bluuebunny/crossref_metadata_2025 huggingface dataset:
Drivers of changes in catchment evapotranspiration in Central Europe over the past 40 years
Authors: Duethmann Doris , Bruno Giulia , Strohmenger Laurent
Published: March 2024

Understanding long-term changes in evapotranspiration and their drivers is crucial due to direct impacts on water availability. Increasing evapotranspiration rates can exacerbate droughts and jeopardise water availability, especially in the summer months with higher water demands. Uncertainties of multi-decadal variations in evapotranspiration at local to regional scale and their drivers are, however, still large. In this data-based study, we derive changes in evapotranspiration from the catchment water balance for a large number of catchments in Central Europe over 1982–2016. We further analyse changes in potential drivers including vegetation and land cover based on a remote-sensing derived vegetation index and a land cover product, water availability based on changes in seasonal precipitation and available energy and atmospheric demand based on changes in reference evapotranspiration. We find wide-spread increases in catchment evapotranspiration until about the year 2000 and only small changes with a decreasing tendency after 2000. The observed variations in regional evapotranspiration are significantly correlated with variations in precipitation, reference evapotranspiration and vegetation activity. High evapotranspiration around 2000 can be related to high values of reference evapotranspiration, precipitation and vegetation activity. Lower evapotranspiration in the early 1980s despite relatively high precipitation is linked to lower values of reference evapotranspiration and vegetation activity, while the halt of further evapotranspiration increases after 2000 despite high values of reference evapotranspiration may be explained by low precipitation. The study contributes to expand our knowledge on the drivers of changes in the water balance in Central Europe over recent decades, which is of great importance for water management in a changing climate.
Incipient particle entraiment prediction with the use of machine learning methods
Authors: Valyrakis Manousos , Ojo Taiwo
Published: March 2024

In natural water bodies, sheared turbulent flows are the forcing agent responsible for particle mobilization near the river bed surface. Several analytical approaches have been used to describe this phenomenon, with ambiguities in the analytical methods employed, resulting in methodological biases. The application of a machine learning technique, namely, Adaptive Neuro-Fuzzy Inference System (ANFIS), is proposed here to model sediment transport dynamics. It is hypothesized that turbulent flow of different magnitudes and sufficient duration or near bed instantaneous flow power is responsible for particle displacement. The entrainment of sediment is modeled using the dynamic incipient motion criteria of impulse and energetic turbulent flow events. Several ANFIS architectures have been developed to relate the hydrodynamic vectorial quantities to particle displacement. ANFIS combines artificial neural networks' adaptation and learning power with the advantage of fuzzy inference (IF-THEN) rules for knowledge representation. To demonstrate ANFIS applicability for near bed threshold conditions, streamwise velocity [1], and particle dislodgement [2], flume-based experimental data sets are obtained as input and output signals to train the ANFIS model of various architecture complexities. The energy-based criterion and impulse criterion are obtained as cubic and quadratic expressions of streamwise velocity, respectively, and they are also used as inputs to train the ANFIS model [3]. Following a trial and error approach, the models developed with these criteria are analyzed and compared in terms of their efficiency and predictability using several performance indices. The optimum performing model is found capable of replicating the complex dynamics of sediment transport.References[1] Liu, D., AlObaidi, K., Valyrakis, M.* (2022). The assessment of an Acoustic Doppler Velocimetry profiler from a user’s perspective, Acta Geophysica, 70, pp. 2297-2310. DOI: 10.1007/s11600-022-00896-3.[2] AlObaidi, K., Valyrakis, M. (2021). Linking the explicit probability of entrainment of instrumented particles to flow hydrodynamics, Earth Surface Processes and Landforms, 46(12), pp. 2448-2465 DOI: 10.1002/esp.5188.[3] Valyrakis, M., Diplas, P., Dancey, C.L. (2011). Prediction of coarse particle movement with adaptive neuro-fuzzy inference systems, Hydrological Processes, 25(22). pp.3513-3524, DOI:10.1002/hyp.8228.
Monitoring an ice-dammed lake outburst using topographic data at Kongsvegen, Svalbard 
Authors: Piermattei Livia , Alexander Andreas , Filhol Simon , Nanni Ugo , Lefeuvre Pierre-Marie , Kohler Jack , Treichler Désirée , Earlie Claire S. , Schmidt Louise S. , Schuler Thomas V.
Published: March 2024

