bluuebunny/crossref_metadata_2025 huggingface dataset:
The C, H2O and S budget of the deep crust (Ossola Valley, Ivrea-Verbano Zone)
Authors: Degen Sarah , Secrétan Alexia , Hermann Jörg , Rubatto Daniela
Published: March 2024

The deep crust in the Ivrea-Verbano zone consists of underplated mafic and metasedimentary rocks. In the metasedimentary rocks, C in the form of residual carbonates and graphite, H2O in hydrous phases and S in sulphides can potentially be stored for millions to billions of years. Yet, the budgets of these volatile elements in the deep crust are poorly quantified.Drill cores obtained from the DIVE-project have been studied for the distribution of C-, H2O- and S-bearing phases in the predominantly felsic metasedimentary upper portion of the lower crust. The drill core samples are mainly biotite-gneisses (75 vol-%, Qtz + Pl + Bt ± Grt ± Sil ± Kfs), metabasites (21 vol-%, Amp + Pl ± Qtz ± Grt ± Px ± Bt) and calcsilicate rocks (2 vol-%, Cc + Grt + Px + Ttn + Pl + Qtz ± Amp) which were metamorphosed at upper amphibolite facies conditions (720-770 ± 50°C at 7.4 ± 1.5 kbar). Varying degrees of partial melting have been observed in all lithologies. Graphite occurs in the matrix of biotite-gneisses and as inclusions in garnet. Moreover, carbonates are present in calcsilicate rocks and occasionally in metabasites. H2O is hosted in biotite and amphibole and S occurs in sulphides.C-N-S analyses revealed that the biotite-gneisses contain an average of 0.26 wt.-% C while the average values for the metabasites and calcsilicate rocks are 0.07 and 2.22 wt.-%, respectively. The majority of the water is stored in the biotite-gneisses (0.41 wt.-%) and metabasites (0.14 wt.-%). In contrast, the calcsilicate rocks contain negligible amounts of H2O (< 0.004 wt.-%). S concentrations are highest in the biotite-gneisses (0.25 wt.-%) and metabasites (0.19 wt.-%) For the 578.7m deep borehole, overall concentrations reach an average of 0.23 wt.-% C, 0.55 wt.-% H2O and 0.23 wt.-% S.C mobilisation in the calcsilicate rocks likely occurred via garnet-forming decarbonation reactions (e.g. Zo + Hed + Cc --> Qtz + Grs + H2O + CO2) as well as via melt, which is indicated by the presence of calcite in leucosomes. FTIR spectra of apatite indicate varying concentrations of CO2 which reach 1500 ppm in case of the calcsilicate rocks. Furthermore, apatites hosted by leucosomes contain several hundreds of ppm CO2, indicating the presence of C-bearing hydrous melts. Therefore, devolatilisation reactions as well as partial melting result in a moderate transfer of C and H2O from the deep crust to the upper crust. Nevertheless, metasedimentary rocks that reside at around 25 km depth at temperatures of ~750°C are an important reservoir for C, H2O and S.
CPG Subsea Power Plant – When solutions come together
Authors: Köhlert Sebastian
Published: March 2024

As oil and gas production will most likely go down in the next decades and CPG is waiting for deployement in a wide scale, future applications should be investigated on a wide scale.CPG (CO2 plume geothermal system) is a great way to improve efficiency and feasibility of geothermal power plants. Subsea Compressors made by MAN Energy Solutions are used to improve efficiency in oil and gas production. At the first glance these are two technologies which have no link.But putting both things together results in a new approach to produce sustainable energy for offshore use. A turbine necessary to run a CPG plant is a technology not very different to a compressor. Feasibility was positively checked by the experts of MAN Energy Solutions. Problems like increased necessary steam temperature in high pressure environments - like the deep sea - in classical geothermal power production are no object with CPG.Works from Martin O. Saar et al. showed the great potential of CPG onshore. A use offshore or even subsea is closely linked to potential apllications and the comparison with other technologies to produce sustainable energy for offshore use.Possible future consumers like fuel production (H2, methanol, ammonium), subsea data centers, CO2 storage or desalination plants can benefit from a sustainable and reliable local power source.The right LCOE (Levelized Cost of Electricity) is the final paramter to be met. Investigations showed that with the right side conditions a competitive LCOE compared to other offshore baseload capable power plants like OTEC (ocean thermal energy conversion), or wave, tidal, wind and solar with energy storage is possible.
Storm surge events and associated dynamics in the North Atlantic
Authors: Barbot Simon , Pineau-Guillou Lucia , Delouis Jean-Marc
Published: March 2024

