bluuebunny/crossref_metadata_2025 huggingface dataset:
Postprocessing East African rainfall forecasts using a generative machine learning model
Authors: Antonio Bobby , McRae Andrew , MacLeod Dave , Cooper Fenwick , Marsham John , Aitchison Laurence , Palmer Tim , Watson Peter
Published: March 2024

Existing weather models are known to have poor skill at forecasting rainfall over East Africa, where there are regular threats of drought and floods. Improved precipitation forecasts could reduce the effects of these extreme weather events and provide significant socioeconomic benefits to the region. We present a novel machine learning based method to improve precipitation forecasts in East Africa, using postprocessing based on a conditional generative adversarial network (cGAN). This addresses the challenge of realistically representing tropical rainfall in this region, where convection dominates and is poorly simulated in conventional global forecast models. We postprocess hourly forecasts made by the European Centre for Medium-Range Weather Forecasts Integrated Forecast System at 6-18h lead times, at 0.1o resolution. We combine the cGAN predictions with a novel neighbourhood version of quantile mapping, to integrate the strengths of both machine learning and conventional postprocessing. Our results indicate that the cGAN substantially improves the diurnal cycle of rainfall, and improves rainfall predictions up to the 99.9th percentile of rainfall. This improvement persists when evaluating against the 2018 March-May season, which had extremely high rainfall, indicating that the approach has some ability to generalise to more extreme conditions. We explore the potential for the cGAN to produce probabilistic forecasts and find that the spread of this ensemble broadly reflects the predictability of the observations, but is also characterised by a mixture of under- and over-dispersion. Overall our results demonstrate how the strengths of machine learning and conventional postprocessing methods can be combined, and illuminate what benefits machine learning approaches can bring to this region.
A near-seafloor study reveal a smaller low magnetization zone of a basalt-hosted hydrothermal field at East Pacific Rise
Authors: Zhou Fei , Tao Chunhui , Wu Tao , Dyment Jerome
Published: March 2024

Rock types of basement determine the magnetic signature of hydrothermal fields. Low magnetization zone (LMZ) is commonly observed at the basalt-hosted hydrothermal fields due to the fluid-rock interaction destroying the magnetic minerals inside basalt. We here report a near-seafloor magnetic survey conducted by the Autonomous Underwater Vehicle (AUV) over a basalt-hosted hydrothermal field on the East Pacific Rise (EPR). Inversed magnetization and Reduced-To-the-Pole (RTP) magnetic anomaly both show negative reduced magnetic signature centered on the hydrothermal field, reflecting enhanced demagnetization alteration process. Meanwhile, we delineate the range of the LMZ and compare it with the previous high-resolution near-seafloor magnetic studies on the fast-spread EPR, slow-spread Mid-Atlantic Ridge, and ultra-slow-spread Southwest Indian Ridge and Mohns Ridge. The statistical result shows that the diameter of LMZ increases with the decreasing spreading rate, suggesting the stable tectonic environment and focused melt supply at slower spreading ridge favor the birth of larger hydrothermal field.
Quantifying the impact of variable solar forcing on Sudden Stratospheric Warmings (SSWs)
Authors: Borthakur Monali , Sinnhuber Miriam
Published: March 2024

Sudden stratospheric warmings (SSWs) are characterised by the rise of polar stratospheric temperatures by several tens of kelvins. Here, we investigate the SSW of 2009 using the ECHAM/MESSy (EMAC) chemistry climate model. We study in particular how the SSWs are affected by variable solar forcing: EUV photo-ionisation that dominates the changes during high solar activity and geomagnetic storms. The warmings are preceded by a slowing then reversal of the westerly winds in the stratospheric polar vortex which then becomes easterly and it is also closely associated to polar vortex breakdown. 20 ensemble members are considered and different onset dates of the free running ensembles for the SSW event in January are tested to see the development of the polar vortex and its breakdown in the different ensemble members. Ionisation rates from the AISSTORM model are used in this case. And the results are compared with a geomagnetic storm (consisting of mostly electrons that are in the range of a few kilo-electron volts (keVs) to about 1 MeV) included on the day of the SSW, i.e., 25th of January. For the experiments considered here, the EUV photoionization was doubled and halved, and in both cases an increase in stratospheric temperature compared to the normal EUV was observed. Overall, effects of both EUV photoionization and particles on the temperature, wind fields, NOy and ozone in the middle atmosphere was observed. As ozone is one of the key species in radiative heating and cooling of the stratosphere, changes in its concentration can be linked to dynamical changes in the middle atmosphere. 
Comparison of probabilistic fault displacement hazard assessment results with observed permanent ground displacements in the aftermath of the 6 February 2023 Kahramanmaras Turkiye Earthquake.
Authors: Cagnan Zehra
Published: March 2024

