Sound travels 1000s of kilometres underwater; depending on its frequency, its variety of wavelengths enables probing of the ocean from millimeters to megameters. In this project, we resource the natural ambient sound as the probe with distributed sensing of optical fibres within legacy seafloor cables as vast arrays of passive acoustic receivers. The amplitude, phase and travel time of acoustic signals are strongly affected by the water temperature and flow velocity fields in their path. To obtain spatially resolved variability in these measurands, tomographic techniques can be used to combine integrals over several acoustic paths that connect a source and a receiver. Access to a higher number of acoustic paths improves estimation of ocean structure. Notable examples of oceanic phenomena already captured by tomographic techniques comprise convective chimneys in the Greenland Sea and basin-scale inversions of thermal structure. Despite these promising examples, use of active acoustic tomography is limited due to i) the economics of maintaining a powerful acoustic source (with noise-pollution consequences on marine life), and ii) the limitations on lateral and temporal resolutions associated with practical constraints on acoustic paths from active sources. Noise interferometry (NI) overcomes these limitations by replacing the use of active sources with diverse and broadband (10^-3 Hz - 10^-5 Hz) ambient marine noise, entails cross-correlating pressure fluctuations at different locations to retrieve an approximation to the acoustic Green's functions of various waves (i.e. the deterministic wave field due to a point source), which is then inverted to obtain ocean structure. This approach transforms any pair of discrete acoustic sensors (say, hydrophones) into virtual acoustic transceivers, which enables the quantification of both path-integrated sound speed (which is a function of temperature and pressure) and velocity. Flow velocity is retrieved from travel time nonreciprocity, i.e. the difference between travel times in opposite directions between two transceivers. Insensitivity of acoustic non-reciprocity to uncertainties in sound speed and transceiver positions enables accurate passive measurements of the oceanic current velocity, despite its absolute magnitude being less than the uncertainty in sound speed. When used with discrete sensors, NI requires maintaining sub-millisecond clock accuracy on underwater moorings for months-long periods and impractically large number of discrete sensors for useful spatio-temporal oceanographic measurements. This work overcomes these problems by replacing sparse point sensors (hydrophones/seismometers) with the data obtained using distributed sensing of optical fibres within offshore legacy seafloor cables. This enables spatially resolved O(10 m), dynamic measurements of relative deformation in optical fibre under the influence of ambient noise fields. Whilst these measurements are fundamentally different from acoustic pressure measured using conventional hydrophones, their sensitivity is comparable. In the NI context, the required time synchronization is greatly simplified as all signals come from the same fiber, with real-time data availability. Moreover, the large number of available sensor pairs and variety of pair-wise sensor separations yields a larger volume of input data for evaluating the noise cross-correlation function which results in the acoustic Green's function extraction, albeit with proportionately reduced noise averaging times, e.g., from hours-days to seconds-minutes. This project builds on the growing number of studies that have demonstrated the basics of the method by comparing inverse estimates from NI with directly measured time series of full ocean depth velocity and temperature. Our overarching aim is to determine the practical limits on spatio (vertical-horizontal) - temporal resolutions with measurand (temperature-velocity) precisions.

In response to global warming, the ice covers of the Arctic and Antarctic are changing, with a significant reduction in the summer extent of Arctic sea ice. The observed recent, rapid reduction of Arctic sea ice is more extreme than the predictions of even the most pessimistic of climate models, which suggests that these models do not present the processes controlling the reduction of sea ice adequately. Satellite observations, field work, and modelling all point to the importance of sea ice dynamics in controlling the mass balance of Arctic sea ice. The greatest uncertainty in sea ice dynamics is in the relationship between internal sea ice stresses and the deformation and state of the sea ice cover, known as the sea ice rheology. The description of sea ice rheology in existing climate models treats the ice cover as isotropic, so that at a given location there is equal resistance to failure in all directions. However, it has been known for over a decade that the ice cover is highly anisotropic, with oriented cracks present at all length scales, and these cracks control the directions of preferential deformation. While researchers have been aware of the importance of anisotropic mechanics, only recently has a model of anisotropic rheology been constructed and incorporated into the sea ice component of a climate model. This project aims to eliminate fundamental uncertainty in the processes controlling anisotropy creation and destruction through a combination of recently produced, high-resolution satellite deformation maps and computer modelling. A major result of the research will be a new representation of anisotropic sea ice rheology incorporated into the CICE sea ice model, which is the sea ice model used in many climate models, including the UK Hadley Centre series of climate models. We will use CICE to investigate the role of anisotropic rheology in producing the recent and rapid reduction of Arctic sea ice.

