An ECOOP web portal for visualising and comparing distributed coastal oceanography model and in situ data

As part of a large European coastal operational oceanography project (ECOOP), we have developed a web portal for the display and comparison of model and in situ marine data. The distributed model and in situ datasets are accessed via an Open Geospatial Consortium Web Map Service (WMS) and Web Feature Service (WFS) respectively. These services were developed independently and readily integrated for the purposes of the ECOOP project, illustrating the ease of interoperability resulting from adherence to international standards. The key feature of the portal is the ability to display co-plotted timeseries of the in situ and model data and the quantification of misfits between the two. By using standards-based web technology we allow the user to quickly and easily explore over twenty model data feeds and compare these with dozens of in situ data feeds without being concerned with the low level details of differing file formats or the physical location of the data. Scientific and operational benefits to this work include model validation, quality control of observations, data assimilation and decision support in near real time. In these areas it is essential to be able to bring different data streams together from often disparate locations

The extreme 2013/2014 winter storms: Beach recovery along the southwest coast of England

publisher: Elsevier articletitle: The extreme 2013/2014 winter storms: Beach recovery along the southwest coast of England journaltitle: Marine Geology articlelink: http://dx.doi.org/10.1016/j.margeo.2016.10.011 content_type: article copyright: © 2016 The Authors. Published by Elsevier B.V

Projected sea level rise and changes in extreme storm surge and wave events during the 21st century in the region of Singapore

Singapore is an island state with considerable population, industries, commerce and transport located in coastal areas at elevations less than 2 m making it vulnerable to sea-level rise. Mitigation against future inundation events requires a quantitative assessment of risk. To address this need, regional projections of changes in (i) long-term mean sea level and (ii) the frequency of extreme storm surge and wave events have been combined to explore potential changes to coastal flood risk over the 21st century. Local changes in time mean sea level were evaluated using the process-based climate model data and methods presented in the IPCC AR5. Regional surge and wave solutions extending from 1980 to 2100 were generated using ~ 12 km resolution surge (Nucleus for European Modelling of the Ocean – NEMO) and wave (WaveWatchIII) models. Ocean simulations were forced by output from a selection of four downscaled (~ 12 km resolution) atmospheric models, forced at the lateral boundaries by global climate model simulations generated for the IPCC AR5. Long-term trends in skew surge and significant wave height were then assessed using a generalised extreme value model, fit to the largest modelled events each year. An additional atmospheric solution downscaled from the ERA-Interim global reanalysis was used to force historical ocean model simulations extending from 1980–2010, enabling a quantitative assessment of model skill. Simulated historical sea surface height and significant wave height time series were compared to tide gauge data and satellite altimetry data respectively. Central estimates of the long-term mean sea level rise at Singapore by 2100 were projected to be 0.52 m (0.74 m) under the RCP 4.5 (8.5) scenarios respectively. Trends in surge and significant wave height 2 year return levels were found to be statistically insignificant and/or physically very small under the more severe RCP8.5 scenario. We conclude that changes to long-term mean sea level constitute the dominant signal of change to the projected inundation risk for Singapore during the 21st century. We note that the largest recorded surge residual in the Singapore Strait of ~ 84 cm lies between the central and upper estimates of sea level rise by 2100, highlighting the vulnerability of the region

Role of Atmospheric Indices in Describing Inshore Directional Wave Climate in the United Kingdom and Ireland

Improved understanding of how our coasts will evolve over a range of time scales (years‐decades) is critical for effective and sustainable management of coastal infrastructure. A robust knowledge of the spatial, directional and temporal variability of the inshore wave climate is required to predict future coastal evolution and hence vulnerability. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980–2017) and show that 73% are directionally bimodal. We find that winter‐averaged expressions of six leading atmospheric indices are strongly correlated (r = 0.60–0.87) with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Regional inshore wave climate classification through hierarchical cluster analysis and stepwise multi‐linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (R2 = 0.45–0.8; p<0.05). We demonstrate for the first time the significant explanatory power of leading winter‐averaged atmospheric indices for directional wave climates, and show that leading seasonal forecasts of the NAO skillfully predict wave climate in some regions. Plain Language Summary Understanding the seasonal variability in wave climate around our coasts is fundamental for improving our understanding of how coasts will respond to climate change and sea‐level rise. Recent research has highlighted the importance of wave direction on coastal response. In this study we specifically explore the seasonal variability in wave direction throughout the inshore regions of the United Kingdom and Ireland at 63 locations between 1980 and 2017. We find that 73% of sites examined are directionally bimodal. We also find that combinations of six of the regions leading climate indices (NAO, AO, WEPA, EA, SCAND, EA/WR) are strongly correlated with both total and directional winter wave power at all the studied sites. We show that regression models using combinations of these climate indices have significant skill in predicting directional wave climates over the period 1980‐2017. For the first time we show that 'seasonal ahead' forecasts of the NAO can skilfully predict wave climate in some regions of the United Kingdom and Ireland, which could be used as tool to support coastal hazard mitigation