Storm surges events are investigated using the ECHAR method, which identify and quantify the different dynamical structures of a typical storm surge event. In the North Atlantic, analysis of 65 tide gauges revealed that storm surge events display 2 majors and 2 minors structures, each of them corresponding to specific ocean dynamics. The 2 major structures refer to a slow-time Gaussian structure, lasting around 24 days, associated with the impact of the atmospheric pressure and a fast-time Laplace structure, lasting around 1.4 days, mainly wind-driven. The absence of the Gaussian structure along the North America coasts is explained by storms of smaller spatial extension, compared to Europe. Concerning the minor structures, a negative  surge of around 6 cm just after the peak surge is observed over North America only. Such a sudden drop of the sea level is explained by the turning winds during the storm event, favored by the smaller spatial extension of storms. Finally, high frequency oscillations, with amplitude typically of 3 cm and up to 25 cm, are observed at some tide gauges. These oscillations refer to tide-surge interactions they are often maximum at a specific phase of the tide and/or enhanced because of resonant basins.
Fate of reservoir storages due to sedimentation in changing climate
Authors: Bartosova Alena , Brendel Conrad , Capell René
Published: March 2024

Retention of sediment in lakes and reservoirs is a major problem that impacts drinking water supplies, irrigation, recreation, hydropower production, and flood control globally. Sediment loads to lakes and reservoirs are likely to increase due to changing climate, e.g. increases in high intensity precipitation events. However, this impact is often not considered in large scale hydrological assessments of climate changes.An ongoing challenge with assessing reservoir sedimentation at a large scale is that many basins are ungauged, and information about sediment management and decision making is not available. Large-scale dynamic hydrological models are fortunately becoming more commonly established as tools not only for flood forecasting and climate impact analyses, but also for estimating time-dynamic water fluxes and their transport into sea basins. One such tool is the dynamic, semi-distributed, process-based rainfall-runoff and water quality model, Hydrological Predictions for Environment (HYPE, see Lindström et al., 2010; https://hypeweb.smhi.se/).While many hydrological models do not explicitly consider the sediments accumulating in reservoirs, HYPE was recently updated to dynamically simulate (1) the effect of sediments on the available volume of lakes and reservoirs and (2) selected sediment management strategies. The new routines were tested on several reservoirs globally using different types of data for calibration: instream sediment concentrations (Banja in Albania), storage capacity loss (Enguri in Georgia), and upstream sediment yield (dams in Greater uMngeni River Basin in South Africa). The current annual rate of storage capacity loss varied greatly among cases (0.004-4.15%). The routines were then incorporated into a pan-European HYPE model, E-HYPE (Brendel et al., 2023), and calibrated against observed sediment concentrations. The change in storage capacity loss in European reservoirs and lakes was then evaluated for 3 climate models. We present current and future losses of lake and reservoir storage and analyze sediment regimes in water bodies with water management structures such as hydropower or drinking water reservoirs. Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., Arheimer, B., 2010. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrol. Res. 41, 295–319. https://doi.org/10.2166/nh.2010.007Brendel, C. E., Capell, R., Bartosova, A. (2023) To tame a land: Limiting factors in model performance for the multi-objective calibration of a pan-European, semi-distributed hydrological model for discharge and sediments. Journal of Hydrology: Regional Studies, 50(2023) 101544. https://doi.org/10.1016/j.ejrh.2023.101544
Evaluating the Impact of Aggregation Scales and Campaign Durations on Land-Use Regression Models for Air Pollution Estimation with Mobile NO2 Monitoring
Authors: Tian Tian , Helbich Marco , Yuan Zhendong , Kerckhoffs Jules , Vermeulen Roel
Published: March 2024

Background: It is common practice in land-use regressions for air pollutant predictions to aggregate mobile measurements into road segments of predefined lengths (e.g., 50 m or 100 m) or raster cell sizes (e.g., 10 m or 25 m) in an ad hoc manner. However, the selection of the segment lengths and cell sizes is arbitrary and possibly affects the prediction accuracy which, in turn, may lead to heterogeneous results in studies using these air pollution surfaces.  Aims: We aimed to 1) assess how different aggregation approaches (i.e., segments and cells) affect the accuracy of air pollution predictions from land-use regression models based on mobile measurements, and 2) assess the impact of various aggregation scales and measurement durations on the accuracy of depicting long-term air pollution concentrations.  Methods: We utilized around 5.6 million mobile nitrogen dioxide (NO2) measurements in Amsterdam, the Netherlands, from May 2019 to February 2020. The mobile measurements were collected across five distinct campaigns of 10, 20, 30, 50, and 70 days. We aggregated mobile measurements from each duration into road segments and cells with varying spatial resolutions (i.e., 25 m, 50 m, 100 m, 150 m, 200 m). A stepwise linear regression (SLR) and a random forest (RF) were trained for each aggregated dataset. Furthermore, 80 long-term stationary NO2 measurements were employed to validate the LUR models.Results: First, in LUR model training. RF consistently outperformed the SLR across all spatial scales and measurement durations. The performance of cell-based LUR models fluctuated more than segment-based models across different scales. The explained variance in the RF-based LUR models decreased with increasing cell sizes (e.g., decreased from 61% to 48%). Conversely, the stepwise LUR models explained larger parts of the variance with increasing cell sizes (e.g., increased from 19% to 31%). Second, in the long-term validation with stationary NO2 measurements, the prediction accuracy varied across different scales, but no clear trend was observable. The segment-based LUR models were less sensitive to changes in the spatial scale than cell-based LUR models. Moreover, our results showed that the duration of the mobile measurements campaign is vital, with longer-duration campaigns (e.g., 50 days and 70 days) producing more accurate predictions than shorter ones (e.g., 10 days and 20 days).  Conclusion: By examining the effects of different spatial and temporal aggregation schemes on LUR models, we found that using different-sized segments leads to less variance in the results for model training and long-term air pollution predictions than cells. Our results suggest that a segment-based approach is more robust and should be used to predict air pollution concentrations.
Spatiotemporal relationships between human population and climate during the last glacial period in China
Authors: Zhu Dan , Lin Zinan , Zhou Jiayi
Published: March 2024

