bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatically detecting avalanches with machine learning in optical SPOT6/7 satellite imagery 
Authors: Hafner Elisabeth D. , Barton Patrick , Caye Daudt Rodrigo , Wegner Jan Dirk , Schindler Konrad , Bühler Yves
Published: March 2022

Safety related applications like avalanche warning or risk management depend on timely information about avalanche occurrence. Knowledge on the locations and sizes of avalanches releasing is crucial for the responsible decision-makers. Such information is still collected today in a non-systematic way by observes in the field, for example from ski resort patrols or community avalanche services. Consequently, the existing avalanche mapping is, in particular in situations with high avalanche danger, strongly biased towards accessible terrain in proximity to (winter sport) infrastructure.

Recently, remote sensing has been shown to be capable of partly filling this gap, providing spatially continuous information on avalanche occurrences over large regions. In previous work we applied optical SPOT 6/7 satellite imagery to manually map two avalanche periods over a large part of the swiss Alps (2018: 12’500 and 2019: 9’500 km2). Subsequently, we investigated the reliability of this mapping and proved its suitability by identifying almost ¾ of all occurred avalanches (larger size 1) from SPOT 6/7 imagery. Therefore, optical SPOT data is an excellent source for continuous avalanche mapping, currently restricted by the time intensive manual mapping. To speed up this process we now propose a fully convolutional neural network (CNN) called AvaNet. AvaNet is based on a Deeplabv3+ architecture adapted to specifically learn how avalanches look like by explicitly including height information from a digital terrain model (DTM) for example. Relying on the manually mapped 24’737 avalanches for training, validation and testing, AvaNet achieves an F1 score of 62.5% when thresholding the probabilities from the network predictions at 0.5. In this study we present the results from our network in more detail, including different model variations and results of predictions on data from a third avalanche period we did not train on.

The ability to automate the mapping and therefor quickly identify avalanches from satellite imagery is an important step forward in regularly acquiring spatially continuous avalanche occurrence data. This enables the provision of essential information for the complementation of avalanche databases, making Alpine regions safer.
Monthly Antarctic-wide surface meltwater evolution between 2006 and 2021, and its links to climate
Authors: Tuckett Peter , Ely Jeremy , Sole Andrew , Livingstone Stephen , Lea James , Jones Julie
Published: March 2022

Understanding the distribution and evolution of surface meltwater on the Antarctic Ice Sheets is vital in enabling us to predict how the ice sheet will respond to a warming climate. The majority of Antarctic surface meltwater studies have typically been limited by either spatial or temporal scale. We have overcome these limitations by using a fully automated method to map surface meltwater across the entire Antarctic continent between 2006 and 2021 at a monthly temporal resolution. Furthermore, by accounting for variability in both cloud cover and satellite image coverage, we have generated the first consistent and continuous multi-year time series of Antarctic-wide surface meltwater to date. Here, we present results from analysis of this dataset, including long-term trends in surface meltwater extent, comparison between surface meltwater area and modelled melt, and associations between surface meltwater area and climatic factors. Regression analysis shows strong correlations between surface meltwater area and modelled snowmelt around the ice sheet margin, increasing our confidence in regional climate models to predict future melt conditions. Synoptic scale climate regimes, such as the Southern Annular Mode, exert a strong controlling factor on surface meltwater area totals on an annual basis. However, regional climatic processes and melt-albedo feedbacks can have strong second-order influences on localised melt rates, resulting in high variability in meltwater coverage, especially in parts of East Antarctica. This multi-year dataset offers the opportunity to explore surface meltwater evolution at local, catchment and continental scales, and will be of widespread use in understanding the operation of surface hydrological systems.
On the potential impact of the Great Green Wall on the West African Monsoon 
Authors: Ingrosso Roberto , Pausata Francesco Rocco Salvatore
Published: March 2022

