ALBERT

Description: We create a deep neural network based approach for the geospatial predicition of total organic carbon percentages in marine sediments. The code in the repository includes jupyter notebooks and python files to pre-process the data, train the models and post-process the outputs.

Description: The macrofauna in soft sediments of the deep seafloor is generally diverse and represents a comparatively well-studied faunal group of deep-sea ecosystems. In the abyss of the Clarion Clipperton Fracture Zone (CCFZ) in the NE Pacific, macrofauna are major contributors to benthic biodiversity. Their distribution, composition, and diversity have been frequently investigated to assess the potential impacts of future mining activities on the resident fauna. In this study, patterns of densities and community structure of CCFZ macrobenthic infauna and their relationships with a range of environmental and climatic variables were examined, with a special focus on communities from the eastern German contract area (referred to as BGR CA). However, comparisons were also made with other contractor areas (e.g., IFREMER, IOM, GSR) and one Area of Particular Environmental Interest (APEI3). Material for this study was obtained by means of a box corer during six expeditions to the CCFZ between 2013 and 2018 resulting in 148 samples. Our study uncovered notable spatial and temporal variations in both faunal densities and community composition. While areas within the BGR CA exhibited a similar community composition, slight differences were observed between the various CAs and APEI3. Surprisingly, we found an unexpected negative correlation between food availability and both macrofaunal density and community structure that may be attributed to differences in sampling methodologies and pronounced temporal variation. Furthermore, we explored the impact of climatic fluctuations associated with the El Niño/Southern Oscillation (ENSO) on macrofaunal densities, observing an increase during warm (El Niño) events. Our findings underscore the challenges of accurately assessing spatial and temporal variations in the absence of standardised sampling protocols. Hence, we emphasize the importance of adopting standardised protocols to enhance data comparability, thereby fostering a deeper understanding of the underlying factors influencing spatial and temporal changes in macrofauna community structure within the CCFZ.

Description: In this paper we describe the implementation of the carbon isotopes 13C and 14C (radiocarbon) into the marine biogeochemistry model REcoM3. The implementation is tested in long-term equilibrium simulations where REcoM3 is coupled with the ocean general circulation model FESOM2.1, applying a low-resolution configuration and idealized climate forcing. Focusing on the carbon-isotopic composition of dissolved inorganic carbon (δ13CDIC and Δ14CDIC), our model results are largely consistent with reconstructions for the pre-anthropogenic period. Our simulations also exhibit discrepancies, e.g. in upwelling regions and the interior of the North Pacific. Some of these differences are due to the limitations of our ocean circulation model setup, which results in a rather shallow meridional overturning circulation. We additionally study the accuracy of two simplified modelling approaches for dissolved inorganic 14C, which are faster (15 % and about a factor of five, respectively) than the complete consideration of the marine radiocarbon cycle. The accuracy of both simplified approaches is better than 5 %, which should be sufficient for most studies of Δ14CDIC.

Description: Oceanic detachment faulting, a major mode of seafloor accretion at slow and ultraslow spreading ridges, is thought to occur during magma‐poor phases and be abandoned when magmatism increases. In this framework, detachment faulting is the result of temporal variations in magma flux, which is inconsistent with recent geophysical observations at the Longqi segment on the Southwest Indian Ridge (49°42′E). In this paper, we focus on this sequentially active detachment faulting system that includes an old, inactive detachment fault and a younger, active detachment fault. We investigate the mechanisms controlling the temporal evolution of this tectonomagmatic system by using 2D mid‐ocean ridge spreading models that simulate faulting and magma intrusion into a visco‐elasto‐plastic continuum. Our models show that temporal variations in magma flux alone are insufficient to match the inferred temporal evolution of the sequentially active detachment system. Rather we find that sequentially active detachment faulting spontaneously occurs at the Longqi segment as a function of lithospheric thickness. This finding is in agreement with an analytical model, which shows that a thicker axial lithosphere results in a smaller fault heave and that a flatter angle in lithosphere thickening away from the accretion axis stabilizes the active fault. A thicker axial lithosphere and its flatter off‐axis angle combined have the potential to modulate sequentially active detachment faulting at the Longqi segment. Our results thus suggest that temporal changes of magmatism are not necessary for the development and abandonment of detachment faults at ultraslow spreading ridges.

