Abstract This study investigates the contribution of mesoscale convective systems (MCSs) to cold cloudiness and rainfall accumulation as simulated by a global model at kilometer-scale resolution. The realism of MCSs simulated by the convection-permitting model AROME-Global, developed at CNRM, in a 120-day global simulation is first assessed through comparisons with satellite observations. A detection and tracking algorithm is applied to infrared brightness temperatures derived from geostationary satellites as well to those computed from the model outputs. The spatial distribution of simulated MCSs is consistent with observations, although there are too numerous. Simulated systems display realistic morphological features but tend to be longer lived, do not reach observed sizes, and travel shorter distances than observed. At the scale of the tropics, AROME-Global shows weak biases in terms of rain amount and cold cloudiness, although it exhibits higher regional biases. Two highly biased regions (with biases of opposite signs) are analyzed: the Indian Ocean (IO) and the eastern Pacific intertropical convergence zone (ITCZ-EP). The IO rainfall deficit is attributed to MCSs that are too small and insufficiently precipitating, while the ITCZ-EP excess stems from an overestimation of small, weakly precipitating MCSs. These findings highlight that large-scale biases in MCS cloudiness and precipitation fields in AROME-Global are strongly tied to the statistical and morphological representations of tropical MCSs. Significance Statement Mesoscale convective systems (MCSs) are organized deep convective systems that contribute significantly to rainfall and cold cloudiness in the tropics. In this study, the ability of the global convective-permitting AROME-Global model in simulating MCS-related cloudiness and rain amount is assessed. In spite of weak biases at the scale of the tropics in terms of rain and radiation budget and rain, regional biases can be high. Simulated MCSs exhibit a realistic spatial distribution, although they are too numerous, have lifetimes that are too short, and underestimate the extent of cold cloud shields. Regional biases in rainfall and cold cloudiness can be attributed to biases in individual MCS properties showing some compensating behaviors.

Abstract. We analyse the evolution of convective diagnostics such as mixed-layer convective available potential energy (CAPE), level of neutral buoyancy and precipitation rate as a function of lead time in the model uncertainty model-intercomparison project. Four model physics packages are exposed to common dynamics to form a large single-column model dataset. We analyse tendencies in an equatorial band over the Indian Ocean out to 6 hr lead time over one month. We prescribe dynamics and initial conditions from an ICON-DYAMOND simulation after coarse-graining to 0.2 degrees. The physics suites represent state-of-the-art global numerical weather and climate prediction models. Correlation analysis shows that the spatial mean change of CAPE is not associated with precipitation rate, but it correlates very well with mean mixed-layer drying across our suites. This systematic drying occurs below 700 hPa in some suites, especially in the first hour. The sub-grid physics adjusts the initialised ICON state towards the native climate of each physics suite, in particular at low levels. We apply a column-by-column empirical orthogonal function (EOF) analysis to a two-layer representation of physics and dynamics tendencies, CAPE tendency and precipitation rate. The first EOF is associated with free-tropospheric tendencies and nearly all precipitation variability, with neat compensation between physics and dynamics tendencies. The second and third EOFs of each suite indicate that a imbalance between these terms in the mixed-layer correlates with the CAPE change at least one of them, which are explained by temperature and humidity adjustments, but with little imprint on precipitation.

Abstract The Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) phase I provided insights into the tropical radiative‐convective equilibrium state across an unprecedented ensemble of models that includes both those with parameterized and explicit convection. RCEMIP phase II (RCEMIP‐II) introduces mock‐Walker simulations to examine how sea surface temperature (SST) gradients and the forced circulation they theoretically induce interact with the structure of convection and mean climate state. This overview paper presents descriptions of and initial results from 17 numerical models regarding clouds, convective organization, and circulation structure. Results regarding warming the mean SST in the presence of an SST gradient generally mirror those from RCEMIP‐I but a larger range of dynamical circulations, along with additional phenomena, are possible in RCEMIP‐II. Increases in the SST gradient while the mean SST is held constant lead to more convective organization across many models, as measured by a number of organization metrics. Increases in the SST gradient also tend to decrease the frequency of high, thin clouds while increasing the frequency of low clouds, including the possibility of stratocumulus, which were not seen in RCEMIP‐I. RCEMIP‐II also provides an estimate of the influence of a pattern effect, suggesting increased negative climate feedbacks if increases in the SST gradient occur alongside mean warming. However, the SST gradient's inability to fully constrain the circulation structure allows simulations to show varying importance of self‐aggregation and forced clustering over the warm pool, resulting in extreme variety in the circulation structures and important climate variables in the organized state across the model ensemble.