Climate variations during the last glacial period had major impacts on plant and animal populations including humans. Yet, relationships between human population levels and climate through time and across space remain elusive. Here, we used the archaeological radiocarbon dates spanning 50 to 10 ka BP in China to indicate fluctuations in human population sizes, and investigated their correlations with climate variables from paleoclimate proxies and climate model outputs using a Bayesian radiocarbon‐dated event count (REC) statistical model. We find that temperature has a significant positive effect on population in China during 50 – 10 ka, while the sensitivity of population size to temperature exhibits a declining trend over time, suggesting a potential gradual adaptation to cold climates. We further used a global ecosystem model that explicitly simulates human population dynamics, the ORCHIDEE-FOEGE model, to reconstruct human densities during the LGM, and investigated the roles of climate and atmospheric CO2 levels in shaping the distribution of human populations in China.
Assessing the relative importance of hydrological and geomorphological controls on river flood conveyance at a global scale
Authors: Vahidi Ellie , Nicholas Andrew , Ashworth Philip , Boothroyd Richard , Bennett Georgie , Cloke Hannah , Darby Stephen , Delorme Pauline , Griffith Helen , Gebrechorkos Solomon , Hawker Laurence , Leyland Julian , Liu Yinxue , McLelland Stuart , Neal Jeffrey , Parsons Daniel , Slater Louise , Wortmann Michel
Published: March 2024

Climate variability is a significant driver of flood events. However, geomorphological changes in river channels, including variations in local and upstream sediment supply, play a crucial role in determining flood conveyance capacity and flood stage variations. The interplay between hydrology and geomorphology, and their relative impact on flood conveyance, can vary in different river systems depending on both the degree of internal channel dynamics and the nature and magnitude of external forcings. For example, rates of bank erosion, vegetation establishment on bar surfaces, and overbank sedimentation control the time required for floodplain reworking, the adjustment of channel morphology and the associated evolution of river flow conveyance capacity and stage-discharge relations.To investigate the relative significance of hydrological and geomorphological controls on flood-stage variability, we employ a new computationally-efficient model of river and floodplain morphodynamics. This model simulates the evolution of river morphology and flow conveyance capacity by representing the interaction between processes of bank erosion, floodplain construction and river bed-level change over multiple centuries. The simple nature of the model enables its application at large spatial scales – e.g., to explore global variations in the controls on flood conveyance and its sensitivity to future environmental change. Simulated changes in conveyance capacity for a range of environmental settings were evaluated against trends in observed river gauging datasets. Convergent cross-mapping analysis was then applied to investigate the cause-and-effect relationships between controlling factors, including: (i) hydrologic regime; (ii) river sediment load; (iii) floodplain composition (e.g., fine versus coarse sediment); and (iv) lateral river dynamics (e.g., rates of erosion and accretion). Our analysis quantifies the causality between these factors and the resulting variability in river morphology (width and bed level), flood stage and channel conveyance capacity. Results indicate that in dynamic river systems, while the importance of climate-driven hydrological changes in driving conveyance capacity changes are acknowledged, geomorphological changes – specifically, variations in sediment supply and lateral sediment sources – may dominate over climate-driven trends.
A study on antecedent rainfall-triggered landslides in the Garhwal Himalayas, India
Authors: Chandna Prachi , Pandit Koushik , Kumar Ganesh , Sarkar Shantanu
Published: March 2024

Landslides are one of the recurrent precarious geological hazards that may prove fatal to life and property. In the Indian Himalayan region, the primary triggering factor contributing to landslides is rainfall. Recent advancement in rainfall threshold related studies have contributed significantly to a better understanding of the problem and the development of more accurate models at the local, regional and global levels. Although there are well-established studies on role of antecedent rainfalls and its criticality in the initiation of landslides, at present, there is no uniformly accepted method to consider effect of antecedent conditions or rainfall duration on stability of slopes. The antecedent period considers the influence of both soil moisture and groundwater on slope once the rainfall has ceased, since its effect is delayed due to hydrological attributes of the soil. Study from Uttarkashi region indicate that 15-day antecedent rainfall of around 109 mm can activates about 99% landslides in the area, highlighting the need to quantitatively estimate the likelihood of landslide incidents. For the present study, a decadal data on rainfall and landslide were curated from the Uttarkashi district of Uttarakhand state in India which comes under the Garhwal Himalayan region. These data were utilized to assess the influence of daily rainfall and antecedent rainfall on slope stability and to develop an empirical equation that predicts the probability of slope failure. The equation can be used as landslide warning for vulnerable zones if forecast precipitation values are available.
Keywords: Landslides, Garhwal Himalayas, Antecedent Rainfall, Landslide prediction.
Rapid chlorine deactivation at very low ozone concentrations in the Antarctic stratosphere
Authors: Grooß Jens-Uwe , Müller Rolf , Lehmann Ralph
Published: March 2024

