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Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Post-processing bias correction approaches utilise observational data to address this challenge, although they are typically criticised for not being physically justified and not considering uncertainty in the correction. This paper proposes a novel Bayesian bias correction framework that robustly propagates uncertainty and models underlying spatial covariance patterns. Shared latent Gaussian processes are assumed between the in situ observations and climate model output, with the aim of partially preserving the covariance structure from the climate model after bias correction, which is based on well-established physical laws. Results demonstrate added value in modelling shared generating processes under several simulated scenarios, with the most value added for the case of sparse in situ observations and smooth underlying bias. Additionally, the propagation of uncertainty to a simulated final bias-corrected time series is illustrated, which is of key importance to a range of stakeholders, such as climate scientists engaged in impact studies, decision-makers trying to understand the likelihood of particular scenarios and individuals involved in climate change adaption strategies where accurate risk assessment is required for optimal resource allocation. This paper focuses on one-dimensional simulated examples for clarity, although the code implementation is developed to also work on multi-dimensional input data, encouraging follow-on real-world application studies that will further validate performance and remaining limitations. The Bayesian framework supports uncertainty propagation under model adaptations required for specific applications, providing a flexible approach that increases the scope of data assimilation tasks more generally. [ABSTRACT FROM AUTHOR]

Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts on the biosphere. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Bias correction approaches using observational data aim to address this challenge. However, approaches are typically criticised for not being physically justified and not considering uncertainty in the correction. These aspects are particularly important in cases where observations are sparse, such as for weather station data over Antarctica. This paper attempts to address both of these issues through the development of a novel Bayesian hierarchical model for bias prediction. The model propagates uncertainty robustly and uses latent Gaussian process distributions to capture underlying spatial covariance patterns, partially preserving the covariance structure from the climate model which is based on well-established physical laws. The Bayesian framework can handle complex modelling structures and provides an approach that is flexible and adaptable to specific areas of application, even increasing the scope of the work to data assimilation tasks more generally. Results in this paper are presented for one-dimensional simulated examples for clarity, although the method implementation has been developed to also work on multidimensional data as found in most real applications. Performance under different simulated scenarios is examined, with the method providing most value added over alternative approaches in the case of sparse observations and smooth underlying bias. A major benefit of the model is the robust propagation of uncertainty, which is of key importance to a range of stakeholders, from climate scientists engaged in impact studies, decision makers trying to understand the likelihood of particular scenarios and individuals involved in climate change adaption strategies where accurate risk assessment is required for optimal resource allocation. [ABSTRACT FROM AUTHOR]

Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses. [ABSTRACT FROM AUTHOR]

The problem of model complexity has been lively debated in environmental sciences as well as in the forest modelling community. Simple models are less input demanding and their calibration involves a lower number of parameters, but they might be suitable only at local scale. In this work we calibrated a simplified ecosystem process model (PRELES) to data from multiple sites and we tested if PRELES can be used at regional scale to estimate the carbon and water fluxes of Boreal conifer forests. We compared a multi-site (M-S) with site-specific (S-S) calibrations. Model calibrations and evaluations were carried out by the means of the Bayesian method; Bayesian calibration (BC) and Bayesian model comparison (BMC) were used to quantify the uncertainty in model parameters and model structure. To evaluate model performances BMC results were combined with more classical analysis of model-data mismatch (M-DM). Evapotranspiration (ET) and gross primary production (GPP) measurements collected in 10 sites of Finland and Sweden were used in the study. Calibration results showed that similar estimates were obtained for the parameters at which model outputs are most sensitive. No significant differences were encountered in the predictions of the multi-site and site-specific versions of PRELES with exception of a site with agricultural history (Alkkia). Although PRELES predicted GPP better than evapotranspiration, we concluded that the model can be reliably used at regional scale to simulate carbon and water fluxes of Boreal forests. Our analyses underlined also the importance of using long and carefully collected flux datasets in model calibration. In fact, even a single site can provide model calibrations that can be applied at a wider spatial scale, since it covers a wide range of variability in climatic conditions. [ABSTRACT FROM AUTHOR]

We present here a high resolution surface water pH dataset obtained in the Northwest European shelf seas in summer 2011. This is the first time that pH has been measured at such a high spatial resolution (10 measurements h-1) in this region. The aim of our paper is to investigate the carbonate chemistry dynamics of the surface water using pH and ancillary data. The main processes controlling the pH distribution along the ship's transect, and their relative importance, were determined using a statistical approach. The study highlights the impact of biological activity, temperature and riverine inputs on the carbonate chemistry dynamics of the shelf seas surface water. For this summer cruise, the biological activity formed the main control of the pH distribution along the cruise transect. Variations in chlorophyll and nutrients explained 29% of the pH variance along the full transect and as much as 68% in the northern part of the transect. In contrast, the temperature distribution explained ca. 50% of the pH variation in the Skagerrak region. Riverine inputs were evidenced by high dissolved organic carbon (DOC) levels in the Strait of Moyle (northern Irish Sea) and the southern North Sea with consequent remineralisation processes and a reduction in pH. The DOC distribution described 15% of the pH variance along the full transect. This study highlights the high spatial variability of the surface water pH in shelf seawaters where a range of processes simultaneously impacts the carbonate chemistry. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports within the issue on topics including hydrology, geophysical investigations, glacier research and environmental sciences.

The article presents a study which focuses on the use of remote sensing method for retrieving aerosol optical thickness (AOT) over snow using infrared (IR) Advanced Along Track Scanning Radiometer (AATSR). A comparison of the method with the ground-based Aerosol Robotic Network (AERONET) data is highlighted. A case study about the AOT retrieval using the method is also outlined.

Despite the importance of fungi in soil ecosystem services, a theoretical framework that links soil management strategies with fungal ecology is still lacking. One of the key challenges is understanding how the complex geometrical shape of pores in soil affects fungal spread and species interaction. Progress in this area has long been hampered by a lack of experimental techniques for quantification. In this paper we use X-ray computed tomography to quantify and characterize the pore geometry at microscopic scales (30 μm) that are relevant for fungal spread in soil. We analysed the pore geometry for replicated samples with bulk-densities ranging from 1.2-1.6 g/cm3. The bulk-density of soils significantly affected the total volume, mean pore diameter and connectivity of the pore volume. A previously described fungal growth model comprising a minimal set of physiological processes required to produce a range of phenotypic responses was used to analyse the effect of these geometric descriptors on fungal invasion, and we showed that the degree and rate of fungal invasion was affected mainly by pore volume and pore connectivity. The presented experimental and theoretical framework is a significant first step towards understanding how environmental change and soil management impact on fungal diversity in soils. [ABSTRACT FROM AUTHOR]