The Great GreenWall (GGW) is a multibillion-dollar African initiative to combat desertification and drought in the Sahel.  In Western Africa, the most important climate feature is the West African Monsoon (WAM), which brings rainfall over the Sahel during the Northern Hemisphere summer. Changes in WAM strength and length could impact climate both regionally and far afield, such as the tropical Atlantic, equatorial Pacific or the Arctic, Potential climatic system response to a greener Sahel are investigated, by means of an atmospheric climate model, looking at changes in the regional atmospheric circulation and climate indices aimed at providing information about changes in extreme events. The analysis shows significant changes in temperature, precipitation and atmopsheric circulation and in the climate indices considered related to the presence of the GGW for the end of this century. 
Bias Correction of Operational Storm Surge Forecasts Using Neural Networks 
Authors: Tedesco Paulina , Rabault Jean , Sætra Martin Lilleeng , Kristensen Nils Melsom , Aarnes Ole Johan , Breivik Øyvind , Mauritzen Cecilie
Published: March 2022

Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute (MET Norway) produces 120-hour regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS). Despite advances in the development of models and computational capability, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest storm events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources spent on mitigation.

Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict, and correct, the error in the ROMS output. For this purpose, sea surface height data from stations around Norway were collected and compared with the ROMS output.

We develop two different residual learning frameworks that can be applied on top of the ROMS output. In the first one, we perform binning of the model error, conditionalized by pressure, wind, and waves. Clear error patterns are visible when the error conditioned by the wind is plotted in a polar plot for each station. These error maps can be stored as correction lookup tables to be applied on the ROMS output. However, since wind, pressure, and waves are correlated, we cannot simultaneously correct the error associated with each variable using this method. To overcome this limitation, we develop a second method, which resorts to Neural Networks (NNs) to perform nonlinear modeling of the error pattern obtained at each station. 

The residual NN method strongly outperforms the error map method, and is a promising direction for correcting storm surge models operationally. Indeed, i) this method is applied on top of the existing model and requires no changes to it, ii) all predictors used for NN inference are available operationally, iii) prediction by the NN is very fast, typically a few seconds per station, and iv) the NN correction can be provided to a human expert who gets to inspect it, compare it with the ROMS output, and see how much correction is brought by the NN. Using this NN residual error correction method, the RMS error in the Oslofjord is reduced by typically 7% for lead times of 24 hours, 17% for 48 hours, and 35% for 96 hours.
Sargassum observations from MODIS: using aggregations context to filter false detections
Authors: Podlejski Witold , Descloitres Jacques , Chevalier Cristèle , Minghelli Audrey , Lett Christophe , Berline Léo
Published: March 2022

Since 2011, the distribution extent of pelagic Sargassum algae has substantially increased and now covers the whole Tropical North Atlantic Ocean, with significant inter-annual variability. The ocean colour imagery has been used as the only alternative to monitor such a vast area. However, the detection is hampered by cloud masking, sunglint, coastal contamination and others phenomena. All together, they lead to false detections that cannot be discriminated with classic radiometric analysis, but may be overcome by considering the shape and the context of the detections. Here, we built a machine learning model based on spatial features to filter false detections. More specifically, Moderate-Resolution Imaging Spectroradiometer (MODIS, 1 km) data from Aqua and Terra satellites were used to generate daily map of Alternative Floating Algae Index (AFAI). Based on this radiometric index, Sargassum presence in the Tropical Atlantic North Ocean was inferred. For every Sargassum detections, five spatial indices were extracted for describing their shape and surrounding context and then used by a random forest binary classifier. Contextual features were most important in the classifier. Trained with a multi-annual (2016-2020) learning set, the classifier performs the filtering of daily false detections with an accuracy of 90%. This leads to a reduction of detected Sargassum pixels of 50% over the domain. The method provides reliable data while preserving high spatial and temporal resolutions (1 km, daily). The resulting distribution on 2016-2020 is consistent with the literature for seasonal and inter-annual fluctuations, with maximum coverage in 2018 and minimum in 2016. In particular, it retrieves the two areas of consolidation in the western and eastern part of the Tropical Atlantic Ocean associated with distinct temporal dynamics. At full resolution, the dataset allowed us to semi-automatically extract Sargassum aggregations trajectories from successive filtered images. Using those trajectories will help to better quantify the drift of aggregations with respect to the currents, the wind and sea state. Overall, this new dataset will be useful for understanding the drivers of Sargassum dynamics at fine and large scale and validate future models.
Automated mapping of Eastern Himalayan glacial lakes using deep learning and multisource remote sensing data
Authors: Kaushik Saurabh , Singh Tejpal , Joshi Pawan Kumar , Dietz Andreas J
Published: March 2022

