bluuebunny/crossref_metadata_2025 huggingface dataset:
Infrasound from lightning measured in northern Romania
Authors: Ghica Daniela , Antonescu Bogdan
Published: March 2024

During thunderstorms in northern Romania, numerous infrasonic signals are emitted due to the process of lightning and thunder. Association between infrasound detections into 0.5 to 7 Hz frequency band and lightning flashes detected by the Arrival Time Difference lightning network (ATDnet) managed by the Met Office within 50 km from the BURARI infrasound station is systematically investigated. Statistical results are presented based on infrasound and lightning observations during summer months (June to August) from 2020 to 2022.Assuming direct wave propagation path, infrasound detections can be successfully correlated with ATDnet lightning detections up to distances of 50 km from the infrasound array. Long-duration trains of frequent sharp spikes in the amplitude observed into infrasound recordings during thunderstorms are associated with lightning discharges. Acoustic signatures of lightning activity show short-lived disturbances with dominant frequency of approx. 3 Hz and amplitudes ranging from 0.01 up to about 0.5 Pa. In order to associate BURARI measurements with ATDnet detections, a relationship between infrasound time-of-arrival and time of discharge signals is applied. A maximum deviation of 10o between observed infrasound back-azimuth and back-azimuth of ATDnet detections is allowed.For several cases (days with the largest number of lightnings), detection conditions of infrasound from lightning are detailed, and some characteristics are analyzed (e.g., amplitude, frequency, trace velocity and spectrograms in the frequency range from 0.5 to 10 Hz). Correlations with synoptic charts, regional lightning activity maps and electric field measurements could be performed.
How Machine Learning and Satellite Data Enhance Near-Real Time Detection of Volcanic Activity Worldwide
Authors: Cariello Simona , Corradino Claudia , Torrisi Federica , Del Negro Ciro
Published: March 2024

Nowadays, several satellite missions provide thermal infrared data at various spatial resolutions and revisit time, enabling nearly continuous monitoring of thermal volcanic activity worldwide. In addition, computer vision techniques, based on Machine Learning (ML) and Deep Learning (DL) models, offer unique advantages in automatically extracting valuable information from large datasets. Here, we propose a Machine Learning approach that leverages the capabilities of such models, combined with nearly continuous high spatial resolution images (20 m) acquired from the Sentinel-2 MultiSpectral Instrument (S2-MSI), to detect high-temperature volcanic features and to quantify volcanic thermal emissions. The impact of the volcanic activity is assessed based on the spatial distribution of the erupted products. Spatially characterizing the detected thermal anomalies allows us to both highlight significant pre-eruptive thermal changes and estimate the areal coverage, length of the erupted products, and the lowest altitude reached by them. We utilize the entire archive of high spatial resolution Sentinel-2 data, which comprises more than 6000 S2-MSI scenes from ten different volcanoes around the world. By employing a “top-down” cascading architecture that integrates two distinct Machine Learning models, a scene classifier (SqueezeNet) and a pixel-based segmentation model (Random Forest), we achieve very high accuracy, specifically 95%. This result comes from overcoming the limitations of the scene-level DL classification model, which compresses the entire spatial and spectral information into one unique label, by relying on the pixel-level Random Forest (RF) model. The use of multiple models allows to create more robust and powerful predictors making this tool suitable for near real time volcanic activity detection. These results demonstrate that the cascading system processes any available S2-MSI image in near-real time, providing a significant contribution to the monitoring, mapping, and characterization of volcanic thermal features worldwide.
The relationship between forest fragmentation and extreme high temperature
Authors: Du Ran , Gao Yanhong
Published: March 2024

Warming lead to a surge in extreme climate events, including heatwaves, droughts, flooding, and wildfires. Numerous studies demonstrate that these occurrences have become more frequent, which exerts notable influences on socio-economic development and human health. Besides natural climate changes, land use and land cover changes (LULCC) play a crucial role in shaping extreme climates. As the most extensive land use type globally, forest has experienced great changes since the industrial evolution. Deforestation is one of the most notable global environmental issues. Besides the decrease of the coverage, fragmentation is one of the appearances of deforestation. Many studies have demonstrated that forest distribution shows high agreements with climate regimes generally, however, the relationship between forest fragmentation and extreme climate events remain unclear. This study analyzes the relation between forest fragmentation and main extreme high temperature indices in 2000-2020. Global continental areas are categorized into regions with increased and decreased forest fragmentation index. Regions with increased index, such as the southeast Amazon, Congo Basin, and parts of the Southeast Asia are emphasized. The 11 extreme temperature indices are analyzed responded to the forest fragmentation index change. This study could provide insights for forest management strategies adapting to climate change in the future.
Identifying effective strategies for cereal cultivation under dry climate in Bavaria
Authors: Shlomi Omer , Schauberger Bernhard , Wiesmeier Martin , Sümmerer Manuel
Published: March 2024

