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Su T, Li Z, Henao NR, Luan Q, Yu F. Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface. SCIENCE ADVANCES 2024; 10:eadl5044. [PMID: 38781324 PMCID: PMC11114194 DOI: 10.1126/sciadv.adl5044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 04/17/2024] [Indexed: 05/25/2024]
Abstract
Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.
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Affiliation(s)
- Tianning Su
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Zhanqing Li
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Natalia Roldan Henao
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Qingzu Luan
- Earth System Science Interdisciplinary Center & AOSC, University of Maryland, College Park, MD, USA
| | - Fangqun Yu
- Atmospheric Sciences Research Center, University at Albany, Albany, NY, USA
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2
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Jo DS, Nault BA, Tilmes S, Gettelman A, McCluskey CS, Hodzic A, Henze DK, Nawaz MO, Fung KM, Jimenez JL. Global Health and Climate Effects of Organic Aerosols from Different Sources. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:13793-13807. [PMID: 37671787 DOI: 10.1021/acs.est.3c02823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
The impact of aerosols on human health and climate is well-recognized, yet many studies have only focused on total PM2.5 or changes from anthropogenic activities. This study quantifies the health and climate effects of organic aerosols (OA) from anthropogenic, biomass burning, and biogenic sources. Using two atmospheric chemistry models, CAM-chem and GEOS-Chem, our findings reveal that anthropogenic primary OA (POA) has the highest efficiency for health effects but the lowest for direct radiative effects due to spatial and temporal variations associated with population and surface albedo. The treatment of POA as nonvolatile or semivolatile also influences these efficiencies through different chemical processes. Biogenic OA shows moderate efficiency for health effects and the highest for direct radiative effects but has the lowest efficiency for indirect effects due to the reduced high cloud, caused by stabilized temperature profiles from aerosol-radiation interactions in biogenic OA-rich regions. Biomass burning OA is important for cloud radiative effect changes in remote atmospheres due to its ability to be transported further than other OAs. This study highlights the importance of not only OA characteristics such as toxicity and refractive index but also atmospheric processes such as transport and chemistry in determining health and climate impact efficiencies.
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Affiliation(s)
- Duseong S Jo
- Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80301, United States
| | - Benjamin A Nault
- Center for Aerosols and Cloud Chemistry, Aerodyne Research, Inc., Billerica, Massachusetts 01821, United States
- Department of Environmental Health and Engineering, The Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Simone Tilmes
- Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80301, United States
| | - Andrew Gettelman
- Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80301, United States
- Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80305, United States
| | - Christina S McCluskey
- Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80305, United States
| | - Alma Hodzic
- Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado 80301, United States
| | - Daven K Henze
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Muhammad Omar Nawaz
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Ka Ming Fung
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Jose L Jimenez
- Department of Chemistry, University of Colorado Boulder, Boulder, Colorado 80309, United States
- Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado 80309, United States
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3
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Sanchez KJ, Painemal D, Brown MD, Crosbie EC, Gallo F, Hair JW, Hostetler CA, Jordan CE, Robinson CE, Scarino AJ, Shingler TJ, Shook MA, Thornhill KL, Wiggins EB, Winstead EL, Ziemba LD, Chambers S, Williams A, Humphries RS, Keywood MD, Ward JP, Cravigan L, McRobert IM, Flynn C, Kulkarni GR, Russell LM, Roberts GC, McFarquhar GM, Nenes A, Woods SF, Reid JS, Small-Griswold J, Brooks S, Kirschler S, Voigt C, Wang J, Delene DJ, Quinn PK, Moore RH. Multi-campaign ship and aircraft observations of marine cloud condensation nuclei and droplet concentrations. Sci Data 2023; 10:471. [PMID: 37474611 PMCID: PMC10359301 DOI: 10.1038/s41597-023-02372-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023] Open
Abstract
In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP2EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosol-cloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.
