In the winter of 2013-2014, the strong winds of the jet stream shifted north, allowing a mass of warm water dubbed 鈥渢he blob鈥� to across more than 1500 kilometres of the north Pacific Ocean.

Floating instruments moored to the seabed off Alaska, Washington and Oregon alerted scientists and the fishing industry to the arrival of this water, which was up to 4掳C hotter than normal.

They were part of the Ocean Observatories Initiative (OOI), five mooring off the US west and east coasts and Greenland. Announcing $220 million in funding for the programme in 2023, the US National Science Foundation (NSF) the OOI was needed to monitor 鈥渃ritical organs of the Earth鈥�. But last month the NSF from the water following funding cuts by the administration of US President Donald Trump.

As a planet-warming El Ni帽o climate phase warmed the water further in 2015-2016, sensors running up and down OOI mooring wires revealed the blob was into the deep sea below 250 metres. The mooring data helped show the blob, which repeated in 2019 and may be happening more frequently due to climate change, spurred toxic algal blooms that California鈥檚 $60 million Dungeness crab fishery for the season.

The removal of most OOI moorings will diminish the accuracy of weather forecasting, including precipitation patterns influencing the record drought in the western US. It will also hinder efforts to monitor a possible weakening in the Atlantic Meridional Overturning Circulation (AMOC) that keeps Europe temperate, as well as the effects of an imminent El Ni帽o.

鈥淲e鈥檙e flying blind, and it will end up costing us more,鈥� says at the University of St. Thomas in Minnesota.

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While the OOI costs $56 million a year to run, the US commercial fishing industry, which relies in part on OOI data, billions of dollars each year. Weather and climate disasters did $183 billion of in 2024. (The US government discontinued this tally in 2025.)

Without the OOI, fleets won鈥檛 know which fishing areas might be less impacted by the coming El Ni帽o, which some models say could be the strongest on record, says at Oregon State University. Oyster, clam and shellfish farms won鈥檛 be able to prepare for heating and reduced nutrients the El Ni帽o could bring, and scientists will lose their view of harms to marine ecosystems. In the past, the OOI has also alerted scientists to the formation of low-oxygen 鈥渄ead zones鈥� on the seafloor.

鈥淭hat is going to be lost at exactly the worst time,鈥� says at Boston College in Massachussetts.

Because satellites can鈥檛 see beneath the surface of the sea, measurements by underwater floats, gliders and moorings are crucial to understand what鈥檚 happening in the 70 per cent of the planet covered by ocean.

While these mostly measure temperature, salinity and flow rate, the OOI moorings also have sensors for parameters like pH, oxygen and CO2 for understanding the biology and chemistry of the ocean. And they do so in remote, little-monitored places where the movement of water masses affects the climate.

Is a vital ocean current just decades away from catastrophic collapse?

Two studies suggest the Atlantic meridional overturning circulation could collapse by the middle of the century and wreak havoc with the climate, but such predictions are controversial

The loss of these sensors will impact the rest of the world, especially by reducing observations of the AMOC. The OOI array in the Irminger Sea, east of Greenland, is part of OSNAP, a line of moorings, gliders and floats stretching from Canada to Greenland to Scotland. It monitors warm, salty water flowing from the tropics to the north Atlantic, where it cools and sinks, driving the AMOC. A collapse in this system could plunge Europe into 鈥渋ce age鈥� winters and disrupt monsoon rains critical for agriculture in Africa and Asia.

鈥淥SNAP has taught us that most of the actual overturning takes place east of Greenland and that the Irminger Sea is key in understanding the overturning variability,鈥� says at the Royal Netherlands Institute for Sea Research.

Removing OOI will create a data gap that will limit understanding of the AMOC, even if it鈥檚 someday replaced, Palevsky adds.

快猫短视频s fear the dismantling of OOI is the start of a massive rollback of US ocean research funding that could see the discontinuation of OSNAP. Some worry it could even undercut Argo, a vital network of almost 4000 descending instrument floats across the global ocean, of which are provided by the US.

In a statement to 快猫短视频, the NSF said the OOI removal was to 鈥減rioritize support for evolving scientific priorities鈥�. But it comes as the Trump administration wages what at the Union of Concerned 快猫短视频s calls an 鈥渁ttack on science鈥�. The administration has or suspended thousands of research grants, and it has proposed slashing the NSF鈥檚 budget by 55 per cent in 2027.

This week, the administration proposed a rule that would cancel peer review of research grant applications, allowing political appointees rather than independent experts to decide the fate of federally funded research. It would also ban international collaborations and research on gender and diversity.

The looming El Ni帽o could be bad 鈥� but much worse is to come

Global warming will amplify the impacts of El Ni帽o events, and could also make them much stronger and more far-reaching

at the Oregon State University, who manages the OOI array off the coast of Washington and Oregon, says the dismantling of OOI and the proposed grant rule are both part of sweeping changes that would 鈥渨eaken peer review and politicise NSF-funded science鈥�.

A last month found that dismantling even one-fifth of the Global Ocean Observing System, a network of instruments that includes the OOI arrays and the Argo floats, would increase the error in the annual rate of ocean heating by 33 per cent. That would be like going from predicting an unemployment rate of 3 per cent this year to only being able to give a range of 2 to 4 per cent, says Abraham, who was part of the team behind the research.