The continuous functionality of critical infrastructure such as oil, gas, and water pipelines, as well as tunnels and bridges following an earthquake is important for the effective management of response actions and influences the seismic resilience of communities. Failure of such infrastructure may result in injuries and human fatalities, environmental pollution as well as significant direct and indirect economic losses. One of the most catastrophic earthquake-induced actions is the permanent fault displacements observed when fault ruptures propagate up to the ground surface in case of large magnitude earthquakes. Any structure but mostly pipelines, tunnels and bridges crossing active faults are prone to such permanent fault displacements as they develop excessive deformations as a result. The earthquake resistant design of pipelines, tunnels and bridges is based on reliable estimation of such excessive deformations.6 February 2023 Kahramanmaras Turkiye Earthquake with Moment Magnitude 7.8 (United States Geological Survey, USGS) ruptured 3 consecutive segments of the East Anatolian Fault Zone: namely Amanos, Pazarcik and Erkenek segments. East Anatolian Fault Zone is one of the most active strike-slip faults in the world. The total observed surface rupture as a result of the Kahramanmaraş Earthquake exceeded 300km with measured permanent displacements reaching 5m near Kahramanmaras and diminishing to 0.5m towards South and North. In this study, by carrying out probabilistic fault displacement hazard assessment for these segments of the East Anatolian Fault Zone expected permanent fault displacements were computed as a function of return period. Influence of multi-segment rupture, selected earthquake recurrence model, adopted maximum magnitude value are studied on the computed permanent displacement results. Computed displacements are critically compared against the observed permanent displacements. Pipeline Systems and Liquid Storage Tanks Earthquake Code of Turkey (2021) and EN1998-4:2006 suggest the use of empirical fault rupture length-permanent fault displacement relationships in design of pipelines crossing active faults. prEN 1998-4:2022 however suggests an alternative methodology that allows approximate calculation of permanent fault displacements corresponding to any given return period based on fault mechanism, fault rupture length and fault productivity. These code-based estimates are compared with measured fault displacements and conducted probabilistic fault displacement hazard results.
Maintenance of MJO Convection by Radiative Feedbacks  
Authors: Maloney Eric , Hsaio Wei-Ting
Published: March 2024

The maintenance mechanisms for the Madden-Julian oscillation (MJO) remain an area of active research, and may include a combination of radiative feedbacks, wind-evaporation feedbacks, and moistening produced by lower tropospheric convective heating. This presentation will revisit the importance of radiative feedbacks for supporting MJO convection with a new GPCP precipitation dataset and NASA CERES radiative heating profiles. Prior work by Adames and Kim with the GPCP v1.3 precipitation product and NOAA OLR indicated that radiative feedbacks are strongly supportive of MJO convection as viewed through the vertically integrated moist static energy budget, and provide a strong scale selection mechanism. This presentation uses the newer GPCP v3.2 product to show that while radiative feedbacks still provide a strong scale selection mechanism, the overall strength of radiative feedbacks are weaker than with GPCP1.3. This suggests that the relative role of other feedbacks such as wind-evaporation feedbacks for supporting MJO convection may be more important than once thought. This presentation also uses NASA CERES radiation profiles in a vertically-resolved moisture budget framework that employs the tropical weak temperature gradient assumption to determine the impact of radiative feedbacks on the MJO moisture budget. It is shown that longwave cloud radiative feedbacks onto MJO moisture anomalies are enhanced in the Indian Ocean and southern Maritime Continent region compared to other parts of the tropics, suggesting stronger support for MJO convection there. This finding is consistent with prior work by Mayta and Adames suggesting that the MJO most closely resembles a moisture mode in that region. It is hypothesized that enhanced vertical shear in the Indian Ocean and southern Maritime Continent supports convective organization that fosters greater cloud-radiative feedbacks.
Sustainable recovery of rare earth elements with eggshell waste calcite
Authors: Rateau Rémi , Drost Kerstin , Maddin Melanie , Szucs Adrienn , Terribili Luca , Guyett Paul , Rodriguez-Blanco Juan Diego
Published: March 2024

In November 2023, the European Union reached a provisional agreement on an European Critical Raw Materials Act, which encourages the local production, processing, and recycling of critical elements, notably the rare earth elements (REE). While indispensable for the green energy transition, their production is notoriously environmentally damaging, and efforts are being made to reduce this environmental footprint, for example via the use of secondary REE sources from waste or by the application of green chemistry and circular economy principles.In this study, we investigated the potential of hen eggshell calcite waste to be recycled for the uptake of REE from industrial and waste streams. We interacted commercial eggshells with 50 mM multi-REE (La, Nd, Dy) solutions at 25 to 205 °C between three hours and three months. The resulting products were characterized by powder XRD and Rietveld refinement for quantitative phase identification; SEM electron imaging for structural characterization; SEM energy dispersive spectroscopy for elemental mapping and quantification of major and minor elements; and laser ablation ICP-MS for trace element mapping.We observe a pervasive diffusion of the REE inside the eggshell calcite, along pathways formed by the intracrystalline organic matrix and calcite crystal boundaries, and without any partitioning of La, Nd and Dy. At 90 °C, calcite is observed dissolving and being replaced by kozoite spherulites, reminiscent of natural kozoite crystals. At 165 °C and 205 °C, an interface coupled dissolution-precipitation mechanism is observed, resulting in the complete dissolution of the calcite and its pseudomorphic replacement by polycrystalline kozoite. At 205 °C, kozoite itself is slowly replaced by hydroxylbastnäsite, the stable form of rare earth hydroxycarbonate, following a crystallization pathway previously established with inorganic calcite. Minor REE zoning at the eggshell grain scale is also observed, hinting at a potential use for REE separation.Our results demonstrate two potential applications of eggshell waste for the sustainable recovery of REE from aqueous solutions: at low temperatures, as a mixed organic-inorganic adsorbent and absorbent; and at higher temperatures as an efficient sacrificial template for the precipitation of rare earth hydroxycarbonates.
Fusion of IIRS and M3 data for the purpouse of finer resolution mineral mapping
Authors: Ivanov Iskren , Filchev Lachezar
Published: March 2024