In the ocean, mesoscale or submesoscale structures and coastal processes are associated with fine scales. The simulation of such features thus requires the hydrodynamic equations to be solved at high-resolution (from a few hundred meters down to a few tens of meters). Therefore, local mesh refinement is a primary issue for regional and coastal modeling. The AGRIF (adaptive grid refinement in Fortran) library is committed to tackling such a challenge for structured grids. It has been implemented in MARS3D (Model for Application at Regional Scale), a semi-implicit, free-surface numerical model developed by Ifremer (the French Research Institute for Exploitation of the Sea) for coastal environmental research and studies. As its time scheme uses an alternating-direction implicit (ADI) algorithm, the two-way nesting implementation differs from the one in explicit models. The present paper describes the specifics of the AGRIF introduction and how the nesting preserves some essential properties (mass, momentum and tracer conservations) along with the induced constraints (bathymetric coherence between grids and increase in computation cost). The use and the performance of this new tool are detailed over two configurations that illustrate the wide range of scales and resolutions typically targeted by coastal applications. The first one is based on multiple high-resolution (500 m) grids that pave the coastal ocean over thousands of kilometers, allowing a continuum between the regional and coastal scales. The second application is more local and has a finer resolution (50 m). It targets a recurrent question for semi-enclosed bays, i.e., the renewal time indicator. Throughout these configurations, the paper intends to compare the two-way nesting method with the traditional one-way approach. It highlights how the MARS3D-AGRIF tool proves to be an efficient way to both improve the physical hydrodynamics and unravel ecological challenges. [ABSTRACT FROM AUTHOR]

The article presents a study which explores various laboratory studies and atmospheric measurements including droplet activation, separation and compositional analysis. An Aerodyne Aerosol Mass Spectrometer was utilized to analyze the residual materials after the droplets produced in a cloud condensation nuclear chamber were evaporated. Particle analysis was conducted using a Laser Mass Spectrometry instrument.

The article presents a study which focuses on the development of the University of Colorado Ground Multi Axis Differential Optical Absorption Spectroscopy (CU GMAX-DOAS) instrument which was established in Pensacola, Florida. The instrument collects various atmospheric compounds including bromine oxide (BrO), iodine oxide (IO) and formaldehyde (HCHO). The root mean square (RMS) values attained by the instrument are highlighted.

The response of ocean phytoplankton community structure to climate change depends upon species competition for nutrients and light, as well as the increase in surface ocean temperature. We propose an analytical framework linking changes in nutrients, temperature and light with changes in phytoplankton growth rates, and we assess our theoretical considerations against model projections (1980-2100) from a global Earth System model. Our proposed "critical nutrient theory" suggests that there is a critical nutrient threshold below (above) which a nutrient change will affect more (less) small phytoplankton biomass than diatom biomass, i.e. the phytoplankton with lower half-saturation coefficient K are influenced more strongly in low nutrient environments. This nutrient threshold broadly corresponds to 45° S and 45° N, poleward of which high vertical mixing and inefficient biology maintain higher surface nutrient concentrations and equatorward of which reduced vertical mixing and more efficient biology maintain lower surface nutrients. In the 45° S-45° N low nutrient region, decreases in limiting nutrients - associated with increased stratification under climate change - are predicted analytically to limit more strongly the net growth of small phytoplankton than the growth of diatoms. In high latitudes, the impact of nutrient decrease on phytoplankton biomass is more significant for diatom biomass than for small phytoplankton biomass, and contributes to diatom declines in the northern marginal sea ice and subpolar biomes. Climate driven increases in surface temperature and changes in light are predicted to have a stronger impact on small phytoplankton than on diatom biomass in all ocean domains. Our analytical predictions explain reasonably well the shifts in community structure under a modeled climate-warming scenario. Further stratification from global warming could result in geographical shifts in the "critical nutrient" threshold and additional changes in ecology. [ABSTRACT FROM AUTHOR]

This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications. [ABSTRACT FROM AUTHOR]

The article presents a study which focuses on the development of a novel algorithm for retrieving black carbon (BC) optical parameters using common thermal-optical (OC/EC) instruments. The analysis of the correlation between the flame ionization detector (FID)-carbon detection and OC/EC laser transmission is emphasized which allows the calculation of mass absorption cross-sections (MACs) for BC. Insights about the properties of BC are also highlighted.

The article focuses on the application of more participative environmental management and social capital in Poland. It notes that the country's low social capital level posed problems when implementing new regulations especially in the environmental policy field. A study of administrative culture and decision-making methods is presented.

The synergies of advances in environmental sensing and modeling and the mainstreaming of immersive technologies lay the foundation for a theoretical grounding of embodied digital twins. Embodied digital twins draw on an established understanding of the importance of place for environmental sciences as well as a paradigmatic shift in the cognitive sciences toward embodied cognition. Nonetheless, the excitement of realizing embodied experiences through immersive technologies such as augmented and virtual reality stands in stark contrast to a lack of consistent terminology and empirical research. In this vision paper, we are proposing to draw more deeply on the theoretical basis of embodied cognition and to establish research frameworks for advancing embodied digital twins. We discuss several examples for turning environmentally sensed and modeled information into high-fidelity immersive experiences and provide a discussion and outlook on critical topics to address. [ABSTRACT FROM AUTHOR]

Understanding soil moisture is essential for earth and environmental sciences especially in geology, hydrology, and meteorology. Remote sensing techniques are widely applied to large-scale monitoring tasks. Among them, DInSAR using multi-temporal spaceborne SAR images is able to derive surface movement up to mm level over an area. One of the factors inducing the movement is variation of soil moisture. Based on this, a semi-empirical approach can be tailored to retrieve the underground water content. However, the derived movement is often contaminated with other irrelevant noise. Besides, a time-series analysis could not be simply implemented without additional fusion and calibration. In this paper, we propose a novel modelling based on advanced DInSAR to solve these problems. The irrelevant noise will be removed as parts of the modelled elements in the DInSAR processing. A forward model on a scene is built by regressing the measured soil moisture on the DInSAR-derived movement series. We tested our approach using Sentinel-1 images in the grasslands of organic soil within State of Brandenburg, Germany. The Pearson correlation coefficients between the measured soil moistures and the DInSAR-derived movements are up to 0.91. The mean square errors of the predicted soil moistures compared with the measurements reach 3.03 % (volumetric water content) at best. Our study shows a promising new concept to develop a global monitoring of soil moisture in the future. [ABSTRACT FROM AUTHOR]

Estimating the impact of different sources of uncertainty along the modelling chain is an important skill graduates are expected to have. Broadly speaking, educators can cover uncertainty in hydrological modelling by differentiating uncertainty in data, model parameters and model structure. This provides students with insights on the impact of uncertainties on modelling results and thus on the usability of the acquired model simulations for decision making. A survey among teachers in the Earth and environmental sciences showed that model structural uncertainty is the least represented uncertainty group in teaching. This paper introduces a computational exercise that introduces students to the basics of model structure uncertainty through two ready-to-use modelling experiments. These experiments require either Matlab or Octave, and use the open-source Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) and the open-source Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) data set. The exercise is short and can easily be integrated into an existing hydrological curriculum, with only a limited time investment needed to introduce the topic of model structure uncertainty and run the exercise. Two trial applications at the Technische Universität Dresden (Germany) showed that the exercise can be completed in two afternoons or four 90 min sessions and that the provided setup effectively transfers the intended insights about model structure uncertainty. [ABSTRACT FROM AUTHOR]

Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km 2 of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, , 10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e,) provide new insights to solve some of the aforementioned issues. [ABSTRACT FROM AUTHOR]

In the ocean, meso / submesoscale structures and coastal processes are associated with fine scales. The simulation of such features thus requires the hydrodynamic equations to be solved at high-resolution (from a few hundred meters down to a few tens of meters). Therefore, local mesh refinement is a primary issue for regional and coastal modelling. As over structured grids, AGRIF (Adaptive Grid Refinement In Fortran) library is committed to tackle this challenge. It has been implemented in MARS3D, which is a numerical model developed by Ifremer (the French research institute for the exploitation of the sea) for coastal environmental researches and studies. The present paper describes how the dedicated implementation preserves some essential principles (mass conservation, constant preserving…) along with the induced constraints. The use and the performance of this new tool are detailed over two configurations that illustrate the wide range of scales and resolutions typically targeted by coastal applications. The first one is based on multiple high-resolution (500m) grids that pave the coastal ocean over thousands of kilometres, allowing a continuum between the regional and coastal scales. The second application is more local and has a finer resolution (50m). It targets a recurrent question for semi-enclosed bays: the renewal time indicator. Throughout these configurations, the paper intends at comparing the two-way nesting method with the traditional one-way approach and highlights how MARS3D-AGRIF tool proves to be an efficient way significantly improve the physical hydrodynamics and bring it biological issues. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau and its surroundings, also known as the Third Pole, play an important role in the global and regional climate and hydrological cycle. Carbonaceous aerosols (CAs), including black carbon (BC) and organic carbon (OC), can directly or indirectly absorb and scatter solar radiation and change the energy balance on the Earth. CAs, along with the other atmospheric pollutants (e.g., mercury), can be frequently transported over long distances into the inland Tibetan Plateau. During the last decades, a coordinated monitoring network and research program named "Atmospheric Pollution and Cryospheric Changes" (APCC) has been gradually set up and continuously operated within the Third Pole regions to investigate the linkage between atmospheric pollutants and cryospheric changes. This paper presents a systematic dataset of BC, OC, water-soluble organic carbon (WSOC), and water-insoluble organic carbon (WIOC) from aerosols (20 stations), glaciers (17 glaciers, including samples from surface snow and ice, snow pits, and 2 ice cores), snow cover (2 stations continuously observed and 138 locations surveyed once), precipitation (6 stations), and lake sediment cores (7 lakes) collected across the Third Pole, based on the APCC program. These data were created based on online (in situ) and laboratory measurements. High-resolution (daily scale) atmospheric-equivalent BC concentrations were obtained by using an Aethalometer (AE-33) in the Mt. Everest (Qomolangma) region, which can provide new insight into the mechanism of BC transportation over the Himalayas. Spatial distributions of BC, OC, WSOC, and WIOC from aerosols, glaciers, snow cover, and precipitation indicated different features among the different regions of the Third Pole, which were mostly influenced by emission sources, transport pathways, and deposition processes. Historical records of BC from ice cores and lake sediment cores revealed the strength of the impacts of human activity since the Industrial Revolution. BC isotopes from glaciers and aerosols identified the relative contributions of biomass and fossil fuel combustion to BC deposition on the Third Pole. Mass absorption cross sections of BC and WSOC from aerosol, glaciers, snow cover, and precipitation samples were also provided. This updated dataset is released to the scientific communities focusing on atmospheric science, cryospheric science, hydrology, climatology, and environmental science. The related datasets are presented in the form of excel files. BC and OC datasets over the Third Pole are available to download from the National Cryosphere Desert Data Center (10.12072/ncdc.NIEER.db0114.2021; Kang and Zhang, 2021). [ABSTRACT FROM AUTHOR]

Citizen science is an efficient tool for data collection for scientific projects. In the framework of SIMILE Interreg Italy-Switzerland project, the citizen's contribution to monitor the quality of lakes water has been envisaged. In the initial phase of this research, state of art of citizen science and water quality monitoring was investigated. The analysis of past and current projects, governed by different organizations and communities, pointed out a variety of tasks that can be accomplished by citizens. In these studies, authors stressed suitability of smartphones for the fulfilment of various assignments given to citizens. Due to that, in this research, different smartphone applications for water quality monitoring were tested and analyzed. Despite the fact that many applications for water quality monitoring are freely available, none of them is open source. Hence, this paper is proposing the design of a new application, for the purposes of SIMILE project, that will be free and open source, addressing not just users but also developers giving them a possibility for customization and improvement. [ABSTRACT FROM AUTHOR]

Particle acidity (aerosol pH) is an important driver of atmospheric chemical processes and the resulting effects on human and environmental health. Understanding the factors that control aerosol pH is critical when enacting control strategies targeting specific outcomes. This study characterizes aerosol pH at a land–water transition site near Baltimore, MD, during summer 2018 as part of the second Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaign. Inorganic fine-mode aerosol composition, gas-phase NH3 measurements, and all relevant meteorological parameters were used to characterize the effects of temperature, aerosol liquid water (ALW), and composition on predictions of aerosol pH. Temperature, the factor linked to the control of NH3 partitioning, was found to have the most significant effect on aerosol pH during OWLETS-2. Overall, pH varied with temperature at a rate of - 0.047 K-1 across all observations, though the sensitivity was - 0.085 K-1 for temperatures > 293 K. ALW had a minor effect on pH, except at the lowest ALW levels (< 1 µgm-3), which caused a significant increase in aerosol acidity (decrease in pH). Aerosol pH was generally insensitive to composition (SO42- , SO42-:NH4+ , total NH3 (Tot- NH3) = NH3 + NH4+), consistent with recent studies in other locations. In a companion paper, the sources of episodic NH3 events (95th percentile concentrations, NH3 > 7.96 µgm-3) during the study are analyzed; aerosol pH was higher by only ∼ 0.1–0.2 pH units during these events compared to the study mean. A case study was analyzed to characterize the response of aerosol pH to nonvolatile cations (NVCs) during a period strongly influenced by primary Chesapeake Bay emissions. Depending on the method used, aerosol pH was estimated to be either weakly (∼ 0.1 pH unit change based on NH3 partitioning calculation) or strongly (∼ 1.4 pH unit change based on ISORROPIA thermodynamic model predictions) affected by NVCs. The case study suggests a strong pH gradient with size during the event and underscores the need to evaluate assumptions of aerosol mixing state applied to pH calculations. Unique features of this study, including the urban land–water transition site and the strong influence of NH3 emissions from both agricultural and industrial sources, add to the understanding of aerosol pH and its controlling factors in diverse environments. [ABSTRACT FROM AUTHOR]

