bluuebunny/crossref_metadata_2025 huggingface dataset:
Mining impact in a coal exploitation under an urban area: detection by Sentinel-1 SAR data
Authors: Cuervas-Mons Jose , Domínguez-Cuesta María José , Mateos-Redondo Félix , Monserrat Oriol , Barra Anna
Published: March 2022

In this work, the A-DInSAR techniques are applied in Central Asturias (N Spain). In this area, the presence of the most important cities in the region is remarkable, as well as industry and port infrastructures and a dense road network. Moreover, this region is specially known for their historical coal exploitation, which was developed mainly on the Central Coal Basin for almost 2 centuries, and is being abandoned from the beginning of the 21st. The main aim of this study is detecting and analysing deformations associated to this underground coal mining activity. For this, the following methodology was realised: 1) Acquisition and processing of 113 SAR images, provided by Sentinel-1A and B in descending trajectory between January 2018 and February 2020, by means of PSIG software; 2) Obtaining of Line of Sight mean deformation velocity map (in mm year-1) and deformation time series (in mm); 3) Analysis of detected terrain displacements and definition of mining impact. The results show a Velocity Line of Sigh (VLOS) range between -18.4 and 37.4 mm year-1, and accumulated ground displacements of -69.1 and 75.6 mm. The analysis, interpretation and validation of these ground motion allow us to differentiate local sectors with recent deformation related to subsidence and uplift movements with maximum VLOS of -18.4 mm year-1 and 9.5 mm year-1. This study represents an important contribution to improve the knowledge about deformations produced by impact of coal mining activity in a mountain and urban region. In addition, this work corroborates the reliability and usefulness of the A-DInSAR techniques like powerful tools in the study and analysis of geological hazards at regional and local scales for the monitoring and control of underground mining infrastructures.
Relationship between atmospheric rivers and landslides in western North America
Authors: Vallejo-Bernal Sara M. , Wolf Frederik , Luna Lisa , Boers Niklas , Marwan Norbert , Kurths Jürgen
Published: March 2022

In this study, we investigate the relationship between land-falling atmospheric rivers (ARs) and landslides in western North America. ARs are channels of enhanced water vapor flux in the atmosphere and play an essential role in the water supply for precipitation in the midlatitudes. However, they can also trigger natural hazards such as floods and landslides. Our objective is to determine if the occurrence of landslides in western North America can be attributed to ARs hitting the western coastline and causing rainfall at the locations of the landslides and to characterize the strength and persistence of the ARs that lead to landslides. To that aim, we use landslide records with daily temporal resolution along with daily rainfall estimates from the ERA5 reanalysis, for the period between 1996 and 2018. We propose and run two attribution models to relate landslides to rainfall and rainfall to ARs and subsequently verify statistically if there is a unique and significant association between the landslides and the ARs. Our results show that the majority of the landslides reported along the western coast of North America are preceded by an AR. In the coastal regions, ARs and landslides are significantly correlated. Further inland, landslides are less likely, but those that do occur are significantly correlated with very intense ARs. Understanding and revealing the impacts of ARs on landslides in western North America will lead to better forecasts and risk assessments of these natural hazards.
A deep learning multimodal method for precipitation estimation: case study of the extreme rainfall from July 2021
Authors: Moraux Arthur , Dewitte Steven , Munteanu Adrian
Published: March 2022

In July 2021, western Europe has been subject to extreme rainfall that lead to severe flooding, incurring heavy property losses and claiming dozens of people’s lives in both Germany and Belgium. This unfortunate disaster is a reminder that carrying out studies about extreme event forecasting is a matter of prime importance in the field of meteorology. The extreme rainfall from July 2021 invites us to study the performance of our deep learning method for precipitation estimation in case of extreme events.

The main novelty of our method resides in its ability to merge different physical measurement modalities in order to improve precipitation estimation accuracy. In specific, the proposed method merges rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network design, composed of an encoder–decoder architecture, performs multiscale analysis of the three input modalities to simultaneously estimate the rainfall probability and the precipitation rate with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Once trained, we evaluate the performance of our model to estimate the extreme precipitation that happened in Belgium and Germany in July 2021 by comparing our results with the measurements from rain gauges and radar estimation.
Towards Coupled Modelling of the Biosphere and Atmosphere for the Archean Climate: the importance of Methane
Authors: Eager Jake , Mayne Nathan , Lenton Tim
Published: March 2022

Life has played a key role in shaping the atmosphere since its origin on Earth, but modelling the biosphere’s impact on climate is complicated by the range of time and spatial scales involved. 3D climate models have successfully been used to spatially resolve key processes, but on relatively short time scales compared to those at which the biosphere interacts with the climate system. Whereas, biogeochemical modelling allows us to estimate biosphere driven gas fluxes in and out of the atmosphere over longer time scales [1], but lacks a sophisticated treatment of a spatially resolved atmosphere. Here, we look to bridge these two modelling approaches to better understand the biosphere’s impact on the climate.

