Found 851 publications. Showing page 1 of 36:
Circulating MicroRNAs in Cord Blood to Predict Attention-Deficit/Hyperactivity Disorder Diagnosis
Background
There are large knowledge gaps in the etiology of attention-deficit/hyperactivity disorder (ADHD), and although it is a prevalent and highly heritable neurodevelopmental disorder, diagnosis can be challenging. We aimed to assess the association of circulating blood plasma microRNAs (miRNAs) at birth with ADHD for use as biomarker candidates and build an miRNA-based prediction model.
Methods
Our study population consisted of 206 children with ADHD (33.0% female), 207 control children (33.8% female), and their parents from the MoBa (Norwegian Mother, Father, and Child Cohort Study). Expression levels of 51 selected miRNAs in plasma from children’s cord blood at birth and from both parents during early pregnancy were quantified by quantitative polymerase chain reaction and tested for association with children’s ADHD diagnosis and ADHD symptom scores based on ratings by parents.
Results
Seven miRNAs were differentially expressed at birth in children with ADHD and control children (false discovery rate < .05), and 31 had a statistically significant linear relationship with parent-rated ADHD symptom score at 8 years. A 19-miRNA ADHD prediction model achieved good discrimination in the test population (area under the receiver operating curve = 0.959, accuracy = 0.893). Functional analysis for the 19-miRNA prediction set revealed involvement in several highly relevant pathways, e.g., dopaminergic synapse, circadian rhythm, and axon guidance. We also found that parental miRNA expression levels significantly associated with children’s ADHD diagnoses and/or ADHD symptoms scores.
Conclusions
We showed that expression levels of circulating miRNAs at birth may be used to predict increased risk of ADHD diagnosis, and our 19-miRNA set should be included in future efforts to develop a biomarker panel.
2025
Building-related symptoms in school environment: Predictability using machine learning approach
Building-related symptoms (BRS) are commonly experienced by students in schools and are potentially affecting academic performance and health. Even though indoor environment quality (IEQ) measurements indicated fair conditions, students still perceived discomfort that led to symptoms, highlighting the necessity of collecting user-feedback about IEQ-complaints. This study aimed to predict and understand the prevalence of BRS (headache, tiredness, cough, dry eyes-hands) experienced by students in classrooms using machine-learning (ML) approach based on measurement data, building factors, and prevalence of IEQ-complaints. We collected measurement data (from indoor and outdoor climate), building factors, and user-feedback by students via online-platform across three sampled classrooms each campaign during three consecutive school semesters. Significant input variables for ML were pre-selected using statistical tests. ML models were evaluated based on accuracy metrics and SHAP analysis for input interpretation. Models using measurement data alone performed poorly (testing R² <50 %) to predict prevalence of BRS, whereas adding building factors and prevalence of IEQ-complaints increased accuracy (R² up to 95 %) of prediction of BRS with lower RMSE. In addition, interpretation from SHAP analysis showed IEQ-complaints especially related with indoor air quality (e.g., heavy air, dust & dirt, and dry air) as significant contributors for predicting prevalence of BRS. We conclude that the framework of combining objective measurements with occupant-reported complaints can be reliable, interpretable predictions of symptom prevalence. This study is limited by single-school setting, health confounders, and symptoms verification. Future research may contribute to exploring wider set of input variables, applicability, and variation of complaints preference.
2025
Abstract Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar techniques such as K‐means, density‐based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100 × 100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present a scalable hierarchical clustering implementation that we apply to two year‐long, hourly atmospheric data sets: model concentration and deposition timeseries at 290,520 locations over Alberta and Saskatchewan (538 540 grid); and 366,427 multi‐pollutant observations from 51 national air pollution surveillance stations located across Canada. When combined with other information such as emissions source locations, orography, and prevailing meteorological conditions, the method yields coherent, interpretable structures. In the case of model time series, the clustering provides regions of similar air quality (airsheds) which can be used to inform air quality monitoring network placement, or regions of similar deposition which can inform critical load assessment as well as monitoring site locations. In the case of the multi‐pollutant observations, we show that a single low‐primary pollutant cluster appears the most frequently at all but one of 51 stations across Canada, accounting for 62% of all station‐hours, while elevated SO 2 appears in factor profiles at certain monitoring locations near industrial and shipping activity. Together, these results demonstrate that hierarchical clustering can efficiently summarize patterns relevant to airshed mapping and source apportionment at previously unreachable scales.