Farmers cultivating cereals in Germany have experienced unfavorable conditions during the recent decade due to more frequent droughts and heat episodes (Lüttger & Feike, 2018). These events are likely to aggravate in the future (Trnka et al., 2014). Drought and heat-related yield reductions were already being seen in cereal crops all over the country (Webber et al., 2020; Schmitt et al., 2022).Franconia covers the northern part of Bavaria, the the most important region for wheat and silage maize cultivation. At the same time, Franconia is the driest region in Bavaria with mean annual precipitation
Very minor ions in the magnetosphere: a hub of the mesospheric, ionospheric, magnetospheric, solar wind, lunar, and meteoroid sciences.
Authors: Yamauchi Masatoshi , Dandouras Iannis , Würz Peter , Kastinen Daniel , Plane John , Schulz Leonard , Yau Andrew , Kistler Lynn , Christon Steve , Haaland Stein , Saito Yoshifumi , Nozawa Satonori , Mann Ingrid , Watanabe Shigeto , Gunnarsdottir Tinna
Published: March 2024

This is the summary of findings by ISSI topical team on the molecular and metallic ions in the magnetosphere.Heavy molecular and metallic ions with mass ≥ 27 (Al+, N2+, NO+, O2++, Fe+, Cu+, Ti+, etc) in the magnetosphere provide independent information on the ion sources and entry route to the magnetosphere from traditional four components (H+, He++, He+, O+). There are four ultimate sources of these heavy molecular and metallic ions: the solar wind (high charge-state metallic ions), the ionosphere (mainly molecular ions), the atmospheric metal layers (low charge-state metallic ions and metal-rich molecular ions that ultimately originating from ablation of meteoroids and possibly space debris), and the surface and exosphere go the Moon (low charge-state metallic and molecular ions). The lunar origin low charge-state metallic ions, if separated from the ionospheric origin, give independent information on the entry route into the magnetosphere for ions of much larger gyroradius than the solar wind ions. The atmospheric-origin molecular ions are essential in understanding energization, ionization altitudes, and upward transport in the ionosphere during various ionospheric and magnetospheric conditions. These ions are also important when considering the evolution of the Earth's atmosphere on the geological timescale. So far, we cannot dismiss any of four possible sources with the existing data because only a few terrestrial missions have been equipped with instrumentation dedicated to separate these molecular and metallic ions, within only a limited energy range (cold ions of < 50 eV and energetic ions of ~100 keV or more) and a limited mass range (mainly ≤ 40 amu). This is far too limited to make any quantitative discussion on the very heavy ions in the magnetosphere.  Under this circumstance, it is worth to re-examine, using available tools, the existing data from the past and on-going missions, including those not designed for the required mass separation, to search for these ions.  We synthesised these patchy observations and combining all sources with updated models. With such knowledge, we re-examined available data and model that actually provided important indications of the sources of these heavy ions and their amounts that have been overlooked to date.  Finally, we note the possible future contamination of specific masses by ablated space debris (Al, but also Li, Fe, Ni, Cu, Ti, and Ge) in the coming decades.
Marine functional connectivity through the ages: geological and historical perspectives
Authors: Agiadi Konstantina , Caswell Bryony , Darnaude Audrey ,   the Q-MARE - SEA-UNICORN workshop participants
Published: March 2024

Marine Functional Connectivity (MFC) refers to all the unimpeded flows of matter, genes and energy that are caused by the movements of marine life that occur at various spatial and temporal scales. Climate, palaeogeography, ocean circulation, biogeochemical cycles, evolution of Life and human activities control MFC over the long term. The geological and historical records offer valuable data on ecological and societal change that can be used to understand the evolution of MFC over time. We explain the links between these long-term drivers and MFC processes, as well as the diverse archives that can be used to study them: the sedimentary record, biogeochemical proxies, fossil assemblages, sclerochronological archives, genetic data, zooarchaeological remains, archaeological artefacts and historical sources.
A Transformer-Based Data-Driven Model for Real-Time Spatio-Temporal Flood Prediction
Authors: Pianforini Matteo , Dazzi Susanna , Pilzer Andrea , Vacondio Renato
Published: March 2024

Among the non-structural strategies for mitigating the huge economic losses and casualties caused by floods, the implementation of early-warning systems based on real-time forecasting of flood maps is one of the most effective. The high computational cost associated with two-dimensional (2D) hydrodynamic models, however, prevents their practical application in this context. To overcome this drawback, “data-driven” models are gaining considerable popularity due to their high computational efficiency for predictions. In this work, we introduce a novel surrogate model based on the Transformer architecture, named FloodSformer (FS), that efficiently predicts the temporal evolution of inundation maps, with the aim of providing real-time flood forecasts. The FS model combines an encoder-decoder (2D Convolutional Neural Network) with a Transformer block that handles temporal information. This architecture extracts the spatiotemporal information from a sequence of consecutive water depth maps and predicts the water depth map at one subsequent instant. An autoregressive procedure, based on the trained surrogate model, is employed to forecast tens of future maps.As a case study, we investigated the hypothetical inundation due to the collapse of the flood-control dam on the Parma River (Italy). Due to the absence of real inundation maps, the training/testing dataset for the FS model was generated from numerical simulations performed through a 2D shallow‐water code (PARFLOOD). Results show that the FS model is able to recursively forecast the next 90 water depth maps (corresponding to 3 hours for this case study, in which maps are sampled at 2-minute intervals) in less than 1 minute. This is achieved while maintaining an accuracy deemed entirely acceptable for real-time applications: the average Root Mean Square Error (RMSE) is about 10 cm, and the differences between ground-truth and predicted maps are generally lower than 25 cm in the floodable area for the first 60 predicted frames. In conclusion, the short computational time and the good accuracy ensured by the autoregressive procedure make the FS model suitable for early-warning systems.
Marine heatwaves: Can we predict them in the Barents Sea?
Authors: Langehaug Helene R. , Sandø Anne Britt , Hordoir Robinson , Counillon Francois , Chiu Ping-Gin , Raj Roshin
Published: March 2024

