Description: Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model-data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include fugacity of carbon dioxide, pH on total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. The evolution of these OA indicators is presented on a global surface ocean 1° × 1° grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850–2010), and to five future Shared Socioeconomic Pathways (2020–2100): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. These OA trajectories represent an improvement over previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs. The generated data product offers a state-of-the-art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/surface-oa-indicators.html. Key Points: - This study presents the evolution of 10 ocean acidification (OA) indicators in the global surface ocean from 1750 to 2100 - By leveraging 14 Earth System Models (ESMs) and the latest observational data, it represents a significant advancement in OA projections - This inter-model comparison effort showcases the overall agreements among different ESMs in projecting surface ocean carbon variables

Description: Submarine landslides pose a hazard to coastal communities and critical seafloor infrastructure, occurring on all of the world's continental margins, from coastal zones to hadal trenches. Offshore monitoring has been limited by the largely unpredictable occurrence of submarine landslides and the need to cover large regions. Recent subsea monitoring has provided new insights into the preconditioning and run-out of submarine landslides using active geophysical techniques. However, these tools measure a small spatial footprint and are power- and memory-intensive, thus limiting long-duration monitoring. Most landslide events remain unrecorded. In this chapter, we first show how passive acoustic and seismologic techniques can record acoustic emissions and ground motions created by terrestrial landslides. This terrestrial-focused research has catalyzed advances in characterizing submarine landslides using onshore and offshore networks of broadband seismometers, hydrophones, and geophones. We discuss new insights into submarine landslide preconditioning, timing, location, velocity, and down-slope evolution arising from these advances. Finally, we outline challenges, emphasizing the need to calibrate seismic and acoustic signals generated by submarine landslides. Passive seismic and acoustic sensing has a strong potential to enable more complete hazard catalogs to be built and open the door to emerging techniques (such as fiber-optic sensing) to fill key knowledge gaps.

Description: Accessible seafloor minerals located near mid‐ocean ridges are noticed to mitigate the projected metal demands of the net‐zero energy transition, promoting growing interest in quantifying the global distributions of seafloor massive sulfides (SMS). Mineral potentials are commonly estimated using geophysical and geological data that lastly rely on additional confirmation studies using sparsely available, locally limited, seafloor imagery, grab samples, and coring data. This raises the challenge of linking in situ confirmation data to geophysical data acquired at disparate spatial scales to obtain quantitative mineral predictions. Although multivariate data sets for marine mineral research are incessantly acquired, robust, integrative data analysis requires cumbersome workflows and experienced interpreters. We introduce an automated two‐step machine learning approach that integrates the mound detection through image segmentation with geophysical data. SMS predictors are subsequently clustered into distinct classes to infer marine mineral potentials that help guide future exploration. The automated workflow employs a U‐Net convolutional neural network to identify mound structures in bathymetry data and distinguishes different mound classes through the classification of mound architectures and magnetic signatures. Finally, controlled source electromagnetic data are utilized together with in situ sampling data to reassess predictions of potential SMS volumes. Our study focuses on the Trans‐Atlantic Geotraverse area, which is among the most explored SMS areas worldwide and includes 15 known SMS sites. The automated workflow classifies 14 of the 15 known mounds as exploration targets of either high or medium priority. This reduces the exploration area to less than 7% of the original survey area from 49 to 3.1 km 2 . Key Points A two‐step machine learning workflow identifies mound structures in bathymetry data and classifies their origins based on auxiliary data Significant increase in potential seafloor massive sulfides (SMS) edifices detected within the trans‐Atlantic geo‐traverse hydrothermal field distributed within latitudinal bands SMS mineral potential is likely lower than previously assumed due to heterogeneously distributed mineralization within mounds

Description: Key Points: - During 1993–2019, the East Greenland Coastal Current is freshest in 2010 and 2012 notably matching years of exceptional Greenland runoff - Freshwater anomalies from sea-ice melt and Arctic export advected along east Greenland are of similar magnitudes as those linked to runoff - Simulation of fresh coastal waters requires improved surface boundary conditions and/or models capable of representing mesoscale dynamics Accelerated melting of the Greenland Ice Sheet is considered a tipping element in the freshwater balance of the subpolar North Atlantic (SPNA). The East Greenland Current (EGC) and Coastal Current (EGCC) are the major conduits for transporting Arctic-sourced and Greenland glacial freshwater. Understanding freshwater changes in the EGC system and drivers thereof is crucial for connecting tipping elements in the SPNA. Using the eddy-rich model VIKING20X (1/20°) and Copernicus GLORYS12 (1/12°), we find that from 1993 to 2019 freshwater remains close to the shelf with interannual extremes in freshwater content (FWC) attributable to the imprint of Greenland melt only in years 2010 and 2012. Runoff increased significantly from 1995 to 2005 and Arctic freshwater export after 2005. Overall, regional wind patterns, sea ice melt and increasingly glacial ice and snow meltwater runoff along with the Arctic-sourced Polar Water set interannual FWC variations in the EGC system. We emphasize that these freshwater sources have different seasonal timing. South of 65°N sea ice melts year round and retreats to north of 65°N, where melt in summer prevails. Greenland runoff peaks in June–August with only some locations of year round discharge. Alongshore winds intensify in fall and winter where reduced onshore Ekman transport allows for freshwater to spread laterally in the EGC. We show that sea ice melt, runoff and wind can cause interannual variations of comparable magnitude. All of which makes attributing ocean freshening events to Greenland meltwater inflow at current magnitudes a major challenge.