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-atmosphere coupling involves multiple processes occurring across different temporal and spatial scales. These complex processes are not yet fully represented in climate or numerical weather prediction models. To evaluate these coupled models and improve or develop new parameterizations, four different single-column model setups are proposed. These setups vary in the type of spatially homogeneous land cover: grassland, wheat field, corn field, and pine forest. They are based on the single-column version of the CNRM-CM6-1 model, which couples the ARPEGE-Climat atmospheric component with the SURFEX surface modeling platform. First, the simulation results are compared with observational data, focusing on the radiative balance, surface fluxes, soil temperature and moisture, as well as air temperature, specific humidity, and wind speed near the surface. The evaluation shows that the ARPEGE-SURFEX single-column model can accurately replicate both surface and atmospheric conditions. Next, the same modeling framework is applied to the Meso-NH-SURFEX large-eddy simulation model. These simulations provide a consistent reference for evaluating the boundary-layer in CNRM-CM6-1, particularly in terms of potential temperature and specific humidity throughout the day. Finally, a sensitivity study on the impact of surface parameters on land-atmosphere coupling reveals that the hydric stress parameterization, by modifying canopy conductance and transpiration, is the most influential driver of temperature and moisture in the mixed layer. This work highlights the advantages of using the ARPEGE-SURFEX single-column configuration as a reliable platform for experimentation and parameterization improvement, and local climate modeling. The study cases developed are made available for further evaluation of land-atmosphere coupling.

Global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) have been shown to underestimate the observed recent warming over western Europe despite their overestimated global warming. Several hypotheses have been proposed to explain this paradox, including the role of anthropogenic aerosols and of large-scale atmospheric circulation. Here, three sets of global atmospheric simulations driven by prescribed radiative forcings and oceanic boundary conditions are used to better understand the observed recent warming over western Europe. In the control experiment, the model underestimates the observed warming despite the use of observed sea surface temperatures (SST). A spectral nugding of the dynamics towards ERA5 reanalysis allows the model to capture accurately both the high-frequency variability and multi-decal trends of the observed near-surface temperature. The use of climatological rather than slowly evolving anthropogenic aerosols suggests a minor radiative contribution to the recent warming. Overall, the results suggest that changes in large-scale atmospheric circulation and regional feedbacks are not mutually exclusive explanations for the amplified summer warming over western Europe. The forced component of the observed circulation changes and the implications for the CMIP6 projections are also briefly discussed.

The effective radiative forcing (ERF) is a robust predictor of future equilibrium warming. It is generally assumed that the ERF depends only on changes in atmospheric constituents and is independent of the background climate state. Building on recent work demonstrating that, in contrast, the instantaneous radiative forcing (IRF) for CO2 is strongly state-dependent, we show that the ERF for CO2 also increases in warmer climate states. We analyse a 4×CO2 atmosphere-only forcing in both control and warmer climate states in eight CMIP6-era models. Four models participated in the Cloud Feedback Model Intercomparison Project (CFMIP) which used pre-industrial SSTs in its control state and SSTs from near the end of the same model’s coupled abrupt-4×CO2 run in its warm state. In the other four models we used an AMIP climatology as the control state and a uniform increase in SSTs of 4 K above this AMIP climatology in the warm state. All eight models show an increase in 4×CO2 ERF, ranging from 0.1-0.5 W m-2, translating to a relative increase of 0.02-0.09 W m-2 K-1 or 0.2-1.1 % K-1. The increase is statistically significant in five of the eight models.Our findings have implications for derivation of simplified relationships of climate warming, for instance in the calculations of global warming metrics and in economic models, from which future climate change risks being underpredicted without a temperature adjustment.We also run aerosol forcing experiments under the +4 K climate, for which there is less agreement between models, but some show large changes in aerosol ERF under the warmer climate state, with potential implications for our ability to discern transient warming even with a more accurate understanding of present-day aerosol forcing.

