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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The Detection of Mesoscale Convective Systems by the GPM Ku-Band Spaceborne Radar

The Global Precipitation Measurement (GPM) core observatory satellite launched in 2014 features more extended latitudinal coverage (65 degree S-65 degree N) than its predecessor Tropical Rainfall Measuring Mission (TRMM, 35 degree S-35 degree N). The Ku-band radar onboard of the GPM is known to be capable of characterizing the 3D structure of deep convection globally. In this study, GPM’s capability for detecting mesoscale convective systems (MCSs) is evaluated. Extreme convective echoes seen by GPM are compared against an MCS database based on tracking convective entities over the contiguous US. The tracking is based on geostationary satellite and ground-based Next Generation Radar (NEXRAD) network data obtained during the 2014-2016 warm seasons. Results show that more than 70% of the GPM-detected Deep-Wide Convective Core (DWC) and Wide Convective Core (WCC) objects are part of NEXRAD identified MCSs, indicating that GPM-classified DWCs and WCCs correlate well with typical MCSs containing large convective features. By applying this method to the rest of the world, a global view of MCS distribution is obtained. This work reveals GPM’s potential in MCS detection at the global scale, particularly over remote regions without dense observation network.

3D reflectivity structure↗
DOE OSTI2012

Development of Ensemble Neural Network Convection Parametrizations for Climate Models Using ARM Data

The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an important practical conclusion of the study: the obtained results on NN ensembles as a stochastic physics parametrization show a realistic possibility of development of NN convection parametrization for climate (and NWP) models based on learning cloud physics from CRM/SAM simulated data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Evaluating and Improving Convective Parameterization for GCMs Using ARM Observations. Final report

This technical report summarizes the achievements during the project period funded by the Atmospheric System Research (ASR) program. The general goal of the project is to improve the representation of atmospheric deep convection in global climate models. By using DOE Atmospheric Radiation Measurement (ARM) program observations and a hierarchy of models from cloud-resolving to global climate models, we evaluated and improved many aspects of convection parameterization schemes, such as the trigger function for convection and closure condition to determine the amount of convection. We also incorporated the stochasticity of convection into a convection scheme. These improvements, when implemented into the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) and the DOE Exascale Energy Earth System Model (E3SM), led to improved simulation of precipitation characteristics, including precipitation intensity, Intertropical Convergence Zone, and Madden-Julian oscillation. The project also improved the representation of microphysical processes in convective clouds. It enables the interaction of cloud microphysics with aerosols in weather and climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Relating Convection to GCM Grid-Scale Fields Using Cloud-Resolving Model Simulation of a Squall Line Observed during MC3E Field Experiment

In this study, a WRF (Weather Research and Forecasting) model is used as a cloud-resolving model to simulate a squall line observed on 20 May 2011 in the Southern Great Plains (SGP) of the United States. The model output is then used to examine the relationships between convective precipitation and coarse-grained variables averaged over a range of subdomain sizes equivalent to various global climate model horizontal resolutions. The objective is to determine to what extent convection within the subdomains can be related to these “large-scale” variables, thus that they can potentially serve as closure in convective parameterization. Results show that convective precipitation is well correlated with the vertical velocity at 500 hPa, column integrated moisture convergence and CAPE change due to large-scale advective forcing (dCAPE) for various subdomain sizes, but the correlation decreases with decreasing subdomain size. dCAPE leads convective precipitation for all subdomain sizes examined; however, the lead time decreases with decreasing subdomain size. Moisture convergence leads convective precipitation for subdomain sizes greater than 32 km but has no lead time for smaller subdomain sizes. Mid-tropospheric vertical velocity has no lead time or slightly lags convective precipitation. The lead/lag composite analysis with respect to maximum precipitation time indicates that peaks of large-scale variables increase with decreasing subdomain size. The peaks of 500 hPa vertical velocity and column integrated moisture convergence occur at the same time as maximum precipitation, but maximum dCAPE leads maximum precipitation by twelve minutes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Magnetically Modulated Heat Transport in a Global Simulation of Solar Magneto-convection