鈥淭his is purposeful to try and remove our eyes and ears in the ocean,鈥� he says of the OOI dismantling. 鈥淏ecause if we don鈥檛 measure something, how do we know we have a problem?鈥�

鈥淚t鈥檚 an absolutely unbelievable scientific achievement,鈥� says Andrew Charlton-Perez, talking to me by video from his office at the University of Reading, UK. His colleague, Simon Driscoll at the University of Cambridge, nods enthusiastically. 鈥淭here are so many different applications and so many different uses for it.鈥�

No, they aren鈥檛 referring to quantum computing or nuclear fusion. They are talking about weather prediction. 鈥淧eople just moan about the weather forecast and how bad it is,鈥� says Charlton-Perez. As a meteorology professor, he hears this a lot. But that is because most people don鈥檛 realise that our ability to predict the weather, given the complexity of the atmosphere, is practically a superpower. 鈥淭his is an incredibly complicated system that we don鈥檛 observe very well. And we can put it onto your phone and it鈥檚 pretty accurate most of the time,鈥� he says.

Driscoll, a maths and physics researcher, has spent a lot of time working with Charlton-Perez on the miracle of 鈥減retty accurate鈥� forecasting. They have sliced and diced the many petabytes of weather data accumulated since the 1990s by satellites, weather balloons, ships and ground sensors. Now, they are testing new AI models that could change the way we predict the weather. No, you moaners, it isn鈥檛 going to become perfectly accurate. But it is about to change how you learn if tomorrow will be sunny.

Some big scientific insights of our time came from attempts to predict the weather. Edward Lorenz discovered chaos theory while modelling atmospheric circulation. He knew the way a storm develops is both chaotic and highly dependent on initial conditions. Lorenz fed those initial conditions into an early digital computer, using variables like temperature and wind speed. He found that a tiny shift in one of those variables led to a wildly different prediction of the storm鈥檚 path. He called it 鈥渄eterministic chaos鈥�. In popular parlance, it is known as the butterfly effect.

Every time you get a weather alert on your phone, it is partly thanks to Lorenz and partly thanks to a daily analysis produced by weather centres. For their starting variables, they use meteorological data gathered by thousands of sensors, on Earth and in orbit, and then feed it into a large computer, which spits out pretty accurate forecasts of the sort that tell you there is a 鈥�30 per cent chance of rain鈥�. This is known as numerical weather prediction and it has ruled the roost for decades.

The problem is that it requires expensive supercomputers to ingest huge amounts of current weather data, compare it with past events and subject all of it to the rules of physics to get an idea of what will happen. Global teams have cooperated to produce your rain forecast. Driscoll, for example, has contributed expertise on how ocean ice is affecting the climate. Ultimately what this means is that only a few countries can afford to generate weather reports, leaving most of the world dependent on the generosity of a small number of government agencies.

We could be about to democratise access to weather prediction, which would help smaller countries

All of that could change with new AI models. In a , Charlton-Perez and Driscoll stress-tested four popular AI models to see how well they could predict an unusual stormy event known as a bomb cyclone. They did decently, but 鈥渢he big difference is that it鈥檚 thousands of times faster鈥�, says Charlton-Perez. Plus, 鈥渢he forecasts we used鈥� I ran them on my laptop鈥�.

So AI could potentially allow forecasters to predict weather with fewer resources and smaller teams, meaning less dependence on, say, the US or the European Union for information about the temperature in Barbados. We could be about to democratise access to weather prediction. This would help smaller countries, but would also allow anyone to track niche weather phenomena. If you love rainbows, you could ask an AI model to predict where the next one might appear.

Still, Charlton-Perez warns there may be new roadblocks. The input data required to make a forecast has traditionally been shared freely. But as the cost of analysing it comes down, 鈥渢he data becomes even more king than it was鈥�, he says. He worries that firms behind AI weather models, such as Google, Microsoft and Nvidia, might enter into exclusivity relationships with meteorological services for such data. In other words, much of the globe would be dependent on tech companies for weather reports instead of government bodies.

Worse still, it could cut public access to free forecasts at a time when we need it most. Heat waves are getting deadlier. Storms that were once inconvenient now cause killer floods. This worries Charlton-Perez, who believes meteorological prediction is humanity鈥檚 鈥減rimary climate change adaptation tool鈥�. In an era when extreme weather is on the rise, we need to know what is coming. Having that information may increasingly be the difference between life and death.

Annalee鈥檚 week

What I鈥檓 reading

Historian Josephine Quinn鈥檚 In Search of the Phoenicians, because I want to understand the Punic world.

What I鈥檓 watching

Murderbot !!! Need I say more?

What I鈥檓 working on

Getting ready to visit Knossos 鈥� one of the great Bronze Age city-states 鈥� on the island of Crete in Greece.

Annalee Newitz is a science journalist and author. Their latest book is Stories Are Weapons: Psychological warfare and the American mind. They are the co-host of the Hugo-winning podcast Our Opinions Are Correct. You can follow them @annaleen and their website is techsploitation.com