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A lower bound on the uncertainty in observational estimates of the aerosol direct radiative effect (DRE; the direct interaction with solar radiation by all aerosols) and the aerosol direct radiative forcing [DRF; the radiative effect of just anthropogenic aerosols (RFari)] is quantified by making the optimistic assumption that global aerosol observations can be made with the accuracy found in the Aerosol Robotic Network (AERONET) sun photometer retrievals. The global-mean all-sky aerosol DRE uncertainty was found to be 1.1 W m−2 (one standard deviation). The global-mean all-sky aerosol DRF (RFari) uncertainty was determined to be 0.31 W m−2. The total uncertainty in both quantities is dominated by contributions from the aerosol single scattering albedo uncertainty. These uncertainty estimates were compared to a literature survey of mostly satellite-based aerosol DRE/DRF values. Comparisons to previous studies reveal that most have significantly underestimated the aerosol DRE uncertainty. Past estimates of the aerosol DRF uncertainty are smaller (on average) than our optimistic observational estimates, including the aerosol DRF uncertainty given in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). This disconnect between our observation-based uncertainty and that found in past aerosol DRF studies that rely, at least in part, on modeling is discussed. Also quantified is a potential reduction in the current observational uncertainty possible with a future generation of satellite observations that would leverage aerosol typing and more refined vertical information.

Both to reconcile the large range in satellite-based estimates of the aerosol direct radiative effect (DRE) and to optimize the design of future observing systems, this study builds a framework for assessing aerosol DRE uncertainty. Shortwave aerosol DRE radiative kernels (Jacobians) were derived using the MERRA-2 reanalysis data. These radiative kernels give the differential response of the aerosol DRE to perturbations in the aerosol extinction coefficient, aerosol single-scattering albedo, aerosol asymmetry factor, surface albedo, cloud fraction, and cloud optical depth. This comprehensive set of kernels provides a convenient way to consistently and accurately assess the aerosol DRE uncertainties that result from observational or model-based uncertainties. The aerosol DRE kernels were used to test the effect of simplifying the full vertical profile of aerosol scattering properties into column-integrated quantities. This analysis showed that, although the clear-sky aerosol DRE can be had fairly accurately, more significant errors occur for the all-sky DRE. The sensitivity in determining the broadband spectral dependencies of the aerosol scattering properties directly from a limited set of wavelengths was quantified. These spectral dependencies can be reasonably constrained using column-integrated aerosol scattering properties in the midvisible and near-infrared wavelengths. Separating the aerosol DRE and its kernels by scene type shows that accurate aerosol properties in the clear sky are the most crucial component of the global aerosol DRE. In cloudy skies, determining aerosol properties in the presence of optically thin cloud is more radiatively important than doing so when optically thick cloud is present.

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night. To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.

We present a detailed evaluation of remotely sensed aerosol microphysical properties obtained from an advanced, multi-wavelength high-spectral-resolution lidar (HSRL-2) during the 2013 NASA DISCOVER-AQ field campaign. Vertically resolved retrievals of fine-mode aerosol number, surface-area, and volume concentration as well as aerosol effective radius are compared to 108 collocated, airborne in situ measurement profiles in the wintertime San Joaquin Valley, California, and in summertime Houston, Texas. An algorithm for relating the dry in situ aerosol properties to those obtained by the HSRL at ambient relative humidity is discussed. We show that the HSRL-2 retrievals of ambient fine-mode aerosol surface-area and volume concentrations agree with the in situ measurements to within 25 and 10 %, respectively, once hygroscopic growth adjustments have been applied to the dry in situ data. Despite this excellent agreement for the microphysical properties, extinction and backscatter coefficients at ambient relative humidity derived from the in situ aerosol measurements using Mie theory are consistently smaller than those measured by the HSRL, with average differences of 31 ± 5 % and 53 ± 11 % for California and Texas, respectively. This low bias in the in situ estimates is attributed to the presence of coarse-mode aerosol that are detected by HSRL-2 but that are too large to be well sampled by the in situ instrumentation. Since the retrieval of aerosol volume is most relevant to current regulatory efforts targeting fine particle mass (PM2. 5), these findings highlight the advantages of an advanced 3β + 2α HSRL for constraining the vertical distribution of the aerosol volume or mass loading relevant for air quality.

Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

For aerosol retrieval from multi-angle polarimetric (MAP) measurements over the ocean it is important to accurately account for the contribution of the ocean-body to the top-of-atmosphere signal, especially for wavelengths <500 nm. Performing online radiative transfer calculations in the coupled atmosphere ocean system is too time consuming for operational retrieval algorithms. Therefore, mostly lookup-tables of the ocean body reflection matrix are used to represent the lower boundary in an atmospheric radiative transfer model. For hyperspectral measurements such as those from Spectro-Polarimeter for Planetary Exploration (SPEXone) on the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission, also the use of look-up tables is unfeasible because they will become too big. In this paper, we propose a new method for aerosol retrieval over ocean from MAP measurements using a neural network (NN) to model the ocean body reflection matrix. We apply the NN approach to synthetic SPEXone measurements and also to real data collected by SPEX airborne during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We conclude that the NN approach is well capable for aerosol retrievals over ocean, introducing no significant error on the retrieved aerosol properties

