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A recent study using artificial intelligence (AI) has revealed that regional climate warming is progressing faster than previously projected. Findings indicate that global warming thresholds critical to ecosystems and human livelihoods could be surpassed within the next two decades, with some regions expected to face extreme temperature rises of up to 3°C by 2060.
The research, published in the journal Environmental Research Letters, presents a stark warning about the urgency of climate adaptation and mitigation. With data from 10 global climate models, researchers utilised a novel AI transfer-learning approach to refine and enhance the accuracy of regional climate projections.
The study predicts that most of the land regions globally will surpass the critical 1.5°C warming threshold by 2040, with many areas accelerating toward 3°C by 2060. Vulnerable regions such as South Asia, the Mediterranean, Central Europe, and parts of sub-Saharan Africa are expected to experience the most quick changes, boosting existing threats to ecosystems, water resources, and humans.
The AI-enhanced analysis revealed alarming projections about regional climate warming. It predicts that 34 regions are likely to exceed the 1.5°C warming threshold by 2040. Among these, 31 regions are expected to reach 2°C of warming within the same timeframe. Furthermore, 26 regions are projected to surpass 3°C of warming by 2060, significantly ahead of earlier estimates.
The research shows an advancement in climate modeling by applying AI transfer-learning techniques. A convolutional neural network (CNN) was trained using historical climate data and refined with limited observational data, enabling exact regional predictions. This AI-based approach bridges gaps in traditional climate modeling by using localized feedback mechanisms and addressing uncertainties in regional climate variability.
The research adapts methodologies from previous studies that applied neural networks to global warming thresholds, enhancing them for regional analyses. The team overcame challenges related to smaller-scale atmospheric, oceanic, and land-surface processes that typically introduce uncertainty in regional forecasts.
The findings underscore the urgency of scaling up global climate action to address the reduction in emissions and adaptation efforts tailored to regional vulnerabilities. Regions projected to reach critical thresholds sooner must prepare for intensified heatwaves, water shortages, and agricultural disruptions.
Researchers caution that while AI has significantly improved the precision of climate forecasts, uncertainty remains due to the variability in climate systems and the complexity of localised processes. Nonetheless, this study represents a vital step forward in bridging gaps between global projections and regional realities.
References: https://iopscience.iop.org/article/10.1088/1748-9326/ad91ca
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