Methods for estimating mean transit times from chemical or isotopic tracers (such as Cl-, δ18O, or δ²H) commonly assume that catchments are stationary (i.e., time-invariant) and homogeneous. Real catchments are neither. In a companion paper, I showed that catchment mean transit times estimated from seasonal tracer cycles are highly vulnerable to aggregation error, exhibiting strong bias and large scatter in spatially heterogeneous catchments. I proposed the young water fraction, which is virtually immune to aggregation error under spatial heterogeneity, as a better measure of transit times. Here I extend this analysis by exploring how nonstationarity affects mean transit times and young water fractions estimated from seasonal tracer cycles, using benchmark tests based on a simple two-box model. The model exhibits complex nonstationary behavior, with striking volatility in tracer concentrations, young water fractions, and mean transit times, driven by rapid shifts in the mixing ratios of fluxes from the upper and lower boxes. The transit-time distribution in streamflow becomes increasingly skewed at higher discharges, with marked increases in the young water fraction and decreases in the mean water age, reflecting the increased dominance of the upper box at higher flows. This simple two-box model exhibits strong equifinality, which can be partly resolved by simple parameter transformations. However, transit times are primarily determined by residual storage, which cannot be constrained through hydrograph calibration and must instead be estimated by tracer behavior. Seasonal tracer cycles in the two-box model are very poor predictors of mean transit times, with typical errors of several hundred percent. However, the same tracer cycles predict time-averaged young water fractions (Fyw) within a few percent, even in model catchments that are both nonstationary and spatially heterogeneous (although they may be biased by roughly 0.1–0.2 at sites where strong precipitation seasonality is correlated with precipitation tracer concentrations). Flow-weighted fits to the seasonal tracer cycles accurately predict the flow-weighted average Fyw in streamflow, while unweighted fits to the seasonal tracer cycles accurately predict the unweighted average Fyw. Young water fractions can also be estimated separately for individual flow regimes, again with a precision of a few percent, allowing direct determination of how shifts in a catchment's hydraulic regime alter the fraction of water reaching the stream by fast flowpaths. One can also estimate the chemical composition of idealized "young water" and "old water" end-members, using relationships between young water fractions and solute concentrations across different flow regimes. These results demonstrate that mean transit times cannot be estimated reliably from seasonal tracer cycles and that, by contrast, the young water fraction is a robust and useful metric of transit times, even in catchments that exhibit strong nonstationarity and heterogeneity. [ABSTRACT FROM AUTHOR]

This study characterizes the impact of the Chesapeake Bay and associated meteorological phenomena on aerosol chemistry during the second Ozone Water-Land Environmental Transition Study (OWLETS-2) field campaign, which took place from 4 June to 5 July 2018. Measurements of inorganic PM 2.5 composition, gas-phase ammonia (NH 3), and an array of meteorological parameters were undertaken at Hart-Miller Island (HMI), a land–water transition site just east of downtown Baltimore on the Chesapeake Bay. The observations at HMI were characterized by abnormally high NH 3 concentrations (maximum of 19.3 µgm-3 , average of 3.83 µgm-3), which were more than a factor of 3 higher than NH 3 levels measured at the closest atmospheric Ammonia Monitoring Network (AMoN) site (approximately 45 km away). While sulfate concentrations at HMI agreed quite well with those measured at a regulatory monitoring station 45 km away, aerosol ammonium and nitrate concentrations were significantly higher, due to the ammonia-rich conditions that resulted from the elevated NH 3. The high NH 3 concentrations were largely due to regional agricultural emissions, including dairy farms in southeastern Pennsylvania and poultry operations in the Delmarva Peninsula (Delaware–Maryland–Virginia). Reduced NH 3 deposition during transport over the Chesapeake Bay likely contributed to enhanced concentrations at HMI compared to the more inland AMoN site. Several peak NH 3 events were recorded, including the maximum NH 3 observed during OWLETS-2, that appear to originate from a cluster of industrial sources near downtown Baltimore. Such events were all associated with nighttime emissions and advection to HMI under low wind speeds (< 1 ms-1) and stable atmospheric conditions. Our results demonstrate the importance of industrial sources, including several that are not represented in the emissions inventory, on urban air quality. Together with our companion paper, which examines aerosol liquid water and pH during OWLETS-2, we highlight unique processes affecting urban air quality of coastal cities that are distinct from continental locations. [ABSTRACT FROM AUTHOR]

The China-Europe Railway Express (CER-Express), not only promoted the cooperation between countries and regions across Asia and Europe but also gave rise to remarkable changes in landcover and had a profound effect on the natural environment along the railway in recent years. Effective ways to monitor and assess ecological changes are urgently needed to ensure sustainable development of CER-Express. There are very few existing environmental monitoring studies focusing on the area along the CERExpress. In this paper, we present a study of environmental degradation, which occurred during the construction and operation of CER-Express from 2010 to 2018, based on a comprehensive evaluation index (CEI), which takes three environmental indicators into account and provides a timely and reliable evaluation of environmental changes at large scales. In addition, the environment conditions of the regions and countries along the CER-Express have been quantified and comparatively studied at different scales over different periods, using histograms of mean CEI values. Furthermore, specific causes of environmental degradation in the rail-intensive countries and small-area countries along the railway are discussed. Our results show that the environmental degradation can be detected in most of the rail-intensive countries, such as Germany, Poland, Austria, and Czech Republic along the railway. Therefore, to ensure sustainability of the CER-Express, environmental protection along the railway should be paid more attention to and a reasonable arrangement for the exploitation of CER-Express devised. [ABSTRACT FROM AUTHOR]

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem wherein the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties, e.g., in measurements or atmospheric transport modeling; hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information: the prior source term, also known as the first guess, and regularization parameters for the shape of the source term. While the first guess is based on information independent of the measurements, such as the physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term and regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3. [ABSTRACT FROM AUTHOR]

Summarizing information from large bodies of scientific literature is an essential but work-intensive task. This is especially true in environmental studies where multiple factors (e.g., soil, climate, vegetation) can contribute to the effects observed. Meta-analyses, studies that quantitatively summarize findings of a large body of literature, rely on manually curated databases built upon primary publications. However, given the increasing amount of literature, this manual work is likely to require more and more effort in the future. Natural language processing (NLP) facilitates this task, but it is not clear yet to which extent the extraction process is reliable or complete. In this work, we explore three NLP techniques that can help support this task: topic modeling, tailored regular expressions and the shortest dependency path method. We apply these techniques in a practical and reproducible workflow on two corpora of documents: the Open Tension-disk Infiltrometer Meta-database (OTIM) and the Meta corpus. The OTIM corpus contains the source publications of the entries of the OTIM database of near-saturated hydraulic conductivity from tension-disk infiltrometer measurements (https://github.com/climasoma/otim-db , last access: 1 March 2023). The Meta corpus is constituted of all primary studies from 36 selected meta-analyses on the impact of agricultural practices on sustainable water management in Europe. As a first step of our practical workflow, we identified different topics from the individual source publications of the Meta corpus using topic modeling. This enabled us to distinguish well-researched topics (e.g., conventional tillage, cover crops), where meta-analysis would be useful, from neglected topics (e.g., effect of irrigation on soil properties), showing potential knowledge gaps. Then, we used tailored regular expressions to extract coordinates, soil texture, soil type, rainfall, disk diameter and tensions from the OTIM corpus to build a quantitative database. We were able to retrieve the respective information with 56 % up to 100 % of all relevant information (recall) and with a precision between 83 % and 100 %. Finally, we extracted relationships between a set of drivers corresponding to different soil management practices or amendments (e.g., "biochar", "zero tillage") and target variables (e.g., "soil aggregate", "hydraulic conductivity", "crop yield") from the source publications' abstracts of the Meta corpus using the shortest dependency path between them. These relationships were further classified according to positive, negative or absent correlations between the driver and the target variable. This quickly provided an overview of the different driver–variable relationships and their abundance for an entire body of literature. Overall, we found that all three tested NLP techniques were able to support evidence synthesis tasks. While human supervision remains essential, NLP methods have the potential to support automated evidence synthesis which can be continuously updated as new publications become available. [ABSTRACT FROM AUTHOR]