We use a biogeochemical model [2] to understand the limits on the potential evolution of the atmosphere, as well as a state-of-the-art 3D climate model [3] to explore potential atmospheric compositions produced by early biospheres. The biogeochemical model, coupled to a 1D photochemical model, has been developed to explore the effects of early biospheres driven by anoxic phototrophs. There is a particular focus on the effect of methane on the early climate, which has predominantly biotic sources. We use the 3D climate model to extend a 1D exploration of methane’s diminished greenhouse potential during the Archean [4] by looking at how methane concentrations affect the cloud distribution, atmospheric dynamics and surface temperature.

We find that global surface temperature peaks for pCH4 between 30-100 Pa, with the peak shifting to higher pCH4 as pCO2 is increased. Equator-to-pole temperature differences also have a peaked response driven by changes in the radiative balance. These changes come about from the balance between the effect of methane and carbon dioxide on atmospheric dynamics due to changes in heating rates vertically and meridionally, which also affects the cloud formation. This work begins to explore how our understanding of early biospheres can be coupled to 3D climate models, to understand the biosphere’s impact on the climate of Earth and terrestrial exoplanets following the origin of life.

References

[1] Kharecha, Kasting & Siefert (2005) Geobiology 3, 53-76.

[2] Lenton & Daines (2017) Ann. Rev. Mar. Sci. 9:1, 31-58.

[3] Mayne et al. (2014) Geosci. Model Dev. 7, 3059–3087.

[4] Byrne & Goldblatt (2015) Clim. Past 11, 559–570.
3D anisotropic P-wave tomography of the Central Mediterranean: new insights into slab geometry and upper mantle flow patterns
Authors: Rappisi Francesco , VanderBeek Brandon Paul , Faccenda Manuele , Morelli Andrea , Molinari Irene
Published: March 2022

Characterized by the coexistence of different compressional and extensional phases associated with episodes of orogenesis, slab rollback, slab tearing and oceanic spreading, the Central Mediterranean represents one of the most interesting convergent margin on Earth. Since the late 1990s, several seismologists have studied this region aiming at imagining the isotropic and anisotropic structures below its surface. Although numerous researchers have demonstrated that performing P-wave tomography neglecting seismic anisotropy can introduce significant imaging artefacts, prior tomographic studies have largely assumed an isotropic Earth. Using the method proposed by VanderBeek & Faccenda (2021), here we discard the isotropic approximation and invert for both P-wave isotropic velocity anomalies and seismic anisotropy and present the first 3D anisotropic P-wave tomography of the upper mantle covering the entire Central Mediterranean. Our results show that inverting for seismic anisotropy strongly reduces the magnitude of the isotropic P-wave anomalies. This suggests that lateral variations in temperature and/or composition are smaller that what can be inferred from purely isotropic tomographies. P-wave fast azimuths orient mostly parallel to the trend of the Balcanic and the Alpine orogens in Eastern and Central Europe, respectively. In the Central Mediterranean the P-wave fast azimuths are sub-parallel to the Oligocene/Miocene-to-present retreating direction of the Ionian trench which led to the opening of the Liguro-Provençal and Thyrrenian basins and rotation of the Corsica-Sardinia block. We find that the pattern of the P-wave fast azimuths is largely consistent with the S-wave fast azimuths determined from the splitting of SKS waves and from Rayleigh waves. This poses further constraints on the interpretation of the regional geodynamic evolution and on the accuracy of the employed inverse method.

References:

VanderBeek, B. P., & Faccenda, M. 2021. Imaging upper mantle anisotropy with teleseismic P-wave delays: insights from tomographic reconstructions of subduction simulations. Geophysical Journal International,225(3), 2097–2119.
Testing methods to constrain future European climate projections in an “out-of-sample” framework
Authors: O'Reilly Christopher , Booth Ben , Brunner Lukas , Qasmi Said , Nogherotto Rita , Ballinger Andrew , Befort Dan , Weisheimer Antje
Published: March 2022

A major problem in climate science is providing reliable regional climate projections. Existing model ensembles are imperfect so a number of different methods for constraining future climate projections using the observational record have been developed, with the aim of improving the reliability of the projections. Whilst these constrained projections displayed some overlap and common characteristics, there were also some distinct differences. However, how these different projections should be interpreted is not clear. For example, are some of the methods more accurate and reliable than others?

To address this question, we performed a novel out-of-sample “perfect model” experiment to test the different constraining methods. In this experiment we took advantage of newly available CMIP6 archive of coupled climate simulations to act as (anonymised) pseudo-observations over only the observational period, to test the methods which were all previously used CMIP5-era model data; for this reason, we refer to this as an out-of-sample test. Five different groups used these pseudo-observations to constrain their CMIP5-era model projections. Detailed results from these projections, verified against the target pseudo-observations, will be presented. The constraining methods all improve the projections for regional summer temperatures but for precipitation and in the winter season the improvements are less clear. There are substantial differences across the constraining methods and reasons for these and the utility of these different methods will be discussed.
Robust multi-objective optimization and probabilistic analysis methods under multiple uncertainties: the CROPAR algorithm.
Authors: Zhang Jitao , Solomatine Dimitri , Dong Zengchuan
Published: March 2022