We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades to advance understanding of Earth system change and its impacts on society and the environment and thereby support international climate policy. We go on to recommend a number of priority research areas for the coming decade, a timescale that encompasses a number of newly starting international modelling activities, as well as the IPCC Seventh Assessment Report (AR7) and the second UNFCCC Global Stocktake. Progress in these priority areas will significantly advance our understanding of Earth system change and its impacts, increasing the quality and utility of science support to climate policy.We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris climate targets - for example, pathways that overshoot 1.5 or 2 degrees C global warming, before returning to these levels at some later date. Earth system models need to be capable of thoroughly assessing such warming overshoots - in particular, the efficacy of mitigation measures, such as negative CO2 emissions, in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 or 2 degrees C above pre-industrial temperatures is also required. We recommend Earth system models run overshoot scenarios in CO2-emission mode to more fully represent coupled climate-carbon-cycle feedbacks and, wherever possible, interactively simulate other key Earth system phenomena at risk of rapid change during overshoot. Regional downscaling and impact models should use forcing data from these simulations, so impact and regional climate projections cover a more complete range of potential responses to a warming overshoot. An accurate simulation of the observed, historical record remains a fundamental requirement of models, as does accurate simulation of key metrics, such as the effective climate sensitivity and the transient climate response to cumulative carbon emissions. For adaptation, a key demand is improved guidance on potential changes in climate extremes and the modes of variability these extremes develop within. Such improvements will most likely be realized through a combination of increased model resolution, improvement of key model parameterizations, and enhanced representation of important Earth system processes, combined with targeted use of new artificial intelligence (AI) and machine learning (ML) techniques. We propose a deeper collaboration across such efforts over the coming decade.With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend an increased focus on high-impact-low-likelihood (HILL) outcomes - in particular, the risk and consequences of exceeding critical tipping points during a warming overshoot and the potential impacts arising from this. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly as a result of climate mitigation actions, it is important that spatially detailed, disaggregated information used to generate future scenarios in integrated assessment models be available for use in impact models. Conversely, there is a need to develop methods that enable potential societal responses to projected Earth system change to be incorporated into scenario development.The new models, simulations, data, and scientific advances proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable by modelling communities across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy.

Abstract. We review how the international modelling community, encompassing Integrated Assessment models, global and regional Earth system and climate models, and impact models, have worked together over the past few decades, to advance understanding of Earth system change and its impacts on society and the environment, and support international climate policy. We then recommend a number of priority research areas for the coming ~6 years (i.e. until ~2030), a timescale that matches a number of newly starting international modelling activities and encompasses the IPCC 7th Assessment Report (AR7) and the 2nd UNFCCC Global Stocktake. Progress in these areas will significantly advance our understanding of Earth system change and its impacts and increase the quality and utility of science support to climate policy. We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris Climate Targets, including pathways that overshoot the 1.5 °C and 2 °C targets, before later returning to them. Earth System models (ESMs) need to be capable of thoroughly assessing such warming overshoots, in particular, the efficacy of negative CO2 emission actions in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 °C or 2 °C above pre-industrial temperatures is also required. We recommend ESMs run overshoot scenarios in CO2-emission mode, to more fully represent coupled climate - carbon cycle feedbacks. Regional downscaling and impact models should also use forcing data from these simulations, so impact and regional climate projections are as realistic as possible. An accurate simulation of the observed record remains a key requirement of models, as does accurate simulation of key metrics, such as the Effective Climate Sensitivity. For adaptation, improved guidance on potential changes in climate extremes and the modes of variability these extremes develop in, is a key demand. Such improvements will most likely be realized through a combination of increased model resolution and improvement of key parameterizations. We propose a deeper collaboration across modelling efforts targeting increased process realism and coupling, enhanced model resolution, parameterization improvement, and data-driven Machine Learning methods. With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend increased attention is paid to High Impact Low Likelihood (HILL) outcomes. In particular, the risk and consequences of exceeding critical tipping points during a warming overshoot. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly from specific mitigation actions, it is important detailed, disaggregated information from the Integrated Assessment Models (IAMs) used to generate future scenarios is available to impact models. Conversely, methods need to be developed to incorporate potential future societal responses to the impacts of Earth system change into scenario development. Finally, the new models, simulations, data, and scientific advances, proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable across all modelling communities and across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy.