Glacial lake outburst floods (GLOFs) from ice-dammed lakes are frequent in Svalbard, impacting local ice dynamics, and subglacial hydrological systems, causing geomorphological changes, and posing flooding hazards. Additionally, GLOFs can influence nutrient dynamics in the fjord of tidewater glaciers, affecting the local ecosystem.In this study, we use high-resolution topographic data to monitor the formation of an ice-dammed lake and identify the drainage mechanisms of a GLOF that occurred in the summer of 2021 on the Kongsvegen glacier, a surge-type tidewater glacier located in Kongsfjorden (Svalbard). Additionally, seismometers were deployed to monitor the subglacial dynamics at the kilometre scale.Over the 2.5-month-long process starting in early June, terrestrial laser scanning (TLS) data and drone images were acquired at nearly daily intervals to monitor the ice-dammed lake formation and drainage. A time-lapse camera and pressure logger installed at the border of the ice-dammed lake allowed us to estimate the drainage timing, occurring from July 23 to July 26, resulting in a total drainage duration of 77 hours. To reconstruct the lake volume, the lake extension was manually digitized from the TLS data and drone orthophotos. Elevation information of the corresponding lake outlines was extracted from a 1 m resolution Digital Elevation Model (DEM) generated from Pléiades stereo satellite images acquired on 20 September 2020, at the end of the thaw season. This DEM serves as bathymetric data, representing the lake bottom. The extracted water level was used to calculate the stage-volume curve. The lake's maximum volume reached approximately 7.17 million m3 with an average discharge rate of 26 m3/s. Analyzing seismic data allowed for monitoring of the development of the subglacial drainage, assessing the transition from an inefficient to an efficient system.This study highlights the importance of very high spatial and temporal resolution data for accurate lake volume quantification and a better understanding of the link between GLOF and subglacial system.
Parameterized internal wave mixing in three ocean general circulation models
Authors: Brüggemann Nils , Losch Martin , Scholz Patrick , Pollmann Friederike , Danilov Sergey , Gutjahr Oliver , Jungclaus Johann , Koldunov Nikolay , Korn Peter , Olbers Dirk , Eden Carsten
Published: March 2024

We evaluate the parameterization IDEMIX for vertical mixing by breaking internal gravity waves in three different non-eddy resolving ocean models, namely ICON-O, FESOM and MITgcm. To assess the impact of the new closure, we prescribe three different products for wave forcing by tidal flow over topography that encompass the current uncertainty of this process. We compare these sensitivity simulations with a reference simulation without IDEMIX of each model and analyze the model-independent effects on the ocean circulation and mixing.In particular, we observe a stronger mixing work once IDEMIX is used which better agrees with observations.Coherent model responses to the stronger mixing work from IDEMIX are a deepening of thermocline depth, a warming of the upper-ocean thermocline water masses and an increased strength of the upper Atlantic overturning cell.
Elucidate the interaction between catchment and in-stream processes by using high frequency multivariate and multisite data
Authors: Gutiérrez Kenneth , Lischeid Gunnar , Rode Michael
Published: March 2024

Hydrological processes perform a fundamental role in ecosystem dynamics. Understanding the complex temporal variations of relevant parameters is essential for the effective management of catchments and watercourses. However, when faced with large data sets and a patterns to disentangle, combined with uncertainty about which variables are causing the most significant impact, valuable insights can be lost in the complexity. The inability to distinguish significant patterns and key variables can limit a thorough understanding of the analyzed phenomenon, thus limiting informed decision making. The primary objective of this research was to explore the application of high-resolution multivariate data, collected from diverse locations, in discerning the complex interplay of watershed and in-stream processes within stream ecosystems. To achieve this goal, Principal Component Analysis (PCA) of time series, also referred to as empirical orthogonal functions, was employed to identify prevalent patterns among the various variables under consideration. The dataset used in this study was derived from five monitoring stations located within the Bode river basin in Saxony-Anhalt, Germany. Six key variables were continuously measured at each station, i.e., electrical conductivity, pH, NO3 concentration, turbidity, water temperature, and water discharge. Consequently, a total of thirty time series were analyzed. Measurements were carried out at 15-minute intervals spanning the temporal range from 2013 to 2020. The first principal component explained 46% of the total variance and described the typical effect of stream discharge fluctuations on water quality compounds. In contrast the second component, explaining 13% of the variance, grasped discharge of upwelling saline groundwater in the downstream reaches of the river during stream low-flow. The third component, contributing 7% of the variance, was closely related to diurnal oscillations of pH. As other effects on pH have been factored out by the first two principal components this pattern could be assigned to the photosynthetic activity of algae. It is remarkable that it did not only show seasonal pattern but also substantial short-term variability as well as large variability between different stream reaches and between years. In particular the hot years of 2019 and 2020 stand out in this regard. The application of high-resolution multivariate data and the utilization of PCA proved to be helpful in disentangling complex interactions within stream ecosystems. These findings emphasize the importance of advanced analytical techniques in unraveling complex hydrobiogeochemical dynamics. As freshwaters continue to face environmental challenges, further studies employing similar methodologies can enhance our ability to predict and mitigate the impacts of anthropogenic and natural factors on aquatic ecosystems.
Insights into the Neoarchean Magmatic Evolution of the Western Nilgiri Block, Southern India.
Authors: Shamil V.T. Muhammed , -Kumar C.Ishwar , -Kumar M.Satish
Published: March 2024

