The Atlantic meridional overturning circulation (AMOC) - part of the so-called 'ocean conveyor belt' - is a key component of Earth's climate system. It involves the northward transport of warm surface waters to the high latitude North Atlantic, where they cool (releasing heat to the atmosphere), sink and flow back southwards at depth. Changes in the AMOC are thought to alter global temperature and precipitation patterns, regional sea-level, and socio-economically important marine ecosystems. There are concerns regarding the strength and stability of AMOC in the future. This is because predicted surface ocean warming and freshening could weaken the formation of dense water that helps drive the AMOC. Earlier research suggests that the AMOC may have different stable states, raising the possibility that the AMOC could rapidly switch to a weaker, or even an 'off', state, having a severe impact on global climate. IPCC models do not predict an abrupt weakening of the AMOC under typical 21st century scenarios; yet there are suggestions that current climate models may be excessively stable. NERC and the international community have invested heavily in monitoring the AMOC, including the implementation of the RAPID array since 2004 and more recently the OSNAP array. Since observations began in 2004, AMOC has weakened at a rate ten times faster than predicted by most models. Yet the extent to which this decline can be attributed to natural multi-decadal variability is uncertain. The limited time span of the RAPID array means we are unable to gain an understanding of the nature of AMOC variability on timescales longer than interannual-to-decadal. Therefore we must turn to geological archives to reconstruct AMOC changes beyond the instrumental record. Yet there are no existing records to provide perspective on recent AMOC variability at multi-decadal and longer timescales. Using recent, novel techniques to constrain past variability, coupled with exceptional sediment archives, ReconAMOC will constrain past AMOC variability on decadal to centennial timescales, generating records for the last 7000 years that will become benchmark constraints on AMOC behaviour. We will focus on the past 7000 years because the climate was not dramatically different to the present day, and remnant glacial ice sheets had melted away so that the major features of deep Atlantic circulation were broadly similar to modern. ReconAMOC deploys a twin approach that utilizes (i) the characteristic subsurface temperature AMOC fingerprint, and (ii) the deep western boundary current response to AMOC change. We have verified these new paleoclimate approaches against variability in the instrumental record and demonstrated their applicability through an extensive pilot study. ReconAMOC is therefore a low risk yet ambitious project, bringing together an international team of collaborators, that will meet a long-sought and much-needed requirement of a wide range of climate scientists and modellers. ReconAMOC will enable testing and improvement of model simulations of AMOC that help facilitate assessment of the vulnerability of the AMOC to climate change, and permit the investigation of the role of AMOC on other components of the climate system. The topics addressed by ReconAMOC are key research targets at national UK (e.g. identified strategic science themes and goals within the NERC strategy) and international (e.g. CMIP6, IMAGESII, SCOR, PAGES, IODP and NSF) levels. Specifically, the ReconAMOC proposal builds on the NERC programmes RAPID, RAPID-WATCH, and RAPID-AMOC, in which interannual to multi-decadal variability in the AMOC is a central focus, as well as NERC programme ACSIS examining interannual to decadal climate variability in the Atlantic.

Our vision for this Transition Award is to leverage and combine key emerging technologies in Artificial Intelligence (AI) and Engineering Biology (EB) to enable and pioneer a new era of world-leading advances that will directly contribute to the objectives of the National Engineering Biology Programme. Realisation of the benefits of Engineering Biology technologies is predicated on our ability to increase our capability for predictive design and optimisation of engineered biosystems across different biological scales. Such a scaled approach to Engineering Biology would serve to significantly accelerate translation of scientific research and innovation into applications of wide commercial and societal impact. Synthetic Biology has developed rapidly over the past decade. We now have the core tools and capabilities required to modify and engineer living systems. However, our ability to predictably design new biological systems is still limited, due to the complexity, noise, and context dependence inherent to biology. To achieve the full capability of Engineering Biology, we require a change in capacity and scope. This requires lab automation to deliver high-throughput workflows. With this comes the challenge of managing and utilising the data-rich environment of biology that has emerged from recent advances in data collection capabilities, which include high-throughput genomics, transcriptomics, and metabolomics. However, such approaches produce datasets that are too large for direct human interpretation. There is thus a need to develop deep statistical learning and inference methods to uncover patterns and correlations within these data. On the other hand, steady improvements in computing power, combined with recent advances in data and computer sciences have fuelled a new era of Artificial Intelligence (AI)-driven methods and discoveries that are progressively permeating almost all sectors and industries. However, the type of data we can gather from biological systems does not match the requirements for off-the-shelf ML/AI methods and tools that are currently available. This calls for the development of new bespoke AI/ML methods adapted to the specific features of biological measurement data. AI approaches have the potential to both learn from complex data and, when coupled to appropriate systems design and engineering methods, to provide the predictive power required for reliable engineering of biological systems with desired functions. As the field develops, there is thus an opportunity to strategically focus on data-centric approaches and AI-enabled methods that are appropriate to the challenges and themes of the National Engineering Biology Programme. Closing the Design-Build-Test-Learn loop using AI to direct the "learn" and "design" phases will provide a radical intervention that fundamentally changes the way that we design, optimise and build biological systems. Through this AI-4-EB Transition Award we will build a network of inter-connected and inter-disciplinary researchers to both develop and apply next-generation AI technologies to biological problems. This will be achieved through a combination of leading-light inter-disciplinary pilot projects for application-driven research, meetings to build the scientific community, and sandpits supported by seed funding to generate novel ideas and new collaborations around AI approaches for real-world use. We will also develop an RRI strategy to address the complex issues arising at the confluence of these two critical and transformative technologies. Overall, AI-4-EB will provide the necessary step-change for the analysis of large and heterogeneous biological data sets, and for AI-based design and optimisation of biological systems with sufficient predictive power to accelerate Engineering Biology.