Wetlands are a C paradox, with huge capacity for C storage but also a strong source of C as methane (CH4). Wetlands worldwide are highly sensitive to climate change, given dependencies on rain and groundwater. Seasonal shifts in saturation have potential to lead to highly dynamic greenhouse gas (GHG) fluxes, in the form of CO2 and CH4. Tropical wetlands are particularly understudied but make outsized contributions to the global CH4 budget. Though Brazil has vast wetlands, previous work largely focused on permanently saturated systems, e.g., Amazon or Pantanal floodplains. In contrast, the poorly studied Cerrado domain includes permanent (peatlands) and seasonally inundated (seasonal grasslands), wetlands and dry grasslands, with saturation shifting seasonally and episodically. Climate change is likely to bring warmer and drier conditions to this region, increasing areas of seasonal and dry grasslands and concomitant potential increases in GHG emissions. Given current paucity of observations and importance of predicting future of these critical seasonal systems, it is vital to determine mechanisms driving spatial and temporal shifts in GHG emissions and C storage across the saturation gradient, as well as changes in spatial extent of these systems through time. Our overall objective is to overcome these knowledge gaps in sites in Brazil. Our combined field and modeling approaches in Cerrado tropical grasslands will focus on answering three questions: Q1. What are the drivers of spatial and temporal heterogeneity in sea storage and flux across saturation gradients? Q2. How does saturation extents (areas and perimeters) in tropical grasslands change over box? Seasonal and decadal scales? Q3. How will rates and forms of sea emissions from tropical grasslands change under future climates? To test Q1, spatially distributed measurements will be coupled with high-temporal resolution measurements to understand GHG, soil and vegetation C dynamics across the saturation gradient. GHG variability will be measured spatially with static flux chambers and temporally with automated chambers. Site changes through time will be determined by initial soil characterization, combined with seasonal measurements of plant phenology, stomatal conductance, porewater chemistry, baseline climate and groundwater seasonally. To test Q2, high resolution remote sensing and field reference data will be combined to map wetland extent, seasonally 14C and 210Pb dating will be used to understand wetlands extent changes at decadal scales. To test Q3, data from Q1 and Q2 will be utilized in simulations with E3SM Land Models coupled to PFLOTRAN reactive transport models. Estimates of C sequestration patterns and GHG emissions will be spatially simulated with projections of C balance changes and GHG with expected shifts in regional climate and hydrology.

Many aquatic and marine organisms have a planktonic phase in their life history and spend the first days or weeks of their life drifting in plankton. Plankton may be carried great distances by ocean currents and enable new areas to be colonised and genes to be exchanged between apparently quite distant populations. Not surprisingly, the occurrence of a planktonic life phase strongly influences many evolutionary and ecological processes including the global distribution of species, the creation of new species, and the persistence of individual populations. The latter is particularly important for conservation. For example, lobsters in Cuba may launch their offspring into the plankton which later arrive in Florida. In this case, the number of lobsters in Florida may be highly dependent on the number of adult lobsters in Cuba and the populations require management at large scales. Understanding levels of larval exchange is vital for biodiversity conservation and fisheries management but few data are available. Two approaches are usually taken to infer levels of larval connectivity. The first uses detailed oceanographic models to predict the dispersal of 'virtual larvae' in ocean and coastal currents. The second examines the genetic structure of populations and identifies scales where little larval exchange occurs (i.e. relatively isolated populations). Rarely have both approaches been integrated, largely because of the challenges in sampling organisms across relevant spatial scales and the computational complexity of creating spatially-realistic models of circulation. However, it is highly desible to combine both approaches as they offer great synergy. In this proposal, we combine large-scale sampling of genetic structure with a state-of-the-art model of larval dispersal (published by our collaborators in Science earlier this year). We examine the genetic structure and larval connectivity of the massive coral Montastraea annularis which is found throughout the Caribbean Sea. Working with this coral has a number of advantages. Perhaps most importantly, its natural history is relatively easy to model which lends itself to modelling larval dispersal. Therefore, we are able to perform one of the clearest tests possible for agreement between modelled larval dispersal and observed genetic diversity. We have sampled the genetic diversity of M. annularis throughout the Caribbean Sea and will compare the observed patterns of gene flow to predicted levels of larval connectivity. Insight from this project will also support on-going activities to model the metapopulation dynamics of this important coral and design more appropriate algorithms for the selection of marine reserve networks.

