estimate between IPCC cycles, useful for calibrating scenario baselines and the pace of warming in risk models. Earth System Science Data (Copernicus Publications), Indicators of Global Climate Change 2025, 11 June 2026

Learning outcomes

  1. Explain what changed in climate science, variability and change since 2024 and cite the studies, data or rules behind each change
  2. Reproduce indicators of Global Climate Change 2025 puts human-induced warming at 1.37 °C on open data and state the uncertainty of the result
  3. Evaluate a 2024 to 2026 study: its question, data, method, limits and what it changes for practice
  4. Design an analysis that combines a frontier method with the faculty's instruments, and justify each choice

Modules

  1. Indicators of Global Climate Change 2025 puts human-induced warming at 1.37 °C
    • The annual peer-reviewed update of IPCC-style indicators estimated human-induced warming reached 1.37 °C in 2025, rising about 0.27 °C per decade, and added a marine heatwave indicator showing heatwave days more than tripled since 1991.
    • It gives an annually updated, traceable estimate between IPCC cycles, useful for calibrating scenario baselines and the pace of warming in risk models.
    • Earth System Science Data (Copernicus Publications), 11 June 2026
  2. WMO decadal update: 91% chance of a year above 1.5 °C
    • The WMO decadal update, produced by the UK Met Office, gave an 86% chance that one year in 2026–2030 beats 2024 as warmest, a 91% chance of a year above 1.5 °C, and a 75% chance the five-year mean exceeds it.
    • Near-term probabilistic outlooks help set planning horizons for heat, water and agriculture risk, though regional skill varies and should be checked locally.
    • World Meteorological Organization, 28 May 2026
  3. WMO State of the Global Climate 2025 adds energy imbalance indicator
    • WMO reported 2025 at about 1.43 °C above 1850–1900, second or third warmest on record, with 2015–2025 the 11 warmest years.
    • The energy imbalance shows heat is still accumulating, so physical risk scenarios should not assume a pause after cooler years in the annual temperature series.
    • World Meteorological Organization, 23 March 2026
  4. Copernicus: 2023–2025 first three-year period above 1.5 °C
    • The Copernicus Climate Change Service found 2025 was the third-warmest year in ERA5 at 1.47 °C above pre-industrial, and that the 2023–2025 average exceeded 1.5 °C, the first three-year period to do so.
    • Practitioners should distinguish single-year and multi-year exceedances from long-term warming when interpreting Paris thresholds in risk disclosures and scenarios.
    • Copernicus Climate Change Service (ECMWF), 14 January 2026
  5. Reproduction lab
    • Extend the discipline's method with one frontier method and compare its results
    • State the uncertainty and the limits of the result
    • Peer review against a shared checklist
  6. Technology that helps and the risks it carries
    • Machine learning forecasting, carbon dioxide removal and climate data infrastructure.
    • The governance of solar radiation modification and the integrity of carbon removal.

Labs with Academy tools

Nexus Standards

Teaches

  • NRS-X1 Climate change risk and adaptation
  • NRS-H5 Hazard family: Thermal extremes
  • NRS-H7 Hazard family: Large-scale circulation anomalies
  • NRS-H28 Hazard family: Climate-state shifts
  • NRS-H15 Hazard family: Glacier, ice-sheet and permafrost change
  • NRS-H65 Hazard family: Planetary boundary transgressions
  • NRS-H66 Hazard family: Ice tipping elements
  • NRS-H69 Hazard family: Carbon-cycle feedbacks

Relies on

  • NS-1 The UNI and the artefact catalogue
  • NS-16 UNI application profile (how articles, documents, code, data, APIs and interfaces carry the UNI)
  • NRS-C1 Risk vocabulary and ontology
  • NRS-C2 Risk assessment method
  • NRS-C3 Hazard, exposure, vulnerability and capacity data
  • NRS-C4 Risk data model, identifiers and exchange
  • NRS-C7 Early warning, triggers and anticipatory action
  • NRS-C8 Evidence, uncertainty and provenance

Assessment

  • 50%Method reproductionReproduce one frontier method on open data and state the uncertainty of the result
  • 30%Study reviewA critical review of a 2024 to 2026 study: question, data, method, limits, what it changes
  • 20%Peer reviewReview two other learners' reproductions against a shared checklist

Delivery

  • Self-paced online, with the labs in the browser
  • Cohort study over six to eight weeks, with a weekly lab review
  • Co-delivered with a partner institution, inside its own courses

Partner on this course

  • Co-teach a module and bring a current case from your work
  • Host a cohort for your learners, with your own schedule and review
  • Contribute a lab: a dataset your institution publishes openly, or a model
  • Review the course content and its references before it opens
  • Translate the course and adapt its cases to your region

How institutions and experts take part

In the Academy LMS

Course on the learning platform
Expert, 6 topics
Cohort group
A community group for each cohort of learners
Achievement
Recorded on completion of the assessed work

References

  1. Earth System Science Data (Copernicus Publications), Indicators of Global Climate Change 2025, 11 June 2026
  2. World Meteorological Organization, Global Annual to Decadal Climate Update 2026–2035, 28 May 2026
  3. World Meteorological Organization, State of the Global Climate 2025, 23 March 2026
  4. Copernicus Climate Change Service (ECMWF), Copernicus: 2025 was the third hottest year on record, 14 January 2026
  5. Philip et al., A protocol for probabilistic extreme event attribution analyses, 2020
  6. IPCC, Climate Change 2021: The Physical Science Basis, Working Group I contribution to the Sixth Assessment Report, 2021
  7. Shepherd, A common framework for approaches to extreme event attribution, 2016
05Take part

Take part in the work

Start from who you are.

06Questions

Questions, answers and key terms

Is the course free?
Yes. Every course and learning material of Risk Academy, led by the Nexus Consortium, is free to study, self-paced or with a cohort. Ordinary participation has no fee; paid work, internships and fellowships are separate arrangements.
What does completing a course give me?
A completion record: the course, its version, the date and what was assessed, private by default and shared by you. It stacks into discipline, department and programme achievements. A record states what was studied and assessed and does not certify competence; it is not a licence, a professional title or an employment status.
Where do the data and the places come from?
Every faculty teaches on a twin of a real place built from open data; each layer carries its provider, licence and date, and every result on the Academy states its source. Maps and figures are indicators for teaching, never an assessment of a place.
How do I take part beyond the course?
Form a competence cell of three to seven people, study a shared pathway, take a supervised assignment in the National Working Groups, or bring a cohort from your institution. For organisations.

Key terms

Term

Climate variability

Variations in the climate around its mean state on timescales from months to decades, such as ENSO.

Term

Shared Socioeconomic Pathways

Scenario narratives of future society used with emissions scenarios to drive climate projections.

Term

Downscaling

Methods that derive local or regional climate information from coarse global model output.

Term

Event attribution

The assessment of how climate change has altered the likelihood or intensity of a specific extreme event.

ReportsContribute

Contribute evidence from this page.

Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

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