Research priorities in support of ocean monitoring and forecasting at the Met Office

Ocean monitoring and forecasting services are increasingly being used by a diverse community of public and commercial organizations. The Met Office, as the body responsible for severe weather prediction, has for many years been involved in providing forecasts of aspects of the marine environment. This paper describes how these have evolved to include a range of wave, surge, and ocean reanalysis, analysis, and forecasts services. To support these services, and to ensure they evolve to meet the demands of users and are based on the best available science, a number of scientific challenges need to be addressed. The paper goes on to summarize the key challenges, and highlights some priorities for the ocean monitoring and forecasting research group at the Met Office. There is a need to both develop the underpinning science of the modelling and data assimilation systems and to maximize the benefits from observations and other inputs to the systems. Systematic evaluation underpins this science, and also needs to be the focus of research

The impact of ocean‐wave coupling on the upper ocean circulation during storm events

Many human activities rely on accurate knowledge of the sea surface dynamics. This is especially true during storm events, when wave-current interactions might represent a leading order process of the upper ocean. In this study, we assess and analyze the impact of including three wave-dependent processes in the ocean momentum equation of the Met Office North West European Shelf ocean-wave forecasting system on the accuracy of the simulated surface circulation. The analysis is conducted using ocean currents and Stokes drift data produced by different implementations of the coupled forecasting systems to simulate the trajectories of surface (iSphere) and 15 m drogued (SVP) drifters affected by four storms selected from winter 2016. Ocean and wave simulations differ only in the degree of coupling and the skills of the Lagrangian simulations are evaluated by comparing model results against the observed drifter tracks. Results show that, during extreme events, ocean-wave coupling improves the accuracy of the surface dynamics by 4%. Improvements are larger for ocean currents on the shelf (8%) than in the open ocean (4%): this is thought to be due to the synergy between strong tidal currents and more mature decaying waves. We found that the Coriolis-Stokes forcing is the dominant wave-current interaction for both type of drifters; for iSpheres the secondary wave effect is the wave-dependent sea surface roughness while for SVPs the wave-modulated water-side stress is more important. Our results indicate that coupled ocean-wave systems may play a key role for improving the accuracy of particle transport simulations

The UKC2 regional coupled environmental prediction system

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere–land–ocean–wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential benefits of coupling between environmental model components. Results also illustrate that the coupling itself is not sufficient to address all known model issues. Priorities for future development of the UK Environmental Prediction framework and component systems are discussed

CYP4F Enzymes Are the Major Enzymes in Human Liver Microsomes That Catalyze the O-Demethylation of the Antiparasitic Prodrug DB289 [2,5-Bis(4-amidinophenyl)furan-bis-O-methylamidoxime]

DB289 [2,5-bis(4-amidinophenyl)furan-bis-O-methylamidoxime] is biotransformed to the potent antiparasitic diamidine DB75 [2,5-bis(4-amidinophenyl) furan] by sequential oxidative O-demethylation and reductive N-dehydroxylation reactions. Previous work demonstrated that the N-dehydroxylation reactions are catalyzed by cytochrome b5/NADH-cytochrome b5 reductase. Enzymes responsible for catalyzing the DB289 O-demethylation pathway have not been identified. We report an in vitro metabolism study to characterize enzymes in human liver microsomes (HLMs) that catalyze the initial O-demethylation of DB289 (M1 formation). Potent inhibition by 1-aminobenzotriazole confirmed that M1 formation is catalyzed by P450 enzymes. M1 formation by HLMs was NADPH-dependent, with a Km and Vmax of 0.5 μM and 3.8 nmol/min/mg protein, respectively. Initial screening showed that recombinant CYP1A1, CYP1A2, and CYP1B1 were efficient catalysts of M1 formation. However, none of these three enzymes was responsible for M1 formation by HLMs. Further screening showed that recombinant CYP2J2, CYP4F2, and CYP4F3B could also catalyze M1 formation. An antibody against CYP4F2, which inhibited both CYP4F2 and CYP4F3B, inhibited 91% of M1 formation by HLMs. Two inhibitors of P450-mediated arachidonic acid metabolism, HET0016 (N-hydroxy-N′-(4-n-butyl-2-methylphenyl)formamidine) and 17-octadecynoic acid, effectively inhibited M1 formation by HLMs. Inhibition studies with ebastine and antibodies against CYP2J2 suggested that CYP2J2 was not involved in M1 formation by HLMs. Additionally, ketoconazole preferentially inhibited CYP4F2, but not CYP4F3B, and partially inhibited M1 formation by HLMs. We conclude that CYP4F enzymes (e.g., CYP4F2, CYP4F3B) are the major enzymes responsible for M1 formation by HLMs. These findings indicate that, in human liver, members of the CYP4F subfamily biotransform not only endogenous compounds but also xenobiotics