Browsing by Author "Sorooshian, Armin"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Identifying Chemical Aerosol Signatures using Optical Suborbital Observations: How much can optical properties tell us about aerosol composition?(Copernicus Publications, 2021-09-24) Kacenelenbogen, Meloë S. F.; Tan, Qian; Burton, Sharon P.; Hasekamp, Otto P.; Froyd, Karl D.; Shinozuka, Yohei; Beyersdorf, Andreas J.; Ziemba, Luke; Thornhill, Kenneth L.; Dibb, Jack E.; Shingler, Taylor; Sorooshian, Armin; Espinosa, Reed W.; Martins, Vanderlei; Jimenez, Jose L.; Campuzano-Jost, Pedro; Schwarz, Joshua P.; Johnson, Matthew S.; Redemann, Jens; Schuster, Gregory L.Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).Item Opportunistic Experiments to Constrain Aerosol Effective Radiative Forcing(Copernicus Publications, 2021-08-20) Christensen, Matthew; Gettelman, Andrew; Cermak, Jan; Dagan, Guy; Diamond, Michael; Douglas, Alyson; Feingold, Graham; Glassmeier, Franziska; Goren, Tom; Grosvenor, Daniel; Gryspeerdt, Edward; Kahn, Ralph; Li, Zhanqing; Ma, Po-Lun; Malavelle, Florent; McCoy, Isabel; McCoy, Daniel; McFarquhar, Greg; Mülmenstädt, Johannes; Pal, Sandip; Possner, Anna; Povey, Adam; Quaas, Johannes; Rosenfeld, Daniel; Schmidt, Anja; Schrödner, Roland; Sorooshian, Armin; Stier, Philip; Toll, Velle; Watson-Parris, Duncan; Wood, Robert; Yang, Mingxi; Yuan, TianleAerosol-cloud interactions (ACI) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The non-linearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can also be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well defined sources provide “opportunistic experiments” (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatio-temporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite data sets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Opportunistic experiments have significantly improved process level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus, demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.Item Uncertainty in aerosol-cloud radiative forcing is driven by clean conditions(EGU, 2023-04-05) Gryspeerdt, Edward; Povey, Adam C.; Grainger, Roy G.; Hasekamp, Otto; Hsu, N. Christina; Mulcahy, Jane P.; Sayer, Andrew; Sorooshian, ArminAtmospheric aerosols and their impact on cloud properties remain the largest uncertainty in the human forcing of the climate system. By increasing the concentration of cloud droplets (Nd), aerosols reduce droplet size and increase the reflectivity of clouds (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol (β), the diversity of which explains much of the variation in radiative forcing in global climate models. This has 5 made measuring β a key target for developing observational constraints of the aerosol forcing. While the aerosol burden of the clean, pre-industrial atmosphere has been demonstrated as a key uncertainty for the aerosol forcing, here we show that the behaviour of clouds under these clean conditions is of equal importance for understanding the spread in radiative forcing estimates between models and observations. This means that the uncertainty in the aerosol impact on clouds is, counterintuitively, driven by situations with little aerosol. Discarding clean conditions produces a close agreement 10 between different model and observational estimates of the cloud response to aerosol, but does not provide a strong constraint on the radiative forcing from aerosol-cloud interactions. This makes constraining aerosol behaviour in clean conditions an important goal for future observational studies.