bluuebunny/crossref_metadata_2025 huggingface dataset:
Country-wide Cross-Year Crop Mapping from Optical Satellite Image Time Series
Authors: Turkoglu Mehmet Ozgur , Aasen Helge , Schindler Konrad , Wegner Jan Dirk
Published: March 2024

Previous works on vegetation mapping from optical satellite images use training and test datasets within the same year. We think that from a practical perspective, this experimental setting is not realistic due to (i) crop growth changes from year to year (also like from region to region), therefore test assessment does not fully reflect real-world cases and (ii) obviously it is not possible to apply the algorithm current year if it is trained with current year data. Thus a cross-year experimental setting should be de-facto for this line of research then we can readily apply developed algorithms in real-world applications. In this work, we evaluate a state-of-the-art crop classification method from optical satellite (Sentinel-2) image time series data - a hierarchical multi-stage deep learning method, i.e. ms-convSTAR which we introduced in [1] - in a cross-year experimental setting. The deep learning model is trained with the entire 2021 crop dataset in Switzerland and during test time it is applied to the 2022 crop dataset. Our results show that our method performs reasonably well in this experimental setting achieving ~83% accuracy at the pixel level. References[1] Turkoglu, M. O., D'Aronco, S., Perich, G., Liebisch, F., Streit, C., Schindler, K., & Wegner, J. D. (2021). Crop mapping from image time series: Deep learning with multi-scale label hierarchies. Remote Sensing of Environment, 264, 112603.
Wave-Particle Interactions near Interplanetary Shocks: Solar Orbiter Observations
Authors: Li Xingyu , Heidrich-Meisner Verena , Wimmer-Schweingruber Robert , Zong Qiu-Gang , Wang Ling-Hua , Yang Liu , Berger Lars
Published: March 2024

The interaction between waves and particles plays a pivotal role in particle acceleration near interplanetary shocks. Previously, detailed investigations about these processes were limited due to data availability and coarse time resolution from interplanetary missions. However, recent observations from the Solar Orbiter mission, with its high-resolution capabilities, have shed new light on this topic. In this work, we conduct a comprehensive study of wave-particle interactions near interplanetary shocks, using four years of data obtained by the Energetic Particle Detector (EPD), Magnetometer (MAG), Radio and Plasma Wave Analyzer (RPW) and Solar Wind Analyzer (SWA) onboard the Solar Orbiter. We analyze the propagation and polarization properties of waves associated with shocks through wavelet analysis. In addition, we reconstruct the pitch angle distributions and gyrophase distributions of particles in the solar wind frame of reference. These reconstructions help us identify wave-particle interactions in the data and investigate the energy transport during these events. We report on results from this ongoing analysis. Our results advance the understanding of particle acceleration induced by waves near interplanetary shocks, highlighting the role of wave-particle interactions in dynamic processes occurring in the inner heliosphere.
Why is  b=1?
Authors: Main Ian , Geffers Gina-Maria
Published: March 2024

The exponent b of the log-linear frequency-magnitude relation for natural seismicity commonly takes values that are statistically indistinguishable from b=1.  There are some exceptions, notably with respect to focal mechanism and for volcanic and induced seismicity, but it is possible these could be explained at least in part by variability in the dynamic range of measurements between the minimum magnitude of complete reporting and the maximum magnitude, especially where the dynamic range of the statistical sample is small.  However, in laboratory experiments and in discrete element simulations a wide range of b-values for acoustic emissions are consistent (after accounting for systematic differences in the transducer response) with systematic variations in the range  as the stress intensity factor increases from its minimum to its maximum, critical value.  The question remains: why is  an attractor stationary state for large-scale seismicity?  Previous attempts to answer this question have relied on a simple geometric ‘tiling’ argument that is inconsistent with the spatial distribution of earthquake locations, or a hierarchical ‘triple-junction’ model that has not been validated by observation.  Here, we derive a closed analytical solution for the maximum entropy -value, conditional on the assumption that earthquake magnitude scales linearly with the logarithm of rupture area. In the limit of infinite dynamic range, the solution is .  The maximum entropy -value converges to this value asymptotically from above as dynamic range increases for large systems at steady state.  This is in contrast to a previous maximum entropy solution based on analysing the spectrum in ‘natural time’ of earthquake catalogues, where larger samples with greater dynamic range lead to a divergence from .  The new theory is consistent with the trend in b-value convergence from above towards an asymptotic limit of in b=1.027±0.015 at 95% confidence from the global CMT earthquake frequency-moment catalogue for data since 1990.
Integrated garnet and zircon petrochronology of corona formation in high‐grade rocks from granulite‐facies rocks in the North China Craton
Authors: Du Kaiyang , Cheng Hao , Dragovic Besim , Cao Chi
Published: March 2024

