Radiation Measurement Program Southern Research Articles

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments

AbstractWe demonstrate the combination of three Doppler lidars (DLs), a water vapor differential absorption lidar and a temperature rotational Raman lidar for the investigation of the interactions between the land‐surface and the atmospheric boundary layer. This combination of scanning lidars was operated for the first time during the Land‐Atmosphere Feedback Experiment at the Atmospheric Radiation Measurement program's Southern Great Plain site, Oklahoma, USA, in August 2017, and provided simultaneous surface layer profiles of horizontal wind, humidity and temperature. The horizontal wind profiles were determined using the dual‐Doppler method with two DLs. The scans were performed above four towers providing atmospheric variables and fluxes. These combined lidar data allowed for the estimation of the friction velocity as well as the surface latent and sensible heat fluxes. For this purpose, profiles calculated with Monin‐Obukhov similarity theory were fitted with the Levenberg‐Marquardt nonlinear least squares curve‐fitting method to the measured surface‐layer lidar profiles. We present case studies of three 50‐min periods to illustrate the new method. The measurements resulted in friction velocities of 0.31, 0.29, and 0.38 ms−1, sensible surface heat fluxes of 312.1, 234.1, and 183.3 Wm−2 and latent surface heat fluxes of 251.8, 227.3, and 274.4 Wm−2. These values were compared with the in‐situ measurements of the towers. Considering the sampling differences and the error analyses, the results agree within 25.1%, 42.5%, and 28.8%, respectively, demonstrating that this synergy of scanning active remote sensing systems can be used to derive surface fluxes with reasonable accuracy.

Methodology to determine the coupling of continental clouds with surface and boundary layer height under cloudy conditions from lidar and meteorological data

Abstract. The states of coupling between clouds and surface or boundary layer have been investigated much more extensively for marine stratocumulus clouds than for continental low clouds, partly due to more complex thermodynamic structures over land. A manifestation is a lack of robust remote sensing methods to identify coupled and decoupled clouds over land. Following the idea for determining cloud coupling over the ocean, we have generalized the concept of coupling and decoupling to low clouds over land, based on potential temperature profiles. Furthermore, by using ample measurements from lidar and a suite of surface meteorological instruments at the U.S. Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains site from 1998 to 2019, we have developed a method to simultaneously retrieve the planetary boundary layer (PBL) height (PBLH) and coupled states under cloudy conditions during the daytime. The new lidar-based method relies on the PBLH, the lifted condensation level, and the cloud base to diagnose the cloud coupling. The coupled states derived from this method are highly consistent with those derived from radiosondes. Retrieving the PBLH under cloudy conditions, which has been a persistent problem in lidar remote sensing, is resolved in this study. Our method can lead to high-quality retrievals of the PBLH under cloudy conditions and the determination of cloud coupling states. With the new method, we find that coupled clouds are sensitive to changes in the PBL with a strong diurnal cycle, whereas decoupled clouds and the PBL are weakly related. Since coupled and decoupled clouds have distinct features, our new method offers an advanced tool to separately investigate them in climate systems.

Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification

AbstractWe define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high‐pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model‐simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state‐dependent. Frontal conditions in the model do not produce enough thick cloud, while fair‐weather conditions produce too much. We find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.

Investigating the dependence of SCM simulated precipitation and clouds on the spatial scale of large‐scale forcing at SGP

AbstractLarge‐scale forcing data, such as vertical velocity and advective tendencies, are required to drive single‐column models (SCMs), cloud‐resolving models, and large‐eddy simulations. Previous studies suggest that some errors of these model simulations could be attributed to the lack of spatial variability in the specified domain‐mean large‐scale forcing. This study investigates the spatial variability of the forcing and explores its impact on SCM simulated precipitation and clouds. A gridded large‐scale forcing data during the March 2000 Cloud Intensive Operational Period at the Atmospheric Radiation Measurement program's Southern Great Plains site is used for analysis and to drive the single‐column version of the Community Atmospheric Model Version 5 (SCAM5). When the gridded forcing data show large spatial variability, such as during a frontal passage, SCAM5 with the domain‐mean forcing is not able to capture the convective systems that are partly located in the domain or that only occupy part of the domain. This problem has been largely reduced by using the gridded forcing data, which allows running SCAM5 in each subcolumn and then averaging the results within the domain. This is because the subcolumns have a better chance to capture the timing of the frontal propagation and the small‐scale systems. Other potential uses of the gridded forcing data, such as understanding and testing scale‐aware parameterizations, are also discussed.

