bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-term effects of restoration strategies on ecosystem functions, soil conservation and afforestation on the Loess Plateau
Authors: Feng Tianjiao , Zhang Yufei , Wang Ping
Published: March 2024

Loess Plateau is a typical ecologically-fragile area worldwide. Vegetation restoration acts as a long-term and important strategic program wherein, while the ecological effects after long-term vegetation restoration need to be assessed in detail to support for ecological restoration in similar areas. Across the Loess Plateau, four representative sites: the P. tabulaeformis artificial forest, P. orientalis artificial forest, R. pseudoacacia artificial forest and natural Secondary forest were selected as typical artificial plantations and natural forests in this study. To analyze ecological benefits under different vegetation restoration types and discuss the optimal model of afforestation in the future, soil properties in 0-100 cm, vegetation attributes at four synusium, hydrological processes of 23 rainfall events and ecosystem functions (10-years soil water storage) were measured and compared. The results showed that: (1) the soil properties of natural forest, especially soil water retention, were better than that of planted forests; (2) the biomass of arborous synuium in natural forest was lower than that in artificial forest, while it was significantly higher at shrub and herbaceous synuium; (3) there were differences in rainfall redistribution between natural and artificial forest with the satisfactory water storage conversion in study, but the natural forest had the highest soil water storage (414.33 mm) during the last decade; (4) according to the structure equation model (SEM), the vegetation attributes at arborous and herbaceous synuium had the most direct and total effects on the hydrological processes and function in ecosystems. The results indicate that afforestation should be conducted in a nature inclusive manner, which can provide guidance for vegetation restoration in the Loess Plateau and similar ecologically-fragile regions in the future.
Quantifying Carbon Dioxide and Methane Hotspots: A Simulation Study with the TANGO Satellite Initiative
Authors: Charuvil Asokan Harikrishnan , Landgraf Jochen , Scheidweiler Leon , Butz André
Published: March 2024

In addressing the challenges caused by climate change, it is crucial to understand the impact of anthropogenic carbon dioxide (CO2) and methane (CH4) emissions. Therefore, it is essential to collect accurate and precise information on the sources of these greenhouse gases to develop effective mitigation strategies. Here, we present a simulation study for quantifying the emission hotspot targets using next-generation satellite sensors. A satellite end-to-end simulator will be used to identify and prioritize targets of CH4 and CO2 sources around the globe.The current study will follow the orbit and satellite parameters of the proposed Twin Anthropogenic Greenhouse Gas Observers (TANGO) mission, representing an innovative CubeSat satellite initiative consisting of two satellites. TANGO-Carbon (1) will measure CO2 and CH4 in the 1.6 μm range, while TANGO-Nitro (2) will detect NO2 and cloud-related data. These satellites offer a spatial resolution of 300x300 m2, covering target areas spanning 30x30 km2. The TANGO mission aims to quantify point sources with CO2 emission rates of at least 2 Mt/yr and CH4 emissions of at least 5 kt/yr. Supported by national Dutch funding, the TANGO mission is scheduled for launch in 2027.After identifying localized point sources through the simulation study, we present performance analyses for these future-generation satellites. From an identified target tile, a simulated measurement study will also be conducted. Expected concentration fields will be used to create synthetic measurements from spectrometer parameters. These synthetic measurements will be used in a physics-based radiative transfer model to estimate emission rates. Additionally, we will examine different performance metrics and potential error sources, such as errors due to aerosol scattering, to understand how they might affect our emissions estimates.
GC5-LFRic: Developing the next Met Office coupled atmosphere-ocean model with a cubed-sphere atmosphere grid.
Authors: Graham Tim , Carvalho Maria , Copsey Dan , Ackerley Duncan , Earnshaw Paul , Marzin Charline , Mittermaier Marion , Narayan Nikesh , Shipway Ben , Willett Martin
Published: March 2024

The Met Office uses its global coupled atmosphere-ocean model for predictions on timescales from days to centuries as part of its seamless prediction framework. The atmospheric component of the coupled system is the Unified Model on a latitude-longitude grid. With ever increasing resolution and significant changes to high performance computing such as new processor types and increased parallelisation, a fundamentally new atmosphere model is needed to meet future requirements.A major effort is underway to develop a new version of the coupled model using the GungHo dynamical core on a cube-sphere grid with atmospheric physics from the existing unified model. This is coupled to the NEMO ocean model and SI3 sea-ice model on a tripolar grid.We will show results from the latest prototype models for numerical weather prediction and climate simulations. We will also discuss the ongoing work and challenges to make these systems operational at the Met Office.
Observations and retrieval of volcanic SO2 emissions from the Sakurajima volcano, Japan, using temperature-stabilised Car-DOAS
Authors: Bittner Simon , Poulidis Alexandros P. , Richter Andreas , Iguchi Masato , Vrekoussis Mihalis
Published: March 2024