Appropriate water resource allocation schemes are essential for the coordinated and stable development of the basin. Identifying the risks existing in a basin and proposing a robust water resource allocation scheme are of great significance for water resource management in a basin. In this study, the Coupled Robust Optimization and Robust Probabilistic Analysis (CROPAR) algorithm is proposed based on the Robust Optimization and Robust Probabilistic Analysis (ROPAR) algorithm, taking into account the multiple uncertainties of water resources allocation in a basin. First, this study calculates the multi-objective optimal allocation of water resources under certainty. In this study, a single Pareto front is obtained by minimizing the water shortage rate and minimizing the typical pollutant emissions as two objective functions. Then, this study analyzes the frequency and uncertainty of inflow based on historical record data. This study assumes that the basin inflows vary within a certain interval, while the basin has multiple inflows. In this study, the joint probability distribution function of the inflows was constructed with the Copula function, and nine scenarios were generated. Then, the ROPAR algorithm was applied to these nine cases. A total of 9,000 Pareto fronts were calculated through 1,000 Monte Carlo samples for each scenario. Finally, a probabilistic analysis is performed for each scenario to reach a robust optimal solution for a specific scenario according to the robustness criterion. The results show that the CROPAR algorithm can adequately tackle the uncertainty of water allocation in the basin. It helps to make a wide range of risk-based decisions.
Investigating the potential of vineyard soils for carbon sequestration and greenhouse gas emission mitigation after incorporation of organic matter into the subsoil 
Authors: Islam Muhammad Saiful , Pätzold Stefan , Wehrle Ralf , Bendel Nele , Herzog Katja , Brüggemann Nicolas
Published: March 2022

Soils in perennial cropping systems, such as vineyards, have good prospects for storing carbon since less management is required with minimum disturbance to the soil that might prevent rapid turnover of organic matter. In addition, incorporation of organic matter into the subsoil instead of conventional topsoil application might increase its resistance to decomposition through physical isolation and the buildup of organo-mineral complexes. However, the stability of organic matter in agricultural land could also be highly dependent on individual systems, soil properties and climatic conditions.

In our study, the stability of high carbon organic materials (i.e., compost and a Terra Preta-like material) after deep (30-60 cm) incorporation into the soil of a vineyard in western Germany was investigated with respect to greenhouse gas emissions. Portable gas analyzers were used for long-term in-situ monitoring of greenhouse gas emissions. Additional parameters quantified were soil redox potential using Pt electrodes and the concentration of greenhouse gases in the pore space of the soil using air samplers.

The deeply incorporated soil organic amendments showed good stability with respect to N2O and CH4 emission, whereas 30.4% and 51.7% of the compost and the Terra Preta-like material, respectively, was decomposed and released as atmospheric CO2 after two years of observation. Oxygen availability at different soil depths throughout the sampling period, indicated by redox potential values of 300 to 700 mV, played a role in the turnover of organic matter in the treatments. Higher CO2 concentration in the treatments in the deeper soil layer (30-50 cm) compared to the control was also consistent with higher CO2 emission at the soil surface.

To investigate the site-specific influence on the stability of organic matter, the emission of greenhouse gases will also be quantified in different vineyards at different locations with similar management.  
Investigating the Sensitivity of North Sea Glacial Isostatic Adjustment during the Last Interglacial to the Penultimate Deglaciation of Global Ice Sheets
Authors: Pollard Oliver , Barlow Natasha , Gregoire Lauren , Gomez Natalya , Cartelle Víctor , Ely Jeremy , Astfalck Lachlan
Published: March 2022

The Last Interglacial (LIG; MIS 5e) period (130 - 115 ka) saw the last time in Earth’s history that polar temperatures reached 3 - 5 °C above pre-industrial values causing the Greenland and Antarctic ice sheets to shrink to sizes smaller than those of today. Similar polar temperature increases are predicted in the coming decades and the LIG period could therefore help to shed light on ice-sheet and sea-level responses to a warming world. 

LIG estuarine sediments preserved in the North Sea region are promising study sites for identification of the Antarctic ice sheet's relative contribution to LIG sea level, as well as for the reconstruction of both the magnitude and rate of LIG sea-level change during the interglacial. For these purposes, sea-level records in the region must be corrected for the impacts of glacial isostatic adjustment (GIA) which is primarily a consequence of two components: the evolution of terrestrial ice masses during the Penultimate Deglaciation (MIS 6), predominantly the near-field Eurasian ice sheet, and the viscoelastic structure of the solid Earth. 

