A klímakutatók és meteorológusok egyre szélesebb körben alkalmazzák a mesterséges intelligenciát a globális felmelegedés és az időjárás modellezésére. Az AI-alapú előrejelző rendszerek a nagy mennyiségű korábbi adatokból tanulva pontosságban már felveszik a versenyt a hagyományos fizikai rendszerekkel, miközben töredéknyi számítási kapacitást igényelnek az üzemeltetéshez.
A technológia kiemelkedő eredményeket ér el a műholdképek elemzésében is. A gépi tanulási modellek képesek automatikusan azonosítani az ipari metánszivárgásokat és az illegális fakitermelést, vagy akár a tengeri jég állapotának változásait. A kutatók jelenleg a hibrid modelleken dolgoznak, amelyek a fizikai törvényszerűségeket ötvözik az AI gyorsaságával.
Az Európai Középtávú Időjárás-előrejelző Központ már élesben, a hivatalos tájékoztatás részeként is használ AI-előrejelző rendszert. A legnagyobb kihívást továbbra is a rekordot döntő, szélsőséges időjárási események pontos előrejelzése jelenti, mivel ezekről nem áll rendelkezésre elegendő korábbi adat.
Az eredeti szöveg (Carbon Brief)
Artificial intelligence (AI) is developing at a rapid pace and is increasingly being used by scientists to better understand climate change.
AI is perhaps best-known for its use in “large language models”, such as ChatGPT, Claude and Gemini.
From weather forecasting to analysing satellite images, climate scientists are using various types of AI to develop faster and more efficient ways to study the warming planet.
However, the technology comes with no shortage of risks and challenges, including overreliance on unverified results, intensive energy use and exacerbating existing inequalities in climate science.
Carbon Brief spoke to 10 experts working across a range of climate-related fields – from developing climate forecasts to analysing changes in the energy grid – about how AI is being used.
Their answers are set out below, grouped by theme.
Dr Doug McNeall, science lead in data-driven climate modelling at the UK Met Office:
One of the most significant recent developments has been the success of AI weather-prediction models, which learn from large volumes of weather data to predict the next few hours ahead and then repeatedly feed those predictions back into themselves to generate forecasts up to two weeks into the future.
These models have demonstrated that AI can provide accurate predictions while requiring far less computing power than existing numerical models [which rely on mathematical equations to simulate the climate].
One of the main challenges is that AI weather-prediction models have been developed for relatively short timescales, where large amounts of observational data [for example, temperature, rainfall or sea ice extent data] is available and model performance can be evaluated quickly and rigorously.
Climate prediction is fundamentally different. To provide useful climate information, we need to make projections for future climates that lie outside the observational record.
One promising route forward is, therefore, to use AI to emulate or accelerate these existing models, rather than relying solely on purely data-driven approaches. This blended approach could make climate risk information far more accessible and affordable to produce, while retaining the physical understanding [based on mathematical and scientific principles] that underpins confidence in climate projections.
Prof William Collins, professor of AI for climate science at the University of East Anglia:
We’re seeing a really exciting renaissance in both weather and climate science…Machine learning [a subset of AI that uses algorithms to let computers learn from data and make predictions without being explicitly programmed] has had an enormous impact on numerical weather prediction.
So, for example, the European Center for Mid-Range Weather Forecasts is now deploying an AI forecast system as part of their official communication of weather predictions to the European community, alongside the traditional forecast system. AI systems have become good enough that they’re absolutely competitive with the world’s leading numerical weather prediction…So that’s been quite remarkable.
Much of machine learning and AI involves learning from data and climate represents a future for which we do not have data. We’re literally trying to predict the future. We don’t have observations of it. So, where does the data come from to inform the models? That is, at the moment, the central challenge that the climate community is attempting to overcome.
Dr Amirpasha Mozaffari, researcher in Earth artificial intelligence at the Barcelona Supercomputing Centre:
AI forecast models do well on typical weather, but underperform physics-based models on record-breaking and unprecedented extremes – precisely the events that matter most for adaptation [to climate change].
Solving that – whether through hybrid models [which combine AI with traditional models], physical constraints [in which rules are implemented based on the laws of physics to help the models make real