Volcanic eruptions in recent years, including the long-lasting eruption of Cumbre Vieja, La Palma (September-December 2021) and the fissure eruption at Reykjanes Peninsula, Iceland (December 2023, currently ongoing), have put a spotlight on the severe impacts volcanic gas emissions can have on human activities and the environment. Observations of such volcanic degassing were the target of the Volcanic Emissions Observation and Modeling (VOLCOM) field campaign, carried out on Sakurajima volcano, Japan, during November 2023, chosen due to the volcano’s long-lasting activity (currently ongoing since 1955) and surrounding population (>1 million residents in the surrounding 20 km). VOLCOM provided an excellent opportunity to employ and test a well-known remote sensing methodology (Car-DOAS). To reduce the noise level in Car-DOAS measurements, we incorporated a temperature stabilization device. The temperature stabilized-Avantes spectrometer was mounted on a vehicle equipped with a UV bandpass filter. The planning of the measurements was supported by a dedicated high-resolution meteorological forecasting approach using the Weather Research and Forecasting (WRF) and FALL3D models. With the presented setup, SO2 emission rates from the vent were monitored, and strong signals (SCD >1018 molecules cm-2) were observed.High concentrations of SO2 in the plume, however, can make proper retrieval of the signal challenging as the strong absorption of SO2 pushes the DOAS method to its limits. It was seen that shifting the fit window depending on the SO2 signals ensures that the weak absorber assumption remains valid, which is used to create a composite product featuring multiple fit windows. Based on the composite product, the passive emissions during that period were estimated using a mass-balance approach with the forecasted wind field over the volcano. Proper retrieval of the SO2 signal will help us establish a new dataset for the volcano and allow us to gain insight into the chemistry and environmental impacts of volcanic gas emissions.
The effect of a strong hurricane on carbon export from the largest contiguous mangrove forest in North America
Authors: Stegehuis Annemiek I. , Ho David T. , Bopp Laurent , Guenet Bertrand
Published: March 2024

Mangroves, one of the most productive ecosystems on Earth, are an important link between land and ocean and are crucial for moderating carbon fluxes to the ocean in the tropics and sub-tropics. Besides their vulnerability to sea-level rise and other human-induced changes, they are regularly exposed to hurricanes. Here we studied the impact of Hurricane Irma on dissolved organic and inorganic carbon (DOC and DIC) fluxes in the mangrove estuary of Shark River in the Everglades National Park, the largest mangrove estuary in North America. We found that both DIC and DOC fluxes decreased severely directly after Irma made landfall as a Category 4 hurricane. The DIC flux recovered quickly, while the DOC flux remained lower up to two months after Irma. Besides the change in absolute numbers, we found that the contribution of estuarine (i.e., mangrove root respiration and calcite dissolution) vs non-estuarine (i.e., from upstream of the estuary) sources to DIC decreased considerably at the site closer to the ocean after the hurricane. Although both DIC and DOC fluxes were still lower up to two years after Irma, the high inter- and intra-variability of both fluxes made it difficult to draw solid, long-term conclusions.
Validation of the CrIS, IASI, AIRS and GOSAT-NH3 products 
Authors: Dammers Enrico ,   FTIR-NH3 and satellite NH3 product teams
Published: March 2024

Ammonia (NH3) is an essential form of reactive nitrogen whose emissions originate primarily from manure and fertilizer. Atmospheric ammonia is a major precursor of anthropogenic aerosols, for example ammonium nitrate, and negatively impacts both human and ecosystem health. While important, the overall budget of ammonia remains highly uncertain, as in-situ measurements of ammonia are still sparse and typically only provide a very coarse spatial-temporal coverage. Satellite ammonia observations provide a tool to study the budget in detail, however NH3 satellite products validation studies are challenging due to the small scale spatiotemporal variability of atmospheric ammonia. Furthermore, the validation studies typically only focus on a single satellite product and/or comparison to in-situ surface concentrations. This limits such studies to more of an evaluation on how well a satellite represents a surface point measurement instead of validating the satellite vertical sounding (e.g. profile and total column) observations. The ground-based Fourier Transform infrared (FTIR)-NH3 product is an excellent dataset that can more directly evaluate the satellite column and profile type products as it shares a similar sounding measurement. Here we present an update to the FTIR-NH3 record, which includes an extension in both the number of sites as well as the observation period of these stations. Furthermore, we present the latest evaluation and intercomparison results for the most current CrIS (1, and 2), IASI(-A, -B and -C), AIRS and GOSAT(1) ammonia products.
Avoided impacts of climate change on compound hot-dry events under sustainable development versus fossil-fueled development
Authors: Hosseinzadehtalaei Parisa , Termonia Piet , Tabari Hossein
Published: March 2024