The relative paucity of geological constraints on characteristics of the MIS 6 Eurasian ice sheet makes it challenging to evaluate its effect on sea level in the North Sea region. In order to model the Eurasian ice extent, thickness, and volume during the Penultimate Deglaciation we use a simple ice sheet model (Gowan et al. 2016), calibrated against models of the Last Glacial Maximum. By employing a gravitationally consistent sea-level model (Kendall et al. 2005), we generate a large ensemble of GIA outputs that spans the uncertainty in parameters controlling both the viscoelastic earth model and the evolution of global ice sheets during the Penultimate Deglaciation. By performing spatial sensitivity analysis with this ensemble, we are able to demonstrate the relative importance of each parameter in controlling North Sea GIA. Our comprehensive approach to exploring uncertainties in both the global ice sheet evolution and solid earth response provides significant advances in our understanding of LIG sea level.
Using in-situ measurements of ice water content to characterize the cloud radiative effect of Arctic cirrus
Authors: Marsing Andreas , Meerkötter Ralf , Heller Romy , Jurkat-Witschas Tina , Kaufmann Stefan , Voigt Christiane
Published: March 2022

The radiative energy budget in the Arctic undergoes a rapid transformation compared to global mean changes. Understanding the role of cirrus in this system is vital, as they interact with short- and long-wave radiation at the top of the tropopause, aside other indirect radiative effects through heterogeneous processes and interaction with humidity. Between autumn and spring, the presence of cirrus can be decisive as to a net gain or loss of radiative energy in the polar atmosphere. To improve modelling capabilities with respect to cirrus, their well observable radiative effect needs to be linked to the occupied atmospheric volume and microphysical properties, accessible through in-situ measurements.

In an effort to derive radiative properties of cirrus in a real scenario in this sensitive region, we use in-situ measurements of ice water content (IWC) performed during the POLSTRACC aircraft campaign in the boreal winter and spring 2015/2016 employing the German research aircraft HALO. A large dataset of IWC measurements of mostly thin cirrus at high northern latitudes was collected in the upper troposphere and also frequently in the lowermost stratosphere. From this dataset we selected vertical profiles that sampled the complete vertical extent of cirrus cloud layers. These profiles exhibit a vertical IWC structure that will be shown to control the instantaneous radiative effect both in the long and short wavelength regimes.

We perform radiative transfer calculations with the UVSPEC model from the libRadtran program package in a one-dimensional column between the surface and the top of the atmosphere (TOA), taking as input the IWC profiles, as well as the state of the atmospheric column (temperature, humidity, trace gases) at the time of measurement, as given by ECMWF IFS and CAMS products. In parameter studies, we vary the surface albedo and solar zenith angle in ranges typical for the Arctic region, we find the strongest (positive) radiative forcing of cirrus over bright snow, whereas the forcing is mostly weaker and even ambiguous over the open ocean in winter and spring. The vertical IWC structure over several kilometres in the vertical affects the irradiance at the TOA, at times by means of symmetrically or asymmetrically distributed effective radiative layers. A strong heating rate profile within the cloud drives dynamical processes and may contribute to the thermal stratification at the tropopause.

Our case studies highlight the importance of a detailed treatment of cirrus clouds for estimations of the radiative energy budget in the Arctic. Furthermore, as they still rely on various assumptions regarding ice crystal microphysics, they provide a path to further substantiate the results using recent observations from the dedicated DLR lead HALO mission CIRRUS-HL on cirrus in high latitudes.
Subduction history of the Caribbean from upper-mantle seismic imaging and plate reconstruction
Authors: Braszus Benedikt , Goes Saskia , Allen Rob , Rietbrock Andreas , Collier Jenny ,   VoiLA Team
Published: March 2022

Even though the Caribbean region is constantly struck by the impact of geological hazards, the details of the Caribbean plate's evolution are still not completely understood. This interdisciplinary study combines and jointly interprets seismic tomography data with trench positions derived from plate reconstruction which constrains some of the most important events governing the evolution of the Caribbean plate. 
Our new teleseismic P-wave tomography model of the upper mantle beneath the Caribbean includes manually processed and analysed data from 32 ocean-bottom seismometers installed for 16 months during the VoiLA experiment as well as recordings from 192 permanent and temporary land stations. Reconstruction tests show improved resolution compared to previous models and a sufficient recovery of a synthetic anomaly assimilating the Caribbean slab. 
Based on reconstructed trench positions we attribute slab fragments residing in depths of 700-1200km to 90–115 Myr old westward subduction along the Great Arc of the Caribbean (GAC) prior to Caribbean Large Igneous Province volcanism, rather than to eastward dipping Farallon subduction. 
In the mantle transition zone, the imaged slab coincides with predicted trench positions from 50-70 Ma with a slab window approximately at the location of the subducted Proto-Caribbean spreading ridge.
Along the otherwise continous slab in the shallow upper mantle from Hispanola to Grenada several tears are interpreted as ruptures along fault zones in the Proto-Caribbean crust as well as the subducted extinct Proto-Caribbean spreading ridge. 
Understanding landscape evolution parameters using global 10Be erosion rates
Authors: Ruetenik Gregory , Jansen John , Val Pedro
Published: March 2022

