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Abstract. Climate change is increasing the 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 regional research concentration. 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 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire 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 Hawai’i (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 by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events 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 are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. 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.

Abstract. The Community Atmosphere Model (CAM6.1), the atmospheric componentof the Community Earth System Model (CESM; version 2.1), simulates thelife cycle (emission, transport, and deposition) of mineral dust and itsinteractions with physio-chemical components to quantify the impacts of duston climate and the Earth system. The accuracy of such quantifications relieson how well dust-related processes are represented in the model. Here weupdate the parameterizations for the dust module, including those on thedust emission scheme, the aerosol dry deposition scheme, the sizedistribution of transported dust, and the treatment of dust particle shape.Multiple simulations were undertaken to evaluate the model performanceagainst diverse observations, and to understand how each update alters themodeled dust cycle and the simulated dust direct radiative effect. Themodel–observation comparisons suggest that substantially improved modelrepresentations of the dust cycle are achieved primarily through the newmore physically-based dust emission scheme. In comparison, the othermodifications induced small changes to the modeled dust cycle andmodel–observation comparisons, except the size distribution of dust in thecoarse mode, which can be even more influential than that of replacing thedust emission scheme. We highlight which changes introduced here areimportant for which regions, shedding light on further dust modeldevelopments required for more accurately estimating interactions betweendust and climate.

Abstract. Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, the relative contributions of the world’s major dust 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 model simulations with observationally informed constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We obtain a data set that constrains the relative contribution of each of nine major source regions to size-resolved dust emission, atmospheric loading, optical depth, concentration, and deposition flux. We find that the 22–29 Tg (one standard error range) global loading of dust with 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). Current models might on average be overestimating the contribution of North African sources to atmospheric dust loading at ~65 %, while underestimating the contribution of Asian dust at ~30 %. However, both our results and current models could be affected by unquantified biases, such as due to errors in separating dust aerosol optical depth from that produced by other aerosol species in remote sensing retrievals in poorly observed desert regions. 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/year, 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.

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 two 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 geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, 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 data sets 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 data set 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.

Abstract. 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.

Atmospheric deposition is a source of potentially bioavailable iron (Fe) and thus can partially control biological productivity in large parts of the ocean. However, the explanation of observed high aerosol Fe solubility compared to that in soil particles is still controversial, as several hypotheses have been proposed to explain this observation. Here, a statistical analysis of aerosol Fe solubility estimated from four models and observations compiled from multiple field campaigns suggests that pyrogenic aerosols are the main sources of aerosols with high Fe solubility at low concentration. Additionally, we find that field data over the Southern Ocean display a much wider range in aerosol Fe solubility compared to the models, which indicate an underestimation of labile Fe concentrations by a factor of 15. These findings suggest that pyrogenic Fe-containing aerosols are important sources of atmospheric bioavailable Fe to the open ocean and crucial for predicting anthropogenic perturbations to marine productivity.

Abstract. This work reports on the current status of the global modeling of iron (Fe)deposition fluxes and atmospheric concentrations and the analyses of thedifferences between models, as well as between models and observations. Atotal of four global 3-D chemistry transport (CTMs) and general circulation(GCMs) models participated in this intercomparison, in the framework ofthe United Nations Joint Group of Experts on the Scientific Aspects of MarineEnvironmental Protection (GESAMP) Working Group 38, “The Atmospheric Inputof Chemicals to the Ocean”. The global total Fe (TFe) emission strength inthe 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 anthropogeniccombustion/biomass burning and wildfires). The mean global labile Fe (LFe)source strength in the models, considering both the primary emissions and theatmospheric processing, is calculated to be 0.7 (±0.3) Tg Fe yr−1,accounting for both mineral dust and combustion aerosols. Themean global deposition fluxes into the global ocean are estimated to be in the rangeof 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 theatmospheric Fe cycle varies among models, in terms of both the magnitude ofnatural and combustion Fe emissions as well as the complexity of atmosphericprocessing parameterizations of Fe-containing aerosols. The model comparisonwith aerosol Fe observations over oceanic regions indicates that most modelsoverestimate surface level TFe mass concentrations near dust sourceregions and tend to underestimate the low concentrations observed in remoteocean regions. All models are able to simulate the tendency of higher Feconcentrations near and downwind from the dust source regions, with the meannormalized bias for the Northern Hemisphere (∼14), largerthan that of the Southern Hemisphere (∼2.4) for the ensemble modelmean. This model intercomparison and model–observation comparison studyreveals two critical issues in LFe simulations that require furtherexploration: (1) the Fe-containing aerosol size distribution and (2) therelative contribution of dust and combustion sources of Fe to labile Fe inatmospheric aerosols over the remote oceanic regions.

