Soil organic carbon (SOC) is one of the most important organic C pools on Earth with C turnover ranging from days to millennium. In Earth system models (ESMs), which are models aiming at simulating all the relevant aspects of the Earth system (climate, ocean circulation, greenhouse gases exchanges, etc.), simulations start from an equilibrium state assuming that the full system was at equilibrium before the simulations. But due to long SOC turnover, soil disturbances (e.g. land use change) that have taken place long time ago may still impact the SOC dynamics today. Thus, this equilibrium state assumption is not valid and therefore all current ESMs simulations are based on an incorrect conceptualization of the system. Moreover, in ESMs, the soil is still represented based on pools that are not measurable, making parameterization and evaluation of this key ESM component’s climate impact difficult. The big challenge of EASIER is to propose a new initialization procedure for soil in ESMs. To fulfil this aim, I need to i) develop a soil model using measurable pools included in an ESM that has contributed to IPCC assessment reports for the past two decades; ii) to carry out field measurements of these soil pools using Rock-Eval analysis, a technology initially developed in oil industry and which I previously helped adapt to soil science ; iii) answer basic questions on the stabilization mechanisms of SOC using a combined measurement of 14C and CO2 with different aggregate compositions; iv) run European-level simulations with this new non-soil-equilibrium ESM to study the climate-carbon feedbacks due to soil . EASIER presents a highly original approach that will propose several innovative solutions leading to numerous breakthroughs in the field of carbon/climate feedbacks.

Nutrition, health, well-being… How can we get policy relevant and multi-thematic information on the typology and evolution of urban systems? Polls, surveys, census and… sewers! Inspired by latests developments in Sewage Chemical-Information Mining, and consolidated by our recent successes, the EGOUT project aims at developing an objective, reactive and integrated observatory of the evolution of Paris by monitoring a large array of geochemical indicators and confront them to socio-economic metrics. We will produce maps of geochemical heterogeneities that will be analysed in the light of socio-economic. We will monitor the evolution of selected indicators to track socio-economic trajectories. Citizens, public policymakers and practitioner’s involvement will be ensured through a co-design and a participatory research action process.

Methane (CH4) emissions have to be reduced to reach the Paris Agreement objectives. Modeling approaches assimilating observations are expected to verify the effectiveness of mitigation policies by monitoring sectoral and regional emission changes based on atmospheric observations. These “top-down” approaches are appropriate to estimate CH4 net flux at the surface; however, they have critical weak points: an incomplete prior knowledge of CH4 natural emissions, and difficulties in disentangling overlapping sources from different sectors. The main natural sources of CH4 are inland water emissions: wetlands, peatlands, lakes, ponds… While global wetland emissions gridded products exist, none are available for other inland water systems, which are thus missing in the prior description of emissions forcing top-down approaches. Global estimates of inland water systems remain not consistent in terms of localization and magnitude. Thus, dynamical maps of these areas and their emissions based on same data sets, are necessary to ensure consistency and reduce uncertainties in CH4 emission estimates. 3D top-down approaches retrieving CH4 emissions are based on total CH4 observations only, at surface stations or from remote sensing, and solve for net fluxes at the surface or few individual sectors. However, CH4 observations are not sufficient to properly distinguished between sectors. Isotopic or co-emitted species such as ethane for oil and gas emissions, or carbon monoxide for biomass burning, could help better discriminating the emissions from different sectors and their trend. For this, the project coordinator’s team has newly developed an inverse system (CIF-LMDz-SACs) that integrates isotopic observations to constrain CH4 emissions. The AMB-M3 project aims at 1) producing the first consistent gridded product of CH4 emissions from wetlands, peatlands and lakes to be used for CH4 sources analysis and as input to global top-down models, and 2) discriminating CH4 sources by improving the capabilities of an inverse systems. In the first work package we will develop dynamical and consistent gridded CH4-centric inland water maps including information on vegetation and soil types (carbon content), over 1995-2020 based on the GIEMS-2 inundated data set. GIEMS-2, covering already 1995-2015, provides inundated extent and dynamics under dense vegetation, produces consistent surface water in semi-arid regions and accounts for satellite inter-calibration issues. Then CH4 emissions based on this new product will be estimated by upscaling density fluxes from the literature. Developments on CH4 emissions from wetlands and northern peatlands have been done in different versions of the ORCHIDEE land surface model. For the project, they will be integrated into the trunk version of the ORCHIDEE model to better simulate CH4 emissions and compared to previous estimations. The second work package will demonstrate the potential of adding ethane and carbon monoxide within the CIF-LMDz-SACs surface data constrained global inversions. Analyses will be performed to optimally combine different tracer constraints from both surface and satellite observations to take advantage of all available observations for regional analysis. Finally, AMB-M3 project will provide new estimates of CH4 sources and sinks at the global and regional scales over 2010-2020 based on up-to-date bottom-up (ORCHIDEE) estimates and top-down modelling system.

