Since shallow clouds over the tropical oceans are organised into mesoscale structures, explaining the role of these clouds in climate requires understanding what governs mesoscale patterns in shallow cumulus convection. Many puzzle pieces have emerged in recent years. These include both external forcings on the boundary layer, such as the import of extratropical eddies and water vapour with the large-scale flow, weak sea-surface temperature anomalies and remotely triggered gravity waves, as well as internal feedbacks between convection and its mesoscale environment, such as cold pool dynamics and self-reinforcing low-level moisture convergence. What all these mechanisms share, is their interaction with the low-level mesoscale vertical motion field, which itself is often organised into shallow circulations that couple tightly to the convection. Here, we will therefore propose to begin assembling the puzzle pieces by analysing the origins of shallow circulations, in a conceptual framework of weak mesoscale virtual temperature gradients. The analysis is enabled by the simultaneous presence of observations of clouds, thermodynamics and mesoscale vertical motion taken during the EUREC4A field campaign, and simulations from EUREC4A-MIP; it will therefore serve as an example of what other puzzle pieces might fall in place by combining observations with other intercomparison projects in the model hierarchy, such as Lagrangian LES MIP, CP-MIP and NextGEMS.

How to cite: Janssens, M.: The puzzle of shallow convection-circulation coupling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9104, https://doi.org/10.5194/egusphere-egu24-9104, 2024.

In current storm-resolving models, the parameterization of shallow cumulus convection is based on the mass-flux framework, originally tailored for coarse mesh sizes O(10-50km). Recent finer grids present a unique opportunity to study the coupling between clouds, convection, and the large-scale circulations. This finer resolution also prompts a critical inquiry into the role of shallow convection parameterization (SCP). Within the context of EUREC4A-MIP, we use HARMONIE-AROME with a grid spacing of 2.5 km to test the mesoscale cloud sensitivity to SCP. While cloud patterns are discernible at this resolution, individual shallow cumuli may not be fully resolved. Our investigation reveals that mesoscale properties of tropical shallow cumulus fields and associated circulations exhibit a pronounced dependence on sub-grid parametrization, with differences in cloud cover up to 20%. We simulate the period from January 1st to February 28th 2020, and compare three configurations of HARMONIE-AROME: 1) the control with active SCP, 2) UVmix-off without momentum mixing by shallow convection, 3) SCP-off without any mixing by shallow convection. Instead of an incremental effect, our results show that UVmix-off and SCP-off can produce opposite responses in the cloud field. UVmix-off produces large anvils, less precipitation, and a cooler lower-troposphere. In contrast, SCP-off produces many smaller clouds which precipitate more in a warmer lower-troposphere due to a more unstable environment, and the buildup of CAPE and turbulent kinetic energy. Importantly, our results underscore that the removal of SCP (and to a lesser extent, the removal of UV mixing) strengthens mesoscale circulations and augments their coverage through increased wind variance, predominantly at scales larger than 25 km. Stronger resolved vertical motion in SCP-off produces stronger circulations, whereas the altered wind mixing in UVmix-off primarily affects the coverage of circulations. The ability to look at parameterized tendencies provides further insight into where the convection is strengthening or weakening the winds. This nuanced exploration contributes valuable insights into the intricate dynamics of mesoscale cloud systems under varying shallow convective parameterizations.

How to cite: Savazzi, A., Nuijens, L., de Rooy, W., and Siebesma, P.: The role of parametrized shallow convection in tropical cloud systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8708, https://doi.org/10.5194/egusphere-egu24-8708, 2024.

Limited understanding of the key factors that govern the lifecycle of cumulus clouds, including the interactions among clouds and with surrounding environments, contributes to climate prediction uncertainty. To investigate these processes, we tracked the lifecycle of thousands of individual shallow cumulus clouds within a large-eddy simulation during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HISCALE) field campaign in the U.S. Southern Great Plains.

