Coupled atmospheric chemistry, radiation, and dynamics of an exoplanet generate self-sustained oscillations.

Nonlinearity in photochemical systems is known to allow self-sustained oscillations, but they have received little attention in studies of planetary atmospheres. Here, we present a unique, self-oscillatory solution for ozone chemistry of an exoplanet from a numerical simulation using a fully coupled, three-dimensional (3D) atmospheric chemistry-radiation-dynamics model. Forced with nonvarying stellar insolation and emission flux of nitric oxide (NO), atmospheric ozone abundance oscillates by a factor of thirty over a multidecadal timescale. As such self-oscillations can only occur with biological nitrogen fixation contributing to NO emission, we propose that they are a unique class of biosignature. The resulting temporal variability in the atmospheric spectrum is potentially observable. Our results underscore the importance of revisiting the spectra of exoplanets over multidecadal timescales to characterizing the atmospheric chemistry of exoplanets and searching for exoplanet biosignatures. There are also profound implications for comparative planetology and the evolution of the atmospheres of terrestrial planets in the solar system and beyond. Fully coupled, 3D atmospheric chemistry-radiation-dynamics models can reveal new phenomena that may not exist in one-dimensional models, and hence, they are powerful tools for future planetary atmospheric research.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3 Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Societal shifts due to COVID-19 reveal large-scale complexities and feedbacks between atmospheric chemistry and climate change.

The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS).

The northern high latitude (NHL, 40°N to 90°N) is where the second peak region of gross primary productivity (GPP) other than the tropics. The summer NHL GPP is about 80% of the tropical peak, but both regions are still highly uncertain (Norton et al. 2019, https://doi.org/10.5194/bg-16-3069-2019). Carbonyl sulfide (OCS) provides an important proxy for photosynthetic carbon uptake. Here we optimize the OCS plant uptake fluxes across the NHL by fitting atmospheric concentration simulation with the GEOS-CHEM global transport model to the aircraft profiles acquired over Alaska during NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (2012-2015). We use the empirical biome-specific linear relationship between OCS plant uptake flux and GPP to derive the six plant uptake OCS fluxes from different GPP data. Such GPP-based fluxes are used to drive the concentration simulations. We evaluate the simulations against the independent observations at two ground sites of Alaska. The optimized OCS fluxes suggest the NHL plant uptake OCS flux of -247 Gg S year-1, about 25% stronger than the ensemble mean of the six GPP-based OCS fluxes. GPP-based OCS fluxes systematically underestimate the peak growing season across the NHL, while a subset of models predict early start of season in Alaska, consistent with previous studies of net ecosystem exchange. The OCS optimized GPP of 34 PgC yr-1 for NHL is also about 25% more than the ensembles mean from six GPP data. Further work is needed to fully understand the environmental and biotic drivers and quantify their rate of photosynthetic carbon uptake in Arctic ecosystems.

Computation by Convective Logic Gates and Thermal Communication.

We demonstrate a novel computational architecture based on fluid convection logic gates and heat flux-mediated information flows. Our previous work demonstrated that Boolean logic operations can be performed by thermally driven convection flows. In this work, we use numerical simulations to demonstrate a different , but universal Boolean logic operation (NOR), performed by simpler convective gates. The gates in the present work do not rely on obstacle flows or periodic boundary conditions, a significant improvement in terms of experimental realizability. Conductive heat transfer links can be used to connect the convective gates, and we demonstrate this with the example of binary half addition. These simulated circuits could be constructed in an experimental setting with modern, 2-dimensional fluidics equipment, such as a thin layer of fluid between acrylic plates. The presented approach thus introduces a new realm of unconventional, thermal fluid-based computation.

Midlatitude atmospheric OH response to the most recent 11-y solar cycle.