The Himalayan glacierized region has experienced a substantial rise in number and area of glacial lakes in the past two decades. These glacial lakes directly influence glacier melt, velocity, geometry, and thus overall response of the glacier to climate change. The sudden release of water from these glacial lakes poses a severe threat to downstream communities and infrastructure. Thereby, regular monitoring and modelling of these lakes bear significance in order to understand regional climate change, and mitigating the anticipated impact of glacial lake outburst flood. Here, we proposed an automated scheme for Himalayan glacial lake extent mapping using multisource remote sensing data and a state-of-the-art deep learning technique. A combination of multisource remote sensing data [Synthetic Aperture Radar (SAR) coherence, thermal, visible, near-infrared, shortwave infrared, Advanced Land Observing Satellite (ALOS) DEM, surface slope and Normalised Difference Water Index (NDWI)] is used as input to a fully connected feed-forward Convolutional Neural Network (CNN). The CNN is trained on 660 images (300×300×10) collected from 11 sites spread across Himalaya. The CNN architecture is designed for choosing optimum size, number of hidden layers, convolutional layers, filters, and other hypermeters using hit and trial method. The model performance is evaluated over 3 different sites of Eastern Himalaya, representing heterogenous landscapes. The novelty of the presented automated scheme lies in its spatio-temporal transferability over the large geographical region (~8477, 10336 and 6013 km2). The future work involves Intra-annual lake extent mapping across High-Mountain Asian region in an automated fashion.

Keywords: Glacial Lake, convolutional neural network, semantic segmentation, remote sensing, Himalaya, SAR and climate change
Using high-resolution climate models to predict increases in atmospheric turbulence 
Authors: Smith Isabel H. , Williams Paul D. , Schiemann Reinhard
Published: March 2022

Atmospheric turbulence has a serious, dangerous, and costly impact on aviation. Turbulence makes up most weather-related in-flight accidents and costs the global aviation sector up to US$1 billion every year. Upper level turbulence can be broken down into four main types: Clear-Air Turbulence (CAT), Convectively Induced Turbulence (CIT), Near-Cloud Turbulence (NCT), and Mountain Wave Turbulence (MWT). Aviation is often impacted by CAT, which is not visible on radar and is therefore extremely hard to detect in advance of an encounter. Previous literature has shown that climate change is strengthening CAT globally, with increased severity particularly over the North Atlantic, a busy flight route, within the winter months. These findings have been based on CMIP3 and CMIP5 climate models, which have now been superseded by CMIP6 (Coupled Model Intercomparison Project Phase 6) models with higher resolution. 

In this presentation we build and develop these previous findings further by using the CMIP6 HighResMIP PRIMAVERA simulations, which have grid spacings from 135km to 25km. CAT has not previously been investigated with models that come this close to resolving individual patches of turbulence. Comparisons between several resolutions have given us a better understanding of how different climate models, and their grid spacings, represent turbulence. Despite some multidecadal and yearly variability, CAT is found to increase in frequency, in all turbulent severities, in time and with increased near-surface temperatures. Interestingly, atmosphere-only global climate models predict a smaller increase in CAT, in comparison to coupled atmosphere-ocean models. Our findings suggest that an increasing mean near-surface temperature over the North Atlantic will lead to further light to severe turbulence events, which results in extremely bumpy air travel, longer travel times, and increased CO2 emissions into the atmosphere. 
Lacustrine diatom oxygen isotopes as Holocene palaeo precipitation proxy for the Polar Urals, Russia
Authors: Meyer Hanno , Kostrova Svetlana S. , Philip Meister , Marlene M. Lenz , Larisa Nazarova , Gerhard Kuhn , Luidmila S. Syrykh , Yuri Dvornikov
Published: March 2022

The diatom oxygen isotope composition (δ18Odiatom) from lacustrine sediments is a reliable proxy to trace hydrological and climate dynamics in individual lake catchments, and is generally linked to lake temperature and water isotope variations. Diatom oxygen isotopes are excellent recorders of these changes, especially in high-latitudes where carbonates are widely absent.