2026
Spatial and temporal assessment of soil degradation risk in Europe
Soil degradation threatens agricultural productivity and ecosystem resilience across Europe, yet spatially consistent assessments of its intensity and drivers remain limited. In this study, we used Soil Degradation Proxy (SDP), that integrates four key indicators of soil degradation, including erosion rate, soil pH, electrical conductivity, and organic carbon content, to quantify soil degradation risk. Using over 38,000 LUCAS topsoil observations and a machine learning model trained on climate, land cover, topographic, soil parent material properties, and spectral variables, we map annual SDP values between years 2000 to 2022 across Europe. Results show soil degradation risk is highest in southern Europe, especially in intensively managed and sparsely vegetated landscapes. Over the past two decades, approximately 7.1% of land area across the EU and the UK has experienced increasing degradation risk (most notably across Eastern Europe), with rainfed croplands emerging as the most affected land cover type. Land cover is the most influential driver, modulating effects of climatic variables such as precipitation and temperature on SDP. This data-driven framework provides a consistent and scalable approach for monitoring soil degradation risk and offers actionable insights to support targeted conservation and EU-wide policy implementation.
2025
2026
Microplastic and other anthropogenic particles in surface waters of the Isfjorden system (Svalbard)
Knowledge of sources and transport mechanisms of anthropogenic particles (APs) such as microplastics (MPs) and related plastic chemicals, in the Arctic marine environment is limited. This study investigates the surface waters of the Isfjordensystem, where Svalbard's largest settlement, Longyearbyen, is located, for the presence of APs. The wastewater from Longyearbyen is released untreated into Adventfjorden, which is a branch of Isfjorden. Samples from the inflowing current of Isfjorden into Adventfjorden, and its outflowing current were sampled and analyzed for APs (>50 μm). APs were classified regarding size, shape, and polymer type via μFTIR spectroscopy. Each location showed an AP burden (Isfjorden: 26 APs/L, Adventfjorden: 20 APs/L). Highest amounts of APs were found in the Isfjorden current (37 APs/L), before entering Adventfjorden. 14 APs/L were indicated near the wastewater effluent in Adventfjorden, and 15 APs/L in the outflowing current in Isfjorden. Plastic related chemicals, polypropylene and other polyolefins had high frequencies, but silk and rayon material dominated each location except the inflowing current from Isfjorden. Local sources like wastewater and other anthropogenic activities, as well as northwards long-range transport from the south into the Arctic, are considered. Oceanographic dynamics, and the time of sampling seems to affect the distribution of APs in the surface waters, besides its characteristics itself (e.g., polymer type and size).
2025
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
Efficacy of individual and combined terrestrial and marine carbon dioxide removal
Abstract Limiting global temperature rise below 2°C requires significant reduction in greenhouse gas emissions and likely large-scale carbon dioxide removal (CDR). This study assesses the CO2 sequestration and efficacy of two CDR approaches, Bioenergy with Carbon Capture and Storage (BECCS) and Ocean Alkalinity Enhancement (OAE), applied individually and in combination. Using the Norwegian Earth System Model (NorESM2-LM), simulations were designed to ramp up deployment of BECCS and OAE, to an additional area of 5.2 million km² by 2100 for bioenergy feedstock for BECCS, and a CaO deployment rate of approximately 2.7 Gt/year for OAE within the exclusive economic zones of Europe, the United States and China. The combined land-ocean CDR simulation revealed a largely additive carbon removal effect. Over 2030-2100, OAE sequestered 7 ppm of CO 22 with an accumulated 82.3 Gt CaO, achieving a CDR effectiveness of 0.08 ppm (~ 0.17 PgC) per Gt CaO, while BECCS reduced 16 ppm of CO2, with CDR effectiveness of 3.1 ppm per million km² of bioenergy crops. Together, the carbon removal achieved by BECCS and OAE corresponds to anthropogenic CO₂ emissions of 5.4 Gt CO₂/year by 2100, slightly more than 60% of current global transport sector emissions. Notably, the efficiency of BECCS and OAE alone was unaffected by their concurrent deployment. Nevertheless, simulations revealed distinct non- linear interactions, such as declines in land and soil carbon sinks in the combined scenario. Furthermore, all simulations show negligible effects on the global annual mean temperature. These results highlight near-additive CDR responses even under net-negative emissions, but feedback on land and ocean carbon sinks must be considered when designing CDR portfolios. This study provides new insights into CDR portfolio design and Earth system feedback under an overshoot scenario, highlighting both their potential and the need for continued emissions cuts and supportive policies.