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Affiliation(s)
| | - David Painemal
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | - Matthew D Brown
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | - Ewan C Crosbie
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | - Francesca Gallo
- NASA Langley Research Center, Hampton, VA, 23681, USA
- NASA Postdoctoral Program, Oak Ridge Associated Universities, Oak Ridge, TN, 837830, USA
| | | | | | - Carolyn E Jordan
- NASA Langley Research Center, Hampton, VA, 23681, USA
- National Institute of Aerospace, Hampton, VA, 23666, USA
| | - Claire E Robinson
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | - Amy Jo Scarino
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | | | | | - Kenneth L Thornhill
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | | | - Edward L Winstead
- NASA Langley Research Center, Hampton, VA, 23681, USA
- Science Systems and Applications, Inc., Hampton, VA, 23666, USA
| | - Luke D Ziemba
- NASA Langley Research Center, Hampton, VA, 23681, USA
| | - Scott Chambers
- Australian Nuclear Science and Technology Organisation, Lucas Heigths, NSW, 2232, Australia
| | - Alastair Williams
- Australian Nuclear Science and Technology Organisation, Lucas Heigths, NSW, 2232, Australia
| | - Ruhi S Humphries
- Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Australia
| | - Melita D Keywood
- Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Australia
| | - Jason P Ward
- Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, Australia
| | - Luke Cravigan
- School of Earth and Atmospheric Sciences, Queensland University of Technology, Brisbane, Australia
| | - Ian M McRobert
- Engineering and Technology Program, CSIRO National Collections and Marine Infrastructure, Hobart, Australia
| | - Connor Flynn
- School of Meteorology, University of Oklahoma, Norman, OK, USA
| | - Gourihar R Kulkarni
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, USA
| | | | - Gregory C Roberts
- Scripps Institution of Oceanography, La Jolla, CA, USA
- Centre National de Recherches Météorologiques, UMR3589, Toulouse, France
| | - Greg M McFarquhar
- School of Meteorology, University of Oklahoma, Norman, OK, USA
- Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma, USA
| | - Athanasios Nenes
- Laboratory of atmospheric processes and their impacts (LAPI), ENAC/IIE, Ecole polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Institute for Chemical Engineering Sciences, Foundation for Research and Technology Hellas (ICE-HT/FORTH), Patra, Greece
| | - Sarah F Woods
- Stratton Park Engineering Company (SPEC), Boulder, CO, 80301, USA
| | | | | | | | - Simon Kirschler
- Institute for Atmospheric Physics, DLR, German Aerospace Center, Oberpfaffenhofen, Germany
- Institute for Atmospheric Physics, University of Mainz, Mainz, Germany
| | - Christianne Voigt
- Institute for Atmospheric Physics, DLR, German Aerospace Center, Oberpfaffenhofen, Germany
- Institute for Atmospheric Physics, University of Mainz, Mainz, Germany
| | - Jian Wang
- Center for Aerosol Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
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Liu Y, Zhang J, Hong L, Fu Y, Xia H, Zhang R. Method for improving the measurement accuracy of binocular stereo vision in a scattering environment. APPLIED OPTICS 2022; 61:6158-6166. [PMID: 36256228 DOI: 10.1364/ao.463391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/24/2022] [Indexed: 06/16/2023]
Abstract
In the scattering environment, binocular stereo vision measurement technology produces large errors due to the change of refractive index of the imaging light path and the decrease in target image contrast. To address this problem, this paper proposes a method for improving the measurement accuracy of binocular stereo vision in a scattering environment combined with polarization imaging theory. First, scattering images with different polarization directions are obtained and filtered by a Gaussian low-pass filter to calculate the degree of polarization and angle of polarization. Then, the scattered light intensity is calculated by using polarization information to obtain images after removing the scattering. Second, feature extraction and matching are carried out for the images after scattering removal. Finally, the target is measured based on the binocular stereo vision measurement model. The experimental results show that when the scattering concentration is high enough, the conventional method can no longer perform measurement, but the method proposed in this paper can still obtain the target parameters at this time, and can also improve measurement accuracy by at least 46.30%. In conclusion, the proposed method provides a valuable reference to improve the accuracy of binocular stereo vision measurement in a scattering environment by reducing the interference of scattering light.
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5
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Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements. REMOTE SENSING 2022. [DOI: 10.3390/rs14143342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosol-cloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (nCCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP nCCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly nCCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global nCCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved nCCN dataset for use in ACI studies.