Marine heatwaves (MHW) can have large negative impacts on life in the ocean, such as kelp forest and corals. These environments are vital for protecting a range of different species in the ocean. With global warming, the occurrence and intensity of MHW are expected to increase, also in the polar regions. The Barents Sea has experienced large climate changes, becoming less influenced by sea ice during the last decades. Being able to predict the likelihood of MHW to occur in the Barents Sea could be highly beneficial to fisheries, aquaculture, and other relevant stakeholders. Such information could be useful in long-term risk assessment. In this study, we assess for the first time the skill of the Norwegian Climate Prediction Model (NorCPM) in predicting the likelihood of MHW. For this analysis, we focus on intense MHW in July 2016 taking place in the Barents Sea, and previously documented by satellite data. We find promising results in the seasonal predictions from NorCPM, where the predictions show increased probability for MHW to occur in July 2016 compared to July 2015 (when the MHW activity was lower than in 2016). The increased probability was already seen six months prior to the event. Furthermore, we downscale the results from the global NorCPM to a more refined grid with a horizontal resolution of 10km. This test case shows that downscaling can provide valuable information on the subsurface signature of MHW. We found the event in July 2016 to be shallow (down to about 50m) compared to another MHW event in July 2013, where warm anomalies occupied the whole water column. These results suggest that the event in July 2016 was atmospheric-driven, consistent with a previous study, whereas the event in 2013 is more likely to be ocean-driven. The results from this case study are promising for future seasonal prediction of MHW using NorCPM, and more in-depth studies are needed to quantify the predictive skill for different cases and different regions.
When winter is weird: Quantifying the change in winters across the Arctic
Authors: Rasmussen Laura Helene , Markussen Bo , Ditlevsen Susanne
Published: March 2024

Arctic winter climate is rapidly changing, with more variable snow depths, spring snowmelt timing, and more frequent midwinter thaw events. Less predictable conditions disrupt ecosystem balances and development in Arctic communities, and understanding winter variability across the Arctic and its influence on climate the whole year is needed to mitigate consequences of changing winters. However, access to in situ measured data has been extremely limited and scattered in local databases. Hence, cross-Arctic winter studies are few and based on remotely sensed data with larger spatial and temporal coverage, but less local sensitivity, and the winter contribution to annual average temperature change has not been investigated across the Arctic.In this project, which runs January 2024-December 2025, we 1) obtain, clean and standardize in situ soil surface temperature, snow depth and soil moisture data from climate monitoring programs across the Arctic and create a unique database with cross-Arctic in situ winter climate data from the last appr. 30 years. We will use this dataset to 2a) estimate the accuracy of remotely sensed soil surface temperature, snow depth and soil moisture data using the regression model with the best fit, and quantify the bias, for each major Arctic region. We further 2b) construct an open access Winter Variability Index (WVI) for each major Arctic region based on the winter phenomena (average snow depth, snowmelt date, frequency of winter thaw events) that are most important drivers of a clustering analysis such as hierarchical clustering or autoencoders. Finally, we 3) use the change in WVI and in annual mean temperatures for each decade in a function-on-function regression analysis, which will quantify the contribution of winter variability change to annual average temperature changes in each Arctic region.  The project will produce a comprehensive dataset with potential for further research and will improve our region-specific understanding of remotely sensed data accuracy, and the WVI allows scientists or local communities to classify Arctic winter data within a quantitative framework of pan-Arctic winter variability also in the future, and to understand how important changes in winter variability is for Arctic climate changes the whole year.
Observations of magnetic reconnection between the crustal magnetic field and the ICB at Mars
Authors: Qiu Xinzhi , Yu Yiqun
Published: March 2024

Based on magnetic field and plasma measurements from the Mars Atmosphere and Volatile EvolutioN (MAVEN) mission, we present the first observation of magnetic reconnection occurring between the closed crustal magnetic field and the Ion Composition Boundry(ICB) at the dayside of Mars. Notably, distinctive typical features typical of reconnection, such as the Hall magnetic field and plasma outflow, have been unambiguously detected. Our findings robustly support the occurrence of reconnection at Mars, specifically highlighting the interaction between the interplanetary magnetic field in the induced magnetosphere and the closed crustal magnetic field. This reconnection event induces significant alterations in the magnetic field topology, exerting a profound influence on the escape dynamics of ions.
Preliminary Study on Minutely Sample Labeling Algorithm with Prior Knowledge Model 
Authors: Leng Liang , Zhang Tong , Xiao Yanjiao , Liu Jie , Gao Chulin , Sun Jing
Published: March 2024