Corona textures in high-grade metamorphic rocks, characterized by specific mineral assemblages, have been extensively studied, yet their direct dating remains a challenge. In an effort to ascertain the formation time and duration of corona textures, we undertook a comprehensive petrochronological study, utilizing garnet Lu–Hf and Sm-Nd alongside zircon U–Pb dating, on a corona-bearing granulite from the Miyun area in the North China Craton. This region, situated at the intersection of the Archean Eastern Block and the Paleoproterozoic Trans-North China Orogen, provides valuable insights into polymetamorphic processes.Through pseudosection modeling and conventional thermobarometry, we determined that the corona texture—predominantly garnet, with minor clinopyroxene and quartz—formed at approximately 0.9 GPa and 740 °C. This was followed by retrogression at ~0.5 GPa and 670 °C, leading to the growth of amphibole and biotite. Lu–Hf dating of garnet gave an age of 1864 ± 3 Ma, significantly younger than the metamorphic zircons' U–Pb age of ca. 2454 Ma. This discrepancy suggests a disequilibrium in rare earth elements (REE) between garnet and zircon. Comparative analysis of REE concentrations in zircon and garnet, along with experimental data and lattice strain modeling, indicated that zircons deviated from the equilibrium array, implying their (re)crystallization independent of garnet. The preservation of Lu zoning in garnet suggests that the Lu–Hf date reflects the period of garnet growth, approximating the time of anhydrous corona assembly formation.The U–Pb zircon age of ca. 2454 Ma indicates an earlier, distinct Archean metamorphic event, which is a common feature in high-grade metamorphic rocks throughout the Eastern Block of the North China Craton. The garnet Sm–Nd dating, yielding an age of 1817 ± 6 Ma, aligns with the amphibole-biotite Lu–Hf date of 1819 ± 16 Ma, indicating a gap of approximately 47 Myr from the garnet Lu–Hf date. This suggests a phase of rapid growth followed by fast cooling. The coinciding ages of garnet Sm–Nd and amphibole-biotite Sm–Nd and Lu–Hf, given their different closure temperatures, point to more than mere cooling, indicating the formation of anhydrous coronae around 1.86 billion years ago, followed by hydrous retrogression ca. 1.82 Ga.The presence of both Paleoproterozoic and Archean ages in the Miyun granulites correlates with significant tectonic episodes in the Trans-North China Orogen and the Eastern Block, respectively. This correlation suggests that the Miyun region experienced overlapping metamorphic events associated with both these tectonic units. Our approach, integrating Lu–Hf and Sm–Nd dating, is particularly effective for rocks with explicit textures, enabling precise extraction of temporal intervals in metamorphic processes.
Improved representation of anthropogenic deposits in 3D urban geological subsurface models
Authors: Schokker Jeroen , Dijkstra Joris
Published: March 2024

The urban subsurface is increasingly disturbed by human activity and/or covered by anthropogenic deposits. This is particularly true for city centres, with thick and heterogeneous subsurface archives related to historical urban development, as well as for modern residential and industrial areas, that are often built on extensive sheets of filling sand. The anthropogenic deposits may be very diverse in nature, ranging from natural aggregates (crushed rock, gravel, sand or clay) to various types of novel anthropogenic materials (e.g. steelworks slags, concrete and rubble), as well as mixtures of these.Although anthropogenic deposits could be represented on subsurface maps and in 3D models, these deposits are often omitted. Their lateral extent and thickness are not well constrained and relevant information on the lithological properties of the deposits is generally lacking. At the same time, the demand for complete and detailed subsurface information in the built environment is increasing and relates to anything from building stability and ground heat extraction to preserving cultural heritage and mitigating the effects of climate change.This presentation therefore focusses on the lithological characterisation and stratigraphical subdivision of anthropogenic deposits in order to improve their representation in 3D geological subsurface models. We will evaluate current lithological standards and stratigraphic approaches and present the principles of the approach that we are developing in the Netherlands. We will discuss the practical consequences and give examples of bringing our approach into practice. Ultimately, a well-thought lithological description and classification system of anthropogenic deposits is a prerequisite to produce reliable subsurface and coupled surface-subsurface models. In that way, we can address the many challenges related to the ever-increasing use of  urban space and thus improve the wellbeing of our citizens.
Antarctic extreme high temperatures across seasons and their response to advection
Authors: Xu Min , Pithan Felix , Yang Qinghua
Published: March 2024

Antarctic extreme high temperatures impact the cryosphere, with very warm extremes driving surface melt on ice shelves. Here, we analyse temperatures exceeding the 90th percentile of the temperature distribution, focusing on the associated circulation patterns and radiation anomalies. ERA5 reanalysis data show high air pressure / positive geopotential height anomalies related to the occurrence of warm extremes. The highest temperature during warm extremes appears on the periphery of high-pressure systems, consistent with anticyclonic advection. Autumn and winter exhibit stronger warm extremes due to the transport of warm and moist air. In summer, the weak meridional gradients of TOA downward solar radiation flux and surface air temperature contribute to weak temperature anomalies by advection of anomalously warm air. Warm extremes are associated with positive longwave radiation anomalies in all seasons, but with negative shortwave radiation anomalies at the surface except during polar night. These relationships are verified by station observations. Our results confirm that Antarctic warm extremes are mostly driven by meridional advection of warm air, and suggest that these warm air masses are predominantly moist and cloudy.
Temperature vs ecosystem structure control over remineralisation length scale of sinking particles in the Global Ocean 
Authors: Sanders Richard , Giering Sarah , Henson Stephanie , Martin Adrian , McDonagh Elaine , Wiedmann Ingrid , Yool Andrew
Published: March 2024

