Search

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from https://doi.org/10.5281/zenodo.11400539 (Jones et al., 2024) and https://doi.org/10.5281/zenodo.11420742 (Kelley et al., 2024a).

Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.

The Community Atmosphere Model (CAM6.1), the atmospheric component of the Community Earth System Model (CESM; version 2.1), simulates the life cycle (emission, transport, and deposition) of mineral dust and its interactions with physio-chemical components to quantify the impacts of dust on climate and the Earth system. The accuracy of such quantifications relies on how well dust-related processes are represented in the model. Here we update the parameterizations for the dust module, including those on the dust emission scheme, the aerosol dry deposition scheme, the size distribution of transported dust, and the treatment of dust particle shape. Multiple simulations were undertaken to evaluate the model performance against diverse observations, and to understand how each update alters the modeled dust cycle and the simulated dust direct radiative effect. The model–observation comparisons suggest that substantially improved model representations of the dust cycle are achieved primarily through the new more physically-based dust emission scheme. In comparison, the other modifications induced small changes to the modeled dust cycle and model–observation comparisons, except the size distribution of dust in the coarse mode, which can be even more influential than that of replacing the dust emission scheme. We highlight which changes introduced here are important for which regions, shedding light on further dust model developments required for more accurately estimating interactions between dust and climate.

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of 2 relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded datasets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this dataset is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world's major source regions to the global dust cycle remain poorly constrained. This problem hinders accounting for the potentially large impact of regional differences in dust properties on clouds, the Earth's energy balance, and terrestrial and marine biogeochemical cycles. Here, we constrain the contribution of each of the world's main dust source regions to the global dust cycle. We use an analytical framework that integrates an ensemble of global aerosol model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth (DAOD). We obtain a dataset that constrains the relative contribution of nine major source regions to size-resolved dust emission, atmospheric loading, DAOD, concentration, and deposition flux. We find that the 22–29 Tg (1 standard error range) global loading of dust with a geometric diameter up to 20 µm is partitioned as follows: North African source regions contribute ∼ 50 % (11–15 Tg), Asian source regions contribute ∼ 40 % (8–13 Tg), and North American and Southern Hemisphere regions contribute ∼ 10 % (1.8–3.2 Tg). These results suggest that current models on average overestimate the contribution of North African sources to atmospheric dust loading at ∼ 65 %, while underestimating the contribution of Asian dust at ∼ 30 %. Our results further show that each source region's dust loading peaks in local spring and summer, which is partially driven by increased dust lifetime in those seasons. We also quantify the dust deposition flux to the Amazon rainforest to be ∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred from satellite data by previous work that likely overestimated dust deposition by underestimating the dust mass extinction efficiency. The data obtained in this paper can be used to obtain improved constraints on dust impacts on clouds, climate, biogeochemical cycles, and other parts of the Earth system.

Abstract Quantification of dust aerosols in Earth System Models (ESMs) has important implications for water cycle and biogeochemistry studies. This study examines the global life cycle and direct radiative effects (DREs) of dust in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1), and the impact of increasing model resolution both horizontally and vertically. The default 1° E3SMv1 captures the spatial and temporal variability in the observed dust aerosol optical depth (DAOD) reasonably well, but overpredicts dust absorption in the shortwave (SW). Simulations underestimate the dust vertical and long‐range transport, compared with the satellite dust extinction profiles. After updating dust refractive indices and correcting for a bias in partitioning size‐segregated emissions, both SW cooling and longwave (LW) warming of dust simulated by E3SMv1 are increased and agree better with other recent studies. The estimated net dust DRE of −0.42 Wm−2 represents a stronger cooling effect than the observationally based estimate −0.2 Wm−2 (−0.48 to +0.2), due to a smaller LW warming. Constrained by a global mean DAOD, model sensitivity studies of increasing horizontal and vertical resolution show strong influences on the simulated global dust burden and lifetime primarily through the change of dust dry deposition rate; there are also remarkable differences in simulated spatial distributions of DAOD, DRE, and deposition fluxes. Thus, constraining the global DAOD is insufficient for accurate representation of dust climate effects, especially in transitioning to higher‐ or variable‐resolution ESMs. Better observational constraints of dust vertical profiles, dry deposition, size, and LW properties are needed.