We present results from a global MHD simulation of solar convection in which the heat transported by convective flows varies in-phase with the total magnetic energy. The purely random initial magnetic field specified in this experiment develops into a well-organized large-scale antisymmetric component undergoing hemispherically synchronized polarity reversals on a 40 year period. A key feature of the simulation is the use of a Newtonian cooling term in the entropy equation to maintain a convectively unstable stratification and drive convection, as opposed to the specification of heating and cooling terms at the bottom and top boundaries. When taken together, the solar-like magnetic cycle and the convective heat flux signature suggest that a cyclic modulation of the large-scale heat-carrying convective flows could be operating inside the real Sun. We carry out an analysis of the entropy and momentum equations to uncover the physical mechanism responsible for the enhanced heat transport. The analysis suggests that the modulation is caused by a magnetic tension imbalance inside upflows and downflows, which perturbs their respective contributions to heat transport in such a way as to enhance the total convective heat flux at cycle maximum. Potential consequences of the heat transport modulation for solar irradiance variability are briefly discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Improving the representation of shallow cumulus convection with the simplified-higher-order-closure–mass-flux (SHOC+MF v1.0) approach

Abstract. Parameterized boundary layer turbulence and moist convection remain some of the largest sources of uncertainty in general circulation models. High-resolution climate modeling aims to reduce that uncertainty by explicitly attempting to resolve deep moist convective motions. An example of such a model is the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) with a target global resolution of 3.25 km, allowing for a more accurate representation of complex mesoscale deep convective dynamics. Yet, small-scale planetary boundary layer turbulence and shallow convection still need to be parameterized, which in SCREAM is accomplished through the turbulent-kinetic-energy-based (TKE-based) simplified higher-order closure (SHOC) – a simplified version of the assumed-double-Gaussian-PDF (probability density function) higher-order-closure method. In this paper, we implement a stochastic-multiplume mass-flux (MF) parameterization of dry and shallow convection in SCREAM to go beyond the limitations of double-Gaussian-PDF closures and couple it to SHOC (SHOC+MF). The new parameterization implemented in a single-column model type version of SCREAM produces results for two shallow cumulus convection cases (marine and continental shallow convection) that agree well with the reference data from large-eddy simulations, thus improving the general representation of the thermodynamic quantities and their turbulent fluxes as well as cloud macrophysics in the model. Furthermore, SHOC+MF parameterization shows weak sensitivity to the vertical grid resolution and model time step.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Using Satellite Observations to Infer the Relationship Between Cold Pools and Subsequent Convection Development

Cold pools are increasingly being recognized as important players in the evolution of both shallow and deep convection; hence, the incorporation of cold pool processes into a number of recently developed convective parameterizations. Unfortunately, observations serving to inform cold pool parameterization development are limited to select field programs and limited radar domains. However, a number of recent studies have noted that cold pools are often associated with arcs-lines of shallow clouds traversing 10 100 km in visible satellite imagery. Boundary layer thermodynamic perturbations are plausible at such scales, coincident with such mesoscale features. Atmospheric signatures of features at these spatial scales are potentially observable from satellites. In this presentation, we discuss recent work that uses multi-sensor, high-resolution satellite products for observing mesoscale wind vector fluctuations and boundary layer temperature depressions attributed to cold pools produced by antecedent convection. The relationship to subsequent convection as well as convective system longevity is discussed. As improvements in satellite technology occur and efforts to reduce noise in high-resolution orbital products progress, satellite pixel level (10 km) thermodynamic and dynamic (e.g. mesoscale convergence) parameters can increasingly serve as useful benchmarks for constraining convective parameterization development, including for regimes where organized convection contributes substantially to the cloud and rainfall climatology.