Climate change is expected to increase the frequency and intensity of compound hot-dry events, which can have significant impacts on human life, economic systems, and agriculture. The extent of this impact depends on the socioeconomic pathway we adopt in the future. While sustainable development aspires to reconcile economic growth, environmental protection, and social equity, thereby ensuring a more sustainable future for all, fossil-fueled development may drive economic growth at the expense of exacerbating climate change, pollution, and resource depletion. This study employs a CMIP6 multi-model ensemble to scrutinize the global-scale potential for mitigating climate change impacts on compound hot-dry events under sustainable development versus fossil-fueled development. These events are quantified by analyzing the joint distribution probability between temperature and soil moisture extremes through bivariate copula functions. The results show that although the likelihood of compound hot-dry events is expected to increase under both scenarios, the increase under fossil-fueled development is anticipated to be twice larger than that under sustainable development. The results show that although the likelihood of compound hot-dry events is expected to increase under both scenarios, the increase under fossil-fueled development is anticipated to be twice as large as that under sustainable development. The mitigated impact through sustainable development is not regionally uniform, with the largest mitigation, up to one-third, expected in the Mediterranean region.
Observing the volume and property changes of the Water Masses in the Nordic Seas
Authors: Almeida Lucas , Kolodziejczyk Nicolas , Lique Camille
Published: March 2024

The Nordic Seas, where cold and fresh Arctic waters mix with warmer and saltier North Atlantic waters, play a crucial role in the ocean circulation system. This region is also the place of intense water mass transformations, with a conversion of lighter waters into denser waters that contribute to the lower limb of the Atlantic Meridional Overturning Circulation. In recent years, the region has experienced Atlantification, characterized by an increased contribution of Atlantic waters, leading to a warming in the upper layers. This study aims to investigate the impact of Atlantification on the properties of water masses in the Nordic Seas. We have used ISAS, an optimal interpolation from ARGO data with a monthly time series spanning 2002 to 2020, the ANDRO dataset for computing geostrophic velocities from ARGO float drift, and the ERA5 dataset for air-sea flux exchanges. The Nordic Seas are divided into four basins: the Greenland Sea (GS), the Icelandic Plateau (IP) in the west, and the Lofoten Basin and Norwegian Basin in the east. The water column is divided into three water masses based on potential density (𝞼0): surface (𝞼0 < 27.8 kg m-3), intermediate (27.8 < 𝞼0 < 28.0 kg m-3), and deeper water mass (28.0 < 𝞼0 < 28.07 kg m-3). Based on the observational datasets, we estimate the variations of the volume of each water mass, the transport within and outside the basins, and the surface-forced Water Mass Transformation (WMT). The eastern basins are experiencing surface warming, particularly after 2013, accompanied by an increase in the volume of the same water mass. Moreover, the volume of intermediate water masses is decreasing. In the Norwegian Basin, surface-forced transformations dominate the volume changes, while the Lofoten Basin experiences a significant influence from both surface-forced transformation and the import of warm waters from the south. In the western basins, both the intermediate and deeper water masses are increasing in volume encompassing a larger depth range , with a smaller trend in the Icelandic Plateau. In the Greenland Sea, the WMT are dominating these changes and the region is mostly exporting denser waters. In contrast, in the Icelandic Plateau the intermediate water is mostly explained by differences in the transports, and the deeper water masses by the surface transformation. We conclude that the changes observed in the Nordic Seas water masses result from a combination of local changes driven by air-sea fluxes and the advection of warmer waters. Monitoring the relative contributions of remote and local processes involved in WMT will help us to better understand and anticipate the ongoing and future shifts in the Nordic Seas conditions. 
Enhancing Space Mission Return through On-Board Data Reduction using Unsupervised Machine Learning
Authors: Gruchola Salome , Keresztes Schmidt Peter , Tulej Marek , Riedo Andreas , Mezger Klaus , Wurz Peter
Published: March 2024

