Explore the vast range of titles available.

Atmospheric sulfate levels are thought to determine the pH of small aerosol particles. Thermodynamic analysis of field aerosol data reveals that fine particles remain acidic in the southeastern United States despite large sulfate reductions. Particle acidity affects aerosol concentrations, chemical composition and toxicity. Sulfate is often the main acid component of aerosols, and largely determines the acidity of fine particles under 2.5 μm in diameter, PM 2.5 . Over the past 15 years, atmospheric sulfate concentrations in the southeastern United States have decreased by 70%, whereas ammonia concentrations have been steady. Similar trends are occurring in many regions globally. Aerosol ammonium nitrate concentrations were assumed to increase to compensate for decreasing sulfate, which would result from increasing neutrality. Here we use observed gas and aerosol composition, humidity, and temperature data collected at a rural southeastern US site in June and July 2013 (ref.  1 ), and a thermodynamic model that predicts pH and the gas–particle equilibrium concentrations of inorganic species from the observations to show that PM 2.5 at the site is acidic. pH buffering by partitioning of ammonia between the gas and particle phases produced a relatively constant particle pH of 0–2 throughout the 15 years of decreasing atmospheric sulfate concentrations, and little change in particle ammonium nitrate concentrations. We conclude that the reductions in aerosol acidity widely anticipated from sulfur reductions, and expected acidity-related health and climate benefits, are unlikely to occur until atmospheric sulfate concentrations reach near pre-anthropogenic levels.

Nitrogen oxides (NOx) and ammonia (NH3) from anthropogenic and biogenic emissions are central contributors to particulate matter (PM) concentrations worldwide. The response of PM to changes in the emissions of both compounds is typically studied on a case-by-case basis, owing in part to the complex thermodynamic interactions of these aerosol precursors with other PM constituents. Here we present a simple but thermodynamically consistent approach that expresses the chemical domains of sensitivity of aerosol particulate matter to NH3 and HNO3 availability in terms of aerosol pH and liquid water content. From our analysis, four policy-relevant regimes emerge in terms of sensitivity: (i) NH3 sensitive, (ii) HNO3 sensitive, (iii) NH3 and HNO3 sensitive, and (iv) insensitive to NH3 or HNO3. For all regimes, the PM remains sensitive to nonvolatile precursors, such as nonvolatile cations and sulfate. When this framework is applied to ambient measurements or predictions of PM and gaseous precursors, the “chemical regime” of PM sensitivity to NH3 and HNO3 availability is directly determined. The use of these regimes allows for novel insights, and this framework is an important tool to evaluate chemical transport models. With this extended understanding, aerosol pH and associated liquid water content naturally emerge as previously ignored state parameters that drive PM formation.

The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity to GHG levels is central for formulating effective climate policies, yet its exact value is shroud in uncertainty. Climate sensitivity is quantitatively expressed in terms of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR), estimating global temperature responses after an abrupt or transient doubling of CO 2 . Here, we represent the complex and highly-dimensional behavior of modelled climate via low-dimensional emergent networks to evaluate Climate Sensitivity (netCS), by first reconstructing meaningful components describing regional subprocesses, and secondly inferring the causal links between these to construct causal networks. We apply this methodology to Sea Surface Temperature (SST) simulations and investigate two different metrics in order to derive weighted estimates that yield likely ranges of ECS (2.35–4.81°C) and TCR (1.53-2.60°C). These ranges are narrower than the unconstrained distributions and consistent with the ranges of the IPCC AR6 estimates. More importantly, netCS demonstrates that SST patterns (at “fast” timescales) are linked to climate sensitivity; SST patterns over the historical period exclude median sensitivity but not low-sensitivity (ECS < 3.0°C) or very high sensitivity (ECS ≥ 4.5°C) models. The Earth’s surface temperature response to increasing CO2 emissions remains uncertain in climate models within the CMIP6 project. The authors present a network-based constraint of climate sensitivity of sea surface temperatures that can be used to evaluate and weight CMIP6 models.

The effect of ocean biological productivity on marine clouds is explored over a large phytoplankton bloom in the Southern Ocean with the use of remotely sensed data. Cloud droplet number concentration over the bloom was twice what it was away from the bloom, and cloud effective radius was reduced by 30%. The resulting change in the short-wave radiative flux at the top of the atmosphere was -15 watts per square meter, comparable to the aerosol indirect effect over highly polluted regions. This observed impact of phytoplankton on clouds is attributed to changes in the size distribution and chemical composition of cloud condensation nuclei. We propose that secondary organic aerosol, formed from the oxidation of phytoplankton-produced isoprene, can affect chemical composition of marine cloud condensation nuclei and influence cloud droplet number. Model simulations support this hypothesis, indicating that 100% of the observed changes in cloud properties can be attributed to the isoprene secondary organic aerosol.

