A new interdisciplinary study led by researchers from Nanyang Technological University, Singapore (NTU Singapore), with collaborators from the City University of Hong Kong, has found that El Niño events significantly reduce life expectancy across high-income Pacific Rim countries, resulting in economic losses of up to US$35 trillion by the end of the 21st century.

Using over six decades of mortality records from 10 high-income Pacific Rim countries, the research team shows that El Niño is a persistent driver of health and economic loss, not just a short-term weather anomaly. El Niño-driven climate extremes, such as heatwaves and air pollution, disrupt healthcare systems and raise long-term mortality risks, particularly among vulnerable populations.

The research, published in the scientific journal Nature Climate Change and part of NTU’s Climate Transformation Programme, shows that El Niño events not only cause immediate health impacts but also persistently slow long-term improvements in mortality rates, leading to enduring reductions in life expectancy.

Rivers cover more than 4 million miles of the U.S., but protections for rivers are piecemeal, accounting for less than 20% of total river length and varying widely by region, shows a new study co-led by the University of Washington.

A record of repeated retreat of the West Antarctic Ice Sheet during the past warm climates has been identified by IODP Exp379 Scientists. By analyzing deep-sea sediments from the Amundsen Sea and tracing their geochemical signatures, the study shows that the ice sheet retreated far inland at least five times during the warm Pliocene Epoch. The findings highlight the ice sheet’s sensitivity to warming and its potential to drive future sea-level rise.

Existing sea surface height prediction models require excessive computing power and training times and suffer from progressive error accumulation, limiting their accuracy to 14–15 days in the future. A team of researchers recently developed a lightweight deep learning model called GTU-Net with unique loss function and physical constraints that improve the reliability of medium- and long-range sea surface height predictions to improve long-term ocean monitoring, climate studies and operational ocean forecasting.