Long-term aerosol-mediated changes in cloud radiative forcing of deep clouds at the top and bottom of the atmosphere over the Southern Great Plains

Abstract. Aerosols can alter the macro- and micro-physical properties of deep convective clouds (DCCs) and their radiative forcing (CRF). This study presents what is arguably the first long-term estimate of the aerosol-mediated changes in CRF (AMCRF) for deep cloud systems derived from decade-long continuous ground-based and satellite observations, model simulations, and reanalysis data. Measurements were made at the US Department of Energy's Atmospheric Radiation Measurement Program's Southern Great Plains (SGP) site. Satellite retrievals are from the Geostationary Operational Environmental Satellite. Increases in aerosol loading were accompanied by the thickening of DCC cores and the expansion and thinning of anvils, due presumably to the aerosol invigoration effect (AIV) and the aerosol microphysical effect. Meteorological variables dictating these cloud processes were investigated. Consistent with previous findings, the AIV is most significant when the atmosphere is moist and unstable with weak wind shear. Such aerosol-mediated systematic changes in DCC core thickness and anvil size alter CRF at the top of atmosphere (TOA) and at the surface. Using extensive observations, ~300 DCC systems were identified over a 10 years period at the SGP site (2000–2011) and analyzed. Daily mean AMCRF at the TOA and at the surface are 29.3 W m−2 and 22.2 W m−2, respectively. This net warming effect due to changes in DCC microphysics offsets the cooling resulting from the first aerosol indirect effect.

Using covariates to model dependence in nonstationary, high‐frequency meteorological processes

Meteorological processes that are measured at high temporal frequencies require nonstandard statistical models to adequately characterize their observed behavior. We examine this problem through the specific example of a nonstationary, bivariate process—the high‐frequency changes in surface temperature and dew point—conditionally on the average hourly level of relative humidity, magnitude of minute‐to‐minute changes in wind direction, and presence of sunlight. One particularly interesting aspect of this process is that dew point is bounded above by temperature, and so a part of the modeler's challenge is to characterize the joint behavior as temperature approaches dew point (or equivalently as relative humidity approaches 100%). The data analyzed are from early May from the years 2003 through 2012 at the Atmospheric Radiation Measurement Program's Southern Great Plains site in Northern Oklahoma, at the central facility near Lamont, Oklahoma. Our model gives a parametric description of how the spectral matrix of the process varies with covariates over blocks of time. The spectral approach allows for convenient and interpretable models of bivariate processes in time, and our model captures many of the observed changes in the behavior of the process. Copyright © 2014 John Wiley & Sons, Ltd.

Quantifying aerosol direct effects from broadband irradiance and spectral aerosol optical depth observations

AbstractWe outline a methodology using broadband and spectral irradiances to quantify aerosol direct effects on the surface diffuse shortwave (SW) irradiance. Best Estimate Flux data span a 13 year timeframe at the Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains (SGP) site. Screened clear‐sky irradiances and aerosol optical depth (AOD), for solar zenith angles ≤ 65°, are used to estimate clear‐sky diffuse irradiances. We validate against detected clear‐sky observations from SGP's Basic Radiation System (BRS). BRS diffuse irradiances were in accordance with estimates, producing a root‐mean‐square error and mean bias errors of 4.0 W/m2 and −1.4 W/m2, respectively. Absolute differences show 99% of estimates within ±10 W/m2 (10%) of the mean BRS observations. Clear‐sky diffuse estimates are used to derive quantitative estimates of aerosol radiative effects, represented as the aerosol diffuse irradiance (ADI). ADI is the contribution of diffuse SW to global SW, attributable to scattering of atmospheric transmission by natural plus anthropogenic aerosols. Estimated slope for the ADI as a function of AOD indicates an increase of ~22 W/m2 in diffuse SW for every 0.1 increase in AOD. Such significant increases in the diffuse fraction could possibly increase photosynthesis. Annual mean ADI is 28.2 W/m2, and heavy aerosol loading at SGP provides up to a maximum increase of 120 W/m2 in diffuse SW over background conditions. With regard to seasonal variation, the mean diffuse forcings are 17.2, 33.3, 39.0, and 23.6 W/m2 for winter, spring, summer, and fall, respectively.