The intensive catalytic chemical ozone loss cycles involving inorganic chlorine compounds are known to cause the ozone hole, that regularly forms in the Antarctic polar vortex each winter and spring.  One key point of the explanation of the ozone hole are heterogeneous reactions on the surface of Polar Stratospheric Cloud (PSC) particles, which are present in the polar stratosphere owing to the very low stratospheric temperatures.Ozone mixing ratios can reach values of a few ppbv which is below detection limit of ozone sonde observations.  Under these extreme conditions, fast chlorine deactivation into the reservoir HCl does occur even though polar stratospheric clouds are still present, that are normally causing chlorine activation.In this study we revisit this issue and investigate the occurring chlorine chemistry in more detail.  The explanation of the chemical mechanism of the fast net HCl formation is based on the automatic determination of reaction pathways by the Pathway Analysis Program (PAP) (Lehmann, 2004).The simulations of chemical composition are performed by the model CLaMS in box model mode along an ensemble of about 600 trajectories in the Antarctic spring 2018.  The simulated rapid complete chlorine deactivation into HCl in the presence of PSCs is in line with satellite  observations by the Microwave Limb Sounder (MLS). ReferenceLehmann, R.: An algorithm for the determination of all significant pathways in chemical reaction systems, J. Atmos. Chem. 47, 45-78 (2004).
SPOCC - a GFZ Software Tool for a Multi-GNSS Orbit and Clock Combination
Authors: Brack Andreas , Mansur Gustavo , Sakic Pierre , Zajdel Radosław , Männel Benjamin
Published: March 2024

Among the core products of the International GNSS Service (IGS) are precise satellite orbits and clocks, which are generated by the Analysis Center Coordinator (ACC) as a combination of the solutions provided by different Analysis Centers (AC). A strategic goal of the IGS is to facilitate multi-GNSS solutions, implying that the currently operational system-wise GPS and GLONASS combinations should be replaced by a consistent set of multi-GNSS products, eventually containing at least GPS, GLONASS, Galileo, BeiDou, and QZSS.Over the past years, the Satellite Precise Orbit and Clock Combination (SPOCC) software tool has been developed at GFZ. It provides a fully consistent multi-GNSS orbit and clock combination that covers all available and possible future constellations and is based on a well-defined unified least-squares framework. The resulting combined orbit and clock products are a weighted average of the individual AC solutions with weights determined through least-squares variance component estimation (VCE). A main objective is to support multi-GNSS precise point positioning (PPP) users.We will introduce the combination workflow, which essentially consists of alignments harmonizing the AC products followed by the VCE and the weighted averaging, and is complemented by quality checks such as outlier detection. For the orbit combination, the alignment consists of Helmert transformations applied to the AC orbits, which is iterated with the VCE-based weighted averaging until convergence. The clock alignments consist of a radial correction from the orbit differences between the AC solutions, a removal of the impact of different reference clocks in the AC solutions, as well as an adjustment of all non-GPS satellite clocks for different inter-system bias (ISB) references at the ACs. The combination can be configured for different weighting schemes, including AC specific weights, AC+constellation specific weights, up to satellite type or even satellite specific weights, and the Helmert transformations can be based on different sets of satellite orbits.The SPOCC software has been extensively tested with the operational IGS products, the IGS Multi-GNSS Experiment (MGEX) products, and the IGS repro3 products. Performance evaluations by means of a comparison of the combination with the input products and the official IGS combination, through a satellite laser ranging (SLR) validation, and with PPP results will be used to show that the software achieves reliable results that are suitable for the users’ high precision GNSS applications.SPOCC is implemented in Python and will be provided as open source software.
Assimilating UAV-based GNSS ZTDs for Numerical Weather Predictions
Authors: Zhang Zhenyi , Liu Mengjie , Huber Valeria , Möller Gregor , Henneberger Jan , Kryenbühl Philipp , Hammerschmidt Lukas , Kłopotek Grzegorz , Soja Benedikt
Published: March 2024