Keywords: Dual-Polarization, Minutely Dynamic Tracking, Prior Knowledge Physical Constraints, Deep Learning
Considering the suddenness and locality of short duration heavy rainfall (SDHR) and the motion of the associated convection precipitation echo, we propose an automatic sample labeling algorithm using minutely automatic weather station data. Firstly, we filtered out the stations with a sliding cumulative hourly rainfall exceeding 20 millimeters, and then conducted dynamic tracking for several hours. The tracking window remains consistent during an event. Then we extract corresponding volume scan data and obtained 21,706 samples.  Compared to grid data, these samples retain the raw radial reflectivity and velocity, which can reveal the dynamic characteristics within precipitation cloud. Moreover, dual-polarization parameters can offer precise advantage in estimating SDHR. 
Based on the novel spatiotemporal prior-informed deep network (STPiDN) that incorporates seismic spatiotemporal prior knowledge into deep neural networks using limited historical samples[1]. We propose an end-to-end SDHR model that considers fundamental physical prior knowledge and spatiotemporal development of heavy rainfall events. We develop a gated convolutional encoder-decoder network that effectively characterizes the spatiotemporal patterns of SDHR. This network is built upon fluid-individualized variation-guided gating convolutional units, and a comprehensive loss function. In particular, a spatiotemporal consistency loss is designed to facilitate the generation of predictive results that conform to the actual spatiotemporal patterns of SDHR formation, development, and dissipation . The model is trained based on gridded data, and the results show that the predictive model outperforms multiple existing models[2].
More efforts will involve training the proposed model based on radial samples, considering the basic physical prior knowledge and spatio-temporal development patterns of precipitation.
This work was supported in part by the Joint Research Project for Meteorological Capacity Improvement under Grant 22NLTSY015, Hubei Province Key R&D Plan 2023BCB119 and Hubei Province Meteorological Bureau Project 2022Y04.
[1] Liu, J., Zhang, T.*, Gao, C., and Wang, P.  (2023). Forecasting Earthquake Magnitude and Epicenter by Incorporating Spatio-temporal Priors into Deep Neural Networks. IEEE Transactions on Geoscience and Remote Sensing 61:5911413. DOI:10.1109/TGRS.2023.3281784.
[2] Zhang, T., Liu, J.*, Gao, C., Wang, P., Leng, L. Xiao, Y. (2023). Prior-Guided Gated Convolutional Networks for Rainstorm Forecasting. Journal of Hydrology. (Under review)
A bottom-up regionalization approach for extreme rainfall events
Authors: Hoch Jannis , Probyn Izzy , Marra Francesco , Lucas Chris , Savage James , Wing Oliver , Sampson Chris , Addor Nans
Published: March 2024

Intensity–duration–frequency (IDF) curves are representations of the probability that a given rainfall intensity over a given duration [GU1] will be exceeded [GU2] within a given period. To construct IDF curves, rainfall observations are required, ideally at the sub-daily temporal resolution. Unfortunately, such measurements are available only for a few locations world-wide. This poses a major challenge for simulations of global pluvial flood hazard and risk which require information of intensity, duration, and probability as boundary conditions.As an alternative to global IDF curves created from remotely sensed rainfall, we here propose a bottom-up approach which departs at the gauge level and employs machine-learning for regionalizing information on IDF curves from gauged to ungauged areas.To that end, we use available quality-controlled sub-daily precipitation data from the GSDR data set to derive Simplified Metastatistical Extreme Value (SMEV) parameters at around 10,000 locations world-wide. After combining these parameters with globally available data of precipitation drivers, a random forest regression model is applied. Results indicate that some SMEV parameters can be better regionalized than others. With globally available SMEV parameters, it is possible to obtain rainfall intensity for any combination of duration and frequency.We then evaluated these IDF maps against analytical intensities derived at the GSDR stations directly. Results show overall good agreement, yet the tails of the distributions are not entirely represented in our simulated intensities.  Additionally, we benchmarked our intensity maps against similar datasets such as PPDIST and GPEX. Last, we assessed practical implications by comparing flood maps created with the various datasets used as pluvial boundary condition. While there are fundamental differences in how each of the datasets is derived, our analysis indicates overall similar spatial patterns and distributions of rainfall intensities.While such data-driven approaches clearly depend on the quality and quantity of available sub-daily rainfall observations, our proposed bottom-up approach seems to be able to scale local data to global data applicable in both flood risk research and practice.
Stratospheric impact of the anomalous 2023 Canadian wildfires
Authors: Khaykin Sergey , Godin-Beekmann Sophie , Bekki Slimane , Tence Florent , Meziane Mehdi , Josse Beatrice , Pelletier Sophie , Hu Qiaoyun , Goloub Philippe , Laeng Alexandra
Published: March 2024