The large uncertainty in the mineral dust direct radiative effect (DRE) hinders projections of future climate change due to anthropogenic activity. Resolving modeled dust mineral speciation allows for spatially and temporally varying refractive indices consistent with dust aerosol composition. Here, for the first time, we quantify the range in dust DRE at the top of the atmosphere (TOA) due to current uncertainties in the surface soil mineralogical content using a dust mineral-resolving climate model. We propagate observed uncertainties in soil mineral abundances from two soil mineralogy atlases along with the optical properties of each mineral into the DRE and compare the resultant range with other sources of uncertainty across six climate models. The shortwave DRE responds region-specifically to the dust burden depending on the mineral speciation and underlying shortwave surface albedo: positively when the regionally averaged annual surface albedo is larger than 0.28 and negatively otherwise. Among all minerals examined, the shortwave TOA DRE and single scattering albedo at the 0.44–0.63 µm band are most sensitive to the fractional contribution of iron oxides to the total dust composition. The global net (shortwave plus longwave) TOA DRE is estimated to be within −0.23 to +0.35 W m−2. Approximately 97 % of this range relates to uncertainty in the soil abundance of iron oxides. Representing iron oxide with solely hematite optical properties leads to an overestimation of shortwave DRE by +0.10 W m−2 at the TOA, as goethite is not as absorbing as hematite in the shortwave spectrum range. Our study highlights the importance of iron oxides to the shortwave DRE: they have a disproportionally large impact on climate considering their small atmospheric mineral mass fractional burden (∼2 %). An improved description of iron oxides, such as those planned in the Earth Surface Mineral Dust Source Investigation (EMIT), is thus essential for more accurate estimates of the dust DRE.

Tropospheric ozone concentrations are sensitive to natural emissions of precursor compounds. In contrast to existing assumptions, recent evidence indicates that terrestrial vegetation emissions in the pre-industrial era were larger than in the present day. We use a chemical transport model and a radiative transfer model to show that revised inventories of pre-industrial fire and biogenic emissions lead to an increase in simulated pre-industrial ozone concentrations, decreasing the estimated pre-industrial to present-day tropospheric ozone radiative forcing by up to 34 % (0.38 to 0.25 W m−2). We find that this change is sensitive to employing biomass burning and biogenic emissions inventories based on matching vegetation patterns, as the co-location of emission sources enhances the effect on ozone formation. Our forcing estimates are at the lower end of existing uncertainty range estimates (0.2–0.6 W m−2), without accounting for other sources of uncertainty. Thus, future work should focus on reassessing the uncertainty range of tropospheric ozone radiative forcing.

Herein, we present a description of the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0). This iron processing module was developed for use within Earth system models and has been updated within a modal aerosol framework from the original implementation in a bulk aerosol model. MIMI simulates the emission and atmospheric processing of two main sources of iron in aerosol prior to deposition: mineral dust and combustion processes. Atmospheric dissolution of insoluble to soluble iron is parameterized by an acidic interstitial aerosol reaction and a separate in-cloud aerosol reaction scheme based on observations of enhanced aerosol iron solubility in the presence of oxalate. Updates include a more comprehensive treatment of combustion iron emissions, improvements to the iron dissolution scheme, and an improved physical dust mobilization scheme. An extensive dataset consisting predominantly of cruise-based observations was compiled to compare to the model. The annual mean modelled concentration of surface-level total iron compared well with observations but less so in the soluble fraction (iron solubility) for which observations are much more variable in space and time. Comparing model and observational data is sensitive to the definition of the average as well as the temporal and spatial range over which it is calculated. Through statistical analysis and examples, we show that a median or log-normal distribution is preferred when comparing with soluble iron observations. The iron solubility calculated at each model time step versus that calculated based on a ratio of the monthly mean values, which is routinely presented in aerosol studies and used in ocean biogeochemistry models, is on average globally one-third (34 %) higher. We redefined ocean deposition regions based on dominant iron emission sources and found that the daily variability in soluble iron simulated by MIMI was larger than that of previous model simulations. MIMI simulated a general increase in soluble iron deposition to Southern Hemisphere oceans by a factor of 2 to 4 compared with the previous version, which has implications for our understanding of the ocean biogeochemistry of these predominantly iron-limited ocean regions.