The efficient use of the provided downlink capacity for scientific data is a fundamental aspect of space exploration. The use thereof can be optimised through sophisticated data reduction techniques and automation of processes on board that otherwise require interaction with the operations centres on Earth. Machine learning-based autonomous methods serve both purposes; yet space-based ML applications remain relatively rare compared to the application of ML on Earth to data acquired in space.In this contribution, we present a potential application of unsupervised machine learning to cluster mass spectrometric data on-board a spacecraft. Data were acquired from a phoscorite rock [1] using a prototype of a laser ablation ionisation mass spectrometer (LIMS) for space research [2]. Two unsupervised dimensionality reduction algorithms, UMAP and densMAP [3,4], were employed to construct low-dimensional representations of the data. Clusters corresponding to different mineral phases within these embeddings were found using HDBSCAN [5]. The impact of data pre-processing and model parameter selection on the classification outcome was investigated through varying levels of pre-processing and extensive grid searches.Both UMAP and densMAP effectively isolated major mineral phases present within the rock sample, but densMAP additionally found minor inclusions present only in a small number of mass spectra. However, densMAP exhibited higher sensitivity to data pre-processing, yielding lower scores for minimally treated data compared to UMAP. For highly processed data, both UMAP and densMAP exhibited high stability across a broad model parameter space.Given that the data were recorded using a miniature mass spectrometric instrument designed for space flight, these methods demonstrate effective strategies for substantial reduction of data similarly to what is anticipated on future space missions. Autonomous clustering of data into groups of different chemical composition, followed by the downlink of a representative mass spectrum of each cluster, aids in identifying relevant data. Mission return can therefore be enhanced through the selective downlink of data of interest. As both UMAP and densMAP, coupled with HDBSCAN, are relatively complex algorithms compared to more traditional techniques, such as k-means, it is important to evaluate the benefits and drawbacks of using simpler methods on-board spacecraft. [1] Tulej, M. et al., 2022, https://doi.org/10.3390/universe8080410.[2] Riedo, A. et al., 2012, https://doi.org/10.1002/jms.3104.[3] McInnes, L. et al., 2018, https://doi.org/10.48550/arXiv.1802.03426.[4] Narayan, A., et al., 2021, https://doi.org/10.1038/s41587-020-00801-7.[5] McInnes, L., et al., 2017, https://doi.org/10.21105/JOSS.00205.
Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced surface classification in sea ice
Authors: Chen Weibin , Tsamados Michel , Willatt Rosemary , Brockley David , Deisenroth Marc , De Rijke-Thomas Claude , Francis Alistair , Hirata Len , Johnson Thomas , Lawrence Isobel , Landy Jack , Lee Sanggyun , Liu Wenxuan , Nasrollahi Shirazi Dorsa , Nelson Connor , Stroeve Julienne , Takao So
Published: March 2024

In our research, we leverage the capabilities of the Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018, respectively, to deepen our understanding of the polar regions. These satellites offer a unique blend of high-resolution Ku-band radar altimetry data, synthetic aperture radar (SAR) mode altimetry, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer. This combination enables the acquisition of both optical imagery and SAR radar altimetry data, extending up to 81 degrees North. Central to our study is the application of deep learning techniques, specifically the Vision Transformers (ViT), which adapt the Transformer algorithm for surface classification in polar environments. This approach is instrumental in distinguishing between sea ice and leads, demonstrating robust performance across various metrics, including accuracy and model roll-out on comprehensive OLCI image datasets. We produce our first lead classification maps at the original OLCI swath level resolution of 300m and a lead fraction prototype mosaic spring pan-Arctic product at gridded level of 1km, 5km and 10km resolution and on daily, weekly and monthly timescales. The use of binned statistics in conjunction with our deep learning classifications provides valuable insights into the spatial distribution and changes of leads within the polar ice. We compare our prototype product with other existing lead products and with auxiliary datasets on thin ice (roughness, thickness). Our work combining different satellite products at pan-Arctic intermediate resolution enhances our capacity to estimate sea ice thickness and aids in forecasting future changes in the Arctic and Antarctic regions, thereby contributing to the field of polar remote sensing with direct applications to the future polar missions CRISTAL and CMIR.
Seasonal speedup of glacier in the southeastern Tibetan Plateau
Authors: zhang Tianzhao , Yang Wei , Ren Shaoting
Published: March 2024

Most glaciers around the world are slowing down, but some marine-terminating outlet glaciers occur and trigger seasonal or shorter-term accelerations (up to 100% greater than the annual mean), and the speedup events of glaciers cause accelerated glacier loss. Although some speedup events have been observed in the Greenland and Antarctica, limited observed in the Tibetan Plateau and the trigger mechanism is also poorly understood. In this study, we used the high-frequency Global Navigation Satellite System (GNSS) observations collected on the Parlung No.4 Glacier in southeastern Tibetan Plateau in 2022 to characterize the seasonal dynamics of glacier velocity and analyze its mechanism. The results show that the velocity has a distinct seasonal variation, with highly fast in summer (1.42 times as fast as winter flow). A total of 9 speedup events were observed in spring and summer, with 3 GNSS stations simultaneously generating 5 acceleration events; the glacier accelerated frequently from 25 June to 3 July, with a total of 3 speedup events; with the maximum intensity occurred at the end of July, which was about 10 times as fast as others. In addition, we find that the speedup of the glacier is mainly consistent with precipitation and the glacial runoff, and we suggest that the speedup of the glacier is mainly due to enhanced meltwater leading to glacier basal motion.
Time parallel integration and phase averaging for the nonlinear shallow water equations on the sphere
Authors: Yamazaki Hiroe , Cotter Colin , Wingate Beth
Published: March 2024

We present a phase-averaging framework for the rotating shallow-water equations and a time-integration methodology for it. Phase averaging consists of averaging the nonlinearity over phase shifts in the exponential of the linear wave operator. Phase averaging aims to capture the slow dynamics in a solution that is smoother in time (in transformed variables), so that larger timesteps may be taken. In our numerical implementation, the averaging integral is replaced by a Riemann sum, where each term can be evaluated in parallel. This creates an opportunity for parallelism in the timestepping method.In this talk, we will show proof-of-concept results and analyse their errors in order to examine the impact of the phase averaging on the rotating shallow-water solution. We will also examine how the averaging allows us to use larger timesteps and where the optimal averaging window is at a chosen timestep size.
Learning Catchment Features with Autoencoders
Authors: Bassi Alberto , Mira Antonietta , Höge Marvin , Fenicia Fabrizio , Albert Carlo
Published: March 2024

