bluuebunny/crossref_metadata_2025 huggingface dataset:
Strong increase of probability of Northwestern European multi-year droughts in a warmer climate
Authors: van der Wiel Karin , Batelaan Thomas , Wanders Niko
Published: March 2022

Three consecutive dry summers in western Europe (2018-2019-2020) had widespread negative impacts on society and ecosystems, and started societal debate on (changing) drought vulnerability and needs to revise adaptation measures. To facilitate that discussion, we investigate multi-year droughts in the Rhine basin, with a focus on event probability in the present climate and in future warmer climates. Additionally, we studied the temporally compounding physical processes leading to multi-year drought events. A combination of multiple reanalysis datasets and multi-model large ensemble climate model simulations was used to robustly analyse the statistics and physical processes of these rare events. In these data, we identify two types of multi-year drought events (consecutive meteorological summer droughts and long-duration hydrological droughts), and show that these occur on average about twice in a 30 year period in the present climate, though natural variability is large (zero to five events in a single 30 year period). Projected decreases in summer precipitation and increases in atmospheric evaporative demand, lead to a doubling of event probability in a world 1 °C warmer than present and an increase in the average length of events. Consecutive meteorological summer droughts are forced by two, seemingly independent, summers of lower than normal precipitation and higher than normal evaporative demand. The soil moisture response to this temporally compound meteorological forcing has a clear multi-year imprint, resulting in a relatively larger reduction of soil moisture content in the second summer and potentially more severe drought impacts. Long-duration hydrological droughts start with a severe summer drought followed by lingering meteorologically dry conditions. This limits and slows down the recovery of soil moisture content to normal levels, leading to long-lasting drought conditions. This initial exploration provides avenues for further investigation of multi-year drought hazard and vulnerability in the region, which is advised given the projected trends and vulnerability of society and ecosystems.
How diverse minerals affect soil organic matter age distribution and chemical composition
Authors: Stoner Shane , Sierra Carlos , Doetterl Sebastian , Schrumpf Marion , Hoyt Alison , Trumbore Susan
Published: March 2022

Soil mineral characteristics have been shown to play a dominant role in stabilizing soil organic matter over medium to long term timescales. However, while great strides have been made (Kleber et al, 2021) toward understanding organic matter stabilization processes, there remain uncertainties about the chemistry, time scales, and age of carbon that is stored on soil minerals. We applied modern thermal analysis methods to investigate soil mineral effects on the thermal stability, chemical composition, and age distribution of soil organic matter. We selected subsoil mineral fractions that contained a single dominant stabilizing pathway (e.g. 2:1 clays, iron oxides, short-range order minerals, crystalline minerals) to isolate effects of individual minerals. We paired thermal fractionation with pyrolysis-GC/MS to describe the relationships of SOM age and chemical composition. Early results show that while certain minerals display heterogeneous thermal stabilities, single mineralogies contain generally narrow age ranges. In addition, organic matter chemistry associated with diverse minerals varies widely and indicates that certain minerals provide higher stability to complex, energy-rich molecules. Associated with this work, we also present novel continuous SOM radiocarbon distributions from thermal fractionation.
Seasonal ice velocity variability of Western Antarctic Peninsula tidewater glaciers from high temporal resolution Sentinel-1 imagery
Authors: Wallis Benjamin , Hogg Anna , Davison Benjamin , van den Broeke Michiel
Published: March 2022

In Antarctica dynamic ice loss dominates the continent’s contribution to sea level rise and the magnitude of dynamic ice loss depends in part on the ice speed at marine-terminating glacier grounding lines. Long term dynamic ice speed variations in Antarctica have been observed on multi-year timescales, most notably in ice speed increases in the Amundsen Sea sector, Getz basin and Antarctic Peninsula. Glacier and ice sheet speed can also be variable on seasonal timescales, due to surface meltwater-induced variations in basal water pressure and changes in the force balance at the terminus due to terminus advance and retreat. While these seasonal changes are well documented on the Greenland Ice Sheet, observations of seasonal ice speed changes in Antarctica are sparse and poorly resolved.

In this study, we show widespread seasonal ice speed fluctuations near the termini of 106 tidewater outlet glaciers across Western Antarctic Peninsula North of 70° S by exploiting the full Sentinel-1 record from 2014 to 2021. The seasonal speed variations were consistent each year, and are characterised by a summertime speed-up, with speed variability on average 13 ± 6.5% of the annual mean. There is good agreement between our observations of seasonal ice speed changes and time-series of potential forcing mechanisms, including surface water flux, terminus position change and reanalyses of ocean temperature. Our results demonstrate that the glaciers of the Western Antarctic Peninsula are sensitive to forcing in the ice-ocean-atmosphere system on seasonal timescales.