The Southern Granulite Terrane (SGT) is an integral part of the Neoproterozoic Gondwana supercontinent. The Nilgiri block situated in the northern part of the SGT is characterized by Neoarchean magmatism. The major rock types found in the western part of the Nilgiri block include charnockite, hornblende biotite gneiss (HBG), pyroxenite, gabbro, amphibolite, mafic granulites, and granite. This study focuses on the geochemical signatures of major lithologies in the western Nilgiri block to understand the geological evolution within the broader Southern Granulite Terrane. Oxide data show charnockite samples with SiO2 from 51 to 67 wt% and HBG with SiO2 from 63 to 71 wt%. Normative Ab-An-Or classification diagrams indicate tonalitic and granodioritic signatures for charnockite and HBG, respectively. Both exhibit high Al2O3 (>15 wt%) and low K2O (
Contribution of latent variables to emulate the physics of the IPSL model
Authors: Crossouard Ségolène , Kageyama Masa , Vrac Mathieu , Dubos Thomas , Thao Soulivanh , Meurdesoif Yann
Published: March 2024

Atmospheric general circulation models include two main distinct components: the dynamical one solves the Navier-Stokes equations to provide a mathematical representation of atmospheric movements while the physical one includes parameterizations representing small-scale phenomena such as turbulence and convection (Balaji et al., 2022). However, computational demands of the parameterizations limit the numerical efficiency of the models. The burgeoning field of machine learning techniques opens new horizons by producing accurate, robust and fast emulators of parts of a climate model. In particular, they can reliably reproduce physical processes, thus providing an efficient alternative to traditional process representation. Indeed, some pioneering studies (Gentine et al., 2018; Rasp et al., 2018) have shown that these emulators can replace one or more parameterizations that are computationally expensive and so, have the potential to enhance numerical efficiency.Our research work aligns with these perspectives, since it involves exploiting the potential of developing an emulator of the physical parameterizations of the IPSL climate model, and more specifically of the ICOLMDZOR atmospheric model (for DYNAMICO, the dynamic solver using an icosahedral grid - LMDZ, the atmospheric component - ORCHIDEE, the surface component). The emulator could improve performance, as currently almost half of the total computing time is given to the physical part of the model.We have developed two initial offline emulators of the physical parameterizations of our standard model, in an idealized aquaplanet configuration, to reproduce profiles of tendencies of the key variables - zonal wind, meridional wind, temperature, humidity and water tracers - for each atmospheric column. The results of these emulators, based on a dense neural network or a convolutional neural network, have begun to show their potential for use, since we easily obtain good performances in terms of the mean of the predicted tendencies. Nevertheless, their variability is not well captured, and the variance is underestimated, posing challenges for our application. A study of physical processes has revealed that turbulence was at the root of the problem. Knowing how turbulence is parameterized in the model, we show that incorporating physical knowledge through latent variables as predictors into the learning process, leading to a significant improvement of the variability.Future plans involve an online physics emulator, coupled with the atmospheric model to provide a better assessment of the learning process (Yuval et al., 2021).
Serpentinized ultramafic rocks of Evia Island (Greece) as potential reservoirs for CO2 mineralization based on a petrological research
Authors: Karkalis Christos , Magganas Andreas , Koukouzas Nikolaos , Koutsovitis Petros
Published: March 2024

In the island of Evia (Aegean, Greece), big peridotitic masses crop out in the central and north parts displaying different extents of serpentinization. These rocks were frequently subjected to CO2-metasomatism [1,2] forming magnesite deposits that have been exploited for several years for industrial purposes (currently by Grecian Magnesite and TERNA Mag companies). The non-carbonated ultramafic rock types mostly appear in the broader areas of Pagondas, Dafni, Psachna, Makrimalli, Artaki and Vatondas. These are predominantly divided into serpentinized harzburgites/lherzolites and serpentinites, whereas scarce occurrences of garnet-bearing serpentinites are rarely evident close to the Pagondas locality in spatial association with rodingites [3]. The mineralogy [mostly serpentine ± (clino- and ortho-) pyroxenes ± olivine ± garnet] and geochemistry (i.e. high MgO, FeO and occasionally CaO contents) of representative rock samples from the Pagondas and Psachna-Makrimalli regions indicate that they can react with carbonated water in order to crystallize carbonate mineral phases. This is also shown by thermodynamic calculations with PERPLE_X software [4], which simulate the natural carbonation of Central Evia ultramafic rocks, revealing that they have the physicochemical potential to form specific carbonate minerals (siderite, magnesite and calcite) at T ≤ ~390oC. Thus, it is concluded that in Central Evia the petrological properties and relatively high quantities of serpentinized peridotites, offer a potentially viable option for their exploitation as reservoir rocks during in-situ CO2-storage. These non-carbonated ultramafic outcrops are located proximal to: (a) the Western and Eastern coastline of Evia Island (i.e. ~6 km far from the Evoikos Gulf and ~20 km far from the Aegean Sea), (b) the Central Evia groundwaters [5],  (c) the Edipsos hot-springs in Northern Evia [6] and (d) areas with industrial activities close to the city of Chalkida. Hence, their geographical distribution could contribute to the financial viability of such storage scenarios lowering the logistic costs and contributing to sustainable development through actions for the mitigation of climate change.References: [1] Karkalis, C. 2022: PhD-Thesis National and Kapodistrian University of Athens, Department of Mineralogy and Petrology, 371p; [2] Grieco et al., 2023: Minerals, 13(2), 159; [3] Karkalis et al., 2022: Lithosphere, 2022 (1): 9507697; [4] Connolly, J.A.D., 2009: Geochem Geophys 10, Q10014; [5] Voutsis et al., 2015: J. Geochem. Explor., 159, 79-92; [6] Kanellopoulos et al., 2016:  Proc. 14th Intern. Congress Geol. Soc. Greece, Thessaloniki, May 2016, 50(2), 720-729
Satellite-based detection of snow wetness and wave-induced surface wetting of Antarctic sea ice
Authors: Stentella Marta , Picard Ghislain , Heil Petra , Corney Stuart , Wille Jonathan , Favier Vincent
Published: March 2024