Hyperspectral images, despite their rich spectral information, often suffer from low spatial resolution due to physical constraints in imaging sensors. However, when higher spatial resolution data of the same scene are available, we can perform data fusion to generate hyperspectral images with high spatial resolution. This fused data can be viewed as the output of a synthetic sensor that combines the high spatial and spectral resolution data acquired by different sensors. This fusion allows for new applications with increased accuracy, such as high-resolution mapping of minerals and surface materials. Imaging spectroscopy facilitates the identification and discrimination of materials and their constituents. Data fusion enhances both the spatial and spectral characteristics of the initial data. It is based on the synergistic exploitation of data from different sources, aiming to produce superior results. By integrating data from The Moon Mineralogy Mapper (M3) by NASA and the Imaging Infrared Spectrometer (IIRS) by ISRO, we can improve the spatial and spectral resolutions, enhance measurement accuracy, and reduce uncertainties. This will enable a more precise assessment of the mineral composition of the area of interest. The objective is to fuse high spatial resolution data, which has discontinuities in the spectral domain, with low spatial resolution data that has continuous spectra. The ultimate goal is to estimate an image with high spatial and spectral content, providing a more comprehensive and accurate understanding of the area of interest. We replaced the noisy bands in the M3 and IIRS data and used cubic convolution to resample the M3 bands to the IIRS band’s native spatial resolution. However, the M3 bandwidth is different from the IIRS bandwidths. Nevertheless, this gap-filling procedure will allow us to identify endmembers. As a followup study we are going to employ a spectral unmixing technique to obtain endmembers information and high-resolution abundance matrices from the initial images. Data fusion helps overcome the limitations of individual datasets, exploit the strengths of different sensors, and extract more valuable information.
Inorganic carbon: an overlooked pool in global soil carbon research  
Authors: Raza Sajjad , Irshad Annie , Margenot Andrew , Zamanian Kazem , Ullah Sami , Kurganova Irina , Zhao Xiaoning , Kuzyakov Yakov
Published: March 2024

Soils are one of the major players in the global carbon (C) cycle and climate change by functioning as a sink or a source of atmospheric carbon dioxide (CO2). The largest terrestrial C reservoir in soils comprises two main pools: organic (SOC) and inorganic C (SIC), each having distinct fates and functions but with a large disparity in global research attention. This study quantified global soil C research trends and the proportional focus on SOC and SIC pools based on a bibliometric analysis. Research on soil C pools started in 1905 and has produced over 42,000 publications (> 1.6 million citations). Although the global C stocks down to 2 m depth are nearly the same for SOC and SIC, the research has dominantly examined SOC (> 96% of publications and citations) with a minimal share on SIC (< 4%). Approximately 39% of the soil C research was focused on climate change. Despite poor coverage and publications, the climate change-related research impact (citations per document) of SIC studies was higher than that of SOC. Machine learning, biochar, soil properties, and climate change were the recent top trend topics for SOC research (2018-2022), whereas soil acidification, organic C, climate change, and Holocene were recent trends for SIC. SOC research was contributed by 150 countries compared to 85 for SIC. As assessed by publications, soil C research was mainly concentrated in a few countries, with only 10 countries accounting for 75% of the research. China and the USA were the major producers (44%), collaborators (36%), and funders of soil C research. SIC is a long-lived soil C pool with a turnover rate of more than 1000 years in natural ecosystems but intensive agricultural practices have accelerated SIC losses, making SIC an important player in global C cycle and climate change. The lack of attention and investment towards SIC research could jeopardize the ongoing efforts to mitigate climate change impacts to meet the 1.5-2.0 oC targets under the Paris Climate Agreement of 2015. This study calls for expanding the research focus on SIC and including SIC fluxes in C budgets and models, without which the representation of the global C cycle is incomplete.
NOAA's Unified Forecast System Research to Operations (UFS R2O) Project Phase II - Accomplishments, Progress and Future Plans
Authors: Tallapragada Vijay , Whitaker Jeffrey , Kinter Jim
Published: March 2024