By observing widespread seasonal ice speed variations on the Antarctic Peninsula for the first time, we demonstrate a previously unknown sensitivity of part of the Antarctic Ice Sheet to external forcing over short timescales. This is particularly relevant for mass balance calculations by the input-output method, which typically rely on annual estimates of ice speed that do not capture these seasonal changes. Our dataset covers the Sentinel-1 epoch (2014-present), however the Antarctic Peninsula has undergone the greatest warming of any Southern Hemisphere terrestrial area in the latter twentieth century and atmospheric temperatures are projected to rise further in a 1.5°C warming scenario. Therefore, it is essential to understand the historic prevalence of seasonal speed changes on the Peninsula and to determine the impact of these seasonal variations on annual ice motion, to improve future projections of the Antarctic response to continued warming and its contributions to sea level rise.
Using interpretable machine learning to identify compound meteorological drivers of crop yield failure
Authors: Sweet Lily-belle , Zscheischler Jakob
Published: March 2022

Extreme weather events, such as droughts, floods or heatwaves, severely impact agricultural yield. However, crop yield failure may also be caused by the temporal or multivariate compounding of more moderate weather events. An example of such an occurrence is the phenomenon of 'false spring', where the combined effects of a warm interval in late winter followed by a period of freezing temperatures can result in severe damage to vegetation. Alternatively, multiple weather events may impact crops simultaneously, as with compound hot and dry weather conditions.

Machine learning techniques are able to learn highly complex and nonlinear relationships between predictors. Such methods have previously been used to explore the influence of monthly- or seasonally-aggregated weather data as well as predefined extreme event indicators on crop yield. However, as crop yield may be impacted by climatic variables at different temporal scales, interpretable machine learning methods that can extract relevant meteorological features from higher-resolution time series data are desirable.

In this study we test the ability of adaptations of random forest models to identify compound meteorological drivers of crop failure from simulated data. In particular, adaptations of random forest models capable of ingesting daily multivariate time series data and spatial information are used. First, we train models to extract useful features from daily climatic data and predict crop yield failure probabilities. Second, we use permutation feature importances and sequential feature selection to investigate weather events and time periods identified by the models as most relevant for crop yield failure prediction. Finally, we explore the interactions learned by the models between these selected meteorological drivers, and compare the outcomes for several global crop models. Ultimately, our goal is to present a robust and highly interpretable machine learning method that can identify critical weather conditions from datasets with high temporal and spatial resolution, and is therefore able to identify drivers of crop failure using relatively few years of data.
Development and application of a novel UHPLC-HRMS method for the analysis of organic wildfire tracers in ice cores. 
Authors: Burgay Francois , Salionov Daniil , Huber Carla , Singer Thomas , Ungeheuer Florian , Eichler Anja , Vogel Alexander , Bjelic Sasa , Schwikowski Margit
Published: March 2022

Wildfires have an important role in affecting the Earth’s radiative balance. Biomass burning aerosols can scatter or absorb the incoming solar radiation, alter the ice and snow albedo and act as cloud condensation nuclei. Overall, their net contribution to the Earth’s radiative forcing is negative, however this estimate has large uncertainties. To better assess the impact of wildfires on climate (and vice versa), it is crucial to reconstruct their past regional and temporal variability on decadal and centennial timescales. Ice cores are excellent archives to perform such palaeofire reconstructions. Previous studies have reconstructed the occurrence of wildfires in ice cores using both inorganic (ammonium, potassium and black carbon) and organic proxies (levoglucosan, vanillic acid and p-hydroxybenzoic acid). However, a more comprehensive view that involved a broader suite of wildfire proxies was missing. Here, we present a new SPE-UHPLC-HRMS method for the determination of five organic biomass burning tracers (syringic acid, vanillic acid, vanillin, syringaldehyde and p-hydroxybenzoic acid) and pinic acid, as biogenic emission proxy, in ice core samples. This method showed average recoveries of 76% (58-88% range), excellent inter-day reproducibility, no significant matrix effects and fast analysis time (13 min per sample). Comparing the published concentration ranges of the selected species from different ice core regions (i.e. Alps, Greenland, Kamchatka, China and Svalbard Archipelago) with the procedural detection limits of this new methodology, we conclude that four of the six targeted compounds can be successfully detected in real ice and snow samples. Only for vanillin and syringaldehyde, no ice-core measurements have been reported in the scientific literature so far. The method development also involved the evaluation of common laboratory practices such as the melting and refreezing of ice samples before the analysis. We found that the melting and refreezing of the samples resulted in a mass loss for the majority of the investigated compounds, which was more evident at lower concentrations. We hypothesize that the reason of this phenomenon is the adsorption of the compounds on the walls of the glass vials used for this study. In light of this, we propose alternative sample storage strategies that can also be extended for the analysis of other compounds.

