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A barometer on the island of Tahiti and a barometer in the Australian city of Darwin show different pressures. Subtract one from the other, and you get a number that climatologists call the Southern Oscillation Index. It changes every day, but within these daily jumps hide steady rhythms, just as the beats of a metronome hide within the noise of an orchestra.

The ocean is like a pendulum with a memory. A push from seasonal winds returns not immediately but months later, when a wave crosses the Pacific. This delay, the authors argue, is what makes El Niño and La Niña repeatedly peak at the same time of year.

Picture a giant spiderweb stretched across thousands of points on Earth's surface. Each strand connects two places where air temperatures move in sync: if one node warms, so does the other. You can build this web from data for any given year, then compare how it looked across different years.

In 1988, the surface temperature of the Pacific Ocean began to change long before meteorologists declared the onset of El Niño. Points on the map scattered along the equator started behaving in concert: if water warmed in one place, it did so in a neighboring one too.

An orchestra without a conductor: the instruments set their own rhythm. That is how one might describe the climate system of the Indo-Pacific region, where sea surface temperatures oscillate across many time scales, from seasonal to multi-decadal. Previously, to hear a single instrument, you had to pre-tune filters, cutting off everything else.

Picture a map of China with sparse green patches scattered across a yellow desert. Those patches are where the GloSea5 climate model can genuinely look three months ahead, while the desert is everything else, where its forecasts are no better than a coin toss.

A translator who hears only the meaning, not the noise in the hall, can convey a conversation accurately. Climatologists have long sought such a translator for the ocean–atmosphere pair: their interaction resembles a dialogue where voices drown in the roar of extraneous sounds.

Picture a web stretched across every corner of the Earth: each strand is a coincidence of temperature fluctuations at two points. In ordinary years, the web is neatly divided into regional clusters, but when El Niño arrives, the strands break and reweave, linking distant continents.

Picture a globe divided into cells: near the poles they shrink into narrow wedges, while at the equator they stretch into wide bands. If you connect random points on such a globe with lines, the compass arrows will lie: connections will point to where there are more cells, not where the flows actually go.

The ocean remembers its weather. Each month, its surface temperature forms a vast map, and if you find a similar map in the past, you can see what happened next. That is exactly how the new method for forecasting El Niño works: the warm current that, every few years, alters the climate of half the planet.

The orchestra has been playing in the Pacific Ocean for over a century. Some instruments carry a fast melody, others draw out a slow bass line, and together they create a pattern of warm and cold patches on the water's surface.

Earth's climate resembles a choir, where each member sings their own part at their own pitch. Some voices hold low notes, like multi-year ocean temperature oscillations, while others produce rapid trills, like short atmospheric waves. The conductor needs to hear each one, but the voices overlap and the notes blend into a mush.

In February 2017, Lima, a city of over ten million people, was hit by El Niño: the warm current off Peru's coast brought torrential rains, floods, and landslides. More than a hundred people died, thousands of homes were destroyed, and roads turned into rivers. The city's economy, like a living organism, fell out of rhythm: shoppers visited stores less often, and their spending patterns shifted almost overnight.

A translator in a meeting room hears not only individual voices but also how they bounce off each other: one speaker trails off, another picks up the thought, a third clarifies. Remove that interplay, and you are left with a set of monologues, and the meaning of the conversation slips away.

In 1960, a meteorologist at a station in Peru records water temperature, while a farmer in India watches the sky waiting for the monsoon. There is no direct link between these two events, but months later one will echo in the other.

In the Pacific Ocean, every few years the water off the coast of Peru warms up, and fishermen call it El Niño. Sometimes the warming is mild, sometimes so strong that it changes weather across the entire planet. Forecasting a strong El Niño six months to a year ahead is a task at the edge of possibility: weather is forgotten within two weeks, while climate remembers for decades.

A meteorologist stares at a pressure map, trying to figure out where the cyclone will go. They have a model, but it is flawed, and fresh measurements, but they are incomplete. Merging the two so that the forecast remains physically meaningful is harder than it seems: classical methods sometimes produce negative temperatures or impossible probabilities.

The Pacific Ocean spans half the planet, and its mood decides whether India gets rain or Australia gets drought. In the center of this giant, in a region that climatologists call NINO3.4, water temperatures rise and fall, affecting millions of lives.

A fisherman pulls on his net and listens to it ring. Each strand quivers in its own way, but before a strong tug, all the strands begin to vibrate in unison. So it is with complex systems: before a sharp state jump, the individual parts suddenly start to oscillate in concert, and that synchrony betrays the coming jump.

