Retrouvez tous les événements.
L'exploitation des données anciennes
24/11/2026 14:00
Le thème « Système Solaire » de l’IPSL vous convie à une demi-journée consacrée à l’exploitation des données anciennes le mardi 24 novembre 2026.
IPSL Masterclass: “(Paleo)Climate Modelling for experimental scientists”
14/10/2026 11:54
The masterclass is aimed at PhD students and postdocs coming from an experimental background who want to understand how a climate model works and how they might use climate model output in their own research. All career stages are welcome to attend, though priority will be given to PhD students/postdocs if capacity becomes limiting.
Retrouvez tous les séminaires.
Prévisions, projections, scénarios : quelles informations climatiques sont utiles pour les opérations humanitaires ?
27/10/2026 18:30
L’ONG Médecins Sans Frontières organise une conférence le mardi 27 octobre prochain, pour croiser le regard de climatologues et d’humanitaires sur les outils de prévision climatique et leur usage opérationnel.
Model forecast representation of hazardous Mediterranean cyclones
27/10/2026 11:00
Séminaire du LMD à l’ENS-PSL
End-to-end learning of river discharge prediction largely resolves the equifinality of distributed river discharge prediction systems over Japan
22/10/2026 11:00
Data-driven models that jointly train runoff generation and river routing to reproduce in-situ discharge achieve high river discharge prediction accuracy while remaining spatially,consistent and mass-conservative. To tackle the problem of equifinality in hydrology modelling, we ask whether ML systems trained end-to-end can nonetheless identify a physically consistent causal structure.
Retrouvez toutes les soutenances de thèses et de HDR.
Deep learning and rare event sampling for the study of weather extremes impacting the power system
20/10/2026 14:00
The transition toward energy systems with a high share of renewable energy increases their vulnerability to rare extreme weather events, which can compromise the stability of power grids.
In the context of climate change, the resilience of these systems depends on our ability to anticipate, characterise, and quantify the uncertainty associated with these phenomena. This thesis develops advanced numerical methods to improve the modelling and prediction of extreme events with high energy impact: winter cold spells, compound events generating high peaks in residual demand, and, more specifically, heatwaves at mid-latitudes.
The study of these rare events faces a structural problem of data scarcity. Historical observations are limited, and traditional physical climate models require massive simulations to robustly sample very low-probability events. This leads to prohibitive computational costs and limits the accuracy of return period estimates.
To address these limitations, this work explores low-cost prediction and simulation approaches for rare events, building on recent advances in deep learning.
Three complementary directions are developed: (i) the design of direct prediction models able to learn from limited data, with particular attention to their physical interpretability; (ii) the design of climate model emulators able to efficiently reproduce their dynamics at reduced cost, examining in particular their ability to extrapolate beyond the training range for extreme events; (iii) the adaptation of rare event simulation algorithms, combining these emulators with physical models to guide the exploration toward trajectories leading to extreme weather events.
The methodological developments are first validated on climate models of intermediate complexity before being applied to state-of-the-art models, opening the way for future applications related to climate change adaptation.
Global food systems’ effects on planetary health
22/10/2026 10:00
Food systems being highly complex socio-environmental systems, the solutions to improve their environmental and health outcomes require employing innovative inter-disciplinary approaches to understand the interactions and dynamics within them.
In my research career, I have used multi-disciplinary approaches to study global food systems and environmental sustainability with a broad objective of identifying feasible solutions to improve the health and environmental outcomes of global food systems. I have acquired extensive experience in combining complex, trans-disciplinary datasets, and now possess a solid understanding of the underlying processes in both Earth system and economic models.
In addition, I have proven expertise in analysis and scenario runs for complex systems (e.g. complex network theory), network modelling, projections and optimization.
I have also obtained independent and collaborative research funding, which enabled me to build a team. I have gained experience in the estimation of other environmental pressures from agricultural production, such as the use of land, fertilisers, and the emissions of greenhouse gases.
