British Oceanographic Data Centre

Accurate modelling and prediction of sediment transport in aquatic environments is essential for sustainable coastal and riverine management. Current capabilities rely on physical process-based numerical models and fine-scale sediment flux measurements. High-resolution hydroacoustic instrumentation has emerged as a promising tool for such measurements. However, challenges arise due to the inherent complexity of ultrasound scattering processes. This study introduces a numerical modelling using a point-particle approach to simulate the echoes backscattered by such instrumentation in sediment-laden flow conditions. The model considers geometric, statistical, particle cloud, and flow-induced effects on sediment velocity, concentration, and flux estimates using an acoustic concentration and velocity profiler as a reference. The model performance is assessed here under unidirectional constant flow conditions in terms of velocity, concentration, and time-resolved sediment flux estimates for a large range of the particles' advection speed and sampled volume sizes. Application to the estimation of the measurement accuracy of sediment flux in these flows is also considered, with a final error on the flux seen to be partially controlled by the residence time of particles within the sampled volumes. The proposed model provides insights into scattering processes and offers a tool for investigating robust sediment flux estimation techniques in various flow conditions.

Abstract The Northwest European shelf (NWS) experienced its longest recorded category II marine heatwave (MHW) in June 2023 (16 days). Locally, it reached category IV north of Ireland (anomalies up to 5 °C). With state-of-the-art observation and modelling capabilities, we show the MHW developed quickly in the first half of June due to strong atmospheric forcing (high level of sunshine, weak winds and waves, tropical air) during neap tides. This shallow MHW then maintained itself by reducing cloud cover in persistent anticyclonic conditions, followed by another week of neap tides. The MHW impacted the weather of northern Europe with stronger, warmer and moister sea breezes generating more rainfall and contributing to the breaking of June mean temperature records. This MHW was additional to climatological sea surface temperatures +0.9 °C warmer over the last 20 years. These MHW temperatures are projected to become commonplace by the middle of the century under a high CO2 emission scenario.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Oceans. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionMapping Arctic Sea Ice Thickness: A New Method for Improved Ice Freeboard Retrieval from Satellite AltimetryAuthorsJack ChristopherLandyiDJérômeBouffardChrisWilsoniDStefanieRyndersiDYevgenyAksenoviDMichelTsamadosSee all authors Jack Christopher LandyiDCorresponding Author• Submitting AuthorUniversity of Tromsø - The Artic University of NorwayiDhttps://orcid.org/0000-0002-7372-1007view email addressThe email was not providedcopy email addressJérôme BouffardEuropean Space Agencyview email addressThe email was not providedcopy email addressChris WilsoniDNational Oceanography CentreiDhttps://orcid.org/0000-0003-0891-2912view email addressThe email was not providedcopy email addressStefanie RyndersiDNational Oceanography CentreiDhttps://orcid.org/0000-0003-1334-4577view email addressThe email was not providedcopy email addressYevgeny AksenoviDNational Oceanographic CenteriDhttps://orcid.org/0000-0001-6132-3434view email addressThe email was not providedcopy email addressMichel TsamadosUniversity College Londonview email addressThe email was not providedcopy email address

This a new major update of the OGGM model. It it the result of one year of development, with several non-backwards compatible changes. We recommend all users to update to this version.

Mean monthly climatological mixed layer depth (MLD) combined with temperature, dissolved oxygen, and apparent oxygen utilization (AOU) are used to produce global estimates of the seasonal variability of ocean heat content anomaly (OHCA), O2 content anomaly (O2CA), and AOU content anomaly (ACA) in the surface mixed layer. Linear regression analyses show that the highest correlation occurs when O2CA lags OHCA by one month, whereas the highest correlation occurs when ACA lags OHCA by 2-3 months. The O2CA is negatively correlated, while the ACA is positively correlated with the OHCA in the mixed layer. The O2-heat ratio in the surface mixed layer is about -1.85 nmol/J in the subtropical and subpolar regions, which is on the same order of magnitude due to the O2 solubility effect alone. The solubility effect is the primary driver for the seasonal cycle of the O2 inventory in the mixed layer, and thus subject to changes in ocean warming. The 1-month lag between O2CA and OHCA suggests the O2 inventory quickly responds to heat content changes on seasonal time scales due to strong mixing in the mixed layer. The 2-3 month lag between ACA and OHCA suggests oxygen changes through biological activities take a longer time following OHC changes in relation to physical changes through O2 solubility. Our analysis indicates that the deoxygenation rate in the mixed layer, estimated from the regression analysis, is approximately -2.2 Tmol/year based on the O2-heat ratio in the mid-latitudes, accounting for 6±2% of the global deoxygenation for the time period 1955-2019.