The frequency of extreme wildfires has increased as a response to the regional and global warming trends and there is an emerging realization of their impact on climate through emissions of smoke aerosols into the stratosphere. The 2023 wildfire season in Canada was unprecedented in terms of its duration, burned area and cumulative fire power, rendering it the most destructive ever recorded. Here we use various satellite observations (TROPOMI, OMPS-LP, OMPS-NM, MLS, CALIPSO, SAGE III) to quantify the stratospheric emissions of smoke aerosols and carbon monoxide by the 2023 Canadian wildfires and to characterize the long-range transport of smoke plumes in the stratosphere. Using multiwavelength lidar observations in Northern France, we show systematically distinct microphysical properties of UTLS smoke aerosols compared to their free-tropospheric counterparts.The analysis of satellite data reveals multiple episodes of smoke intrusions into the stratosphere through pyroconvection (PyroCb) and synoptic-scale processes (warm conveyor belt, WCB). Model simulations using MOCAGE chemistry-transport model, which included emission data from GFAS (Global Fire Assimilation System) are shown to accurately capture the synoptic-scale uplift of smoke into the UTLS and reproduce the spatial evolution of the aerosol plumes.We show that the multiple episodes of wildfire-driven stratospheric intrusions during Boreal Summer 2023 through PyroCb and WCB mechanisms are altogether responsible for the record-high and persistent season-wide smoke pollution at the commercial aircraft cruising altitudes and the lowermost stratosphere.
Geological risk analysis of geothermal developments in the Buda thermal karst area
Authors: Tóthi Tamara , Markó Ábel , Szilágyi Imre , Mádl-Szőnyi Judit
Published: March 2024

The Buda Thermal Karst system is one of the best-known hypogenic karst areas. In addition, it is located below the city of Budapest. The groundwater of the confined and unconfined carbonate system is characterised by complex gravity and buoyancy-driven flow. It is associated with a complex geological setup characterised by significant heterogeneity in permeability distribution, resulting in exploration uncertainty. The utilisation of thermal water in the area goes back to the Roman Empire through springs, but from the 19th century, wells have been deepened. In Budapest, outstanding thermal wells and projects have been developed. However, several unsuccessful attempts also occurred in SE Budapest due to geological uncertainty, lack of knowledge and proper pre-analysis.Motivated by the green transition and the energy crisis, the geothermal utilisation of the thermal water in the area has become the focus of interest nowadays. The geothermal risk assessment can be a helpful tool to quantify the uncertainties of the exploration of such a complex system. In this research, we develop a novel methodology to analyse and quantify the geological risk of geothermal developments, focusing on the deep and thick carbonate reservoir. The new approach is adapted from the petroleum industry and follows the risk assessment scheme of petroleum play analysis.The methodology was tested in a local study area in northeast Budapest, where the required geothermal capacity for heating was previously determined. Here, the Neogene siliciclastic sediments cover the Triassic carbonates, which serve as thermal water reservoirs. To estimate whether a future geothermal system can provide sufficient capacity, in this study, the probability of success is assessed by evaluating potential geological risk factors which can hinder geothermal production and reinjection. This is done by combining and evaluating datasets on aquifer and water quality, temperature and flow conditions using lithological data, geophysical logs (cavities), temperature-elevation, and pressure-elevation profiles. The outcomes of the research help in decision-making while preparing new geothermal developments.This research was supported and financed through the KDP-2021 Cooperative Doctoral Programme of the Ministry of Culture and Innovation of Hungary from the source of the National Research, Development and Innovation Fund, grant number: KDP 2021 _ELTE_C 1789026. The research is funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project.
Quantitatively evaluating the Community Earth System Model version 2 regarding the physical processes leading to heat extremes
Authors: Röthlisberger Matthias , Sprenger Michael , Beyerle Urs , Fischer Erich M. , Wernli Heini
Published: March 2024

Do heat extremes in climate model simulations form for the right physical reasons? Addressing this question is essential to further our confidence in heat extreme projections, to pinpoint potential model biases and thus for contributing to model improvements.
Here, we perform a detailed process-based evaluation of the Community Earth System Model version 2 (CESM2) regarding heat extremes and employ a Lagrangian approach to quantify advective, adiabatic and diabatic contributions to near-surface temperature anomalies (T’) during heat extremes in CMIP6-type CESM2 simulations.
Comparison of these results with results of an analogous analysis in ERA5 reveals that, qualitatively and at very large scales, heat extremes in CESM2 form in a physically similar way as in ERA5: Advective contributions dominate in storm track regions, diabatic contributions dominates over tropical and subtropical land regions, and adiabatic warming contributes significantly to heat extremes over subtropical oceans as well as extratropical land regions.
However, quantitatively and at regional scales, there are considerable differences: For instance, in Europe, the diabatic contribution to heat extremes in CESM2 roughly doubles that in ERA5, while the adiabatic contribution to CESM2 heat extremes is only about half of that in ERA5. Thus, in Europe the dominant physical processes that form near-surface T’ during heat extremes differ between CESM2 and ERA5. Preliminary analyses of the underlying causes suggest that differences in climatological temperature gradients (i.e., a mean-state differences) as well as systematic differences in the trajectories of air parcels involved in heat extremes in Europe (i.e., a difference in the circulation leading up to heat extremes) both contribute to these discrepancies. Differences in the contributions to heat extremes are not uniform across space, e.g., in Australia, heat extremes in CESM2 feature much larger advective contributions than in ERA5, while the diabatic contribution is comparable for heat extremes in CESM2 and ERA5 there.
We argue that such a detailed understanding of the physical function of simulated and observed heat extremes, and in particular their differences, is highly relevant to assessing the robustness of heat extreme projections. Our results thus call for analogous investigations also with other state of the art models.
Geoengineering's role in reducing future Antarctic mass loss is unclear
Authors: Adhikari Mira , Martin Daniel , Edwards Tamsin , Payne Antony , O'Neill James , Irvine Peter
Published: March 2024