The UFS-R2O Project, which began in July 2020 as a five-year plan with deliverables for the first three years funded, has made significant progress in developing the medium-range and sub-seasonal to seasonal (MRW/S2S) predictions, a regional, high-resolution hourly-updating and convection-allowing ensemble system for prediction of short range severe weather (CAM/SRW), and a Hurricane Application developing a very high-resolution Hurricane Analysis and Forecast System (HAFS) with storm following moving nests.  The Project is organized with Application Teams and Development Teams interacting with each other to reflect the cross-cutting nature of the UFS components and infrastructure. It  fostered successful collaborations between the National Centers for Environmental Prediction (NCEP) Environmental Modeling Center, several NOAA research labs, the National Center for Atmospheric Research (NCAR), the Naval Research Lab (NRL), and multiple universities and cooperative institutes.  Most sIgnificant outcomes of the project thus far are the implementation of the HAFSv1 ahead of the schedule, and the development of a six-way global coupled (atmosphere/ ocean/ land/ sea-ice/ wave/ aerosol) modeling system, both within the UFS framework, major accomplishments from the community modeling perspective.  The UFS-R2O Project has entered into its second phase (2023-2024), albeit with reduced funding, to continue the momentum built during the first phase.  While the first three years of the project were focused on engineering and infrastructure, Phase II is primarily targeting systematic testing and evaluation of the prototype UFS configurations for selecting the candidates for potential transition to operations in the next few years.  In addition, Phase II of the project includes a new Seasonal Forecast System (SFS) Application Tean established to develop SFS v1 that will replace the legacy Climate Forecast System (CFSv2) currently in operations since 2011.This presentation describes the outcomes of the UFS R2O Project for the first three years, and highlights the progress and plans for the Phase II.
Probabilistic streamflow forecasting using generative deep learning models
Authors: Jahangir Mohammad Sina , Quilty John
Published: March 2024

The significance of probabilistic hydrological forecasting has grown in recent years, offering crucial insights for risk-based decision-making and effective flood management. This study explores generative deep learning models, specifically the conditional variational autoencoder (CVAE), for probabilistic streamflow forecasting. This innovative approach is applied for forecasting streamflow one to seven days (s) ahead in 75 Canadian basins included in the open-source Canadian model parameter experiment (CANOPEX) database. CVAE is compared against two benchmark quantile-based deep learning models: the quantile-based encoder-decoder (ED) and the quantile-based CVAE (QCVAE).Over 9000 deep learning models are developed based on different input variables, basin characteristics, and model structures and evaluated regarding point forecast accuracy and forecast reliability. Results highlight CVAE‘s superior reliability, showing a median reliability of 92.49% compared to 87.35% for ED and 84.59% for QCVAE (considering a desired 90% confidence level). However, quantile-based forecast models exhibit marginally better point forecasts, as evidenced by Kling-Gupta efficiency (KGE), with a median KGE of 0.90 for ED and QCVAE (compared to 0.88 for CVAE). Notably, the CVAE model provides reliable probabilistic forecasts in basins with low point forecast accuracy.The developed generative deep learning models can be used as a benchmark for probabilistic streamflow forecasting due to the use of the open-source CANOPEX dataset. Overall, the results of this study contribute to the expanding field of generative deep learning models in hydrological forecasting, offering a general framework that applies to forecasting other hydrological variables as well (precipitation and soil moisture).
Use of Remote Sensing Flood Inundation Maps (FIM) for Evaluating Model-predicted FIM: Challenges and Strategies
Authors: Cohen Sagy , Tian Dan , Baruah Anupal , Liu Hongxing , Nikrou Parvaneh
Published: March 2024

Remote sensing (RS)-derived Flood Inundation Maps (RS-FIM) are, in principle, a desirable source of observed data for the development, calibration, and validation of (model) predicted FIM. Advantages of using RS-FIM for evaluating predicted FIM include its spatial continuity (compared to point observations), and the representation of real flooding events (compared to synthetic events (e.g. 100-yr) or other models). Disadvantages may include low/mismatched spatial resolution, insufficient classification accuracy, lack of water depth information, and gaps in coverage (due to dense vegetation, buildings, clouds, etc.). Gaps in inundation coverage are very common in RS-FIM. While these may not be a major issue for some RS-FIM applications, they are a major, yet unacknowledged, issue for fair and robust evaluation of predicted FIM. This is because the evaluated model may correctly predict flooding in these gaps while the (RS-FIM) benchmark data is classified as non-flooded (leading to inaccurate identification of 'False-positives'). Techniques for 'closing the gaps' in RS-FIM using hydraulic models or terrain analysis can yield improved FIM but, depending on the scale of the 'gap-filling', can result in an RS-model hybrid which undermines the observational nature of RS-FIM. Here we will demonstrate and discuss the challenges in using RS-FIM for the evaluation of predicted FIM and present tools and analysis demonstrating a new framework for fair and robust evaluation of FIM predictions using RS-FIM.
Terrestrial carbon dioxide removal from the atmosphere: Ecological Principles
Authors: Luo Yiqi
Published: March 2024