2025
Temporal changes in per and polyfluoroalkyl substances and their associations with type 2 diabetes
We assessed temporal changes of PFAS and associations with T2DM over a period of 30 years in a nested case–control study with repeated measurements. Logistic regression was used to assess associations between 11 PFAS and T2DM at five time-points in 116 cases and 139 controls (3 pre- and 2 post-diagnostic time-points in cases). Mixed linear models were applied to assess if changes in PFAS were related to T2DM status. In the pre-diagnostic time-point T3 (2001), future cases had higher concentrations of PFHpA, PFNA, PFHxS and PFHpS compared to controls. In the post-diagnostic time point T5 (2015/16), PFNA and PFOS were higher in prevalent cases. PFHxS and PFHpS were positively associated with future T2DM at the pre-diagnostic time-point T3, whereas PFTrDA were inversely associated with future T2DM at T1 (1986/87) and prevalent T2DM at T4 (2007/8). Temporal changes in PFAS across the study period showed that cases experienced a greater increase in pre-diagnostic concentrations of PFHpA, PFTrDA, PFHxS and PFOSA, as well as a larger post-diagnostic decrease in PFOSA, compared to controls. This study is the first to show that temporal changes in PFAS are associated with T2DM status for certain PFAS, and associations between PFAS and T2DM vary according to sample year.
2025
Killer whales (Orcinus orca) accumulate high levels of persistent organic pollutants (POPs), which have been linked to immunomodulation. Over the past decades, large-scale mortality events associated with cetacean morbillivirus (CeMV) have affected cetacean populations, and concerns have been raised about the role of contaminants in exacerbating these outbreaks. However, establishing cause-effect relationships in free-roaming cetaceans remains a significant challenge. In vitro approaches present unique potential for furthering our understanding of the effects of multiple environmental stressors in marine mammal health. In this study, we used primary fibroblasts cultured from wild Norwegian killer whale skin biopsies (n = 6) to assess how exposure to POP mixtures affects cell viability and CeMV replication. Our findings demonstrate that CeMV successfully replicates in killer whale fibroblasts, with the viral replication significantly increasing over the duration of the experiment. POP exposure led to a concentration-dependent decrease in cell viability and a significant increase in viral replication. These results validate killer whale primary fibroblasts as a valuable in vitro tool for the study of co-exposure of POPs and morbillivirus on toothed cetaceans. Moreover, these findings support the need for further research to confirm the role of contaminants in intensifying the severity of CeMV infections in the wild.
2025
Measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) combined with a global 12-box model of the atmosphere have long been used to estimate global emissions and surface mean mole fraction trends of atmospheric trace gases. Here, we present annually updated estimates of these global emissions and mole fraction trends for 42 compounds through 2023 measured by the AGAGE network, including chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, nitrogen trifluoride, methane, nitrous oxide, and selected other compounds. The data sets are available at https://doi.org/10.5281/zenodo.15372480 (Western et al., 2025). We describe the methodology to derive global mole fraction and emissions trends, which includes the calculation of semihemispheric monthly mean mole fractions, the mechanics of the 12-box model and the inverse method that is used to estimate emissions from the observations and model. Finally, we present examples of the emissions and mole fraction data sets for the 42 compounds.
2025
2025
Etablering av vindkraftverk på land kan medføre en risiko for drikkevann når installasjonene ligger i eller nær vanntilsigsområder til drikkevannskilder. Denne rapporten, utarbeidet av VKM på oppdrag fra Mattilsynet, gir Mattilsynet et kunnskapsbasert grunnlag for å stille krav til konsekvensutredninger og detaljplan for å beskytte drikkevannet.