The hydroxyl radical (OH) plays an important role in middle atmospheric photochemistry, particularly in ozone (O(3)) chemistry. Because it is mainly produced through photolysis and has a short chemical lifetime, OH is expected to show rapid responses to solar forcing [e.g., the 11-y solar cycle (SC)], resulting in variabilities in related middle atmospheric O(3) chemistry. Here, we present an effort to investigate such OH variability using long-term observations (from space and the surface) and model simulations. Ground-based measurements and data from the Microwave Limb Sounder on the National Aeronautics and Space Administration's Aura satellite suggest an ∼7-10% decrease in OH column abundance from solar maximum to solar minimum that is highly correlated with changes in total solar irradiance, solar Mg-II index, and Lyman-α index during SC 23. However, model simulations using a commonly accepted solar UV variability parameterization give much smaller OH variability (∼3%). Although this discrepancy could result partially from the limitations in our current understanding of middle atmospheric chemistry, recently published solar spectral irradiance data from the Solar Radiation and Climate Experiment suggest a solar UV variability that is much larger than previously believed. With a solar forcing derived from the Solar Radiation and Climate Experiment data, modeled OH variability (∼6-7%) agrees much better with observations. Model simulations reveal the detailed chemical mechanisms, suggesting that such OH variability and the corresponding catalytic chemistry may dominate the O(3) SC signal in the upper stratosphere. Continuing measurements through SC 24 are required to understand this OH variability and its impacts on O(3) further.

Leading role of Saharan dust on tropical cyclone rainfall in the Atlantic Basin.

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

New Estimates of Nitrogen Fixation on Early Earth.

Fixed nitrogen species generated by the early Earth's atmosphere are thought to be critical to the emergence of life and the sustenance of early metabolisms. A previous study estimated nitrogen fixation in the Hadean Earth's N2/CO2-dominated atmosphere; however, that previous study only considered a limited chemical network that produces NOx species (i.e., no HCN formation) via the thermochemical dissociation of N2 and CO2 in lightning flashes, followed by photochemistry. Here, we present an updated model of nitrogen fixation on Hadean Earth. We use the Chemical Equilibrium with Applications (CEA) thermochemical model to estimate lightning-induced NO and HCN formation and an updated version of KINETICS, the 1-D Caltech/JPL photochemical model, to assess the photochemical production of fixed nitrogen species that rain out into the Earth's early ocean. Our updated photochemical model contains hydrocarbon and nitrile chemistry, and we use a Geant4 simulation platform to consider nitrogen fixation stimulated by solar energetic particle deposition throughout the atmosphere. We study the impact of a novel reaction pathway for generating HCN via HCN2, inspired by the experimental results which suggest that reactions with CH radicals (from CH4 photolysis) may facilitate the incorporation of N into the molecular structure of aerosols. When the HCN2 reactions are added, we find that the HCN rainout rate rises by a factor of five in our 1-bar case and is about the same in our 2- and 12-bar cases. Finally, we estimate the equilibrium concentration of fixed nitrogen species under a kinetic steady state in the Hadean ocean, considering loss by hydrothermal vent circulation, photoreduction, and hydrolysis. These results inform our understanding of environments that may have been relevant to the formation of life on Earth, as well as processes that could lead to the emergence of life elsewhere in the universe.

Tropical mid-tropospheric CO2 variability driven by the Madden-Julian oscillation.

Carbon dioxide (CO(2)) is the most important anthropogenic greenhouse gas in the present-day climate. Most of the community focuses on its long-term (decadal to centennial) behaviors that are relevant to climate change, but there are relatively few discussions of its higher-frequency forms of variability, and none regarding its subseasonal distribution. In this work, we report a large-scale intraseasonal variation in the Atmospheric Infrared Sounder CO(2) data in the global tropical region associated with the Madden-Julian oscillation (MJO). The peak-to-peak amplitude of the composite MJO modulation is ∼1 ppmv, with a standard error of the composite mean < 0.1 ppmv. The correlation structure between CO(2) and rainfall and vertical velocity indicate positive (negative) anomalies in CO(2) arise due to upward (downward) large-scale vertical motions in the lower troposphere associated with the MJO. These findings can help elucidate how faster processes can organize, transport, and mix CO(2) and provide a robustness test for coupled carbon-climate models.