Art–science partnerships offer valuable opportunities to enhance inclusive engagement with research through collaborative creative practice. Here, we present two case studies of interdisciplinary approaches to contextualising environmental science for wider audiences. We synthesise lessons learnt from these case studies and associated stakeholders to provide advice for conducting successful art–science collaborations that help to broaden interactions with environmental geoscience research. [ABSTRACT FROM AUTHOR]

This paper reports on Intertidal – a collaborative project to demonstrate integrated workflows to 3D spatial data infrastructure (SDI), simulations and geovisual interfaces - as integrated approaches to support the 3D characterization of coastal morphology, intertidal dynamics, potential sea level rise, and mitigation responses to them. Specifically, this project emphasized the potential of emerging 3D data, new analytical visualization methods, and emerging 3D interface technologies as ingredients of emerging and future environmental data science and visualization practice of coastal/intertidal environments. [ABSTRACT FROM AUTHOR]

Disasters including flash floods, earthquakes, and landslides have huge economic and social losses besides their impact on environmental disruption. Studying environmental changes due to climate change can improve public and expert sector's awareness and response towards future disastrous events. Synthetic Aperture Radar (SAR) data and Interferometric Synthetic Aperture Radar (InSAR) technologies are valuable tools for flood modeling and surface deformation modeling. This paper proposes an efficient approach to detect the flooded area changes using Sentinel-1A over Ramsar flood on 5th October 2018. For detection of the flooded area due to flash flood SARPROZ in MATLAB programming language is used and discussed. Flooded areas in Ramsar are detected based on the change detection modeling using normalized difference values of amplitude belonging to the master image (on 28th September 2018) and the slave image (on 10th October 2018). [ABSTRACT FROM AUTHOR]

Research projects now rely on an array of different channels to increase impact, including high-level scientific output, tools, and equipment, but also communication, outreach, and educational activities. This paper focuses on education for children aged 5-12 years and presents activities that aim to help them (and their teachers) grasp some of the complex underlying issues in environmental science. More generally, it helps children to become familiarized with science and scientists, with the aim to enhance scientific culture and promote careers in this field. The activities developed are focused on rainfall: (a) designing and using a disdrometer to observe the variety of drop sizes; (b) careful recording of successive dry and rainy days and reproducing patterns using a simple model based on fractal random multiplicative cascades; and (c) collaboratively writing a children's book about rainfall. These activities are discussed in the context of current state-of-the-art pedagogical practices and goals set by project funders, especially in a European Union framework. [ABSTRACT FROM AUTHOR]

The impact of large dams is a popular topic in environmental science, but the importance of altered water quality as a driver of ecological impacts is often missing from such discussions. This is partly because information on the relationship between dams and water quality is relatively sparse and fragmentary, especially for low-latitude developing countries where dam building is now concentrated. In this paper, we review and synthesize information on the effects of damming on water quality with a special focus on low latitudes. We find that two ultimate physical processes drive most water quality changes: the trapping of sediments and nutrients, and thermal stratification in reservoirs. Since stratification emerges as an important driver and there is ambiguity in the literature regarding the stratification behavior of water bodies in the tropics, we synthesize data and literature on the 54 largest low-latitude reservoirs to assess their mixing behavior using three classification schemes. Direct observations from literature as well as classifications based on climate and/or morphometry suggest that most, if not all, low-latitude reservoirs will stratify on at least a seasonal basis. This finding suggests that low-latitude dams have the potential to discharge cooler, anoxic deep water, which can degrade downstream ecosystems by altering thermal regimes or causing hypoxic stress. Many of these reservoirs are also capable of efficient trapping of sediments and bed load, transforming or destroying downstream ecosystems, such as floodplains and deltas. Water quality impacts imposed by stratification and sediment trapping can be mitigated through a variety of approaches, but implementation often meets physical or financial constraints. The impending construction of thousands of planned low-latitude dams will alter water quality throughout tropical and subtropical rivers. These changes and associated environmental impacts need to be better understood by better baseline data and more sophisticated predictors of reservoir stratification behavior. Improved environmental impact assessments and dam designs have the potential to mitigate both existing and future potential impacts. [ABSTRACT FROM AUTHOR]

Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of environmental studies as it provides a suite of tools and access to data that facilitate large-scale environmental monitoring projects through its powerful parallel processing capabilities. In this study, we use GEE to access multi-source remote sensing datasets and implement an object-based image analysis, and random forest algorithm for the classification of wetlands in the state of Minnesota. Emergent, forested, and scrub-shrub wetland classes, water, as well as urban, forest, and agriculture land cover types were classified using Sentinel-2, Sentinel-1, USGS 3D Elevation Program 10-meter DEM, and gridded soil data. NDVI, EVI, BSI, NDBI, and NDWI spectral indices were calculated from Sentinel-2 imagery, VV and VH polarization channels, and their ratio, as well as span parameters, were calculated from Sentinel-1 imagery, and slope and aspect features were extracted from DEM. Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), Principal Components Analysis (PCA), and random forest algorithms were implemented to classify wetlands from the GEE platform. Emergent wetlands, water, urban, and agriculture classes performed well with producer accuracies greater than 90%. Sentinel-1, DEM, and soil datasets improve the identification of wetland classes and highlight the importance of multi-source approaches for wetland mapping. [ABSTRACT FROM AUTHOR]

