Climate change is not only an environmental problem but a technological one, and it bears directly on how AI is built and used. The rapidly developing El Nino is sharply raising the odds that 2024 becomes the warmest year on record. That creates new challenges for AI-driven climate modelling and forces strategy changes across a range of industries.
What happened
Carbon Brief now puts the probability that 2024 becomes the warmest year on record at 35%, nearly double its estimate four months ago. Carbon Brief's central estimate has 2024 as the second warmest year, roughly 1.51C above pre-industrial levels. El Nino conditions set in during April and by June crossed the "strong" event threshold, with the Nino3.4 index hitting 1.6C. More than 91% of the 667 models analysed forecast an El Nino peak late this year that would exceed the strongest El Nino on record. The first six months of 2024 were the third warmest start to a year ever recorded, about 1.4C above pre-industrial levels, behind only 2023 and 2016. The median forecast for the El Nino peak in the second half of 2024 is 3.1C, against a previous record of 2.69C in 1982-83.
Why it matters
For AI and software development this means AI-based climate models face ever tougher tests. Accurately forecasting extreme weather events and their consequences for agriculture, energy and logistics is becoming critical. A developing El Nino also affects resource availability, which can ripple into supply chains for technology companies. Rising global temperatures demand AI systems that are more adaptive and able to chew through unprecedented volumes of data to detect new patterns and predict future shifts. It is also driving research into AI for climate science, including new models and algorithms for climate data analysis.
What it means in practice
Founders and developers working with AI should weigh the following:
- Better AI climate models: invest in research and development of AI models that forecast extreme weather events and their long-term consequences more accurately. That is critical for agriculture, insurance and logistics.
- Infrastructure adaptation: consider how climate change may affect your IT infrastructure, data centres in particular. Higher temperatures can raise cooling costs and outage risk.
- New openings for AI in sustainability: demand is growing for AI solutions aimed at mitigating climate change, such as optimising energy use, managing water resources and developing sustainable materials.
- Supply chain risk analysis: use AI to analyse and forecast climate-related supply chain risks so the business keeps running.
- Adaptive algorithms: build AI systems that adjust quickly to changing environmental conditions and new data so they stay relevant in a fast-shifting climate.