Decadal decrease in Los Angeles methane emissions is much smaller than bottom-up estimates.

Methane, a powerful greenhouse gas, has a short atmospheric lifetime ( ~ 12 years), so that emissions reductions will have a rapid impact on climate forcing. In megacities such as Los Angeles (LA), natural gas (NG) leakage is the primary atmospheric methane source. The magnitudes and trends of fugitive NG emissions are largely unknown and need to be quantified to verify compliance with emission reduction targets. Here we use atmospheric remote sensing data to show that, in contrast to the observed global increase in methane emissions, LA area emissions decreased during 2011-2020 at a mean rate of (-1.57 ± 0.41) %/yr. However, the NG utility calculations indicate a much larger negative emissions trend of -5.8 %/yr. The large difference between top-down and bottom-up trends reflects the uncertainties in estimating the achieved emissions reductions. Actions taken in LA can be a blueprint for COP28 and future efforts to reduce methane emissions.

Atmospheric pressure as a natural climate regulator for a terrestrial planet with a biosphere.

Lovelock and Whitfield suggested in 1982 that, as the luminosity of the Sun increases over its life cycle, biologically enhanced silicate weathering is able to reduce the concentration of atmospheric carbon dioxide (CO(2)) so that the Earth's surface temperature is maintained within an inhabitable range. As this process continues, however, between 100 and 900 million years (Ma) from now the CO(2) concentration will reach levels too low for C(3) and C(4) photosynthesis, signaling the end of the solar-powered biosphere. Here, we show that atmospheric pressure is another factor that adjusts the global temperature by broadening infrared absorption lines of greenhouse gases. A simple model including the reduction of atmospheric pressure suggests that the life span of the biosphere can be extended at least 2.3 Ga into the future, more than doubling previous estimates. This has important implications for seeking extraterrestrial life in the Universe. Space observations in the infrared region could test the hypothesis that atmospheric pressure regulates the surface temperature on extrasolar planets.

Evaluation of Modeled Hyperspectral Infrared Spectra Against All-Sky AIRS Observations Using Different Cloud Overlap Schemes.

Hyperspectral infrared sounding contains information about clouds, which plays an important role in modulating Earth's climate. However, there is a great deal of uncertainty in modeling the radiative effect of clouds due to its complex dependence on various parameters. Therefore, cloudy scenarios are often neglected in retrievals of infrared spectral measurements and in data assimilation. One-dimensional radiative transfer (RT) models have a limited capability to represent the cloud three-dimensional multilayer structure. This issue is typically resolved by using a multiple independent column approach, which is computationally demanding. Therefore, it is necessary to find a balance between computational speed and accuracy for infrared RT all-sky radiance simulations. In this study, we utilize the Community Radiative Transfer Model with four different cloud overlap schemes and compare against observations made by the Atmospheric Infrared Sounder (AIRS) using a statistical metric called the first Wasserstein distance. Our results show that the average cloud overlap scheme performs the best and successfully predicts the overall probability distribution of brightness temperature over nonfrozen oceans for a wide range of wavelengths. The mean absolute differences are less than 0.7 K for 846 selected AIRS channels between 790 cm-1 and 1231 cm-1.

Seasonal Variations of Solar-Induced Fluorescence, Precipitation, and Carbon Dioxide Over the Amazon.