In recent decades, various studies have demonstrated that assimilating tropospheric parameters from ground-based GNSS receivers benefits numerical weather predictions (NWPs). However, the achieved performance is limited by the spatial resolution of GNSS, especially in the vertical direction. With the rapidly developing and growing market of unmanned aerial vehicles (UAVs) and the facilitates of integrating low-cost GNSS hardware into various autonomous systems over the last years, there is a potential to address this problem by utilizing UAVs to collect airborne GNSS data and generate zenith total delays (ZTDs). The airborne GNSS ZTDs can act as a potential complementary source to radiosonde data for obtaining vertical profiles of the troposphere, making it promising to investigate the impact of assimilating GNSS ZTDs of high spatio-temporal resolution in NWPs.In this study, we explored the use of GNSS data collected by a vertically ascending UAV, with ZTDs processed using the software CamaliotGNSS. Based on the airborne GNSS ZTDs, we conducted not only data assimilation but also weather predictions using the Weather Research and Forecasting Model (WRF). With the onboard meteorology observations as references, we found that assimilating airborne GNSS ZTDs positively impacted humidity and temperature forecasts, with their forecasting root-mean-square errors decreasing by about 19% and 29%, respectively. Moreover, by selecting and comparing different subsets of data, we found that better forecasts can be obtained with airborne GNSS ZTDs of higher spatio-temporal resolution. The positive results invite further exploration of applications of airborne platforms such as UAVs in the field of GNSS meteorology. 
Spatial variation of future trends in Atlantic upwelling cells from CMIP6 models
Authors: Flügel Raquel , Herbette Steven , Treguier Anne Marie , Waldman Robin , Roberts Malcolm
Published: March 2024

Eastern Boundary Upwelling Systems (EBUS) are characterised by wind-triggered upwelling of deep waters along the coast. They are hotspots of biological productivity and therefore have a high economic, ecological and social importance. Here we investigate the evolution of the two Atlantic EBUS during the historical period and in a future high-emission scenario in CMIP6 models from two modelling centres, with spatial resolutions ranging from 1° to 1/12° in the ocean. The decomposition of the upwelling systems into subregions reveals differences between the equatorward and poleward parts. Our analysis is focused on the modelled vertical transport, which is shown to be consistent with the wind-derived Ekman index. Integrating the vertical transport provides a synthetic view of the upwelling cells, their strength, depth and distance to the coast. The models show high interannual variability over the 21st century century, which explains why significant trends could only be found in few subregions of the Atlantic EBUS. The results suggest a poleward migration of upwelling systems with climate change and a change of the upwelling cells, rather than the uniform intensification which had been hypothesised by Bakun in 1990.
Evaluation of soil-structure interaction by combining deconvolution of building and field earthquake recordings with polarization analysis: application to the Matera experiment (Italy)
Authors: Parolai Stefano , Sklodowska Anna Maria , Petrovic Bojana , Romanelli Fabio
Published: March 2024

The study of soil-structure or even city-soil interaction is attracting the attention of many researchers, as both numerical simulation results and preliminary results from empirical data indicate their significant effect in determining the level of seismic hazard.In this study, the wave field radiated from a building to its surroundings, which is due to the interaction of the building with the ground, is identified and extracted using a novel approach. The proposed approach, which is valid for seismic data analysis, combines deconvolution and polarization analysis. It consists of four steps: (1) estimation of building resonance frequencies, (2) deconvolution of seismic recordings of sensors installed in a building and in the surrounding environment, (3) identification of seismic phases, reconstruction of seismic phases, reconstruction of the signal transmitted from the building to the surrounding environment and estimation of its energy, and (4) polarization analysis.The application of the approach to recordings of an M4.6 earthquake collected by sensors installed in a building and on a nearby athletic field in Matera, Italy, showed that the particle motion of the wave field radiated from the building to the ground was mostly linearly polarized in the radial and transverse planes, while a clear elliptical polarization was observed only in the horizontal plane. The analysis showed that the wave field radiated from the building and recorded on the ground could be dominated by unconventionally polarized surface waves, i.e. quasi-Rayleigh waves or a combination of quasi-Rayleigh and quasi-Love waves. The results indicated that the energy transmitted from the analyzed vibrating building to the surrounding environment was significant and decreased ground shaking due to the out-of-phase motion between the incoming seismic wave field and that radiated from the building.
Input and output fluxes of surface CO2 over the Late Quaternary
Authors: Castrogiovanni Luca , Sternai Pietro , Pasquero Claudia , Piana Agostinetti Nicola
Published: March 2024