Our examination of these clouds followed two paths. First, we compared two distinct groups of clouds—those with growing cloud neighbors and those with decaying cloud neighbors. Clouds with growing neighbors were found to form over areas with larger surface heterogeneity than clouds with decaying neighbors. Clouds with growing neighbors also had less instability, less moisture and warmer air below cloud base than decaying neighbor clouds. This suggests that evaporation below the cloud base likely occurs before the formation of these clouds with decaying neighbor clouds due to the colder and moister air below cloud base. Larger instability leads to higher vertical velocity and convergence within the cloud, which causes stronger downdrafts and water vapor removal in the surrounding area. The latter appears to be the reason for the decaying neighboring clouds.

Second, we introduced two new metrics to assess the relationships between cloud shape and these processes: one reflecting the irregularity of cloud edges and another emphasizing the cloud horizontal aspect ratio. During the lifecycle of simulated cumulus clouds, cloud edge irregularity increased with minimal changes in aspect ratio. Irregularity-driven growth of the cloud perimeter was a strong indicator of cloud splitting, more so than growth driven by aspect ratio changes. Additionally, clouds with more irregular edges exhibited smaller gradients of properties at their boundaries, suggesting more intense mixing with the surrounding cloud-free environment.

These results advance insights into the interactions between cumulus clouds and their nearby environment entrainment that influence the evolution of cloud populations. Such knowledge can support the development of more accurate shallow cumulus parameterizations in the new generation of climate models.

How to cite: Chen, J., Hagos, S., Feng, Z., Xiao, H., Fast, J., Lu, C., and Varble, A.: The role of cloud-cloud interactions and entrainment-mixing in the lifecycle of shallow cumulus clouds, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2919, https://doi.org/10.5194/egusphere-egu24-2919, 2024.

Cloud modelling is a very important tool for climate research. However, it is not an easy task to validate model data and assess a model’s performance. Since cloud model data can not be expected to be an exact match of corresponding satellite data, there is no immediate method of comparison available.

We use a cloud tracking algorithm [1] to find the lifetime and cloud size distributions of the cloud datasets. This enables us to provide a unique quality assessment of the model data. Lifetime information is interesting because it encompasses multiple dynamic scales from micro to planetary regimes, while cloud size and cloud cover are important factors for the radiative properties of the clouds in a region and characterise the clouds’ general behavior.

Here we compare satellite data from the EUREC⁴A campaign [2] (observed by the Advanced Baseline Image onboard the GOES-16 satellite) and model output from ICON-LEM tailored for the EUREC⁴A campaign [3], where two resolutions are available. All datasets are located east of Barbados in the Caribbean Sea. We build on previous cloud tracking analyses for the GOES satellite dataset [1].

For the comparison between the three datasets, we first show the temporal development of cloud cover and number of clouds as an overview for the datasets. Secondly, we show the distributions of clouds lifetimes and sizes for all trajectories. The linear regression exponent for the logarithmic cloud size distribution can be expected to be around -2 on the global scale [4], which all three datasets come close to. However for this region, we would expect small clouds to have a bigger influence compared to the global view. This effect can be observed in the model data which have slightly more negative exponents for both resolutions. Thirdly, we show the average development of cloud size over the lifetime of the tracked clouds as a further metric for evaluating how well the model can represent the cloud-development processes.

References

[1] Seelig et al. (2023) “Do optically denser trade-wind cumuli live longer?”, in Geophysical Research Letters, doi: 10.1029/2023GL103339

[2] EUREC⁴A campaign: www.eurec4a.eu

[3] Schulz, Hauke & Stevens, Bjorn (2023) “Evaluating Large-Domain, Hecto-Meter, Large-Eddy Simulations of Trade-Wind Clouds Using EUREC4A Data” in Journal of Advances in Modeling Earth Systems, doi: 10.1029/2023MS003648

[4] Wood, Robert & Field, Paul (2011) “The Distribution of Cloud Horizontal Sizes”, in J. Climate, doi: 10.1175/2011JCLI4056.1

How to cite: Müller, F., Seelig, T., and Tesche, M.: Tracking Clouds: Comparing Geostationary Satellite Observations and Model Data in the EUREC4A domain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9928, https://doi.org/10.5194/egusphere-egu24-9928, 2024.