Long-term environmental research networks are one approach to advancing local, regional, and global environmental science and education. A remarkable number and wide variety of environmental research networks operate around the world today. These are diverse in funding, infrastructure, motivating questions, scientific strengths, and the sciences that birthed and maintain the networks. Some networks have individual sites that were selected because they had produced invaluable long-term data, while other networks have new sites selected to span ecological gradients. However, all long-term environmental networks share two challenges. Networks must keep pace with scientific advances and interact with both the scientific community and society at large. If networks fall short of successfully addressing these challenges, they risk becoming irrelevant. The objective of this paper is to assert that the biogeosciences offer environmental research networks a number of opportunities to expand scientific impact and public engagement. We explore some of these opportunities with four networks: the International Long-Term Ecological Research Network programs (ILTERs), critical zone observatories (CZOs), Earth and ecological observatory networks (EONs), and the FLUXNET program of eddy flux sites. While these networks were founded and expanded by interdisciplinary scientists, the preponderance of expertise and funding has gravitated activities of ILTERs and EONs toward ecology and biology, CZOs toward the Earth sciences and geology, and FLUXNET toward ecophysiology and micrometeorology. Our point is not to homogenize networks, nor to diminish disciplinary science. Rather, we argue that by more fully incorporating the integration of biology and geology in long-term environmental research networks, scientists can better leverage network assets, keep pace with the ever-changing science of the environment, and engage with larger scientific and public audiences. [ABSTRACT FROM AUTHOR]

Access to mountainous regions for monitoring streamflow, snow and glaciers is often difficult, and many rivers are thus not gauged and hydrological measurements are limited. Consequently, cold-region watersheds, particularly heavily glacierized ones, are poorly represented in large-sample hydrology (LSH) datasets. We present a new LSH dataset for Iceland, termed LamaH-Ice (LArge-SaMple DAta for Hydrology and Environmental Sciences for Iceland). Glaciers and ice caps cover about 10 % of Iceland and, while streamflow has been measured for several decades, these measurements have not previously been published in a consistent manner. The dataset provides daily and hourly hydrometeorological time series and catchment characteristics for 107 river basins in Iceland, covering an area of almost 46 000 km2 (45 % of Iceland's area), with catchment sizes ranging from 4 to 7500 km2. LamaH-Ice conforms to the structure of existing LSH datasets and includes most variables contained in these datasets as well as additional information relevant to cold-region hydrology, e.g., time series of snow cover, glacier mass balance and albedo. LamaH-Ice also includes dynamic catchment characteristics to account for changes in land cover, vegetation and glacier extent. A large majority of the watersheds in LamaH-Ice are not subject to human activities, such as diversions and flow regulations. Streamflow measurements under natural flow conditions are highly valuable to hydrologists seeking to model and comprehend the natural hydrological cycle or estimate climate change trends. The LamaH-Ice dataset (Helgason and Nijssen, 2024) is intended for the research community to improve the understanding of hydrology in cold-region environments. LamaH-Ice is publicly available on HydroShare at https://doi.org/10.4211/hs.86117a5f36cc4b7c90a5d54e18161c91 (Helgason and Nijssen, 2024). [ABSTRACT FROM AUTHOR]

The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC). [ABSTRACT FROM AUTHOR]

The purpose of this paper is to provide a method for perturbing Weather Generators (WGs) for future decades and to assess its effectiveness. Here the procedure is applied to the WG implemented within the UKCP09 package and assessed using data from a Regional Climate Model (RCM) simulation which provides a significant "climate change" between a control run period and a distant future. The WG is normally calibrated on observed data. For this study, data from an RCM control period (1961-1990) was used, then perturbed using the procedure. Because only monthly differences between the RCM control and scenario periods are used to perturb the WG, the direct daily RCM scenario may be considered as unseen data to assess how well the perturbation procedure reproduces the direct RCM simulations for the future. [ABSTRACT FROM AUTHOR]

Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) and its surroundings, known as the Third Pole, play an important role in the regional and global climate and hydrological cycle. Carbonaceous aerosols (CAs), including black carbon (BC) and organic carbon (OC), can directly/indirectly absorb and scatter solar radiation, and change the energy balance on Earth. CAs, along with other atmospheric pollutants (e.g., mercury), can frequently be transported over long distances into the inland TP. During the last decade, a coordinated monitoring network and research program on Atmospheric Pollution and Cryospheric Change (APCC) has been gradually setup and continuously operated within the Third Pole regions to investigate the linkage between atmospheric pollutants and cryospheric change. This paper presents a systematic dataset of BC, OC, water soluble organic carbon (WSOC), and water insoluble organic carbon (WIOC) from aerosols (19 stations), glaciers (17 glaciers, including samples from surface snow/ice, snowpit, and two ice cores), snow cover (2 stations continuous observed, and 138 sites surveyed), precipitation (6 stations), and lake sediment cores (7 lakes) collected across the TP and its surroundings, as the first dataset released from this APCC program. These data were created based on online (in-situ) and laboratory measurements. High-resolution (daily scale) atmospheric equivalent BC (eBC) concentrations were obtained by using an aethalometer (AE-33) in the Mt. Everest (Qomolangma) region, which can provide a new insight into the mechanism of BC transportation over the Himalayas. Spatial distributions of BC, OC, WSOC and WIOC from aerosols, glaciers, snow cover, and precipitation indicated different features among the different regions of the TP, which were mostly influenced by emission sources, transport, and deposition processes. Several hundred years of refractory BC (rBC) records from ice cores and BC from lake sediment cores revealed the strength of human activities since the industrial revolution. BC isotopes from glaciers and aerosols identified the relative contributions of biomass and fossil fuel combustion to BC deposition on the Himalayas and TP. Mass absorption cross section of BC and WSOC from aerosol, glaciers, snow cover, and precipitation samples were also provided. This updated dataset is released to the scientific communities focusing on atmospheric science, cryospheric science, hydrology, climatology and environmental science. The related datasets are presented in the form of excel files. These files are available to download from the State Key Laboratory of Cryosphere Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences at Lanzhou https://doi.org/10.12072/ncdc.NIEER.db0114.2021, Kang and Zhang, 2021). In the future, datasets of mercury, heavy metals, and POPs will be reported. [ABSTRACT FROM AUTHOR]

Chen et al. (2022) analyzed the event of rapid nocturnal O 3 enhancement (NOE) observed on 31 July 2021 at the surface level in the North China Plain and proposed transport of substantial stratosphere ozone to the surface by Typhoon In-fa followed by downdraft of shallow convection as the mechanism of the NOE event. The analysis seems to be valid from the viewpoint of atmospheric physics. This comment revisits the NOE phenomenon on the basis of the China National Environmental Monitoring Center (CNEMC) network data used in Chen et al. (2022), together with the CNEMC data from Zibo (ZB) and O 3 , NO x , PAN (peroxyacetic nitric anhydride), and VOC (volatile organic compound) data from the Zibo supersite operated by the China Research Academy of Environmental Sciences (CRAES). We found (a) O x (O 3 + NO 2) levels during the NOE period approaching those of O 3 during 14:00–17:00 LT, (b) levels of PAN and the relationship between O 3 and PAN consistent with dominance of chemical and physical processes within the boundary layer, and (c) estimated photochemical ages of air mass shorter than 1 d and showing no drastic increases during the NOE. We argue that the NOE was not caused by typhoon-induced stratospheric intrusion but originated from fresh photochemical production in the lower troposphere. Our argument is well supported by the analysis of atmospheric transport as well as ground-based remote sensing data. [ABSTRACT FROM AUTHOR]