Using the BISICLES ice sheet model, we compare the Antarctic ice sheet’s response over the 22nd century in a scenario where idealised large scale, instantaneous geoengineering is implemented in 2100 or 2050 (geoengineering), with scenarios where the climate forcing is held constant in the same year (stabilisation). Results are highly climate model dependent, with larger differences between models than between geoengineering and stabilisation scenarios, but show that geoengineering cannot prevent significant losses from Antarctica over the next two centuries. If implemented in 2050, sea level contributions under geoengineering are lower than under stabilisation scenarios. If implemented in 2100, under high emissions, geoengineering produces higher sea level than stabilisation scenarios, as increased surface mass balance in the warmer stabilisation scenarios offsets some of the dynamic losses. Despite this, dynamic losses appear to accelerate and may eventually negate this initial offset, indicating that beyond 2200, geoengineering could eventually be more effective.
Tracing the origin of Lago Mare biota: ostracods and mollusks from the late Neogene of the Slavonian mountains in the southern Pannonian Basin (NE Croatia) 
Authors: Mužek Katja , Mandic Oleg , Hajek Tadesse Valentina , Harzhauser Mathias , Kovačić Marijan , Kurečić Tomislav , Pezelj Đurđica
Published: March 2024

Lake Pannon was a huge central European long-lived endorheic lake settled in the Pannonian Basin System and surrounded by the Alps, Carpathians and Dinarides mountain ranges during the late Neogene. The rise of brackish Lake Pannon enabled establishment of specific environmental conditions which triggered a spectacular adaptive radiation of a great number of autochthonous mollusk and ostracod species. The latter species represent excellent regional paleoecological proxies and biostratigraphic markers due to their good preservation-potential and taxonomic richness. Although dominantly endemic to Lake Pannon, some of its taxa managed to migrate into the Eastern Paratethys and have also been reported from the Mediterranean. These species are restricted to the Lago Mare interval, representing the ultimate stage of the Messinian Salinity Crisis, a significant environmental perturbation characterized by massive evaporite deposition. The Lago Mare interval was apparently forced by a drainage of the Eastern Paratethys brackish water into the Mediterranean. The Bozara section is situated in the southern Pannonian Basin at the southern slopes of Mt. Papuk and carries a well-preserved benthic fauna representative of Lake Pannon. The 27 -m-thick section consists of alternating pelitic sediments and sand packages divided into 4 facies: silty marl and calcareous silt (F1), sand (F2), intercalation of sand and sandy silt (F3) and clayey silt (F4). According to the regional stratigraphic division it belongs to the Nova Gradiška Formation. We detected therein 25 ostracod and 17 mollusk taxa allowing an integrated evaluation of the depositional setting, biostratigraphic position and paleogeographic distribution pattern. The paleoecology of Bozara fauna documents a general shallowing upward trend along the section from calm deep-water sublittoral to deltaic high-energy littoral conditions. Based on presence of several biostratigraphic markers, such as the bivalve Rhombocongeria rhomboidea and the ostracod Caspiocypris pontica the stratigraphic position of the Bozara section is constrained to the Portaferrian substage (8.0-4.5 Ma).From 16 ostracod taxa determined at species level, 10 can be found in the Eastern Paratethys deposits, whereas only 3 are shared with the Mediterranean Lago Mare. In contrast, among 12 corresponding mollusk taxa, only 4 are shared with the Eastern Paratethys, while being completely absent from the Lago Mare interval. Such a paleobiogeographic pattern suggests that the Lake Pannon outflow and faunal migration into the Eastern Paratethys, ceased distinctly before the Lago Mare phase and the corresponding migration of Paratethys biota into the Mediterranean basin. 
Deriving the interaction point of an Interplanetary Coronal Mass Ejection and High-Speed stream : A case study
Authors: Remeshan Akshay Kumar , Dumbović Mateja , Temmer Manuela
Published: March 2024

Understanding the propagation and evolution of Coronal Mass Ejections (CMEs) is one of the fundamental problems in Heliospheric Physics. An important factor in comprehending the evolution of CMEs in interplanetary space (ICMEs) is studying the different interactions they undergo, such as with high-speed streams (HSS) originating from coronal holes (CH). We study the interaction of an ICME detected in situ at the L1 Lagrange point on 12th October 2016 with a trailing HSS. The in-situ measurements indicate a Magnetic Obstacle (MO) with a symmetric flux rope structure and reconnection exhaust signatures at the ICME-HSS boundary. The ICME is associated with a Halo CME recorded in the LASCO CME catalogue on 9th October 2016. We use Graduated Cylindrical Shell (GCS) reconstruction to obtain 3D CME characteristics and SDO AIA 193 Å measurements to analyse basic CH properties. We next use "a two-step Drag Based Model (DBM)" together with EUropean Heliospheric FORecasting Information Asset (EUHFORIA) to model and analyse the interaction and estimate where in the heliosphere the interaction takes place. We find that the results from the two-step DBM model give better justification for the observed in-situ signatures compared to the EUHFORIA run; this could be due to the lack of reliable magnetogram data from the backside of the Sun. Our analysis indicates that the interaction between ICME and HSS initiated relatively close to Earth (~0.9 AU), providing a benchmark to study ICME-HSS interaction at an early phase.
Detecting climate milestones on the path to climate stabilization
Authors: MacDougall Andrew H. , Rogelj Joeri , Jones Chris D. , Liddicoat Spencer K. , Grassi Giacomo
Published: March 2024

