Shallow convective cloud cover prediction is a critical element of climate modeling. In most global climate models (GCMs), the cloud scheme relies on a statistical description of the atmospheric water content. While the mean value of specific humidity is a standard output of the climate model, the upper moments, in particular the variance and skewness, must be prescribed by a dedicated model. Observations and large eddy simulations (LES) have shown that the asymmetry of water distribution is linked to the presence of organized convective cells. To capture this asymmetry, the boundary layer convective cells are represented in this work by an eddy diffusivity mass flux model, coupled to a bi-Gaussian statistical cloud scheme. Previously, the variance of each component has been prescribed diagnostically from the difference in specific humidity between updrafts and environment. This cloud scheme has demonstrated its capacity to represent dry shallow convective boundary layers, however, it reveals significant inaccuracies in deep convection situations and in the prediction of skewness. We propose here to develop a prognostic model for the total specific humidity variance. We show in particular that the transport of specific humidity and its square in the mass-flux scheme permits to implement the generic prognostic equations of the variance into a GCM. Furthermore, model adjustment is carried out using recently added automatic tuning methods. Based on this adjustment, we show that the prognostic model is consistent with previous work on shallow convective cases while providing significant improvements in the description of variance and third-order moment profiles.

Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consistent with real-world observations. When calibrating models with high-dimensional output (e.g. a spatial field), it is common to represent the output as a linear combination of a small set of basis vectors. Often, when trying to calibrate to such output, what is important to the credibility of the model is that key emergent physical phenomena are represented, even if not faithfully or in the right place. In these cases, comparison of model output and data in a linear subspace is inappropriate and will usually lead to poor model calibration. To overcome this, we present kernel-based history matching (KHM), generalising the meaning of the technique sufficiently to be able to project model outputs and observations into a higher-dimensional feature space, where patterns can be compared without their location necessarily being fixed. We develop the technical methodology, present an expert-driven kernel selection algorithm, and then apply the techniques to the calibration of boundary layer clouds for the French climate model IPSL-CM.

Land exchanges carbon with the atmosphere through numerous processes, such as photosynthesis and vegetation respiration. As land carbon uptake is greater than land carbon emissions, land surface represents nowadays a carbon sink, absorbing around a third of total anthropogenic carbon emissions every year. Land surface models, used in global climate models, model these processes and provide estimates of net carbon fluxes between the atmosphere and land surfaces. However, simulations of these models still contain significant uncertainties. Other methods, known as Atmospheric CO2 Inversion, exist to estimate these fluxes, and are not consistent with estimates given by land surface models which usually provide a stronger carbon sink in the tropics than Atmospheric CO2 Inversions. These methods cannot provide simulations of the future that are needed for future projections of Climate Models. For that reason, it is important to understand and improve key processes controlling ecosystem carbon budgets, and to embed this understanding in predictive models. Land surface models typically use many free parameters to describe vegetation, which need to be rigorously calibrated or tuned. Here, we aim at calibrating ORCHIDEE, the land surface model used by the IPSL Earth system model, on the atmospheric inversion fluxes (taken as data-driven constraints) in order to study ORCHIDEE's ability to reconcile with Atmospheric CO2 Inversion by finding physically acceptable parameter sets, or detect models’ inability to recover the same spatio-temporal distribution of carbon fluxes. Calibration usually requires many model simulations, which are very costly. Emulators, and especially Gaussian processes, can replace the computationally time-consuming model and help us to run a large number of simulations to fill the parameter space and rule out parameter subspace that give inconsistent simulation. This method, called History Matching, is emerging in the climate community and has shown many advantages. We show the capacity of History Matching to calibrate ORCHIDEE on global simulations using different targets: Known, using twin experiments, which leads to a very rich source of information on parameter sensitivity, uncertainty, equifinality and global and specific knowledge of the model. Unknown, using fluxes from atmospheric CO2 inversion which could also be combined with vegetation activity data (i.e, such as vegetation fluorescence) to add a physical constraint to parameter calibration. This calibration can provide parameter sets that reconcile to a certain extent bottom-up and top-down approaches, or key information on missing processes in ORCHIDEE that need to be added or modified. In both cases, this is highly instructive and leads to a better understanding of the model and processes being modeled and highlights the potential of current land surface models to simulate carbon flux distribution compatible with existing atmospheric CO2 observation (in situ or from satellite).