Marine boundary layer clouds stand out because of their importance for Earth's planetary albedo and their central role in determining Earth's sensitivity to forcing. The new global-coupled simulations at kilometer-scale resolution in both the atmosphere and the ocean in the framework of the H2020 nextGEMS project offer new opportunities to study cloud processes and their environmental factors, as well as provide unprecedented realism and new opportunities for comparison to observations. We examine the representation of (sub)tropical stratocumulus and trade-wind cumulus clouds by the IFS and ICON models configured with kilometer-scale resolution and global domains. The simultaneous consideration of ICON and IFS allows us to compare two strategies. The former simplifies parameterizations to understand process interactions better, sacrificing degrees of freedom to tune the model. The latter considers more sophisticated parameterizations, which allow for better tuning. The results of this study show the value of both. The performance of the four-year simulations is assessed in terms of the top-of-atmosphere (TOA) albedo and the vertical structure of the atmospheric boundary layer in eight regions where low-clouds are climatologically found. The stratocumulus regions are located in the eastern subtropical ocean basins, and the trade-wind cumulus regions are located west and equatorward from the stratocumulus ones. As an observational reference for the TOA albedo, we used satellite data from the CERES-EBAF TOA dataset.
Both models captured the mean horizontal distribution and seasonal cycle of TOA albedo and the typical vertical structure of the low atmosphere over the stratocumulus regions. Despite its relatively simplistic approach to sub-grid parameterizations, particularly turbulence mixing treated with the Smagorinsky scheme, ICON performed comparably well to IFS, which employs more sophisticated solutions, including eddy-diffusivity mass flux and convection schemes. Regarding trade-wind cumulus, both models overestimate the mean TOA albedo. To validate the simulated vertical structure of the atmospheric boundary layer in the northwestern Atlantic trade-wind regime, we used the radiosondes launched at the Barbados Cloud Observatory (BCO) during the EUREC⁴A field campaign. The ICON and IFS models represent the main characteristics of the vertical structure of wind speed, temperature, and moisture observed at the BCO. We also find some discrepancies between the model representation and the observations. The simulations represented a colder (1 K) vertical profile than the observations. The ICON represented a drier cloud layer between 1–2 km and a moister layer above it, which is attributed to too much vertical mixing across the top of the cloud layer and suggests some revision of the stability correction function. The IFS model represented this region better than ICON, which was expected because IFS uses a shallow convection scheme, which allows better control of this region. However, IFS represented slightly drier the lowest 500 m.

How to cite: D'Amato Dragaud, I., Nowak, J., Dziekan, P., Lee, J., Mellado, J. P., and Stevens, B.: Representation of marine low‐level clouds in global-coupled kilometer-scale simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12952, https://doi.org/10.5194/egusphere-egu24-12952, 2024.

Stratocumulus clouds contribute significantly to the global energy budget as they are the Earth’s predominant cloud type and contribute strongly to Earth’s albedo. They are known to predominate in the subtropics, especially on the eastern edge of the subtropical highs. Previous studies have confirmed the importance of these highs for stratocumulus clouds, but how much it varies can influence the cloudiness hasn’t been quantified, yet. Our study investigates this relation for both the annual cycle and deseasonalized time series for the five major subtropical high-pressure regions. It has been shown that the estimated cloud top entrainment index (ECTEI) is a useful predictor for the stratocumulus cloud fraction for both time scales. We show, however, that the variation of the highs provides additional information on the fraction change on an annual cycle. The Northern Hemisphere is more sensitive to the highs change compared to the Southern Hemisphere. Variations in the structure, area, and location of subtropical highs are not considered the dominant influencing factors (correlations about 0.3~0.4). Nevertheless, we found a qualitative preference that stratocumulus clouds prefer a flatter, large, and westward subtropical high.

How to cite: Ding, H., Stevens, B., and Schmidt, H.: Links between subtropical high-pressure systems and stratocumulus clouds variation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9182, https://doi.org/10.5194/egusphere-egu24-9182, 2024.