By employing Machine Learning techniques on the US-CAMELS dataset, we discern a minimal number of streamflow features. Together with meteorological forcing, these features enable an approximate reconstruction of the entire streamflow time-series. This task is achieved through the application of an explicit noise conditional autoencoder, wherein the meteorological forcing is inputted to the decoder to encourage the encoder to learn streamflow features exclusively related to landscape properties. The optimal number of encoded features is determined with an intrinsic dimension estimator. The accuracy of reconstruction is then compared with models that take a subset of static catchment attributes (both climate and landscape attributes) in addition to meteorological forcing variables. Our findings suggest that attributes gathered by experts encompass nearly all pertinent information regarding the input/output relationship. This information can be succinctly summarized with merely three independent streamflow features. These features exhibit a strong correlation with the baseflow index and aridity indicators, aligning with the observation that predicting streamflow in dry catchments or with a high baseflow index is more challenging. Furthermore, correlation analysis underscores the significance of soil-related and vegetation attributes. These learned features can also be associated with parameters in conceptual hydrological models such as the GR model family.
Policies for Aquifer Thermal Energy Storage (ATES)
Authors: Stemmle Ruben , Hanna Richard , Menberg Kathrin , Østergaard Poul Alberg , Jackson Matthew , Staffell Iain , Blum Philipp
Published: March 2024

Aquifer thermal energy storage (ATES) is a promising technology for sustainable and climate-friendly space heating and cooling. Compared to conventional heating and cooling techniques, ATES-based systems offer several benefits such as lower greenhouse gas emissions and reduced primary energy consumption. Despite these benefits and the availability of suitable aquifers in many places around the world, ATES has yet to see a widespread global utilization. Currently the vast majority of installed systems is located in the Netherlands, Belgium, Sweden and Denmark. Besides technical and hydrogeological feasibility, appropriate national policies driving ATES deployment are therefore of high importance. Hence, this study provides an international comparison of ATES policies, highlighting best practice examples and revealing where appropriate policy measures are missing. To this end, multi-disciplinary views from experts in geothermal energy and ATES from academia, companies, government authorities, national geological surveys and industrial associations in 30 countries were obtained through an online survey. Subsequent semi-structured interviews with a smaller selection of experts revealed further insights. The online survey results show significant differences regarding the existence and the strength of supporting policy elements between countries of different ATES market maturity. Going beyond these descriptive findings, the interviews provided more country-specific details on how favorable conditions came into effect and what obstacles have still to be overcome for an increased ATES deployment. Based on the lessons learned from the online survey and the expert interviews, recommendations for sophisticated ATES policies are derived which address the following areas: legislative and regulatory issues, raising awareness and expertise, the role of ATES in local energy transitions, and social engagement. This work aims at steering energy policy towards a wider international ATES deployment and better harnessing the potential of ATES to decarbonize buildings.
Seasonal Variations in Particle Exposure: A Study at an Indian Highway Toll Station Investigating Coarse, Fine, Ultrafine, and Black Carbon Particles
Authors: Nazneen Nazneen , Patra Aditya Kumar
Published: March 2024

Particulate matter (PM2.5 and PM10), black carbon (BC), and ultrafine particle (UFP) exposures among toll station workers on a highway in India were measured in both summer and winter seasons. Results showed that toll workers inside the toll collection booths (Tinside) were exposed to higher concentrations of air pollutants than those working outside the booths (Toutside), except for UFP. The concentrations of PM2.5 were higher during winter than summer: 152.3 > 86.1 µg m-3 (Tinside) and 136.6 > 79.2 µg m-3 (Toutside), while PM10 concentrations were 205.8 > 169.5 µg m-3 (Tinside) and 185.3 > 156.4 µg m-3 (Toutside), and BC concentrations were 38.8 > 34.5 µg m-3 (Tinside) and 22.2 > 18.5 µg m-3 (Toutside). In contrast, UFP concentrations were higher at Toutside (31312 > 22000 pt cm-3) than Tinside (21610 > 18000 pt cm-3). The diurnal variation of pollutants showed higher concentrations in the evening hours due to increased traffic, low wind speed and less atmospheric dispersion. Further, a significant correlation was found between pollutants and meteorological parameters (temperature, relative humidity, wind speed, solar radiation and boundary layer height) and traffic volume. Using Multiple-path particle dosimetry model (MPPD), mass deposition in the lungs were determined. Mass deposition found to be higher inside the toll booths workers. The study also revealed that PM particles consisted of soot, mineral and fly ash, which are proxies of fresh exhaust emissions, re-suspended road dust, and industrial emissions, respectively. The presence of Si, Al, Ca and Pb, as confirmed by EDX analyses, indicated the sources of pollutants to be re-suspended road dust, brake/tire wear, and construction dust. The results underscore the importance of implementing policies to control air pollutant levels, especially in workplaces near busy roads.
Temperature impact of open-loop systems on groundwater in an urban area: A case study of Murska Sobota, NE Slovenia
Authors: Adrinek Simona , Janža Mitja , Brenčič Mihael
Published: March 2024

