Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4 percent of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights the potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes.

Published as a workshop paper at "Tackling Climate Change with Machine Learning", ICLR 2025 Accurate ocean modeling and coastal hazard prediction depend on high-resolution bathymetric data; yet, current worldwide datasets are too coarse for exact numerical simulations. While recent deep learning advances have improved earth observation data resolution, existing methods struggle with the unique challenges of producing detailed ocean floor maps, especially in maintaining physical structure consistency and quantifying uncertainties. This work presents a novel uncertainty-aware mechanism using spatial blocks to efficiently capture local bathymetric complexity based on block-based conformal prediction. Compared to conventional techniques, experimental results over several ocean regions show notable increases in both reconstruction quality and uncertainty estimation reliability. This framework increases the reliability of bathymetric reconstructions by preserving structural integrity while offering spatially adaptive uncertainty estimates, so opening the path for more solid climate modeling and coastal hazard assessment.Figure 1: Learning Enhanced Structural Representations with Block-Based Uncertainties 1 Simple diffusion equations to complex Navier-Stokes equations used in computational fluid dynamics (CFD) span these physical models, all of which depend on thorough bathymetric data to properly forecast tsunami propagation, storm surges, and the effects of sea level rise on coastal communities. The GEBCO project (General Bathymetric Chart of the Oceans), fuses multibeam sonar, satellite altimetry, and shipborne soundings, yet filling in sub-kilometer details globally would take on the order of two centuries at current survey rates Mayer et al. (2018). Enhancement is further complicated by three interrelated factors: (1) heterogeneous data sources with distinct error characteristics and regional resolution gaps; (2) the need to preserve sharp morphological boundaries, such as ridges, canyons, and trenches, that are critical for physical simulations; and (3) spatially varying data quality arising from different acquisition techniques (direct soundings vs. altimetry) that induce nonuniform uncertainty patterns.

Tsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The GNSS are constellations of satellites providing signals from Earth orbit - Europe's Galileo, the United States' Global Positioning System (GPS), Russia's Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) and China's BeiDou. The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Gramian Angular Difference Fields (from Computer Vision) and Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd. Our work represents a new frontier in detecting tsunami-driven IGWs in open-ocean, dramatically improving the potential for natural hazards detection for coastal communities.

War in Europe, a momentous volcanic eruption and a surprise finding that could rewrite our understanding of reality – 2022 really has been a busy year for science, technology, health and environment news, and all that happened in just the first few months. From stunning space imagery to pig heart transplants, here are the New Scientist news editors' picks of the biggest scientific developments, discoveries and events of the year. Russia's invasion of Ukraine in February has sparked devastation across the country and affected many areas of life around the world, as both nations play a key role in the global supply chains for energy, food and more. It has also raised the spectre of nuclear weapons, with Russian president Vladimir Putin making not-so veiled threats about deploying his atomic arsenal. Thankfully, Armageddon has been avoided, but Russia's offensive has sparked discussion of a new kind of nuclear war, as Ukraine's nuclear power plants became a battleground this year.

The enormous underwater volcano off Tonga last month not only caused record plumes of ash into the air, but also led to one of the largest volcanic lightning events ever seen. According to GLD360, the ground-based global lightning detection network owned and operated by Vaisala, the eruption triggered nearly 590,000 lighting strikes that were'unlike anything on record.' The lightning almost engulfed the surrounding islands in the Tonga archipelago, according to Chis Vagasky, a meteorologist at Vaisala. 'I can't imagine what the people on the islands would have been going through, with a huge ash cloud overhead, a tsunami flooding everything they own, and cloud-to-ground lightning coming down around them,' he said. 'It must have felt apocalyptic.' Ash sent spewing into the air from the massive underwater volcanic eruption in Tonga was photographed by International Space Station astronauts.