Landscape evolution models simulate erosional and depositional changes in terrain surface over time and have proven useful for studying surface processes at a variety of scales. These models rely on several input parameters such as a coefficient of hillslope diffusion (D), as well as stream power exponents of drainage basin area (m) and slope (n), a value of minimum drainage area (Ac) below which advective fluvial processes dominate over diffusive hillslope processes, and an effective stream power/advection coefficient of rock ‘erodibility’ (k). In spite of the widespread application of landscape evolution models, values of these input parameters and their variation through space and time are generally poorly constrained in large part due to the large number of processes and physical properties which are amalgamated into the advection-based SP equation. Several recent studies have looked at global controls on erosion rates using stream power parameters and other river metrics by making use of sophisticated stream profile analysis tools, and we aim to build on these past studies by using a landscape evolution modelling framework.  Here, we make use of a global catalog of basin-averaged cosmogenic 10Be-derived apparent erosion rates to tune several landscape evolution model parameters. We employ an Approximate Bayesian Computation (ABC) approach which is based on the performance of many combinations of randomly selected parameters with respect to a likelihood function that measures how well a model fits a sample of observations for a given set of parameter values. Prescribing the commonly observed stream concavity ratio (m/n) of 0.5, maximum agreement between LEM-predicted and 10Be apparent erosion rates is obtained when the free parameters of stream power slope coefficient (n) is approximately 2, the ratio of hillslope diffusivity (D) to effective stream power coefficient (K) is between 103 and 104 mn-1 yr-1 and when critical drainage area (Ac) is ~0.1 km2. Additionally, we find that models can be optimized to a greater degree when the diffusive component of the LEMs is squared, in line with recent studies. Finally, we perform a search for optimal parameters in the face of variable stream concavity, climate, and geology which are encompassed in k, D, m, and n,  all of which show considerable variability over different climatic, lithologic, and ecologic regimes. Ultimately, this demonstrates that globally optimal parameters may not be applicable at the local to regional scale, but continent to global scale analyses could benefit from understanding these optimal parameters.
Forecasting electrical conductivity of a coastal karstic aquifer with artificial intelligence methods
Authors: Pisinaras Vassilios , Nikolaidiou Aikaterini , Semertzidis Theodoros , Daras Petros , Balacco Gabriella , Fidelibus Maria Dolores , Tziritis Evangelos
Published: March 2022

Groundwater resources are inevitably considered as the primary source of high-quality water for the Mediterranean region. In critical cases, groundwater is essential to complement inadequate, uncertain, expensive, or even lacking surface-water sources. Especially in coastal areas of the Mediterranean, which show increasing development and population growth, karst aquifers represent vital freshwater sources. Karst aquifers are rather complex water systems, and therefore, though to manage, predict, and protect. Groundwater modeling has proved to be a very effective tool for groundwater management. Physically-based modeling is usually applicable to porous aquifers; numerical modeling application to karst aquifers is very challenging because of their complexity, which combines discontinuity, conduit, and porous medium domains. The need to forecast groundwater quantity and quality in karst aquifer systems is high, with groundwater salinity being very critical. Artificial Intelligence (AI) algorithms have been proved to be an effective alternative in simulating groundwater quality and quantity variables. This study aims to develop and test the performance of 6 AI algorithms to forecast groundwater electrical conductivity (EC) in the highly complex, coastal karst aquifer system of Salento (Puglia, Southern Italy). The AI algorithms applied were: 1) Multilayer Perceptron (MLP), 2) Long short-term memory (LSTM), 3) Bidirectional LSTM (BiLSTM), 4) Convolutional Neural Network (CNN), 5) Recurrent Neural Networks (RNN), and 6) Support Vector Machine (SVR). Except for SVR, which is considered a machine learning (ML) algorithm, all the other approaches are deep learning (DL) neural network architectures. Models’ development was based on 3-year groundwater EC daily data from 7 sensors. Other variables used for EC modeling were groundwater level and temperature, precipitation, and air temperature. The above variables were combined in 11 input variable experiments. In addition, various realizations of training times windows were developed under five scenarios. The total number of trained EC models was 2184. The results show AI models can efficiently provide a 30-day groundwater EC forecast for a wide range of EC values varying from slightly saline (0.7-2 mS/cm) to very saline (25-45 mS/cm). BiLSTM proved to be the most effective algorithm, while the least but still effective algorithm was SVR, thus showing the superior performance of DL algorithms compared to legacy ML approaches. Experimental results showed that increasing the number of input variables did not improve the performance of models. In contrast, including 2-time windows for training (one short-term and one long-term) increased it.
A comparison of explainable AI solutions to a climate change prediction task
Authors: Bommer Philine Lou , Kretschmer Marlene , Bareeva Dilyara , Aksoy Kadircan , Höhne Marina
Published: March 2022

In climate change research we are dealing with a chaotic system, usually leading to huge computational efforts in order to make faithful predictions. Deep neural networks (DNNs) offer promising new approaches due to their computational efficiency and universal solution properties. However, despite the increase in successful application cases with DNNs, the black-box nature of such purely data-driven approaches limits their trustworthiness and therefore the useability of deep learning in the context of climate science.