Une très grande variété de climats existe sur la Terre et des méthodes dédiées sont nécessaires pour comprendre leur variabilité et évolution dans un contexte de réchauffement climatique global. Au LMD, sont développées des approches numériques pour étudier les climats régionaux. Il s’agit historiquement du modèle global zoomé LMDZ et plus récemment du modèle régional du système Terre Reg-IPSL. Ces modèles ont permis l’étude de climats de nombreuses régions du globe, telles que l’Afrique de l’Ouest, la Méditerranée, l’Amérique du Sud, l’Asie, l’Antarctique et bien d’autres. Seules les études sur la région Méditerranée ont appliqué et comparé toutes les méthodes de régionalisation développées au laboratoire et sont développées dans cet article.

With the growing complexity of land surface models used to represent the terrestrial part of wider Earth system models, the need for sophisticated and robust parameter optimisation techniques is paramount. Quantifying parameter uncertainty is essential for both model development and more accurate projections. In this study, we assess the power of history matching by comparing results to the variational data assimilation approach commonly used in land surface models for parameter estimation. Although both approaches have different setups and goals, we can extract posterior parameter distributions from both methods and test the model–data fit of ensembles sampled from these distributions. Using a twin experiment, we test whether we can recover known parameter values. Through variational data assimilation, we closely match the observations. However, the known parameter values are not always contained in the posterior parameter distribution, highlighting the equifinality of the parameter space. In contrast, while more conservative, history matching still gives a reasonably good fit and provides more information about the model structure by allowing for non-Gaussian parameter distributions. Furthermore, the true parameters are contained in the posterior distributions. We then consider history matching's ability to ingest different metrics targeting different physical parts of the model, thus helping to reduce the parameter space further and improve the model–data fit. We find the best results when history matching is used with multiple metrics; not only is the model–data fit improved, but we also gain a deeper understanding of the model and how the different parameters constrain different parts of the seasonal cycle. We conclude by discussing the potential of history matching in future studies.

L’étude des climats et des atmosphères des mondes extraterrestres constitue une activité importante du Laboratoire de Météorologie Dynamique depuis plus de trois décennies. L’équipe « Planétologie » est reconnue internationalement pour le développement de modèles numériques de climat appliqués à l’ensemble des dix atmosphères du système solaire, et maintenant aux exo-atmosphères atour des autres étoiles. Les applications scientifiques sont innombrables. Forte de cette expertise, l’équipe s’est aussi impliquée activement dans les diverses missions spatiales qui ont ponctué ces dernières décennies, et le développement instrumental. Au sein d’un laboratoire comme le LMD, toutes ces recherches s’effectuent dans un esprit de « climatologie comparée », où l’étude des autres mondes permet de tester et améliorer les modèles et les théories développées dans le cadre terrestre.

This study presents the development of a TKE-l parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large-scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and their number minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has also been paid to the numerical stability at typical time steps used in General Circulation Models. Test, parametric sensitivity assessment and preliminary tuning are performed on single-column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are also assessed through its implementation in the LMDZ General Circulation Model and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.