The era of anthropogenic climate change can be described by defined climate milestones. These milestones mark changes in the historic trajectory of change, and include peak greenhouse gas emissions, peak CO2 concentration, deceleration of warming, net-zero emissions, and a transition to global cooling. However, given internal variability in the Earth system and measurement uncertainty, definitively saying that a milestone has passed requires rigour, with the statistical illusion of the 2010s global warming hiatus being a recent cautionary tale of the need for robust methods.Here we use CMIP6 simulations of peak-and-decline scenarios to examine the time needed to robustly detect three climate milestones: 1) the slowdown of global warming; 2) the end of global surface temperature increase; and 3) peak concentration of CO2. To detect these climate milestones we use a modified version of the Monte-Carlo based method of Rahmstorf et al. 2017, developed to test whether the global warming hiatus was an illusion. The method has been modified to account for auto-correlated noise characteristic of the climate system.We estimate that it will take 40 to 60 years after a simulated slowdown in warming rate, to robustly detect the signal in the global average temperature record. Detecting when warming has stopped will also be difficult and for the one peak-and-decline scenario that has model simulations extended to the year 2300, it takes until the mid 22nd century to have enough data to conclude that  warming has stopped. Detecting that CO2 concentration has peaked is far easier, and a drop in CO2 concentration of 3 ppm is consistent with a greater than 99% chance that CO2 has peaked in all scenarios examined.  Overall it is sobering that even under aggressive mitigation scenarios a conclusive end to global warming is at the very outer edge of the living future, with only a small number of the very youngest children alive today likely to witness detection of the end of global warming.
Quantifying dynamic water fluxes and origin at the land-sea interface from days to weeks using temperature and stable isotopes: An example from Königshafen, Sylt
Authors: Gilfedder Benjamin , Böttcher Michael E. , von Ahn Catia M. E. , Frei Sven
Published: March 2024

Beach faces form the interface between terrestrial and marine systems. They act as a reactive zone between these two compartments, transporting and biogeochemically modifying chemical constituents such as nutrients, pollutants and carbon. Mixing between saline seawater and fresh terrestrial groundwater in the subsurface is complicated by catchment morphology, variable density flow and very dynamic boundary conditions across temporal scales (e.g. tides, storms, yearly variations in terrestrial groundwater levels). Thus, tracing water and nutrients fluxes through the subterranean estuary is not trivial, especially when attempting to quantify temporal dynamics on time scales from days to weeks. In this work we use long-term (months) temperature profile measurements and numerical heat modelling to investigate the dynamics of water fluxes through the beach sediments into the Königshafen Bay, Sylt Island, North Germany. Temperature measurements were complemented by stable isotope (δ18O, δ2H)  and pore water chemical measurements to infer the origin of water discharging into the bay. The results showed that the temporal fluxes vary considerable depending on season, location and catchment characteristics. The freshwater flow paths are complex, with dune morphology influencing the focal point for fresh groundwater discharge. Moreover, it appears that either the isotope signature of the islands fresh groundwater is variable or there are at least two end-members contribute to the freshwater signature. Seaward, saline and brackish discharge occurs into the tidal creek draining the bay. Overall temperature measurements and heat modelling combined with pore water chemistry show potential to understand the dynamics in water and element exchange through the subterranean estuary and thus help to understand local water and material fluxes and transformations at the land-ocean interface.
TerraceM 3.0: Advancing marine terrace mapping through integrated machine learning methods
Authors: Jara Muñoz Julius , Mey Jurgen , Freisleben Roland , Pedoja Kevin , Melnick Daniel
Published: March 2024

TerraceM is an open-source software written in MATLAB for mapping and analyzing marine terraces. In this latest release, TerraceM-3 has undergone significant evolution, which leverages the capabilities of machine learning to introduce an automated marine terrace mapping feature. This new version includes a neural network that has been meticulously trained with over 1000 mapped marine terraces. This allows TerraceM-3 users to effortlessly map marine terraces and precisely determine their elevation through the automated mapping of their shoreline angles. In addition, TerraceM-3 incorporates two new functionalities: 1) Photon profile mapping, which includes mapping of satellite LiDAR profiles from the IceSat-2 mission, which broadens the applicability of TerraceM-3 beyond the availability of topographic data. 2) Indicative meaning calculator that accounts for the factors that can alter the initial sea-level position using global datasets (wave conditions and tidal ranges). This method facilitates the direct assessment of uncertainties in the reconstructions of the paleo-sea-level based on marine terraces. TerraceM-3 is a complete toolkit for researchers and students engaged in marine terrace analysis by offering a unique blend of numerical methods, statistical analyses techniques and additional enhanced functionalities to precisely map marine terraces and using them as markers of tectonic deformation.
Advances in measuring low N2O fluxes by a portable gas analyser and manual chambers
Authors: Triches Nathalie Ylenia , Marushchak Maija , Virkkala Anna , Vesala Timo , Heimann Martin , Göckede Mathias
Published: March 2024