Ocean biological processes, principally the surface production, sinking and interior conversion of organic carbon to CO2 store enough carbon in the ocean interior to keep atmospheric CO2 concentration substantially lower than it would otherwise be. The size of this effect is linked to the depth at which sinking organic matter is remineralised in the ocean, with a deeper mineralisation causing a greater storage. Two prominent hypotheses regarding the control over the depth at which sinking material is lost are Temperature and Ecosystem Structure, specifically the proportion of diatoms in the surface community. These are both theoretically valid (temperature controls respiration, diatoms control density) and have some support in the literature, however to date have been considered in isolation. In this paper we firstly compute the strength of these effects in isolation from simple theory and show that they produce relationships consistent with existing literature thus suggesting that both factors may play a role. We use these relationships to produce an equation linking mineralisation depth, parameterised as remineralisation length scale, to community structure and temperature, thus uniting the two factors. An analysis of this equation suggests that community structure exerts a stronger control over remineralisation length scale than does temperature.
Decrypting Sea Level Variations in Deep Time
Authors: Haq Bilal
Published: March 2024

Earth’s sedimentary record holds physical and chemical clues that allow us to decipher sea level variations in the deep past in fair amount of detail and time resolution, even though many issues remain that stifle precision. On the longer time scales (multiple million years) sea level trajectories can also be reconstructed from paleogeographic data (marine floodings of continents) and geophysical modeling (sea floor geodynamics and basin volume changes). The last few decades have seen significant advances in our understanding of the behavior of sea level at both regional (eurybatic) and global (eustatic) spatial scales and updated sea-level curves have been published for the entire Phanerozoic, albeit with different degree of accuracy and resolution that generally decrease further back in deep time due to increasing uncertainties about timing and amplitudes of eustatic variations. One major recent discovery made through geophysical modeling has been the long-wavelength and relatively slow (multiple million years) warping of continental margins due to mantle driven dynamic topographic changes that significantly affect amplitudes estimates and often go undetected. On the positive side, for some parts of the Mesozoic and for all of the Cenozoic oxygen-isotopic data (δ18O) of marine benthic foraminifera have proven useful in constraining both the timings and (to a lesser extent) the amplitudes of sea-level rises and falls. Digitizing sea-level variations in deep time poses a challenge, as multiple streams of parallel data have to be correlated with meaningful precision, each dataset having its own inherent degree(s) of uncertainties. Nonetheless, For most portions of the Phanerozoic sea-level variations data, especially the timing of sea-level withdrawals,  has been arrived at through calibration to several different scales (i.e., magnetic reversal, biochronologic, isotopic), as well as absolute time. The Cenozoic (the last 66 Myr.) has also been fine tuned through astronomical (orbital) cyclostratigraphy. Nevertheless deep time eustatic history should be regarded as work in advanced progress and, with periodic revisions (as new bio-chronological data and new technologies become available), it can be kept current for enhanced utility. In this presentation, the advances made in recent years and the state of the art of our current understanding of sea level in deep time will follow the discussion of the technological limitations.   
The research landscape of wildfire vulnerability in Austria
Authors: Fuchs Sven , Echtler Pia , Hausharter David , Schlögl Matthias , Papathoma-Köhle Maria
Published: March 2024

Changes in temperature and precipitation in Austria due to climate change are expected to increase the days of fire weather in the near future. Extreme wildfire events are not common in Austria, nevertheless, given climate change and an increase in the number of events in the last years, authorities and decision-makers require tools to identify vulnerable hotspots and to reduce the upcoming risk in the Wildland Urban Interface. A number of projects ran by the University of Natural Resources and Life Sciences in Vienna focus on the assessment of vulnerability at different levels (national and local) and different elements at risk (industrial and residential buildings, free spaces and infrastructure). We present herein the current research landscape on the field in Austria and more specifically the projects PHLoX (StartClim), REVEAL (Waldfonds), and FIREPRIME (DG ECHO). Each project is based on expert knowledge and data-driven approaches and deals with different elements at risk and vulnerability indicators emphasising the need for participatory methods for wildfire risk management. We demonstrate how these projects can serve as a blueprint for increased wildfire resilience in Europe and beyond.
Ecosystem services supply-demand mismatches for urban heat mitigation 
Authors: Aznarez Celina , Kumar Sudeshna , Márquez-Torres Alba , Pascual Unai , Baró Francesc
Published: March 2024