The method was successfully tested on nine ice core samples from the Colle Gnifetti (European Alps) and it will be applied on ice cores from the Alps and the Russian Altai, contributing to the better understanding of wildfire temporal evolution and their relations with climate.
A century of sedimentation in a reservoir in central Europe – sedimentation rate and characteristics
Authors: Stauch Georg , Esch Alexander , Dörwald Lukas , Esser Verena , Lechthaler Simone , Lehmkuhl Frank , Schulte Philipp , Walk Janek
Published: March 2022

The sediments of the artificial Urft reservoir preserve a century of environmental information due to undisturbed sedimentation conditions. The Urft reservoir is located in the Eifel Mountains in western Germany and was built between 1900 and 1905. At the time of its construction, the Urft reservoir was the largest reservoir (45.51 million m³) and drove, with 12 MW, the most powerful water storage power plant in Europe. During construction works in November 2020, the reservoir was nearly completely drained. This offered the unique possibility to analyse the sediment volume and the composition deposited during the last 115 years.

We used high resolution maps with a scale of 1:1,000 from 1898 which were compiled to calculate the original storage volume of the reservoir. To assess the present-day surface, the entire lake area was photogrammetrically surveyed using an Unmanned Aerial Vehicle (UAV). Additionally, 10 drill cores were retrieved in 2020 to quantify the anthropogenic influence on the sediments in the form of mining-induced sediment-bound pollutants (e.g., heavy metals) and to relate this to the history of use in the catchment area. Furthermore, microplastics were studied in the sediments. To derive the sediment ages, a detailed Cs-137 chronology was created for one of the cores.

In summer 2021, the northern Eifel Mountains were impacted by a catastrophic flooding event, resulting in massive destructions in the catchment of the Urft and strong relocation of sediments in the floodplain. To assess these geomorphologic changes in the Urft reservoir, the water level was lowered again in December 2021. Consequently, an additional digital elevation model was produced by UAV surveying. Furthermore, additional sediment cores were taken to get information on changes in the sediment composition due to the flood event. In the upper part of the reservoir, up to 30 cm of sediments were deposited in summer 2021 while channels below the water surface experienced strong modifications.
Are we ready for an ecological transition? A survey between young geoscientists in Italy
Authors: Egidio Elena , Gerbaudo Andrea , Lasagna Manuela , Lozar Francesca , Tonon Marco Davide
Published: March 2022

During the last decades, especially the last one, the importance of geoethics has been highlighted by the major consciousness about the role of geoscience, and  geoscientists, on environmental and social challenges that the world has to face.This awareness has been raised in accordance with the 17 sustainable development goals of the UNESCO 2030 Agenda. This document led to the wider popularity of the concept of education for sustainability (EfS) as an evolution of what was formerly known as environmental education (EE) or education for sustainable development (ESD). The main purpose of EfS is to promote an inter and trans-disciplinary knowledge, capable of framing the complexity of the current crisis.In one of the last publications about geoethics (Di Capua, Peppoloni 2019) the authors encourage geoscientists to be aware of the geological community role and to reflect on the ethical responsibility that this implies. Moving from this call for engagement, this study focuses on the Italian situation.Does the Italian Earth Sciences community have enough knowledge and a real shared interest on sustainability, in order to shift to a geoethical perspective? This study shows the results of a survey conducted between the participants of BeGeo21, the first Italian national congress dedicated to young geoscientists (MSc and PhD), held in Napoli in October 2021. The data analysis shows that the majority of respondents has scarce knowledge about sustainability and the 17 SDGs and only a few had the possibility to attend courses and seminars about these topics in their department. Nonetheless, the importance of the sustainability, and thus geoethic, point of view for the future of Geosciences is well recognized and there’s a wide request for our higher education institutions to increase the number of activities connected to EfS.

 
Revisiting earthquake-tsunami models of the 2018 Palu events using near-fault high-resolution imaging and 3D fully-coupled earthquake-tsunami modeling
Authors: Ulrich Thomas , Marconato Léo , Gabriel Alice-Agnes , Klinger Yann
Published: March 2022

The 2018 Mw 7.5 Palu earthquake struck the Sulawesi island, Indonesia, in 2018 and was followed by an unexpected tsunami. Using a physics-based, coupled earthquake-tsunami model, Ulrich et al. (2019) showed that direct earthquake-induced uplift could have sourced the tsunami. The 3D dynamic rupture model of the earthquake captures key observations, including the supershear rupture speed and the deformation pattern derived from satellite data. Stress state and fault conditions were tightly constrained by observations combined with simple static analyses based on Mohr-Coulomb theory of frictional failure and a few trial models. The earthquake scenario predicts a combination of up to 6 m of left-lateral slip and 2 m of normal slip on a straight fault segment dipping 65 degrees beneath Palu Bay.

