Browsing by Author "Yuan, Tianle"
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Item Anthropogenic Decline of African Dust inferred from Insights From the Holocene Records and Beyond: are dust purely natural?(Copernicus Publications, 2021-04-19) Yuan, Tianle; Yu, Hongbin; Chin, Mian; Remer, Lorraine; McGee, David; Evan, AmatoItem Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial Network(2019-05-21) Yuan, TianleHere we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate two-dimensional (2D) marine low cloud reflectance fields. AI-CD uses a conditional generative adversarial net (cGAN) framework to model distribution of 2-D cloud reflectance in nature as observed by the MODerate resolution Imaging Spectrometer (MODIS). Specifically, the AI-CD models the conditional distribution of cloud reflectance fields given a set of largescale environmental conditions such as instantaneous sea surface temperature, estimated inversion strength, surface wind speed, relative humidity and large-scale subsidence rate together with random noise. We show that AI-CD can not only generate realistic cloudy scenes but also capture known, physical dependence of cloud properties on large-scale variables. AI-CD is stochastic in nature because generated cloud fields are influenced by random noise. Therefore, given a fixed set of large-scale variables, an ensemble of cloud reflectance fields can be generated using AI-CD. We suggest that AI-CD approach can be used as a data driven framework for stochastic cloud parameterization because it can realistically model sub-grid cloud distributions and their sensitivity to meteorological variables.Item Current and Future Perspectives of Aerosol Research at NASA Goddard Space Flight Center(AMS, 2014-10-01) Matsui, Toshi; Ichoku, Charles; Randles, Cynthia; Yuan, Tianle; Silva, Arlindo M. da; Colarco, Peter; Kim, Dongchul; Levy, Robert; Sayer, Andrew; Chin, Mian; Giles, David; Holben, Brent; Welton, Ellsworth; Eck, Thomas; Remer, LorraineAerosols are tiny atmospheric particles that are emitted from various natural and anthropogenic sources. They affect climate through direct and indirect interactions with solar and thermal radiation, clouds, and atmospheric circulation (Solomon et al. 2007). The launch of a variety of sophisticated satellite-based observing systems aboard the Terra, Aqua, Aura, SeaWiFS (see appendix for all acronym expansions), CALIPSO, and other satellites in the late 1990s to mid-2000s through the NASA EOS and other U.S. and non-U.S. programs ushered in a golden era in aerosol research. NASA has been a leader in providing global aerosol characterizations through observations from satellites, ground networks, and field campaigns, as well as from global and regional modeling. AeroCenter (http://aerocenter.gsfc.nasa.gov/), which was formed in 2002 to address the many facets of aerosol research in a collaborative manner, is an interdisciplinary union of researchers (~200 members) at NASA GSFC and other nearby institutions, including NOAA, several universities, and research laboratories. AeroCenter hosts a web-accessible regular seminar series and an annual meeting to present up-to-date aerosol research, including measurement techniques; remote sensing algorithms; modeling development; field campaigns; and aerosol interactions with radiation, clouds, precipitation, climate, biosphere, atmospheric chemistry, air quality, and human health. The 2013 annual meeting was held at the NASA GSFC Visitor Center on 31 May 2013, which coincided with the seventh anniversary of the passing of Yoram Kaufman, a modern pioneer in satellite-based aerosol science and the founder of AeroCenter. The central theme of this year's meeting was “current and future perspectives” of NASA's aerosol science and satellite missions.Item Detecting ship-produced NO₂ plumes and shipping routes in TROPOMI data with a deep learning model(2023-06-25) Yuan, Tianle; Liu, Fei; Lamsal, Lok; Song, HuaShip emissions are important contributor to air pollution and the climate through interacting with clouds. They are the dominant anthropogenic source over the oceans. However, their magnitudes still have large uncertainty. Here we develop a deep learning model to detect ship-emitted NO2 plumes at the pixel level in TROPOMI tropospheric NO2 data. The trained model performs well and, when applied to a year of data, it finds major shipping routes, but misses several other routes. We show that high cloudiness in these shipping routes is the culprit because clouds block signals from reach the sensor. Indeed, detected shipping routes in this study complements