Urban areas, characterized by dense construction, often exhibit elevated land surface temperatures, leading to the formation of urban heat islands (UHIs). These UHIs pose significant environmental hazards, contributing to issues such as heat-related mortality, degraded air quality, and elevated heat stress on biodiversity and ecosystems. Moreover, the impact of UHIs is not uniformly distributed due to the heterogeneous nature of urban landscapes and socio-spatial inequities influencing factors like impervious surfaces and vegetation cover. Urban green infrastructure is increasingly valued as a nature-based solution to mitigate UHIs, offering essential ecosystem services (ES) like urban heat mitigation. To analyze the relationship between users' access and dependence on these benefits, we propose a modeling approach that integrates remote sensing, field, and socio-demographic data, along with Artificial Intelligence for Environment and Sustainability (ARIES) and GIS tools. This approach incorporates: i) indicators of UHI exposure and urban heat vulnerability indices; ii) spatial quantification of the supply and demand of urban green infrastructure related to ES for UHI mitigation; iii) spatially explicit (mis)matches of ES supply and demand balance and iv) coupled modelling. We applied it in the ‘green’ city of Vitoria-Gasteiz, in the Basque Country as a case study. Our findings evidence the unequal distribution of UHI burdens, with individuals vulnerable to heat experiencing disproportionate impacts, including higher exposure and limited access to temperature-regulating ES. This mismatch between the supply and demand of ES particularly affects disadvantaged communities. Conversely, areas associated with higher income levels indicate reduced vulnerability to heat. Incorporating environmental justice principles into UHI mitigation strategies is essential to ensure equitable outcomes for all residents. By considering the socio-spatial inequalities associated with supply-demand mismatches in ES and their impact on vulnerability to heat, our approach enables evidence-based decision-making and spatial prioritization to address the specific needs of vulnerable populations.
Dynamic earthquake source inversion with GAN priors, with application to the 2023 Mw 7.8 Kahramanmaraş, Turkey earthquake
Authors: Premus Jan , Ampuero Jean-Paul
Published: March 2024

Dynamic source inversion of earthquakes consists of inferring frictional parameters and initial stress on a fault consistent with co-seismic seismological and geodetic data and dynamic earthquake rupture models. In a Bayesian inversion approach, the nonlinear relationship between model parameters and data (e.g. seismograms) requires a computationally demanding Monte Carlo (MC) approach. As the computational cost of the MC method grows exponentially with the number of parameters, dynamic inversion of a large earthquake, involving hundreds to thousands parameters, shows problems with convergence and sampling. We introduce a novel multi-stage approach to dynamic inversions. We divide the earthquake rupture into several successive temporal (e.g. 0-10 s, 10-20 s, …) and spatial stages (e.g., 100 km, 200 km, …). As each stage requires only a limited number of independent model parameters, their inversion converges relatively fast. Stages are interdependent: earlier stage inversion results are a prior for a later stage inversion. Our main advancement is the use of Generative Adversarial Networks (GAN) to transfer the prior information between inversion stages, inspired by Patel and Oberai (2019). GAN are a class of machine learning algorithms originally used for generating images similar to the training dataset. Their unsupervised training is based on a contest between a generator that generates new samples and a critic that discriminates between training and generator’s images. The resulting generator should generate synthetic images/samples with noise in a low-dimensional latent space as an input. We train GANs on samples of dynamic parameters from an earlier stage of the inversion and use the GAN to suggest the dynamic parameters in a later stage of inversion. We show a proof of concept dynamic inversion of a synthetic benchmark, comparing performance of direct MC dynamic inversion with parallel tempering with our GAN approach. We efficiently handle large ruptures by adopting a 2.5D approximation that solves for source properties averaged across the rupture depth. The 2.5D modeling approach accounts for the 3D effect of the finite rupture depth while keeping the computational cost the same as in 2D dynamic rupture simulations. Additionally we show current results on the dynamic inversion of 2023 Mw 7.8 Kahramanmaraş, Turkey, earthquake.
An exploratory bottom-up resilience assessment framework for coupled human-water systems
Authors: Mijic Ana , Liu Leyang
Published: March 2024

Resilience has been defined as the ability of a system to withstand stressors while preserving its structure and functions. Various resilience assessment frameworks and metrics have been developed for understanding individual water system behaviour. However, in coupled human-water systems, the increased complexity presents new challenges in the application of these frameworks. This exploratory study first conducted a literature review on system performance indicators, failure thresholds, and resilience metrics, across urban water supply, drainage, wastewater, groundwater, and river systems. Challenges are identified in intercomparison between system performance indicators, robustness of thresholds selection, and resilience metrics synthesis as well as their applicability to inform water management. Based on the insights, a bottom-up resilience assessment framework for coupled human-water systems is developed. This framework sets double thresholds to characterise the vulnerable and critical systems state during a disruptive period. Four shape-based resilience metrics are designed and uniformly applied to various performance indicators to facilitate intercomparison between subsystems. The application of the metrics crosses temporal scales, from event-level assessments for understanding system behaviour to annual-level evaluations of system reliability, which are ultimately synthesised at the system level for multi-stakeholder decision-making. The efficacy of this framework is demonstrated through its application with the integrated water system model (WSIMOD) in Luton, UK, serving as a case study. The findings highlight river water quality as the least resilient subsystem that needs prioritised management. Sensitivity analysis is conducted to examine the robustness of results, with subsequent interpretation linking these metrics to specific design variables for enhanced management. This framework can be further applied with stakeholder engagement and multi-criteria analysis for more effective decision-making to achieve better system performance under deep uncertainties. 