While most studies (e.g. Bai et al., 2018, Ulrich et al., 2019, Oral et al., 2019) suggest a very early supershear transition, the exact timing of the onset of supershear rupture and the driving mechanism of the supershear transition are elusive. Here we revisit the earthquake dynamic rupture modeling based on new high-resolution near-fault deformation maps derived from correlation of optical satellite data. We vary nucleation radius, fault geometry, and off-fault plasticity parametrization to obtain alternative dynamic rupture scenarios. Specific inputs allow delayed transition to supershear. The obtained scenarios are evaluated based on near-fault damage inference.

Additionally, we revisit the tsunami model, adopting advanced strategies for earthquake-tsunami linking and tsunami modeling. In Ulrich et al. (2019), a one-way linking approach with a shallow water equations solver allowed translating the time-dependent seafloor displacements into a tsunami model with wave amplitudes and periods matching those measured at the Pantoloan wave gauge and inundation that is consistent with field survey data. Such modeling workflow yet neglects tsunami generation complexity, acoustic waves, and dispersion, and only approximates horizontal momentum transfer.  We present a 3D fully coupled earthquake-tsunami model (Krenz et al., 2021), that releases these limitations. This allows us to assess how the standard earthquake-tsunami workflow affects our results, and to revisit our conclusions.
Applying water requirements into metadata in the era of SDGs and Essential Variables: semantics, quality parameters and discoverability in the GEM+
Authors: Serral Ivette , Masó Joan , Julià Núria , Manakos Ioannis , Milis George , Pesquer Lluís
Published: March 2022

WQeMS aims to provide an operational Water Quality Emergency Monitoring Service to the water utilities industry in relation with the quality of the ‘water we drink’. This Copernicus service focuses on monitoring lakes for the delivery of drinking water and will provide open geospatial data products structured in Essential Water Variables. While Essential Climate variables are fully defined, a set of Essential Water Variables was proposed by GEOSS but was never fully adopted. In this communication we will present a metadata manager tool called GeM+ that adopts a general framework for Essential Variables (EV) that includes a renewed proposal for Essential Water Variables. EVs are included in a keyword library that relates them to SDG indicators. In addition, the GEM+ includes a library of quality measures defined in QualityML (that inherits and extends the UncertML approach) vocabulary that will be used and tested for the Water Quality Emergency Monitoring Service. The quality need for a vocabulary proposed by QualityML is now being adopted by the ISO 19157-3 proposal. GEM+ also implements the ISO 19115-1 approach for lineage (provenance) that allows to carefully document data product workflows used to create the geospatial products as a mechanism to describe the traceability, quality and reproducibility of the dada. Examples of water related dataset have been prepared showing how EVs, quality measures and provenance is used to semantically tag data, document quantitative quality estimations and present the sources and processes used to elaborate this Copernicus service candidate products.

 

WQeMS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004157.
Conditions for detecting early warning of tipping.
Authors: Ditlevsen Peter
Published: March 2022

The warning of tipping to an undesired state in a complex system, such as the climate, when a control parameter slowly approaching a critical value ($\lambda(t) \rightarrow \lambda_0$) relies on detecting early warning signals (EWS) in observations of the system. The primary EWS are increase in variance, due to loss of resilience, and increased autocorrelation due to critical slow down. They are statistical in nature, which implies that the reliability and statistical significance of the detection depends on the sample size in observations and the magnitude of the change away from the base value prior to the approach to the tipping point. Thus the possibility of providing useful early warning depends on the relative magnitude of several interdependent time scales in the problem. These are (a) the time before the critical value $\lambda_c$ is reached, (b) the (inverse) rate of approach to the bifurcation point (c) The size of the time window required to detect a significant change in the EWS and finally, (d) The escape time for noise-induced transition (prior to the bifurcation). Here we investigate under which conditions early warning of tipping can be provided. 
Characteristics of the First Ground Level Enhancement (GLE) of Solar Cycle 25 on 28 October 2021
Authors: Papaioannou Athanasios , Kouloumvakos Athanasios , Mishev Alexander , Vainio Rami , Usoskin Ilya , Herbst Konstantin , Rouillard Alexis P. , Anastasiadis Anastasios , Giesler Jan , Wimmer-Schweingruber Robert , Kuhl Patrick
Published: March 2022