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations ( %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

This work reports on the current status of the global modeling of iron (Fe) deposition fluxes and atmospheric concentrations and the analyses of the differences between models, as well as between models and observations. A total of four global 3-D chemistry transport (CTMs) and general circulation (GCMs) models participated in this intercomparison, in the framework of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) Working Group 38, The Atmospheric Input of Chemicals to the Ocean. The global total Fe (TFe) emission strength in the models is equal to ∼ 72 Tg Fe yr−1 (38–134 Tg Fe yr−1) from mineral dust sources and around 2.1 Tg Fe yr−1 (1.8–2.7 Tg Fe yr−1) from combustion processes (the sum of anthropogenic combustion/biomass burning and wildfires). The mean global labile Fe (LFe) source strength in the models, considering both the primary emissions and the atmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1, accounting for both mineral dust and combustion aerosols. The mean global deposition fluxes into the global ocean are estimated to be in the range of 10–30 and 0.2–0.4 Tg Fe yr−1 for TFe and LFe, respectively, which roughly corresponds to a respective 15 and 0.3 Tg Fe yr−1 for the multi-model ensemble model mean.The model intercomparison analysis indicates that the representation of the atmospheric Fe cycle varies among models, in terms of both the magnitude of natural and combustion Fe emissions as well as the complexity of atmospheric processing parameterizations of Fe-containing aerosols. The model comparison with aerosol Fe observations over oceanic regions indicates that most models overestimate surface level TFe mass concentrations near dust source regions and tend to underestimate the low concentrations observed in remote ocean regions. All models are able to simulate the tendency of higher Fe concentrations near and downwind from the dust source regions, with the mean normalized bias for the Northern Hemisphere ( ∼ 14), larger than that of the Southern Hemisphere ( ∼ 2.4) for the ensemble model mean. This model intercomparison and model–observation comparison study reveals two critical issues in LFe simulations that require further exploration: (1) the Fe-containing aerosol size distribution and (2) the relative contribution of dust and combustion sources of Fe to labile Fe in atmospheric aerosols over the remote oceanic regions.

Atmospheric processing of iron in dust and combustion aerosols is simulated using an intermediate-complexity soluble iron mechanism designed for Earth system models. The solubilization mechanism includes both a dependence on aerosol water pH and in-cloud oxalic acid. The simulations of size-resolved total, soluble and fractional iron solubility indicate that this mechanism captures many but not all of the features seen from cruise observations of labile iron. The primary objective was to determine the extent to which our solubility scheme could adequately match observations of fractional iron solubility. We define a semi-quantitative metric as the model mean at points with observations divided by the observational mean (MMO). The model is in reasonable agreement with observations of fractional iron solubility with an MMO of 0.86. Several sensitivity studies are performed to ascertain the degree of complexity needed to match observations; including the oxalic acid enhancement is necessary, while different parameterizations for calculating model oxalate concentrations are less important. The percent change in soluble iron deposition between the reference case (REF) and the simulation with acidic processing alone is 63.8 %, which is consistent with previous studies. Upon deposition to global oceans, global mean combustion iron solubility to total fractional iron solubility is 8.2 %; however, the contribution of fractional iron solubility from combustion sources to ocean basins below 15° S is approximately 50 %. We conclude that, in many remote ocean regions, sources of iron from combustion and dust aerosols are equally important. Our estimates of changes in deposition of soluble iron to the ocean since preindustrial climate conditions suggest roughly a doubling due to a combination of higher dust and combustion iron emissions along with more efficient atmospheric processing.