 
The asymmetric evolution of three-dimensional gravity currents in cold, fresh water
Authors: Castro-Folker Nicolas , Grace Andrew P. , Stastna Marek
Published: March 2024

In a single-component system density depends only on temperature. If this dependence is linear, then we can expect a pair of floating and sinking gravity currents with i) the same absolute initial density difference between intruding and ambient fluids, and ii) initial conditions that (under a certain non-dimensionalisation) obey reflectional symmetry across mid-depth to maintain that symmetry throughout their evolution. However, water attains its maximum density at approximately 4°C, so the density of a cold (
Simulation of the Impact of Instantaneous Solar UV Radiation Enhancements on the Middle Atmosphere via UV Radiation Reconstruction
Authors: Wang Jianmei , Liu Dan , Yang Junfeng
Published: March 2024

This study reconstructed the ultraviolet (UV) radiation band ranging from 0.01 to 399.5 nm with a 5-minute time resolution during the solar minimum conditions on October 18, 2006. The aim was to examine the impact of solar flare events on the middle atmosphere. Instead of utilizing daytime scale data, a 5-minute resolution of 0.01–399.5 nm UV radiation was employed to observe how the middle atmosphere responds to sudden enhancements in solar UV radiation. The findings suggest minor temperature increases of 0.05 K in low latitudes, observed in both the lower thermosphere and the stratosphere. The UV radiation enhancement resulted in a 0.6% ozone increase in the stratosphere, causing subtle temperature elevations. Ozone increased by 4% at 80 km, with a -2% change at 60–70 km and a -6% change in the low thermosphere. Above 60 km, atmospheric density experienced a 0.05% increase, reaching up to 0.15% at 80–90 km. Thus, the response of the middle atmosphere's temperature and density to instantaneous UV radiation enhancements can be captured through UV radiation reconstruction. The simulation results were comparatively weaker than those of the previous study.
Investigating the hydrological behaviour of a shallow karst aquifer flooding intermittently: the Pivka Karst Aquifer (Slovenia)
Authors: Mayaud Cyril , Kogovšek Blaž , Petrič Metka , Ravbar Nataša , Blatnik Matej , Gabrovšek Franci
Published: March 2024

The Pivka Karst Aquifer is a shallow karst aquifer located under the Upper Pivka Valley, about 40 km SW from Ljubljana (Slovenia). This aquifer is connected to the larger Javorniki Karst Aquifer that borders the Upper Pivka Valley on the NE. While the geometry of the conduit system in the Pivka Karst Aquifer is practically unknown, the geometry of the Javorniki Karst Aquifer is better characterized. Under low water conditions, water from the Pivka Karst Aquifer drains through the Javorniki Karst Aquifer towards the Unica and Malenščica Springs in the N, which are the terminal outlets of the region. Under high-water situations, the regional groundwater level rises up to 45 m, and the regional flow direction is modified. The Pivka Karst Aquifer receives water from the Javorniki Karst Aquifer which provides in the meantime autogenic water to the Unica and Malenščica Springs. The rise of water level in the Pivka Karst Aquifer result in the appearance of 17 intermittent lakes in the Upper Pivka Valley. This work aims establishing a conceptual hydrological model of the Pivka Karst Aquifer to better understand its interaction with the Javorniki Karst Aquifer. To do so, a network of automatic stations recording water level, specific electrical conductivity and water temperature at a 30 min interval has been progressively established in the Upper Pivka Valley since 2020. The four years dataset were analysed with data collected in the water active caves of the Javorniki Karst Aquifer and at the Unica and Malenščica Springs. The interpretation of water level records suggest that the Javorniki Karst Aquifer is a large recharge contributor of the Pivka Karst Aquifer, which act as an overflow of the whole system. However, the southern and western parts of the Pivka Karst Aquifer are also recharged locally. Such finding is supported by the analysis of specific electric conductivity data, which suggests the existence of several preferential flow paths in the Pivka Karst Aquifer that activate during flooding.
Exciting times for Climate Change Education – from global opportunities to local challenges
Authors: Guilyardi Eric , Wilgenbus David
Published: March 2024

