Demi-journée du thème "Systèmes solaires" : retour sur Vénus

29/11/2022 13:30

Climat et Impacts

23/11/2022 09:00

Webinales de la plateforme PRAMMICS de l'OSU-EFLUVE : présentation du pôle inorganique (4e édition)

22/11/2022 10:30

Aviation climate impact and mitigation

13/10/2022 14:00

Bernd Kärcher est chercheur au DLR travaillant sur les nuages de glace dans la haute troposphère.

Methane in the climate system: mapping emissions from satellites

12/10/2022 14:00

Daniel Jacob de l’Université d’Harvard est en visite au LATMOS-IPSL.

La perliculture de Polynésie française menacée par les microplastiques

06/10/2022 13:00

Tony GARDON est post-doctorant IRD à l’UMR LEMAR.

Apprentissage statistique pour les modèles climatiques

04/12/2024 10:00

Apprentissage statistique pour les modèles climatiques

Les modèles climatiques peinent à représenter précisément les structures de circulation atmosphérique liées aux événements extrêmes, et notamment leurs variations régionales. Cette thèse explore comment l’Allocation Latente de Dirichlet (LDA), une méthode d’apprentissage statistique issue du traitement du langage naturel, peut être utilisée pour évaluer la représentation par modèles climatiques de données telles que la pression au niveau de la mer (SLP). La LDA identifie un jeu de structures locales (ou motifs) à l’échelle synoptique, interprétables physiquement comme des cyclones et des anticyclones. La même base de motifs peut servir à décrire les données issues des modèles et des réanalyses, permettant de représenter toute carte SLP par une combinaison parcimonieuse de ces motifs. Les coefficients, ou poids, de ces combinaisons fournissent une information locale sur la configuration synoptique de la circulation. Les analyser permet de caractériser la structure de la circulation dans les réanalyses et les modèles, et ainsi d’identifier localement des biais globaux ou spécifiques aux événements extrêmes. Une erreur dynamique globale peut être définie à partir des différences de poids des données modèles avec les réanalyses.

Cette méthodologie a été appliquée à quatre modèles de CMIP6. Bien que les modèles représentent correctement en général la circulation à grande échelle, leurs erreurs sont plus élevées pour les vagues de froid et de chaleur. Une source d’erreur dans tous les modèles est liée aux motifs méditerranéens. Des critères d’évaluation supplémentaires ont été proposés. L’un s’appuie sur la fréquence d’apparition des motifs dans la représentation des cartes de pression. L’autre consiste à combiner l’erreur dynamique globale avec l’erreur de température, ce qui permet de différentier entre les modèles. Ces résultats démontrent le potentiel de la LDA pour l’évaluation et la préselection des modèles.

 

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Statistical Learning for Climate Models

Climate models face challenges in accurately representing atmospheric circulation patterns related to extreme weather events, especially regarding regional variability. This thesis explores how Latent Dirichlet Allocation (LDA), a statistical learning method originating from natural language processing, can be adapted to evaluate the ability of climate models to represent data such as SeaLevel Pressure (SLP). LDA identifies a set of local synoptic-scale structures, physically interpretable as cyclones and anticyclones, referred to as motifs. A common basis of motifs can be used to describe reanalysis and model data so that any SLP map can be represented as a sparse combination of these motifs. The motif weights provide local information on the synoptic configuration of circulation. By analyzing the weights, we can characterize circulation patterns in both reanalysis data and models, allowing us to identify local biases, both in general data and during extreme events. A global dynamic error can be defined for each model run based on the differences between the average weights of the run and reanalysis data.

This methodology was applied to four CMIP6 models. While large-scale circulation is well predicted by all models on average, higher errors are found for heatwaves and cold spells. In general, a major source of error is found to be associated with Mediterranean motifs, for all models. Additional evaluation criteria were considered: one was based on the frequency of motifs in the sparse map representation. Another one involved combining the global dynamic error with the temperature error, thus making it possible to discriminate between models. These results show the potential of LDA for model evaluation and preselection.

Modeling the Dynamics of Water and CO2 Ice on the Planet Mars

21/11/2024 14:00

Mars is surrounded by a thin atmosphere composed mostly of CO₂ with a small amount of water. Every night and every winter, CO₂ and water can locally condense as frost. In the geologically recent past, when the obliquity of Mars was larger, the Martian atmosphere was enriched in water and a perennial mantle of water ice formed from the poles to the mid-latitudes. When the obliquity decreased again, these ices sublimated and got buried under a lag deposit in the Martian subsurface, while much of the atmospheric CO₂ condensed at the poles to form massive glaciers. Today, we can observe the remains of these periods via the water ice buried in the subsurface at mid-latitudes and the perennial CO₂ cap at the South Pole. This thesis aims to improve our understanding of the stability and dynamics of these ices over time, from the diurnal cycle to the million-year scale. I used space observations and, even more, numerical models such as the global climate model « Mars Planetary Climate Model » (PCM) and the new « Planetary Evolution Model » (PEM) for which I actively invested to develop new capabilities.