Satellite-based estimations of dry-air column-averaged mixing ratios of methane (XCH4) contribute to a better understanding of changes in CH4 emission sources and variations in its atmospheric growth rates. High accuracy of the satellite measurements is required, and therefore, extensive validation is performed, mainly against the Total Carbon Column Observing Network (TCCON). However, validation opportunities at open-ocean areas outside the coastal regions are sparse. We propose a new approach to assess the accuracy of satellite-derived XCH4 trends and variations. We combine various ship and aircraft observations with the help of atmospheric chemistry models, mainly used for the stratospheric column, to derive observation-based XCH4 (obs. XCH4). Based on our previously developed approach for the application to XCO2 , we investigated three different advancements, from a simple approach to more elaborate approaches (approaches 1, 2, and 3), to account for the higher tropospheric and stratospheric variability in CH4 as compared to CO2. Between 2014 and 2018, at 20–40° N of the western Pacific, we discuss the uncertainties in the approaches and the derived obs. XCH4 within 10° by 20° latitude–longitude boxes. Uncertainties were 22 ppb (parts per billion) for approach 1, 20 ppb for approach 2, and 16 ppb for approach 3. We analyzed the consistency with the nearest TCCON stations and found agreement of approach 3 with Saga of 1±12 ppb and -1±11 ppb with Tsukuba for the northern and southern latitude box, respectively. Furthermore, we discuss the impact of the modeled stratospheric column on the derived obs. XCH4 by applying three different models in our approaches. Depending on the models, the difference can be more than 12 ppb (0.6 %), showing the importance for the appropriate choice. We show that our obs. XCH4 dataset accurately captures seasonal variations in CH4 over the ocean. Using different retrievals of the Greenhouse Gases Observing Satellite (GOSAT) from the National Institute for Environmental Studies (NIES), the RemoTeC full-physics retrieval operated at the Netherlands Institute for Space Research (SRON), and the full-physics retrieval of the University of Leicester (UoL-OCFP), we demonstrate the applicability of the dataset for satellite evaluation. The comparison with results of approach 3 revealed that NIES showed a difference of - 0.04 ± 13 ppb and strong scatter at 20–30° N, while RemoTeC and OCFP have a rather systematic negative bias of - 12.1 ± 8.1 and - 10.3 ± 9.6 ppb. Our new approach to derive XCH4 reference datasets over the ocean can contribute to the validation of existing and upcoming satellite missions in future. [ABSTRACT FROM AUTHOR]

The presented study investigates the efficiency of heterogeneous ice formation in natural clouds over Lauder, New Zealand / Aotearoa. Aerosol conditions in the middle troposphere above Lauder are subject to huge contrasts. Clean, pristine air masses from Antarctica and the Southern Ocean arrive under southerly flow conditions, while high aerosol loads can occur when air masses are advected from nearby Australia. This study assesses how these contrasts in aerosol load affect the ice formation efficiency in stratiform midlevel clouds in the heterogeneous freezing range (- 40 to 0 ∘C). For this purpose, an 11-year dataset was analyzed from a dual-wavelength polarization lidar system operated by National Institute of Water and Atmospheric Research (NIWA), Taihoro Nukurangi, at Lauder in collaboration with the National Institute for Environmental Studies in Japan and the Meteorological Research Institute of the Japan Meteorological Agency. These data were used to investigate the efficiency of heterogeneous ice formation in clouds over the site as a function of cloud-top temperature as in previous studies at other locations. The Lauder cloud dataset was put into context with lidar studies from contrasting regions such as Germany and southern Chile. The ice formation efficiency found at Lauder is lower than in polluted midlatitudes (i.e., Germany) but higher than, for example, in southern Chile. Both Lauder and southern Chile are subject to generally low free-tropospheric aerosol loads, which suggests that the low ice formation efficiency at these two sites is related to low ice-nucleating-particle (INP) concentrations. However, Lauder sees episodes of continental aerosol, more than southern Chile does, which seems to lead to the moderately increased ice formation efficiency. Trajectory-based tools and aerosol model reanalyses are used to relate this cloud dataset to the aerosol load and the air mass sources. Both analyses point clearly to higher ice formation efficiency for clouds which are more strongly influenced by continental aerosol and to lower ice formation efficiency for clouds which are more influenced by Antarctic/marine aerosol and air masses. [ABSTRACT FROM AUTHOR]

The high potential of nuclear power to combat climate change and support sustainable development plays a critical role in achieving the goal of the Paris Agreement. Moreover, with the ever-increasing demand for electricity, it is essential to consider nuclear power as a reliable source of energy. However, it should be noted that although the risk of a radiological accident is small, it always exists, and therefore it is crucial to have a complete understanding of the management of nuclear power. Herein, we present a statistical review of nuclear accident research at Chernobyl and Fukushima Nuclear Power Plants using bibliometric analysis to identify key patterns in scientific results and current issues in accident research. More than 10,000 articles have been collected from the SCOPUS database covering the periods 1986–2022 and 2011–2022 on Chernobyl and Fukushima, respectively. The results were obtained from two perspectives: first, we identified stages through which pre- and post-accident researches have evolved, and then we analysed the spatial correlation between energy-economic performance and scientific literature for the leading productive countries. From the analysis of research trends, it was found that the number of articles increased sharply immediately after the accident and slightly decreased over time. Among the most attractive subject categories in terms of the largest number of publications and received citations were environmental sciences, medicine and energy policy. The productive country ranking was mostly topped by the country in which the accident occurred. Spatial statistical analysis revealed a strong correlation between the scientific productivity and energy-economic performance of the leading countries. [ABSTRACT FROM AUTHOR]

Using numerical models which require large meteorological data sets is sometimes difficult and problems can often be traced back to the Input/Output functionality. Complex models are usually developed by the environmental sciences community with a focus on the core modelling issues. As a consequence, the I/O routines that are costly to properly implement are often error-prone, lacking flexibility and robustness. With the increasing use of such models in operational applications, this situation ceases to be simply uncomfortable and becomes a major issue. The MeteoIO library has been designed for the specific needs of numerical models that require meteorological data. The whole task of data preprocessing has been delegated to this library, namely retrieving, filtering and resampling the data if necessary as well as providing spatial interpolations and parameterizations. The focus has been to design an Application Programming Interface (API) that (i) provides a uniform interface to meteorological data in the models, (ii) hides the complexity of the processing taking place, and (iii) guarantees a robust behaviour in the case of format errors, erroneous or missing data. Moreover, in an operational context, this error handling should avoid unnecessary interruptions in the simulation process. A strong emphasis has been put on simplicity and modularity in order to make it extremely easy to support new data formats or protocols and to allow contributors with diverse backgrounds to participate. This library is also regularly evaluated for computing performance and further optimized where necessary. Finally, it is released under an Open Source license and is available at http://models.slf.ch/p/meteoio. This paper gives an overview of the MeteoIO library from the point of view of conceptual design, architecture, features and computational performance. A scientific evaluation of the produced results is not given here since the scientific algorithms that are used have already been published elsewhere. [ABSTRACT FROM AUTHOR]