Science indicates that human impact on the planet's climate is clear. Over the past 30 years, climate change has shifted from being primarily a scientific concern to emerging as one of the defining environmental challenges within our society. However, science alone cannot guide us on how to address this crisis. This challenge is also about how we envision living together, what we collectively value, and the level of risk we are prepared to assume. It fundamentally pertains to the kind of society we aspire to, making education a pivotal component. Inspired by the Paris Agreement, the time has arrived for Climate Change Education. It derives its momentum from the aspirations and mobilization of the youth, making it the most potent transformative action in response to climate change.Climate Change Education comes with unique and exciting opportunities. Firstly, it offers a chance to learn about science in general and climate science specifically, drawing from authoritative sources like IPCC reports. Secondly, it provides an avenue to acquire life skills, humanities knowledge, and insights into global citizenship, imparting a holistic perspective to the young generation on a global scale. Lastly, it fosters critical thinking, hopeful hearts, and empathy in an ever-evolving educational landscape. However, Climate Change Education presents numerous challenges as it strives to balance the development of cognitive, emotional, and practical aspects within existing educational systems. Educators need to be prepared for this unique combination of ‘head’, ‘heart’, and ‘hands’.The mission of the Office for Climate Education (OCE) is precisely to empower educators in preparing young generations with a robust understanding of climate change and the skills needed to act as global citizens in a changing world. The OCE, driven by collaboration between climate science and educational communities, develops sets of pedagogical resources, offers teacher professional development opportunities, and facilitates networks of practice worldwide. As a pivotal participant in the newly established Greening Education Partnership, the OCE serves as a bridge between the global landscape of IPCC-based science and the specific needs of local primary and secondary educational systems in over 20 countries.
Going beyond the river long profile
Authors: Clubb Fiona
Published: March 2024

Quantifying landscape form can provide crucial insight into the interactions between tectonics and climate. River long profile morphology, quantified by metrics such as channel steepness, is the most commonly used tool to investigate topographic form, with many studies relating long profile morphology to uplift rate, precipitation, sediment properties, or lithology, for example. River long profiles record the signal of external forcing over large spatial scales (i.e. tens of kilometres). This has many advantages: for example, it is a convenient scale for analysing variations in large-scale processes, such gradients in tectonic uplift. It also means that high resolution digital elevation models (DEMs) are not required and therefore river long profiles can be extracted globally. However, analysis of river long profiles over tens of kilometres can also result in signal smoothing and subsequent loss of finer scale tectonic or climatic signatures encoded into the landscape.Tectonic and climatic processes do not only leave their fingerprint in the long profiles of rivers. Hilltops, hillslopes, and valleys make up the majority of Earth’s landscapes by area, yet their morphology has received much less attention than that of rivers. This is in part due to the difficulty in accurately extracting hilltops and valley morphology from DEMs, especially on a global scale. Here, I show that we can now extract hilltop and valley metrics from high-resolution (< 15 m) DEMs over orogenic to continental scales using new topographic analysis techniques and high-performance computing facilities. I argue that by combining hilltop, hillslope, and valley metrics, we can obtain more information about tectonic and climatic processes than from river profiles alone.
A multi-modal high spatial resolution aerial imagery scene classification model with visual enhancement
Authors: He Lin , Lin Yi , Song Yufei
Published: March 2024

Remote sensing image scene classification is to annotate semantic categories for image areas covering multiple land cover types, reflecting the spatial aggregation of relevant social resources among feature objects, which is one of the remote sensing interpretation tasks with higher challenges for algorithms to understand the images. Nowadays, scene semantic information extraction of images using deep neural networks is also one of the hot research directions. In comparison to other algorithms, deep neural networks can better capture semantic information in images to achieve higher classification accuracy involved in applications such as urban planning. In recent years, multi-modal models represented by image-text have achieved satisfactory performance in downstream tasks. The introduction of "multi-modal" in the field of remote sensing research should not be limited to the use of multi-source data, but more importantly to the coding of diverse data and the extracted deep features based on the huge amount of data. Therefore, in this paper, based on an image-text matching model, we establish a multi-modal scene classification model (Fig. 1) for high spatial resolution aerial images which is dominated by image features and text provides facilitation for the representation of image features. The algorithm first employs self-supervised learning of the visual model, to align the expression domain of the image features obtained from training on natural images with that of our particular dataset, which will help to improve the feature extraction effectiveness of the aerial survey images on the visual model. The features generated by the pre-trained image encoding model and the text encoding model will be further aligned and some of the parameters in the image encoder will be iteratively updated during training. A valid classifier is designed at the end of the model to implement the scene classification task. Through experiments, it was found that our algorithm has a significant improvement effect on the task of scene categorization on aerial survey images compared to single visual models. The model presented in the article obtained precision and recall of above 90% on the test dataset, contained in the high spatial resolution aerial survey images dataset we built with 27 categories (Fig. 2).Fig 1. Diagram of the proposed model structure. Blue boxes are associated with the image, green boxes with the text, and red boxes with both image and text.Fig 2. Samples in our high spatial resolution aerial survey images dataset.
Assessing ECMWF Lightning Forecast in Portugal during fire seasons
Authors: Campos Cátia , Couto Flavio T. , Santos Filippe L.M. , Rio João , Ferreira Teresa , Purificação Carolina , Salgado Rui
Published: March 2024