AbstractThe increase of carbon-dioxide-doubling-induced warming (climate sensitivity) in the latest climate models is primarily attributed to a larger extratropical cloud feedback. This is thought to be partly driven by a greater ratio of supercooled liquid-phase clouds to all clouds, termed liquid phase ratio. We use an instrument simulator approach to show that this ratio has increased in the latest climate models and is overestimated rather than underestimated as previously thought. In our analysis of multiple models, a greater ratio corresponds to stronger negative cloud feedback, in contradiction with single-model-based studies. We trace this unexpected result to a cloud feedback involving a shift from supercooled to warm clouds as climate warms, which corresponds to greater cloud amount and optical depth and weakens the extratropical cloud feedback. Better constraining this ratio in climate models – and thus this supercooled cloud feedback – impacts their climate sensitivities by up to 1 ˚C and reduces inter-model spread.

Clouds affect the Earth climate with an impact that depends on the cloud nature (solid and/or liquid water). Although the Antarctic climate is changing rapidly, cloud observations are sparse over Antarctica due to few ground stations and satellite observations. The Concordia station is located on the eastern Antarctic Plateau (75∘ S, 123∘ E; 3233 m above mean sea level), one of the driest and coldest places on Earth. We used observations of clouds, temperature, liquid water, and surface irradiance performed at Concordia during four austral summers (December 2018–2021) to analyse the link between liquid water and temperature and its impact on surface irradiance in the presence of supercooled liquid water (liquid water for temperature less than 0 ∘C) clouds (SLWCs). Our analysis shows that, within SLWCs, temperature logarithmically increases from −36.0 to −16.0 ∘C when liquid water path increases from 1.0 to 14.0 g m−2. The SLWC radiative forcing is positive and logarithmically increases from 0.0 to 70.0 W m−2 when liquid water path increases from 1.2 to 3.5 g m−2. This is mainly due to the downward longwave component that logarithmically increases from 0 to 90 W m−2 when liquid water path increases from 1.0 to 3.5 g m−2. The attenuation of shortwave incoming irradiance (that can reach more than 100 W m−2) is almost compensated for by the upward shortwave irradiance because of high values of surface albedo. Based on our study, we can extrapolate that, over the Antarctic continent, SLWCs have a maximum radiative forcing that is rather weak over the eastern Antarctic Plateau (0 to 7 W m−2) but 3 to 5 times larger over West Antarctica (0 to 40 W m−2), maximizing in summer and over the Antarctic Peninsula.

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.

