In a paper published in National Science Review, an international team led by Prof. Xuanmei Fan presents a deep learning-based framework that utilizes a novel global database of nearly 400,000 earthquake-triggered landslides to predict landslide probability for any earthquake worldwide with ~82% spatial accuracy in less than one minute without requiring prior local field data.

A research team led by Prof. ZHANG Haijiang from the University of Science and Technology of China (USTC), in collaboration with Dr. Robert Myhill from the University of Bristol, utilized tele-seismic double-difference tomography technology to uncover the morphological changes of the Pacific subducting slab in the mantle transition zone beneath Northeast China. In addition, they also reported its controlling effects on Changbaishan volcanic field (CVF) volcanism and deep earthquakes. This study was published in Nature Communications.

Across the western U.S., wildfires are becoming larger and more severe — and even trees that initially survive are dying in subsequent years, making it harder for forests to regenerate, according to new research from Portland State University.

In a paper published in Science China Earth Sciences, a team of scientists present a novel application of data-driven AI model to real-time predictions of the 2023–2024 climate conditions in the tropical Pacific. As configured, this AI-based model can adequately represent the coupled ocean-atmosphere interactions, with its prediction procedure being executed in a rolling manner, in which the related key fields during multi-month time intervals (TIs) are taken for input predictors serving as initial conditions. Sensitivity experiments are conducted to examine how prediction skills are affected by the input predictor specifications, including TIs. A comparison with other dynamic coupled models is also made to demonstrate the prediction performance for the 2023–2024 El Niño event.

A study conducted by researchers from the University of Plymouth and Plymouth Marine Laboratory, who have spent more than a decade examining the impact of artificial light at night (ALAN) on the world’s coasts and oceans, has shown that more than one-fifth of the global ocean – an area spanning more than 75million sq km – has been the subject of ocean darkening over the past two decades. Ocean darkening occurs when changes in the optical properties of the ocean reduce the depth of its photic zones, home to 90% of all marine life and places where sunlight and moonlight drive ecological interactions.