Sea ice is a fundamental, highly variable element of the polar environments. Its variability deeply affects, not only the local climate- and ecosystem but also the global Earth system. Until recently Arctic sea ice experienced a general retreat as expected under global warming whilst Antarctic sea ice extent increased up to 2014. However, Antarctic sea-ice extent at maximum annual cover shifted from a record high (2014) to a record minimum extent (2023), begging to explore the relationship between sea ice and ocean/atmospheric forcing. In this work, we pinpoint some extreme atmospheric events, specifically, atmospheric rivers (ARs) to analyse their influence on sea ice and snow properties. ARs can have a direct impact on the nature of oceanic surface gravity waves. Increasing wind speed causes an increase in wave height and energy, leading to greater repercussions on snow and sea ice. The sea ice area most affected by this forcing is the one that separates the pack ice from the open oceans, known as the marginal ice zone (MIZ). Our analysis aims to understand wave-sea ice interaction and its effect on accelerating snow melt or changing sea ice morphology. To accomplish this we focus on the influence of wave overwash on sea ice surfaces during the spring season in the Weddell Sea. The MIZ is identified by posing the limits of sea ice concentration (SIC) ranging from 15% to 80%. ARs events are identified using ERA5 reanalysis data, estimating their integrated water vapour transport (IWV) and vertically integrated vapour transport (vIVT) values, which are considered extreme if they exceed 95% of historical norms for the same location and time of year over a time interval that spreads from 1980 to 2022. Data obtained from AMSRE, AMSR2 & SMOS passive microwave sensors are used to generate time series and local maps of brightness temperature. These microwave signatures serve in the analysis of the possible spatial and temporal correlation between ARs events and sea ice and snow characteristics. Initial findings suggest that ARs and their subsequent gravity waves may significantly affect the wetting of sea ice and of the snow on it leading to increased melting of the MIZ. This study will improve the methods to inform models used to forecast the impact that extreme atmospheric events can have on sea ice and snow, offering new directions to investigate the coupled ocean-sea ice-atmosphere system in a changing climate.
  A glacier-based reconstruction of the Last Glacial Maximum climate in the southern European Alps    
Authors: Rettig Lukas , Monegato Giovanni , Kamleitner Sarah , Spagnolo Matteo , Ribolini Adriano , Ivy-Ochs Susan , Rea Brice R. , Gianotti Franco , Mozzi Paolo
Published: March 2024

Improved records of precipitation and temperature are crucial to understand the evolution of Alpine glaciers during the Last Glacial Maximum (LGM). Palaeoclimate models and proxy data have suggested an increased moisture supply to the southern face of the Alps during the LGM, following a south-ward shift of the North-Atlantic jet stream. Ground control for such models, however, has been lacking for many sectors of the Alps, and regional climatic gradients have therefore remained poorly constrained. Here, we present new insights into the LGM palaeoclimate in the southern Alps, using the Equilibrium Line Altitudes (ELAs) of marginal glaciers as proxy. Marginal glaciers include ice caps, cirque, and valley glaciers that throughout the LGM remained isolated from larger outlet lobes connected to the Alpine ice network. Several sites of marginal glaciation were investigated through a combination of geomorphological mapping, surface exposure dating (both 10Be and 36Cl dating), and numerical reconstructions of palaeoglacier geometries and ELAs.The chronological data indicate that marginal glaciers across the southern Alps reached their maximum extent by ca. 24 ka and that an important readvance occurred at 19 ka, at the end of the LGM. Reconstructed palaeoglacier ELAs show considerable variations, from ca. 1100 m a.s.l. in the Julian and Carnic Prealps (SE-Alps) up to almost 2000 m a.s.l. in the Maritime Alps (SW-Alps). Minor differences between the sites can be attributed to topoclimatic factors (i.e., received solar radiation related to catchment aspect). Spatial trends in ELA, however, primarily reflect regional climatic gradients. More specifically, we recognised: (1) a N-S gradient related to increasing summer temperatures with lower latitudes, and (2) a strong E-W gradient driven by precipitation. For all sites, our data indicate little to no reduction in LGM precipitation compared to the present day, highlighting the importance of substantial precipitation for the build-up of marginal LGM glaciers in the southern Alps.
Modelling the North Atlantic: How parameterizations affect model biases and uncertainties
Authors: Juricke Stephan , Bagaeva Ekaterina , Danilov Sergey , Koldunov Nikolay
Published: March 2024