Portugal is one of the European countries that faces significant challenges with wildfires. While lightning-triggered natural fires constitute a minority compared to anthropogenic ones, accurate forecasting of lightning occurrences is crucial for effective prevention. The study assesses the ECMWF model's capability to predict lightning in Portugal over four fire seasons [2019-2022]. Observed lightning data was obtained from the national lightning detector network, aggregated into 0.5° and 1° resolutions over 3-hour periods. The evaluation employs statistical indices from a contingency table to analyze the model's performance. Results indicate an overestimation of lightning occurrences by the ECMWF model, with a Bias greater than 1. The success rate for lightning prediction was 57.7% for a horizontal resolution of 1° and 49% for 0.5°. Additionally, the temporal analysis reveals a time lag between both data, with the model starting to predict lighting before its occurrence and finishing the prediction earlier. These findings are complemented by analyzing the spatial lightning distribution, which led us to identify some weather patterns associated with lightning activity during the study period. For instance, lightning activity was associated with the Iberian thermal low development overlapped by an Upper Level Low and the passage of large-scale features, such as frontal systems. The insights gained from this study have implications for the ECMWF lightning forecast applicability in the context of forecasting natural forest fires in Portugal. The research was funded by the European Union through the CILIFO project (0753-CILIFO-5-E) and also by national funds through FCT Foundation for Science and Technology, I.P. under the PyroC.pt project (PCIF/MPG/0175/2019).
Can climate change training promote pro-environmental behavior in the long term? A pilot study with teenagers
Authors: Munerol Francesca , Parodi Antonio , Polo Lara , Milelli Massimo , Loglisci Nicola , Rania Nadia , Bracco Fabrizio , Coppola Ilaria
Published: March 2024

Environmental education (EE) programs are critically important. The EE within the EU Project "I-CHANGE" (https://ichange-project.eu/) aims at global citizenship, in order to generate new knowledge and new, more participatory and conscious ways of acting in the environment. The present study aims to verify the effectiveness of a training intervention based on education on the issues of climate change and on the active participation of the subjects in the small psychological group. 309 students participated in the intervention, equally distributed by gender (52.1% males), 64.4% enrolled in primary school and 35.6% enrolled in lower secondary school. A quantitative protocol was administered to evaluate the effectiveness of the intervention. The study shows an increase in pro-environmental behaviors and their stability even after 15-30 days. The intervention appears to be effective in triggering pro-environmental behaviors and maintaining them over time. The results of this study highlight the need to develop environmental education programs in schools to increase levels of knowledge and awareness on the topic of climate change.
Improving rain cell tracking for convective rainfall nowcasting: a two-stage analogue model approach
Authors: Shu Zhou , Onof Christian , Wang Lipen
Published: March 2024

Rain cell tracking methods are essential to the object-based rainfall nowcasting of convective storms. These methods identify rain cells from radar images and the tracks associate cells between any two successive time steps. Based upon the identified cells and tracks, the positions of the cells in the next few time steps can be forecasted. Many existing nowcasting methods assume Lagrangian persistence. That is, they generally lack the mechanisms to predict the temporal evolution of cell properties and their types. This deficiency may have a great impact to the accuracy of the convective storm nowcasting. To improve cell tracking methods, a two-stage analogue model is proposed to address the limits of existing cell tracking methods.Predicting cell type: three machine learning classifiers –KNN, logistic regression and random forest—are employed to predict the cell types based on rain cell properties.
Predict temporal evolution of cell properties: an ensemble forecast (0-1h lead times) of cell mean intensity, maximum intensity, size and major axis length is obtained using an analogue method. This method assumes that rainfall cells with similar conditions will evolve similarly. Analogues are chosen based on the predicted cell type from the previous step.
In this study, a dataset of rainfall cells from a total of 165 convective storms between 2005 and 2017 is used. These rainfall cells are identified using enhanced TITAN. The study area is centred at Birmingham city, with an area of 512 × 512 km². Results show that the random forest classifier has the best performance in predicting track types. As the temporal profile of the selected cell properties is incorporated into the prediction process, the prediction accuracy of the random forest classifier can be higher than 80%. Results also show that predicting cell type prior to the selection of analogues improves the forecasting of temporal evolution of cell properties at a lead time of 5 minutes. Overall, the analogue method enhances the prediction of temporal evolution of cell properties compared with assuming Lagrangian persistence. At the moment, cell types are predicted for a 5-minute lead time.
Reflect sunlight or use it to store carbon?
Authors: Graf Alexander , Wohlfahrt Georg , Desai Ankur , team the FLUXNET ALBEDO
Published: March 2024