Oceanic flows have been traditionally decomposed into two main components, "large-scale" and "mesoscale/small-scale" ("eddy"), which is in part motivated by the fact that most numerical ocean models under-resolve the eddy component. The eddy fluxes then have to be parameterized, and the most common approach is to use the flux-gradient relation with the eddy diffusivity (tensor) coefficient. The simplicity of this relation is appealing, but the main challenge lies in finding the appropriate diffusivity tensor, which varies with geographical location and depth (i.e., inhomogeneous) and time, and is direction-dependent (i.e., anisotropic), as evidenced by observation- and GCM-based estimates. The overarching goal of the proposed study is to explore properties of the inhomogeneous and anisotropic eddy-induced transport at mid-latitudes, and to examine their importance for tracer distribution. The main hypotheses are that the eddy-induced transport can be succinctly quantified by a spatio-temporal map of the eddy diffusivity tensor, and that this complexity can be systematically reduced to its most essential properties. Specifically, we will objectively calculate and analyze the corresponding eddy-diffusivity tensor maps and explore the dependence in the results on the spatial and temporal scales. This diffusivity map will then be used to produce tracer distributions in flows that do not fully resolve the eddy-driven tracer advection, and the resulting skill will be quantified using relevant metrics. We will employ a hierarchy of eddy-resolving numerical simulations, real-ocean drifter trajectories and a wide range of scale-aware flow decompositions, as well as several novel methods for estimating and interpreting the eddy diffusivity tensors for both fundamental understanding and practical purposes. The study will capitalize on the synergy of existing intensive and efficient collaboration between the US and UK members of the research group.

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial component of the climate system due to its role in heat and salt transports, as well as its role in transporting and storing carbon. Variability in the strength of AMOC has been linked to important climate impacts, for instance, the number of Atlantic Hurricanes, anomalous Sahel precipitation, and European weather. Therefore, improved predictions of the AMOC would have important societal benefits. Despite its importance, the predictability of the AMOC remains relatively unexplored on timescales from one season to 10 years ahead, and many uncertainties persist in our understanding of AMOC variability. For example, we are unsure of the relative importance of different processes in driving AMOC variability on different timescales and latitudes, nor how predictable they are in state-of-the-art forecasting systems. Recent studies have provided considerable evidence that the atmospheric circulation in the North Atlantic is much more predictable than previously thought on these timescales. However, the predicted signals are far too small (the so-called signal-to-noise paradox) and predictions need to be calibrated to provide credible forecasts of society relevant variables, such as surface temperature. Given that atmospheric circulation is a key driver of AMOC, then it follows that AMOC predictions on these timescales may also suffer from similar signal-to-noise issues. Furthermore, predictions of AMOC, and its climate impact, could be improved by extending the published statistical calibrations to the ocean circulation. ALPACA will utilise AMOC observations (RAPID and OSNAP) and observation-based AMOC reconstructions to assess the quality of current AMOC forecasts in state-of-the-art seasonal and decadal prediction systems. Furthermore, we will evaluate the processes that contribute to skill and assess their consistency across models. We will also use new simulations to better understand the relative roles of different processes in driving observed variability on different timescales, and we will leverage new large ensemble simulations to quantify the role of external forcing in driving AMOC variability and change. Finally, by exploiting this new understanding, we will determine whether seasonal-to-decadal predictions of AMOC and its climate impacts can be improved through physically-consistent statistical calibrations that reduce the signal-to-noise errors in predictions. ALPACA is a collaboration between the National Centre for Atmospheric Science at the University of Reading, The National Oceanography Centre Southampton, The University of Exeter, and the Met Office Hadley Centre from the U.K., and The National Center for Atmospheric Research and the University of Miami, from the U.S, and the Barcelona Supercomputing Center from Spain.

The Atlantic Meridional Overturning Circulation (AMOC) is a system of ocean currents that circulate water around the Atlantic Ocean. It is a vital part of the Earth's climate system, playing a significant role in regulating global climate and weather patterns. We need to Continuously observe the AMOC for several reasons: 1. Understanding Climate Change: Continuous observations of the AMOC help scientists better understand how climate change affects the ocean's circulation and heat transport. By continuously monitoring the AMOC, researchers can identify changes in its intensity, speed, and location, which can help them make more accurate predictions about future changes in the climate. 2. Improving Climate Models: Continuous observations of the AMOC help improve climate models by providing data to validate and refine model predictions. This information can help scientists make more accurate projections about the effects of climate change on various aspects of the Earth's ecosystem, such as sea levels, ocean acidity, and weather patterns. 3. Detecting Abrupt Changes: Abrupt changes in the AMOC could have significant impacts on the Earth's climate and weather patterns. Continuous monitoring of the AMOC can help scientists detect such changes early, allowing for timely intervention and mitigation strategies to be put in place. 4. Understanding Ecosystems: The AMOC plays a critical role in regulating oceanic ecosystems, and continuous observations can help researchers better understand how changes in the AMOC affect marine life, such as plankton, fish, and other organisms. 5. Predicting Extreme Weather Events: The AMOC has a significant impact on weather patterns, and continuous monitoring can help researchers make more accurate predictions about extreme weather events, such as hurricanes, floods, and droughts. Overall, continuous observations of the AMOC are essential for understanding the Earth's climate system and its impact on various aspects of our planet, including the environment, ecosystems, and human societies. By continuously observing the AMOC, we can improve our understanding of the Earth's climate system, and better predict and prepare for the effects of climate change. The AMOC has been observed at 26N between Florida and Africa since 2004. This is heavily reliant on tall moorings in the water and research ships to collect the data and replace the moorings. In this programme, we will exploit new technologies to design a fit-for-purpose sustainable AMOC observing system at substantially lower cost than at present and deliver data back via satellite. This will allow us to deploy an optimised lower-cost 26N AMOC observing system from 2027.