Terrestrial ecosystems sequester about a third of anthropogenic CO2 emissions by natural processes and, thus, play a critical role in mitigating climate warming. As climate change become more aggravated, the need to remove CO2 from the atmosphere to the terrestrial and aquatic ecosystems becomes more urgent over time. A score of new techniques of carbon dioxide removal (CDR) have been recently proposed based on a notion of actively managing land carbon cycle processes to increase carbon sequestration and/or reduce greenhouse gas (GHG) emissions. These actively managed climate solutions by human (i.e., human-based) should be complementary to the nature-based climate solutions to combat climate change together with concurrent and dramatic economy-wide decarbonization. However, what are ecological principles behind the terrestrial CDR techniques? How can the ecological principles identified from these removal techniques be used to guide the design of more effective, future CDR techniques? These questions remain unanswered. This presentation will show ecological principles we identified from our analysis of these existing CDR techniques and propose more effective techniques for carbon dioxide removal. We analyzed a dozen of existing CDR techniques, such as afforestation and reforestation, biochar from crop residues or slashed woods, and peatland restoration. All these existing CDR techniques manage carbon residence time more than carbon input. As carbon storage is jointly determined by carbon input and residence time, elongation of residence time or increase in carbon input or both all result in increased carbon sequestration (i.e., increased carbon dioxide removal from the atmosphere).  It appears that there are more rooms to manage carbon residence time than carbon input as carbon residence time can change from a few months or years to thousands of years. Thus, we can evaluate and design CDR techniques using methods that can substantially elongate carbon residence time. 
Investigating the Kuan Kreng Peat Swamp Forest using Electrical Resistivity and Ground Penetrating Radar for Carbon Stock Estimation
Authors: Phiranram Theethach , Chenrai Piyaphong , Jirapinyakul Akkaneewut , Rachukan Narongsak
Published: March 2024

Peatlands, also known as bogs, fens, and especially peat swamp forests in tropical regions, are wetland ecosystems where peat layers are present due to anoxic conditions. Immense amounts of carbon are stored in peat layers, making it an important carbon sink for atmospheric carbon and playing a major role in carbon cycle. These peat layers are vulnerable to becoming a carbon emission source due to the disturbance of the peat layer by natural and anthropogenic processes. Southern Thailand comprises several peatlands that have encountered degradation due to cultivation and forest fires, especially the Kuan Kreng peat swamp forest, which is the second largest peatland in Thailand and serves for carbon storage. To evaluate the impact of peatland degradation, carbon stock estimation is necessary; thus, the thickness and distribution of the peat layers are necessary. This study utilizes ground penetrating radar and electrical resistivity imaging, along with conventional core studies, to investigate physical and chemical characteristics and also delineate peat layer.Significant relationships between chemical and physical properties of the peat layer are represented, which is useful for geophysical interpretation. The resistivity profiles show a high resistivity response, in the range of 21.9 to 145.0 ohm-m, which is interpreted as peat layers in the shallow subsurface. The high amplitude, contorted to sub-parallel reflection from GPR profiles, represents a peat layer that has a relatively lowest velocity with the highest dielectric constant. In order to evaluate carbon stock, average values of bulk density (0.19 g/cm­3) and TOC (31.18 wt. %) from the drilling core samples are advocated, resulting in 59.24 Kg C/m3 of carbon density. Then the peat layer average thickness of 18.00 cm from the geophysical survey and core samples are used to estimate the carbon stock per unit area. Therefore, considering the entire area of the Kuan Kreng peat swamp forest, the carbon stock is estimated at a minimum of 7.53 Mt.
Unify Different Interferograms to Get the Unique InSAR Time Series
Authors: Ma Zhangfeng , Jiang Yu , Li Chenglong , Wei Shengji
Published: March 2024

“Long or short baseline interferogram” is a well-known concept in InSAR time series analysis. In different cases, scientists often choose interferograms with different baselines based on some specific criteria, such as the level of coherence, the degree of atmospheric delay, and the length of the spatiotemporal baseline to get the best results. However, a question behind the selection of interferograms still keeps intact, that is, why do different interferograms get different results? The recent case of deriving the postseismic deformation of 2021 Maduo Mw7.4 Earthquake illustrates just how important this question is, in which almost every team achieved different results. We highlight the roles of unwrapping error and fading signal in this case, which can explain why different interferograms can get different results. We also proposed a new method to correct these two error sources. After the correction, we unify the time series results from different interferograms. In addition, we also explored the relationship between fading signal and soil moisture, and successfully mapped the liquefaction related to earthquake using InSAR, which to our knowledge is the first time in the world.
Divergent microbial phosphorous acquisition strategies between active layer and permafrost deposits
Authors: Wang Lu , Yang Yuanhe
Published: March 2024