thermodynamics↗
NASA NTRS2021

Improved Convective Ice Microphysics Parameterization in the NCAR CAM Model

Partitioning deep convective cloud condensates into components that sediment and detrain, known to be a challenge for global climate models, is important for cloud vertical distribution and anvil cloud formation. In this study, we address this issue by improving the convective microphysics scheme in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 (CAM5.3). The improvements include: (1) considering sedimentation for cloud ice crystals that do not fall in the original scheme, (2) applying a new terminal velocity parameterization that depends on the environmental conditions for convective snow, (3) adding a new hydrometeor category, “rimed ice,” to the original four-class (cloud liquid, cloud ice, rain, and snow) scheme, and (4) allowing convective clouds to detrain snow particles into stratiform clouds. Results from the default and modified CAM5.3 models were evaluated against observations from the U.S. Department of Energy Tropical Warm Pool-International Cloud Experiment (TWP-ICE) field campaign. The default model overestimates ice amount, which is largely attributed to the underestimation of convective ice particle sedimentation. By considering cloud ice sedimentation and rimed ice particles and applying a new convective snow terminal velocity parameterization, the vertical distribution of ice amount is much improved in the midtroposphere and upper troposphere when compared to observations. The vertical distribution of ice condensate also agrees well with observational best estimates upon considering snow detrainment. Comparison with observed convective updrafts reveals that current bulk model fails to reproduce the observed updraft magnitude and occurrence frequency, suggesting spectral distributions be required to simulate the subgrid updraft heterogeneity.

terminal velocity↗
NASA NTRS2020

Observed Modulation of the Tropical Radiation Budget by Deep Convective Organization and Lower-Tropospheric Stability

This study analyses the observed monthly deseasonalized and detrended variability of the tropical radiation budget, and suggests that variations of the lower-tropospheric stability and of the spatial organization of deep convection both strongly contribute to this variability. Satellite observations show that on average over the tropical belt, when deep convection is more aggregated, the free troposphere is drier, the deep-convective cloud coverage is less extensive, and the emission of heat to space is increased; an enhanced aggregation of deep convection is thus associated with a radiative cooling of the tropics. An increase of the tropical-mean lower-tropospheric stability is also coincident with a radiative cooling of the tropics, primarily because it is associated with more marine low clouds and an enhanced reflection of solar radiation, although the free-tropospheric drying also contributes to the cooling. The contributions of convective aggregation and lower-tropospheric stability to the modulation of the radiation budget are complementary, largely independent of each other, and equally strong. Together, they account for more than sixty percent of the variance of the tropical radiation budget. Satellite observations are thus consistent with the suggestion from modeling studies that the spatial organization of deep convection substantially influences the radiative balance of the Earth. This emphasizes the importance of understanding the factors that control convective organization and lower-tropospheric stability variations, and the need to monitor their changes as the climate warms.

convective aggregation↗
NASA NTRS2024

Precipitation -17 O-Excess Altered During Tropical Convection: Evidence From Monsoon Cold Surges in Singapore

Despite the recent recognition of -17 O-excess as a promising new tracer for hydrological processes, our knowledge of the control mechanisms underlying -17 O-excess in tropical regions remains limited. To understand how microphysical processes during tropical convection affect precipitation isotope ratios, particularly -17 O-excess, in Singapore, we collected precipitation samples at minute intervals from six rain events associated with cold surges during the Northeast Monsoon seasons and analyzed their triple oxygen isotopes. Our results show that precipitation δ -18 O decreases in the convective zones and then gradually increases in the stratiform zones, while d-excess exhibits an inverse trend. This correlation between δ -18 O and d-excess indicates that rain evaporation plays a crucial role in regulating precipitation isotopes. Moreover, the rain events with a higher upstream rainout amount have lower δ -18 O and higher -17 O-excess values, suggesting that precipitation δ -18 O and -17 O-excess likely reflect the integrated upstream convective activity. Microphysical processes associated with upstream convection, such as rain evaporation and vapor recycling, are potential mechanisms that increase -17 O-excess values along moisture transport pathways for a rain event, and hence, undermine the effectiveness of -17 O-excess as a tracer of moisture source humidity. Contrary to the negative correlation observed in monthly precipitation, there is generally a positive correlation between d-excess and -17 O-excess at the event scale. However, this correlation weakens as convective rain intensifies, suggesting that stronger convection can attenuate the positive correlation between d-excess and -17 O-excess. Therefore, it is crucial to consider how tropical convection alters -17 O-excess when utilizing this tracer to interpret atmospheric dynamics and hydrological processes.