We present an overview of the first ground-level enhancement (GLE) event of solar cycle 25, recorded on 28 October 2021 (GLE73), based on the available neutron monitor (NM) network observations and on data from near-Earth spacecraft (GOES, SOHO, SolO). The maximum increase was ~7.3% for DOMC (Dome C NM at Concordia station) and 5.4% for SOPO (South Pole) conventional NMs located on the Antarctic plateau. Bare (lead-free) NMs at the same sites detected a higher response (14.0% for DOMB and 6.6% for SOPB). The Fort Smith (FSMT) NM shows the earliest increase among the high-latitude NMs, indicating a moderate anisotropy in the first phase of the GLE event. The maximum rigidity of accelerated protons did not exceed 2.4 GV. We estimated the solar release time (SRT) of ≥1 GV protons into open magnetic field lines at ~15:40 UT.  In-situ proton observations from near-Earth spacecraft were combined with the detection of a solar flare in soft X-rays (SXRs), a coronal mass ejection (CME), radio bursts and extreme ultraviolet (EUV) observations to identify the solar origin of the GLE. Around the ≥1 GV proton SRT the CME-driven shock was located at a height of ~2.33 Rs. The timing of the EUV wave evolution towards the field lines magnetically connected to Earth seem to be in good agreement with the inferred release time of ≥1 GV protons.
Beyond crossovers: Predicting ice thickness uncertainties in ice penetrating radar data from geometric controls
Authors: Young Duncan , Kempf Scott , Ng Gregory
Published: March 2022

Ice thickness is a key parameter for predictive ice sheet modeling, geological interpretation of the underlying bed rock, and site selection for deep ice sheet and bed rock sampling.  However, the uncertainties typically reported are in terms of crossover statistics, and ice thickness uncertainties are generally not formally integrated into ice sheet models.  Here we examine what crossover statistics reveal and conceal for the actual uncertainty in reported ice thickness, examine the impact of system and geometric parameters on uncertainties, and place these parameters in the context of the observed subglacial roughness.  We provide a predictive model for uncertainties as a function of ice thickness, sensor height, and subglacial roughness parameters, evaluate it from the perspective of ground based, airborne and orbital sounding and make recommendations for parameters that should be reported in ice thickness data products.
Comparing prediction algorithms in FTIR-based chemometric analysis to predict soil variables within centuries-old charcoal rich Technosols
Authors: Ramezany Shaghayegh , Bonhage Alexander , Raab Thomas
Published: March 2022

As an alternative to the costly wet chemical analysis of soil, Machine Learning (ML) algorithms can be applied for the quantification of soil properties through prediction models. In this study, we evaluate the performances of the Random Forest (RF) algorithm and Partial Least Square Regression (PLSR), in the prediction of soil variables, including CEC, pH, total contents of C and N as wells as other elements (Al, Fe, Ca, Mn, Mg, K, and Na) based on FTIR-spectra of Relict Charcoal Hearth (RCH) soils and reference forest soils (Non-RCH). We investigate the effect of high quantities of charcoal in the soil on the prediction models. Preliminary results suggest that there is no significant difference in the results of prediction models for total N, C, and Fe contents, while the accuracy of PLSR in the prediction of pH, Mg, and Ca, and RF performance in prediction of pH and C decreased for RCH soils. Both algorithms demonstrate higher accuracies in the prediction of Al within the RCH soils. Prediction of CEC, Na, K, Mg (RF) within the RCH soils, Al (PLSR) for Non-RCH soils, and Mn and Ca (RF) for both soil types resulted in lower quality of predictions. 

It can be inferred from the results that the performance of FTIR-based prediction models can be affected by the presence of charcoal in soil due to the nature of spectral features reflecting the soil composition. The presence of charcoal in soil likely alters the absorption interference and peak overlaps, which can result in lower accuracy of the prediction models. In addition to the accuracy of the prediction models, we evaluate the reliability of the weighted wavenumbers (as important variables) in each prediction, which provides information about the correlation of spectral features and chemical properties. It can be studied through the Variable Importance Plot in RF and Variable Importance on Projection through PLSR (VIPs), which show high potential for studying soil composition and metal distribution in mineral and organic soil fractions despite the observed weaknesses in weighing wavenumbers in predictions. Therefore, we assess and compare the quality of information gained regarding soil chemical properties from the algorithms besides a sole quantification of soil parameters. Furthermore, we applied the developed prediction models on a large number (n > 600) of FTIR-spectra of RCH soils to investigate the practical application of the models and thereby compare spectral derived chemical properties of the studied Technosols and reference forest soils.
Hydrological model adaptivity to inputs of varied quality 
Authors: Wang Jiao , Zhuo Lu , Han Dawei
Published: March 2022

Hydrological models serve as useful tools to describe current conditions and to predict future conditions in a catchment. However, the errors from input data including precipitation and potential evapotranspiration (PET) and model parameterization can lead to huge uncertainties on the model outputs. Although it is challenging to quantify the effect from individual sections due to the high non-linearity of hydrological processes, the potential compensations among different inputs and model parameters may provide valuable insights into the input data correction and the model calibration.

 

In this study, we aim to improve the understanding of the adaptation mechanisms between model parameters and the quality of inputs during the hydrological simulation. The objectives are to investigate: (1) the most effective metrics needed to characterize the hydrological applicability of input sources; (2) the hydrological model adaptivity to input sources of varied quality; (3) the compensating interaction of different inputs on the hydrological modelling. We demonstrate our approach to the widely used conceptual Xin’anjiang (XAJ) hydrological model. Rainfall estimations from multiple sources are collected for a headwater catchment in the Southern United States and the Brue catchment in Southwest England, from rain gauges, weather radars, satellites, reanalysis products, and Weather Research and Forecasting (WRF) model dynamic downscaling.

