Philip Stier

Abstract:

<jats:p>Abstract. Deep convective clouds substantially modify the balance of shortwave and longwave radiative energy at the top of the atmosphere. Although in the present-day these effects approximately balance out, projected changes in deep convective clouds could alter the future top-of-atmosphere energy balance. Past studies have found relationships between convection and anvil clouds, but our understanding of how convection typically controls the properties and evolution of anvil clouds that determine anvil radiative effects remains incomplete, limiting our ability to explain or justify projected changes in cloud optical properties. This manuscript presents a new method to track the lifecycle of deep convective clouds and their convective cores in three-dimensional space in km-scale global climate models. An analysis of how convective organisation, intensity and area relate to anvil properties in the ICOsahedral Non-hydrostatic (ICON) model is then presented. Approximately 1000 deep convective clouds are tracked over one simulation week in the tropical Amazon region. We find that while both updraft intensity and area correspond to larger anvils, the correlation between convective area and anvil size is stronger than that between anvil size and updraft intensity. Updraft intensity was associated with a 4-fold increase in anvil extent when convective cores were larger, compared to when they were in the bottom 50th size percentile. This result could not be explained by associated changes in peak convective mass flux or organisation. These results indicate how changes in the frequency or typical size of convective updrafts may link to changes in anvil development, extent and, ultimately, radiative effects.</jats:p>

Abstract:

The radiative response of deep convective anvil clouds to anthropogenic aerosols is a major source of uncertainty. While aerosol-cloud interactions (ACI) in the convective core have been extensively studied, the microphysical mechanisms governing the full anvil lifecycle, from detrainment to dissipation, remain poorly constrained.This study examines the Cloud Radiative Effect (CRE) of deep convection through a microphysical process-rate lens. We perform three regional simulations with interactive aerosol using ICON-HAM-lite, comprising baseline, clean, and polluted runs. The simulations follow the TRACER-MIP protocol for a sea-breeze event over Houston, Texas. Using Lagrangian tracking with the tobac cloud tracking algorithm, we isolate individual convective cells and track their evolution from convective onset to the detrainment and dissipation of the resulting anvils. We then assess aerosol-cloud interactions over the lifecycle of the tracked cells by aligning their evolution with the onset of freezing, to ensure a consistent lifecycle comparison.Our results show that a 9-fold increase in aerosol concentration leads to a 2.5-fold increase in cloud droplet number concentration (CDNC). This suppresses warm-rain processes and enhances upward mass flux above the melting layer. As a result, it also lofts higher droplet concentrations, which can shape anvil characteristics by modulating the total ice surface area available for deposition and the net cross-section for riming. This creates a competition between enhanced riming, which promotes mass fallout, and increased vapour deposition, which sustains smaller ice crystals aloft. We conclude by investigating how these competing factors change the lifetime of the anvil and its net CRE.

Abstract:

The top-of-atmosphere net radiative effect of convective anvils is estimated to be close to zero and arises from a balance of significant short-wave cooling and long-wave warming over a complex diurnal cycle. When anvils are optically thick, the cooling due to daytime scattering of shortwave solar radiation dominates. In contrast, optically thin anvils have weaker scattering of solar radiation, so longwave warming becomes the dominant effect. Hence, it is essential to understand the controls of anvil radiative properties over the convective lifecycle, which arises from a complex interplay of convective cloud dynamics and microphysics. The convective mass flux modulates anvil extent, and changes in ice crystal size and morphology affect anvil lifetime and radiative properties. Convective anvils have been proposed to respond to global warming (cloud feedbacks) and anthropogenic aerosols (aerosol-cloud interactions). However, the associated uncertainties remain large and key relevant processes are not represented in the current generation of climate models. Emerging kilometre-scale climate models present new opportunities to examine these effects at the process level.In this work we bring together multiple research strands to quantify the controls of convective anvil clouds and associated radiative effects over the convective lifecycle towards understanding its sensitivity to climate and air pollution changes. We use the tobac cloud tracking framework to track convective cores and associated anvils in 4D across regional and global km-scale ICON model simulations which allows us to quantify the link between convective mass flux, anvil extent and anvil radiative properties. We apply this framework to regional high-resolution simulation of ICON coupled to HAM-lite, our reduced complexity aerosol model derived from the microphysical aerosol scheme HAM [Weiss et al., GMD, 2025], to explore the sensitivity of anvils and their radiative effects to aerosol perturbations in the context of the ORCHESTRA/EarthCARE Model Intercomparison Project (ECOMIP) as well as the TRACER campaign MIP. We find that an increase in aerosol increases cloud droplet numbers, suppresses warm rain formation, increases convective mass flux and thereby upper tropospheric ice water content and will discuss how these changes translate into anvil cloud radiative effects. Prototype next generation km-scale climate models are implicitly already including such anvil radiative effects; however, these currently remain unconstrained by observations. We develop novel observational constraints on the convective anvil cloud lifecycle through consistent tracking of convection using the tobac-flow cloud tracking framework [Jones et al., 2024] between MSG SEVIRI observations and forward simulated geostationary satellite radiances from ICON model output.  This reveals that deep convective systems in ICON grow too fast and show a faster dissipation of thick to thin anvils than observations, which affects their radiative effects. Our work provides novel approaches to improve our understanding of aerosol effects on convective clouds and climate.

Abstract:

Understanding the driving forces behind mesoscale cloud organization is fundamental to reducing uncertainties in cloud climate feedbacks. Traditional climate models cannot explicitly resolve mesoscale cloud structures due to their limited resolution, leading to large uncertainties in cloud climate feedback estimates. Storm-resolving models that simulate the atmosphere at kilometre resolution have the potential to reduce these uncertainties. Yet, these models are still biased in their organizational structure when compared to satellite observations. Approaches constraining cloud feedbacks directly from the satellite records are promising but often rely on manually chosen cloud controlling factors (CCFs) that do not necessarily capture all the information necessary to explain mesoscale organizational structures and generally only utilise linear models to predict cloud radiative properties from CCFs.We present CloudDiff, a probabilistic machine learning model that generates mesoscale cloud structures at kilometre resolution conditioned on environmental conditions in the atmosphere, namely the temperature and humidity profiles as well as vertical and horizontal winds. The model is trained on MODIS Level 1 satellite data and environmental conditions from ECMWF ERA5 reanalysis data. CloudDiff is able to reconstruct realistic MODIS observations from matching ERA5 environmental conditions and achieves a lower reconstruction error compared to generating MODIS observations solely from pre-defined CCFs. In CloudDiff’s generation stage, the environmental conditions are compressed into a latent representation using an attention mechanism. This latent representation can be interpreted as a set of CCFs that have been learned purely from data. We’ll discuss the properties of the learned CCFs including how they relate to existing CCFs, their geographical distribution, and their predictive power of the radiative properties of cloud fields.

Abstract:

The top-of-atmosphere radiative effect of tropical anvil clouds varies with cloud opacity, and can range from substantially negative to largely positive. Recent climate model assessments have found a decrease in the proportion of thick, or opaque, anvil cloud with warming, resulting in a positive climate feedback. However, the mechanism for this change remains obscure.Lifecycle analysis of deep convective clouds tracked using tobac in the convection-permitting global ICOsahedral Non-hydrostatic model (ICON) shows how anvil area and opacity respond to convection. We find that both properties increase in response to increased convective intensity and convective area, but that their sensitivity to each is not equal. To interpret these results, we independently develop a simple analytical model that links anvil expansion and opacity to convective mass flux (CMF). The model predicts that higher CMF leads to greater anvil expansion, increasing the area of thick anvil cloud. But when anvil opacity also depends on convective intensity, we find a strong, non-linear increase in thick anvil amount in response to increasing CMF, consistent with the response observed in ICON. This implies a strong sensitivity of thick anvil amount to changes in the upper tail of the distribution of CMF and illustrates a possible mechanism by which changes in the distribution of cloud CMF could drive anvil thinning in a warming climate.