In this study, Lake Bolshoye Shchuchye (67°53'N; 66°19' E; 186 m a.s.l) is explored being the largest and deepest freshwater reservoir in the Polar Urals, Arctic Russia. The recent isotope geochemistry helps identifying Lake Bolshoye Shchuchye as a well-mixed monomictic lake, covered more than half of the year by ice, and with negligible evaporative effects. Its Holocene δ18Odiatom record generally follows a decrease in summer insolation and the northern hemisphere (NH) temperature history interpreted as Holocene cooling. However, it is overprinted by short-term variations in δ18Odiatom exceeding 5‰, especially in Mid and Late Holocene. This centennial-scale variability is similar to Holocene NH glacier advances. However, presently glaciers in the Lake Bolshoye Shchuchye catchment are not known, nor have left any significant imprint on the lake sedimentary record. As the lake is deep (max water depth 160 m) and voluminous, about 30−50% of its volume need to be exchanged with isotopically different water within decades to account for the short-term shifts in the δ18Odiatom record. A plausible source of water with light isotope composition inflow is snow, known to be transported in surplus by snow redistribution from the windward to the leeward side of the Polar Urals. We propose snow melt and influx changes being the dominant mechanism responsible for the observed short-term changes in the δ18Odiatom record. This is the first time such drastic, centennial-scale hydrological changes in a catchment have been identified in Holocene lacustrine diatom oxygen isotopes, which, for Lake Bolshoye Shchuchye, are interpreted as proxy for summer temperatures and palaeo precipitation.
New insights in the lithospheric configuration of the Ligurian-Provençal Basin derived from gravity field interpretation
Authors: Götze Hans-Jürgen , Bott Judith , Kaus Boris , Scheck-Wenderoth Magdalena , Schuler Christian
Published: March 2022

The area of the western Mediterranean between the French and Italian coasts and Corsica-Sardinia is still of great interest in terms of its structural development, which remains incompletely understood. The resolution of geophysical data was not always high enough to explore detailed structures in the lithosphere. After completion of the new AlpArray gravity maps, a high-resolution gravity field is available. The intended 3D modelling of the lithosphere requires the search for reliable constraints for the density/susceptibility models (seismic, bathymetry, gravity fields, gradients). The calculation of residual gravity fields is difficult due to uncertainties in the calculation of regional fields which are characterized by pronounced gravity highs and lows in a very limited spatial area. The residual fields calculated here provide new insights into the lithospheric structure and suggest that the mass distribution in the Ligurian-Provençal Basin does not monotonously follow the known major geological units. A broad belt of local gravity highs (25 - 40 x 10-5 m/s2) extends off the French coast to the northwest of the basin where it merges with NW-SE directed gravity highs (up to 45 x 10-5 m/s2) near the Italian coast. Hitherto unknown is the residual field anomaly south of Marseille with max. 100 x 10-5 m/s2. Euler deconvolution and correlations with maps of focal depths of earthquakes resulted in source depths that lie in the mantle. The results of further processing techniques (curvature calculations, third derivative of potential, terracing and cluster analysis) were superimposed on geological maps to make visual correlations clear. Results of dynamic modelling of the surrounding subduction zones, as well as newly inferred Moho and LAB depths, are also available for interpreting gravity field components of deeper regions of the Earth's mantle in the study area. Previously performed investigations (magnetic field modelling and recent seismic campaigns, e.g., LOBSTER and AlpArray seismic tomography models) were also added to the research.
Microbial survival through high metabolic rates in a deep and hot subseafloor environment
Authors: Schubert Florian , Beulig Felix , Adhikari Rishi Ram , Glombitza Clemens , Heuer Verena , Hinrichs Kai-Uwe , Homola Kira , Inagaki Fumio , Jørgensen Bo Barker , Kallmeyer Jens , Krause Sebastian , Morono Yuki , Sauvage Justine , Spivack Arthur , Treude Tina
Published: March 2022

