The goal of this innovative, inter-disciplinary project is to explain the role of mineral dust during key climate periods through its feedbacks on the global carbon cycle and climate. Mineral (desert) dust is a major component of the natural aerosol load globally. It impacts the climate system directly by interacting with solar and terrestrial radiation, and indirectly impacts clouds, surface albedo, and biogeochemical cycles. Paleodust records from land, ice and oceans show significant variability, both in the alternation of glacial/interglacial cycles, and on shorter time scales, with potential impacts on climate change in the 21st century. The state-of-the-art IPSL-CM5 Earth System Model will be our main research tool, with simulations grounded in an extensive compilation of observational data, constraining the mass balance as well as the physical and compositional properties of dust. Mentoring from supervisors will provide fertile grounds for training the candidate through mobility, ensuring both the realization of this project and his career development. The consistent organization of size- and time-resolved dust mass accumulation rates data since the last interglacial period (130 ka) into an innovative database, will provide a research tool fulfilling an emerging necessity in the climate scientific community, that will be freely shared/disseminated. An integrated observation-modelling approach will allow reconstructing the dust cycle for the Last Glacial Maximum period$. The expected output of the project will fill a gap in the quantitative understanding of the global dust cycle, studying its impacts in the future with direct impacts on reducing the uncertainty in aerosol feedbacks on climate. Therefore, it fits the goals of the key Horizon 2020 Environment & Climate Action: “Developing climate modelling and science for climate services to help provide trustworthy science-based information to government, public and private decisiion-makers".

Soil is both the largest sink and source of organic carbon (C) exchanged with the atmosphere. These exchanges result from biological processes, the primary source being the decomposition of soil organic matter (SOM), which is controlled by physical factors such as climate. As such, soil C emissions are very vulnerable to climate change but can also be reduced with new land management practices if we can predict the outcomes of soil carbon-climate feedbacks. However, predictions from the existing large-scale soil C models strongly diverge, and reveal large uncertainties in the processes and controls at play. One of these uncertainties is the effect of change in precipitation regimes on SOM decomposition mediated by soil microorganisms. Functions describing the decomposition response of soil carbon to soil moisture are static in current large-scale models, yet recent empirical studies show that decay responses under new soil moisture conditions can change due to shifts in microbial communities. Recent evidence suggests that evolution is a key processes driving these shifts in microbial communities. This project proposes to integrate variable decomposition-moisture functions into a large-scale soil C model to reflect precipitation history and carbon substrate influence on microbial responses to changing soil moisture. These functions will be calculated from a mechanistic microbial model that accounts for both ecological and evolutionary processes. The mechanistic model will be an updated version of the trait-based model DEMENT developed by the fellow’s supervisor at the partner institution (UC Irvine). The moisture response functions will be integrated into a commonly used soil carbon model, RothC, that has been incorporated into the global land surface model (ORCHIDEE) of the host institution (LSCE).

Soil organic matter is the largest land carbon (C) pool, vulnerable to land-use change and climate change. Soil C models are used to assess current organic C stocks and make predictions under future conditions. These models are typically developed to make predictions over centennial timescales. Given the ‘4 per mil’ initiative, there is now a critical need for annual-to-decadal soil C stock predictions to evaluate land management decisions and hold participants accountable to stated goals. The project proposes a new soil model framework to make predictions at annual-to-decadal timescales by developing a Bayesian forecasting model from a deterministic soil carbon model with the capacity to ingest multiple data types, propagate uncertainty from data and parameters into predictions, and update predictions when new data become available. The main focus is probabilistic prediction of soil C changes under land use and climate change for the next two decades. Specifically, the project plans build a forecasting model version of the Millennial model, recently developed by the researcher with University colleagues. The Millennial model is an evolution of the commonly used soil C model Century – also incorporated in the global land surface model (ORCHIDEE) of the host institution (LSCE) - but in contrast to Century, Millennial includes soil pools that correspond directly to measurements. First, we will develop the Bayesian calibration of modeled temperature response against warming experiment data, using the Millennial model to integrate measurements from the multi-national, collaborative whole-soil warming experiments FORHOT and BBSFA. Then, we will develop the Bayesian calibration of the modeled land management response against field data with different amounts and quality of added litter. We will then incorporate this new model into ORCHIDEE to predict soil C storage for near term land-based mitigation objectives of the Paris Climate Agreement.

The presence of organic compounds was essential to the emergence of life on Earth 3.5 to 3.8 billion years ago. Such compounds may have had several different origins; amongst them the ocean-atmosphere coupled system (the primordial soup theory), or exogenous inputs by meteorites, comets and Interplanetary Dust Particles. Titan, the largest moon of Saturn, is the best known observable analogue of the Early Earth. I recently identified a totally new source of prebiotic material for this system: the upper atmosphere. Nucleobases have been highlighted as components of the solid aerosols analogues produced in a reactor mimicking the chemistry that occurs in the upper atmosphere. The specificity of this external layer is that it receives harsh solar UV radiations enabling the chemical activation of molecular nitrogen N2, and involving a nitrogen rich organic chemistry with high prebiotic interest. As organic solid aerosols are initiated in the upper atmosphere of Titan, a new question is raised that I will address: what is the evolution of these organic prebiotic seeds when sedimenting down to the surface? Aerosols will indeed undergo the bombardment of charged particles, further UV radiation, and/or coating of condensable species at lower altitudes. I expect possible changes on the aerosols themselves, but also on the budget of the gas phase through emissions of new organic volatiles compounds. The aerosols aging may therefore impact the whole atmospheric system. An original methodology will be developed to address this novel issue. The successive aging sequences will be experimentally simulated in chemical reactors combining synchrotron and plasma sources. The interpretation of the experimental results will moreover be supported by a modelling of the processes. This complementary approach will enable to decipher the aerosols evolution in laboratory conditions and to extrapolate the impact on Titan atmospheric system.