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (HS), mean wave period (Tm), and mean wave direction (θm) computed over time-slices 1979–2004 and 2081–2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

<div> <p>More than 40% of the human population live within 100 km of the sea. Many of these communities intimately rely on the oceans for their food, climate and economy. However, the oceans are increasingly being adversely affected by human-driven activities such as climate change and pollution. Many targeted, marine monitoring programmes (e.g. GOSHIP, OceanSITES) and pioneering observing technologies (e.g. autonomous underwater vehicles, Argo floats) are being used to assess the impact humans are having on our oceans. Such activities and platforms are deployed, calibrated and serviced by state-of-the-art research ships, multimillion-pound floating laboratories which operate diverse arrays of high-powered, high-resolution sensors around-the-clock (e.g. sea-floor depth, weather, ocean current velocity and hydrography etc.). These sensors, coupled with event and environmental metadata provided by the ships logs and crew, are essential for understanding the wider context of the science they support, as well as directly contributing to crucial scientific understanding of the marine environment and key strategic policies (e.g. United Nation’s Sustainable Development Goal 14). However, despite their high scientific value and cost, these data streams are not routinely brought together from UK large research vessels in coordinated, reliable and accessible ways that are fundamental to ensuring user trust in the data and any products generated from the data.  </p> </div><div> <p>The National Oceanography Centre (NOC) and British Antarctic Survey (BAS) are currently working together to improve the integrity of the data management workflow from sensor systems to end-users across the UK National Environment Research Council (NERC) large research vessel fleet, making cost effective use of vessel time while improving the FAIRness of data from these sensor arrays. The solution is based upon an Application Programming Interface (API) framework with endpoints tailored towards different end-users such as scientists on-board the vessels as well as the public on land. Key features include: Sensor triage using real-time automated monitoring systems, assuring sensors are working correctly and only the best data are output; Standardised digital event logging systems allowing data quality issues to be identified and resolved quickly; Novel open-source, data transport formats that are embedded with well-structured metadata, common standards and provenance information (such as controlled vocabularies and persistent identifiers), reducing ambiguity and enhancing interoperability across platforms; An open-source data processing application that applies quality control to international standards (SAMOS, or IOOS Qartod); Digital notebooks that manage and capture processing applied to data putting data into context; Democratisation and brokering of data through open data APIs (e.g. ERDDAP, Sensor Web Enablement), allowing end-users to discover and access data, layer their own tools or generate products to meet their own needs; Unambiguous provenance that is maintained throughout the data management workflow using instrument persistent identifiers, part of the latest recommendations by the Research Data Alliance (RDA).  </p> </div><div> <p>Access to universally interoperable oceanic data, with known quality and provenance, will empower a broad range of stakeholder communities, creating opportunities for innovation and impact through data use, re-use and exploitation.</p> </div>

Abstract Coastal flood warning and design of coastal protection schemes rely on accurate estimations of water level and waves during hurricanes and violent storms. These estimations frequently use numerical models, which, for computational reasons, neglect the interaction between the hydrodynamic and wave fields. Here, we show that neglecting such interactions, or local effects of atmospheric forcing, causes large uncertainties, which could have financial and operational consequences because flood warnings are potentially missed or protection schemes underdesigned. Using the Severn Estuary, SW England, we show that exclusion of locally generated winds underestimates high water significant wave height by up to 90.1%, high water level by 1.5%, and hazard proxy (water level + 1/2 significant wave height) by 9.1%. The uncertainty in water level and waves is quantified using a system to model tide‐surge‐wave conditions, Delft3D‐FLOW‐WAVE in a series of eight model simulations for four historic storm events.