Ocean monitoring will improve outcomes if ways of knowing and priorities from a range of interest groups are successfully integrated. Coastal Indigenous communities hold unique knowledge of the ocean gathered through many generations of inter-dependent living with marine ecosystems. Experiences and observations from living within that system have generated ongoing local and traditional ecological knowledge (LEK and TEK) and Indigenous knowledge (IK) upon which localized sustainable management strategies have been based. Consequently, a comprehensive approach to ocean monitoring should connect academic practices (“science”) and local community and Indigenous practices, encompassing “TEK, LEK, and IK.” This paper recommends research approaches and methods for connecting scientists, local communities, and IK holders and their respective knowledge systems, and priorities, to help improve marine ecosystem management. Case studies from Canada and New Zealand (NZ) highlight the emerging recognition of IK systems in natural resource management, policy and economic development. The in-depth case studies from Ocean Networks Canada (ONC) and the new Moana Project, NZ highlight real-world experiences connecting IK with scientific monitoring programs. Trial-tested recommendations for successful collaboration include practices for two-way knowledge sharing between scientists and communities, co-development of funding proposals, project plans and educational resources, mutually agreed installation of monitoring equipment, and ongoing sharing of data and research results. We recommend that future ocean monitoring research be conducted using cross-cultural and/or transdisciplinary approaches. Vast oceans and relatively limited monitoring data coupled with the urgency of a changing climate emphasize the need for all eyes possible providing new data and insights. Community members and ocean monitoring scientists in joint research teams are essential for increasing ocean information using diverse methods compared with previous scientific research. Research partnerships can also ensure impactful outcomes through improved understanding of community needs and priorities.
Physical wave basin tests with a focus on uncertainty estimation have been conducted on a sphere subjected to wave loads at Aalborg University as part of the effort of the OES Wave Energy Converters Modeling Verification and Validation (formerly, OES Task 10) working group to increase credibility of numerical modeling of WECs. The tests are referred to as the Kramer Sphere Cases, and the present note is dealing with wave excitation force tests on a fixed model. The present note is including details to facilitate CFD models which replicate the physical setup in detail.
Physical model tests are often conducted during the design process of coastal structures. The wave climate in such tests often includes short-crested nonlinear waves. The structural response is related to the incident waves measured in front of the structure. Existing methods for separation of incident and reflected short-crested waves are based on linear wave theory. For analysis of nonlinear waves, the existing methods are limited to separation of nonlinear long-crested waves. For short-crested waves, the only options so far have been to use estimates without the structure in place. The present paper thus presents a novel method for directional analysis of nonlinear short-crested waves: Non-Linear Single-summation Oblique Reflection Separation (NL-SORS). The method is validated on numerical model data, as for such data, the target is well defined as simulations may be performed with fully absorbing boundaries. Second- and third-order wave theory is used to demonstrate that small errors on the celerity of nonlinear components in the mathematical model of the surface elevation can be obtained if a double narrow-banded directional spectrum is assumed, ie the primary frequency and the directional spreading function must be narrow banded. As the increasing nonlinearity of the waves often arise from waves shoaling on a sloping foreshore, the directional spreading of the waves will decrease due to refraction, and a broad directional spreading function will thus not be experienced in highly nonlinear conditions. The new NL-SORS method is shown to successfully decompose nonlinear short-crested wave fields and estimate the directional spectrum thereof.
This paper highlights the urgent need to accelerate research and action on ocean carbon sinks through human intervention, known as Global Ocean Negative Carbon Emissions (Global-ONCE) Programme, as a vital strategy in global efforts to mitigate climate change. Achieving 'net zero' by 2050 cannot rely on emission reductions alone, emphasising the necessity of complementary approaches. Global-ONCE's mission extends beyond scientific exploration. It embodies a profound commitment to protecting and restoring blue carbon ecosystems, as well as implementing ocean-based solutions that are sustainable, equitable, and inclusive. Early Career Ocean Professionals (ECOPs) are at the heart of these efforts, and their innovative approaches, technical expertise, and passion make them indispensable leaders in advancing ONCE initiatives. ECOPs bridge the gap between science and society, playing a relevant role in integrating cutting-edge research, technological advancements, and community-driven action to address climate threats. By bringing together diverse perspectives and leveraging their interdisciplinary expertise, ECOPs ensure ONCE strategies are grounded in scientific rigour and practical feasibility. Through advocacy, education, and collaboration, ECOPs not only spearhead research and innovation but also inspire collective action to safeguard our oceans. This paper amplifies the critical role of ECOPs as agents of change and calls for a unified global commitment to harness the ocean's potential for a climate-resilient future.
This paper presents an assessment of three methods used for sea state estimation via the wave buoy analogy, where measured ship responses are processed. The three methods all rely on Machine Learning exclusively but they have different output; Method 1 provides bulk parameters, Method 2 yields a point wave spectrum and the wave direction, while Method 3 gives the directional wave spectrum in non-parametric form. The assessment is made using full-scale data from an in-service container ship in cross-Atlantic service. Training and testing of the methods are made using data from a wave radar, and the three methods perform well. An uncertainty measure, equivalently, a trust level indicator, based on the variation between the post-processed outputs of the methods is proposed, and this facilitates determination of estimates with small errors; without knowing the ground truth.