In this presentation, we discuss the role of a variety of parameterizations for simulating ocean dynamics in the North Atlantic and how they contribute to biases and model uncertainties. Their effect is analyzed via a range of diagnostics and model setups.Many of the crucial processes in the ocean still need to be parameterized in state-of-the-art global ocean and climate models. Among those processes are mesoscale ocean eddies and mixed layer dynamics which cannot be fully resolved in most multidecadal simulations. However, they play a crucial role in setting the dynamic and hydrographic conditions in the North Atlantic and the global oceans. Increasing resolution tends to improve some of the long-standing ocean biases, but is very costly and makes it difficult to disentangle which specific processes or boundary conditions are driving certain improvements.A consequence of imperfect process parameterizations are systematic errors resulting in large model biases. Furthermore, they can lead to inaccurate representation of the chaotic evolution of the ocean system, leading to insufficient representations of forecast uncertainties via ensemble simulations. In the North Atlantic, both of these consequences play a large role, leading to strong model biases and a general underdispersion of ensemble forecasts.Classical biases of ocean models at so called eddy-permitting resolution, where mesoscale eddies are barely resolved, are related to overdissipation of kinetic energy and enhanced diffusion of tracers. We introduce a set of parameterizations that tackle the overdissipation of kinetic energy via specific viscosity schemes, including schemes that reinject some of the overdissipated energy back into the system. A combination of such schemes reduces classical ocean biases such as the North Atlantic cold bias by enhancing eddy activity and improving the path of mean currents such as the Gulf Stream. In addition, we demonstrate how stochastic methods can be used to account for parameterization uncertainties in the North Atlantic, quantifying the role of parameterization errors in ocean and climate simulations. These new schemes come at a small additional computational cost, especially compared to higher resolution simulations, and provide a means of understanding the origin of model biases and uncertainties.
Investigation of climatic changes for hailstorms over the Alps using spatiotemporal satellite imagery and self-supervised machine learning
Authors: Bigalke Paula , Acquistapace Claudia , Corradini Daniele
Published: March 2024

Severe hailstorms are becoming more frequent in Central Europe showing increasing interannual variability. The Pre-Alpine and Alpine region seems to be especially affected due to its complex terrain, that initiates convection and can intensify many hail favoring processes. This results in increasingly strong large hail events, which are often very local phenomena. Ground-based observations from weather radars are most reliable for detecting hail, however, prove to be challenging in the Alpine region due to interference at mountain ranges.Passive Microwave satellite observations offer a useful alternative for detecting hail: a probability for hail can directly be derived from Passive Microwave channels with a high spatial coverage. However, this data is only available at certain times during satellite overpasses, thus, capturing only a few of these events. The highest temporal coverage is given by visible, near-infrared and infrared data from MSG. Though not directly sensitive to hail its high spatiotemporal resolution can identify early stages of severe storm developments.Recently, self-supervised machine learning approaches have been used to classify spatial cloud patterns from satellite measurements from MSG over the Atlantic and Germany. The model learns to sort similar cloud organization patterns into the same classes.In this work, we aim at adapting this model to also include the temporal component to then classify the evolution of typical cloud patterns leading to severe hailstorms over the Alpine region. The framework will later be used to characterize changes in spatiotemporal evolution of large hail bearing systems and associated environmental conditions across a multi-year dataset. First steps are presented here including the investigation of the optimal training dataset using the available data sources.
Models underestimate the deceleration of North Atlantic Ocean ventilation from 1985 to 2014
Authors: Guo Haichao , Koeve Wolfgang , Kriest Iris , Frenger Ivy , Oschlies Andreas
Published: March 2024

The deceleration of the North Atlantic Ocean ventilation reduces its ability to supply oxygen to the deep ocean and to absorb anthropogenic carbon dioxide from the atmosphere, bringing far-reaching impacts on the marine ecosystems and the climate system. Here we assessed the temporal variations of ocean ventilation age inferred from chlorofluorocarbon-12 (CFC-12) based on Inverse Gaussian Transit Time Distribution mean ages within the North Atlantic Ocean. Our study utilizes Global Ocean Data Analysis Project products (GLODAPv2.2022) as well as simulations conducted with seven Earth System Models in the framework of CMIP6. These simulations were subsampled analogously to the observational sampling schedules. The findings illuminate a noteworthy difference between observations and simulations. Between 1985 and 2014 observations suggest that the North Atlantic Ocean experienced a 13.2% increase in ventilation age, while the model simulations showed only a 4.8%±3.8% increase. This striking difference indicates that the real-world North Atlantic is undergoing a more rapid deceleration in ventilation than what state-of-the-art climate models simulated. We propose that this systematic underestimation of ventilation deceleration in models contributes to their failure to accurately estimate the rate of ocean deoxygenation.
Ionospheric Disturbance Index for GNSS Precise Positioning: Comparison between GIX and ROTI over China 
Authors: Fu Zhihao , Shen Xuhui , Wang Ningbo , Li Ang
Published: March 2024