The impact of biomass burning aerosols (BBA) emitted in central Africa on the tropical African climate is studied using the ocean-atmosphere global climate model CNRM-CM, including prognostic aerosols. The direct BBA forcing, cloud feedbacks (semi-direct effects), effects on surface solar radiation, atmospheric dynamics and precipitation are analysed for the 1990-2014 period. During the June-July-August (JJA) season, the CNRM-CM simulations reveal a BBA semi-direct effect exerted on low-level clouds with an increase in the cloud fraction of similar to 5 %-10 % over a large part of the tropical ocean. The positive effect of BBA radiative effects on low-level clouds is found to be mainly due to the sea surface temperature response (decrease of similar to 0.5 K) associated with solar heating at 700 hPa, which increases the lower-tropospheric stability. Over land, results also indicate a positive effect of BBA on the low-cloud fraction, especially for the coastal regions of Gabon and Angola, with a potentially enhanced impact in these coupled simulations that integrates the response (cooling) of the sea surface temperature (SST). In addition to the BBA radiative effect on SST, the ocean-atmosphere coupled simulations highlight that the oceanic temperature response is noticeable (about -0.2 to -0.4 K) down to similar to 80 m depth in JJA between the African coast and 10 degrees W. In parallel to low-level clouds, reductions of similar to 5 %-10 % are obtained for mid-level clouds over central Africa, mainly due to BBA-induced surface cooling and lower-tropospheric heating inhibiting convection. In terms of cloud optical properties, the BBA radiative effects induced an increase in the optical depth of about similar to 2-3 over the ocean south of the Equator. The result of the BBA direct effect and feedback on tropical clouds modulates the surface solar radiation over the whole of tropical Africa. The strongest surface dimming is over central Africa (similar to-30 W m-2), leading to a large reduction in the continental surface temperature (by similar to 1 to 2 K), but the solar radiation at the oceanic surface is also affected up to the Brazilian coast. With respect to the hydrological cycle, the CNRM-CM simulations show a negative effect on precipitation over the western African coast, with a decrease of similar to 1 to 2 mm d-1. This study also highlights a persistent impact of BBA radiative effects on low-level clouds (increase in cloud fraction, liquid water content and optical depth) during the September-October-November (SON) period, mainly explained by a residual cooling of sea surface temperature over most of the tropical ocean. In SON, the effect on precipitation is mainly simulated over the Gulf of Guinea, with a reduction of similar to 1 mm d-1. As for JJA, the analysis clearly highlights the important role of the slow response of the ocean in SON and confirms the need to use coupled modelling platforms to study the impact of BBA on the tropical African climate.

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.

Radiation in the atmosphere provides the energy that drives atmospheric dynamics and physics on all scales, from cloud particle growth to global weather and climate. Radiation schemes in global weather and climate models have to simplify the complex interaction of radiation with the Earth system. Capturing the interactions of gases and clouds with radiation is particularly challenging, since gas effects are extremely wavelength-dependent, while clouds vary strongly on small spatial and temporal scales, and they both interact strongly with radiation. Uncertainties in the radiation scheme and the cloud, aerosol and gas and inputs lead to uncertainties in weather and climate processes, such as energy balance, cloud development and dynamics.The radiation scheme ecRad (Hogan & Bozzo 2018) has been operational in the IFS model at ECMWF since 2017 and in ICON at Deutscher Wetterdienst (DWD) since 2021 and will be the next radiation scheme in the operational numerical weather prediction models AROME and ARPEGE, the climate model ARPEGE-Climat and the regional research model Méso-NH at Météo-France. As a modular scheme, ecRad provides the opportunity to vary parametrisations and assumptions individually. Several options are available for the radiation solver, cloud vertical overlap and horizontal inhomogeneity treatment and cloud hydrometeor optical property parametrisations. The solver SPARTACUS is the only radiation solver in a global model that can treat 3D radiative effects. The new gas optics model ecCKD can improve both precision and cost of the gas optics calculation, as can recent code optimisations.We will present the status of and future plans for implementation in the Météo-France models, and show first evaluation results for radiation, energy balance and clouds on various scales scales. We will also investigate the impact of cloud and aerosol input and search for the best settings for radiation balance, model energy and physics and forecast performance. Finally, we will present future plans for radiation work in the Météo-France models. Reference:Hogan, R. J., & Bozzo, A. (2018), A flexible and efficient radiation scheme for the ECMWF model. Journal of Advances in Modeling Earth Systems, 10, 1990-2008. https://doi.org/10.1029/2018MS001364

Abstract Due to their severity and lack of predictability, understanding and forecasting extreme precipitation events (EPEs) is critical for disaster risk reduction. The present work documents the large‐scale environment of tropical EPEs based on a 42‐year data set combining dense rain‐gauge networks that cover several tropical small islands and coastal regions. Approximately 10%–30% of EPEs are associated with a tropical storm or cyclone (TC), except for Reunion, for which its high topography makes it reach 55%. TCs multiply the EPE probability by a factor of 4–15, especially during TCs of category 1 or higher. A composite analysis demonstrates that the remaining large part of EPEs occurs within large‐scale and strong moist, convective, and cyclonic wind anomalies resulting from the superimposition of intraseasonal, seasonal‐to‐annual, and interannual timescales. These intense anomalies come essentially from intraseasonal variability, and lower frequencies improve the effect of intraseasonal events in creating a favorable environment for EPEs.