A fourth of the global seabed sediment volume is buried at depths where temperatures exceed 80 °C, a previously proposed thermal barrier for life in the subsurface. Here, we demonstrate, utilizing an extensive suite of radiotracer experiments, the prevalence of active methanogenic and sulfate-reducing populations in deeply buried marine sediment from the Nankai Trough subduction zone, heated to extreme temperature (up to ~120 °C). Sediment cores were recovered during International Ocean Discovery Program (IODP) Expedition 370 to Nankai Trough, off the cost of Moroto, Japan. The steep geothermal gradient of ~100 °C km-1 allowed for the exploration of most of the known temperature range for life over just 1 km of drill core. Despite the high temperatures, microbial cells were detected almost throughout the entire sediment column, albeit at extremely low concentration of <500 cells per cm³ in sediment above ~50 °C. In millions of years old sediment a small microbial community subsisted with high potential cell-specific rates of energy metabolism, which approach the rates of active surface sediments and laboratory cultures. Even under the most conservative assumptions, potential biomass turnover times for the recovered sediment ranges from days to years and therefore many orders of magnitude faster than in colder deep sediment.

Our discovery is in stark contrast to the extremely low metabolic rates otherwise observed in the deep subseafloor. As cells appear to invest most of their energy to repair thermal cell damage in the hot sediment, they are forced to balance delicately between subsistence near the upper temperature limit for life and a rich supply of substrates and energy from thermally driven reactions of the sediment organic matter.
An Assimilative Version of TIE-GCM using PDAF
Authors: Corbin Armin , Vielberg Kristin , Kusche Jürgen
Published: March 2022

The TIE-GCM (Thermosphere Ionosphere Electrodynamics General Circulation Model) is a numerical model of the upper atmosphere, providing extensive information about the neutral and charged particles therein. It enables simulations of the neutral density that is required to compute the drag acceleration acting on satellites. We have developed an assimilative version of the TIE-GCM using PDAF (Parallel Data Assimilation Framework), to improve the neutral density modeling and the derived drag acceleration. Here, we present an experiment in which we assimilate neutral densities from a calibrated empirical model into the TIE-GCM: In a first step, we have scaled the NRLMSIS2.0 with densities derived from the CHAMP accelerometer, then evaluated it on a regular grid and finally assimilated the neutral densities into the TIE-GCM. We assess the performance of the assimilation framework using densities derived from the CHAMP and GRACE acceleromerters. The investigated time period includes quiet and stormy conditions.
Thermogravimetric-calorimetric characterisation of organic matter in oxide-rich tropical soils
Authors: Schnee Laura , Kaufhold Stephan , Ngakou Albert , Filser Juliane
Published: March 2022