The spatial gradient of Total Electron Content (TEC) demonstrates a notable correlation with occurrences of ionospheric scintillation and degradation in Global Navigation Satellite Systems (GNSS) performance. To assess the degree of the ionosphere perturbation over China, we utilize two ionospheric disturbance indices, i.e., the Gradient Ionospheric Index (GIX) and Rate of Total Electron Content Index (ROTI). Analyzing data from over 230 GNSS stations in China, we investigated the consistency and differences in ionospheric irregularities as indicated by GIX and ROTI, focusing particularly on the impact of horizontal gradients of vertical TEC (VTEC) on GNSS positioning. Experimental results indicate that the occurrence of ionospheric irregularities and the degradation of kinematic Precise Point Positioning (PPP) performance are more closely aligned with the strong horizontal VTEC gradients indicated by GIX, both in evolutionary characteristics and temporal variations, compared to ROTI. Notably, severe position errors and strong horizontal gradient of VTEC were consistently observed throughout the entire duration of St. Patrick's Day storm, including the prestorm period (06-16 UT on 16th March), the main phase (06-13 UT on 17th March), and the late recovery phase (05-14 UT on March 19th). Furthermore, significant PPP degradation (3-D PPP>1m) occurred not only preceded the intensification of ROTI but also in periods of stable ionospheric conditions indicated by ROTI values below 0.4 TECU/min, particularly on March 19th. The degradation primarily occurred in regions with significant irregularities, especially when the horizontal VTEC gradient surpassed a specific threshold (GIXx, P95+ > 55 mTECU/km, 1 mTECU/km = 10−3 TECU/km). These findings indicate the potential of GIX in effectively capturing the effects of ionospheric disturbances and enhancing the safety and accuracy of GNSS navigation and positioning.
Development of a global and dynamic map of wetland and inundated areas based on microwave remote sensing product (GIEMS-2) over 1992-202
Authors: Bernard Juliette , Prigent Catherine , Jimenez Carlos , Saunois Marielle , Frappart Frederic , Normandin Cassandra , Zeiger Pierre , Peng Shushi , Xi Yi , Fluet-Chouinard Etienne , Zhang Zhen
Published: March 2024

Wetlands and inundated areas cover only a few percent of the Earth's surface. However, they play an important role in freshwater regulation, biodiversity, and climate. In particular, a significant proportion of atmospheric methane is emitted from these areas [1]. There is therefore a need for data that can reliably capture surface water interannual variability over the past decades. The Global Inundation Extent from Multi-Satellites (GIEMS-2) [2] is based on microwave remote sensing data (SSM/I and SSMIS). It provides a 0.25° global monthly estimate of inundated and saturated areas and has been extended to 2020 to cover three decades (1992-2020). First, an evaluation of GIEMS-2 together with other products is presented. Key findings include consistent spatial patterns, seasonal cycles and time series anomalies observed by GIEMS-2 with the other observational datasets studied (MODIS-derived surface water, CYGNSS-derived surface water, river discharge). This highlights the interest of such a product for the calibration of hydrological models, as has been achieved for example by Xi et al. (2022) for TOPMODEL [3]. In a second part, the use of GIEMS-2 for the estimation of methane emissions from wetlands and inundated areas is discussed. GIEMS-2 has been processed with other data sources to derive a dynamic map of wetlands (including peatlands), open water (lakes, rivers, reservoirs) and rice paddies. This comprehensive product allows a consistent view of the area between the different classes, limiting problems of double counting and miss counting. This new database can then be used to constrain the extent of the water surface in models estimating methane flux rates, in order to study the influence of surface water changes on interannual variations in methane emissions. [1] Marielle Saunois et al. “The Global Methane Budget 2000–2017”. In: Earth System Science Data 12.3 (July 2020), pp. 1561–1623. doi: 10.5194/essd-12-1561-2020. url: https://doi.org/10.5194/essd-12-1561-2020.[2] C. Prigent, C. Jimenez, and P. Bousquet. “Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2)”. In: Jour-nal of Geophysical Research: Atmospheres 125.3 (Feb. 2020). doi: 10.1029/2019jd030711. url: https://doi.org/10.1029/2019jd030711.[3] Yi Xi et al. “Gridded Maps of Wetlands Dynamics over Mid-Low Latitudes for 1980–2020 Based on TOPMODEL”. In: Scientific Data 9.1 (June 2022), p. 347. issn: 2052-4463. doi: 10.1038/s41597-022-01460-w
Subseasonal to seasonal forecasts using Masked Autoencoders
Authors: Thümmel Jannik , Schlör Jakob , Strnad Felix , Goswami Bedartha
Published: March 2024

Subseasonal to seasonal (S2S) weather forecasts play an important role as a decision making tool in several sectors of modern society. However, the time scale on which these forecasts are skillful is strongly dependent on atmospheric and oceanic background conditions. While deep learning-based weather prediction models have shown impressive results in the short- to medium range, S2S forecasts from such models are currently limited, partly due to fewer available training data and larger fluctuations in predictability. In order to develop more reliable S2S predictions we leverage Masked Autoencoders, a state-of-the-art deep learning framework, to extract large-scale representations of tropical precipitation and sea-surface temperature data.  We show that the learned representations are highly predictive for the El Niño Southern Oscillation and the Madden-Julian Oscillation, and can thus serve as a foundation for identifying windows of opportunity and generating skillful S2S forecasts.
Understanding the influence of soil compaction on greenhouse gas emissions
Authors: Lewis Elysia , Longo Matteo , Rocco Sebastiano , Dal Ferro Nicola , Cabrera Miguel , Lazzaro Barbara , Morari Francesco
Published: March 2024