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6
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Machine-Learning Based Analysis of Liquid Water Path Adjustments to Aerosol Perturbations in Marine Boundary Layer Clouds Using Satellite Observations. ATMOSPHERE 2022. [DOI: 10.3390/atmos13040586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (Nd, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and Nd are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and Nd is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: Nd–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.
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7
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Won WS, Oh R, Lee W, Ku S, Su PC, Yoon YJ. Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility. Sci Rep 2021; 11:16401. [PMID: 34385551 PMCID: PMC8361198 DOI: 10.1038/s41598-021-95834-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 07/26/2021] [Indexed: 11/09/2022] Open
Abstract
The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, \documentclass[12pt]{minimal}
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\begin{document}$$f\left( {RH} \right)$$\end{document}fRH, and the hygroscopic mass growth, \documentclass[12pt]{minimal}
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\begin{document}$$GM_{VIS}$$\end{document}GMVIS, which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled \documentclass[12pt]{minimal}
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\begin{document}$$f\left( {RH} \right)$$\end{document}fRH agreed well with the observed \documentclass[12pt]{minimal}
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\begin{document}$$f\left( {RH} \right)$$\end{document}fRH in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.
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Affiliation(s)
- Wan-Sik Won
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Rosy Oh
- Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea
| | - Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, 08826, Korea
| | - Sungkwan Ku
- Department of Aviation Industrial and System Engineering, Hanseo University, Seosan-si, Chungcheongnam-do, 32158, Korea
| | - Pei-Chen Su
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
| | - Yong-Jin Yoon
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore. .,Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Korea.
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Dadashazar H, Painemal D, Alipanah M, Brunke M, Chellappan S, Corral AF, Crosbie E, Kirschler S, Liu H, Moore RH, Robinson C, Scarino AJ, Shook M, Sinclair K, Thornhill KL, Voigt C, Wang H, Winstead E, Zeng X, Ziemba L, Zuidema P, Sorooshian A. Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors. ATMOSPHERIC CHEMISTRY AND PHYSICS 2021; 21:10499-10526. [PMID: 34377145 PMCID: PMC8350960 DOI: 10.5194/acp-21-10499-2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cloud drop number concentrations (N d) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosol optical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak in spring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH), low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol-N d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N d.
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Affiliation(s)
- Hossein Dadashazar
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
| | - David Painemal
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Majid Alipanah
- Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, USA
| | - Michael Brunke
- Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
| | - Seethala Chellappan
- Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Andrea F. Corral
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
| | - Ewan Crosbie
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Simon Kirschler
- Institute of Atmospheric Physics, German Aerospace Center, Oberpfaffenhofen, Germany
| | - Hongyu Liu
- National Institute of Aerospace, Hampton, VA, USA
| | | | - Claire Robinson
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Amy Jo Scarino
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | | | - Kenneth Sinclair
- NASA Goddard Institute for Space Studies, New York, NY, USA
- Universities Space Research Association, Columbia, MD, USA
| | | | - Christiane Voigt
- Institute of Atmospheric Physics, German Aerospace Center, Oberpfaffenhofen, Germany
| | - Hailong Wang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Edward Winstead
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Xubin Zeng
- Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
| | - Luke Ziemba
- NASA Langley Research Center, Hampton, VA, USA
| | - Paquita Zuidema
- Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USA
| | - Armin Sorooshian
- Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA
- Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA
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Jia H, Ma X, Yu F, Quaas J. Significant underestimation of radiative forcing by aerosol-cloud interactions derived from satellite-based methods. Nat Commun 2021; 12:3649. [PMID: 34131118 PMCID: PMC8206093 DOI: 10.1038/s41467-021-23888-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 05/17/2021] [Indexed: 02/05/2023] Open
Abstract
Satellite-based estimates of radiative forcing by aerosol-cloud interactions (RFaci) are consistently smaller than those from global models, hampering accurate projections of future climate change. Here we show that the discrepancy can be substantially reduced by correcting sampling biases induced by inherent limitations of satellite measurements, which tend to artificially discard the clouds with high cloud fraction. Those missed clouds exert a stronger cooling effect, and are more sensitive to aerosol perturbations. By accounting for the sampling biases, the magnitude of RFaci (from -0.38 to -0.59 W m-2) increases by 55 % globally (133 % over land and 33 % over ocean). Notably, the RFaci further increases to -1.09 W m-2 when switching total aerosol optical depth (AOD) to fine-mode AOD that is a better proxy for CCN than AOD. In contrast to previous weak satellite-based RFaci, the improved one substantially increases (especially over land), resolving a major difference with models.