Equatorward excursions of cold polar air masses during cold air outbreaks (CAOs) result in the development of mesoscale convective circulations that significantly affect surface fluxes. Air masses undergo intense transformations as they transition from the ice to the warmer ocean.  Initially strong surface heat fluxes and strong shear result in the formation of helical convective rolls and associated cloud streets that can extend for hundreds of kilometers. Further downwind helical convective rolls evolve into convective cells forming open cell clouds.

We study an intense CAO observed on 13 March 2020 during Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) [1]. COMBLE deployed the Department of Energy Atmospheric Radiation Measurement (ARM) Mobile Facility 1 (AMF1) at Andenes, Norway to observe a range of CAO conditions. We simulate the evolution of a CAO using coupled mesoscale to microscale simulations with the Weather Research and Forecasting (WRF) model. The coupled simulation using WRF includes a mesoscale domain with 1050 m horizontal grid cell coupled online with a cloud-resolving LES domain with horizontal grid cell size of 150 m that stretches through the full ~1000 km extent of a CAO, from the ice edge to Andenes. Within the cloud-resolving domain are nested two LES domains with 30 m grid cells. One of these domains is focused on the region of convective rolls while the other one is focused on convective cells. This configuration enables us to study the transformation of airmass at high resolution, providing unprecedented insight into the mixed phase cloud (MPC) transition from rolls to cells. We study the interaction between large-scale forcing, surface fluxes, radiative transfer, and cloud processes in the formation and evolution of mesoscale organization and MPCs. As part of this effort, we utilize the Cloud Resolving Model Radar Simulator (CR-SIM) to compare WRF more directly to the measurements. Our CR-SIM analysis suggests that convective cell structures and properties are well modeled at the AMF1 site when using turbulence-resolving resolutions.

How to cite: Kosovic, B., Juliano, T., xue, L., Geerts, B., Lackner, C., and Abrokwah Oteng, N.: Coupled Mesoscale to Microscale Simulations of Mixed-Phase Convective Clouds Observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6203, https://doi.org/10.5194/egusphere-egu24-6203, 2024.

The Intertropical Convergence Zone (ITCZ) in the Atlantic is typically described as a narrow band of precipitation and deep convection. However, this description often stems from long-term averaging of precipitation or outgoing longwave radiation in the ITCZ. On shorter time scales, the ITCZ is much more dynamic and various classifications of different patterns can be attempted. One possibility is based on satellite images collected during the GATE campaign in 1974 where four patterns - Line, Double Line, Broad and Cluster - were previously identified. In our analysis, we build on the pioneering results from GATE, supplement the category No Clouds, and validate the patterns based on 43 years of harmonized, equal-angle grid geostationary satellite images. In a first attempt, manual classification of these patterns in the visible spectrum proved feasible for 1000 km wide cutouts and for manually defining the extent of the pattern on the entire Atlantic ITCZ. Manual classification for July 2021 has already shown that all classes neither occur with the same frequency nor the same spatial ditribution over all regions of the Atlantic. For further analysis on the appearance of these patterns on longer time scales the satellite images have been classified by a machine learning algorithm, and their frequency dependence on season and region have been analyzed. These results now enable us to ask why these different patterns occur.

How to cite: Hayo, L., Windmiller, J., Schulz, H., Acquistapace, C., and Crewell, S.: Pattern Recognition of Convection in the Atlantic Intertropical Convergence Zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17171, https://doi.org/10.5194/egusphere-egu24-17171, 2024.