Ice core archives allow us to retrieve the atmospheric CO2 concentration of the past 800,000 years characterized by periodically lower and higher CO2 levels corresponding to ice ages and interglacials, respectively. However, there is no broadly accepted consensus regarding the leading drivers of such variability. To a large extent, what prevents us from identifying the mechanisms that underlie changes in atmospheric CO2 concentrations is our inability to split the overall atmospheric CO2 budget into its sources and sinks terms, thereby assessing the fluxes of carbon among different reservoirs. Here, we use a reversible-jump Markov chain Monte Carlo (rj-McMC) algorithm to invert the atmospheric CO2 concentration dataset provided by the EPICA ice core based on a general forward formulation of the geological carbon cycle. We can quantify the most likely temporal variability of atmospheric carbon fluxes in ppm/yr throughout the last 800,000 years. Results suggest that temperature changes have been driving the variations of atmospheric CO2 until the Mid-Brunhes Event (MBE), when the onset of a progressive cyclic increase of  the atmospheric carbon fluxes marks a distinct behavioral change of the climate system. We ascribe such change to mechanisms internal to the Earth system, possibly related to the deglacial triggering of global volcanism and associated feedbacks on climate or a combination of geological, biological, and physical processes. Regardless, our unprecedented quantification of past atmospheric input and output CO2 fluxes provide (1) new constraints for climate carbon cycle and paleoclimate models to assess dominant climate-driving mechanisms, and (2) the benchmark for climate models intercomparison projects and better assessing the anthropogenic perturbation to the geological carbon cycle an associated climatic effect. 
Elucidating the role of brown carbon in HONO and NOx production from renoxification of nitrate-containing aerosol
Authors: Bao Fengxia , Bell David , Tronconi Arianna , Iezzi Lucia , Chen Yanfang , Cheung Ka Yuen , Dällenbach Kaspar Rudolf , Ammann Markus
Published: March 2024

Oxides of nitrogen (NOx = NO + NO2) and nitrous acid (HONO) play crucial roles in forming tropospheric ozone (O3), hydroxyl radicals (·OH), and secondary aerosols. The photochemical reactions of nitrate aerosol are of significant atmospheric interest as they produce HONO and NOx, a process termed renoxification. Light-absorbing organic species, particularly chromophoric Brown Carbon (BrC) predominantly derived from biomass burning, are suggested to be key players in renoxification, though the mechanism remains controversial. Here, we investigate BrC-associated renoxification upon irradiation of films containing BrC extracts from authentic biomass-burning aerosols and BrC model compounds using the coated wall flow tube (CWFT) technique. We mimic real-world aerosol conditions by adjusting the pH, nitrate concentration, and relative humidity of the CWFT films, ensuring atmospheric relevance. We show that the renoxification rate is enhanced in the presence of BrC. This is likely due to the photosensitizing effect of BrC, which enhances the reduction of nitrate, rather than the previously proposed surface-enhanced direct photolysis of adsorbed nitrate. Given the efficient use of the solar spectrum from UV to visible light by this photosensitized mechanism and the widespread coexistence of nitrates and BrC in various environmental systems, we suggest BrC-photosensitized renoxification could be a substantial source of HONO and NOx. This process may significantly influence the trends and distributions of tropospheric O3, ·OH and secondary aerosols, marking an important, yet largely unexplored, area in atmospheric chemistry.
Optimizing Weather Windows for the Deployment, Operation, and Maintenance of Marine Renewable Energy Devices in Irish Coastal Waters
Authors: Eftekhari Alireza , Moore Dylan , Nash Stephen
Published: March 2024

This study presents a detailed analysis of weather window accessibility for Marine Renewable Energy (MRE) sites along Ireland's coast, utilizing a robust 12-year met-ocean dataset. The research focuses on key test sites - the Atlantic Marine Energy Test Site (AMETS), the Galway Bay Test Site (GBTS), and the Westwave Demonstration Site - and expands to a broader spatial analysis of Irish coastal waters. By integrating significant wave height and wind data, the study evaluates site accessibility, emphasizing the paramount role of wave height in determining access. Findings reveal substantial spatial variability in accessibility, with high-resource areas like AMETS facing greater access challenges due to harsher conditions, as opposed to the more accessible GBTS. The study underscores the need for a nuanced, region-specific approach to MRE development in Ireland, highlighting how strategic planning and technological advancements are crucial in exploiting the country's significant MRE potential. The results also stress the importance of long-term data for accurate environmental variability assessment, offering vital insights for future MRE site viability and strategy development.
In-flight Measurements of Contrails in the Low Soot Regime during the ecoDemonstrator Experiment 
Authors: Bräuer Tiziana , Märkl Raphael , Scheibe Monika , Sauer Daniel , Dischl Rebecca , Heckl Christopher , Aufmhoff Heinfried , Stremming Luca , Voigt Christiane , Digangi Joshua , Diskin Glenn , Baughcum Steven , Griffin William , Rahmes Tim , Miller Cassandra , Moore Richard
Published: March 2024

Analyzing the formation of contrails on emissions from the most recent generation of aircraft engines is key to understand the climate impact from aviation. As for conventional Rich-Quench-Lean engines contrail ice crystals mainly form on a high number of emitted soot particles, the question arises which and how many particles are activated during the contrail formation process if soot emissions are strongly reduced through the Lean Combustion technology.The ecoDemonstrator experiment is a collaboration between Boeing, GE, NASA, DLR and other international partners and took place in October 2023. For the first time, we present in-flight measurements of contrail ice crystals in the exhaust of the ultra-low soot emitting CFM International LEAP-1B engine. This experiment combines the lean-burning engine technology with the use of Sustainable Aviation Fuels (SAF) and ultra-low sulfur kerosene (LS-Jet-A) in newly manufactured engines to achieve close to soot-free emissions. During the experiment, we performed measurements on board the NASA DC-8 research aircraft, sampling emissions behind a Boeing 737 MAX 10 aircraft. In addition to gas and particle emissions, we also measured the number concentrations of ice crystals that formed behind the 737 MAX 10 at a distance between 3 and 8 kilometers with two Cloud and Aerosol Spectrometers (CAS) that were positioned on the upper and lower fuselage of the DC-8. We show a first analysis of the contrail ice data for selected flights and present apparent ice emission indices (AEI) in relation to ambient conditions.
Assessing the area of applicability of spatial prediction models through a local data point density approach
Authors: Schumacher Fabian , Knoth Christian , Ludwig Marvin , Meyer Hanna
Published: March 2024