Several lines of evidence suggest that fire activity was much greater in the preindustrial era than currently assumed in climate models. Here the authors show that greater emission of aerosols from fires leads to a substantial reduction in the magnitude of aerosol radiative forcing over the Industrial Era.

Contemporary methods for synthesizing thioesters often necessitate expensive catalysts and harsh conditions, making their synthesis from chemical feedstocks challenging. Herein, we report a sustainable metal-, photocatalyst-, and oxidant-free electron donor–acceptor (EDA) mediated synthesis of thioesters via site-selective C–H functionalization using aryl sulfonium salts (acceptor) with potassium thioacid salts (donor) under visible light irradiation. Our approach enables rapid access to thioesters from a wide variety of arenes, including pharmaceutical and agrochemical compounds, as well as a diverse range of alkyl, aryl, and heteroaryl potassium thioacid salts with excellent efficiency and regioselectivity. Mechanistic studies supported the formation of an EDA-complex, and radical trapping experiments corroborated the involvement of a radical-based mechanism for the product formation. Moreover, our method demonstrates excellent atom economy and E-factor scores, which are considered excellent in terms of safety, economic and ecological yardsticks. [ABSTRACT FROM AUTHOR]

Scholars of critical geography and geopolitics have long noted the intersection between research and strategic interest in the polar areas. This article focuses on how this link is displayed in the case of Svalbard, an Arctic archipelago under Norwegian sovereignty. While Norway' research policy in Svalbard has traditionally been welcoming and supportive of international research activity with little interference in the organization of research, the Norwegian government recently introduced a new policy for the research community Ny-Ålesund. The article reviews the changes introduced as well as the response from involved research organizations. It shows interdependence between political ambitions and research priorities and, given the status of Svalbard, a more complex relationship than is the case for research in polar areas outside national jurisdiction. [ABSTRACT FROM AUTHOR]

Many phenolic compounds (PhCs) in biomass burning and fossil fuel combustion emissions can partition into atmospheric aqueous phases (e.g., cloud/fog water and aqueous aerosols) and undergo reactions to form secondary organic aerosols (SOAs) and brown carbon (BrC). Redox-active transition metals, particularly Fe and Cu, are ubiquitous species in atmospheric aqueous phases known to participate in Fenton/Fenton-like chemistry as a source of aqueous ˙OH. However, even though the concentrations of water-soluble Cu are close to those of water-soluble Fe in atmospheric aqueous phases in some areas, unlike Fe, the effects that Cu have on SOA and BrC formation in atmospheric aqueous phases have scarcely been studied and remain poorly understood. We investigated the effects of Cu(II) on PhC reaction rates and BrC formation during the aqueous oxidation of four PhCs (guaiacol, catechol, syringol, and vanillin) by ˙OH generated from Fenton-like chemistry under different pH conditions. While the PhCs reacted when both H2O2 and Cu(II) were present in the absence (i.e., dark oxidation) and presence (i.e., photooxidation) of light, the reaction rates were at least one order of magnitude higher during photooxidation. Higher PhC reaction rates were measured at higher pH during both dark oxidation and photooxidation as a result of higher ˙OH concentrations produced by Fenton-like chemistry. Only water-soluble BrC was formed during dark oxidation and photooxidation when Cu(II) was present. Mass absorption coefficients (103 to 104 cm2 g−1) comparable to those of biomass burning BrC were measured during dark oxidation and photooxidation when Cu(II) was present. Light absorption was enhanced at higher pH during dark oxidation and photooxidation, which indicated that higher quantities and/or more absorbing BrC chromophores were formed at higher pH. The effects that Cu(II) had on the PhC reaction rates and the composition of SOAs and BrC formed depended on the PhC base structure (i.e., benzenediol vs. methoxyphenol). Overall, these results show how aqueous reactions involving Cu(II), H2O2, and PhCs can be an efficient source of daytime and nighttime water-soluble BrC and SOAs, which can have significant implications for how the atmospheric fates of PhCs are modeled for areas with substantial concentrations of water-soluble Cu in highly to moderately acidic cloud/fog water and aqueous aerosols. [ABSTRACT FROM AUTHOR]

