Guide: how to use ENSOscope
A short walkthrough, from "what is this" to "what do I do with it." No background needed.
Watch: a 4-minute walkthrough
The whole platform end to end, narrated as if you were a health team in Kenya. Captions are on by default; use the CC button in the player to turn them off.
ENSOscope in one picture
The platform follows one chain: forecast the ENSO state, map where it shifts rainfall and heat, see your region's impacts, and act on the lead time.
Using it in four steps
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See what's coming
Check the ENSO outlook: how strong an El Niño or La Niña is likely, and how many months ahead.
Open the Forecast → -
Find your region's risk
Map where the event shifts rainfall, drought and heat, and drill down to your country.
Open Teleconnections → -
Weigh the confidence
See how reliable the forecast is at that lead and season, verified against the observed record.
Open Skill → -
Act on the lead time
Pre-position supplies, brief teams and trigger anticipatory-action plans in the months of warning.
The four steps, made real: an example, a health team in Kenya (see video)
Imagine you are a health team in Kenya and you hear a strong El Niño is forecast. Here is exactly what to do, in about five minutes, and why each step matters.
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Start with the forecast
Open the Forecast tab. The top line tells you in plain words what is coming, for example a strong El Niño strengthening over the next few months, and the bars show how sure the models are. Why it matters: a big event is likely, so it is worth preparing now.
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Find Kenya on the map
Open Teleconnections, drag the globe to East Africa, and click Kenya to zoom in. Leave the "Source" on its default, that simply means the map is built from real, observed rainfall records.
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Check the rain
Keep Wet selected and choose the season that matters to you, SON (the short rains, September to November). Blue means wetter than usual, brown and red mean drier. For you: in a strong El Niño the Kenyan short rains usually turn wetter, which raises the risk of flooding and the disease risk that can follow it.
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Double-check it against the climate model
Still on Kenya, switch the Source from observations to the climate model, CESM2. The wetter pattern barely changes, which is reassuring, and small robustness dots appear where most events do not agree on the sign of the change, so the signal is not robust on that part of the map. Why: observations give us only a handful of past strong El Niños, too few to be sure; the model turns those into hundreds of simulated events, so it can show where the signal is solid and where it is not. When the observations and the model tell the same story, you can trust it more. (See the two kinds of models below for what CESM2 is and how it differs from the forecast.)
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Check for dry spells
Switch to Drought / Dry. This shows the longest run of dry days, so you can see whether other seasons bring dry spells that strain water supply and health services.
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Check the heat
Switch the hazard to Heat stress. This map uses WBGT, which is simply a "feels-like" heat number that combines temperature and humidity. A high value means dangerous heat for the body, which matters most for patients, the elderly and young children.
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Ask: can I trust it?
Open the Skill tab. The grid grades how well the model predicted ENSO in the past: greener and closer to 1.0 means more reliable. If your lead time sits in the reliable part, you can act with confidence.
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Act on the lead time
You now know, months ahead, that wetter short rains and flood risk are likely. So you pre-position emergency and clean-water supplies, check drainage and access routes, brief your teams, and trigger your anticipatory-action plan before the season, not during it. That early warning is the whole point of the tool.
Swap "Kenya" and "short rains" for your own context: the same six clicks work for any country, season and hazard.
Reading the forecast
The Forecast page opens with a plain-language outlook line, then a table of probability bars, one row per lead time. Lead time is how many months ahead the forecast looks (L1 = this month, L6 = six months out). Each bar is split by colour into the seven ENSO classes (from extreme La Niña in deep blue to extreme El Niño in deep red); the width of a segment is the share of model members in that class, which is the probability. Two forecast centres are shown (ECMWF SEAS5 and Météo-France); when they agree, confidence is higher.
Two kinds of models, two different jobs
It is easy to mix these up, so here is the difference in plain terms.
Forecast models: SEAS5 (ECMWF) and MF9 (Météo-France). Their job is to predict what ENSO will most likely do over the next one to six months, and how confident to be. That is the Forecast tab: what is coming, and how likely.
Climate model: CESM2, run 50 times over 1850 to 2100. We use 1850 to 2014 as the "historical" period of the model. CESM2 does a different job, and it is not a forecast. It turns the too few observed events into hundreds of simulated ones we can choose from. For each ENSO phase, intensity and season we can see the typical impact pattern across the indices, compare it to the observed events, and check with statistics how robust that pattern really is when the real record has too few events. That is the "check against a model" step on Teleconnections.