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Affiliation(s)
- Hailing Jia
- grid.260478.fCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, and Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, China ,grid.265850.c0000 0001 2151 7947Atmospheric Sciences Research Center, University at Albany, Albany, NY USA ,grid.9647.c0000 0004 7669 9786Institute for Meteorology, Universität Leipzig, Leipzig, Germany
| | - Xiaoyan Ma
- grid.260478.fCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, and Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, China
| | - Fangqun Yu
- grid.265850.c0000 0001 2151 7947Atmospheric Sciences Research Center, University at Albany, Albany, NY USA
| | - Johannes Quaas
- grid.9647.c0000 0004 7669 9786Institute for Meteorology, Universität Leipzig, Leipzig, Germany
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10
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A First Case Study of CCN Concentrations from Spaceborne Lidar Observations. REMOTE SENSING 2020. [DOI: 10.3390/rs12101557] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We present here the first cloud condensation nuclei (CCN) concentration profiles derived from measurements with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), for different aerosol types at a supersaturation of 0.15%. CCN concentrations, along with the corresponding uncertainties, were inferred for a nighttime CALIPSO overpass on 9 September 2011, with coincident observations with the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft, within the framework of the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign over Thessaloniki, Greece. The CALIPSO aerosol typing is evaluated, based on data from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. Backward trajectories and satellite-based fire counts are used to examine the origin of air masses on that day. Our CCN retrievals are evaluated against particle number concentration retrievals at different height levels, based on the ACEMED airborne measurements and compared against CCN-related retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard Terra and Aqua product over Thessaloniki showing that it is feasible to obtain CCN concentrations from CALIPSO, with an uncertainty of a factor of two to three.
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11
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Gryspeerdt E, Mülmenstädt J, Gettelman A, Malavelle FF, Morrison H, Neubauer D, Partridge DG, Stier P, Takemura T, Wang H, Wang M, Zhang K. Surprising similarities in model and observational aerosol radiative forcing estimates. ATMOSPHERIC CHEMISTRY AND PHYSICS 2020; 20:613-623. [PMID: 33204244 PMCID: PMC7668122 DOI: 10.5194/acp-20-613-2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model and observational differences is thus vital to reduce uncertainty in the impact of aerosols on the climate. These reported discrepancies arise from the different methods of separating the components of aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model (GCM) output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties. Cloud adjustments remain a significant source of uncertainty, particularly for ice clouds. However, they are consistent with the uncertainty from observation-based methods, with the liquid water path adjustment usually enhancing the Twomey effect by less than 50%. Depending on different sets of assumptions, this work suggests that model and observation-based estimates could be more equally weighted in future synthesis studies.
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Affiliation(s)
- Edward Gryspeerdt
- Space and Atmospheric Physics Group, Imperial College London, London, UK
| | | | | | - Florent F. Malavelle
- College of Engineering Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Met Office, Fitzroy Road, Exeter, UK
| | - Hugh Morrison
- National Center for Atmospheric Research, Boulder, USA
| | - David Neubauer
- Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
| | - Daniel G. Partridge
- College of Engineering Mathematics and Physical Sciences, University of Exeter, Exeter, UK
| | - Philip Stier
- Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Oxford, UK
| | - Toshihiko Takemura
- Research Institute for Applied Mathematics, Kyushu University, Fukuoka, Japan
| | - Hailong Wang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, USA
| | - Minghuai Wang
- Institute for Climate and Global Change Research, Nanjing University, Nanjing, China
- School of Atmospheric Sciences, Nanjing University, Nanjing, China
- Collaborative Innovation Center of Climate Change, Nanjing, China
| | - Kai Zhang
- Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, USA
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Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements. REMOTE SENSING 2019. [DOI: 10.3390/rs11232877] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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
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