To achieve sustainable and efficient use of shallow geothermal resources, it is important to understand the heat transfer in the subsurface of the planned geothermal system. In the City Municipality of Murska Sobota, NE Slovenia, the use of geothermal open-loop systems has increased in recent years. Their high spatial density raises the question of potential mutual interference between the systems. By compiling geological, hydrogeological, and thermal data, obtained from the monitoring network, fieldwork, and knowledge of regional hydrogeological conditions, we have developed a transient groundwater flow and heat transfer model to evaluate the impact of the open-loop systems on the subsurface and surrounding systems. Time series data cover time span ranging from December 2019 to the end of December 2021. The sensitivity analysis showed the highest composite sensitivity values for hydraulic conductivity, porosity and dispersivity parameters, which were further calibrated with FePEST to minimize the error between groundwater level and temperature. The results of the groundwater flow model correspond well with the measured average groundwater levels. On the other hand, the thermal model shows higher deviations from the measured data, especially in the summer months when the simulated groundwater temperatures do not exceed 14.3 °C, while the measured temperatures reached even 15.4 °C. These deviations could be related to the effects of local thermal sources on the surface (e.g., sewage pipes, plumbing, buildings with more than one basement and roads), which were not considered in our model. The transient simulation showed that the thermal state in the observed area is restored over the summer, when the systems are not in operation. Also, the systems do not have significant mutual interference that would affect their efficiency. However, as interest in installing new systems in the area increases, simulations of the thermal plumes of new geothermal systems are needed to ensure sustainable and efficient use of shallow geothermal energy in the future.
Net primary production rather than saturation of mineral surfaces limits soil carbon sequestration 
Authors: Poeplau Christopher , Dechow Rene , Begill Neha , Don Axel
Published: March 2024

Accrual of soil organic carbon (SOC), and especially the formation of mineral associated organic carbon (MAOC), has a large theoretical potential to act as sink for atmospheric CO2. For reliable estimates of this potential and efficient policy advice, the major limiting factors need to be understood. The positive correlation between the content of fine mineral particles (silt + clay) and the content of MAOC is widely used to estimate a general maximum MAOC storage capacity of soils, based on the notion that mineral surfaces get C saturated. However, recent literature raised doubts about the concept of C saturation and it remains unclear, if and to what extent the mineral capacity of soils to stabilise C limits SOC accrual. Here, using large scale soil datasets and the model RothC, we provide two independent lines of evidence, that the upper boundary line of the correlation between MAOC and silt and clay does not resemble a maximum mineralogical SOC stabilisation capacity. 1. In coarse-textured soils, the C loading of the silt and clay fraction was found to strongly overshoot the mentioned upper boundary line and thus exceed previous C saturation estimates. 2. A global modelling exercise revealed that only for 28.8 % of all soils, local net primary production (NPP) would be sufficient to reach a C loading of 80 g C kg-1 silt and clay, which is currently assumed to be the maximum capacity of soils to stabilise C. This proportion decreased strongly with increasing silt and clay content, which revealed that high C loadings can hardly be reached in more fine-textured soils. We conclude that SOC accrual is mainly limited by C inputs and strongly modulated by texture, mineralogy and climatic conditions. Taken together, those factors could be used to be formulate an ecosystem capacity to stabilise SOC. However, a potential C saturation point of MAOC appears still unknown and of no relevance for strategies of climate mitigation via SOC sequestration.
Autocorrelation – A Simple Diagnostic for Tropical Precipitation in Global Kilometer-Scale Climate Models
Authors: Spät Dorian , Voigt Aiko , Biasutti Michela , Schuhbauer David
Published: March 2024

Tropical precipitation is the result of a complex interplay of processes across a wide range of atmospheric scales and is highly variable from place to place. A particularly interesting geographical pattern is obtained for the lag 1 autocorrelation of daily precipitation. Generally, this metric displays a relatively uniform distribution of positive values throughout the tropics. However, certain land regions, such as the Sahel, stand out due to exceptionally low autocorrelation values. These low values correspond to a dominance of high frequency precipitation events in the power spectrum.In accordance with previous work, we show that CMIP6 climate models struggle to create a similar autocorrelation pattern. Global kilometer-scale models circumvent many of the shortcomings of the conventional coarse models, by resolving deep convection. We find that the two global kilometer-scale models developed as part of the nextGEMS project produce an autocorrelation pattern that is quite similar to the observations. These models also provide an opportunity to study the processes associated with the autocorrelation pattern.We compare simulations with deep convection parameterization turned on and off to investigate how the parameterization scheme affects the autocorrelation pattern and the underlying power spectrum. Additionally, we perform a precipitation variance analysis based on filtering of convectively coupled equatorial waves to study the genesis of the autocorrelation pattern.
Rock Reconstruction with Deep Generative Network
Authors: Cao Qinglong , Chen Yuntian
Published: March 2024

