forecast

At a landfill on the outskirts of town, the wind shifts direction, and a couple of hours later the nearest village smells of rotten eggs. That's hydrogen sulfide — a gas produced when waste decomposes, and in large doses it's hazardous to health. Usually, authorities only learn about the problem after residents have already breathed it in and called to complain.

Meteorologists have grown used to trusting neural networks: the GraphCast model predicts Earth's weather days ahead by drawing on global snapshots of the atmosphere. But this 'brain' can be transplanted to another planet — where the air is thin and the Sun warms differently.

On a scorching afternoon, a stifling haze hangs over the city, and not a single cloud is on the horizon. Usually, in such weather, all you can do is wait for nature to take pity and send some cool relief. But what if shade and rain could be switched on schedule, like a light in a room?

The Sun doesn't burn steadily: it erupts in series. Like a campfire that dies down and then suddenly throws out showers of sparks, these "fiery seasons" last from six months to a year and a half, and during them the star shows an unusually high number of sunspots.

The aurora borealis is like a distant theater: the show may be on, but whether the audience sees it depends on the curtain. Here, the curtain is clouds and moonlight. Earlier forecasts tried to predict both the occurrence of auroras and viewing conditions at once, mixing everything together.

A forecaster issues a week-long forecast in seconds but cannot explain a single decision. That is roughly how modern AI weather models work: they already match classical numerical methods, yet their internal logic is hidden behind millions of parameters.

By day in Chile's Atacama Desert, the air shimmers with heat like the haze above a hot skillet. Sunlight passing through this shimmer begins to dance and flicker. Astronomers call this airy trembling "seeing" — and it keeps telescopes from getting a sharp view of the stars. To fight this nuisance, engineers have built a special instrument — a solar seeing monitor.

Two observers watch the Pacific Ocean. One studies surface heat maps, the other listens to the rhythm of temperature and heat stored in the water column. On their own, each notices only part of the picture, but together they can sense the approach of El Niño months before the warm current alters weather across the planet.

Weather forecasting is like trying to predict where a rock will roll down a slope, knowing the shape of every bump and the force of the wind. Make a mistake at the start, and a week later that error grows into a full-blown storm. In the past, accurate forecasts required massive computers and days of computation.

In 2023, the ocean off the coast of Peru warmed so much that fishermen came back empty-handed, while meteorologists argued over the strength of the upcoming El Niño. The sea surface temperature map they were looking at was the only one available, but how much could they trust it?

The weather on the next street can differ from yours: cooler by the river, hotter over asphalt, windier between buildings. A regular forecast misses this: its grid cell is the size of a city, not a backyard.

Two barometers hang on the wall. One reads clear, the other says partly cloudy. Which weather do you trust? That is roughly the question facing a group of climate scientists when two independent methods predicted different futures for the Pacific Ocean in 2026.

The tropical Pacific Ocean heats and cools like a giant battery, and its rhythm affects weather from India to Canada. This rhythm is called El Niño–Southern Oscillation, or ENSO for short. Forecasting it months ahead is like predicting when a pot will boil without knowing the strength of the flame.

A camera on a tripod shoots the same landscape for decades. If you don't touch the lens, the images get hazy: the landscape changes, but the focus stays put. That's roughly how climate norms work. According to World Meteorological Organization rules, they are calculated as the average over the last 30 years.

Every spring, El Niño forecasts lose their edge, like a translator who gets distracted by every rustle in the room and turns a one-hour meeting into a two-hour one. Scientists call this the spring predictability barrier: in March and April, models that confidently predicted a warm phase in the Pacific back in January start making mistakes twice as often.

A team of forty forecasters prepares an El Niño forecast two years ahead. Each works with the same model but slightly different initial conditions and settings, so their outputs diverge. Typically, forecasting centers average all forty opinions, assuming each member is equally useful.

For 36 days, tropical cyclone Freddy raged over the Indian Ocean, the longest-lived cyclone on record. Meteorologists tracked it from satellites, aircraft, and ground stations. But one detail eluded all instruments: the precise air density deep within the vortex. And that density determines where the cyclone will turn tomorrow.

In a room with 80 advisors, each shouts their own opinion, and if you simply listen to everyone and take the average, you get mush. So it is with El Niño forecasts: a model produces 80 possible futures, but not all are equally useful. Some err less often, others more, and still others talk about something else entirely.

In May 2024, thermometers in Jalisco seemed to go haywire: on May 10, one station recorded 38.4°C, nearly matching the historical maximum from 1994. The heat was so intense that even agave plants, accustomed to scorching conditions, slowed their growth. And the rains, which usually arrive in June, were delayed, leaving farmers nervously glancing at the sky.

Imagine a translator sitting in on a conversation between two parties: the ocean and the atmosphere. Usually, the translator conveys the meaning with a six-month delay, and in spring it loses the thread entirely. The new model, called CTEFNet, translates up to two years ahead and, on top of that, explains which remark turned the tide.