The DYAMOND project (Stevens et al. 2019) provides an intercomparison framework for state-of-the-art global convection-permitting models with km-scale horizontal grid spacing that can directly simulate convective storms. We recently assessed the fidelity of the convective storms simulated by DYAMOND models using a novel feature tracking technique (Feng et al. 2023) and found a surprisingly large inter-model spread in the simulated frequency of ordinary deep convection and mesoscale convective systems (MCSs), as well as their associated precipitation. Recent works also showed that different feature tracking algorithms have significant impacts on estimating MCS characteristics including frequency, size, lifetime and precipitation (Prein et al. 2023). To further investigate how feature tracking methods affect the evaluation of global MCS simulations and our understanding of convective organization in observations and DYAMOND simulations, we are organizing a new international initiative called MCSMIP (MCS tracking Method Intercomparison Project). Preliminary results from several different feature trackers show that DYAMOND models generally underestimate observed MCS precipitation amount and their contribution to total precipitation in the tropics (Fig. 1), and the simulated MCS precipitation is too intense. However, some models have notable differences in MCS frequency and characteristics among the trackers. Potential paths towards more process-oriented model diagnostics to better understand the differences in simulated MCS and precipitation characteristics will be discussed.

Figure 1. (a) Observed MCS contribution to total precipitation during DYAMOND Phase II, (b) model relative mean difference (%) from observations in the tropics. Each group of bars in (b) is from a feature tracker: PyFLEXTRKR, MOAAP, TOOCAN, tobac, TAMS, and simpleTrack, and each bar denotes a DYAMOND model.

References

Feng, Z. et al. (2023). Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations. Geophys. Res. Lett., doi: 10.1029/2022GL102603.

Prein, A. et al. (2023). Km-Scale Simulations of Mesoscale Convective Systems (MCSs) Over South America – A Feature Tracker Intercomparison. DOI: 10.22541/essoar.169841723.36785590/v1.

How to cite: Feng, Z., Leung, R., Prein, A., Fiolleau, T., Jones, W., Moon, Z., Maybee, B., Song, F., Song, J., Núñez Ocasio, K., Klein, C., Varble, A., Roca, R., and Li, P.: Mesoscale Convective Systems in DYAMOND Models: A Feature Tracking Intercomparison., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1219, https://doi.org/10.5194/egusphere-egu24-1219, 2024.

The characteristic large-scale and strongly precipitating cloud band in extratropical cyclones is associated with the so-called warm conveyor belt (WCB), which is a coherent cyclone-relative airstream that ascends cross-isentropically from the boundary layer into the upper troposphere. Cloud microphysical processes along this ascending airstream determine the total diabatic heating, cloud structure, and associated surface precipitation characteristics.

We disentangle uncertainty related to the representation of cloud microphysical processes in the two-moment microphysics scheme of the ICOsahedral Nonhydrostatic (ICON) modeling framework in a convection-permitting simulation setup for an extratropical cyclone case study in the North Atlantic. To quantify uncertainty, we employ a perturbed parameter ensemble (PPE) approach, whereby five selected uncertain parameters in the cloud microphysics scheme and environmental conditions relevant for cloud formation are perturbed simultaneously and systematically. Specifically, cloud microphysical uncertainty is quantified along Lagrangian WCB trajectories which are calculated online during the ICON simulations from the resolved 3D wind fields at every model time step for each of the 70 PPE members. The Lagrangian perspective not only facilitates the characterisation of the airstream’s ascent behaviour but also provides detailed insight in cloud and precipitation formation along the ascent. The application of the Lagrangian diagnostics to all PPE members enables the quantification of dominant contributions of uncertainty from the perturbed parameters for WCB ascent characteristics, such as ascent timescales and tracks, as well as for precipitation formation along the ascent.

For example, we show that the precipitation efficiency along the ascending airstream is most strongly influenced by cloud condensation nuclei (CCN) concentrations modifying the cloud droplet to rain drop conversion. Moreover, a trajectory-based airstream-relative composite analysis shows that increased CCN concentrations result in a downstream shift of the surface precipitation relative to the eastward propagating airstream as the precipitation efficiency is reduced. In addition, the Lagrangian diagnostics can illustrate the feedback between diabatic heating from cloud microphysical processes in the mixed-phase and local vertical velocity. In this contribution we present our analysis framework and show how the perturbed parameters influence various Lagrangian diagnostics for WCB ascent and associated cloud and precipitation formation.

How to cite: Oertel, A., Miltenberger, A. K., Grams, C. M., and Hoose, C.: Quantifying cloud microphysical uncertainties in an extratropical cyclone’s ascending airstream using Lagrangian diagnostics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6252, https://doi.org/10.5194/egusphere-egu24-6252, 2024.