Estimation of the temporal profile of an atmospheric release, also called the source term, is an important problem in environmental sciences. The problem can be formalized as a linear inverse problem where the unknown source term is optimized to minimize the difference between the measurements and the corresponding model predictions. The problem is typically ill-posed due to low sensor coverage of a release and due to uncertainties e.g. in measurements or atmospheric transport modeling, hence, all state-of-the-art methods are based on some form of regularization of the problem using additional information. We consider two kinds of additional information, the prior source term, also known as the first guess, and regularization parameters for shape of the source term. While the first guess is based on information independent of the measurements, such as physics of the potential release or previous estimations, the regularization parameters are often selected by the designers of the optimization procedure. In this paper, we provide a sensitivity study of two inverse methodologies on the choice of the prior source term as well as regularization parameters of the methods. The sensitivity is studied in two cases: data from the European Tracer Experiment (ETEX) using FLEXPART v8.1 and the caesium-134 and caesium-137 dataset from the Chernobyl accident using FLEXPART v10.3. [ABSTRACT FROM AUTHOR]

Hydro-meteorological hazard is often estimated by university-based scientists using publicly funded climate models, whilst the ensuing risk quantification uses proprietary insurance sector models, which can inhibit the effective translation of risk-related environmental science into modified practice or policy. For co-occurring hazards, this work proposes as an interim solution open R-code that deploys a metric (i.e., correlation coefficient r) obtainable from scientific research, usable in practice without restricted data (climate or risk) being exposed. This tool is evaluated for a worked example that estimates the impact on joint risk at an annual 1-in-200 year level of wet and windy weather in the UK co-occurring rather than being independent, and the approach can be applied to other multi-hazards and compound events in various sectors (e.g. road, rail, telecommunications). [ABSTRACT FROM AUTHOR]

Human actions induce and modify droughts. However, scientific gaps remain with respect to how hydrological processes, anthropogenic dynamics, and individuals' perceptions of impacts are intrinsically entangled in drought occurrence and evolution. This adds complexity to drought assessment studies that cannot be addressed by the natural and environmental sciences alone. Furthermore, it poses a challenge with respect to developing ways to evaluate human behaviour and its pattern of co-evolution with the hydrological cycle – mainly related to water use and landscape modifications. During fieldwork in Brazil, we observed how drought impacts were experienced by people who were exposed to a multi-year drought. Evaluating our data, it appeared that prospect theory, a behavioural economic theory that is usually applied to explain decision-making processes under uncertainty, has explanatory power regarding what we observed in the field. Therefore, we propose an interdisciplinary approach to improve the understanding of drought impact emergence using this theory. When employing prospect theory in this context, drought impacts are considered failed welfare expectations ("prospects") due to water shortage. A shifting baseline after prolonged exposure to drought can therefore mitigate experienced drought impacts. We demonstrate that this theory can also contribute to explaining socio-hydrological phenomena, such as reservoir effects. This new approach can help bridge natural science and social science perspectives, resulting in integrated drought management that considers the local context. [ABSTRACT FROM AUTHOR]

The presented study investigates the efficiency of heterogeneous ice formation in natural clouds over Lauder, New Zealand/Aotearoa. Aerosol conditions in the middle troposphere above Lauder are subject to huge contrasts. Clean, pristine airmasses from Antarctica and the Southern Ocean arrive under southerly flow conditions while high aerosol loads can occur when air masses are advected from nearby Australia. This study assesses how these contrasts in aerosol load affect the ice formation efficiency in stratiform midlevel clouds in the heterogeneous freezing range (−40 °C to 0 °C). For this purpose, an 11-year dataset was analyzed from a dual-wavelength polarization lidar system operated by National Institute of Water & Atmospheric Research (NIWA) at Lauder in collaboration with the National Institute for Environmental Studies in Japan and the Meteorological Research Institute of the Japan Meteorological Agency. These data were used to investigate the efficiency of heterogeneous ice formation in clouds over the site as a function of cloud-top temperature as in previous studies at other locations. The Lauder cloud dataset was put into context with lidar studies from contrasting regions such as Germany and southern Chile. The ice formation efficiency found at Lauder is lower than in polluted mid-latitudes (i.e., Germany) but higher than for example in southern Chile. Both, Lauder and southern Chile are subject to generally low free-tropospheric aerosol loads, which suggests that the low ice formation efficiency at these two sites is related to low ice-nucleating particle (INP) concentrations. However, Lauder sees episodes of continental aerosol, more than does southern Chile, which seems to lead to the moderately increased ice formation efficiency. Trajectory-based tools and aerosol model re-analyses are used to relate this cloud dataset to the aerosol load and the air mass sources. Both analyses point clearly to higher ice formation efficiency for clouds which are more strongly influenced by continental aerosol, and to lower ice formation efficiency for clouds which are more influenced by Antarctic/marine aerosol and air masses. [ABSTRACT FROM AUTHOR]

Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO2) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by retrieval uncertainties and insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented hourly resolution during the daytime. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO–GEMS (where POMINO is the Peking University OMI NO2 algorithm). We develop a hybrid retrieval method combining GEMS, TROPOMI (TROPOspheric Monitoring Instrument) and GEOS-CF (Global Earth Observing System Composition Forecast) data to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for surface reflectance anisotropy and aerosol optical effects through parallelized pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O2 – O2 algorithm by using ancillary parameters consistent with those used in NO2 AMF calculations. The initial retrieval of POMINO–GEMS tropospheric NO2 VCDs for June–August 2021 exhibits strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO–GEMS NO2 VCDs agree with the POMINO–TROPOMI v1.2.2 product (R=0.98 ; NMB = 4.9 %) over East Asia, with slight differences associated with satellite viewing geometries and cloud and aerosol properties affecting the NO2 retrieval. POMINO–GEMS also shows good agreement with the following: OMNO2 (Ozone Monitoring Instrument (OMI) NO2 Standard Product) v4 (R=0.87 ; NMB = - 16.8 %); and GOME-2 (Global Ozone Monitoring Experiment-2) GDP (GOME Data Processor) 4.8 (R=0.83 ; NMB = - 1.5 %) NO2 products. POMINO–GEMS shows small biases against ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) NO2 VCD data at nine sites (NMB = - 11.1 %), with modest or high correlation in diurnal variation at six urban and suburban sites (R from 0.60 to 0.96). The spatiotemporal variation in POMINO–GEMS correlates well with mobile car MAX-DOAS measurements in the Three Rivers source region on the Tibetan Plateau (R=0.81). Surface NO2 concentrations estimated from POMINO–GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China for spatiotemporal variation (R=0.78 ; NMB = - 26.3 %) and diurnal variation at all, urban, suburban and rural sites (R≥0.96). POMINO–GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2. [ABSTRACT FROM AUTHOR]