It has been advocated that nitrogen (N) availability plays an essential role in mediating plant and microbial growth in cold environment, and could thus regulate the direction and magnitude of permafrost carbon (C)-climate feedback. However, compared to widely concerned N, little is known about soil phosphorous (P) availability and its biological acquisition strategies in permafrost environment. Here we explored soil microbial P acquisition strategies using shotgun metagenomics across the Tibetan permafrost area, encompassing a large scale survey spanning 1,000 km. In contrast to the traditional opinion that microorganisms in cold area usually obtain P mainly through mineralization process, our results revealed that the P cycling genes responsible for solubilization, mineralization and transportation were widespread, illustrating multiple microbial strategies for acquiring P in permafrost regions. Moreover, the higher gene abundance related to solubilization and mineralization as well as an increased ration of MAGs carrying these genes were detected in the active layer, while the greater abundance of low affinity transporter gene (pit) and proportions of MAGs harbouring pit gene were observed in permafrost deposits, reflecting a stronger potential for P activation in active layer but an enhanced P transportation potential in permafrost deposits. Taken together, these results highlight that besides microbial P mineralization, multiple P-related acquisition strategies and their differences among various soil layers should be considered simultaneously to improve model prediction for the responses of biogeochemical cycles in permafrost ecosystems to climate change.
Community resilience in China in the context of disaster management
Authors: Liu Jie , Los Alexander
Published: March 2024

In recent years, the frequent occurrence of natural disasters due to global climate change has brought devastating impacts on many countries’ economies, societies, resources, and environment. How decision makers and practitioners respond before, during, and after a disaster occurs is critical to reducing disaster impacts, restoring system functionality, and improving the adaptive capacity for future events, that is, enhancing CR. Currently, there is a lack of empirical data and process-based approaches to assess the dynamic characteristics of CR and to understand what contributes to CR evolution during the disaster management process (i.e., early warning, emergency response, recovery and adaptation). This research is based on the 7.20 heavy rainstorms that happened in Zhengzhou City. To quantify CR, a Dynamic Community Resilience Assessment Framework (DCRAF) is firstly developed. Then, the disaster management meta-network (DMMN), hazard evolution, and disaster management background are identified as the influencing factors of the dynamics of CR. In the next stage, an analytical model is built to explore the quantitative relationship between the dynamics of CR with its influencing factors, and based on this, propose CR enhancement strategies for different phases of future events. Furthermore, the analytical model is expected to have the ability for CR prediction, whereby decision-makers could adjust actions accordingly to mitigate disaster impacts and recover quickly. This presentation will (1) explain the indicators of the DCRAF in the infrastructure domain (including communication, transportation, water, power, municipal infrastructure, and protection works) and the approach to quantify and integrate them to get CR evolutions; (2) use the empirical data to explain how the DMMN, hazard evolution, and disaster management background influence CR at different stages of disaster management; (3) elaborate on the structure of the analytical model, the approach of extracting CR enhancement strategies, and the approach of CR prediction.
Sea ice distribution in the northeastern Nordic Seas during the Dansgaard-Oeschger events
Authors: Wong Wanyee , Risebrobakken Bjørg , Fahl Kirsten , Stein Rüdiger , Jansen Eystein , Supiot Tessier Juliette
Published: March 2024

Sea ice conditions in the eastern Fram Strait during the Dansgaard-Oeschger (D-O) events are reconstructed using biomarkers. The role of the Nordic Seas sea ice has long been recognized as a significant factor influencing the D-O climate changes. Two full D-O cycles between 40 and 34 ka b2k, covering Greenland Stadial (GS) 9, also known as Heinrich Stadial (HS) 4, to Greenland Interstadial (GI) 7 are investigated in high resolution (20-40 yr/sample). Preliminary results suggest an extended sea ice cover in the eastern Fram Strait during both stadials. Repeated polynyas were present during GS-9/HS-4, but not during GS-8. The retreat of the extended sea ice cover in the eastern Fram Strait occurred later during the GS-9/HS-4 to GI-8 transition than during the GS-8 to GI-7 transition. Both interstadials were characterized by a seasonal sea ice cover, with the summer/fall sea ice edge occasionally moving farther north than the study site. Our findings provide new insights on the importance of ocean-ice processes in driving the D-O events.
Local insights into community participation and Eco-DRR strategies for sustainable geopark management at Caota Sand Dunes Geopark, Taiwan
Authors: Lin Tsung-Yi , van Onselen Viola
Published: March 2024

The integration of Eco-DRR practices in geopark management is a promising method to strengthen community resilience. In the context of the Caota dunes geopark, the level of engagement and support from local residents could significantly contribute to the effectiveness of Eco-DRR approaches through fostering awareness, strengthening the local economy, enhancing ecosystem services, promoting sustainable resource use and facilitating the adoption of resilient livelihood practices. However, it is essential to first understand the needs of the community before implementing these strategies. This study aimed to identify critical factors by exploring community needs and challenges through interviews and focus groups to identify pathways for an inclusive and community-centered approach to the geopark's implementation and conserve the essential functions of the dune landscape.  Capacity building has contributed to an increased sense of responsibility and commitment from the local community. Nevertheless, insights from local community members highlighted the urgent need for economic benefits and future stability for the geopark through more defined development plans and increased community involvement. This study highlights the importance of incorporating community insights early in the development process  and promote bottom-up approaches, providing greater opportunities for active participation in geopark planning, management, and monitoring.
El Niño Southern Oscillation and Tropical Basin Interaction in Idealized Worlds
Authors: Dommenget Dietmar , Hutchinson David
Published: March 2024