In a first study, by comparing the different pressure data acquired on the surface of Mars by space probes over the last 50 years, I proved that the average mass of the Martian atmosphere had not varied at these scales and therefore that the perennial CO₂ cap had a neutral mass balance. I then showed that the diurnal cycle of CO₂ frost formation in dusty areas could prevent dust from aggregating, maintaining the presence of reservoirs of mobilizable dust on the surface.

At the same time, I developed a new numerical tool for modeling microclimates on Martian slopes within the PCM and then the PEM.This tool is able to predict the presence of Martian ice on cold slopes in agreement with the detections from orbit. In a dedicated study, using observations from the THEMIS visible and thermal camera and this new model, I showed that the water frost accumulating on these slopes cannot melt in the current climate. It may eventually form brines if this frost is in contact with a significant amount of salts.

I then studied the stability of water ice in the subsurface. In particular, I proved that buried water ice at tropical latitudes under poleward slopes was not stable. The presence of such a buried water ice had previously been presented as essential to explain the absence of seasonal CO₂ ice on these slopes, because of its high thermal inertia. However, my model showed that this absence of CO₂ ice could also be explained by atmospheric heat transport, previously neglected. By adapting a new comprehensive model of exchange between permafrost and the Martian atmosphere, I revisited the effect of the water cycle and near-surface atmospheric conditions on the stability of buried ice. In parallel, we reconstructed the history of buried water ice in the mid-latitude subsurface and suggested that it may be a remnant of the last ice ages of 630,000 years ago. Finally, as for the surface frost, I showed that this buried water ice could not melt, even under the most favorable conditions we could imagine for Mars: the case where a ground flow suddenly exposes the underground ice to solar heating.

In a final study, using the new « Planetary Evolution Model », I simulated the formation of CO₂ glaciers when the entire atmosphere condenses in the polar regions during periods of low obliquity. By decreasing the obliquity from the current value of 25.2° to 15°, I showed that the mean pressure drops to a value of 250 Pa. The humidity of the atmosphere is reduced to a few precipitable microns. Massive CO₂ glaciers form on poleward slopes at high latitudes. Despite the loss of more than half of the Martian atmosphere, CO₂ remains the major component. This work constitutes a new step in our understanding of Martian paleoclimates and in particular of the geological activity induced by recent climatic variations.

Microphysical and optical characterization of fresh and aged combustion aerosol particles: a simulation chamber study

20/11/2024 14:00

Carbonaceous soot particles are formed during the incomplete combustion of fossil fuels, biofuels and biomass burning and are considered to contribute to a significant part of aerosol emission, especially in polluted areas. Soot particles are known to contain the light-absorbing carbon fractions of Black Carbon (BC) and Brown Carbon (BrC) making them a key species when trying to understand and estimate the interaction between aerosols and atmospheric radiation, i.e. the direct radiative effect (DRE). Current estimations of the DRE of soot and its BC and BrC components remain uncertain due to the difficulties in representing their microphysical and spectral optical properties in models. In particular, gaps persist in describing the variability of the soot optical properties at the source, due to different combustion conditions, and their change during atmospheric lifetime, due to mixing with different aerosol components. Further, differences between laboratory observations and field measurements remain and are not understood.

The present work aims to provide new measurements and descriptions of the physical, chemical, and spectral optical properties of BC- and BrC-containing soot aerosol in order to improve its representation in models. The focus of this work is set on advancing the understanding and description of the variability of the soot properties 1) at generation, from changing combustion conditions, and 2) during atmospheric ageing, in particular, due to the internal mixing with inorganic and organic compounds forming coating at the soot surface. To provide a mechanistic study of soot aerosol properties a coherent set of experiments was set up using the large atmospheric simulation chamber CESAM (French acronym for Multiphase Atmospheric Experimental Simulation Chamber) and a controllable propane-based soot generator.

The experiments were used to determine key optical parameters used in modelling and remote sensing applications like the mass absorption, extinction and scattering cross-section (MAC, MEC, MSC), the single scattering albedo (SSA), and the complex refractive index (CRI). The varying properties of the soot from different combustion conditions allowed the investigation and support of a generalized relationship between the MAC and the particle chemical composition for fresh-emitted particles. Optical calculations based on two descriptions of the particle’s morphology were performed to determine the soot’s CRI and discuss the usability and pertinence of data from different shape representations assumptions.

Soot aerosols were subjected to different simulated atmospheric ageing processes to determine the effects of ageing on their absorbing capacity and physico-chemical properties. Especially, the formation of a coating and the enhancement of the absorption due to this internal mixing were studied using two precursors and coating processes. This enabled the investigation of the relationship between absorption enhancement, particle processing, and coating thickness, relevant across the soot lifecycle.