The field of explainable artificial intelligence (XAI) has been established to enable a deeper understanding of the complex, highly-nonlinear methods and their predictions. By shedding light onto the reasons behind the predictions made by DNNs, XAI methods can serve as a support for researchers to reveal the underlying physical mechanisms and properties inherent in the studied data. Some XAI methods have already been successfully applied to climate science, however, no detailed comparison of their performances is available. As the number of XAI methods on the one hand, and DNN applications on the other hand are growing, a comprehensive evaluation is necessary in order to understand the different XAI methods in the climate context.

In this work we provide an overview of different available XAI methods and their potential applications for climate science. Based on a previously published climate change prediction task, we compare several explanation approaches, including model-aware (e.g. Saliency, IntGrad, LRP) and model-agnostic methods (e.g. SHAP). We analyse their ability to verify the physical soundness of the DNN predictions as well as their ability to uncover new insights into the underlying climate phenomena. Another important aspect we address in our work is the possibility to assess the underlying uncertainties of DNN predictions using XAI methods. This is especially crucial in climate science applications where uncertainty due to natural variability is usually large. To this end, we investigate the potential of two recently introduced XAI methods —UAI+ and NoiseGrad, which have been designed to include uncertainty information of the predictions into the explanations. We demonstrate that those XAI methods enable more stable explanations with respect to model noise and can further deal with uncertainties of network information. We argue that these methods are therefore particularly suitable for climate science application cases.
Assessment of the rainfall forecasts from extrapolation-based INCA nowcasting and AROME forecasts in Austria
Authors: Ghaemi Esmail , Foelsche Ulrich , Kann Alexander , Fuchsberger Juergen
Published: March 2022

As one of the essential inputs, accurate precipitation forecasts play a significant role in having a reliable flood warning system. Due to the variability of precipitation in time and space, forecast models need to use the latest observational data as input. However, the accumulation of noise and the imbalance in analyses can decrease the performance of forecasts (Seity et al., 2011). The convection-permitting numerical limited area model AROME (Applications of Research to Operations at Mesoscale) is a non-hydrostatic convective-scale limited-area model with 2.5 km resolution. The three-hourly update cycle AROME has been replaced by the hourly update cycle since 2019 in Austria.

The aim of this study is to assess the operational AROME and INCA (Integrated Nowcasting through Comprehensive Analysis) forecasts with the three-hourly and one-hourly update cycles for three events over Austria. We use the object-oriented Structure-Amplitude-Location (SAL) approach to evaluate the performance of precipitation forecasts. The SAL approach is based on a minimum threshold method to detect precipitation objects for the forecast verification (Wernli et al., 2009). Here, we use a multi-threshold approach to define precipitation objects using the thunderstorm detection and tracking algorithm (Feldmann et al., 2021). Three heavy precipitation events with different characteristics are selected to investigate the performance of AROME and INCA using the SAL metric. The result of this study helps to understand the role of the update frequency in forecasting.
A global climatology of ice nucleating particles derived from model simulations with EMAC-MADE3
Authors: Beer Christof , Hendricks Johannes , Righi Mattia
Published: March 2022

Atmospheric aerosols can act as ice nucleating particles (INPs) and thereby influence the formation and the microphysical properties of cirrus clouds, resulting in distinct climate modifications. From laboratory experiments several types of aerosol particles have been identified as effective INPs at cirrus conditions. However, the understanding of the global atmospheric distribution of INPs in the cirrus regime is still highly uncertain as in situ observations are scarce and limited in space and time.

We perform global model simulations with the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model including the aerosol microphysics submodel MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, third generation) coupled to a two-moment cloud microphysical scheme and a parametrization of aerosol-induced ice formation in cirrus clouds. We present a global climatology of INPs in the cirrus regime, that includes, besides mineral dust and soot, also crystalline ammonium sulfate and glassy organics as INPs at cirrus conditions. The model representation of ammonium sulfate and organic ice nucleating particles includes a formulation of the particle phase state, as recent laboratory measurements suggest that only crystalline ammonium sulfate and glassy organics initiate ice nucleation.  

After implementing the different INP types into the microphysical cirrus cloud scheme, their ice nucleation potential at cirrus conditions is analysed, considering the possible competition mechanisms between different INPs. The simulated INP concentrations in the range of about 1 to 100 L−1 agree well with in situ observations and other global model studies. Our model results suggest that glassy organic particles probably have only minor influences, as ambient conditions often inhibit the glassy phase. On the other hand, crystalline ammonium sulfate often shows large INP concentrations, has the potential to influence ice nucleation, and should therefore be taken into account in future model applications.
Monitoring of Agricultural Hydrodynamics and Investigation of Geophysical Relationships in African Alfisols using Electrical Resistivity Tomography
Authors: Swift Russell , Chambers Jonathan , Boyd Jimmy , Mtangadura Tongai , Phiri Elijah , Sandram Innocent , Meldrum Phil , Harrison Harry , Chimungu Joseph , Watlet Arnaud , Wilkinson Paul , Nguyen Frédéric , Lark Murray ,   CEPHaS Near-Surface Geophysics Team
Published: March 2022

A full understanding of the moisture regimes in agricultural soils is critical when considering a climatic future that is potentially both drier and hotter. The study of near-surface hydrodynamics necessitates a high temporal frequency of measurements to allow the capture of fast-moving processes. In addition to this, heterogeneities in soils, coupled with spatially variable moisture contents, mean that point sensors alone can miss valuable information when studying soil hydrodynamics.