In considerations about land management and global climate, biophysical effects like those of albedo are known to modify biochemical effects of greenhouse gas release or uptake. In particular, the cooling effect of afforestation via creation of carbon sinks has been shown to be partly offset by the low albedo and snow-masking effect of tree canopies.In this presentation, we give a global overview on the relationship between albedo and CO2 uptake (net ecosystem productivity NEP and net biome productivity NBP). We focus on a recent study (Graf et al. 2023, https://doi.org/10.1038/s43247-023-00958-4) and the questions:(i) Do ecosystems sequestering more CO2 have a lower albedo as a rule?(ii) How close would such a relation be and how much room does it leave for climate-smart land use?(iii) Given the different immediacy of albedo and NBP based radiative forcing, are there different mitigation policies to be preferred at different points in time?To empirically investigate these questions with direct in-situ measurements, we identified 176 FLUXNET stations with sufficient coverage of NEP, incoming and outgoing shortwave radiation and ancillary data. A method to fill gaps in outgoing shortwave radiation and identify snow cover periods was developed and validated against available data and PI-provided snow statistics. We found a hyperbola-like decrease in maximum achievable effective (flux-weighted) long-term albedo as NEP increases, and vice versa. Apart from this joint limit, which also applied to non-forest and snow-free sites, the relation scattered strongly, indicating some room for climate-smart land use considering both albedo and carbon sequestration.A conceptual model based on a paired-site permutation approach showed that maximizing each site’s NEP without considering albedo, leads to albedo-based positive radiative forcing (warming) during the first approximately 20 years, before being offset by an even stronger NBP-based cooling. However, the fact that most sites are currently far below their possible maximum albedo-NEP combination also allows for a balanced scenario in which both parameters are improved simultaneously. It avoids warming on all timescales, but provides less cooling than pure NEP maximization in the long term. We discuss how these timelines would interact with current emission reduction policies, the reasons underlying the relationship and real-world examples of joint NEP and albedo change.
Early warning signals of AMOC collapse from North Atlantic array observations
Authors: Smolders Emma , van Westen René , Dijkstra Henk
Published: March 2024

The Atlantic Meridional Overturning Circulation (AMOC), one of the most prominent climate tipping elements on Earth, can potentially collapse as a consequence of surface freshwater input in the North Atlantic. A collapse from its current strong northward overturning state would have major impacts for the global climate system. Although available reconstructions appear to indicate a gradual weakening of the AMOC over the last century, the proximity of the climate system to a potential future collapse of the AMOC remains unknown. Here, we use the results of the first AMOC tipping event modelled in a state-of-the-art Global Climate Model, the Community Earth System Model (CESM), to identify regions and variables that play a key role in a forthcoming AMOC collapse and can therefore serve as early-warning signals (EWS). We analyse the statistical EWS properties using two steady state simulations with the same CESM version, the steady state simulations differ in the distance to the AMOC tipping point. These results will subsequently be used to assess the usefulness of observations from the SAMBA, RAPID and OSNAP arrays to determine whether the present-day AMOC is approaching a tipping point.
Which bias correction methods are suited to represent hydrologic extremes in the Alps?
Authors: Astagneau Paul C. , Wood Raul , Brunner Manuela I.
Published: March 2024

Projections from regional climate models are traditionally bias-corrected with ground observations before being used for hydrological modelling in order to improve the representation of local climate features. The choice of correction method affects hydrological projections, especially in mountain regions where the relationship between precipitation and temperature is a key property of the hydrological cycle as it controls the partitioning between solid and liquid precipitation. While several studies have investigated the sensitivity of hydrological projections (i.e., projections generated by feeding a hydrological model with climate projections) to the choice of bias correction technique, none have focused on single-model initial-condition large ensembles (SMILEs), which are a suitable tool for disentangling the response of hydrological extremes to climate change from their natural variability. In addition, the interaction between hydrological model choice and bias-correction method needs to be investigated in order to obtain more reliable hydrological projections.The objective of this work is to identify the most appropriate statistical techniques to adjust climate SMILEs for studying changes in hydrological extremes in mountainous terrain. For this purpose, we bias-corrected the climate projections of two high-resolution SMILEs (0.11°) under the Representative Concentration Pathway 8.5 for the domain of Switzerland. Specifically, we used a 2 km gridded reanalysis derived from ground observations and three techniques to correct the precipitation and temperature time series: (1) univariate quantile mapping, (2) trend-preserving univariate quantile mapping, and (3) trend-preserving multivariate quantile mapping. Then, we used the bias-corrected time series as inputs to an ensemble of 11 hydrological models to simulate streamflow at the outlet of 93 near-natural Swiss catchments for the period 1955 - 2099. We compared the performance of the three bias-correction techniques with respect to their ability to simulate historical floods and droughts. In addition, we determined the most fit-for-purpose method by examining both the robustness of the corrections (e.g. towards model choice, transferability in time) and the sensitivity of future hydrological projections to the choice of bias correction technique.