Tropical soils are increasingly subjected to both site conversion and intensification of agricultural practices, leading to cultivation-induced losses of soil organic matter (SOM) and associated nutrients. Hence, robust techniques for the qualitative characterisation of SOM in heavily weathered tropical soils are required. While thermogravimetric methods are widely used for the characterisation of temperate soils, thermal degradation features of pedogenic oxides typical for many tropical soils can confound the analyses, particularly in thermolabile SOM fractions. We used thermogravimetry coupled to differential scanning calorimetry and mass spectrometry (DSC-TGA-MS) to discern mineral and organic thermal degradation patterns in a kaolinitic soil from Cameroon receiving different mineral and organic amendments. We quantified endothermic mineral degradation features overlapping with OM combustion and thus corrected the exothermic OM degradation signal for pedogenic oxide dehydroxylation. The addition of thermostable biochar interfered with the identification and quantification of clay mineral dehydroxylation features. Between three and four thermal OM fractions of different energy density were identified, among which a distinct cellulosic fraction marked the continuous C4 vegetation on the site. The addition of compost led to a reduction of the thermolabile fraction, while the absence of organic input resulted in a reduction of the thermostable fraction. We conclude (I) that the addition of nutrient-rich fresh OM (compost) may lead to faster OM turnover as indicated by a reduction of the thermolabile OM fraction, and (II) while DSC-TGA-MS is generally suitable for OM characterisation in tropical soils, the presence of pyrogenic C represents a challenge if clay dehydroxylation is to be determined simultaneously.
Agro-hydrological modeling of soil water retention measures to increase crop system resilience to extreme events
Authors: Turek Maria Eliza , Prasuhn Volker , Holzkämper Annelie
Published: March 2022

An increase in the occurrence of drought events potentially aggravate conflicts between agricultural water use and other human and ecological demands for water resources. Increasing the natural soil water retention capacity can help to defuse these conflicts and at the same time strengthen climate mitigation, biodiversity, and food security. Although a variety of measures may be taken to increase soil water retention in agricultural systems, their effects in response to climate extremes are largely unknown. As part of the OPTAIN initiative (OPTimal strategies to retAIN and re-use water and nutrients in small agricultural catchments across different soil-climatic regions in Europe, www.optain.eu), this project aims to evaluate the soil water dynamics affected by these measures and their extent of influence on the cropping system, looking for possibilities to increase the resilience to drought stress under current and future climatic conditions. The steps include (1)  utilizing information from a long-term lysimeter experiment to setup, calibrate and validate a detailed model of soil water dynamics (SWAP) for a typical Swiss cropping system, (2) specifying soil water retention measures through modifications of input parameters based on a literature review (and additional field measurements), and (3) apply the model to conduct a series of simulation experiments with varying combinations of soil water retention measures and future climate scenarios. Study findings will identify soil water retention measures with the largest potential to mitigate drought stress limitations to agricultural productivity, helping to make future arable production systems in Switzerland less dependent on supplement irrigation.
NenuFAR performances for solar radio observations at high spectral and temporal resolutions
Authors: Briand Carine , Carley Eoin , Cecconi Baptiste , Reid Hamish , Zarka Philippe
Published: March 2022

NenuFAR is the tied-array radio instrument recently deployed in France. It is the low-frequency extension of LOFAR. It covers frequencies between 10 and 85 MHz. Its large collecting surface (53000m2 at 25MHz) makes it very sensitive. Spectral and temporal resolution can be very high, respectively, at <5kHz and < 3ms. Such resolution, associated with high sensitivity, is unique at low frequency. Each antenna is composed of two perpendicularly orientated antennas allowing polarization measurements in the four Stokes parameters. Observations were performed between December 16 and 25, 2021, for two hours around the maximum of Sun elevation. Several sunspot groups were present on the solar surface. In terms of flares, the activity was low during the observing time. Still, many Type III bursts were recorded, some with exceptional fine structures as stria or slowly drifting emission, others with a very weak signal. The capabilities of NenuFAR observations with such high resolution and polarimetric modes are presented. At the beginning of the solar cycle 25, the instrument provides unprecedented possibilities to study the solar corona.
A new high-resolution zoom over the North-East Atlantic based on NEMO 4.2 (IMMERSE) version
Authors: Brivoal Théo , Chanut Jérome , Hamon Mathieu
Published: March 2022