Modelling transient combined heat transfer in complex urban geometry is a key step to predict human exposure or energy consumption and to quantify the effect of climate change mitigation and adaptation measures. A difficulty lies in the possibility for a model to scale up and integrate large and complex urban morphology. We develop a probabilistic approach to solve heat transfers with the Monte Carlo method that is insensitive to the complexity of both the urban geometry and the boundary conditions. The integral formulation that includes random walks for each heat transfer mode is presented and the computation of absorbed solar irradiations at walls with the double randomization technique is detailed. Numerical validations are given through comparisons with deterministic method results for single and two-layer slabs, but also a three-dimensional thermal bridge geometry. The developed probabilistic heat transfer model is then used in a demonstration heat wave scenario where are computed: the outdoor mean radiant temperature showing the influence of trees; and the indoor average wall temperature showing the influence of solar gains through windows.

Dans les années 1980, Robert Sadourny décide de réécrire le noyau dynamique du modèle climatique global LMD. Ce nouveau noyau est d'abord utilisé pour l'étude et les simulations numériques des atmosphères planétaires. Dans les années 90, son couplage avec les paramétrisations physiques de l'ancien modèle climatique du LMD donne naissance au nouveau modèle climatique global LMDZ, rapidement adopté comme composante atmosphérique du modèle climatique global de l'IPSL. Ce modèle, intimement lié à l'histoire de la fédération IPSL, est largement utilisé dans la communauté nationale et internationale pour la compréhension du système climatique et l'anticipation des conséquences climatiques du réchauffement global induit par l'augmentation du dioxyde de carbone dans l'atmosphère, due à la consommation de pétrole et de charbon. Ce chapitre retrace les 35 ans d'histoire de l'équipe LMDZ à partir du point de vue subjectif de l'un de ses acteurs. Cette histoire est rythmée et marquée par l'importance de l'alerte climatique. L'histoire de l'équipe LMDZ a été guidée tout au long de cette période par la conviction de la pertinence des modèles climatiques globaux à physique paramétrée pour éclairer les choix politiques face à la crise climatique, et par une volonté continue de promouvoir le lien entre la compréhension de la physique atmosphérique et le développement de modèles.

AbstractThis study presents the development of a so‐called Turbulent Kinetic Energy (TKE)‐l, or TKE‐l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large‐scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single‐column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers.

Saharan dust represents more than 50% of the total desert dust emitted around the globe and its radiative effect significantly affects the atmospheric circulation at a continental scale. Previous studies on dust vertical distribution and the Saharan Air Layer (SAL) showed some shortcomings that could be attributed to imperfect representation of the effects of deep convection and scavenging. The authors investigate here the role of deep convective transport and scavenging on the vertical distribution of mineral dust over Western Africa. Using multi-year (2006–2010) simulations performed with the variable-resolution (zoomed) version of the LMDZ climate model. Simulations are compared with aerosol amounts recorded by the Aerosol Robotic Network (AERONET) and with vertical profiles of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements. LMDZ allows a thorough examination of the respective roles of deep convective transport, convective and stratiform scavenging, boundary layer transport, and advection processes on the vertical mineral dust distribution over Western Africa. The comparison of simulated dust Aerosol Optical Depth (AOD) and distribution with measurements suggest that scavenging in deep convection and subsequent re-evaporation of dusty rainfall in the lower troposphere are critical processes for explaining the vertical distribution of desert dust. These processes play a key role in maintaining a well-defined dust layer with a sharp transition at the top of the SAL and in establishing the seasonal cycle of dust distribution. This vertical distribution is further reshaped offshore in the Inter-Tropical Convergence Zone (ITCZ) over the Atlantic Ocean by marine boundary layer turbulent and convective transport and wet deposition at the surface.

Les données issues de l’analyse des carottes de glace polaires dans la communauté française ont constitué l’un des premiers moteurs d’un intérêt particulier porté à la modélisation des climats et paléoclimats des régions polaires au LMD. Le passage d’une grille horizontale figée et peu adaptée aux régions polaires à une grille « zoomable » au début début des années 1990, permettant de simuler dans le modèle global des climats régionaux à une résolution horizontale de plus en plus fine a été particulièrement bénéfique pour la région Antarctique. Des travaux plus récents d’évaluation et d’amélioration d’une physique pas toujours bien conçue et/ou calibrée pour les climats extrêmes des régions polaires ont aboutie à un modèle aujourd’hui bien reconnu pour ses capacités dans ces régions.