The reconstruction of Digital Rock is a crucial challenge in understanding the microstructure of rocks and its impact on pore-scale flow through numerical modeling. This is particularly significant due to the typically large samples required to address inherent uncertainties. Despite notable advancements in traditional process-based techniques, statistical methods, and recent popular deep learning models, there is a limited focus on deep learning approaches specifically tailored for reconstructing rocks with predefined properties, such as porosity. To address this gap, our research employs Artificial Intelligence Generative Component (AIGC) technologies to precisely generate rock structures with specified properties. Our experimental results demonstrate the successful application of our method in reconstructing rock images based on target properties. The generation of randomly reconstructed samples with distinct rock properties holds promise for advancing research in pore-scale multiphase flow and uncertainty quantification in subsequent studies.
A novel gravity wave transport parametrization for global chemistry climate models: description and validation 
Authors: Feng Wuhu , Guarino Maria-Vittoria , Gardner Chester S. , Funke Bernd , Garcıa-Comas Maya , Lopez-Puertas Manuel , Kupilas Marcin , Marsh Daniel R. , Plane John M.C.
Published: March 2024

The gravity wave drag parametrization of the Whole Atmosphere Community Climate Model (WACCM) in the NCAR Community Earth System Model version 2 (CESM2) has been modified to include the wave-driven atmospheric vertical mixing caused by propagating, non-breaking, gravity waves. The strength of this atmospheric mixing is represented in the model via the “effective wave diffusivity” coefficient (Kwave).  Using Kwave, a new total dynamical diffusivity (KDyn) is defined. KDyn represents the vertical mixing of the atmosphere by both breaking (dissipating) and vertically propagating (non-dissipating) gravity waves. Here we show that, when the new diffusivity is used, the downward fluxes of Fe and Na between 80 and 100 km are largely increased. Larger meteoric ablation injection rates of these metals (within a factor 2 of measurements) which were reduced by a factor of 5 in the WACCM, can now be used in the developed WACCM version, which produce Na and Fe layers in good agreement with lidar observations. Mesospheric CO2 is also significantly impacted, with the largest CO2 concentration increase occurring between 80-90 km, where model-observations agreement improves. However, in regions where the model overestimates CO2 concentration, the new parametrization exacerbates the model bias. The mesospheric cooling simulated by the new parametrization, while needed, is currently too strong almost everywhere. The summer mesopause in both hemispheres becomes too cold by about 30K compared to observations, but it shifts upward, partially correcting the WACCM low summer mesopause.Our results highlight the far-reaching implications and the necessity of representing vertically propagating gravity waves in climate models. This novel method of modelling gravity waves contributes to growing evidence that it is time to move away from dissipative-only gravity wave parametrizations.
A general model for the seasonal to decadal dynamics of leaf area
Authors: Zhou Boya , Cai Wenjia , Zhu Ziqi , Wang Han , Prentice I.Colin
Published: March 2024

Seasonal changes in leaf display, indicated by variations in leaf area index (LAI), play a crucial role in influencing the exchange of CO2 and energy between terrestrial ecosystems and the atmosphere. Accurate simulation of leaf phenology is essential for both land surface models (LSMs) and dynamic global vegetation models (DGVMs). But there is no agreement on how leaf phenology should be modelled. A common approach invokes specific physiological triggers for budburst and senescence, but the domain of application of such models is restricted to specific plant types and/or climatic zones. Recent theoretical advances suggest the existence of a more general relationship between gross primary production (GPP) and the seasonal variation of ‘steady-state LAI’ (i.e., the LAI that would be supported if environmental conditions were held constant). The dynamics of LAI can then be predicted from the time course of potential GPP, given their interdependence through Beer's law and the necessity for GPP to support LAI development. We have developed a model based on this principle in two steps. First, the principle was implemented using the P model, a universal first-principles light use efficiency (LUE) model for GPP. Second, we used a simple moving average method to represent the time lag between leaf allocation and steady-state LAI. The model requires a prediction of annual peak LAI, which we simulate based on the energy and water requirements of GPP. The model captures satellite-derived LAI dynamics across biomes at both site and global levels, except for some remaining problems in arid biomes. The model outperforms 15 DGVMs participating in the TRENDY project. This study thus provides a prognostic vegetation leaf phenology model that can be used to forecast the seasonal dynamics of LAI under climate change in LSMs and DGVMs. 
Magnetospheric whistler-mode waves detected simultaneously by the DEMETER spacecraft and the Kannuslehto Station
Authors: Drastichova Kristyna , Němec František , Manninen Jyrki
Published: March 2024