In this study we discuss a set of fully coupled general circulation model simulations with idealised geometries of the tropical ocean basins and land with a focus on important characteristics of El Niño Southern Oscillation (ENSO) type of variability and tropical basin interaction. In a series of 15 simulations we first vary the zonal width of a single tropical ocean basin from 50o to 360o, while the rest of the tropical zone is set as land. Further we discuss different simplified configurations of two or three tropical ocean basins. The results show remarkable changes in ENSO characteristics as function of basin width and due to the interaction with other basins that challenge our current understanding of ENSO dynamics. A single basin ENSO has an optimal basin width of about 150o at which ENSO preferred period is the longest, the wind stress feedback is the strongest and variability is stronger than in all other basin widths, expect for the 350o basin. Tropical basin interactions substantially affect ENSO strength, periodicity, feedbacks, non-linearity, spatial scale and pattern. In experiments with two or three identical ocean basins we find highly synchronized ENSO modes that are identical between basins and far more energetic and oscillatory then the single basin modes. The results suggest that tropical basin interaction is an essential part of ENSO. The framework of these experiments can help to better understand the atmospheric dynamics of ENSO and should help to formulate an ENSO theory that incorporates tropical basin interactions as a core element.
Assessing the impact of Ocean Alkalinity Enhancement on the zooplankton community
Authors: Bhaumik Amrita , Henning Merle , Faucher Giulia , Kittu Leila , Schneider Julieta , Meunier Cédric L. , Riebesell Ulf , Boersma Maarten
Published: March 2024

Ocean alkalinity enhancement (OAE) can help mitigate climate change impacts by increasing the carbon storage capacity of the ocean. The technique involves addition of alkaline substances to the seawater to accelerate the natural rock weathering process. However, this will lead to sudden seawater chemistry changes, such as increased pH that might directly and/or indirectly (through trophic pathways) affect zooplankton, an important trophic link, by altering its metabolic state and community composition. In addition, varying dilution times of alkaline substances might impact organisms differently. To date, the possible influences of OAE on zooplankton communities are largely unexplored. To bridge the knowledge gap, we conducted mesocosm and laboratory experiments in simulated non-equilibrated, calcium-based (Ca(OH)2) OAE setups. An incrementally enhanced alkalinity gradient from 0 to 1250 µmol kg-1 in steps of 250 µmol kg-1 was used in all experiments. The wide-ranging enhanced total alkalinity (∆TA) was selected to assess the safety threshold. In addition, we compared immediate versus delayed dilution scenarios in our mesocosm study, where each scenario ended up with the same ∆TA gradient after mixing. We examined the multitrophic community response by monitoring twelve mesocosms for 39 days including the natural spring bloom community of Helgoland roads waters in the North Sea. Subsequently, the direct effect of alkalinity enhancement on the physiology (i.e., respiration and grazing) of Temora longicornis (predominant copepod in the mesocosms) was evaluated in the laboratory. The species-specific bottom-up effect was examined by culturing Rhodomonas salina in aforementioned ∆TA gradient and feeding them to the T. longicornis. We observed relatively lower zooplankton abundance, and growth rate in mesocosms with ∆TA1000 and 1250 µmol kg-1, which might be a bottom-up effect. In our lab experiments, though, we observed a negative impact on R. salina growth rate and nutritional quality from ∆TA750 µmol kg-1, we did not detect any substantial direct or indirect impact on the physiological performance of T. longicornis. Overall, our laboratory study provided a preliminary understanding of the direct and indirect effects of OAE on a key copepod species, and the mesocosm study gave insight into the zooplankton community response.
Responses of ecosystem productivity to anthropogenic ozone and aerosols at 2060 under different emission scenarios
Authors: Zhou Xinyi , Yue Xu , Tian Chenguang
Published: March 2024

Anthropogenic emissions contribute a significant percentage to surface ozone (O3) and aerosols globally. The rate of plant photosynthesis, which represents the capability of terrestrial ecosystems to sequester carbon dioxide (CO2), is significantly affected by anthropogenic air pollutants. While the changes in anthropogenic emissions perturb atmospheric components, their consequent impacts on ecosystem productivity in the future climate remain unclear. Here, we apply a fully coupled climate-vegetation-chemistry model, ModelE2-YIBs, to explore the effects of O3 and aerosols from anthropogenic emissions on global gross primary productivity (GPP) under both present-day and different future emission scenarios at 2060. At the present day, anthropogenic air pollutants induce a GPP loss of -1.67 Pg[C] (-4%) in boreal summer with the contributions of -2.18 Pg[C] by O3 and +0.52 Pg[C] by aerosols. At 2060, the detrimental effect of air pollutants on GPP is exacerbated to -1.85 Pg[C] under a high emissions scenario but alleviated to -0.59 Pg[C] under a low emission scenario. The mitigated GPP loss in the latter scenario is owing to the effective control of anthropogenic emissions that on average reduces surface O3 concentrations by 8.14 ppbv globally relative to 2010. Although the CO2 fertilization effect is weaker in the low emission scenario, the strong decline in air pollutants brings additional GPP gains compared to the high scenario. Regionally, such GPP amelioration is close to or even outweighs the CO2 fertilization effect in eastern China and U.S., suggesting that the deep cut of anthropogenic emissions can effectively promote future ecosystem productivity through the reduction of O3 and aerosols over the nowadays polluted regions.
Chemoautotrophic and photoautotrophic inorganic carbon fixation rates in marine oligotrophic systems; The Mediterranean and Red seas as case studies
Authors: Rahav Eyal , Reich Tom , Berman-Frank Ilana
Published: March 2024