Description: As a variant of Support Vector Machine (SVM), Large Margin Distribution Machine (LDM) has been validated to outperform SVM both theoretically and experimentally. Due to the inevitable noise in real applications, the credibility of different samples is not necessarily the same, which is neglected by most existing LDM models. To tackle the above problem, this paper first introduces fuzzy set theory into LDM, and proposes a Fuzzy Large Margin Distribution Machine (FLDM) with better robustness and performance. Considering the noise and uncertainty in datasets, sample points farther from the center of homogenous class are less reliable. Therefore, a fuzzy membership function based on the distance to the class center is utilized to characterize the confidence of each sample, i.e., the degree to which the sample belongs to a certain category. Furthermore, different strategies are developed to obtain class centers for linearly separable and linearly inseparable problems. Experiments conducted on both artificial and UCI datasets verified the superiority of FLDM from different perspectives.

Description: The problem of network disintegration, such as suppression of an epidemic spread and destabilization of terrorist networks, possesses extensive applications and has lately been the focus of growing interest. Many real-world complex systems are represented by spatial networks in which nodes and edges are spatially embedded. However, existing disintegration approaches for spatial network disintegration focus on singular aspects such as geospatial information or network topography, with insufficient modeling granularity. In this paper, we propose an effective and computationally efficient virtual node model that essentially integrates the geospatial information and topology of the network by modeling edges as virtual nodes with weights. Moreover, we employ Kernel Density Estimation, a well-known non-parametric technique for estimating the underlying probability density function of samples, to fit all nodes, comprising both network and virtual nodes, to identify the critical region of the spatial network, which is also the circular geographic region where disintegration occurs. Extensive numerical experiments on synthetic and real-world networks demonstrate that our method outperforms existing methods in terms of both effectiveness and efficiency, which provides a fresh perspective for modeling spatial networks.

Description: Bioenergy with carbon capture and storage (BECCS) can help stabilize the climate by extracting carbon dioxide from the atmosphere while producing renewable energy. However, biomass availability would limit the potential of BECCS, and biomass cropland expansion may threaten biodiversity, food security, and water supply. Replacing land-intensive foods can help unlock sustainable biomass production. Here, we estimated BECCS energy and negative emissions using biomass grown on freed-up land when replacing animal-source foods. Biomass production excludes agricultural expansion to protect biodiversity, ensures enough food supply globally to safeguard food security, and constrains irrigation to secure water for people and ecosystems. Negative emissions consider supply chain emissions and the forgone sequestration from natural revegetation. Results show that replacing 50% of animal products by 2050 could release enough land for BECCS to generate 26.4–39.5 EJelec/year, the scale of coal power today, while removing 5.9–9.3 GtCO2e/year from the atmosphere, almost what coal power emits today.

Description: We investigated the influence of the El Niño-Southern Oscillation (ENSO) on inter-annual precipitation variability in the far-eastern Pacific (FEP) and northern South America (NSA) using an approach based on phase synchronization (PS). First, we carried out a detailed analysis of observational data to define the inter-annual variability, eliminate the seasonal residual frequencies in hydro-climatic anomalies, and assess the statistical significance of PS. Additionally, we characterized the seasonality of regional patterns of sea surface temperature, surface pressure levels, low-level winds and precipitation anomalies associated with the ENSO states. We found that the positive (negative) precipitation anomalies experienced in the FEP and NSA differ from those previously reported in the literature. In particular, the Guianas (northeastern Amazon) and the Caribbean constitute two regions with negative (positive) rainfall anomalies during El Niño (La Niña), separated by a zone of non-significant anomalies along the Orinoco Low-level Jet corridor. Moreover, we showed that the ENSO signal is phase-locked with inter-annual rainfall and low-level wind variability in most of the study regions. Furthermore, we found consistency in the PS between the Central and Eastern Pacific El Niño indices and hydroclimatic anomalies over the Pacific. However, some areas exhibited PS, although they did not show significant precipitation anomalies, suggesting that the influence of ENSO on tropical climatology manifests not only in terms of the magnitude of anomalies but also in terms of the phases only. Our approach advances the understanding of climatic anomalies in tropical regions and provides new insights into the non-linear interactions between ENSO and hydroclimatic processes in tropical Americas.

Description: Lack of nitrogen limits food production in poor countries while excessive nitrogen use in industrial countries has led to transgression of the planetary boundary. However, the potential of spatial redistribution of nitrogen input for food security when returning to the safe boundary has not been quantified in a robust manner. Using an emulator of a global gridded crop model ensemble, we found that redistribution of current nitrogen input to major cereals among countries can double production in the most food insecure countries, while increasing global production of these crops by 12% with no notable regional loss or reducing the nitrogen input to the current production by one third. Redistribution of the input within the boundary increased production by 6–8% compared to the current relative distribution, increasing production in the food insecure countries by two thirds. Our findings provide georeferenced guidelines for redistributing nitrogen use to enhance food security while safeguarding the planet.

Description: Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP 8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.