Nitrous oxide (N2O) is one of the most important greenhouse gases with a global warming potential of about 298 times stronger than carbon dioxide (CO2) over a period of 100 years. From 1800 to 2023, the atmospheric concentration of N2O has increased from 273 to 336 ppbv, whereby more than half of this rise is due to the addition of fertilisers and manure on agricultural soils. Whilst these managed, nutrient-rich soils have been relatively well studied, little is known about N2O fluxes in nutrient-poor ecosystems (e.g., the Arctic).Since many Arctic soils contain very low amounts of available nitrogen, in the past it has been generally assumed that Arctic soils are not a significant source of N2O. Only recently, several studies have reported significant N2O emissions from organic-rich Arctic soils; however, due to methodological challenges, extensive investigations on N2O fluxes in Arctic soils have been limited. As a result, the importance of N2O fluxes from this region to the global budget remains highly uncertain. With the recent advances in portable GHG analyser technology, extensive manual chamber measurements based on in-situ N2O concentration measurements can provide novel information to close this knowledge gap. However, guidelines on measuring techniques (e.g., chamber closure time) and data quality (e.g., no flux vs. low flux) are still lacking. In this study, we provide new insights on N2O fluxes in a nutrient-poor ecosystem and give general practical guidelines for measuring low N2O fluxes with a portable gas analyser and manual chambers. In May, July, and September 2023, we used a portable N2O/CO2 analyser to measure N2O fluxes in a thawing sub-Arctic permafrost peatland in northern Sweden. Recommendations on practical use in the field are given to support future N2O research with portable gas analysers. 
Identifying cloud objects in the km-scale earth system model ICON
Authors: Rieger Vanessa , Splechtna Paul , Voigt Aiko
Published: March 2024

Clouds crucially impact Earth’s climate. The distribution of clouds, horizontally and vertically, influences the radiative transfer through the atmosphere. Hence, to correctly compute the radiative transfer, it is important to understand the horizontal and vertical distribution of clouds.  Km-scale earth system models enable to resolve convection explicitly and offer the potential to represent cloud patterns more realistically. We investigate simulations of the earth system model ICON with a horizontal resolution of 5 km performed within the project nextGEMS. We identify cloud objects using connected component labelling. The method is applied to the vertically integrated cloud field as well as to the global three-dimensional cloud field. We analyse the distribution of cloud objects, their water and ice content as well as their fractal dimension on a global and regional scale. The choice of the threshold for identifying cloud objects strongly influences the analysis of the objects.
Comparative Study of Spring and Summer Light Effects on Phytoplankton Communities in Semi-Enclosed Fresh- and Brackish Aquatic Ecosystems.
Authors: Kaharuddin Aminah , Kaligatla Rokhya
Published: March 2024

Semi-enclosed freshwater and brackish ecosystems, characterised by restricted water outflow and prolonged residence times, often accumulate nutrients, influencing their productivity and ecological dynamics. These ecosystems exhibit significant variations in bio-physical-chemical attributes, ecological importance, and susceptibility to human impacts. Untangling the complexities of their interactions remains challenging, necessitating a deeper understanding of effective management strategies adapted to their vulnerabilities. This research focuses on the bio-physical aspects, investigating the differential effects of spring and summer light on phytoplankton communities in semi-enclosed freshwater and brackish aquatic ecosystems.Through extensive field sampling and comprehensive environmental parameter analysis, we explore how phytoplankton respond to varying light conditions in these distinct environments. Sampling campaigns were conducted at Müggelsee, a freshwater lake on Berlin's eastern edge, and Barther Bodden, a coastal lagoon northeast of Rostock on the German Baltic Sea coast, during the springs and summers of 2022 and 2023, respectively. Our analysis integrates environmental factors such as surface light intensity, diffuse attenuation coefficients, nutrient availability, water column dynamics, meteorological data, Chlorophyll-a concentration, and phytoplankton communities. Sampling encompassed multiple depths at continuous intervals lasting three days.Preliminary findings underscore significant differences in seasonal light availability, with summer exhibiting extended periods of substantial light penetration. These variations seem to impact phytoplankton abundance and diversity uniquely in each ecosystem. While ongoing analyses are underway, early indications suggest distinct phytoplankton responses in terms of species composition and community structure, influenced by the changing light levels. In 2022 the clear water phase during spring indicated that bloom events have occurred under ice cover much earlier than spring, while in the summer there were weak and short-lived blooms of cyanobacteria. The relationship between nutrient availability and phytoplankton dynamics, however, remains uncertain according to our data.This ongoing study contributes to understanding the role of light as a primary driver shaping phytoplankton community structures and dynamics in these environments.  Our research findings offer insights for refining predictive models, aiding in ecosystem-specific eutrophication management strategies, and supporting monitoring efforts of Harmful Algal Blooms.