It was recently shown that radiation, conduction and convection can be combined within a single Monte Carlo algorithm and that such an algorithm immediately benefits from state-of-the-art computer-graphics advances when dealing with complex geometries. The theoretical foundations that make this coupling possible are fully exposed for the first time, supporting the intuitive pictures of continuous thermal paths that run through the different physics at work. First, the theoretical frameworks of propagators and Green's functions are used to demonstrate that a coupled model involving different physical phenomena can be probabilized. Second, they are extended and made operational using the Feynman-Kac theory and stochastic processes. Finally, the theoretical framework is supported by a new proposal for an approximation of coupled Brownian trajectories compatible with the algorithmic design required by ray-tracing acceleration techniques in highly refined geometry.

The aim of this study is to present a new methodology to compare LES resolved and large-scale parametrized thermals. A conditional sampling based on the combination of a passive tracer emitted at the surface and thermodynamic variables is proposed to qualify the organized structures in cloud-free and cloudy boundary layers. The sampling is validated against more traditional samplings of clouds or dry thermals. It enables to characterize convective updrafts from the surface to the top of the boundary layer (or the top of cumulus clouds) describing in particular the transition from the sub-cloud to the cloud layer. It retrieves plume characteristics, entrainment and detrainment rates, variances and fluxes. With this sampling, the contribution of boundary-layer thermals to fluxes and variances is analyzed in the subcloud and cloud layer.

Documenting the uncertainty of climate change projections is a fundamental objective of the inter-comparison exercises organized to feed into the Intergovernmental Panel on Climate Change (IPCC) reports. Usually, each modeling center contributes to these exercises with one or two configurations of its climate model, corresponding to a particular choice of "free parameter" values, resulting from a long and often tedious "model tuning" phase. How much uncertainty is omitted by this selection and how might readers of IPCC reports and users of climate projections be misled by its omission? We show here how recent machine learning approaches can transform the way climate model tuning is approached, opening the way to a simultaneous acceleration of model improvement and parametric uncertainty quantification. We show how an automatic selection of model configurations defined by different values of free parameters can produce different "warming worlds," all consistent with present-day observations of the climate system.

The cold pools, created below cumulonimbus from the evaporation of precipitation, generate the strong winds responsible for the large dust storms called “haboobs” which appear in the Sahel in summer. Most global climate models do not take into account these types of dust emissions due to lack of parameterization of cold pools and associated gusts (Marsham et al. 2011 ; Pantillon et al. 2015). The introduction of a parameterization of cold pools in the LMDZ climate model has improved the representation of convection, and in particular of the diurnal cycle of continental precipitation in the tropics (Rio et al. 2009). The aim of this work is to develop a parameterization of gusts related to cold pools in order to take into account ‘‘hoobobs’’ in LMDZ model. To do this, we use Large Eddy Simulations (LES) performed on an oceanic domain and in Radiative-Convective Equilibrium (RCE) mode. We use a LES of an oceanic RCE case, easier to analyze because the temperatures are uniform on the surface and therefore the cold pools easier to detect. Before developing a gust parameterization, we evaluate the cold pools parameterization in LMDZ on this RCE case, which has never been done so far. If the comparison confirms the relevance of the scheme and its qualitative match to the LES behavior, is also led to substantial improvements and adjustments to this scheme. Next, we analyze the wind distributions in the LES in order to construct a parametrization based on a probability distibution function of the subgrid scale distribution of the wind which will allow us to take into account the effect of gusts on dust storms. The parametrization relates the moments of the distribution to large-scale wind speed, the spreading speed of the cold pools and the surface fraction covered by the latter. In the following, we will test the parametrization on the LMDZ model by focusing on dust storms in the Sahel during the rainy season.