Land‐atmosphere coupling manifested in warm‐season observations on the U.S. southern great plains

AbstractThis study examines several observational aspects of land‐atmosphere coupling on daily average time scales during warm seasons of the years 1997 to 2008 at the Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains (SGP) Central Facility site near Lamont, Oklahoma. Characteristics of the local land‐atmosphere coupling are inferred by analyzing the covariability of selected land and atmospheric variables that include precipitation and soil moisture, surface air temperature, relative humidity, radiant and turbulent fluxes, as well as low‐level cloud base height and fractional coverage. For both the energetic and hydrological aspects of this coupling, it is found that large‐scale atmospheric forcings predominate, with local feedbacks of the land on the atmosphere being comparatively small much of the time. The relatively weak land feedbacks are manifested especially by (1) the inability of soil moisture to comprehensively impact the coupled land‐atmosphere energetics and (2) the limited recycling of local surface moisture under conditions where most of the rainfall derives from convective cells that originate at remote locations. There is some evidence, nevertheless, that the local land feedback becomes stronger as the soil dries out in the aftermath of precipitation events, or on days when the local boundary layer clouds are influenced by thermal updrafts associated with convection that originates at the surface. Potential implications of these results for climate‐model representation of regional land‐atmosphere coupling also are discussed.

Multiyear Summertime Observations of Daytime Fair-Weather Cumuli at the ARM Southern Great Plains Facility

Abstract A long data record (14 yr) of ground-based observations at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site is analyzed to document the macroscopic and dynamical properties of daytime fair-weather cumulus clouds during summer months. First, a fuzzy logic–based algorithm is developed to eliminate insect radar echoes in the boundary layer that hinder the ability to develop representative cloud statistics. The refined dataset is used to document the daytime composites of fair-weather cumulus clouds properties. Doppler velocities are processed for lower reflectivity thresholds that contain small cloud droplets having insignificant terminal velocities; thus, Doppler velocities are used as tracers of air motion. The algorithm is implemented to process the entire 14-yr dataset of cloud radar vertical velocity data. Composite diurnal variations of the cloud vertical velocity statistics, surface parameters, and profiles of updraft and downdraft fractions, bulk velocity of updrafts and downdrafts, and updraft and downdraft mass flux are calculated. Statistics on the cloud geometrical properties such as cloud thickness, cloud chord length, cloud spacing, and aspect ratios are calculated on the cloud scale. The present dataset provides a unique insight into the daytime evolution and statistical description of the turbulent structure inside fair-weather cumuli over land.

Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method

AbstractWe describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof‐of‐concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5‐day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short‐ and long‐time means, the 5‐day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction.

Cloud fraction at the ARM SGP site

The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site has a rich history of actively sensed cloud observations. Fourteen years (1997–2010) of observations from the Millimeter Cloud Radar (MMCR), Micropulse Lidar (MPL), and Belfort/Vaisala Ceilometers are used to understand how instrument selection and sampling impacts estimates of Cloud Fraction (CF) at this location. Although all instruments should be used in combination for the best estimates of CF, instrument downtime limits available samples and increases observational errors, demanding that users make sacrifices when calculating CF at longer intervals relevant to climate studies. Selection of MMCR or MMCR + MPL cloud masks changes very little in the overall understanding of total CF. Addition of the MPL increases the 14-year average CF by 9 %, mainly through an increase in optically thin high clouds year-round, and mid-level clouds during the summer months. Splitting the period into two equal 7-year periods reveals negligible change in MMCR + MPL CF. For the MMCR, however, CF deceases by 6.1 %. This sudden change in CF occurs around the time the radar was upgraded, suggesting that this decrease is tied to hardware sensitivity or scanning strategy changes. Users must be cognizant of this and other issues when calculating CF from the variety of observations available at the ARM SGP site.