Conjugate observations of magnetospheric whistler-mode waves provided by the DEMETER spacecraft at an altitude of about 660 km and the Kannuslehto station located near Sodankyla, Finland are analyzed. More than 500 DEMETER half-orbits between November 2006 and March 2008 overlap with the Kannuslehto data. The analysis includes waves at frequencies up to 16 kHz. We aim to determine the characteristic spatial scales of the waves and their propagation to the ground. For this purpose, correlations of wave intensities measured by both the spacecraft and the ground-based station are evaluated using two different approaches: i) direct correlations of wave intensities measured simultaneously at the same frequencies, and ii) correlations of wave patterns over a moving frequency-time window of a predefined size. The resulting correlations are studied as a function of the L-shell/geomagnetic longitude separation between the spacecraft and the ground-based station. The corresponding correlation lengths are determined as a function of frequency. Additionally, correlations of the wave intensities and the geomagnetic activity indices (Kp/AE) are calculated, demonstrating rather different dependence on the ground and at low altitudes.
Air quality monitoring across Europe using IoT/low-cost sensors within the AD4GD project
Authors: Borger Christian , Letertre Julie , Hodson Thomas , Falk Ulrike , Ananasso Cristina , Peuch Vincent-Henri
Published: March 2024

Despite longstanding awareness of its risks, air pollution remains one of the biggest challenges for humanity with profound health impacts affecting the lives of billions globally. However, while effective monitoring of air pollution is critically important, it is still inadequate: on one hand ground-based measurement networks of air pollutants often lack sufficient spatial coverage, partly due to the high costs and maintenance requirements involved; on the other hand the process complexity in air chemistry complicates modelling of regional air quality on high-resolution. This is a pressing issue particularly in urban areas where pollutant levels can vary drastically.In this context, measurements from Internet of Things (IoT)/low-cost sensors, for instance from citizen science projects, offer a valuable opportunity to overcome these challenges and can provide deeper insights into local-scale air pollution.In a pilot study of the Horizon Europe project "All Data 4 Green Deal" (AD4GD), the objective is to explore how existing IoT data from various sources can be effectively utilized and how they might contribute to air quality monitoring, particularly regarding health impacts.Here, we present the first preliminary results of this pilot study, highlighting the effectiveness of IoT sensors in selected cities across Europe. We also compare our results with various reference datasets from in situ and analysis models, demonstrating that IoT sensors can significantly improve coverage in these specific urban areas. Furthermore, we discuss the challenges associated with these sensors and potential strategies for addressing them.
Examining the temporospatial patterns of thermal risk for the elderly in an aging rural area in Taiwan
Authors: Juang Jehn-Yih
Published: March 2024

In recent years, the escalation of climate change has led to a discernible increase in occurrences of extreme heat events, subjecting individuals to heightened and more recurrent heat stress. This phenomenon is particularly pronounced among socioeconomically disadvantaged populations, the elderly, and other vulnerable groups. Prolonged exposure to elevated temperatures has been identified as a significant factor contributing to adverse health outcomes within these demographics. Concurrently, alterations in land use and land cover (LULC) exert a notable influence on the thermal environment. While rural areas experience relatively modest changes in land use compared to urban counterparts, the expansion of non-vegetative zones in these regions still engenders temperature fluctuations. This study endeavors to ascertain the spatial-temporal characteristics of the thermal environment and heat-related comfort levels in aging rural areas, a facet that has been largely overlooked in prior research. The focal area of investigation is Yunlin County in Taiwan, a pivotal agricultural region characterized by a noteworthy aging demographic. The research methodology involves an analysis of the spatial distribution of meteorological parameters to discern the thermal landscape in Yunlin. Additionally, a thermal comfort index, Physiological Equivalent Temperature (PET), is employed to gauge the impact of spatial characteristics on human comfortability within this aging rural enclave. LULC data are acquired through supervised classification of remote sensing images. The computation of PET and associated parameters in the Yunlin region is facilitated by the Python package, Pythermalcomfort. The analysis reveals thermal comfort and environmental conditions for the elderly in Yunlin, identifying specific hotspots and periods characterized by elevated PET. Notably, the influence of LULC on thermal conditions is discerned, with built-up and bare soil areas exhibiting elevated temperatures. Furthermore, projections of future scenarios indicate an escalating trend of discomfort for the elderly, manifesting in a heightened frequency of PET exceeding 42°C. The study also undertakes an exposure analysis to identify individuals susceptible to heat injuries under diverse scenarios. Ultimately, a set of preventive measures and recommendations are delineated, encompassing the augmentation of green spaces, provision of adequate shading, identification of cooler areas during heatwaves, and scheduling physically demanding activities during cooler hours (between 4:00 and 7:00). In summation, this research utilizes PET to pinpoint high-risk periods and locations for aging rural areas in Yunlin during the summer, with the overarching aim of furnishing valuable insights into heat-related risks.