Proposition de Sujet de thèse 2020
(1 page recto maximum) Laboratoire (et n° de l’unité) dans lequel se déroulera la thèse : CNRM - UMR 3589
Titre du sujet proposé :
Using hyperspectral infrared observations to evaluate and calibrate general circulation models
Nom et statut (PR, DR, MCf, CR, …) du (des) responsable (s) de thèse (préciser si HDR) : Cyril Crevoisier, CR (HDR)
Quentin Libois, IPEF
Coordonnées (téléphone et e-mail) du (des) responsable(s) de thèse : cyril.crevoisier@lmd.polytechnique.fr : 01 69 33 51 35
quentin.libois@meteo.fr : 05 61 07 96 91 Résumé du sujet de la thèse
Context :
The climate of the Earth is controlled by the balance of radiative fluxes at the top-of- atmosphere, where the sum of reflected solar radiation and infrared (IR) emission from the Earth has to balance incoming solar radiation. Such broadband balance is key in the elaboration of stable and reliable General Circulation Models (GCMs), which would otherwise quickly derive to unrealistically cool or warm worlds. The spectrally detailed radiative balance is on the contrary less constrained in these models. As a consequence, GCM parameterizations are tuned to ensure broadband balance, but such balance can result from the combination of compensating errors in the representation of atmospheric processes, which are generally hard to disentangle. Spectrally resolved observations in the IR, which have been routinely performed in the mid-IR for more than a decade and are likely to extend to the far-IR in the next decade, could put a strong constraint on GCMs (Huang et al., 2007).
However so far IR sounders (e.g. IASI, AIRS) have essentially been used through data assimilation in numerical weather prediction models and for atmospheric composition monitoring. Due to their radiometric stability and lifetime, such observations could be extremely valuable for GCM evaluation and calibration, and might help reducing the sustained uncertainty range of climate projections.
Objectives :
This thesis aims at exploiting IASI and IASI-NG total-sky radiances for GCM evaluation and calibration, starting with the french models LMDz and CNRM-CM. To compare IASI observations to atmospheric outputs of historical GCM runs, a dedicated simulator will be developed based on the 4A radiative transfer model. This will provide a global set of IASI-like synthetic radiances at GCM spatial resolutions. Statistical tools will be developed to compare these synthetic observations to real observations, properly averaged to match GCM resolution. Rather than focusing on a point-to-point comparison, these tools will investigate the spatio-temporal features of TOA radiances, as well as the temporal trends due to climate change. Metrics will be derived from this statistical information to quantify the difference between GCM outputs and satellite observations, which will provide an objective and integrative way of evaluating and comparing GCMs. The processed IASI observations will then serve as a reference fo a variety of GCMs participating in the CMIP6 intercomparison exercise, allowing to rank models in terms of their ability to correctly simulate the Earth spectral radiative budget in the IR. The similarity between GCMs in present-day conditions will be compared to the similarity in warming experiments to assess whether current
hyperspectral IR observations can help reducing the range of climate predictions based on the emergent constraints approach (Hall et al., 2019). The proposed framework will also be extended to synthetic observations of FORUM, ESA 9th Earth Explorer that will measure far- infrared (FIR) radiance starting in 2026. This will demonstrate the added value of FIR observations for the evaluation of GCMs. The proposed strategy will overall help identify structural defaults in current GCMs and will greatly constrain the tuning of the next generation of such models (Hourdin et al., 2017).
Nature du travail attendu et compétences souhaitées
Le travail de thèse impliquera l’analyse de données satellitaires et de sorties de GCMs. Le développement d’outils statistiques pour comparer des spectres IR dans le temps et l’espace constituera une étape essentielle du travail. Des connaissances en physique de l’atmosphère et en transfert radiatif seront indispensables pour l’interprétation des résultats.
Une bonne aptitude au codage informatique et des compétences en communication écrite et orale sont également essentielles.
Références bibliographiques
Hall, A., Cox, P., Huntingford, C., & Klein, S. (2019). Progressing emergent constraints on future climate change. Nature Climate Change, 1.
Huang, Y., Ramaswamy, V., Huang, X., Fu, Q., & Bardeen, C. (2007). A strict test in climate modeling with spectrally resolved radiances: GCM simulation versus AIRS observations. Geophysical Research Letters, 34(24).
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J. C., Balaji, V., Duan, Q., ... & Rauser, F. (2017).
The art and science of climate model tuning. Bulletin of the American Meteorological Society, 98(3), 589-602.