Descriptions are provided of the aerosol classification algorithms and the extinction-to-backscatter ratio (lidar ratio) selection schemes for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) aerosol products. One year of CALIPSO level 2 version 2 data are analyzed to assess the veracity of the CALIPSO aerosol-type identification algorithm and generate vertically resolved distributions of aerosol types and their respective optical characteristics. To assess the robustness of the algorithm, the interannual variability is analyzed by using a fixed season (June–August) and aerosol type (polluted dust) over two consecutive years (2006 and 2007). The CALIPSO models define six aerosol types: clean continental, clean marine, dust, polluted continental, polluted dust, and smoke, with 532-nm (1064 nm) extinction-to-backscatter ratios Sa of 35 (30), 20 (45), 40 (55), 70 (30), 65 (30), and 70 (40) sr, respectively. This paper presents the global distributions of the CALIPSO aerosol types, the complementary distributions of integrated attenuated backscatter, and the volume depolarization ratio for each type. The aerosol-type distributions are further partitioned according to surface type (land/ocean) and detection resolution (5, 20, and 80 km) for optical and spatial context, because the optically thick layers are found most often at the smallest spatial resolution. Except for clean marine and polluted continental, all the aerosol types are found preferentially at the 80-km resolution. Nearly 80% of the smoke cases and 60% of the polluted dust cases are found over water, whereas dust and polluted continental cases are found over both land and water at comparable frequencies. Because the CALIPSO observables do not sufficiently constrain the determination of the aerosol, the surface type is used to augment the selection criteria. Distributions of the total attenuated color ratios show that the use of surface type in the typing algorithm does not result in abrupt and artificial changes in aerosol type or extinction.

Aerosol modulation of atmospheric convection remains an important topic in ongoing research. A key challenge in evaluating aerosol impacts on cumulus convection is isolating their effects from environmental influences. This work investigates aerosol effects on maritime tropical convection using airborne observations from NASA's Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex). Eight environmental parameters with known physical connections to cloud and storm formation were identified from dropsonde data, and 92 dropsondes were matched with corresponding CAMP2Ex flight “scenes.” To constrain environmental conditions, scenes were binned based on their association with “low,” “medium,” or “high” values for each dropsonde-derived parameter. In each scene and environmental bin, eight radar- and radiometer-based parameters with physical implications for convective intensity and/or prevalence were correlated with lidar-derived aerosol concentrations to examine trends in convective characteristics under different aerosol conditions. Threshold values used to stratify the environments were varied across four sensitivity tests to examine how the convective–aerosol correlations within each environmental bin responded. The results were generally inconclusive, with relatively weak correlations observed with limited statistical significance in many cases. Some interesting and potentially impactful comparisons identified in the convective–aerosol analyses support the idea of warm-phase convective invigoration trends and suggest that higher aerosol concentrations were correlated with stronger and/or more-prevalent convection in some cases, while other cases saw a “Goldilocks” zone of medium aerosol concentration favoring enhanced convection. Our results also stress the importance of considering environmental conditions when evaluating aerosol impacts.

The improvement of satellite-derived calculations of direct aerosol radiative effects (DARE) is essential for reducing the uncertainty in the impact of aerosol on solar radiation. We develop a framework to compute DARE at the top of the Earth's atmosphere, in the shortwave part of the electromagnetic spectrum, and in all-sky conditions along the track of the A-Train constellation of satellites. We use combined state-of-the-art aerosol and cloud properties from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors. We also use a global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) to provide vertical distribution of aerosol properties and atmospheric conditions. Diurnal mean satellite DARE values range from −25 W m−2 (cooling) to 40 W m−2 (warming) over the southeast Atlantic during 3 days from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) aircraft campaign. These 3 days indicate agreement between our satellite-calculated DARE and co-located airborne Solar Spectral Flux Radiometer (SSFR) measurements. This paper constitutes the first step before applying our algorithm to more years of combined satellite and model data over more regions of the world. The goal is to ultimately assess the order of importance of atmospheric parameters in the calculation of DARE for specific aerosol and cloud regimes. This will inform future missions about where, when, and how accurately the retrievals should be performed to reduce all-sky DARE uncertainties.

High Spectral Resolution Lidars (HSRLs) have been successfully deployed from a variety of platforms: ground based, airborne, and now satellite. These lidars are uniquely valuable for characterizing atmospheric aerosol and clouds, benefiting from the ability to characterize vertical variability in more detail than any passive instruments, and, compared to elastic backscatter lidars, provide additional channels of measurements that permit the direct retrieval of particulate extinction. Although analytic solutions exist for deriving particulate backscatter, extinction, and linear depolarization ratio, in the case of extinction, the analytic technique greatly magnifies measurement noise. Low signal-to-noise measurements stress the traditional inversion methods. Accordingly, algorithms for the retrieval of HSRL backscatter and extinction are re-examined and optimized to reduce the noise propagation. Here we explore an Optimal Estimation methodology and compare it with an implementation of the direct differentiation method like that historically used for the processing of airborne HSRL data from NASA Langley Research Center.