Rapporten identifiserer potensielle farer for kjemisk og fysisk forurensning av drikkevann gjennom hele livsløpet til et vindkraftverk – fra planlegging og anleggsfase, til drift og avvikling. Den beskriver relevante lover og forskrifter, sentrale aktører og deres roller, og legger vekt på når og hvordan Mattilsynet kan involveres og komme med innspill i den kommunale planprosessen etter plan- og bygningsloven og i konsesjonsprosessen etter energiloven som forvaltes av NVE. Det er av stor betydning at Mattilsynet varsles og involveres tidlig i prosessen. Tiltakshaver må sørge for at risiko for forurensning av drikkevann og vanntilsigsområde utredes på en etterprøvbar måte, slik at Mattilsynet kan gi tydelige innspill til utredningen for å sikre at drikkevannshensyn er ivaretatt.
2025
Abstract. Airborne microplastics are a recently identified atmospheric aerosol species with potential air quality and climate impacts, yet they are not currently represented in global climate models. Here, we describe the addition of microplastics to the aerosol scheme of the UK Earth System Model (UKESM1.1): the Global Model of Aerosol Processes (GLOMAP). Microplastics are included as both fragments and fibres across a range of aerosol size modes, enabling interaction with existing aerosol processes such as ageing and wet and dry deposition. Simulated microplastics have higher concentrations over land, but can be transported into remote regions including Antarctica despite no assumed emissions from these regions. Lifetimes range between ∼17 d to ∼1 h, with smaller, hydrophilic microplastics having longer lifetimes. Microplastics are present throughout the troposphere, and the smallest particles are simulated to reach the lower stratosphere in small numbers. Dry deposition is the dominant microplastic removal pathway, but greater wet deposition occurs for smaller hydrophilic microplastic, due to interactions with clouds. Although microplastics currently contribute a minor fraction of the total aerosol burden, their concentration is expected to increase in future if plastic production continues to increase, and as existing plastic waste in the environment degrades to form new microplastic. Incorporating microplastics into UKESM1.1 is a key step toward quantifying their current atmospheric impact and offers a framework for simulating future emission scenarios for an assessment of their long term impacts on air quality and climate.
2025
Airborne microplastics on the move: Urban Europe as a source to remote regions
This study presents a comprehensive assessment of unique parallel measurements of surface airborne and deposited microplastics (AMPs) across urban and remote sites in Norway, employing pyrolysis-GC/MS for polymer-specific analysis. MPs were detected in nearly all samples, with significantly higher concentrations and fluxes observed in urban areas like Oslo, where tire wear particles (TWP) dominated (>90 % of AMP mass). Seasonal peaks in TWP coincided with the transition to winter tires, while remote sites showed consistent but lower AMP levels, indicating long-range transport (LRT) from European source regions. Parallel measurements of suspended and deposited AMPs revealed consistent polymer signatures, highlighting common sources and transport pathways. Although urban TWP contributions to PM2.5 were generally low, episodic events reached up to 30 %, raising concerns about human exposure. The dual dataset enabled a robust cross-validation of atmospheric loading estimates and facilitated integration into advanced transport models for remote sites. Our findings confirm AMPs as significant components of urban air pollution and subsequent carriers of chemical and biological contaminants to remote regions, emphasizing the need for targeted monitoring and mitigation strategies.
2025
The semi-annual oscillation (SAO) dominates seasonal variability in the equatorial stratosphere and mesosphere. However, the seasonally dependent modulation of the SAO in the stratosphere (SSAO) and mesosphere (MSAO) by sudden stratospheric warmings (SSWs) in the Arctic has not been investigated in detail. In this study, we examine the seasonal evolution of the SAO during 16 major SSW events spanning 2004 to 2024 using the Japanese Atmospheric General Circulation Model for Upper Atmosphere Research Data Assimilation System Whole Neutral Atmosphere Re-analysis (JAWARA). Basic features of the SAO are well captured by JAWARA, as evidenced by the SSAO and MSAO appearing at around 50 km and 85 km, respectively. The different responses of the SAO to early and late winter SSWs are particularly strong during the Northern Hemisphere winter of 2023/24. Early winter SSWs tend to significantly intensify the westward SSAO, while late winter SSWs tend to weaken the eastward SSAO. Similarly, the eastward MSAO is amplified during early winter SSWs, whereas the westward MSAO is slightly weakened during late winter SSWs. The weak MSAO response is probably due to its smaller climatological magnitude. Modulation of the SAO by SSWs is related to meridional temperature changes during SSWs through the thermal wind balance. Our findings contribute to the understanding of coupling between the tropics and high latitudes, as well as interhemispheric coupling.