The Canadian Arctic Archipelago (defined here to include Nares Strait which borders Greenland) is a key gateway that links the Arctic Ocean to the Atlantic. Changes in the flux of freshened seawater and ice through its channels have the potential to significantly affect the distribution of sea-ice in the Arctic and the strength of the Atlantic meridional overturning circulation, and hence influence both regional and global climate. Yet the ocean circulation in this region is poorly modelled, monitored and understood. This project will use a regional, high-resolution coupled ocean-sea-ice model to determine the dynamics that govern the flow through the archipelago. The processes by and timescales on which this flow adjusts to change in atmospheric forcing will also be identified. There will be a particular focus on Nares Strait, one of the three major channels, a key exporter of multi-year ice from the Arctic, and the subject of a recent intensive observational campaign. This project is timely in that it will provide fundamental physical understanding to aid in the interpretation and extrapolation of results from observational projects funded under the International Polar Year of 2007-2009. The results will allow us to make more confident estimates of the total freshwater flux currently exported through the Canadian Arctic Archipelago, and better founded predictions of future change. The inability of global climate models to represent the important dynamical processes that occur in the high-latitude ocean poses a serious problem for predictions of climate change. Determination of the underlying physical mechanisms and sensitivities is a crucial step towards understanding how these flows might be better represented.

Lancaster University, together with a formidable consortium of industrial and third-sector partners, proposes a Centre for Doctoral Training (CDT) aimed at cultivating international research leaders in Statistics and Operational Research (STOR) through a programme in which real-world challenge is the catalyst for cutting-edge methodological advancement. Our partners face a challenging reality: the demand for highly-trained STOR data specialists consistently exceeds the available supply. This situation is exacerbated by the ever-growing significance of data in both the economy and society. Our proposal directly addresses this pressing demand, focussing on the priority area "meeting a user-need". The newly envisioned Centre builds upon the strengths and knowledge derived from an existing, internationally recognised EPSRC CDT. Expanding upon this foundation and with the input of an enlarged partner network, including blue-chip companies, SMEs, and third-sector organisations, we propose a Centre poised to recruit and train 70 students across five cohorts. This program will harness industrial and charitable challenges as inspirational springboards for conducting the highest calibre research. The new programme will innovate by * Developing a new MRes programme co-designed and delivered with our partners; * Including a comprehensive training programme on advanced, reproducible programming for STOR, co-ordinated by the Centre's dedicated, industry-funded, Research Software Engineer; * Embedding industrial and third-sector collaboration throughout the student experience; * Hosting seeded research clusters: vibrant, cross-cohort, cross-sector retreats to explore and develop early-stage challenges emerging from the shared interests of STOR-i and its partners; * Developing an ambitious doctoral exchange programme with highly regarded international university partners, comprising student exchanges, co-supervision and shared training activities. Our partners play an integral role in the Centre's plans, with 80% of doctoral projects adopting a CASE-like approach, receiving co-funding and co-supervision from industrial partners. All other students will engage in industrial research internships. Additionally, partners will lead problem-solving events, data immersion experiences, and contribute to Continuing Professional Development (CPD) activities such as leadership talks, fireside chats, and advanced programming training. The partnership is deeply committed to ensuring the broader impact of STOR-i as a national resource. To this end, the Centre will establish a suite of funded activities open to all UK STOR doctoral students. These include an annual STOR summer school with an emphasis on leadership skills, advanced programming, and a data dive focused on charitable endeavours. Additionally, students will have access to masterclasses and research visits. STOR-i will deliver a wide range of benefits and scientific outcomes to the end-user community, underpinned by three fundamental pillars: 1. People: Our CDT will inject 70 highly talented, diverse PhD graduates into the field, armed with the technical, interpersonal, and leadership skills essential for flourishing careers in STOR across a range of sectors. These graduates will serve as catalysts for innovation, driving cutting-edge research, and enhancing the UK's economic competitiveness. 2. Knowledge: The CDT will generate a wealth of cutting-edge research, disseminated in top STOR journals, and presented at major international conferences. This research will tackle substantial real-world challenges, yielding fresh insights and breakthroughs in STOR. 3. Impact: Our CDT will make a tangible difference in society and the economy by producing (i) case studies and (ii) a repository of documented and reproducible software, available to the public. This will facilitate widespread adoption of our research, leading to meaningful societal and economic impact.