In the context of the IMMERSE project, we present here a new high-resolution configuration over the North-East Atlantic based on the NEMO model in its version 4.2. The configuration is based on already existing eNEATL36 (extended North – East ATLantic) configuration, which covers the Iberian – Biscay – Ireland area and the western Mediterranean Sea at a 1/36° resolution. It incorporates a kilometric resolution (1/108°) AGRIF zoom that covers the Atlantic and Mediterranean French and Spanish coasts and includes the Gibraltar Strait, Corsica and Sardinia. Two-way exchanges are enabled between the nest and the parent configuration. We will present a description of the configuration, alongside some initial results. First, we compare the performance of the NEMO 4.2 version with the pre-IMMERSE version (NEMO 4.0) on our configuration. The configuration is then validated over the 1.5 year target period from January 2017 to June 2018 against satellite data and in-situ observations. Finally, the impact of the high-resolution nest is evaluated by comparing the simulation with a twin experiment over the same period but without nest.
Shallow Lakes Water Level Estimation using Satellite Optical Imagery and Digital Elevation Models Over the Persian Plateau, Iran 
Authors: Ahrari Amirhossein , Patro Epari Ritesh , Akbari Mahdi , Klöve Björn , Torabi Haghaighi Ali
Published: March 2022

Lakes' water levels have a dynamic behavior, and their variations are an essential subject for water resources research and management. These variations have a wide range of time scales, from short-term (daily) to long-term (yearly) scales. However, access to hydrological data is limited due to scarce observation stations, fragmented data holdings, and low data quality in developing countries. Satellite altimeters are considered the main source of water level estimation among remote sensing data. Although many seas and oceans are covered by altimetry satellites, currently, they have a huge gap in covering inland lakes. Accordingly, we proposed an alternative approach to estimate shallow lakes' water levels using typical optical imageries and digital elevation models. The water level is estimated based on the Area Elevation Model (AEM) approach, using MODIS surface reflectance product, ALOS DSM and Landsat JRC product as inputs to the model. The AEM helps extract the water level time series based on the information about water area obtained from satellite products using various spectral indices (NDWI_GNIR, NDVI, NDWI_RSWIR and MNDWI). The methodology was applied to eight shallow lakes in Iran using Google Earth Engine (GEE) platform. These lakes are located across the arid and semi-arid regions of the Persian Plateau, Iran. The lakes' water level in these regions is declining, and there is a great need for taking important measures by regional authorities for sustainable water management. Spectral indices and the effect of satellite resolutions were evaluated. Overall, this methodology can be the alternative approach for water level estimation for lakes with minimum or no ground observation and altimetry coverage.
Is charcoal reflectance a palaeofire intensity proxy?
Authors: Bonk Alicja , Belcher Claire M. , Tylmann Wojciech
Published: March 2022

Variations in wildfire are a hot topic due to concerns linked to the current climate crisis. Although an increasing number of studies focus on how the coupled climate and anthropogenic forcings influence fire frequency and behaviour in highly fire-prone regions, there has been less research in more temperate ecosystems.

Analyzing charcoal records yields insights into the fire regimes in a given area, including the fire frequency, type, and intensity. Recent research suggests that measuring the amount of light reflected from charcoals can provide information about the energy flux during the pyrolysis of plant material, which can be further translated into a proxy for fire intensity. To assess the use of reflectance as a post-fire tool for palaeofire intensity reconstructions, we used annually laminated (varved) sediment record from Lake Żabińskie, north-eastern Poland.

We provide both quantitative macrocharcoal, and charcoal reflectance measurements of individual charcoal particles as well as semi-quantitative morphotypes analysis. These data were compared with palynological analysis of the vegetation shifts and evidence for the impact of humans in the lake area. Our preliminary research indicates that lower reflectance measurements (mean value around 1%) most likely relate to the litter fires as the charcoals show features of biodegradation before the fire. Higher values (mean 1-3%) represent surface understory fires (undergrowth burning) while the highest reflectance values (mean >3%) can be related to the crown fires. Our measurements are the first of this kind in this part of the world.
The Plio-Quaternary activity of the Yusuf Fault System (Alboran Sea; Westernmost Mediterranean): From 3D deep structure to seafloor geomorphology
Authors: Perea Hector , Martínez-Loriente Sara , Llopart Jaume , Canari Ariadna , Gómez de la Peña Laura , Bartolomé Rafael , Gràcia Eulàlia
Published: March 2022