In some regions, reducing aerosol ammonium nitrate (NH4NO3) concentrations may substantially improve air quality. This can be accomplished by reductions in precursor emissions, such as nitrogen oxides (NOx) to lower nitric acid (HNO3) that partitions to the aerosol, or reductions in ammonia (NH3) to lower particle pH and keep HNO3 in the gas phase. Using the ISORROPIA-II thermodynamic aerosol model and detailed observational data sets, we explore the sensitivity of aerosol NH4NO3 to gas-phase NH3 and NOx controls for a number of contrasting locations, including Europe, the United States, and China. NOx control is always effective, whereas the aerosol response to NH3 control is highly nonlinear and only becomes effective at a thermodynamic sweet spot. The analysis provides a conceptual framework and fundamental evaluation on the relative value of NOx versus NH3 control and demonstrates the relevance of pH as an air quality parameter. We find that, regardless of the locations examined, it is only when ambient particle pH drops below an approximate critical value of 3 (slightly higher in warm and slightly lower in cold seasons) that NH3 reduction leads to an effective response in PM2.5 mass. The required amount of NH3 reduction to reach the critical pH and efficiently decrease NH4NO3 at different sites is assessed. Owing to the linkage between NH3 emissions and agricultural productivity, the substantial NH3 reduction required in some locations may not be feasible. Finally, controlling NH3 emissions to increase aerosol acidity and evaporate NH4NO3 will have other effects, beyond reduction of PM2.5 NH4NO3, such as increasing aerosol toxicity and potentially altering the deposition patterns of nitrogen and trace nutrients.

Accurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms. Plain Language Summary Being able to correctly simulate the amount of ice and liquid in clouds is essential for accurate predictions of the cloud radiative forcing in the climatologically sensitive polar regions. A number of collisional processes between ice and liquid particles in clouds, known as secondary ice production, can significantly enhance the ice crystal number concentrations contained in them. This enhancement is often accompanied by a decrease in the cloud liquid water content, resulting in less opaque clouds to incoming solar radiation, which, in turn, can cause a cloud‐induced warming at the surface. Currently most global climate models are missing the description of the most important secondary ice production processes, which can lead to a biased radiative impact of clouds at the surface. To address this, we propose using a machine learning algorithm trained on high‐resolution model outputs to include the effect of ice multiplication in large‐scale climate models. The machine learning framework effectively captures the physical processes underlying secondary ice production in stratiform clouds using only a few inputs readily available in model frameworks. This approach has the potential to improve model predictions bringing them closer to the observed cloud phase partitioning. Key Points A random‐forest parameterization for secondary ice production is developed using outputs from a 10‐km horizontal grid spacing simulation Cloud phase partitioning agrees within a factor of 3, with radiative biases below 3 Wm−2 compared to the detailed microphysics simulation The scheme can be adjusted to coarser resolutions typical of climate models without losing computational efficiency and numerical stability

Oxidized organic aerosol (OOA) is a major component of ambient particulate matter, substantially impacting climate, human health, and ecosystems. OOA is readily produced in the presence of sunlight, and requires days of photooxidation to reach the levels observed in the atmosphere. High concentrations of OOA are thus expected in the summer; however, our current mechanistic understanding fails to explain elevated OOA during wintertime periods of low photochemical activity that coincide with periods of intense biomass burning. As a result, atmospheric models underpredict OOA concentrations by a factor of 3 to 5. Here we show that fresh emissions from biomass burning exposed to NO₂ and O₃ (precursors to the NO₃ radical) rapidly form OOA in the laboratory over a few hours and without any sunlight. The extent of oxidation is sensitive to relative humidity. The resulting OOA chemical composition is consistent with the observed OOA in field studies in major urban areas. Additionally, this dark chemical processing leads to significant enhancements in secondary nitrate aerosol, of which 50 to 60% is estimated to be organic. Simulations that include this understanding of dark chemical processing show that over 70% of organic aerosol from biomass burning is substantially influenced by dark oxidation. This rapid and extensive dark oxidation elevates the importance of nocturnal chemistry and biomass burning as a global source of OOA.

