Abstract. 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.

The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication 1 Changing storminess and global capture fisheriesClimate change-driven alterations in storminess pose a significant threat to global capture fisheries. Understanding how storms interact with fishery social-ecological systems can inform adaptive action and help to reduce the vulnerability of those dependent on fisheries for life and livelihood.

Abstract. It is hypothesised that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, requires 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-west 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 6 contrasting case studies of 5-day duration is presented. Results demonstrate that at least comparable performance can be achieved with the UKC2 system to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and peak 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.

Abstract.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 United Nations Intergovernmental Panel on Climate Change Fifth Assessment Report (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 to 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 Representative Concentration Pathway (RCP)4.5 (8.5) scenarios. 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.

Three shelf sea models are compared against observed surface temperature and salinity in Liverpool Bay and the Irish Sea: a 7 km NEMO model, and 12 km and 1.8 km POLCOMS models. Each model is run with two different surface forcing datasets of different resolutions. <br></br> Comparisons with a variety of observations from the Liverpool Bay Coastal Observatory show that increasing the surface forcing resolution improves the modelled surface temperature in all the models, in particular reducing the summer warm bias and winter cool bias. The response of surface salinity is more varied with improvements in some areas and deterioration in others. <br></br> The 7 km NEMO model performs as well as the 1.8 km POLCOMS model when measured by overall skill scores although the sources of error in the models are different. NEMO is too weakly stratified in Liverpool Bay, whereas POLCOMS is too strongly stratified. The horizontal salinity gradient, which is too strong in POLCOMS, is better reproduced by NEMO which uses a more diffusive horizontal advection scheme. This leads to improved semi-diurnal variability in salinity in NEMO at a mooring site located in the Liverpool Bay ROFI area