Observed impacts of vertical velocity on cloud microphysics and implications for aerosol indirect effects

The simultaneous measurements of vertical velocity and cloud droplet size distributions in cumuli collected during the RACORO field campaign over the Atmospheric Radiation Measurement Program's Southern Great Plains site near Lamont, Oklahoma, US, are analyzed to determine the effects of vertical velocity on droplet number concentration, relative dispersion (the ratio of standard deviation to mean radius), and their relationship. The results show that with increasing vertical velocity the droplet number concentration increases while the relative dispersion decreases. The data also exhibit a negative correlation between relative dispersion and droplet number concentration. These empirical relationships can be fitted well with power law functions. This observational study confirms the theoretical and numerical expectations of the effects of vertical velocity on cloud microphysics by analyzing the data of vertical velocity directly. The effects of vertical velocity on relative dispersion and its relationship with droplet number concentration are opposite to that associated with aerosol loading, posing a confounding challenge for separating aerosol indirect effects from dynamical effects.

The influence of observed cirrus microphysical properties on shortwave radiation: A case study over Oklahoma

[1] The shortwave radiative effect of an ice cloud observed over the Atmospheric Radiation Measurement program's Southern Great Plains site in Oklahoma is investigated. Airborne microphysical data from a cloud particle imager, optical array probes, and forward scattering probes are used to construct vertical profiles of the size and shape distributions of ice crystals. Due to uncertainties associated with measuring the sizes and shapes of small ice crystals with maximum dimensions less than 120 μm, five alternate size-shape distributions are derived and combined with existing databases of wavelength-dependent single-scattering properties of idealized ice crystals to obtain vertical profiles of optical properties. The dependence of the surface and the top-of-the-atmosphere fluxes on these uncertainties is simulated with a radiative transfer model. In addition, surface fluxes are compared against measurements at the surface. It is found that the differences between the modeled and measured fluxes are too large to be explained by uncertainties in the shape and concentrations of small ice crystals. Sensitivity tests suggest that the discrepancies occur because the real optical thickness is larger than that derived from the aircraft profiles most of the time. When the optical thickness was derived based on modeled and measured direct fluxes, the modeled total downward flux agreed well with the measurements. Slightly (less than 10%) reducing the asymmetry parameter, which is possibly associated with the presence of surface roughness, air bubble inclusions or other nonidealities in ice crystals, may further improve the agreement with observations.

Vertical velocity structure of nonprecipitating continental boundary layer stratocumulus clouds

Continental boundary layer (BL) stratocumulus clouds affect the local weather by modulating the surface energy and moisture budgets and are also intimately tied to the diurnal cycle of the turbulence in the BL. Vertical velocity structure of these clouds is studied using data from the Atmospheric Radiation Measurement Program's Southern Great Plains observing facility located near Lamont, Oklahoma. Data from vertically pointing cloud radar from eleven cases of nonprecipitating BL stratocumulus clouds are used to obtain half‐hourly values of in‐cloud vertical velocity variance, skewness, updraft fraction, downdraft fraction, and mass flux. The variance showed a general decrease with height, while the skewness was weakly positive in the cloud layer and negative near cloud top on half‐hourly time scales. Vertically coherent structures spanning through the entire cloud layer were responsible for ∼40% of the observed vertical velocity variance, while horizontally (temporally) coherent structures lasting at least 20 s were responsible for ∼77% of the vertical velocity variance. The half‐hour periods were then classified on the basis of the surface buoyancy flux (B) as small B periods (B < 10 Wm−2) and large B periods (B > 60Wm−2). Small B periods exhibited lower variance near cloud base compared to large B periods with the differences being reversed near cloud top. The skewness was positive in the cloud layer during large B periods and negative during small B periods, while the skewness was negative near cloud top in both scenarios. The small B periods had about ten times more updrafts greater than 1 m s−1 near cloud top compared to large B periods.