tropical weather systems↗
DOE OSTI2017

Simulation of Precipitation Extremes Using a Stochastic Convective Parameterization in the NCAR CAM5 Under Different Resolutions

With the incorporation of the Plant-Craig stochastic deep convection scheme into the Zhang-McFarlane deterministic parameterization in the Community Atmospheric Model version 5 (CAM5), its impact on extreme precipitation at different resolutions (2°, 1°, and 0.5°) is investigated. CAM5 with the stochastic deep convection scheme (experiment (EXP)) simulates the precipitation extreme indices better than the standard version (control). At 2° and 1° resolutions, EXP increases high percentile (>99th) daily precipitation over the United States, Europe, and China, resulting in a better agreement with observations. However, at 0.5° resolution, due to enhanced grid-scale precipitation with increasing resolution, EXP overestimates extreme precipitation over southeastern U.S. and eastern Europe. The reduced biases in EXP at each resolution benefit from a broader probability distribution function of convective precipitation intensity simulated. Among EXP simulations at different resolutions, if the spatial averaging area over which input quantities used in convective closure are spatially averaged in the stochastic convection scheme is comparable, the modeled convective precipitation intensity decreases with increasing resolution, when gridded to the same resolution, while the total precipitation is not sensitive to model resolution, exhibiting some degree of scale-awareness. Sensitivity tests show that for the same resolution, increasing the size of spatial averaging area decreases convective precipitation but increases the grid-scale precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Observing Convective Aggregation

Convective self-aggregation, the spontaneous organization of initially scattered convection into isolated convective clusters despite spatially homogeneous boundary conditions and forcing, was first recognized and studied in idealized numerical simulations. While there is a rich history of observational work on convective clustering and organization, there have been only a few studies that have analyzed observations to look specifically for processes related to self-aggregation in models. Here we review observational work in both of these categories and motivate the need for more of this work. We acknowledge that self-aggregation may appear to be far-removed from observed convective organization in terms of time scales, initial conditions, initiation processes, and mean state extremes, but we argue that these differences vary greatly across the diverse range of model simulations in the literature and that these comparisons are already offering important insights into real tropical phenomena. Some preliminary new findings are presented, including results showing that a self-aggregation simulation with square geometry has too broad distribution of humidity and is too dry in the driest regions when compared with radiosonde records from Nauru, while an elongated channel simulation has realistic representations of atmospheric humidity and its variability. We discuss recent work increasing our understanding of how organized convection and climate change may interact, and how model discrepancies related to this question are prompting interest in observational comparisons. We also propose possible future directions for observational work related to convective aggregation, including novel satellite approaches and a ground-based observational network.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Convective Heating Leads to Self‐Aggregation by Generating Available Potential Energy

Abstract The moisture‐entrainment‐convection (MEC) feedback posits that a moist environment favors deep convection, which further moistens the atmosphere through its associated circulation and detrainment. The MEC feedback has been proposed to be crucial to spontaneous convective aggregation. Here we test this hypothesis by performing minimal cloud‐resolving simulations, without the buoyancy effect due to water vapor, evaporation of rain, or radiative and surface‐flux feedbacks. Convection can self‐aggregate in this minimal simulation, in which the MEC feedback is active. We then switch off this feedback by relaxing moisture to its horizontal mean over a time scale of 3 hr. Convection still self‐aggregates in this mechanism‐denial experiment, suggesting that the MEC feedback is not essential to self‐aggregation. We further show that convective heating coincides with positive temperature anomalies, generating available potential energy. Therefore, we propose that this convective heating‐overturning circulation feedback can lead to spontaneous development of large‐scale circulations.