Machine learning is frequently used in the field of earth and environmental sciences to produce spatial or spatio-temporal predictions of environmental variables based on limited field samples - increasingly even on a global scale and far beyond the extent of available training data. Since new geographic space often goes along with new environmental properties, the spatial applicability and transferability of models is often questionable. Predictions should be constrained to environments that exhibit properties the model has been enabled to learn.Meyer and Pebesma (2021) have made a first proposal to estimate the area of applicability (AOA) of spatial prediction models. Their method is based on distances - in the predictor space - of the prediction data point to the nearest reference data point to derive a dissimilarity Index (DI). Prediction locations with a DI larger than DI values observed through cross-validation during model training are considered outside of the AOA. As a consequence, the AOA is defined as the area where the model has been enabled to learn about relationships between predictors and target variables and where, on average, the cross-validation performance applies. The method, however, is only based on the distance - in the predictor space - to the nearest reference data point. Hence, a single data point in an environment may define a model as “applicable” in this environment. Here we suggest extending this approach by considering the densitiy of reference data points in the predictor space, as we assume that this is highly decisive for the prediction quality.We suggest extending the methodology with a newly developed local data point density (LPD) approach based on the given concepts of the original method to allow for a better assessment of the applicability of a model. The LPD is a quantitative measure for a new data point that indicates how many similar (in terms of predictor values) reference data points have been included in the model training, assuming a positive relationship between LPD values and prediction performance. A reference data point is considered similar if it defines a new data point as being within the AOA, i.e. the model is considered applicable for the corresponding prediction location. We implemented the LPD approach in the R package CAST. Here we explain the method and show its applicability in simulation studies as well as real-world applications.Reference:Meyer, H; Pebesma, E. 2021. ‘Predicting into unknown space? Estimating the area of applicability of spatial prediction models.’ Methods in Ecology and Evolution 12: 1620–1633. doi: 10.1111/2041-210X.13650.
Consolidated Sentinel-1 SAR-based index provides robust annual forest loss assessment in tropical forests with semi-permanent cloud cover 
Authors: Delhez Baptiste , Radoux Julien , Toussaint François , Collet Thibauld , Defourny Pierre
Published: March 2024

Tropical moist forests are one of the richest ecosystems on earth and provide multiple ecosystem services, but are also supporting many human activities and thus undergo continuous threats from logging, fire, shifting cultivation, road development or urban expansion. From this perspective, continuous monitoring and mapping those ecosystems keeps emphasize the importance of their preservation for the 21st century.Satellite remote sensing has proven to be essential to assess the deforestation in wide and remote areas at regional scale. Nevertheless, annual-based assessments using optical images tend to show inconsistency in tropical regions where the semi-persistent cloud coverage prevents steady periodic cloud-free acquisitions. The 8-year-old+ C-band SAR archive from Sentinel-1 provides now robust material to produce consistent yearly-based forest loss assessment.Previous studies showed that a sudden decrease of the backscattered signal in an intact tropical forest canopy indicates a forest loss. However, SAR-based forest loss detection is sensitive to commission errors due to the speckle, showing their limitations as the thresholding of significant land cover change may include stable targets. Moreover, recent near-real-time detection systems focus on mapping forest loss alerts from a temporal early-warning perspective but are less reliable in providing accurate quantification of the degraded surfaces.In this study, we introduce an annual index called MinB-Q10, computed pixel-based with a statistical probabilistic approach that combines VV and VH features. The result is represented as a chi-squared distribution based on Euclidean distance. The index is designed to highlight statistical deviation from stable forest distribution. The study has been developed in Democratic Republic of the Congo. It was calibrated in study area surrounding Yangambi (2.000 km²) and validated in a disconnected study area located around Kisangani (12.000 km²). The probabilistic approach and statistical considerations are developed to design an early-warning system as a second step, where the annual index would consolidate spatial assessment.The preliminary results indicate a marked reduction of the commission errors compared with standard thresholding methods. Object-based accuracy assessment from optical independent imagery (in progress) enables to identify and distinguish the proportion of lost surface from the geometric accuracy of the detections. Moreover, combining pixel-counting with statistical estimates of the false detection rate generates unbiased prediction of the regional forest loss.
A Globally Trained Deep Learning Model for Estimation of Seasonal Residual Signals in GNSS displacement time series
Authors: Çökerim Kaan , Bedford Jonathan , Dobslaw Henryk
Published: March 2024