In 2015, downpours in Chennai turned streets into rivers overnight. The city was caught unprepared: seasonal models offered only a vague warning. Yet the monsoon is not a whim of the sky; it is the pulse of a vast machine linking oceans and continents.

An interpreter at negotiations hears only the words already spoken, yet still guesses the next phrase. This is how the new El Niño forecasting method works: it doesn't peek into the future but relies solely on the past, like an honest listener.

In a meeting room, an interpreter listens not only to the speaker but to the entire buzz of voices, catching who is looking at whom and who falls silent before an important remark. That's how SEA-ViT reads the ocean: it sees not isolated points from buoys but the whole current field at once, like a single conversation of water.

Imagine an orchestra playing without a conductor. Each section takes its turn: first the winds introduce the theme, then the strings join in, and sometimes an unexpected percussionist solos.

Imagine an interpreter in a huge hall who hears not only the person next to them but also a whisper in the far corner, and from those fragments reconstructs the entire conversation. That's how the CAS-Canglong neural network works: it picks up connections between pressure, wind, and heat across the whole planet and uses them to predict what the ocean will look like three months from now.

Two dancers stand facing each other but cannot see each other's faces. One is the atmosphere, fast and noisy, forgetting its moves after just a couple of weeks. The other is the ocean, slow and heavy, remembering every touch for months. For the dance to work, they need to learn to lead each other while only looking at their partner's hands.

In the summer of 2023, thermometers around the world registered numbers that even the boldest forecasts had not anticipated. The planet's average temperature rose to 1.48°C above pre-industrial levels, a jump of 0.17°C that broke the previous record as if the climate had suddenly shifted gears.

A translator at negotiations picks up not only the words but also the tone in which they are spoken. If they focus only on vocabulary, they might miss sarcasm or fatigue in the voice. If only on intonation, they could mistranslate the details of the contract.

A translator at an important negotiation starts by simply repeating words, but gradually begins to anticipate what the counterpart will say. The new weather model works similarly: physics provides the framework, and machine learning learns from errors and fills in the gaps.

Over warm ocean waters, air rises, spins, and turns into a giant vortex that carries winds faster than a race car. For such a vortex to form, several conditions must align: rotation, heat, moisture, and calm winds aloft. Previously, meteorologists combined these conditions into complex indices, like a kitchen recipe with a dozen ingredients.

Every few years, the Pacific Ocean changes its character. Sometimes it warms up off the coast of South America, which is called El Niño, and sometimes it cools down, bringing La Niña. For someone on the other side of the planet, this is not just a weather report item. The ocean acts as a conductor, setting the tempo for heat and drought over Eurasia.

An orchestra without a conductor: the musicians take their seats, tune their instruments, and begin to play in harmony because each one hears the common rhythm. That is how TreeDOX works, a method that predicts chaotic time series without requiring the user to choose parameters or fine-tune anything.

The Pacific Ocean breathes, and that breath has a pulse. Every few years, its surface warms more than usual, bringing torrential rains to Peru and drought to Australia. This is El Niño, the warm phase of the ocean temperature oscillation.

A blindfolded sapper searches for a mine by tossing pebbles at random. He cannot see the map or calculate where the charge is buried. But if he throws enough pebbles and listens for which one lands with a duller thud, he can pinpoint the danger. That is roughly how a new method for predicting El Niño works: the warm current that every few years alters weather across the entire planet.

In the mountains, an echo repeats any shout, and you can't immediately tell who shouted: a person or the wind. That's roughly how climate scientists work when they try to understand why an unusual heat wave or drought happened. They look for a human fingerprint in a weather disaster, but nature has its own voices that can sound similar.

Over the warm waters of the Atlantic, air begins to swirl like water spiraling down a drain. That is a future hurricane, and its fate depends on what happens thousands of kilometers away in the Pacific Ocean. When the El Niño current warms up there, storms in the Atlantic become fewer. The models that forecast hurricane season know about this link.

A meteorologist looks at a map where clouds gather in flocks, like birds before a storm. Each spot on the radar could be a future downpour or just noise. Earth system forecasting is like trying to assemble a puzzle with thousands of pieces that keep changing shape.

A flashlight beam in fog reaches some objects and fades before touching others. So it is with El Niño forecasts: for events in the central Pacific, the light reaches almost two years ahead, but for eastern events it gets lost in random wind gusts within six months.

A thunderstorm front is like a rubber band stretched between two fingers. The harder you pull, the wider the strip where lightning strikes. Meteorologists have long been able to measure storm intensity at a single point, but until now they didn't understand why severe weather sometimes compresses into a compact blob and sometimes covers half a continent.

A long conversation between two people can be condensed into a few key phrases that preserve the essence. That is exactly how a macrovariable works: temperature stands in for billions of moving molecules, and sea level for thousands of waves.