Lancaster University (LU) proposes a Centre for Doctoral Training (CDT) whose goal is the development of international research leaders in statistics and operational research (STOR) through a programme in which industrial challenge is the catalyst for methodological advance. The proposal brings together LU's considerable academic strength in STOR with a formidable array of external partners, both academic and industrial. All are committed to the development of graduates capable of either leadership roles in industry or of taking their experience of and commitment to industrial engagement into academic leadership in STOR. The proposal develops an existing EPSRC-funded CDT (STOR-i) by a significant evolution of its mission which takes its degree of industrial engagement to a new level. This considerably enhanced engagement will further strengthen STOR-i's cohort-based training and will result in a minimum of 80% of students undertaking doctoral projects joint with industry, up from 50% in the current Centre. Industrial internships will be provided for those not following a PhD with industry. Industry will (i) play a role in steering the Centre, (ii) has co-designed the training programme, (iii) will co-fund and co-supervise industrial doctoral projects, (iv) will lead a programme of industrial problem-solving days and (v) will play a major role in the Centre's programme of leadership development. Industry's financial backing is providing for stipend enhancement and a range of infrastructure and training support as well as helping to bring STOR-i benefits to a wide audience. The total pledged support for STOR-i is over £5M (including £1.1M cash). The proposal addresses the priority area 'Industrially-Focussed Mathematical Modelling'. Within this theme we specifically target 'Statistics' (itself a priority area) and Operational Research (OR). This choice is motivated first by the pervasive need for STOR solutions within modern industrial problems and second by the widely acknowledged and long standing skills-shortage at doctoral level in these areas. Our partners' statements of support attest that the substantial recent growth in data acquisition and data-driven business and industrial decision-making have signalled a step change in the demand for high level STOR expertise and have opened the skills gap still wider. The current Centre has demonstrated that a high quality, industrially engaged programme of research training can create a high demand for places among the very ablest mathematically trained students, including many who would otherwise not have considered doctoral study in STOR. We believe that the new Centre will play a yet more strategic role than its predecessor in meeting the persistent skills gap. Our training programme is designed to do more than solve a numbers problem. There is an issue of quality of graduating doctoral students in STOR as much as there is one of quantity. Our goal is to develop research leaders who are able to secure impact for their work across academic, scientific and industrial boundaries; who can work alongside others who are differently skilled and who can communicate widely. Our external partners are strongly motivated to join us in achieving this through STOR-i's cohort-based training programme. We have little doubt that our graduates will be in great demand across a wide range of sectors, both industral and academic. The need for a Centre to deliver the training resides primarily in its guarantee of a critical mass of outstanding students. This firstly enables us to design a training programme around student cohorts in which peer to peer learning is a major feature. Second, we are able to attract and integrate the high quality contributions (both internal and external to LU) we need to create a programme of quality, scope and ambition.