The acidity of aqueous atmospheric solutions is a key parameter driving both the partitioning of semi-volatile acidic and basic trace gases and their aqueous-phase chemistry. In addition, the acidity of atmospheric aqueous phases, e.g., deliquesced aerosol particles, cloud, and fog droplets, is also dictated by aqueous-phase chemistry. These feedbacks between acidity and chemistry have crucial implications for the tropospheric lifetime of air pollutants, atmospheric composition, deposition to terrestrial and oceanic ecosystems, visibility, climate, and human health. Atmospheric research has made substantial progress in understanding feedbacks between acidity and multiphase chemistry during recent decades. This paper reviews the current state of knowledge on these feedbacks with a focus on aerosol and cloud systems, which involve both inorganic and organic aqueous-phase chemistry. Here, we describe the impacts of acidity on the phase partitioning of acidic and basic gases and buffering phenomena. Next, we review feedbacks of different acidity regimes on key chemical reaction mechanisms and kinetics, as well as uncertainties and chemical subsystems with incomplete information. Finally, we discuss atmospheric implications and highlight the need for future investigations, particularly with respect to reducing emissions of key acid precursors in a changing world, and the need for advancements in field and laboratory measurements and model tools.

Overprediction of fine-particle ammonium-sulfate molar ratios (R) by thermodynamic models is suggested as evidence for interactions with organic constituents that inhibit the equilibration of gas-phase ammonia with aerosol sulfate and questions the equilibrium assumption long thought to apply for submicron aerosol. This hypothesis is tested through thermodynamic analysis of ambient observations. We find that the deviation between R from a molar ratio of 2 is strongly correlated with the concentration of sodium (Na+), a nonvolatile cation (NVC), but exhibits no correlation to organic aerosol (OA) mass concentration or mass fraction. Thermodynamic predictions of both R and ammonia gas–particle partitioning can accurately reproduce observations when small amounts of NVCs are included in the calculations, whereas exclusion of NVCs results in a predicted R consistently near 2. The sensitivity of R to small amounts of NVCs arises because, when the latter are present but not included in the thermodynamic calculations, the missing cations are replaced with ammonium in the model (NH3–NH4+ equilibrium shifts to the particle), resulting in an R that is biased high. Results and conclusions based on bulk aerosol considerations that assume all species are internally mixed are not changed even if NVCs and sulfate are largely externally mixed; fine-particle pH is found to be much less sensitive to mixing state assumptions than molar ratios. We also show that the data used to support the “organic inhibition” of NH3 from equilibrium, when compared against other network and field campaign datasets, display a systematically and significantly lower NH4+ (thought to be from an evaporation bias), that is of the order of the effect postulated to be caused by organics. Altogether, these results question the postulated ability of organic compounds to considerably perturb aerosol acidity and prevent ammonia from achieving gas–particle equilibrium, at least for the locations considered. Furthermore, the results demonstrate the limitations of using molar ratios to infer aerosol properties or processes that depend on particle pH.

Biomass burning is a major source of atmospheric brown carbon (BrC), and through its absorption of UV/VIS radiation, it can play an important role in the planetary radiative balance and atmospheric photochemistry. The considerable uncertainty of BrC impacts is associated with its poorly constrained sources, transformations, and atmospheric lifetime. Here we report laboratory experiments that examined changes in the optical properties of the water-soluble (WS) BrC fraction of laboratory-generated biomass burning particles from hardwood pyrolysis. Effects of direct UVB photolysis and OH oxidation in the aqueous phase on molecular-weight-separated BrC were studied. Results indicated that the majority of low-molecular-weight (MW) BrC (<400 Da) was rapidly photobleached by both direct photolysis and OH oxidation on an atmospheric timescale of approximately 1 h. High MW BrC (≥400 Da) underwent initial photoenhancement up to ∼15 h, followed by slow photobleaching over ∼10 h. The laboratory experiments were supported by observations from ambient BrC samples that were collected during the fire seasons in Greece. These samples, containing freshly emitted to aged biomass burning aerosol, were analyzed for both water- and methanol-soluble BrC. Consistent with the laboratory experiments, high-MW BrC dominated the total light absorption at 365 nm for both methanol and water-soluble fractions of ambient samples with atmospheric transport times of 1 to 68 h. These ambient observations indicate that overall, biomass burning BrC across all molecular weights has an atmospheric lifetime of 15 to 28 h, consistent with estimates from previous field studies – although the BrC associated with the high-MW fraction remains relatively stable and is responsible for light absorption properties of BrC throughout most of its atmospheric lifetime. For ambient samples of aged (>10 h) biomass burning emissions, poor linear correlations were found between light absorptivity and levoglucosan, consistent with other studies suggesting a short atmospheric lifetime for levoglucosan. However, a much stronger correlation between light absorptivity and total hydrous sugars was observed, suggesting that they may serve as more robust tracers for aged biomass burning emissions. Overall, the results from this study suggest that robust model estimates of BrC radiative impacts require consideration of the atmospheric aging of BrC and the stability of high-MW BrC.