The sensitivity of Electrical Resistivity Tomography (ERT) to soil water content means that it is ideally suited to studying soil hydrodynamics. As a non-invasive technique, it is able to effectively capture spatial heterogeneities, while repeating ERT measurements at short time intervals enables the monitoring of rapid soil moisture changes.

In order to study Conservation Agriculture (CA) - an agricultural technique shown to have increased water and heat stress resilience - we collect frequent ERT measurements (≤ daily) over a long period of time (> 2 years) using PRIME - a low-cost, low-power, ERT monitoring instrument developed by the British Geological Survey. Our equipment is permanently installed at three agricultural observatories in southern Africa (Malawi, Zimbabwe, and Zambia), and is complemented by many co-located point sensors monitoring soil temperature, water content, and matric potential.

We compare the hydrodynamics - derived from ERT data - found under CA, with those under conventional agricultural tillage methods, to better understand the root of CA’s resilience to hot and dry weather conditions. We use laboratory measurements together with the high number of co-located point sensor measurements to build pedological / geophysical relationships for each site, comparing those derived from laboratory scale samples to those derived from in-field measurements.
Lysimeter experiments reveal effects of elevated atmospheric carbon dioxide on soil-water fluxes and biomass production of alpine grassland under drought
Authors: Birk Steffen , Vremec Matevž , Forstner Veronika , Herndl Markus , Schaumberger Andreas
Published: March 2022

Elevated atmospheric carbon dioxide (eCO2) has led to global warming and thus increased evaporative demand of the atmosphere. Yet, the vegetation response to eCO2 may counter the effect of warming by improving plant water-use efficiency (WUE). Here we use a lysimeter-based experimental approach to investigate the effect of eCO2 on the evapotranspiration (ET), soil-water availability, and aboveground biomass (AGB) production of managed alpine grassland under drought. For this purpose, we use data from six weighable high-precision lysimeters at the ClimGrass experimental site operated by the Agricultural Research and Education Centre Raumberg-Gumpenstein (Austria). While one of these lysimeters is operated under ambient conditions, mini-T-FACE systems are used for controlled manipulation of the other lysimeters. Two lysimeters are operated under constant warming of +3 K relative to the ambient surface temperature, two under constant eCO2 of +300 ppm relative to the ambient atmospheric concentration, and one with a combination of elevated temperature and eCO2.

Considering the observations from 2018 to 2020, eCO2 is found to lower ET relative to ambient conditions. Yet, biomass production does not appear to benefit from the water savings resulting from the reduced ET, because plant growth at this humid alpine site generally is energy-limited rather than water-limited (Forstner et al., Hydrol. Earth Syst. Sci., 2021). During summer 2019, however, a distinct dry spell was observed in which actual ET was well below potential ET. This suggests a depletion of the soil-water availability, potentially limiting plant growth in this time period. Under these drought conditions, ET was temporarily higher at the lysimeters with eCO2 compared to those with ambient carbon dioxide concentrations. This corresponded to higher soil water contents and matric potentials resulting from the water savings in the pre-drought period at the lysimeters treated with carbon dioxide. As opposed to other time periods, under the drought in summer 2019 AGB and WUE were found to be higher at the lysimeters with eCO2 than at those with ambient carbon dioxide concentrations. This effect appears to be most evident at the heated plots. It can be concluded that the water savings resulting from eCO2 enabled prolonged water consumption into the drought period, thus mitigating the water limitation and benefiting plant growth. In summary, our results suggest that elevated atmospheric carbon dioxide can help mitigate water stress in alpine grassland during drought.
A probabilistic hydrometeorological forecasting chain for operational warning procedures in Marche Region 
Authors: Gabellani Simone , Libertino Andrea , Delogu Fabio , Ercolani Giulia , Darienzo Matteo , Sini Francesca , Giordano Valentino
Published: March 2022

An operational hydrometeorological forecasting chain has been developed and implemented to support the civil protection activities of the Multi-risk Functional Centre of Marche Region. The chain includes a distribute hydrological model (Continuum) feed by observed meteorological variables from different sources: ground stations, weather radars and satellites (snow cover from Sentinel 2, MODIS and HSAF, and soil moisture from ASCAT). The precipitation field is obtained using a merging algorithm that fuse rain gauge data and weather radars (Modified Conditional Merging). In the forecasting configuration the chain ingests weather forecast (QPF and other meteorological variables) from different sources producing an ensemble of streamflow forecast (COSMO-LAMI 5 km, WRF 1.5 km, HRES 9 km). An interesting feature of the hydrometeorological forecasting systems on small and medium catchments is the possibility to feed the model with quantitative prediction issued by expert forecasters. They consider the meteorological uncertainty by using the output of various meteorological models combined with their knowledge of the territory, of its climatic peculiarities and on the meteorological situation to give their best quantitative estimate of expected precipitation amount and maxima. Part of the forecasting chain is an interactive tool that allows to create different scenarios to mitigate floods by acting in advance on some of the dams present in the area and used for hydropower production and water supply. Modelling upgrade, an activity of the STREAM project, was financed by the European Regional Development Fund  inside the  Interreg IT-HR programme.  In this work the performances of the forecasting chain will be presented on a set of several past events. 