Remote sensing of cirrus cloud vertical size profile using MODIS data

This paper describes an algorithm for inferring cirrus cloud top and cloud base effective particle sizes and cloud optical thickness from the Moderate Resolution Imaging Spectroradiometer (MODIS) 0.645, 1.64 and 2.13, and 3.75 μm band reflectances/radiances. This approach uses a successive minimization method based on a look‐up library of precomputed reflectances/radiances from an adding‐doubling radiative transfer program, subject to corrections for Rayleigh scattering at the 0.645 μm band, above‐cloud water vapor absorption, and 3.75 μm thermal emission. The algorithmic accuracy and limitation of the retrieval method were investigated by synthetic retrievals subject to the instrument noise and the perturbation of input parameters. The retrieval algorithm was applied to three MODIS cirrus scenes over the Atmospheric Radiation Measurement Program's southern Great Plain site, north central China, and northeast Asia. The reliability of retrieved cloud optical thicknesses and mean effective particle sizes was evaluated by comparison with MODIS cloud products and qualitatively good correlations were obtained for all three cases, indicating that the performance of the vertical sizing algorithm is comparable with the MODIS retrieval program. Retrieved cloud top and cloud base ice crystal effective sizes were also compared with those derived from the collocated ground‐based millimeter wavelength cloud radar for the first case and from the Cloud Profiling Radar onboard CloudSat for the other two cases. Differences between retrieved and radar‐derived cloud properties are discussed in light of assumptions made in the collocation process and limitations in radar remote sensing characteristics.

A Comparison of Forecast Errors in CAM2 and CAM3 at the ARM Southern Great Plains Site

Abstract The authors compare short forecast errors and the balance of terms in the moisture and temperature prediction equations that lead to those errors for the Community Atmosphere Model versions 2 and 3 (CAM2 and CAM3, respectively) at T42 truncation. The comparisons are made for an individual model column from global model forecasts at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site for the April 1997 and June–July 1997 intensive observing periods. The goal is to provide insight into parameterization errors in the CAM, which ultimately should lead to improvements in the way processes are modeled. The atmospheric initial conditions are obtained from the 40-yr ECMWF Re-Analysis (ERA-40). The land initial conditions are spun up to be consistent with those analyses. The differences between the model formulations that are responsible for the major differences in the forecast errors and/or parameterization behaviors are identified. A sequence of experiments is performed, accumulating the changes from CAM3 back toward CAM2 to demonstrate the effect of the differences in formulations. In June–July 1997 the CAM3 temperature and moisture forecast errors were larger than those of CAM2. The terms identified as being responsible for the differences are 1) the convective time scale assumed for the Zhang–McFarlane deep convection, 2) the energy associated with the conversion between water and ice of the rain associated with the Zhang–McFarlane convection parameterization, and 3) the dependence of the rainfall evaporation on cloud fraction. In April 1997 the CAM2 and CAM3 temperature and moisture forecast errors are very similar, but different tendencies arising from modifications to one parameterization component are compensated by responding changes in another component to yield the same total moisture tendency. The addition of detrainment of water in CAM3 by the Hack shallow convection to the prognostic cloud water scheme is balanced by a responding difference in the advective tendency. A halving of the time scale assumed for the Hack shallow convection was compensated by a responding change in the prognostic cloud water. Changes to the cloud fraction parameterization affect the radiative heating, which in turn modifies the stability of the atmospheric column and affects the convection. The resulting changes in convection tendency are balanced by responding changes in the prognostic cloud water parameterization tendency.

