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DOE OSTI2021

Aerosol characteristics at the Southern Great Plains site during the HI-SCALE campaign

Abstract. Large uncertainties exist in global climate model predictions of radiative forcing due to insufficient understanding and simplified numerical representation of cloud formation and cloud–aerosol interactions. The Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the DOE's Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in north-central Oklahoma to provide a better understanding of land–atmosphere interactions, aerosol and cloud properties, and the influence of aerosol and land–atmosphere interactions on cloud formation. The HI-SCALE campaign consisted of two intensive observational periods (IOPs) (April–May and August–September, 2016), during which coincident measurements were conducted both on the G-1 aircraft platform and at the SGP ground site. In this study we focus on the observations at the SGP ground site. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an Ionicon proton-transfer-reaction mass spectrometer (PTR-MS) were deployed, characterizing chemistry of non-refractory aerosol and trace gases, respectively. Contributions from various aerosol sources, including biogenic and biomass burning emissions, were retrieved using factor analysis of the AMS data. In general, the organic aerosols at the SGP site was highly oxidized, with oxygenated organic aerosol (OOA) identified as the dominant factor for both the spring and summer IOP though more aged in spring. Cases of isoprene-epoxydiol-derived secondary organic aerosol (IEPOX SOA) and biomass burning events were further investigated to understand additional sources of organic aerosol. Unlike other regions largely impacted by IEPOX chemistry, the IEPOX SOA at SGP was more highly oxygenated, likely due to the relatively weak local emissions of isoprene. Biogenic emissions appear to largely control the formation of organic aerosol (OA) during the HI-SCALE campaign. Potential HOM (highly oxygenated molecule) chemistry likely contributes to the highly oxygenated feature of aerosols at the SGP site, with impacts on new particle formation and global climate.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Diagnosing the Nature of Land-Atmosphere Coupling During the 2006-7 Dry/Wet Extremes in the U. S. Southern Great Plains

The degree of coupling between the land surface and PBL in NWP models remains largely undiagnosed due to the complex interactions and feedbacks present across a range of scales. In this study, a framework for diagnosing local land-atmosphere coupling (LoCo) is presented using a coupled mesoscale model with observations during the summers of 2006/7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which enables a suite of PBL and land surface model (LSM) options along provides a flexible and high-resolution representation and initialization of land surface physics and states. This coupling is one component of a larger project to develop a NASA-Unified WRF (NU-WRF) system. A range of diagnostics exploring the feedbacks between soil moisture and precipitation are examined for the dry/wet extremes, along with the sensitivity of PBL-LSM coupling to perturbations in soil moisture.

Santanello, Joseph A., Jr.↗

NASA NTRS2015

A Prototype Physical Database for Passive Microwave Retrievals of Precipitation over the US Southern Great Plains

An accurate understanding of the instantaneous, dynamic land surface emissivity is necessary for a physically based, multi-channel passive microwave precipitation retrieval scheme over land. In an effort to assess the feasibility of the physical approach for land surfaces, a semi-empirical emissivity model is applied for calculation of the surface component in a test area of the US Southern Great Plains. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) are utilized along with coincident brightness temperatures (Tbs) from the TRMM Microwave Imager (TMI), and cloud-resolving model profiles. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled top of the atmosphere Tbs show correlations to observations of 0.9, biases of 1K or less, root-mean-square errors on the order of 5K, and improved agreement over the use of climatological emissivity values. The synthesis of these models and data sets leads to the creation of a simple prototype Tb database that includes both dynamic surface and atmospheric information physically consistent with the land surface model, emissivity model, and atmospheric information.