<p>The intrinsic failure of eddy-diffusion parameterizations in representing upward transport of heat in the convective boundary layer, recognized since the 70s, has lead to various propositions of parameterizations like counter-gradient terms and third order closures to account for the asymmetry of the vertical transport. An approach that is now well recognized consists in combining a mass flux parameterization of the organized structures of the convective boundary layer with a local TKE closure for small scale turbulence. The idea traces back to a proposition by Chatfield and Brost (1987) and is since often referred to as the Eddy Diffusion Mass Flux (EDMF) approach. The “thermal plume model” developed for LMDZ was the first EDMF scheme published and tested in a climate model (Hourdin et al., 2002). It was first introduced in the LMDZ5B atmospheric component of the IPSL model for CMIP5. However, this first version suffered from youth problems. It is only for CMIP6A, about 20 years after the development of the parameterization, that a first satisfactory version of the model was delivered. Through years, and more often with this last version, the key role of the representation of shallow convection on many component of the system has been realized: 1) the ventilation of air by the subsiding air around thermal plumes dries the surface, reinforcing the near surface evaporation. Representing this convection correctly both over trade winds and subsiding regions in the tropics, together with the associated cumulus and stratocumulus clouds, is one of the key for the reduction of the East Tropical Ocean warm bias; 2) the preconditioning of the deep convection by a phase of shallow convection is a key for a correct representation of the phasing of the diurnal cycle of convective rainfall over continents; 3) the strong diurnal cycle of the convective boundary layer in desert areas is essential to well represent the maximum of near surface wind in the morning, responsible for a maximum of dust emission, when the momentum of the nocturnal low level jet is brought suddenly back toward the surface when reached by the developing dry convection. 4) the thermal plume model being active about on half of the globe all the time, it controls the transport of all trace elements, with some non linear effects when the emissions themselves show a diurnal cycle. In this presentation, we review these lessons learned with LMDZ, identify the issues which should require further developments, and expose how new machine assisted techniques allow to reconcile improvement of parameterizations at process scale and climate model improvement.</p> <p>Chatfield, R. B., & Brost, R. A. (1987). A two-stream model of the vertical transport of trace species in the convective boundary layer. Journal of Geophysical Research, 92, 13,263–13,276</p> <p>Hourdin, F., Couvreux, F., & Menut, L. (2002). Parameterisation of the dry convective boundary layer based on a mass flux representation of thermals. Journal of the Atmospheric Sciences, 59, 1105–1123</p>

Urban areas are a high-stake target of climate change mitigation and adaptation measures. To understand, predict, and improve the energy performance of cities, the scientific community develops numerical models that describe how they interact with the atmosphere through heat and moisture exchanges at all scales. In this review, we present recent advances that are at the origin of last decade's revolution in computer graphics, and recent breakthroughs in statistical physics that extend well-established path-integral formulations to nonlinear coupled models. We argue that this rare conjunction of scientific advances in mathematics, physics, computer, and engineering sciences opens promising avenues for urban climate modeling and illustrate this with coupled heat transfer simulations in complex urban geometries under complex atmospheric conditions. We highlight the potential of these approaches beyond urban climate modeling for the necessary appropriation of the issues at the heart of the energy transition by societies.

Saharan dust represents more than 50% of the total desert dust emitted around the globe and its radiative effect significantly affects the atmospheric circulation at a continental scale. Atmospheric models often fail to represent the dust vertical distribution and the Saharan Air Layer. They underestimate the effects of deep convection on the vertical transport and of the role of scavenging on the confinement of dust aerosols in this layer. Using multi-year simulations performed with a variable-resolution climate model and processed-based analysis, we show that scavenging in deep convection and further re-evaporation of dusty rainfall in the lower troposphere are critical processes for explaining the vertical distribution of desert dust. They play a key role in maintaining a well-defined dust layer with sharp transition at the top of the SAL and in establishing the seasonal cycle of dust distribution.