 

Results suggest that: (1) The total water balance is a poor indicator of rainfall data quality for hydrological simulations. Instead, the event-based water balance shows a stronger influence on representing the differences in hydrological applicability, especially for heavy storm events; (2) A high compensation relation exists among the quality of rainfall data, the model’s initial soil moisture state, and water balance-related parameters in the XAJ model, allowing the poor WRF rainfall datasets to generate good streamflow simulations; (3) A new hydrological proxy (term), called Compensating Interaction Angle  (CIA) between different inputs is diagnosed to quantitatively measure the trade-off between their quality in producing satisfactory hydrological performance. The proposed CIA is recommended to apply in other regions and hydrological models to validate if a general pattern exists and how it varies regionally.
Present and future synoptic circulation patterns associated with cold and snowy spells over Italy
Authors: Pons Flavio , D’Errico Miriam , Yiou Pascal , Tao Soulivanh , Nardini Cesare , Lunkeit Frank , Faranda Davide
Published: March 2022

Cold and snowy spells are compound extreme events with the potential of causing high socioeconomic impacts. Gaining insight on their dynamics in climate change scenarios could help anticipating the need for adaptation efforts. We focus on winter cold and snowy spells over Italy, reconstructing 32 major events in the past 60 years from documentary sources. Despite warmer winter temperatures,  very recent cold spells have been associated to abundant, and sometimes exceptional snowfall.
Our goal is to analyse the dynamical weather patterns associated to these events, and understand whether those patterns would be more or less recurrent in different emission scenarios using an intermediate complexity model (PlaSim). Our results, obtained by considering RCP2.6, RCP4.5 and RCP8.5 end-of-century CO2 concentrations, suggest that the likelihood of analogous synoptic configurations of these extreme cold spells would grow substantially under increased emissions.

This work was supported by the ANR-TERC grant BOREAS and by the Horizon 2020 research and innovation programme XAIDA (grant agreement No 101003469)
Towards integration of LCLM feedbacks within climate models: an emulator approach
Authors: Nath Shruti , Lejeune Quentin , Seneviratne Sonia , Schleussner Carl-Friedrich
Published: March 2022

Land Cover and Land Management (LCLM) changes display complex interactions with climate conditions, and can in particular modulate regional-scale extreme climate events such as heat waves and droughts. Relatedly, the potential of LCLM for not only climate mitigation but also adaptation has been underlined; it could thus play a role in achieving the corresponding goals of the Paris Agreement. It is thus essential to account for LCLM processes and their climate feedbacks within climate models, in order to inform land-use scenarios that help comply with climate and broader environmental objectives in a comprehensive manner. Emulators represent a computationally cheap but effective way of approximating climate models with an added advantage of agility in scenario exploration. Here we outline a Generalised Additive Model (GAM) based emulator approach to represent LCLM-Climate feedbacks simulated in Earth System Models (ESMs). The emulator is to be used in the LAnd MAnagement for CLImate Mitigation and Adaptation (LAMACLIMA) project, and is trained on dedicated ESM simulations which isolate the effects of de/afforestation, wood harvest and irrigation.

We showcase the emulator’s ability to represent local, monthly surface temperature responses to de/afforestation using input variables of tree cover change, longitude, latitude and orography. Spatial cross-validation is used to fit and tune the emulator, thus considering spatial autocorrelations within the training material. The resulting emulator can be used to estimate surface temperature changes over a major part of the globe and for a variety of possible tree cover changes. Such also enables us to identify the geographical areas and types of tree cover changes which are of high uncertainty within the emulator. This provides us with valuable insight into the additional ESM simulations that would be required to improve its representation of temperature responses to de/afforestation. Extending this framework to wood harvest and irrigation could then provide more clarity on the uncertainties underlying LCLM-Climate feedbacks as represented within ESMs.
A landslide in Heifangtai, Northwest of Chinese Loess Plateau: Triggered Factors, Movement Characteristics and Failure Mechanism
Authors: jiaxu kong , jianqi zhuang , jianbing peng , jia zheng , jiaqi mu , shibao wang , yuting fu
Published: March 2022