microwave↗

NASA NTRS2010

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

Mascaro, Giuseppe↗

DOE OSTI2021

Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity

The activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and hydrological cycle studies, but their properties are not well known. We investigated the long-term measurements of atmospheric aerosol properties, CCN concentrations (N CCN ) at supersaturation (SS = 0.1%–1.0%), and hygroscopicity at the Department of Energy's Southern Great Plains (SGP) site to illustrate the dependence of N CCN on aerosol properties and transport pathways. Cluster analysis was applied to the back trajectories of air masses to investigate their respective source regions. The results showed that aged biomass burning aerosols from Central America were characterized by higher accumulation mode particles (N accu ; median value 805 cm -3 ) and relatively high aerosol hygroscopicity (κ; median value ~0.25) values that result in the higher CCN activation and relatively high N CCN (median value 258–1578 cm -3 at a SS of 0.1%–1.0%). Aerosols from the Gulf of Mexico were characterized by higher N accu (~35%), and N CCN (230–1721 cm -3 at a SS of 0.1%–1.0%) with the lowest κ (~0.17). In contrast, relatively high nucleation mode particles (N nucl ; ~20%) and low N CCN (128–1553 cm -3 at a SS of 0.1%–1.0%) with higher κ (~0.30) values were observed on the aerosols associated with a westerly wind. The results indicate particle size as the most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical composition was secondary. Our CCN closure analysis suggests that chemical composition and mixing state information are more crucial at lower SS, whereas at higher SS, most particles become activated regardless of their chemical composition and size. This study affirms that soluble organic fraction information is required at higher SS for better N CCN prediction, but both the soluble organics fraction and mixing state are vital to reduce the N CCN prediction uncertainty at lower SS.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Climatic Implications of the Observed Temperature Dependence of the Liquid Water Path of Low Clouds in the Southern Great Plains

Satellite observations of low-level clouds have challenged the assumption that adiabatic liquid water content combined with constant physical thickness will lead to a negative cloud optics feedback in a decadal climate change. We explore the reasons for the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurement Program Cloud and Radiation Testbed site in the Southern Great Plains of the United States. We find that low cloud liquid water path is approximately invariant with temperature in winter but decreases strongly with temperature in summer, consistent with the satellite inferences at this latitude. This behavior occurs because liquid water content shows no detectable temperature dependence while cloud physical thickness decreases with warming. Thinning of clouds with warming is observed on seasonal, synoptic, and diurnal time scales; it is most obvious in the warm sectors of baroclinic waves. Although cloud top is observed to slightly descend with warming, the primary cause of thinning is the ascent of cloud base due to the reduction in surface relative humidity and the concomitant increase in the lifting condensation level of surface air. Low cloud liquid water path is not observed to be a continuous function of temperature. Rather, the behavior we observe is best explained as a transition in the frequency of occurrence of different boundary layer types: At cold temperatures, a mixture of stratified and convective boundary layers is observed, leading to a broad distribution of liquid water path values, while at warm temperatures, only convective boundary layers with small liquid water paths, some of them decoupled, are observed. Our results, combined with the earlier satellite inferences, imply that the commonly quoted 1.50 C lower limit for the equilibrium global climate sensitivity to a doubling of CO2, which is based on models with near-adiabatic liquid water behavior and constant physical thickness, should be revised upward.

DelGenio, Anthony D.↗

NASA NTRS1999

Climatic Implications of the Observed Temperature Dependence of the Liquid Water Path of Low Clouds in the Southern Great Plains

Satellite observations of low-level clouds have challenged the assumption that adiabatic liquid water content combined with constant physical thickness will lead to a negative cloud optics feedback in a decadal climate change. We explore the reasons for the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurement Program Cloud and Radiation Testbed site in the Southern Great Plains of the United States. We find that low cloud liquid water path is approximately invariant with temperature in winter but decreases strongly with temperature in summer, consistent with the satellite inferences at this latitude. This behavior occurs because liquid water content shows no detectable temperature dependence while cloud physical thickness decreases with warming. Thinning of clouds with warming is observed on seasonal, synoptic, and diurnal time scales; it is most obvious in the warm sectors of baroclinic waves. Although cloud top is observed to slightly descend with warming, the primary cause of thinning, is the ascent of cloud base due to the reduction in surface relative humidity and the concomitant increase in the lifting condensation level of surface air. Low cloud liquid water path is not observed to be a continuous function of temperature. Rather, the behavior we observe is best explained as a transition in the frequency of occurrence of different boundary layer types. At cold temperatures, a mixture of stratified and convective boundary layers is observed, leading to a broad distribution of liquid water path values, while at warm temperatures, only convective boundary layers with small liquid water paths, some of them decoupled, are observed. Our results, combined with the earlier satellite inferences, imply that the commonly quoted 1.5C lower limit for the equilibrium global climate sensitivity to a doubling of CO2 which is based on models with near-adiabatic liquid water behavior and constant physical thickness, should be revised upward.