Warm conveyor belts (WCB) are regions of large-scale coherent airflow within extratropical cyclones that rapidly ascend from the boundary layer to the upper troposphere. During their ascent, WCB air parcels experience various microphysical processes that produce mixed-phase clouds and large amounts of precipitation. They also transport water vapour and cloud condensate to the upper troposphere/lower stratosphere (UTLS), which is important for Earth’s radiative budget. Recent studies have found that deep and embedded convection play an important role in WCBs. This points to the necessity of high-resolution simulations, that are well validated with observational data to provide a “benchmark” for coarser-resolution global (climate) models. We conduct a Lagrangian investigation of the physical processes governing WCB moisture transport and cloud composition with a particular focus on (i) the microphysical processes controlling moisture loss from the WCB, and (ii) the cloud microphysical properties of the cirrus clouds in the WCB outflow.

To this end we conducted a case-study from the HALO-WISE campaign and ran a high-resolution doubly nested ICON simulation with a maximum (convection permitting) resolution of ~3km. Online trajectories are calculated that capture convective ascent and allow for a Lagrangian analysis of WCB moisture transport and WCB cloud structure.

The Lagrangian metrics show large differences in the behaviour of moisture transport to the UTLS for trajectories with different ascent timescales. Fast ascending trajectories ascend further south and to much lower pressures and temperatures than their slower counterparts. They also produce much more precipitation and have markedly different hydrometeor contents throughout the ascent. In the ice phase, slow ascending trajectories mainly produce ice and snow through depositional growth, whereas fast trajectories also produce graupel and hail by collision-coalescence. Warm rain processes dominate the moisture loss for all ascent timescales, but for fast ascending trajectories the conversion of moisture to precipitation by microphysical processes in the ice phase increases. These findings are important because widely used coarse-resolution simulations with convection parameterization run the risk of missing the physical processes we see for the fastest ascending trajectories.

How to cite: Schwenk, C. and Miltenberger, A.: A Lagrangian investigation of the (micro)physical processes controlling warm conveyor belt moisture transport and cloud properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10425, https://doi.org/10.5194/egusphere-egu24-10425, 2024.

Most of the Earth's atmosphere is covered with clouds, which significantly affect incoming and outgoing radiation and thus the Earth's energy balance. Clouds are a source of considerable uncertainty in weather and climate models. Their size ranges from submillimeters, where cloud microphysics is important, to hundreds of kilometers, where they affect weather and climate. The complex coupling of cloud and turbulent flow dynamics at these scales makes clouds difficult to understand. In addition, several long-standing important puzzles, such as the existence and/or presence of cloud holes (regions without droplets) and the sharpness of cloud boundaries, remain unsolved. Given the high Reynolds numbers in atmospheric clouds (Re~10^6-10^9), laboratory-generated flows (with few exceptions) and direct numerical simulations are not yet capable of achieving cloud-like flow dynamics. Therefore, field studies and, in particular, airborne measurements performed far from the Earth's topographic influences can approach the correct range of parameter space relevant to naturally occurring clouds.
We have developed the Max Planck CloudKite, which consists of a balloon-kite aerostat and a suite of scientific instruments for simultaneous measurements of aerosols and turbulence features in the atmospheric boundary layer and in clouds. Cloudkite is an independent platform capable of characterizing the atmospheric boundary layer and low-lying clouds within the boundary layer (<2 km) at almost any location on Earth. It has been successfully deployed in remote regions of the Atlantic aboard research vessels and also in northern Finland within the Arctic Circle. The cloud-resolving probe is equipped with Particle Image/Tracking Velocimetry (PIV/PTV), Inline Holographic Particle Imaging, Fast Cloud Droplet Probe (FCDP), multi-hole pitot tubes, and humidity, temperature and pressure sensors. In addition, 10 WinDart units, including aerosol spectrometers and 3D ultrasonic sensors, are installed on the tether to fully characterize the atmospheric boundary layer and clouds simultaneously. Overall, the results will greatly improve our understanding of cloud evolution and spatial structure, as well as cloud-aerosol interactions, which is urgently needed to address climate change challenges.