Previous studies suggested that the Amazon, the largest rainforest on Earth, changes from a CO2 sink to a CO2 source during the dry/fire season. However, the biospheric contributions to atmospheric CO2 are not well understood during the two main seasons, the dry/fire season and the wet season. In this article, we utilize Orbiting Carbon Observatory 2 (OCO-2) Solar-Induced Fluorescence (SIF) to explore photosynthetic activity during the different seasons. The spatiotemporal variability of OCO-2 SIF, OCO-2 CO2, precipitation, and burned area are investigated over the Amazon from September 2014 to December 2019. Averaging over the entire Amazon region, we found a positive temporal correlation (0.94) between OCO-2 SIF and Global Precipitation Climatology Project precipitation and a negative temporal correlation (-0.64) between OCO-2 SIF and OCO-2 CO2, consistent with the fact that precipitation enhances photosynthesis, which results in higher values for SIF and rate of removal of CO2 from the atmosphere above the Amazon region. We also observed seasonality in the spatial variability of these variables within the Amazon region. During the dry/fire (August-October) season, low SIF values, low precipitation, high vapor pressure deficit (VPD), large burned areas, and high atmospheric CO2 are mainly found over the southern Amazon region. In contrast, during the wet season (January-March), high SIF values, high precipitation, low VPD, smaller burned areas, and low CO2 are found over both the central and southern Amazon regions. The seasonal difference in SIF suggests that photosynthetic activity is reduced during the dry/fire season relative to the wet season as a result of low precipitation and high VPD, especially over the southern Amazon region, which will contribute to more CO2 in the atmosphere during the dry/fire season.

A bimodal distribution of haze in Pluto's atmosphere.

Pluto, Titan, and Triton make up a unique class of solar system bodies, with icy surfaces and chemically reducing atmospheres rich in organic photochemistry and haze formation. Hazes play important roles in these atmospheres, with physical and chemical processes highly dependent on particle sizes, but the haze size distribution in reducing atmospheres is currently poorly understood. Here we report observational evidence that Pluto's haze particles are bimodally distributed, which successfully reproduces the full phase scattering observations from New Horizons. Combined with previous simulations of Titan's haze, this result suggests that haze particles in reducing atmospheres undergo rapid shape change near pressure levels ~0.5 Pa and favors a photochemical rather than a dynamical origin for the formation of Titan's detached haze. It also demonstrates that both oxidizing and reducing atmospheres can produce multi-modal hazes, and encourages reanalysis of observations of hazes on Titan and Triton.

Sulfur monoxide dimer chemistry as a possible source of polysulfur in the upper atmosphere of Venus.

The abundance of SO dimers (SO)2 in the upper atmosphere of Venus and their implications for the enigmatic ultraviolet absorption has been investigated in several studies over the past few years. However, the photochemistry of sulfur species in the upper atmosphere of Venus is still not well understood and the identity of the missing ultraviolet absorber(s) remains unknown. Here we update an existing photochemical model of Venus' upper atmosphere by including the photochemistry of SO dimers. Although the spectral absorption profile of SO dimers fits the unknown absorber, their abundance is found to be too low for them to contribute significantly to the absorption. It is more likely that their photolysis and/or reaction products could contribute more substantively. Reactions of SO dimers are found to be important sources of S2O, and possibly higher order SnO species and polysulfur, Sn. All of these species absorb in the critical ultraviolet region and are expected to be found in both the aerosol and gas phase. indicating that in-situ high resolution aerosol mass spectrometry might be a useful technique for identifying the ultraviolet absorber on Venus.

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves.

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate goal of this protocol is to determine whether these planetary worlds harbor geological features and/or climate systems. We present a method of extracting information from multi-wavelength single-point light curves and retrieving surface maps. It uses singular value decomposition (SVD) to separate sources that contribute to light curve variations and infer the existence of partially cloudy climate systems. Through analysis of the time series obtained from SVD, physical attributions of principal components (PCs) could be inferred without assumptions of any spectral properties. Combining with viewing geometry, it is feasible to reconstruct surface maps if one of the PCs are found to contain surface information. Degeneracy originated from convolution of the pixel geometry and spectrum information determines the quality of reconstructed surface maps, which requires the introduction of regularization. For the purpose of demonstrating the protocol, multi-wavelength light curves of Earth, which serves as a proxy exoplanet, are analyzed. Comparison between the results and the ground truth is presented to show the performance and limitation of the protocol. This work provides a benchmark for future generalization of exoplanet applications.