A translator who gets distracted by every rustle in the room turns a one-hour meeting into two. That is how old statistical El Niño models worked: they picked out individual numbers from ocean maps but missed how a warm patch off Peru connects to a cool tongue near Australia. The new neural network behaves differently: it follows not every word but the meaning of the paragraph.

An orchestra without a conductor plays a complex piece. The listener hears the overall music but wants to understand who leads the melody and who merely echoes it in unison.

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.

Climate is like an orchestra without a conductor: each musician hears the others with a delay, and the score changes mid-concert. The ocean, atmosphere, ice, and land continuously exchange energy, and the slightest hiccup in one instrument can escalate into a shift of the entire melody.

An artist painting the ocean does not limit themselves to two colors: warm and cold. Likewise, climatologists studying El Niño increasingly speak of a whole palette of shades. El Niño is a warm current off the coast of Peru that, every few years, alters weather across the planet. Its counterpart, La Niña, brings cold.

Each Atlantic hurricane season is like a game of dice, where the number of spots depends on three major climate forces: El Niño, ocean temperature in the 'hurricane nursery,' and the North Atlantic Oscillation.

You can sit on a seesaw and pump your legs, or you can just tip from one side to the other. The Pacific Ocean does both. Every few years, warm water off the coast of Peru gives way to cooler water, and rainfall patterns, winds, and entire ecosystems across half the planet reshuffle. These shifts are called El Niño and La Niña, together known as ENSO: Earth's climate seesaw.

Paraglider, hang glider, and glider — three different wings, but in the sky they behave the same. Pilots catch invisible warm air currents, rise on them, then glide to the next. Such flight is like a game of hopscotch: a jump upward, then a glide forward, and again a search for a place to jump. Scientists analyzed thousands of real routes and found a common law.

The Tibetan Plateau is a giant sponge that soaks up rain and snow, then releases water to Asia's rivers. Since 2000, the sponge has been getting wetter. Scientists decided to check: does this moisture make the sponge stronger, or does it soften it? They used a measure of disorder — entropy.

A group of people searches for a lost item in a large park. Some get tired and leave, others join along the way. The number of searchers is constantly changing. How does this affect the search time? Until now, science had no clear answer. In a new study, a general model of such a process has been built. In it, independent participants randomly enter and leave the search.

In the Large Hadron Collider's detector, each proton collision leaves thousands of points — tracks of flying particles. A neural network learned to spot patterns in this scatter and predict what happened at the moment of impact.

A neural network originally trained to distinguish real gravitational waves from noise in a gravitational wave detector recognizes bearing wear from vibrations without retraining.

To tell one protein from another, you need to describe its shape and composition. There are dozens of such descriptions: molecular length, charge, solubility. But not all are equally useful. A method called Differentiable Information Imbalance (DII) can pick out the most informative features while discarding the rest.

A neural network can be compared to a dormitory where each resident gets their own room. Usually, each feature in the data occupies a separate neuron, and learning proceeds sequentially: first one feature, then another. This is slow and depends on which data you happen to get. But if you allow several features to share a room, overlap them, the picture changes.

Thousands of models simmer in a single cauldron, and the steam rises thick and impenetrable. Each whispers something about the future of El Niño, but in the general din you cannot tell who is right and who is just boiling over.

In August 2017, gravitational wave detectors caught a signal from the merger of two neutron stars — the ultra-dense remnants of exploded stars. At that very moment, life on Earth was business as usual: planes were landing, cars were driving, and navigators were plotting routes.

Light from a distant star, after passing through air and telescope lenses, loses its sharpness, like looking at a streetlamp through wet glass. Astronomers call this blur the telescope's "fingerprint": each instrument has its own, and you need to know it down to the smallest detail.

On a silicon wafer the size of a dinner plate, you can place not just one substance but an entire map of thousands of different compositions. A laser knocks atoms off targets and deposits them in thin layers, varying the proportions from point to point. The result is a materials library: more of one metal here, more of another there, and all combinations visible at once.

A solar panel on a roof produces little current in the morning, a lot at noon, and stays silent at night. Wind doesn't blow randomly either: it's often stronger in the morning and evening than during the day. Energy providers need to know in advance how much energy will be available in an hour or three, so they can switch on backup plants or store electricity in batteries in time.

When a typical computer searches for a law of nature from data, it tries to guess a complete formula in one go. It's like a cook who, without knowing the recipe, dumps all the ingredients into a pot at once and hopes for soup. Most often, the result is an inedible mess: the formula is long but meaningless, and it fails on new data.

In a dark mine under the Italian Alps, a tank holds three tons of liquid xenon. When a particle hits it, it knocks faint flashes of light and electron clouds out of the atoms. Following these tracks, physicists hope to see an ultra-rare decay: a xenon-136 nucleus turning into barium-136, emitting two electrons and no neutrinos.