Yang, Da↗
DOE OSTI2021

Using TRMM Latent Heat as a Source to Estimate Convection Induced Gravity Wave Momentum Flux in the Lower Stratosphere

Due to a lack of observations, it is a big challenge to quantify the momentum flux transported from the troposphere into the lower stratosphere by gravity waves generated by convection. This limits our understanding of the dynamics of general circulation in the stratosphere and the realism of simulations of the Quasi Biennial Oscillation. In the past decade, some general circulation models have linked the momentum flux to the latent heating from subgrid scale convective precipitation with some success. However, there is still a large uncertainty in the sources of subgrid scale convection in these models. This study applies the links between the momentum flux and convective latent heating directly to the convective precipitation derived from 16 years of TRMM precipitation radar observations in the tropics and subtropics. The total and directional momentum flux at 100 hPa are derived at individual convective pixels by using the TRMM Spectrum Latent Heating product and large-scale wind profiles from the ERA-Interim dataset. For the first time, we are able to estimate the geographical distribution of momentum flux at 100 hPa in tropics and subtropics from the observed convective sources. The diurnal, seasonal, and interannual variations of the derived momentum flux are presented and discussed. These results could provide a reference to validate the dynamic coupling between the tropical troposphere and the stratosphere in the general circulation models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

WE-AB-204-07: Spatiotemporal Distribution of the FDG PET Tracer in Solid Tumors: Contributions of Diffusion and Convection Mechanisms

Purpose: In this study, a mathematical model is utilized to simulate FDG distribution in tumor tissue. In contrast to conventional compartmental modeling, tracer distributions across space and time are directly linked together (i.e. moving beyond ordinary differential equations (ODEs) to utilizing partial differential equations (PDEs) coupling space and time). The diffusion and convection transport mechanisms are both incorporated to model tracer distribution. We aimed to investigate the contributions of these two mechanisms on FDG distribution for various tumor geometries obtained from PET/CT images. Methods: FDG transport was simulated via a spatiotemporal distribution model (SDM). The model is based on a 5K compartmental model. We model the fact that tracer concentration in the second compartment (extracellular space) is modulated via convection and diffusion. Data from n=45 patients with pancreatic tumors as imaged using clinical FDG PET/CT imaging were analyzed, and geometrical information from the tumors including size, shape, and aspect ratios were classified. Tumors with varying shapes and sizes were assessed in order to investigate the effects of convection and diffusion mechanisms on FDG transport. Numerical methods simulating interstitial flow and solute transport in tissue were utilized. Results: We have shown the convection mechanism to depend on the shape and size of tumors whereas diffusion mechanism is seen to exhibit low dependency on shape and size. Results show that concentration distribution of FDG is relatively similar for the considered tumors; and that the diffusion mechanism of FDG transport significantly dominates the convection mechanism. The Peclet number which shows the ratio of convection to diffusion rates was shown to be of the order of 10−{sup 3} for all considered tumors. Conclusion: We have demonstrated that even though convection leads to varying tracer distribution profiles depending on tumor shape and size, the domination of the diffusion phenomenon prevents these factors from modulating FDG distribution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Spectral Signatures of Moisture–Convection Feedbacks over the Indian Ocean