Iron emissions from human activities, such as oil combustion and smelting, affect the Earth's climate and marine ecosystems. These emissions are difficult to quantify accurately due to a lack of observations, particularly in remote ocean regions. In this study, we used long‐term, near‐source observations in areas with a dominance of anthropogenic iron emissions in various parts of the world to better estimate the total amount of anthropogenic iron emissions. We also used a statistical source apportionment method to identify the anthropogenic components and their sub‐sources from bulk aerosol observations in the United States. We find that the estimates of anthropogenic iron emissions are within a factor of 3 in most regions compared to previous inventory estimates. Under‐ or overestimation varied by region and depended on the number of sites, interannual variability, and the statistical filter choice. Smelting‐related iron emissions are overestimated by a factor of 1.5 in East Asia compared to previous estimates. More long‐term iron observations and the consideration of the influence of dust and wildfires could help reduce the uncertainty in anthropogenic iron emissions estimates. Plain Language Summary: Human activities, such as smelting and oil combustion, release smoke and particles into the atmosphere. These particles often contain iron, which not only absorbs sunlight, contributing to atmospheric warming, but also serves as a nutrient for phytoplankton in various ocean regions. However, the precise extent of human‐induced iron emissions remains uncertain due to a lack of comprehensive monitoring data. In this study, we leverage a global data set of iron observations to refine our estimates of iron emissions attributed to human activities. Additionally, we examine other co‐released substances, such as carbon and nickel, to identify specific emission sources of iron. We employ statistical techniques to distinguish human‐caused iron emissions from those originating from natural sources like dust and wildfires. Moreover, we utilize iron oxide observations to constrain emissions originating from East Asia and Norway, which are estimated to originate largely from smelting emissions. Through the analysis of long‐term data sets, we provide lower and upper bounds to human‐caused iron emissions. Furthermore, we investigate the impact of reduced observation numbers and a sparse network on the range of estimated iron emissions. Our findings highlight the critical role of observation quality in accurately assessing iron emissions from human activities. Key Points: Anthropogenic total iron emissions are constrained to a factor of 3 in most global regions using long‐term aerosol observationsThe number of sites, interannual variability, and site selection filter can affect the model‐observation comparison uncertainty by 15%–50%Smelting‐related emissions are constrained to a factor of 1.5 using iron oxide observations from East Asia [ABSTRACT FROM AUTHOR]

Metal dissolution from atmospheric aerosol deposition to the oceans is important in enhancing and inhibiting phytoplankton growth rates and modifying plankton community structure, thus impacting marine biogeochemistry. Here we review the current state of knowledge on the causes and effects of the leaching of multiple trace metals from natural and anthropogenic aerosols. Aerosol deposition is considered both on short timescales over which phytoplankton respond directly to aerosol metal inputs, as well as longer timescales over which biogeochemical cycles are affected by aerosols.

Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as variable in size and composition. Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High aerosol concentrations at the surface represent an important public health hazard. There are substantial datasets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modelling community. Here we present a new compilation of PM2.5 and PM10 aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations, and especially particle composition to understand the current distribution of aerosol particles. Most climate models exclude 10-30% of the aerosol particles in both PM2.5 and PM10 size fractions across large swaths of the globe in their current configurations, with ammonium nitrate and agricultural dust aerosol being the most important omitted aerosol types. The dataset is available on Zenodo (https://zenodo.org/records/10459654, Mahowald et al., 2024). [ABSTRACT FROM AUTHOR]

Suppression of afterglow in co-doped CsI:T1 is found to be an order of magnitude more effective in CsI:T1,Sm than in CsI:T1,Eu. Rate equations predict that deep electron traps introduced by co-doping with samarium effectively scavenge electrons from shallow traps associated with thallium, thus suppressing afterglow in the time domain of tens of milliseconds. In addition, combined radioluminescence and thermoluminescence experiments suggest that electrons released by samarium recombine non-radiatively with trapped holes, thus providing a mechanism for suppression of hysteresis. Ab initio quantum chemistry calculations support the conclusion that non-radiative charge-transfer transitions in CsI:T1,Sm are enabled by the presence of low-energy [sup.5][D.sub.J] excited states of [Sm.sup.2+] within the ground configuration. Index Terms--Afterglow, cesium iodide, non-radiative recombination, radioluminescence.