Dark inorganic carbon fixation (DCF) by chemoautotrophs is thought to play a significant role in marine systems, especially in oligotrophic marine ecosystems where photosynthesis is typically low. We investigated DCF and its contribution to the total primary productivity (PP) in the ultra-oligotrophic eastern Mediterranean Sea (2021-2024) and the meso/oligotrophic northern Red Sea (2010-2023). Our results show that DCF is indeed substantial, and corresponds to ~25-40% of the annual primary productivity rates in both areas. The contribution of DCF to PP was high during the summer and increased from the coast to the offshore water. During wintertime, the contribution of DCF to PP was typically low, without clear spatial or vertical trends. Additionally, aphotic DCF rates were similar to those found in the photic zone. Lastly, our results show that organic nutrient amendments significantly elevate dark inorganic carbon fixation, whereas the addition of inorganic nutrients elevates photosynthesis and to a lesser extent DCF. These results suggest that DCF may be an important biochemical process throughout the water column of oligotrophic seas, and thus should be incorporated into oceanic carbon production estimates.
Application of anomalous transport modeling for karst aquifer discharge response to rainfall
Authors: Elhanati Dan , Frank Simon , Goeppert Nadine , Berkowitz Brian
Published: March 2024

Discharge in many karst aquifers exhibits distinctive long tails during recession that follow recharge events, a phenomenon often associated with the intricate flow paths that develop due to the underground structure of karst systems. This complexity poses a unique task from the perspective of modeling the flow and discharge patterns. In this study, we propose a novel approach to address long tail discharge during base-flow conditions, by adapting the continuous time random walk (CTRW) framework, known as a robust tool for modeling the long-tailed behavior observed in breakthrough curves of chemical species during transport, under diverse flow conditions. By establishing a theoretical analogy between partially saturated karst flow and chemical transport, we develop and implement a particle tracking (CTRW-PT) model that provides robust fits of three years of data from the Disnergschroef high alpine study site in the Austrian Alps, underscoring the predominance of slow diffusive flow over the rapid conduit flow. The agreement between measured and simulated data not only validates the proposed analogy between partially saturated karst flow and chemical transport but also highlights the utility of the CTRW-PT model, offering valuable insights and enhanced modeling capabilities for future research in this complex field.
Change in the distribution of heavy 1h precipitation due to temperature changes in measured values, model reanalyses and model simulations of future climate
Authors: Sokol Zbyněk , Popova Jana
Published: March 2024

This contribution focuses on the change in 1h heavy precipitation distribution in response to the increasing air temperature in Czechia (Central Europe). The air temperature, the dew point temperature and the temperature of lifting condensation level are used as temperature characteristics. The change in the distribution of 1h precipitation measurements is compared with the results of reanalyses based on simulations of ALADIN-CZ NWP model and with the results of future climate simulations by ALADIN-CLIMAT-CZ climate model. In general, the increase in heavy precipitation appears clearly in the very upper part of precipitation distribution. Values of the upper percentiles of precipitation increase up to a certain temperature threshold and then they decrease, which is in line with other studies. This is also visible in the simulations of future climate.
Applying Mobile Phone Data on Seismic Disaster Reduction
Authors: Liu Sheu-Yien , Huang Ming-Wey
Published: March 2024

To grasp specific population distribution information is crucial for accurate impact assessments and preparedness planning on natural disasters. With the high popularization of mobile phones, it is possible to know the distribution trend of the people movement in different regions. The mobile phone data from Chunghwa Telecom (the telecommunications company with largest market share in Taiwan) displayed in 500m×500m grids gives the spatiotemporal distribution of people around the Taiwan area on the geographic information system (GIS). Combined with immediate reception of earthquake intensity distribution map, not only can the number of people at risk be more accurately estimated, but also the abnormal flow of people can be highlighted in areas, and then provide real-time warning messages. Except for the real-time crowd data, the historical data from one year of 2018, which is converted into weekly crowd data, are also provided for the purpose of seismic disaster scenarios to improve the precision of relief needs by the grid-base earthquake impact assessment technology of TERIA (Taiwan Earthquake Impact Research and Information Application Platform, established by NCDR) for enhancing the disaster resilience against future major earthquakes.