Getting the right precipitation probability density function in the Mediterranean area is a challenge for most global and regional climate models with parametrized convection. Over land in particular, the intensity of heavy‐precipitating events is often underestimated. In the present study, we provide a process‐based analysis of the representation by the CNRM‐ALADIN63 regional climate model of one of these events, which occurred in the southeast of France on November 1–2, 2008. The CNRM‐ALADIN63 model, when run in a configuration where the large‐scale dynamics is nudged towards that of the ERA‐Interim reanalysis, is first shown to capture the location and intensity of the heavy‐precipitating event appropriately. Then, using a reference convection‐permitting simulation of the same event and a conditional sampling approach to identify and characterize convective updraughts, the ability of the model convection parametrization to capture further convective details is assessed. The model misses the occurrence of the updraught mass flux largest values, despite a significant and systematic overestimation of the updraught vertical velocity. The area covered by the convective updraught is in fact found to be severely underestimated, suggesting an inappropriate approach for convective closure: the event occurred along the foothills of the Massif Central, where the subgrid‐scale features of the topography interact strongly with the impinging large‐scale flow and thereby drive the position and large area fractions of convective updraughts, at least during the mature phase of the event. A preliminary topography‐based convective closure is proposed and implemented in the model to assess this hypothesis further. The results confirm that the parametrization deficiencies can be significantly reduced with a proper inclusion of subgrid‐scale topographic features.

Abstract Extreme precipitation events (EPE) are often associated with severe floods and significant damages in Central Sahel. To better understand their formation and improve their forecasts, we investigate the sub‐seasonal drivers of EPEs. A composite analysis reveals that moist, cyclonic and upper‐level divergence anomalies are found on average as a result of several tropical waves. The equatorial Rossby wave (ER) dominates at large scale providing a moist and convectively‐active anomaly over the northern Sahel together with a smaller‐scale African Easterly Wave (AEW). The Madden‐Julian Oscillation provides upper‐level divergence anomalies and a Kelvin wave increases convection during the EPE. Statistics show the prevalence of AEW and emphasize ER as a key driver of EPE. The co‐occurrences of several tropical waves, especially those involving AEW, ER, and Kelvin waves, increase the probability of EPE. Monitoring these tropical waves combinations could improve EPEs forecasts.

This study investigates the spontaneous self‐aggregation of convection in non‐rotating Radiative‐Convective Equilibrium (RCE) simulations performed by the CNRM‐CM6‐1 General Circulation Model within the framework of the RCE Model Intercomparison Project (RCEMIP). In this model, the level of convection self‐aggregation at equilibrium, as quantified by metrics based on moisture or moist static energy, strongly increases with sea surface temperature (SST). As it gets warmer, the troposphere gets drier, high cloud cover diminishes in dry regions, the top of high cloud rises and their thickness increases in moist regions, and low‐cloud cover increases. At high SSTs, the large‐scale circulation exhibits a shallow component, stronger than its deep counterpart. The transition toward self‐aggregation has a similar first 20‐day phase for all SSTs within the 295–305 K range. It primarily involves radiative positive feedback processes. Then, for SSTs above approximately 298 K, a new, slower, transition toward higher levels of self‐aggregation occurs. It is concomitant with a shift from a top‐heavy to a more bottom‐heavy large‐scale circulation, a strengthening of the shallow circulation and a reduced mobility of convective aggregates. This second transition is mostly driven by the dry regions, still involves longwave radiative positive feedbacks, but also advective positive feedbacks in the driest regions. It is argued that boundary‐layer radiative cooling difference between moist and dry regions, which is stronger at high SSTs, is instrumental in this second phase of self‐aggregation. The sensitivity of deep convection to environmental dry air also likely acts as a positive feedback on the system.