Climate Experts Issue Stark Warning About Global Warming Timeline
A new report from more than 60 of the world's top climate scientists paints a sobering picture: humanity could exhaust its carbon budget for limiting global warming to 1.5 degrees Celsius in just three years.
If current emissions continue unchecked, the long-established target set by the Paris Agreement may soon slip permanently out of reach, BBC News reported.
Since the late 1800s, average global temperatures have climbed steadily, driven by relentless carbon dioxide emissions from coal, oil, and gas, as well as widespread deforestation. While the 1.5 °C threshold was meant to avoid the worst impacts of climate change, scientists say we're now hurtling toward it faster than anyone expected.
"We're seeing some unprecedented changes," said lead author Professor Piers Forster of the University of Leeds. "The heating of the Earth and sea-level rise are accelerating, and it's tied directly to emissions."
The updated analysis, released this week, estimates that only 130 billion metric tons of CO₂ can still be emitted globally to stay under the 1.5 °C limit with a 50 percent chance. At the current rate of around 40 billion tons per year, that leaves just over three years before the budget is spent.
Adding to the urgency, last year marked the first time global temperatures exceeded 1.5 °C for an entire 12-month period. While one year doesn't constitute a formal breach of the Paris goal, the trend is alarming.
Scientists warn that continued warming will bring more extreme weather, rising sea levels, and ecosystem disruptions, affecting millions of people worldwide.
Much of the excess heat has been absorbed by the oceans, leading to faster sea-level rise and marine ecosystem damage. The rate of sea-level rise has doubled since the 1990s.
Still, there's a sliver of hope. The report notes that while emissions remain high, their rate of increase has slowed, thanks in part to clean energy technologies. Experts stress that aggressive emission cuts now could still blunt the worst impacts.
"Every fraction of a degree matters," said climate scientist Joeri Rogelj. "Reducing emissions today will ease suffering tomorrow."Climate Experts Issue Stark Warning About Global Warming Timeline first appeared on Men's Journal on Jun 19, 2025
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
CNN
2 hours ago
- CNN
How your AI prompts could harm the environment
AI Sustainability Climate change EconomyFacebookTweetLink Follow Sign up for CNN's Life, But Greener newsletter. Our limited newsletter series guides you on how to minimize your personal role in the climate crisis — and reduce your eco-anxiety. Whether it's answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people's lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called 'token IDs' and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. So, for each prompt you give AI, what's the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, 'smarter' LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. 'This shows us the tradeoff between energy consumption and the accuracy of model performance,' said Maximilian Dauner, a doctoral student at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. 'You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,' Dauner said. Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said. 'AI expends a lot of energy being polite, especially if the user is polite, saying 'please' and 'thank you,'' Dauner explained. 'But this just makes their responses even longer, expending more energy to generate each word.' For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don't need an explanation at all. Most important, Dauner's study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task. 'Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,' Luccioni explained. If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator. Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said. When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks. Putting a number on the environmental impact of AI has proved challenging. The study noted that energy consumption can vary based on the user's proximity to local energy grids and the hardware used to run AI partly why the researchers chose to represent carbon emissions within a range, Dauner said. Furthermore, many AI companies don't share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI's water consumption. 'You can't really say AI consumes this much energy or water on average — that's just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,' Ren said. One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested. 'Generally, if people were more informed about the average (environmental) cost of generating a response, people would maybe start thinking, 'Is it really necessary to turn myself into an action figure just because I'm bored?' Or 'do I have to tell ChatGPT jokes because I have nothing to do?'' Dauner said. Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said. 'We don't need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,' Luccioni said. 'This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.' With less available information about AI's resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy. 'Overall, I'm still positive about (the future). There are many software engineers working hard to improve resource efficiency,' Ren said. 'Other industries consume a lot of energy too, but it's not a reason to suggest AI's environmental impact is not a problem. We should definitely pay attention.'