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain‐general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain‐general predictive network that encompasses all well‐established cognitive domains exists remains unanswered. The present meta‐analysis aims to address this gap by testing the hypothesis that PP relies on a large‐scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta‐analytic approach was employed, along with Meta‐Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena‐independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross‐domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large‐scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross‐domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta‐Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain‐general, large‐scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context‐ and stimulus‐dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world. [ABSTRACT FROM AUTHOR]

Biomass burning plays an important role in climate-forcing and atmospheric chemistry. The drivers of fire activity over the past two centuries, however, are hotly debated and fueled by poor constraints on the magnitude and trends of preindustrial fire regimes. As a powerful tracer of biomass burning, reconstructions of paleoatmospheric carbon monoxide (CO) can provide valuable information on the evolution of fire activity across the preindustrial to industrial transition. Here too, however, significant disagreements between existing CO records currently allow for opposing fire histories. In this study, we reconstruct a continuous record of Antarctic ice coreCObetween 1821 and 1995CE to overlap with direct atmospheric observations. Our record indicates that the Southern Hemisphere CO burden ([CO]) increased by 50% from a preindustrial mixing ratio of ca. 35 ppb to ca. 53 ppb by 1995 CE with more variability than allowed for by state-of-the-art chemistry-climate models, suggesting that historic CO dynamics have been not fully accounted for. Using a 6-troposphere box model, a 40 to 50% decrease in Southern Hemisphere biomass-burning emissions, coincident with unprecedented rates of early 20th century anthropogenic land-use change, is identified as a strong candidate for this mismatch. [ABSTRACT FROM AUTHOR]

A modified Brillouin intensity measurement method was developed in this study. A means of adjusting a modern Brillouin scattering system to apply the methods presented in previous literature was identified, an analysis model of the geometric factors for a given light path in a Brillouin scattering system was proposed, and ambiguous discussions in the literature due to the idealized modeling of geometric factors were addressed. The proposed model was expanded into a 60° platelet symmetric geometry by measuring the elastic and elasto-optical coefficients of MgO, KTaO3, and TeO2 samples. To avoid deviation, the geometric factor in experiments was considered, and various ways to circumvent it were identified. [ABSTRACT FROM AUTHOR]

In this study were synthesized non-Michael acceptor cyclopentenones (CP) from biomass derivative furfural as anticancer agents. Cyclic enones, both from natural sources and synthetic analogues, have been described as cytotoxic agents. Most of these agents were unsuccessful in becoming valuable therapeutic agents due to toxicity problems derived from unselective critical biomacromolecule alkylation. This may be caused by Michael addition to the enone system. Ab initio studies revealed that 2,4-substituted CPs are less prone to Michael additions, and as such were tested three families of those derivatives. We prepare the new CPs from furfural through a tandem furan ring opening/Nazarov electrocyclization and further functionalization. Experimentally the 2,4-substituted CPs exhibited no reactivity towards sulphur nucleophiles, while maintaining cytotoxicity against HT-29, MCF-7, NCI-H460, HCT-116 and MDA-MB 231 cells lines. Moreover, the selected CP are non-toxic against healthy HEK 293T cell lines and present proper calculated drug-like properties., (© 2023 The Authors. ChemMedChem published by Wiley-VCH GmbH.)