DelGenio, Anthony↗

DOE OSTI2022

Molecular Characterization of Organosulfate-Dominated Aerosols over Agricultural Fields from the Southern Great Plains by High-Resolution Mass Spectrometry

The molecular composition of organic aerosols, especially for day/nighttime variations of organosulfates above agricultural fields, is not well understood despite profound impacts on regional climate, crop production, air quality, and human health. Here, nanospray desorption electrospray ionization with high-resolution mass spectrometry (nano-DESI-HRMS) is used to interrogate the molecular composition of organic aerosols collected at the Southern Great Plains, located in an agricultural region of Oklahoma. Identified molecular formulae featured carbon, hydrogen, oxygen (CHO), nitrogen (CHNO), and/or sulfur (CHOS, CHNOS), with higher organosulfate proportions during daytime (41%) compared to nighttime (30%). In this work, nighttime aerosols featured increases in CHO, CHNO, and extremely low volatility organic carbon (ELVOC) species. However, due to high relative humidity, the nighttime aerosols phase state was found to be more liquid-like than daytime aerosols using parametrized glass transition temperatures. Aerosol molecular composition from an anthropogenically influenced plume (southerly winds) showed significant increases in CHOS and ELVOC species. By comparison with chamber studies, CHOS species are suspected to be of mixed biogenic and anthropogenic origin, whereas CHNOS species (not identified in the southerly winds) are suggested to predominately be of biogenic origin. Overall, this study provides key insight into organosulfates above agricultural fields, demonstrating dependence upon day/night cycles and episodic anthropogenic emissions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Assessment of Extinction‐, Satellite‐, and Model‐Based Vertical Cloud Condensation Nuclei (CCN) Retrieval Methods Using Airborne CCN Measurements Over the Southern Great Plains

Abstract Accurate estimates of the vertical profile of cloud condensation nuclei (CCN) concentration are crucial to better quantify aerosol‐cloud interactions. We assessed the correlation between the vertical CCN concentrations obtained from extinction‐, satellite‐, and model‐based retrieval methods and airborne CCN concentrations collected at 0.24% supersaturation within the 3, 9, 27, and 81 km regions centered over the U.S. Department of Energy's Atmospheric Radiation Measurement User Facility Southern Great Plains (SGP) site during the spring and summer of 2016. The extinction profiles at a wavelength 355 nm were provided by the ground‐based Raman lidar. Our analysis showed moderate correlation between dry‐corrected extinction and airborne CCN data. We found the retrieved number concentration of CCN (RNCCN) method showed regression best‐fit slopes close to unity and consistent prediction errors for the majority of the data. The Lenhardt et al. (2023, https://doi.org/10.5194/amt‐16‐2037‐2023 ) method showed similar conclusions but only during spring, whereas the Mamouri and Ansmann (2016, https://doi.org/10.5194/acp‐16‐5905‐2016 ) method showed poor correlation. The Shinozuka et al. (2015, https://doi.org/10.5194/acp‐15‐7585‐2015 ) satellite‐based method exhibited reasonable agreement during summer but poor correlation during periods where both high (∼1,400 #/cm 3 ) and low (∼50 #/cm 3 ) airborne CCN concentrations were observed. The Copernicus Atmosphere Monitoring Service reanalysis modeled 3‐D CCN data set showed a moderate to weak positive correlation but performed poorly at high airborne CCN concentrations. Our analysis suggests the extinction‐based RNCCN method performed better than other methods across most observation periods under the diverse meteorological conditions observed at the SGP site.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Evaluation of Monin‐Obukhov Similarity Theory Wind Profiles in Convective Storm Environments and Cold Pools at the ARM Southern Great Plains Atmospheric Observatory