On 27 January 2021, at 21:00 (UTC+8), a shallow loess landslide occurred in Heifangtai, Yongjing County, Gansu Province, northwest of the Chinese Loess Plateau. Fortunately, the independently developed GNSS system predicted the landslide 7 hours in advance. Although farmland and channels were buried and destroyed, no damage has been done to the lives and houses of residents. In order to explore the triggering factors and movement process of the landslide, based on the field investigation, we collected the precipitation and temperature data more than one year before the landslide and comprehensively used UAV photogrammetry, numerical simulation, and laboratory test for comprehensive research. It was found that as the temperature rose and freeze-thaw cycles, changes in mechanical properties of loess and unique stratum structure were the main factors triggering the landslide. The rise of temperature led to an increase in groundwater levels, and the strength of soil decreased gradually until shear liquefaction occurred. This landslide caused a substantial topographic change, which provided conditions for slope instability in the future. The process of landslide movement can be divided into three stages: start-up stage, severe sliding stage, and deceleration stage. Simulation results show that the maximum velocity was 22 m/s, and the maximum sliding distance was 393 m. The main movement period was 40 s, and the apparent friction angle was 5°. Finally, this study provides a reliable basis for studying dynamic process and failure mechanism of loess landslide.
Multiple phyla, one time resolution? Time averaging in benthic foraminifera, mollusk, echinoid, crustacean and otolith fossil assemblages
Authors: Nawrot Rafał , Berensmeier Michaela , Gallmetzer Ivo , Haselmair Alexandra , Tomašových Adam , Zuschin Martin
Published: March 2022

Time averaging – mixing of remains of organisms separated by decades, centuries or millennia in a single sedimentary layer – affects not only paleoecological or geochronological inferences, but also the resolution of geochemical proxy records extracted from their biomineralized hardparts. Taxonomic differences in intrinsic skeletal durability are expected to produce temporal mismatch between co-occurring species, but the importance of this effect is difficult to assess due to lack of direct estimates of time averaging for many higher taxa. Moreover, burial below the taphonomic active zone and early diagenetic processes may alleviate taxonomic differences in disintegration rates in subsurface sediments.

Here, we compare time averaging across five phyla of major carbonate producers co-occurring in a sediment core from the northern Adriatic shelf. We dated individual bivalve shells, foraminiferal tests, tests and isolated plates of irregular and regular echinoids, crab claws and fish otoliths. In spite of different skeletal architecture, mineralogy and life habit, all taxa showed very similar time averaging varying from ~1800 to ~3600 yrs (interquartile age ranges). Thus, remains of echinoids and crustaceans – two groups with multi-elemental skeletons assumed to have low preservation potential – can still undergo extensive age mixing comparable to that of the co-occurring mollusk shells. Moreover, just like macrofossil assemblages, benthic foraminifera from shallow shelf settings can be time-averaged over 103 yrs, which may limit their utility as high-resolution environmental proxies. In spite of similar time averaging of the studied taxa, their median ages differed by up to ~3700 yrs, reflecting species-specific timing of seafloor colonization during the Holocene transgression.

Our results suggest that taxa of different skeletal durability can have comparable temporal resolution due to sequestration of fragile remains below the taphonomic active zone. However, significant age offsets between co-occurring species can complicate paleoecological analyses of multi-taxic fossil assemblages and make both age models and geochemical proxy records sensitive to taxon choice.
Compatibility between the preliminary ITRF2020 solution and GNSS antenna phase center offsets
Authors: Villiger Arturo , Dach Rolf , Prange Lars , Arnold Daniel , Kalarus Maciek , Schaer Stefan , Stebler Pascal , Jäggi Adrian
Published: March 2022

The release of the next International Terrestrial Reference Frame (ITRF) 2020 is based on the four geodetic techniques, namely Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), Global Navigation Satellite Systems (GNSS), and Doppler Orbitography and Radio-positioning Integrated by Satellite instrument (DORIS). The upcoming release will presumably define the scale based on the first two techniques.

The GNSS scale is mainly driven by the z-component of the satellite antenna phase center offsets. With the disclosure of the Galileo metadata by the European GNSS Agency (GSA) the satellite antenna pattern became available to the public and enabled the GNSS scale determination.

We aim to analyze the consistency between the preliminary ITRF 2020 solution and the GNSS based scale. In addition, we will extend our study with all available TRF solutions and compare their consistency with the GNSS derived scale and discuss the resulting comparisons. 
Modeling of injection-induced seismicity in fractured rock masses with TOUGH3-seed hybrid solver
Authors: Ciardo Federico , Rinaldi Antonio Pio
Published: March 2022

Injection of fluid in fractured reservoirs triggers seismicity that migrates away from injection point. The enlarging cloud of (micro-)seismicity can be driven by pore-fluid diffusion within fractured rock mass, thus propagating in space proportional to square root of time for an effective isotropic and homogenous medium, or by elastic-stress interactions between over-stressed pre-existing fractures.

In this contribution we adopt an hybrid approach to model seismicity evolution driven by pore-fluid propagation into a Discrete Fracture Network and apply it to a large-scale injection experiment at FORGE Test Site in Utah (USA). We couple a statistical seed model for seismicity with a physic-based solver for non-linear pore-fluid diffusion into a three-dimensional DFN (using TOUGH3). Local inelastic permeability changes mimick irreversible deformations and affect pore-fluid evolution and hence seismicity cloud.

