Browsing by Author "Thornhill, Kenneth L."
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Item Assessment of landscape-scale fluxes of carbon dioxide and methane in subtropical coastal wetlands of South Florida(2024-04-16) Delaria, Erin Rose; Wolfe, Glenn; Blanock, Kaitlyn; Hannun, Reem; Thornhill, Kenneth L.; Newman, Paul A.; Lait, Leslie; Kawa, Stephan Randolph; Alvarez, Jessica; Blum, Spencer; Castañeda-Moya, Edward; Holmes, Christopher D.; Lagomasino, David; Malone, Sparkle Leigh; Murphy, Dylan; Oberbauer, Steven F.; Pruett, Chandler; Sere, Aaron; Starr, Gregory; Szot, Robert; Troxler, Tiffany; Yannick, David; Poulter, BenjaminCoastal wetlands play a significant role in the storage of ‘bluecarbon’, indicating their importance in the carbon biogeochemistry inthe coastal zone and in global climate change mitigation strategies. Wepresent airborne eddy-covariance observations of CO2 and CH4 fluxescollected in southern Florida as part of the NASA BlueFlux missionduring April 2022, October 2022, February 2023, and April 2023. The fluxdata generated from this mission consists of over 100 flight hours andmore than 6000 km of horizontal distance over coastal saline andfreshwater wetlands. We find that the spatial and temporal heterogeneityin CO2 and CH4 exchange is primarily influenced by season, vegetationtype, ecosystem productivity, and soil inundation. The largest CO2uptake fluxes of more than -20 µmol m-2 s-1 were observed over mangrovesduring all deployments and over swamp forests during flights in April.The greatest CH4 effluxes of more than 250 nmol m-2 s-1 were measured atthe end of the wet season in October 2022 over freshwater marshes andswamp shrublands. Although the combined Everglades National Park and BigCypress National Preserve region was a net sink for carbon, CH4emissions reduced the ecosystem carbon uptake capacity (net CO2 exchangerates) by 11-91%. Average total net carbon exchange rates during theflight periods were -4 to -0.2 g CO2-eq m-2 d-1. Our results highlightthe importance of preserving mangrove forests and point to potentialavenues of further research for greenhouse gas mitigation strategies.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 Intercomparison of aerosol volume size distributions derived from AERONET ground-based remote sensing and LARGE in situ aircraft profiles during the 2011–2014 DRAGON and DISCOVER-AQ experiments(EGU, 2019-10-07) Schafer, Joel S.; Eck, Thomas; Holben, Brent N.; Thornhill, Kenneth L.; Ziemba, Luke D.; Sawamura, Patricia; Moore, Richard H.; Slutsker, Ilya; Anderson, Bruce E.; Sinyuk, Alexander; Giles, David M.; Smirnov, Alexander; Beyersdorf, Andreas J.; Winstead, Edward L.Aerosol volume size distribution (VSD) retrievals from the Aerosol Robotic Network (AERONET) aerosol monitoring network were obtained during multiple DRAGON (Distributed Regional Aerosol Gridded Observational Network) campaigns conducted in Maryland, California, Texas and Colorado from 2011 to 2014. These VSD retrievals from the field campaigns were used to make comparisons with near-simultaneous in situ samples from aircraft profiles carried out by the NASA Langley Aerosol Group Experiment (LARGE) team as part of four campaigns comprising the DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) experiments. For coincident (±1 h) measurements there were a total of 91 profile-averaged fine-mode size distributions acquired with the LARGE ultra-high sensitivity aerosol spectrometer (UHSAS) instrument matched to 153 AERONET size distributions retrieved from almucantars at 22 different ground sites. These volume size distributions were characterized by two fine-mode parameters, the radius of peak concentration (rpeak_conc) and the VSD fine-mode width (widthpeak_conc). The AERONET retrievals of these VSD fine-mode parameters, derived from ground-based almucantar sun photometer data, represent ambient humidity values while the LARGE aircraft spiral profile retrievals provide dried aerosol (relative humidity; RH <20 %) values. For the combined multiple campaign dataset, the average difference in rpeak_conc was 0.033±0.035 µm (ambient AERONET values were 15.8 % larger than dried LARGE values), and the average difference in widthpeak_conc was 0.042±0.039 µm (AERONET values were 25.7 % larger). For a subset of aircraft data, the LARGE data were adjusted to account for ambient humidification. For these cases, the AERONET–LARGE average differences were smaller, with rpeak_conc differing by 0.011±0.019 µm (AERONET values were 5.2 % larger) and widthpeak_conc average differences equal to 0.030±0.037 µm (AERONET values were 15.8 % larger).