shipping routes detected using ship-tracks that is only available in cloudy regions. Our method can find application in several areas such as improving ship emission estimates and compliance verifications. Our method will benefit from improved tropospheric NO₂ retrievals since their quality is critical for plume detection.Item Effect of volcanic emissions on clouds during the 2008 and 2018 Kilauea degassing events(Copernicus Publications, 2020-10-22) Breen, Katherine H.; Barahona, Donifan; Yuan, Tianle; Bian, Huisheng; James, Scott C.Aerosol emissions from volcanic eruptions in otherwise clean environments are regarded as natural experiments where the aerosol effects on clouds and climate can be partitioned from other effects like meteorology and anthropogenic emissions. In this work, we combined satellite retrievals, reanalysis products, and atmospheric modeling to analyze the mechanism of aerosol-cloud interactions during two degassing events at the Kilauea Volcano in 2008 and 2018. The eruptive nature of the 2008 and 2018 degassing events was distinct from long-term volcanic activity for Kilauea. For both events, we performed a comprehensive investigation on the effects of aerosol emissions on macro and microphysical cloud processes for both liquid and ice clouds. This is the first time such an analysis has been reported for the 2018 event. Similarities between both events suggested that aerosol-cloud interactions related to the cloud albedo modification were likely decoupled from local meteorology. In both events the ingestion of aerosols within convective parcels enhanced the detrainment of condensate in the upper troposphere resulting in deeper clouds than in pristine conditions. Accounting for ice nucleation on ash particles led to enhanced ice crystal concentrations at cirrus levels and a slight decrease in ice water content, improving the correlation of the model results with the satellite retrievals. Overall, aerosol loading, plume characteristics, and meteorology contributed to observed and simulated changes in clouds during the Kilauea degassing events.Item Global Reduction in Ship-tracks from Sulfur Regulations for Shipping Fuel(2022-06-11) Yuan, Tianle; Song, Hua; Wood, Robert; Wang, Chenxi; Oreopoulos, Lazaros; Platnick, Steven; Hippel, Sophia von; Meyer, Kerry; Light, Siobhan; Wilcox, EricShip-tracks are produced by ship-emitted aerosols interacting with marine low clouds. Here we apply deep learning models on satellite data to produce the first multi-year global climatology map of ship-tracks. We show that ship-tracks are at the nexus of cloud physics, maritime shipping, and fuel regulation. Our map captures major shipping lanes while missing others, reflecting the influences of background cloud and aerosol properties. Ship-track frequency is more than 10 times higher than expected from a previous survey. Interannual fluctuations in ship-track frequency reflect variations in cross-ocean trade, shipping activity, and fuel regulations. Fuel regulation can alter both detected ship-track density and pattern of shipping routes due to cost economics. The new fuel regulation, together with the COVID-19 pandemic, reduced ship-track frequency in 2020 to its lowest level in recent decades across the globe and may have ushered in a new era of low ship-track frequency. We estimate the aerosol indirect forcing induced by the fuel regulation to be between 0.02 and 0.22 Wm-2.Item Identifying meteorological influences on marine low cloud mesoscale morphology using deep learning classifications(Copernicus Publications, 2020-11-03) Mohrmann, Johannes; Wood, Robert; Yuan, Tianle; Song, Hua; Eastman, Ryan; Oreopoulos, LazarosMarine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.Item MISR Radiance Anomalies Induced by Stratospheric Volcanic Aerosols(MDPI, 2018-11-23) Wu, Dong L.; Wang, Tao; Várnai, Tamás; Limbacher, James A.; Kahn, Ralph A.; Taha, Ghassan; Lee, Jae; Gong, Jie; Yuan, TianleThe 16-year MISR monthly radiances are analyzed in this study, showing significant enhancements of anisotropic scattering at high latitudes after several major volcanic eruptions with injection heights greater than 14 km. The anomaly of deseasonalized radiance anisotropy between MISR’s DF and DA views (70.5° forward and aft) is largest in the blue band with amplitudes amounting to 5–15% of the mean radiance. The anomalous radiance anisotropy is a manifestation of the stronger forward scattering of reflected sunlight due to the direct and indirect effects of stratospheric volcanic aerosols (SVAs). The perturbations of MISR radiance anisotropy from the Kasatochi (August 2008), Sarychev (June 2009), Nabro (June 2011) and Calbuco (April 2015) eruptions are consistent with the poleward transported SVAs observed by CALIOP and OMPS-LP. In a particular scene over the Arctic Ocean, the stratospheric aerosol mid-visible optical depth can reach as high as 0.2–0.5. The enhanced global forward scattering by SVAs has important implications for the shortwave radiation budget.Item Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks(2020-02-10) Schmidt, Victor; Alghali, Mustafa; Sankaran, Kris; Bengio, Yoshua; Yuan, TianleWe introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large scale meteorological variables such as sea surface temperature and relative humidity. We show that our trained model can generate realistic CRFs from the corresponding meteorological observations, which represents a step towards a data-driven framework for stochastic cloud parameterization.Item Observational evidence of strong forcing from aerosol effect on low cloud coverage(AAAS, 2023-11-08) Yuan, Tianle; Song, Hua; Wood, Robert; Oreopoulos, Lazaros; Platnick, Steven; Wang, Chenxi; Yu, Hongbin; Meyer, Kerry; Wilcox, EricAerosols cool Earth’s climate indirectly by increasing low cloud brightness and their coverage (Cf), constituting the aerosol indirect forcing (AIF). The forcing partially offsets the greenhouse warming and positively correlates with the climate sensitivity. However, it remains highly uncertain. Here, we show direct observational evidence for strong forcing from Cf adjustment to increased aerosols and weak forcing from cloud liquid water path adjustment. We estimate that the Cf adjustment drives between 52% and 300% of additional forcing to the Twomey effect over the ocean and a total AIF of −1.1 ± 0.8 W m−². The Cf adjustment follows a power law as a function of background cloud droplet number concentration, Nd . It thus depends on time and location and is stronger when Nd is low. Cf only increases substantially when background clouds start to drizzle, suggesting a role for aerosol-precipitation interactions. Our findings highlight the Cf adjustment as the key process for reducing the uncertainty of AIF and thus future climate projections.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 Opportunistic experiments to constrain aerosol effective radiative forcing(EGU, 2022-01-17) Christensen, Matthew W.; Gettelman, Andrew; Cermak, Jan; Dagan, Guy; Yuan, Tianle; et alAerosol–cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity 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 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 spatiotemporal 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 datasets 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. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. 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 Self Supervised Cloud Classification(AMS, 2023-11-17) Geiss, Andrew; Christensen, Matthew W.; Varble, Adam C.; Yuan, Tianle; Song, HuaLow-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including: training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets.Item Substantial Radiative Warming by an Inadvertent Geoengineering Experiment from 2020 Fuel Regulations(2023-10-30) Yuan, Tianle; Song, Hua; Oreopoulos, Lazaros; Wood, Robert; Bian, Huisheng; Breen, Katherine; Chin, Mian; Yu, Hongbin; Barahona, Donifan; Meyer, Kerry; Platnick, StevenHuman activities affect the Earth’s climate through modifying the composition of the atmosphere, which then creates radiative forcing that drives climate change. The warming effect of anthropogenic greenhouse gases has been partially balanced by the cooling effect of anthropogenic aerosols. In 2020, fuel regulations abruptly reduced the emission of sulfur dioxide from international shipping by more than 80% and created an inadvertent geoengineering experiment with global scale. Here we show the regulation leads to a radiative forcing of 0.12 Wm-² averaged over the global ocean using a combination of modeling and satellite data. The forcing is estimated to effectively double the warming rate of global mean temperature in this decade with strong spatiotemporal heterogeneity. The warming effect contributes 50% to the measured increase in planetary heat uptake since 2020. The radiative forcing also has strong hemispheric contrast of 0.12 Wm-² and contributes to the measured hemispheric contrast in absorbed solar radiation, which has important implications for precipitation patterns. Our result suggests marine cloud brightening may be a viable geoengineering method in temporarily cooling the climate.