Yahoo
2 hours ago
- Yahoo
Meet the tiny Australian Moth that travels 1,000 km and navigates using the stars
An Australian moth follows the stars during its yearly migration, using the night sky as a guiding compass, according to a new study. When temperatures heat up, nocturnal Bogong moths fly about 1,000 kilometres to cool down in caves by the Australian Alps. They later return home to breed and die. Birds routinely navigate by starlight, but the moths are the first known invertebrates, or creatures without a backbone, to find their way across such long distances using the stars. Scientists have long wondered how the moths travel to a place they've never been. A previous study hinted that Earth's magnetic field might help steer them in the right direction, along with some kind of visual landmark as a guide. Related Does cutting off rhinos' horns protect them from poachers? New study supports controversial approach Since stars appear in predictable patterns each night, scientists suspected they might help lead the way. They placed moths in a flight simulator that mimicked the night sky above them and blocked out the Earth's magnetic field, noting where they flew. Then they scrambled the stars and saw how the moths reacted. When the stars were as they should be, the moths flapped in the right direction. But when the stars were in random places, the moths were disoriented. Their brain cells also got excited in response to specific orientations of the night sky. The findings were published Wednesday in the journal Nature. It 'was a very clean, impressive demonstration that the moths really are using a view of the night sky to guide their movements,' said Kenneth Lohmann, who studies animal navigation at the University of North Carolina at Chapel Hill and was not involved with the new research. Researchers don't know what features of the night sky the moths use to find their way. It could be a stripe of light from the Milky Way, a colourful nebula or something else entirely. Whatever it is, the insects seem to rely on that, along with Earth's magnetic field, to make their journey. Related Rare snail that can 'slurp up earthworms like noodles' caught on camera laying an egg from its neck Scientists use special 'squeezing' and electrical probes to collect sperm from endangered kākāpō Other animals harness the stars as a guide. Birds take celestial cues as they soar through the skies, and dung beetles roll their remains short distances while using the Milky Way to stay on course. It's an impressive feat for Bogong moths, whose brains are smaller than a grain of rice, to rely on the night sky for their odyssey, said study author David Dreyer with Lund University in Sweden. 'It's remarkable that an animal with such a tiny brain can actually do this,' Dreyer said.
Yahoo
3 hours ago
- Yahoo
Parkinson's Discovery Suggests We Could Have an FDA-Approved Treatment Already
Researchers have discovered how a surface protein on brain cells, called Aplp1, can play a role in spreading material responsible for Parkinson's disease from cell to cell in the brain. Promisingly, an FDA-approved cancer drug that targets another protein – Lag3 – which interacts with Aplp1 – was found to block this process in mice. This suggests a potential treatment for Parkinson's may already exist. In a paper published last year, an international team of scientists detailed how the two proteins work together to help toxic clumps of alpha-synuclein protein get into brain cells. "Now that we know how Aplp1 and Lag3 interact, we have a new way of understanding how alpha-synuclein contributes to the disease progression of Parkinson's disease," neuroscientist Xiaobo Mao from Johns Hopkins University said in June 2024. "Our findings also suggest that targeting this interaction with drugs could significantly slow the progression of Parkinson's disease and other neurodegenerative diseases." More than 8.5 million people globally have Parkinson's, the second most common neurodegenerative disease after Alzheimer's. The disease is progressive and currently incurable, and it's usually only diagnosed when symptoms show. These include tremors, stiffness, balance issues, speech difficulties, disturbed sleep patterns, and mental health issues. In advanced stages, patients may eventually struggle to walk or speak. Most Parkinson's symptoms stem from the loss or dysfunction of dopamine-producing neurons in a brain region involved in fine motor control, called the substantia nigra. This is thought to be caused by Lewy bodies – abnormal clumps of protein, primarily made up of misfolded alpha-synuclein, that move from neuron to neuron. Although alpha-synuclein normally helps neurons communicate, when it misfolds and becomes insoluble, it can cause serious damage. That said, identifying whether this is a cause of Parkinson's or a symptom is difficult. Past studies on mice found Lag3 binds to alpha-synuclein proteins and spreads Parkinson's disease pathology in neurons. While deleting Lag3 significantly impedes this process, it does not completely prevent it, indicating another protein was also implicated in neurons taking in misfolded alpha-synuclein. "Our work previously demonstrated that Lag3 wasn't the only cell surface protein that helped neurons absorb alpha-synuclein, so we turned to Aplp1 in our most recent experiments," said Johns Hopkins neuroscientist Valina Dawson. The scientists conducted tests with genetically modified mice that were missing either Aplp1 or Lag3, or both. They found Aplp1 and Lag3 can each independently help brain cells absorb harmful alpha-synuclein, but together they significantly increase the uptake. When mice were missing both Aplp1 and Lag3, 90 percent less of the harmful alpha-synuclein entered healthy brain cells, meaning a greater amount of the harmful protein clumps was blocked with both proteins missing compared with a deletion of just one. The researchers gave normal mice the drug nivolumab/relatlimab, a melanoma medication that contains a Lag3 antibody, and found that it also stopped Aplp1 and Lag3 from interacting, again almost completely blocking the formation of disease-causing alpha-synuclein clumps in neurons. "The anti-Lag3 antibody was successful in preventing further spread of alpha-synuclein seeds in the mouse models and exhibited better efficacy than Lag3-depletion because of Aplp1's close association with Lag3," said Ted Dawson, a neuroscientist at Johns Hopkins University. The next step will be to test the Lag3 antibody on mouse models of Parkinson's disease and Alzheimer's – where research has pointed to Lag3 as a target too. The research has been published in Nature Communications. An earlier version of this article was published in June 2024. Microplastics Have a Concerning Effect on The Microbes in Our Gut Something in Your Poop May Predict an Imminent Death Long-Term Contraceptive Pill Use Linked With Brain Tumor Risk