2025
Quantifying European SF6 emissions from 2005 to 2021 using a large inversion ensemble
Abstract. Sulfur hexafluoride (SF6) is a highly potent and long-lived greenhouse gas whose atmospheric concentrations are increasing due to human emissions. In this study, we determine European SF6 emissions from 2005 to 2021 using a large ensemble of atmospheric inversions. To assess uncertainty, we systematically vary key inversion parameters across 986 sensitivity tests and apply a Monte Carlo approach to randomly combine these parameters in 1003 additional inversions. Our analysis focuses on high-emitting countries with robust observational coverage – UK, Germany, France, and Italy – while also examining aggregated EU-27 emissions. SF6 emissions declined across all studied regions except Italy, largely attributed to EU F-gas regulations (2006, 2014), however, national reports underestimated emissions: (i) UK emissions dropped from 68 (47–77) t yr−1 in 2008 to 19 (15–26) t yr−1 in 2018, aligning with the reports from 2018 onward; (ii) French emissions fell from 78 (51–117) t yr−1 (2005) to 35 (19–54) t yr−1 (2021), exceeding reports by 88 %; (iii) Italian emissions fluctuated (25–48 t yr−1), surpassing reports by 107 %; (iv) German emissions declined from 182 (155–251) t yr−1 (2005) to 97 (88–104) t yr−1 (2021), aligning reasonably well with reports; (v) EU-27 emissions decreased from 403 (335–501) t yr−1 (2005) to 225 (191–260) t yr−1 (2021), exceeding reports by 20 %. A substantial drop from 2017 to 2018 mirrored the trend in southern Germany, suggesting regional actions were taken as the 2014 EU regulation took effect. Our sensitivity tests highlight the crucial role of dense monitoring networks in improving inversion reliability. The UK system expansions (2012, 2014) significantly enhanced result robustness, demonstrating the importance of comprehensive observational networks in refining emission estimates.
2025
Biomethanol as a Marine Fuel Within Land Use Sustainability Boundaries
Global shipping is an essential, energy-efficient enabler of trade, yet it remains a hard-to-abate sector. With shipping demand projected to continue to rise in the coming decades, identifying scalable and sustainable fuel alternatives is critical. Biofuels, and particularly biomethanol, offer a promising option due to their compatibility with existing infrastructure. However, their sustainability critically hinges on land use impacts. From this Perspective, we argue that biomethanol derived from a dedicated crop could contribute to maritime decarbonisation, with ~71–77% well-to-wake greenhouse gases (GHG) reductions under cropland-only constraints. We further point to the fact that a wider adoption faces challenges such as higher costs, limited availability, and lower energy density relative to fossil fuels. Continued research and monitoring are essential to ensure that biofuel production does not inadvertently contribute to deforestation or biodiversity loss. We underscore the need for spatially sensitive biofuel deployment strategies that align maritime decarbonisation with land-system sustainability and climate objectives.
2025
Fine particulate matter (PM) poses a major threat to public health, with organic aerosol (OA) being a key component. Major OA sources, hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and oxygenated OA (OOA), have distinct health and environmental impacts. However, OA source apportionment via positive matrix factorization (PMF) applied to aerosol mass spectrometry (AMS) or aerosol chemical speciation monitoring (ACSM) data is costly and limited to a few supersites, leaving over 80% of OA data uncategorized in global monitoring networks. To address this gap, we trained machine learning models to predict HOA, BBOA, and OOA using limited OA source apportionment data and widely available organic carbon (OC) measurements across Europe (2010–2019). Our best performing model expanded the OA source data set 4-fold, yielding 85 000 daily apportionment values across 180 sites. Results show that HOA and BBOA peak in winter, particularly in urban areas, while OOA, consistently the dominant fraction, is more regionally distributed with less seasonal variability. This study provides a significantly expanded OA source data set, enabling better identification of pollution hotspots and supporting high-resolution exposure assessments.