Application of aerosol hygroscopicity measured at the Atmospheric Radiation Measurement Program's Southern Great Plains site to examine composition and evolution

A Differential Mobility Analyzer/Tandem Differential Mobility Analyzer (DMA/TDMA) was used to measure submicron aerosol size distributions, hygroscopicity, and occasionally volatility during the May 2003 Aerosol Intensive Operational Period (IOP) at the Central Facility of the Atmospheric Radiation Measurement Program's Southern Great Plains (ARM SGP) site. Hygroscopic growth factor distributions for particles at eight dry diameters ranging from 0.012 μm to 0.600 μm were measured throughout the study. For a subset of particle sizes, more detailed measurements were occasionally made in which the relative humidity or temperature to which the aerosol was exposed was varied over a wide range. These measurements, in conjunction with backtrajectory clustering, were used to infer aerosol composition and to gain insight into the processes responsible for evolution. The hygroscopic growth of both the smallest and largest particles analyzed was typically less than that of particles with dry diameters of about 0.100 μm. It is speculated that condensation of secondary organic aerosol on nucleation mode particles is largely responsible for the minimal hygroscopic growth observed at the smallest sizes considered. Growth factor distributions of the largest particles characterized typically contained a nonhygroscopic mode believed to be composed primarily of dust. A model was developed to characterize the hygroscopic properties of particles within a size distribution mode through analysis of the fixed size hygroscopic growth measurements. The performance of this model was quantified through comparison of the measured fixed size hygroscopic growth factor distributions with those simulated through convolution of the size‐resolved concentration contributed by each of the size modes and the mode‐resolved hygroscopicity. This transformation from size‐resolved hygroscopicity to mode‐resolved hygroscopicity facilitated examination of changes in the hygroscopic properties of particles within a size distribution mode that accompanied changes in the sizes of those particles. This model was used to examine three specific cases in which the sampled aerosol evolved slowly over a period of hours or days.

Evaluating parameterizations of the autoconversion process using a single‐column model and Atmospheric Radiation Measurement Program measurements

A single‐column model is used to evaluate the performance of two types of autoconversion parameterizations. The model results are compared to data collected at the Atmospheric Radiation Measurement Program's Southern U.S. Great Plains site. The model is run over a period covering 2 years (2000–2001), and the results are analyzed for time periods varying from hourly to seasonal. During a relatively short 27‐hour period during March 2000 characterized primarily by shallow frontal clouds, modeled values of cloud liquid water were better simulated using a Manton‐Cotton‐type autoconversion parameterization. However, over longer timescales representing a multitude of different cloud types and meteorological conditions, a Sundqvist‐type parameterization produced better results. Analysis of the model results indicates that the Manton‐Cotton‐type parameterization does better during periods when shallow clouds are present without any overlying clouds, while the Sundqvist‐type parameterization is preferred during periods when high and low clouds coexist. A possible explanation is that precipitation from high clouds may not be represented well by the SCM, thus affecting the precipitation formation rates in any lower clouds. Sensitivity tests using the Manton‐Cotton parameterization indicate that the autoconversion rate is sensitive to the specification of the cloud droplet number concentration (Nc). The single‐column model, as well as many general circulation models, specify Nc as a constant value. However, limited in situ measurements suggest that Nc varies significantly in time. The mean modeled top‐of‐atmosphere cloud radiative forcing during the 2‐year period 2000–2001 differed by 3 W m−2 as the cloud droplet concentration was varied between minimum and maximum values suggested by the in situ measurements. These results imply that model‐produced hydrological cycle and cloud‐radiation interactions could be better modeled using an accurate time‐dependent measure of the cloud droplet concentration.

Light Scattering by Quasi-Spherical Ice Crystals

Abstract The shapes and single-scattering properties of small, irregular, quasi-spherical ice crystals, with equivalent radii between approximately 8 and 90 μm and size parameters from about 90 to 1000, are studied using two-dimensional images measured by a cloud particle imager in midlatitude cirrus during the 2000 Cloud Intensive Operation Period conducted over the Atmospheric Radiation Measurement program's Southern Great Plains site. A statistical shape analysis of the ice crystal images is carried out to obtain size-dependent relative standard deviations of radius and correlation functions of logradius, which together define the shape statistics of the sample ice crystals. The former describes the overall variation in the lengths of radius vectors defining the particle surface from a given origin, whereas the latter describes correlations of lengths of radius vectors as functions of angular distance between them. The logradius is essentially the natural logarithm of radius. There is no strong depende...