Displacement time series from Global Navigation Satellite System (GNSS) at daily rates are used commonly to investigate and understand the processes controlling Earth's surface deformation originating from tectonic processes such as postseismic slip, slow slip events and viscoelastic relaxation, but also non-tectonic processes such as reflectometry, atmospheric sensing and remote sensing. For each individual research field, different parts of the total recorded GNSS displacement time series are of intrest. A major difficulty is the modeling and isolation of non-tectonic seasonal signals that are established to be related with non-tidal surface loading.In the past, many methods were developed with some success based on Kalman filters, matrix factorization and various approaches using curve fitting to separate the tectonic and non-tectonic contributions. However, these methods still have some difficulties in  isolating the seasonal loading signals especially in the presence of interannual variations in the seasonal loading pattern and steps in the time series.We present here a deep learning model trained on a globally distributed, continuous 8-10 years long dataset of ~8000 stations PPP-GNSS displacement time series from NGL to estimate the seasonal loading signals using a global non-tidal surface loading model developed at ESM-GFZ. We compare our model to other statistical methods for isolation of the seasonal with the established method of subtraction of the non-tidal surface loading signals (hydrological loading, and non-tidal atmospheric and oceanic loading) as our baseline. We also present the evaluation of our model and its capabilities in reducing the seasonal loading signal as well as parts of the high-frequency scattering in the original GNSS time series. 
Hourly Precipitation Prediction: Integrating Long Short-Term Memory (LSTM) Neural Networks with Granger Causality
Authors: Sreedhar Rahul , Sunil Akshay , L Murthy Raghu
Published: March 2024

Precipitation events are one of the most crucial processes governing the water cycle and therefore acts as a major input in majority of water resource studies. Furthermore, the modern era is witnessing an unprecedented increase in the frequency of extreme events highlighting the importance of understanding and predicting the precipitation events. Current precipitation prediction methods utilise complex physics-based models that require large number of input parameters as well as powerful computational facilities, making precipitation prediction a complex task. This scenario has not improved much over the years as advancements are often limited to either improving the model’s physics or input data quality. Hourly precipitation prediction is even more challenging due to increasing complexity and non-linearity with decreasing scale and therefore studies on understanding hourly precipitation is limited. Recent trends have shown a shift towards utilizing deep learning models in weather prediction owing to the ability of neural networks to capture complex patterns leading to high accuracy predictions. The current research introduces a Long Short Term Memory (LSTM) neural network adept at forecasting fine-scale hourly precipitation patterns up to two hours ahead, a critical development for real-time rain predictions. The Bi-LSTM's architecture, with its forward and backward processing capabilities, is particularly suited to capture the dynamic temporal relationships among the limited meteorological variables helping in effective precipitation prediction. Granger Causality analysis is done to capture relevant information for improving model performance. The model's performance is evaluated on its ability to accurately forecast weather conditions by learning from the historical inter-variable influences that were clearly detailed in the causality diagram. The findings from this study and the interlinks observed is expected to enhance our understanding of variable impact and improve the predictive power of precipitation models for future weather forecasting
Downscaling precipitation simulations from Earth system models with generative deep learning
Authors: Hess Philipp , Gelbrecht Maximilian , Aich Michael , Pan Baoxiang , Bathiany Sebastian , Boers Niklas
Published: March 2024

Accurately assessing precipitation impacts due to anthropogenic global warming relies on numerical Earth system model (ESM) simulations. However, the discretized formulation of ESMs, where unresolved small-scale processes are included as semi-empirical parameterizations, can introduce systematic errors in the simulations. These can, for example, lead to an underestimation of spatial intermittency and extreme events. Generative deep learning has recently been shown to skillfully bias-correct and downscale precipitation fields from numerical simulations [1,2]. Using spatial context, these methods can jointly correct spatial patterns and summary statistics, outperforming established statistical approaches.However, these approaches require separate training for each Earth system model individually, making corrections of large ESM ensembles computationally costly. Moreover, they only allow for limited control over the spatial scale at which biases are corrected and may suffer from training instabilities.Here, we follow a novel diffusion-based generative approach [3, 4] by training an unconditional foundation model on the high-resolution target ERA5 dataset only. Using fully coupled ESM simulations of precipitation, we investigate the controllability of the generative process during inference to preserve spatial patterns of a given ESM field on different spatial scales.[1] Hess, P., Drüke, M., Petri, S., Strnad, F. M., & Boers, N. (2022). Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nature Machine Intelligence, 4(10), 828-839.[2] Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022).A generative deep learning approach to stochastic downscaling of precipitation forecasts. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS003120.[3] Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J. Y., & Ermon, S. (2021).  Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073.[4] Bischoff, T., & Deck, K. (2023). Unpaired Downscaling of Fluid Flows with Diffusion Bridges. arXiv preprint arXiv:2305.01822.