Quantifying Mineral-Ligand Structural Similarities: Bridging the Geological World of Minerals with the Biological World of Enzymes.

Metal compounds abundant on Early Earth are thought to play an important role in the origins of life. Certain iron-sulfur minerals for example, are proposed to have served as primitive metalloenzyme cofactors due to their ability to catalyze organic synthesis processes and facilitate electron transfer reactions. An inherent difficulty with studying the catalytic potential of many metal compounds is the wide range of data and parameters to consider when searching for individual minerals and ligands of interest. Detecting mineral-ligand pairs that are structurally analogous enables more relevant selections of data to study, since structural affinity is a key indicator of comparable catalytic function. However, current structure-oriented approaches tend to be subjective and localized, and do not quantify observations or compare them with other potential targets. Here, we present a mathematical approach that compares structural similarities between various minerals and ligands using molecular similarity metrics. We use an iterative substructure search in the crystal lattice, paired with benchmark structural similarity methods. This structural comparison may be considered as a first stage in a more advanced analysis tool that will include a range of chemical and physical factors when computing mineral-ligand similarity. This approach will seek relationships between the mineral and enzyme worlds, with applications to the origins of life, ecology, catalysis, and astrobiology.

Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China.

The absence of motor vehicle traffic and suspended manufacturing during the coronavirus disease 2019 (COVID-19) pandemic in China enabled assessment of the efficiency of air pollution mitigation. Up to 90% reduction of certain emissions during the city-lockdown period can be identified from satellite and ground-based observations. Unexpectedly, extreme particulate matter levels simultaneously occurred in northern China. Our synergistic observation analyses and model simulations show that anomalously high humidity promoted aerosol heterogeneous chemistry, along with stagnant airflow and uninterrupted emissions from power plants and petrochemical facilities, contributing to severe haze formation. Also, because of nonlinear production chemistry and titration of ozone in winter, reduced nitrogen oxides resulted in ozone enhancement in urban areas, further increasing the atmospheric oxidizing capacity and facilitating secondary aerosol formation.

Non-Monotonic Aerosol Effect on Precipitation in Convective Clouds over Tropical Oceans.

Aerosol effects on convective clouds and associated precipitation constitute an important open-ended question in climate research. Previous studies have linked an increase in aerosol concentration to a delay in the onset of rain, invigorated clouds and stronger rain rates. Here, using observational data, we show that the aerosol effect on convective clouds shifts from invigoration to suppression with increasing aerosol optical depth. We explain this shift in trend (using a cloud model) as the result of a competition between two types of microphysical processes: cloud-core-based invigorating processes vs. peripheral suppressive processes. We show that the aerosol optical depth value that marks the shift between invigoration and suppression depends on the environmental thermodynamic conditions. These findings can aid in better parameterizing aerosol effects in climate models for the prediction of climate trends.

Ice nucleation by aerosols from anthropogenic pollution.

The formation of ice particles in the atmosphere strongly affects cloud properties and the climate. While mineral dust is known to be an effective ice nucleating particle, the role of aerosols from anthropogenic pollution in ice nucleation is still under debate. Here we probe the ice nucleation ability of different aerosol types by combining 11-year observations from multiple satellites and cloud-resolving model simulations. We find that, for strong convective systems, ice particle effective radius near cloud top decreases with increasing loading of polluted continental aerosols, because the ice formation is dominated by homogeneous freezing of cloud droplets that are smaller under more polluted conditions. In contrast, an increase in ice particle effective radius with polluted continental aerosols is found for moderate convection. Our model simulations suggest that this positive correlation is explained by enhanced heterogeneous ice nucleation and prolonged ice particle growth at larger aerosol loading, indicating that polluted continental aerosols contain a significant fraction of ice nucleating particles. Similar aerosol-ice relationships are observed for dust aerosols, further corroborating the ice nucleation ability of polluted continental aerosols. By catalyzing ice formation, aerosols from anthropogenic pollution could have profound impacts on cloud lifetime and radiative effect as well as precipitation efficiency.