Polycystic ovary syndrome (PCOS) is an endocrine disorder characterized by abnormal steroid hormone levels in peripheral blood and poor-quality oocytes. In the ovary, androgen is produced by theca cells, and estrogen is produced by granulosa cells. Androgen is converted to estrogen in granulosa cells, with cytochrome P450 aromatase as the limiting enzyme during this process. Estrogen receptors (ER) include ER alpha, ER beta, and membrane receptor GPR30. Studies have demonstrated that the abnormal functions of estrogen and its receptors and estradiol synthesis-related enzymes are closely related to PCOS. In recent years, some estrogen-related drugs have made significant progress in clinical application for subfertility with PCOS, such as letrozole and clomiphene. This article will elaborate on the recent advances in PCOS caused by abnormal expression of estrogen and its receptors and the application of related targeted small molecule drugs in clinical research and treatment. [ABSTRACT FROM AUTHOR]

The international GEOTRACES program has allowed unprecedented access to aerosols in the remote marine environment, moved the community toward standardized methodologies, and contributed to expanding research capabilities around the globe. A key aspect of GEOTRACES aerosol research is the quantification of deposition fluxes of trace elements and isotopes to the surface ocean as a source for biolimiting nutrients and anthropogenic contaminants. New methods for quantifying flux have been developed that leverage radioisotope tracers in the surface ocean to estimate bulk deposition rates on longer and more relevant timescales. The question as to what fraction of aerosols is soluble remains and is a source of continuing uncertainty. To that end, the community has engaged in coordinated study to address questions regarding the fractional solubility of aerosol trace elements and isotopes through the evaluation of widely used dissolution and extraction techniques. Intercalibration efforts, method standardization, and normalization of open-access data archiving will be the legacy of GEOTRACES-era aerosol research. [ABSTRACT FROM AUTHOR]

Ocean biogeochemical models have become critical tools for interpreting trace element and isotope (TEI) distributions observed during the GEOTRACES program and understanding their driving processes. Models stimulate new research questions that cannot be addressed with observations alone, for instance, concerning processes that occur over vast spatial scales and linkages between TEIs and other elemental cycles. A spectrum of modeling approaches has been applied to date, including (1) fully prognostic models that couple TEIs to broader biogeochemical frameworks, (2) simpler element-specific mechanistic models that allow for assimilation of observations, and (3) machine learning models that have no mechanistic underpinning but allow for skillful extrapolation of sparse data. Here, we evaluate the strengths and weaknesses of these approaches and review three sets of novel insights they have facilitated. First, models have advanced our understanding of global-scale micronutrient distributions, and their deviations from macronutrients, in terms of a "ventilation-regeneration-scavenging" balance. Second, models have yielded global-scale estimates of TEI inputs to and losses from the ocean, revealing, for instance, a rapid iron (Fe) cycle with an oceanic residence time on the order of decades. Third, models have identified novel links among various TEI cycling processes and the global ocean carbon cycle, such as tracing the supply of hydrothermally sourced Fe to iron-starved microbial communities in the Southern Ocean. We foresee additional important roles for modeling work in the next stages of trace element research, including synthesizing understanding from the GEOTRACES program in the form of TEI state estimates, and projecting the responses of TEI cycles to global climate change. [ABSTRACT FROM AUTHOR]

Dissolved iron (dFe) is an essential micronutrient for phytoplankton, with vanishingly low oceanic dissolved concentrations (pico- to nanomoles per kg) known to limit growth--and thus influence primary productivity and carbon cycling-- over much of the surface ocean. However, because of the considerable challenges associated with contamination-free sample collection and accurate analysis of such low dFe concentrations, the first reliable dFe measurements came only in the 1980s. Further, by 2003, despite several decades of research, there were only ~25 full-depth oceanic dFe profiles worldwide, with dust considered to be the main oceanic dFe source. Since 2008, facilitated by the extensive field campaign and rigorous intercalibration of the international GEOTRACES program, there has been an "explosion" in the availability of oceanic dFe data, with hundreds of profiles now available. Concurrently, there has been a paradigm shift to a view of the marine Fe cycle where multiple sources contribute, and some forms of dFe can be transported great distances through the intermediate and deep ocean. Here, we showcase the GEOTRACES dFe datasets across the different ocean basins, synthesize our current multi-source view of the oceanic Fe cycle, spotlight sediments as an important dFe source, and look to future directions for constraining oceanic dFe boundary exchange. [ABSTRACT FROM AUTHOR]