 
What controls the fate of carbohydrates in meromictic lake sediments throughout the Holocene?
Authors: Gajendra Niroshan , Berg Jasmine , Vogel Hendrik , Schubert Carsten , Lever Mark
Published: March 2022

Carbohydrates are a ubiquitous constituent of living organisms and an important contributor to global sedimentary carbon pools. Yet, the factors that control the pool size and degradation of sedimentary carbohydrates are not well understood. Here, we investigated carbohydrate cycling over a complete Holocene sedimentary succession in high-altitude, meromictic Lake Cadagno (Switzerland). This succession includes sedimentary records of a stepwise evolving lake redox history from oxic, hypoxic to euxinic anoxic, as well as intercalated layers of lacustrine and event deposits. Event deposits differ from lacustrine layers in organic carbon content, stable isotope signatures, carbohydrate contents, and carbohydrate macromolecular compositions (pyrolysis gas/mass spectrometry) indicating significant terrestrial inputs. However, past changes in redox conditions, implied by redox-sensitive elements (e.g. Mn, Fe, Mo, and S), are not reflected in carbon isotope and carbohydrate data. Carbohydrate contributions are stable, or even increase, with sediment age and show compound-specific variations. Certain carbohydrate pyrolysis products of likely aquatic origin, such as levoglucosan, increase in percentage with sediment age, whereas others, such as furaldehyde, decrease with age. On the other hand, pyrolysis products that are of likely terrestrial origin, such as 3-acetamido-methylfuran, decrease strongly with age. In contrast to carbohydrate macromolecules, gas chromatography with flame-ionization detection analyses showed no clear changes in total hydrolyzed carbohydrate monomer compositions in relation to sediment age or between aquatic- and terrestrial-dominated layers. Our results indicate that carbohydrate degradation in sediments is mainly controlled at the macromolecular level. Notably, our findings suggest that carbohydrates can be well-preserved over the entire Holocene in lake sediments and that aquatic carbohydrates are in some cases selectively preserved over their terrestrial counterparts in these sediments.  

 
Different varieties of black marble from “Monregalese” district in Piedmont (Italy)
Authors: Bellopede Rossana , Marini Paola , Chiappino Claudia , Vigna Bartolomeo
Published: March 2022

In Piedmont, where five main quarry districts  can  be recognized (Barale et al 2020), Maritime and Ligurian Alps,  corresponding to the mountain area of southern Piedmont and characterized by a  wide variety of marble and sedimentary carbonate rocks, includes the historic ‘Monregalese’ district (G.A.L. Mongioie, 2005).

Among the several varieties of “Monregalese” marble exploited in the past, Nero di Frabosa, Nero Nuvolato di Miroglio, Nero Vallone , Nero di Ormea and Nero di Garresio are  black marble known and identified in Piedmont Cultural Heritage. These varieties of marble come from a small portion of the Mesozoic carbonate succession called Calcari di Rio di Nava and referable to the Middle Jurassic (Dogger). The entire stratigraphic series of the Brianzonese area consists of a limestone-dolomitic sequence of Trias, greater than 300 m in thickness, followed by pure Jurassic limestones (about 100 m thick) and Cretaceous marly-arenaceous limestones (thickness greater than 150 m). In the innermost sector of the Alpine chain, metamorphism and tectonic deformation were much more intense than in the outer portion. The result of this deformation is a high fragmentation of the entire stratigraphic succession with the formation of flakes and bands of carbonate rocks with sharply vertical geometries and small dimensions that alternate with the quartzites and metavulcanites of the base. The succession of Rio di Nava limestones consisting of dark limestones with abundant organic substance and the presence of levels rich in bioclasts, with an original thickness of about 50 meters, was affected by this metamorphic phase, originating the aforementioned Monregalese black marble varieties.

Nero di Frabosa has been employed in lower part of the Chapel of the Shroud (Turin), Vicoforte Sanctuary; Church of San Giorgio (Frabosa Sottana); Nero Nuvolato in the Mussocco cemetery (Milan), in the Church of San Madre di Dio (Turin); Nero Vallone in the Church of Santa Maria Assunta (Montaldo).

From the technical documents there are informations only on the employ in the past years of Nero Garessio (and none related to Nero di Ormea), characterized by numerous and thin white veins. Nero di Ormea differs from it for the darker color of the veins. All these black marble were used inside therefore their resistance to weathering is not well known. Only for Nero di Ormea, the last quarried one, there are data on ageing tests in order to assess their suitability to be used outside and not only for inner and aesthetical purpose.

 