How to cite: Bodenschatz, E., Bagheri, M., Khodamoradi, H., Kupitzek, A., Nordsiek, F., Schettler, C., and Thiede, B.: The Max Planck Cloud Kite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8740, https://doi.org/10.5194/egusphere-egu24-8740, 2024.

This study investigates the influence of sub-cloud rain evaporation on the decoupling of sub-tropical marine cumulus-topped raining boundary layers. Using 24-hour wind lidar and Ka-band radar observations on February 9, 2020 from the Barbados Cloud Observatory (BCO), along with in-situ rain microphysical observations from the ATR aircraft during the EUREC⁴A field campaign, we extract rain microphysical parameters - raindrop number concentration (N₀) and geometric mean diameter (D_g). These parameters, alongside surface relative humidity measurements, serve as inputs to initialize a single-column rain evaporation model, allowing us to derive vertical profiles of rain evaporation fluxes and evaporation cooling rates. Our analysis identifies 'top-heavy' profiles characterized by maximum evaporative cooling near the cloud base, featuring smaller D_g and larger N₀. Conversely, 'bottom-heavy' profiles exhibit larger D_g and smaller N₀, with maximum evaporative cooling closer to the surface. Notably, our findings reveal that top-heavy profiles, especially when cloud bases are higher, tend to be more decoupled than bottom-heavy profiles. The higher decoupling of the top-heavy profiles is attributed to the stable configuration of the evaporatively-cooled moisture layer just below the warmer cloud layer, hindering moisture transport to the cloud. In contrast, for a bottom-heavy profile where the evaporatively-cooled moisture layer is accumulated closer to the surface over a warmer sea surface, surface-driven mixing promotes moisture transport to cloud bases, resulting in less decoupling. The decoupling index, independently estimated from the difference between ceilometer-based cloud base height and empirically determined lifting condensation level, enhances the robustness of our results. While emphasizing the significant influence of sub-cloud rain evaporation on the decoupling of cumulus-topped raining boundary layers, our study has not explored other factors like surface and radiative fluxes, which could also contribute to the boundary layer decoupling.

How to cite: Sarkar, M., Zheng, Y., and Vogel, R.: Role of Sub-Cloud Rain Evaporation on Boundary Layer Decoupling over Barbados Island, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12792, https://doi.org/10.5194/egusphere-egu24-12792, 2024.

Recent observations of the trades highlight the covariability between cold pool (CP) properties and cloud cover, suggesting a potential impact of CPs on the cloud radiative effect (CRE). To explore this, we use an ensemble of 103 large-domain, high-resolution, large-eddy simulations (Cloud Botany). We investigate the extent to which the variability in CPs is driven by external conditions or convective self-organization. Our findings show that CPs are notably controlled by large-scale conditions, specifically (horizontal) wind speed and subsidence. The temporal evolution of CPs is tightly related to the diurnality in radiation. To understand the extent to which CPs vary with self-organization, we switch off the diurnality in radiation. Despite the absence of the diurnal cycle, CP time series still exhibit fluctuations. These fluctuations result from the recharge-discharge of thermodynamic and dynamic properties of the sub-cloud layer owing to CP-cloud interactions. Our results demonstrate that circulations induced by CPs reinforce the parent clouds, resulting in deepening and scale growth, followed by mesoscale arcs enclosing clear-sky areas. Finally, we show that CPs influence CRE, but only when they exist during the day. Our findings emphasize the importance of the relationship between the timescales of self-organization and the diurnal cycle of external conditions, greatly influencing the CRE dependency on self-organizing CPs.

How to cite: Alinaghi, P., Janssens, M., Jansson, F., Siebesma, A. P., and Glassmeier, F.: Cold Pools in the Trades: External Drivers and Self-Organization Impact, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1281, https://doi.org/10.5194/egusphere-egu24-1281, 2024.