Several synthetic catalogs are generated and compared with one generated with a pure physic-based numerical solver.

 
Gender and social inclusion in disaster risk reduction and management: Key learning and effective practices
Authors: Sneddon Alison , Budimir Mirianna , Brown Sarah , Nelder Issy
Published: March 2022

Resilience to natural hazards varies widely within and between populations. People living in the same area affected by the same hazard event will experience it differently depending on their specific vulnerabilities and capacities. The social inequalities which drive differential resilience vary based on the norms of a given context, but result in resources being harder for some people to reach and use than others.

These inequalities are often invisible in traditional data, and therefore the needs of the most vulnerable are not addressed in disaster risk reduction and management policy and practice. The impacts of disasters therefore reinforce and worsen existing inequalities as already vulnerable people are left further and further behind.

This presentation will focus on new learning about the relationship between gender and social vulnerabilities and resilience to natural hazard-related disasters in a range of contexts with three key aims:

To share key learning about differential disaster resilience and requirements of early warning and disaster risk management implementation

To explore key tools which have been piloted, tested, and developed to improve knowledge and understanding of resilience

To discuss effective and practical ways to apply these tools going forward in research, policy, and practice.

The presentation will draw on experiences and findings from projects conducted in the Philippines, Bangladesh, Malawi, Nepal, and Dominica to research gender and social inclusion in relation to early warning systems, disaster preparedness and response, and disaster risk financing.

The session will examine the drivers of social inequalities and their impacts relating to risk knowledge, monitoring and warning, communication and dissemination, and response capability, sharing examples of the different needs, considerations, and priorities relating to early warning and disaster risk management within communities.

We’ll then explore approaches to data layering and our Missing Voices methodology as key tools to identify and understand factors, including intersectional factors, influencing social and economic resilience to natural hazards.
Thermal response timescales and associated spatial patterns in ESMs to pulse injections of climate forcers
Authors: Baur Susanne , Sanderson Benjamin , Séférian Roland , Terray Laurent
Published: March 2022

The Earth system response to climate forcers can be broken down to multiple timescales, with the land surface responding within a few years to a change in forcing while the deep ocean layers have only fully equilibrated after several hundreds to thousands of years. In this work we assume that there is a number of distinct timescales represented in the thermal response to pulse injections of different climate forcers in the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth System Models (ESMs), which can be estimated by fitting a sum of decaying exponential responses  to a set of non-noisy Empirical Orthogonal Functions of each model. Using these exponential decay functions and a regression-based pattern scaling approach we are able to emulate the gridded transient surface temperature response to an input forcing timeseries. We determine that for the abrupt-4xCO2 experiment the thermal response in most CMIP6 ESMs can be represented by a similar set of timescales, but early results suggest diverse spatial warming patterns. This work introduces the concept that the evolving spatial patterns associated with the thermal response on different timescales for pulse injections of different climate forcers can be simply and accurately emulated and ultimately be used to predict transient simulations.
Dust particles size distributions in snow and its importance for optical properties of snow
Authors: voisin didier , Voiron Celine , Denis Hervé , Darfeuil Sophie , Ginot Patrick , Dumont Marie , Reveillet Marion , Gascoin Simon
Published: March 2022

Light absorbing particles (LAPs) from various origins get deposited to the snow where they greatly influence its physical evolution, and most particularly its melt by changing the energy absorbed from solar radiation. Black Carbon and Dust are the most important such particles. How those particles change snow physics depends on their optical properties, which are a function of their chemical composition and size distribution.

In European mountain ranges, part of the deposited dust comes as sporadic Saharan dust outbreaks, which cause important dust layers in the snowpack. One such event was the object of a citizen science collaborative sampling campaign, which resulted in 150 samples collected over the Pyrenees, the Jura, and the French and Swiss Alps.

Dust in these samples was filtered out and weighted, in order to get total deposition fluxes. Size distributions were measured between 4 and 60 µm. This size fraction represents less than 10% of the total mass measured in those samples. Assuming lognormal distributions to extend the measured size distributions beyond 60 µm only explained a fraction of the missing mass.

The relative importance of the particles not measured between 4 and 60 µm for the optical properties of the snowpack depend strongly on their size. A rough estimate of the importance of the missing fraction was attempted by assuming that the overall optical effect scales with the surface area of the particles. Depending on the assumed diameter of particles in this fraction, the missing mass (~90% of the total mass) overall optical impact is estimated between 80 and 40% of the estimated total optical impact of the dust present in the snow.

A limited set of samples was used to assess the size of this missing fraction, using different methods. This preliminary assessment suggests the potential importance of grain aggregation in natural snow and the importance of unbiased size distribution measurements for dust in snow.