Monin-Obukhov similarity theory (MOST) is widely used in numerical weather prediction to model surface fluxes of momentum, heat, and water vapor. However, MOST is based on assumptions of steady state and horizontally homogeneous turbulence that can lead to prediction errors in and around convective storms. To understand the nature of these errors, we used wind and eddy covariance flux measurements from the Atmospheric Radiation Measurement Southern Great Plains Atmospheric Observatory to evaluate MOST wind profiles in fair-weather and convective storm environments, specifically those of mesoscale convective systems and ordinary thunderstorms. MOST wind profiles agreed well with observations in fair-weather cases, while in convective storm cases the theory systematically overestimated shear in cold pools after gust front passage. Surface layer stability was found to be important in assessing MOST within convective storm environments. The overestimation of wind shear in cold pools suggests the role of non-local fluxes in transferring momentum downward. We discuss reasons for differences and agreement with past studies, and conclude with recommendations to improve prediction of surface winds and fluxes in convective storm simulations.

58 GEOSCIENCES↗

DOE OSTI2021

Impact of Lateral Flow on Surface Water and Energy Budgets over the Southern Great Plains – A Modeling Study

As we see the horizontal grid spacing decrease, treatment of hydrologic processes in land surface models, such as the lateral flow of surface and subsurface flow, need to be explicitly represented. Unlike previous studies that mainly focused on the mountainous regions, in this study the offline WRF-Hydro model is employed to study the impact of lateral flow on soil moisture and energy fluxes over the relatively flat southern Great Plains (SGP). The vast amount of measurements over the SGP provide an unique opportunity to assess the model behavior. In addition, newly developed land surface properties and input forcing are ingested into the model, in an attempt to reduce uncertainties associated with the initial and boundary forcing and help to identify model deficiencies. Our results show that the more realistic inputs (parameters, soil types, forcing) lead to larger underestimation of latent heat flux and dry bias, indicating the existence of model structural uncertainty (embedded errors) in WRF-Hydro that need to be characterized to inform future model development efforts. Including lateral flow processes partly mitigates the model deficiencies in representing hydrologic processes and alleviates the dry bias. In particular, both surface and subsurface lateral flow increase soil moisture mainly over the lower elevations, except that subsurface flow also affects soil moisture over steeper terrains. Additional simulations are performed to assess the effect of routing resolution on model results. When LSM resolution is high, noticeable differences in soil moisture are produced between different routing resolutions especially over steep terrain. Whereas when LSM resolution is coarse, differences between routing resolutions become negligible, especially over flat terrain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. Here, satellite and ground-based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground-based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground-based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2004

Comparison Between Lidar and Nephelometer Measurements of Aerosol Hygroscopicity at the Southern Great Plains Atmospheric Radiation Measurement Site

Aerosol hygroscopicity has a significant effect on radiative properties of aerosols. Here a lidar method, applicable to cloud-capped, well-mixed atmospheric boundary layers, is employed to determine the hygroscopic growth factor f(RH) under unperturbed, ambient atmospheric conditions. The data used for the analysis were collected under a wide range of atmospheric aerosol levels during both routine measurement periods and during the intensive operations period (IOP) in May 2003 at the Southern Great Plains (SGP) Climate Research Facility in Oklahoma, USA, as part of the Atmospheric Radiation Measurement (ARM) program. There is a good correlation (approx. 0.7) between a lidar-derived growth factor (measured over the range 85% RH to 96% RH) with a nephelometer-derived growth factor measured over the RH range 40% to 85%. For these RH ranges, the slope of the lidar-derived growth factor is much steeper than that of the nephelometer-derived growth factor, reflecting the rapid increase in particle size with increasing RH. The results are corroborated by aerosol model calculations of lidar and nephelometer equivalent f(RH) based on in situ aerosol size and composition measurements during the IOP. It is suggested that the lidar method can provide useful measurements of the dependence of aerosol optical properties on relative humidity, and under conditions closer to saturation than can currently be achieved with humidified nephelometers.