Forecast is a major task of statistics in many domains of application. It often takes the form of a probabilistic forecast where the so-called predictive istribution represents the uncertainty of the future outcome given the information available today. Of particular interest is the distributional forecast of rare and extreme events, for instance environmental hazards such as flooding or heat waves, that can have major socio-economic consequences but for which current prediction are often inaccurate and unsatisfactory. In meteorology and weather forecast, the current numerical weather prediction (NWP) models are rather skillful for non-extreme weather events but often fail to provide accurate predictions on extreme weather events. One objective is to derive new statistical post-processing methods that are tailored to the output of existing NWP models in order to improve the forecast of extreme weather. Tree-based method such as generalized random forest for extremes will be developed. Data sets required to develop and validate the new proposed methods will be provided by the Centre National de Recherches Météorologiques (CNRM). In energy and electricity consumption forecast, balancing production and demand is a major concern and forecasting the demand and especially its peaks is crucial. Electricit\'e de France (EDF) has developed a strong expertise where one key tool is the possibility to combine different models to improve prediction. Aggregation of experts is another main direction of the T-REX project with an emphasis on specialized/sleeping experts that focus on extreme regimes. EDF will provide relevant data sets for electricity consumption forecast. Extreme value theory (EVT) provides a theoretical framework for risk assessment and mathematically justified estimation of rare event probabilities. The T-REX project is rooted in EVT and will bridge different research fields to improve probability forecasts of extremes from complex and high dimensional systems.

On the Eastern Steppe of Central Asia, the period spanning the Late Bronze Age (1200–700 BCE) and the Early Iron Age (700–400 BCE) witnessed major societal changes, characterised by an increase in social differentiation and interconnectivity. The adoption of mounted pastoralism during the Late Bronze Age may have played a major role in this transformation. At that time, the horse had already been domesticated. It was harnessed, ridden, and probably used for traction, making it possible for the population to travel greater distances. The overwhelming presence of the horse in funerary and ritual monuments clearly indicates that this animal was of central importance and may have been an integrative component of ceremonial activities linking regionally distinct groups. MOBISTEPPE aims to provide an unprecedented understanding of the role played by horses in interactions among the pastoral communities of Mongolia in the Late Bronze Age. This aim will be achieved through the multi-isotopic (C, O, Sr) analysis of the teeth of ancient horses to investigate pastoral mobility. Our hypothesis is that isotopic analysis can be used to reconstruct the size of the territory used by local human communities, and to thereby illuminate the social differentiation and multi-scalar connections among them. However, current limitations in knowledge on the incorporation of Sr isotopes into enamel, the geospatial distribution of isotopic signatures in the environment, and the availability of ancient horse material have prevented testing of this hypothesis. To remove these barriers and thus address the impact of domestic animals in the structuration of past human societies, MOBISTEPPE will combine animal feeding experiments, archaeology, movement ecology, social anthropology, and state-of-the-art isotopic and geospatial analyses and modelling. Specifically, we will (1) conduct controlled feeding experiments on sheep to document how fast the C and Sr isotopic composition of the diet is recorded in the animals’ tissues; (2) characterise and map the variability in Sr isotope values and mobility patterns of different nomadic herder families living in Central Mongolia today using GPS collars to infer the isotope record of mobility in the teeth of the modern herd animals; and (3) excavate, study, and isotopically analyse ancient horse remains from a large number of archaeological structures in that same region. Controlled feeding experiments will provide ground-breaking theoretical results that will be widely applicable in archaeology and beyond. Our work, which is set in an archaeologically rich region, will produce the first Sr isoscape in Eurasia that will be permanently stored and made freely available. This rigorous mapping and modelling approach is at the frontier of Sr isotope research and will be of great interest to the wider scientific community. MOBISTEPPE will bridge the social, natural, and geological sciences and produce new data in at least four disciplines (archaeology, biogeochemistry, ecology, and social anthropology), which will be made available to the widest possible audience. Besides traditional activities aimed at the scientific community (publication and presentation of the scientific results, organisation of an international workshop), MOBISTEPPE contains an ambitious dissemination programme, including (1) training and teaching of students and young researchers (recruited by the project and participating in excavations) and (2) transfer of knowledge to the general public (through a public website, documentary film, miniseries, and photo exhibition).