2025
2025
Reliable quantification of polychlorinated alkanes (PCAs) remains a major challenge, hindering environmental research across diverse matrices. Each sample can contain over 500 homologue groups, collectively producing >1000 m/z ratios that require interference checks. High-resolution mass spectrometry methods vary in ionization signals and data formats and require specialized algorithms for quantification. CPxplorer streamlines data processing through the integration of three modules: (1) CPions generates target ion sets and isotopic thresholds for compound identification into the next module; (2) Skyline performs instrument-independent data integration, interference evaluation, and homologue profiling; and (3) CPquant deconvolves homologues and reports concentrations using reference standards and homologue profiles from Skyline. Evaluation of the workflow with NIST-SRM-2585 dust and ERM-CE100 fish tissue material yielded comparable results across raw data formats from different instruments. Further applications of CPxplorer across diverse matrices, including indoor dust, organic films, silicone wrist bands, and food samples, demonstrated the usefulness in biological and environmental monitoring. Compared to existing tools limited to qualitative detection, CPxplorer enables quantitative outputs, reduces processing time, and expands functionality to PCA-like substances (e.g., BCAs) and PCA degradation products (e.g., OH-PCAs). CPxplorer reduces learning barriers, empowers users to quantify PCAs across various analytical instruments, and contributes to generating comparable results in the field.
2025
Tidal Amplification in the Lower Thermosphere During the 2003 October–November Solar Storms
Abstract Using the National Center for Atmospheric Research's vertically extended version of the Whole Atmosphere Community Climate Model nudged with reanalyses, we examine the impact of the 2003 Halloween solar storms on atmospheric tides and planetary waves in the lower thermosphere (LT). One of the largest solar flares and fastest coronal mass ejections on record occurred on 30 October, resulting in significant energy transfer via Joule heating and auroral particle precipitation in the Earth's higher latitude thermosphere. In the simulation, that occurrence creates large zonally asymmetric heating perturbations, amplifying the diurnal migrating tide (DW1), semidiurnal migrating tide (SW2), as well as non‐migrating westward and eastward tides between 120 and 200 km. Large‐amplitude bursts of DW1 in the Northern Hemisphere and non‐migrating westward tides in the Southern Hemisphere lead to westward wave forcings, which strengthen the thermospheric wind. Planetary waves are also amplified, but their forcing is much weaker than the forcing exerted by tides in the LT. Non‐migrating tides are generated by nonlinear interactions between tides, or between tides and quasi‐stationary planetary waves, and in situ processes in the LT linked to Joule heating and auroral particle precipitation. The induced disruptions of the thermospheric mean meridional circulation reinforce the Spring thermospheric branch in the Southern Hemisphere at high latitudes and oppose the Fall branch in the Northern Hemisphere. Our examination could be relevant to understand the dynamical impact of recent geomagnetic storms that occurred in May 2024 and October 2024.
2025
The role of the tropical carbon balance in determining the large atmospheric CO2 growth rate in 2023
Abstract. The global annual mean atmospheric CO2 growth rate in 2023 was one of the highest since records began in 1958, comparable to values recorded during previous major El Niño events. We do not fully understand this anomalous growth rate, although a recent study highlighted the role of boreal North American forest fires. We use a Bayesian inverse method to interpret global-scale atmospheric CO2 data from NASA's Orbiting Carbon Observatory (OCO-2). The resulting a posteriori CO2 flux estimates reveal that from 2022 to 2023, the biggest changes in CO2 fluxes of net biosphere exchange (NBE) – for which positive values denote a flux to the atmosphere – were over the land tropics. We find that the largest NBE increase is over eastern Brazil, with small increases over southern Africa and Southeast Asia. We also find significant increases over southeastern Australia, Alaska, and western Russia. A large NBE increase over boreal North America, due to fires, is driven by our a priori inventory, informed by independent data. The largest NBE reductions are over western Europe, the USA, and central Canada. Our NBE estimates are consistent with gross primary production estimates inferred from satellite observations of solar-induced fluorescence and from satellite observations of vegetation greenness. We find that warmer temperatures in 2023 explain most of the NBE change over eastern Brazil, with hydrological changes more important elsewhere across the tropics. Our results suggest that the ongoing environmental degradation of the Amazon is now playing a substantial role in increasing the global atmospheric CO2 growth rate.
2025