I also worked on evaluating the impacts of food systems on biodiversity (via land use, fertiliser use and greenhouse gas emissions) and on nutritional health aspects. I used inter-disciplinary methods: biophysical models, trade modelling, resource efficiency and sustainability; and statistics applied to large spatially-explicit datasets. This expertise is demonstrated in publications in world-leading academic journals, and recognised by several prizes, awards, funding successes (including a UK NERC Independent Research Fellowship and an ERC Starting Grant), membership of the Editorial Board of a major interdisciplinary environmental science journal, and invitations to deliver keynote talks at international scientific conferences.
Here, I outline the research achievements I have attained during my Ph.D., postdoctoral research, and as PI (since 2016), along four main research avenues centered on the spatially-detailed assessment of global food systems’ effects on planetary health:
(i) Water for food: virtual water trade networks,
(ii) Sustainability of agricultural water use and trade,
(iii) Multi-dimensional assessment of food systems environmental sustainability, and
(iv) Food systems and planetary health.
Interaction between Callisto’s neutral and ionized environments and Jupiter’s magnetosphere: observations and modeling
01/10/2026 14:00
Among Jupiter’s four Galilean moons, Callisto orbits at the greatest distance from the giant planet. With its ancient, heavily cratered surface, its still poorly understood internal structure, and probable subsurface ocean, Callisto is undoubtedly the most mysterious of the Galilean satellites to date. The flybys of the icy moon performed by the Galileo spacecraft, supplemented by remote observations, have provided insights into its characteristics, notably its tenuous atmosphere, dominated by O2 and CO2, its relatively dense ionosphere and its subsurface ocean. As it orbits through Jupiter’s magnetosphere, Callisto encounters varying magnetospheric environments with which it interacts through processes such as surface erosion and the formation of Alfvén wings. This variability in the magnetospheric environment at Callisto’s orbit is primarily driven by the tilt of the Jovian magnetic dipole relative to the gas giant’s rotation axis, resulting in significant excursions in magnetic latitude. Temporal variations in the magnetic field surrounding the Galilean moon trigger electromagnetic induction within its conductive layers, specifically its ionosphere and subsurface ocean. Measurements of the magnetic field induced in the latter by the Galileo mission’s magnetometer confirmed the existence of a liquid water layer beneath the icy crust.
With ESA’s JUICE and NASA’s Europa Clipper missions currently en route to Jupiter and its icy moons, and scheduled to perform numerous flybys of Callisto, it is essential to prepare for their arrival by combining observations and simulations. This approach will help advance our understanding of the icy moon and its interaction with Jupiter’s magnetosphere.
Following the Galileo mission in the late 1990s, the ongoing Juno mission became the second to orbit the giant planet. Although it did not perform any flybys of Callisto, unlike its predecessor, it crossed the moon’s orbit numerous times in its absence. These crossings, often referred to as “ghost flybys”, provide an opportunity to probe the pristine magnetospheric conditions the moon encounters along its orbital path. By combining measurements from magnetometers and particle detectors (electrons, heavy ions) on both missions, it is possible to quantify plasma and magnetic field parameters and study their variability. This work has highlighted significant variability linked to magnetic position, itself modulated by local time, with increased variability observed on Jupiter’s dawnside.
Simulations describing the interaction between Callisto’s neutral and ionized environments and the Jovian magnetosphere have also been developed. These use the parallelized LatHyS code, enabling 3D multi-species hybrid simulations of the interaction. Given the large gyroradii of ionospheric ions captured by the Jovian field at Callisto, a kinetic description of the plasma’s ionic component is essential to capture the asymmetries of the plasma interaction. Two key developments were achieved: a self-consistent model of ionospheric resistivity was developed, along with a coupling established between LatHyS and an exospheric model. Simulations performed with LatHyS highlight the diverse interaction modes experienced by Callisto depending on its magnetic position within the Jovian magnetosphere. The developed model will effectively support preparations for the upcoming flybys planned for the JUICE and Europa Clipper missions.
