We examine the impact of El Niño–Southern Oscillation (ENSO) on satellite observed aerosol optical depths (AODs) over the equatorial Pacific. AODs are found to decrease close to the International Dateline and increase in the vicinity of the Maritime Continent during the El Niño developing phase (and vice versa during La Niña) to form an AOD dipole. The positive AOD anomaly over the Maritime Continent is caused by increases in fire-induced fine smoke aerosols in Indonesia and coarse dust particles transported from Australia via the strengthening winds when El Niño develops and affects the Pacific walker circulation. The negative AOD anomaly around the International Dateline is caused by decreases in the emission of marine aerosols, as the developing El Niño is associated with weakening surface wind speeds in the region. This aerosol dipole is found particularly strong during the Central-Pacific type of ENSO, which has most of its sea surface temperature and wind variations around the International Dateline. The Eastern-Pacific type of ENSO produces positive AOD anomalies over the Maritime Continent but weaker negative AOD anomalies near the International Dateline. As a result, the AOD dipole is less obvious during the EP ENSO. The shortwave radiative forcing associated with the aerosol dipole is 29% (21%) as large as the ENSO-induced outgoing longwave radiation in the Maritime Continent (International Dateline) region and represents an important positive feedback to help sustain the ENSO development that may be potentially important in Central Pacific ENSO studies and predictions.

A method to compute the similarity between different plants is proposed, using features of a plant׳s topological structure and peripheral contour, as well as its geometry. The topological structures are described using tree graphs, and their similarity can be calculated based on the edit distance of these graphs. The peripheral contour of a plant is abstracted by its three-dimensional convex hull, which is projected in several directions. The similarity of the different projections is calculated by an algorithm to compute the similarity of two-dimensional shapes. The similarity of the geometrical detail is computed by considering the geometrical properties of different level branches. Finally the overall similarity between different plants is calculated by combining these different similarity measures. The validity of proposed method is evaluated by detailed experiments.

Density functional theory was employed in order to elucidate the mechanism and factors that lead to the observed regioselectivity in the dialkylbiarylphosphine (Phos)/Pd-catalyzed C-C cleavage/cross-coupling of an N-fused bicyclo α-hydroxy-β-lactam, 1. We have identified that (a) a complex [(1)(Cs₂CO₃)]-PdL(PhBr) forms prior to a "base-mediated oxidative addition"; (b) Cs-carbonate (rather than a halide) deprotonates the alcohol substrate in the lowest energy pathway en route to Pd-alcoholate formation; (c) reactions using Phos ligands bearing OCF₃ and OCF₂H substituents on the "B"-ring are predicted to be selective toward proximal ring opening of 1; (d) steric repulsion between the bottom "B"-ring of the Phos ligand and the piperidine moiety of 1 controls the regioselectivity of the C-C cleavage followed by cross-coupling; and (e) the α- vs β-selective functionalization of the piperidine moiety in 1 is influenced by the bulkiness of the R²-substituent of the coupling partner. These studies will aid in the design of selective functionalizations of the piperidine moiety in 1.

We examine the impact of El Niño–Southern Oscillation (ENSO) on surface ozone abundance observed over the continental United States (U.S.) during 1993–2013. The monthly ozone decreases (increases) during El Niño (La Niña) years with amplitude up to 1.8 ppb per standard deviation of Niño 3.4 index. The largest ENSO influences occur over two southern U.S. regions during fall when the ENSO develops and over two western U.S. regions during the winter to spring after the ENSO decays. ENSO affects surface ozone via chemical processes during warm seasons in southern regions, where favorable meteorological conditions occur, but via dynamic transport during cold seasons in western regions, where the ENSO-induced circulation variations are large. The geographic dependence and seasonality of the ENSO impacts imply that regulations regarding air quality and its exceedance need to be adjusted for different seasons and U.S. regions to account for the ENSO-driven patterns in surface ozone.

Frost flowers are clusters of highly saline ice crystals growing on newly formed sea ice or frozen lakes. Based on observations of particles derived from frost flowers in the Arctic, we formulate an observation-based parameterization of salt aerosol source function from frost flowers. The particle flux from frost flowers in winter has the order of 106 m-2 s-1 at the wind speed of 10 m s-1, but the source flux is highly localized to new sea ice regions and strongly dependent on wind speed. We have implemented this parameterization into the regional Weather Research and Forecasting model with Chemistry initialized for two wintertime scenarios. The addition of sea salt aerosol emissions from frost flowers increases averaged sea salt aerosol mass and number concentration and subsequent cloud droplet number. This change of cloud droplet number concentration increases downward longwave cloud radiative forcing through enhanced cloud optical depth and emissivity. The magnitude of this forcing of sea salt aerosols from frost flowers on clouds and radiation, however, contributes negligibly to surface warming in Barrow, Alaska, in the wintertime scenarios studied here. Key Points We evaluate a parameterization of salt aerosol from frost flowers in WRF-Chem The modeled salt explains half of the observed submicron salt aerosol Longwave cloud forcing increases but does not add to Arctic surface warming ©2013. American Geophysical Union. All Rights Reserved.

Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90g% higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2Combining double low line0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

African and South American (ASA) wildfires account for more than 70g% of global burned areas and have strong connection to local climate for sub-seasonal to seasonal wildfire dynamics. However, representation of the wildfire-climate relationship remains challenging due to spatiotemporally heterogenous responses of wildfires to climate variability and human influences. Here, we developed an interpretable machine learning (ML) fire model (AttentionFire_v1.0) to resolve the complex controls of climate and human activities on burned areas and to better predict burned areas over ASA regions. Our ML fire model substantially improved predictability of burned areas for both spatial and temporal dynamics compared with five commonly used machine learning models. More importantly, the model revealed strong time-lagged control from climate wetness on the burned areas. The model also predicted that, under a high-emission future climate scenario, the recently observed declines in burned area will reverse in South America in the near future due to climate changes. Our study provides a reliable and interpretable fire model and highlights the importance of lagged wildfire-climate relationships in historical and future predictions.

Methods that functionalize the periphery of azacylic scaffolds have garnered increasing interest in recent years. Herein, we investigate the selectivity of a solid-state Norrish-Yang cyclization (NYC) and subsequent C-C cleavage/cross-coupling reaction of a strained cyclopropane-fused azacyclic system. Surprisingly, the NYC primarily furnished a single lactam constitutional and diastereo-isomer. The regioselectivity of the C-C cleavage of the α-hydroxy-β-lactam moiety could be varied by altering the ligand set used in the coupling chemistry. Experimental and computational observations are discussed.

The import of plant-derived small sugars by sugar transporters (STs) has received increasing interest due to its important biological role and great industrial potential. STs are important targets of genetic engineering to improve fungal plant biomass conversion. Comparatively analysis of the genome-wide prevalence and transcriptomics of STs was performed in four filamentous fungi: Aspergillus niger, Aspergillus nidulans, Penicillium subrubescens and Trichoderma reesei. Using phylogenetic analysis and literature mining, their predicted STs were divided into ten subfamilies with putative sugar specificities assigned. In addition, transcriptome analysis revealed complex expression profiles among different STs subfamilies and fungal species, indicating a sophisticated transcriptome regulation and functional diversity of fungal STs. Several STs showed strong co-expression with other genes involved in sugar utilization, encoding CAZymes and sugar catabolic enzymes. This study provides new insights into the diversity of STs at the genomic/transcriptomic level, facilitating their biochemical characterization and metabolic engineering.