Soil structure plays a crucial role in determining greenhouse gas (GHG) emissions from agricultural activities. Changes in soil structure, such as compaction, can alter the factors that govern GHG fluxes, leading to an increased potential for emissions. The extent to which soil compaction explains GHG emissions is still under investigation. To address this knowledge gap, a two-year experiment was conducted in Northeast Italy to examine the influence of soil compaction on GHG emissions. The experimental site comprised of 20 lysimeters representing five different cultivation systems, each with four replicates: bare soil (BS), conventional (CV), conventional + with cover crop (CC), conservation with shallow compaction (0-25 cm, CA1), and conservation with deep soil compaction (25-45 cm, CA2). Maize was cultivated as the main crop in 2022, followed by grain sorghum in 2023, with solid digestate (300 kg N ha-1) originated from mixed agricultural waste used for fertilization. Winter wheat served as a cover crop where necessary. Continuous automatic measurements of CO2, N2O, and CH4 emissions were collected using a non-steady state through-flow chamber system and an FTIR gas analyzer, enabling the capture of up to seven fluxes per day for each replicate. Additionally, water-filled pore space (WFPS) and soil temperature were continuously monitored in the 0-30 cm soil profile using Time Domain Reflectometry (TDR) sensors and thermocouples. Cumulative CO2 reached its peak under CV, followed by CC. Notably, observable N2O emissions were predominantly detected in the two weeks following fertilization with peaks reaching 0.8 kg N-N2O ha-1d-1 under CC, while CA1 and CA2 exhibited lower emissions. Conversely, CH4 emissions were negligible, and the soil primarily acted as a sink. The study provides crucial insights for sustainable agriculture by highlighting the impact of soil compaction on GHG.
CMIP6 models evaluation using multi-resolution analysis and satellite observations : study of the atmospheric water vapor 
Authors: Ngoungue Langue Cedric Gacial , Brogniez Hélène , Naveau Philippe
Published: March 2024

Water vapor is one of the fundamental atmospheric components, and as such, is one  Essential Climate Variable  (ECV) monitored by the Global Climate Observing System. In this work, the global water vapor Climate Data Record (CDR) generated within the ESA Water Vapor climate change initiative project (WV_cci) is used as reference (daily, 0.1°, 2003-2014) to evaluate a sample of the Coupled Model Intercomparison Project phase 6 (CMIP6) as well as the fifth generation ECMWF reanalysis (ERA5), with a focus on temporal signal decomposition. This temporal decomposition is performed using multi-resolution analysis (MRA). MRA is a mathematical tool which consists of decomposing a signal into its subcomponents on different time scales. Using this tool, the representation of the total column water vapor over the tropics in the CMIP6 models and ERA5 can be assessed separately from daily to annual and decadal time scales, including monthly and seasonal time scales. This approach is powerful for the  identification of  the relevant time scales for which CMIP6 predictions are most reliable. Hence, at the global-tropical scale, the MRA decomposition of the water vapor signal shows a good correlation between CMIP6 and WV_cci on both seasonal (2 - 8 months) and annual (1 - 1.4 year) time scales. Using a linear regression, we attempt to reconstruct the WV_cci signal using the CMIP6 models and ERA5 as explanatory variables based on the correlation found between the products and WV_cci at each level of decomposition. Such reconstruction highlights the scales of variability that are closest to the observed one. The presentation will detail the MRA approach and the most prominent results, as well as an extension to other parameters linked to atmospheric water vapor distribution, namely cloud cover and types and sea surface temperature. 
Critical zone modelling for alpine catchments : towards a calibration-light model of snow hydrology
Authors: Reverdy Alix , Gupta Aniket , Le Lay Matthieu , Cohard Jean-Martial , Voisin Didier , Lafaysse Matthieu , Rapp-Henry Lucie
Published: March 2024