Pahlow, M.↗

DOE OSTI2024

Enrichment of Phosphates, Lead, and Mixed Soil–Organic Particles in INPs at the Southern Great Plains Site

Ice nucleating particles (INPs) are rare particles that initiate primary ice formation, a critical step required for subsequent important cloud microphysical processes that ultimately govern cloud phase and cloud radiative properties. Laboratory studies have found that organic-rich dusts, such as those found in soils, are more efficient INPs compared to mineral dust. However, the atmospheric relevance of these organic-rich dusts are not well understood, particularly in regions with significant agricultural activity. The Agricultural Ice nuclei at the Southern Great Plains field campaign (AGINSGP) was conducted in rural Oklahoma to investigate how soil dusts contribute to INP populations in the Great Plains. We present chemical characterization of ambient and ice crystal residual particles from a single day of sampling, using single particle mass spectrometry (SPMS) and scanning microscopy. Ambient particles were primarily carbonaceous or secondary aerosol, while the fraction of dust particles was higher in the residual particles. We also observed an unusual particle type consisting of a carbonaceous core mixed with dust fragments on the surface, which was found in higher proportion in residuals. Dust particles measured during residual sampling contained greater proportions of phosphate $(^{63}\text{PO}^-_2$ and $^{79}\text{PO}^-_3)$ and lead ( 206 Pb + ). Strong sulfate signals were not seen in the residual dust particles measured by the SPMS, while nitrate was slightly depleted relative to ambient dust. This study shows that organic-rich soils may be important contributors to the ambient INP population in agricultural regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Dataset for "Enrichment of phosphates, lead, and mixed soil-organic particles in INPs at the Southern Great Plains site"

Ice nucleating particles (INPs) are rare particles that initiate primary ice formation, a critical step required for subsequent important cloud microphysical processes that ultimately govern cloud phase and cloud radiative properties. Laboratory studies have found that organic-rich dusts, such as those found in soils, are more efficient INPs compared to mineral dust. However, the atmospheric relevance of these organic-rich dusts are not well understood, particularly in regions with significant agricultural activity. The Agricultural Ice nuclei at the Southern Great Plains field campaign (AGINSGP) was conducted in rural Oklahoma to investigate how soil dusts contribute to INP populations in the Great Plains. We present chemical characterization of ambient and ice crystal residual particles from a single day of sampling using single particle mass spectrometry (SPMS) and scanning microscopy. Ambient particle concentrations were primarily carbonaceous or secondary aerosol, while dust particles were enhanced in the residual particles. Dust particles measured during residual sampling contained elevated signals for phosphate ($^{63}$PO$_2^-$ and $^{79}$PO$_3^-$) and lead ($^{208}$Pb$_2^+$). Nitrate was slightly depleted relative to ambient dust, and strong sulfate signals were not seen in the residual particles measured by the miniSPLAT. The residual and ambient particles measured by the miniSPLAT were approximately the same size, though this may be a reflection of the SPMS size-dependent transmission efficiency and not that of physical truth. This study shows that organic-rich soils may be important contributors to the ambient INP population in agricultural regions.

Cornwell, Gavin C↗

DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Aerosol Activation in Radiation Fog at the Atmospheric Radiation Program Southern Great Plains Site

Environmental supersaturation is a key parameter in the formation of fog and clouds, yet it cannot be measured directly and must be inferred. Calculating the ambient supersaturation in fog requires knowledge of the aerosol hygroscopicity as well as particle size distribution, and relatively few values have been reported in the literature. Here we use κ-Köhler theory to derive aerosol activation properties based on particle hygroscopicity and dry particle size distributions, and then estimate the effective peak supersaturation during eight cases of radiative fog at the Southern Great Plains site in rural north-central Oklahoma, USA. The mean hygroscopicity parameter κ of particles likely to act as cloud condensation nuclei varied from 0.14 to 0.43. Ambient effective peak supersaturation during the fog episodes was between 0.01%–0.07%, with most values below 0.04%. Furthermore, the minimum 50% dry activation diameter generally ranged between 300–400 nm with little activation of particles with diameters below 300 nm.

54 ENVIRONMENTAL SCIENCES↗