Abstract Positive feedbacks between the cloud population and the environmental moisture field are central to theoretical expositions on the Madden–Julian oscillation (MJO). This study investigates the statistical incidence of positive moisture–convection feedbacks across multiple space and time scales over the tropical Indian Ocean. This work uses vertically integrated moisture budget terms from the ECMWF interim reanalysis [ERA-Interim (ERA-I)] in a framework proposed by Hannah et al. Positive moisture–convection feedbacks are primarily a low-frequency, low-wavenumber phenomenon with significant spectral signatures in the 32–48-day time scale. The efficacy of these feedbacks, however, is subject to horizontal moisture advection variations, whose relative importance varies with scale. Wave-filtered Tropical Rainfall Measuring Mission (TRMM) satellite precipitation is used to show that these moisture–convection feedbacks contribute more to moisture increases in the MJO than in other equatorial waves. A moving-window correlation analysis suggests that instances of moisture–convection feedbacks are more frequent in drier conditions, when column water vapor (CWV) is below its climatological mean value, with the implication that positive moisture–convection feedbacks shape the mean CWV field by moistening drier air columns, but that they are less effective in moistening already moist environments. Ground radar observations show that stratiform rain damps local CWV increases on short time scales (<2 days) and therefore precludes positive moisture–convection feedbacks in high-CWV environments. Vertical coherence structures from ERA-I confirm that relatively bottom-heavy cloud ensembles (i.e., peaks between 700 and 850 hPa) are more effective in inducing low-frequency positive moisture–convection feedbacks than ensembles with other vertical structures. Low-frequency horizontal advective drying damps moisture increases and is strongly coherent with upper-level rising motion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Updraft Constraints on Entrainment: Insights from Amazonian Deep Convection

Abstract Mixing of environmental air into clouds, or entrainment, has been identified as a major contributor to erroneous climate predictions made by modern comprehensive climate and numerical weather prediction models. Despite receiving extensive attention, the ad hoc treatment of this convective-scale process in global models remains poor. On the other hand, while limited-area high-resolution nonhydrostatic models can directly resolve entrainment, their sensitivity to model resolution, especially with the lack of benchmark mass flux observations, limits their applicability. Here, the dataset from the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign focusing on radar retrievals of convective updraft vertical velocities is used with the aid of cloud-resolving model simulations of four deep convective events over the Amazon to provide insights into entrainment. Entrainment and detrainment are diagnosed from the model simulations by applying the mass continuity equation over cloud volumes, in which grid cells are identified by some thresholds of updraft vertical velocity and cloud condensates, and accounting for the sources and sinks of the air mass. Entrainment is then defined as the environmental air intruding into convective cores causing cloud volume to shrink, while detrainment is defined as cloudy grid cells departing the convective core and causing cloud volume to expand. It is found that the diagnosed entrainment from the simulated convective events is strongly correlated to the inverse of the updraft vertical velocities in convective cores, which enables a more robust estimation of the mixing time scale. This highlights the need for improved observational capabilities for sampling updraft velocities across diverse geographic and cloud conditions. Evaluation of a number of assumptions used to represent entrainment in parameterization schemes is also presented, as contrasted against the diagnosed one.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Do Ultrafine Cloud Condensation Nuclei Invigorate Deep Convection?

Numerical simulations of the impact of ultrafine cloud condensation nuclei (CCN) on deep convection are analyzed herein to investigate the idea proposed by Fan et al. that addition of ultrafine CCN to an otherwise pristine environment leads to convective invigoration. The piggybacking methodology is applied, allowing rigorous separation of the impact of aerosols from different flow realizations that typically occur when even a small element of the model physics or modeling setup is changed. The setup follows the case of daytime convective development over land based on observations during the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Overall, the simulated impacts of ultrafine CCN are similar to the previous study by the authors on the impact of pollution on deep convection. There is no convective invigoration above the freezing level, but there is a small invigoration (increase in vertical velocities) below due to the supersaturation and buoyancy differences in conditions with additional ultrafine CCN compared to unperturbed pristine conditions. As in the previous study, the most significant impact is on the upper-tropospheric convective anvils that feature higher cloud fractions in conditions with ultrafine CCN. The increase comes from purely microphysical considerations as the increased cloud droplet concentrations from ultrafine CCN lead to increased ice crystal concentrations and, consequently, smaller sizes and fall velocities, and longer residence times. Mesoscale organization due to low-level shear has a small effect on the simulated ultrafine CCN impacts. Finally, an alternative explanation of increased lightning above oceanic shipping lines seen in satellite observations and argued to result from convective invigoration is provided.

54 ENVIRONMENTAL SCIENCES↗