The current operational hydrological modelling of mountainous catchments mostly relies on conceptual and semi-distributed models, which are calibrated based on historical discharge measurements. As a consequence, in spite of their good performance in seasonal river runoff prediction, they often fail to project the impact of climate change on the hydrological cycle over decades. This is due to a limited representation of physical processes and their inability to simulate water paths and their modification.To overcome such limitations, we applied the data-intensive and calibration-light critical zone model ParFlow-CLM, to a highly instrumented mid-elevation catchment (0.15 km² area between 1950 and 2150 m.a.s.l) close to the Lautaret Pass, in the French Alps. Our setup showed promising results with good correlation when compared to the observed discharge (KGEnp = 0.91), consistent evapotranspiration compared to local Eddy-Covariance measurements and realistic snow disappearance patterns. This simulation served as the proof of concept towards the feasibility of physical-based critical zone hydrological modeling in alpine terrain. It highlighted the necessity of a careful redistribution of the locally observed meteorological forcing including solid precipitation and incoming radiations. It also pointed out the necessity of well simulating the snow aging and albedo to represent streamflow regimes all along the snow period. It was achieved through manually incorporating the impact of grain growth, refreezing and dust in the albedo parameterization (snow age) within the current CLM version of ParFlow (CLM 3.5).In this presentation we will introduce our strategy for a calibration-light model of mountain watersheds by developing realistic but data-parsimonious strategies of data collection and processing. Specifically, we aim at taking into account the impact of complex topography on meteorological forcing (shading, radiation incidence angle, wind acceleration, snow redistribution, altitude gradient). In this framework we will compare several land surface snow schemes (CLM3.5, CLM5, Crocus and MORDOR) using ESM-SnowMIP data. This will help to quantify the improvement expected for moving from CLM3.5 to CLM5 and compare it further with a complex snow scheme (Crocus) and an advanced degree-day approach (MORDOR snow module). Implementation of CLM5 in ParFlow will also enable the representation of dynamic vegetation processes. This will be the first step towards a functional critical zone modeling for a mid-elevation alpine catchment, which will allow the reanalysis and projection of hydrological conditions with minimum calibration.
Insight in Antarctic aerosol particle composition regarding free amino acids
Authors: Breitenstein Christina , van Pinxteren Manuela , Zeppenfeld Sebastian , Herrmann Hartmut
Published: March 2024

As a pristine region, the Antarctic peninsula can be a model for the preindustrial atmospheric environment and, accordingly, give insights in processes related to climate change. Most studies performed in this region focus on either aerosol sources, for example the ocean, or the chemical composition of aerosol particles. Wind and wave driven physical mechanisms for particle mobilization (e.g. bubble bursting) lead to the formation of sea-spray aerosol particles (SSA) consisting of sea salt together with primary organic aerosol (POA), which is rich in organic matter (OM). The molecular nature of this OM is not fully understood to this day. The second-largest fraction of OM are likely proteins, which consist of amino acids (AA). AA contribute massively to the global nitrogen cycle and have impact on cloud chemistry, for example by acting as cloud condensation nuclei (CCN) or ice nucleating particles (INP).             To date, chemical analysis of AAs is often provided as sum parameter, as robust methods for their analysis in original form are lacking. Therefore, individual differences between sample sets cannot be determined and information on biotic or abiotic transfers are lacking. For that reason, we developed a hydrophilic interaction liquid chromatography electrospray ionization time-of-flight mass spectrometry (HILIC-ESI-TOF-MS) method, utilizing the potential of HILIC to separate more polar analytes, compared to standard LC methods. Advantages of the developed method are not only its broad window of analytes, but also its robustness as it can be applied to complex marine samples with a short sample preparation, as derivatization steps are not needed.This new method was applied to Antarctic low volume size segregated aerosol samples. Due to the nature of HILIC, the polar analytes show a good retention and separation from matrix components. Through these measurements, further insights can be gained on the enrichment and chemo-selective transfer of AA from the ocean to the atmosphere and their respective degradation processes. A higher variation and concentration of FAA than in previous literature was observed, with dominating marine derived FAAs. First results, also regarding the influence of air masses to the composition of different AA and comparison with other constituents, will be shown.SourcesJaber et al. (2021) Biogeosciences, 18, 1067–1080.Zeppenfeld et al. (2021), ACS Earth Space Chem. 5, 1032−1047
Development of mineral sector cooperation in Eastern and Northern Finland
Authors: Pihlaja Jouni
Published: March 2024

On a global level, clean transition, energy transition and electrification of transport, among other things, require a lot of strategic or critical raw materials. In Europe, too, the importance of increasing the self-sufficiency of raw material production has come to light in order to secure the operating conditions of industry supporting the green transition.For promoting the sustainable use of raw materials in Eastern and Northern Finland, it has been decided to intensify cooperation between regional, national and international actors.In the regional level, the development of the mineral industry is carried out by the regional Mining Hubs. Nationally, it will be significant in Finland during 2024 to develop a new mineral strategy to promote the growth of the mineral and battery cluster in order to enable a clean and digital transition. The aim of the new strategy is to produce a common view of the current situation of the Finnish mineral cluster, policy objectives, main lines and necessary measures, including the development of a circular economy. At the European level the new Critical Raw Materials Act aims to ensure the EU's access to a secure, diversified, affordable and sustainable supply of critical raw materials and also strengthening Europe's strategic autonomy.Between the actors of regional, national and EU level, the R&I and training organisations play a key role in developing cooperation. In the Eastern and Northern Finland region, the Geological Survey of Finland, the University of Oulu, Kajaani University of Applied Sciences and The Federation of Education in Central Ostrobothnia have decided to join forces to enable this collaboration. A special tool for this is the JTF project “Development of the mining sector in Lapland, Northern Ostrobothnia, Kainuu and Central Ostrobothnia” (KAKE).  The planned measures include e.g. international networking, competence development, workshops, innovating new R&I projects and linking companies to project activities.