Reading the teleconnection maps
A teleconnection is how a shift in the tropical Pacific reaches regions far away:
On the Teleconnections globe, pick a phase, season and hazard; the map shows how that indicator typically departs from normal. Colour tells you the direction:
"Anomaly" is the difference from ENSO-neutral years (it isolates the El Niño / La Niña effect); "absolute" is the raw value during that phase. Robustness dots mark pixels where fewer than 60% of events agree on the direction, so treat dotted areas with caution: the colour there is an average of events that pulled both ways, not a signal you can plan on. The dots appear in the anomaly view only, because they are a statement about a change, and the absolute view shows no change, just the average level during that phase. Click a country to drill all the way down to it. Behind the maps, observations are combined with a large climate-model ensemble so the patterns are robust even though few strong events sit in the observed record.
Replaying a past event
The teleconnection maps answer what does an El Niño usually do. The Event replay page answers a different question, and often the more useful one before a season: what did one particular El Niño actually do here, month by month.
Pick an event and a country. You get the sea surface temperature that defined the event, and underneath it rainfall, the heaviest ten-day downpour, the longest dry spell, dry days, maximum temperature, heat stress and warm nights, each as a map and as a line through time.
Three things are drawn on every chart, and they answer three different questions:
- The coloured line is the event you chose.
- The grey lines are every other event of the same kind, lined up on January of their own first year. If the coloured line sits outside the grey band, this event was doing something the others did not. Point at a grey line to see which year it is.
- The dashed green line is an ordinary year: the average of the years with no El Niño and no La Niña. Comparing an El Niño only with other El Niños tells you whether it was worse than the last one. It cannot tell you whether it was worse than normal, which is usually what decides whether to act.
The month slider moves everything at once. The world map, both country panels and the marker on every chart step together, so you can follow a season as it develops rather than reading one average for the whole event. That matters because two seasons often differ less in how much rain fell than in when it fell: rain that arrives early and rain that arrives late produce the same total and very different consequences.
The two country panels sit side by side on one shared colour scale, so you can put your event next to any other and see the difference directly. Point anywhere on a map to read the exact value at that spot. Save as PDF produces a one-page sheet of whatever you are looking at.
Everything here is observed, from CHIRPS rainfall and ERA5 heat. The one exception is drawn dashed and labelled: where an event is still running, the sea-surface-temperature panel also carries the live forecast. There is no forecast of country rainfall or heat behind this site, so those panels stop where the observations do.
Reading the skill (how much to trust it)
Skill is measured by replaying past forecasts and scoring how close they were:
The Skill page grades how well the model has predicted ENSO in the past. The heatmap shows the correlation for every start month and lead time: 1.0 is perfect, about 0.6 and up is useful, near 0 is no skill. Skill is highest at short lead and fades with lead time and across the boreal-spring barrier. Below it, "the actual track" shows the model line against observations year by year, and the intensity panel asks whether the model gets the strength of an event right, not just its sign.
The words you'll see, in plain English
Every technical term on the platform, explained in one line. You do not need any of these to use the tool.
- El Niño / La Niña (ENSO)
- The natural warming (El Niño) and cooling (La Niña) of the tropical Pacific Ocean. It shifts rainfall and heat worldwide, which is why it is worth watching.
- Teleconnection
- The far-away knock-on effect: how that Pacific change reaches your region as more or less rain, drought or heat.
- Climate model
- A computer simulation of the atmosphere and ocean, a "what if" laboratory used to study how El Niño changes rainfall and heat.
- Ensemble (and "large ensemble")
- Running the model many times from slightly different starts. The spread between runs is the uncertainty; a large ensemble is many runs, which gives lots of El Niño examples to average over.
- CESM2
- The large-ensemble climate model we use. Why: the real world has had only a few strong El Niños, too few to draw a clear map, so the model supplies hundreds of events and the pattern becomes reliable.
- CHIRPS
- A long record of observed rainfall, from satellites and rain gauges. It is what actually fell.
- ERA5
- A reanalysis dataset, not raw observations: a complete, consistent record of past conditions (temperature, humidity, and more), reconstructed by blending a numerical model with worldwide observations. Used here for the heat maps.
- Why observations and a model together?
- Observations are real but short and noisy for rare events; the model fills the gap with many events. When the two agree, the pattern is trustworthy.
- RX10day
- The heaviest 10-day rainfall total, a flood-risk indicator.
- CDD (consecutive dry days)
- The longest run of days with no rain, a drought or dry-spell indicator.
- WBGT
- A "feels-like" heat number that combines temperature and humidity. A high value means dangerous heat for the body.
- UTCI
- A similar "feels-like" comfort index.
- Anomaly
- The difference from normal (from ENSO-neutral years), so it isolates the El Niño or La Niña effect.
- Lead time
- How many months ahead the forecast looks. L1 is this month, L6 is six months out.
- Skill / correlation
- How closely past forecasts matched what actually happened, from 0 (no skill) to 1 (perfect).
For the full methods and data sources, see the Methodology page.