The identification and seismic characterization of the active structures in the Alboran Sea (westernmost Mediterranean) are essential to evaluate better the exposure of the South Iberian Peninsula and Maghreb coasts to different natural hazards. The Alboran Sea accommodates part of the present-day crustal deformation related to the NW-SE convergence (4-5 mm/yr) between the African and Eurasian plates. The area is characterized by low to moderate magnitude instrumental seismicity. However, large earthquakes (I > IX and M > 6.0) have occurred in this region in historical and recent times (i.e., 1522 Almeria, 1790 Oran, 1910 Adra, 1994 and 2004 Al-Hoceima or 2016 Al-Idrissi earthquakes). The dextral strike-slip Yusuf Fault System (YFS) is one of the largest active faults in the Alboran Sea and its seismogenic and tsunamigenic hazard needs to be characterized. The fault system trends WNW-ESE and has a length of ~150 km. Using multi-scale bathymetric (ranging from m to cm) and seismic data and different morphological and seismic analysis tools (i.e., slope or relief image maps), we have imaged and characterized the fault system. The analysis of this dataset reveals that the YFS is a complex structure composed of an array of strike-slip faults. The 3D structural model shows that most of the identified faults reach up and offset the seafloor and the Upper Quaternary sedimentary units. The current morphology of the seafloor is a consequence of the Plio-Quaternary tectonic evolution that have resulted in the formation of a large pull-apart basin, which is deeper than the surrounding areas, a topographic ridge, an elongated depression and morphologic lineaments following its trend. The dataset also images several submarine landslides scars, mainly on the steeper slopes surrounding the pull-apart basin. In addition, the analysis of ultra-high resolution data acquired along the Yusuf lineament with AUV has revealed the presence of a series of en-echelon scarps with heights ranging from few centimeters to less than 10 meter. Seismic profiles across these scarps show that they are related to different fault strands of the YFS that are offsetting the seafloor, possibly because of an earthquake occurred in historical times.
Accounting for Uncertainties in MSIS 2.0
Authors: Mehta Piyush , Licata Richard , Weimer Daniel , Drob Douglas , Tobiska W Kent , Yoshi Jean
Published: March 2022

Modeling of the upper atmosphere, specifically the thermosphere mass density, remains the primary source of uncertainty in satellite drag and orbital operations in low Earth orbit (LEO). The variations in mass density are dominated by changes in solar irradiance on the timescales of the solar cycle, however, short-term space weather changes can significantly impact the state of the thermosphere, especially during geomagnetic storms. Because of our limited understanding of such variations and the resulting inaccurate modeling, quantifying the uncertainty in density specification and forecasting becomes critical for space operations including decision making for collision avoidance and safeguarding of our space assets.

The Naval Research Laboratory’s MSIS model is one of the most widely used models in operations, especially in the commercial industry. Several different versions of the models have been developed, the most recent being MSIS 2.0. A new methodology for calibration of the MSIS model with exospheric temperatures inverted using accelerometer-derived density estimates has recently been developed. In this work, we apply a similar but updated methodology to the MSIS 2.0 model and use machine learning, specifically a neural network, to develop a version of the MSIS 2.0 model calibrated to the accelerometer-derived density estimates  that also provides reliable uncertainty estimates.
Assessing ExxonMobil’s global warming projections
Authors: Supran Geoffrey , Oreskes Naomi , Rahmstorf Stefan
Published: March 2022

The performance of mainstream climate models has received extensive scrutiny. By contrast, climate projections by the fossil fuel industry have never been assessed. Based on recent archival discoveries, we quantitatively evaluate all available global warming projections documented by – and in many cases modeled by – Exxon and ExxonMobil Corp scientists between 1977 and 2003. We find that most of their projections accurately forecast warming consistent with subsequent observations. Their projections were also consistent with, and at least as skillful as, those of independent academic and government models. Our results show that ExxonMobil predicted global warming correctly and that by the 1980s, they already knew how much global warming the company’s products were likely to cause.
Changes in pasture productivity may affect potential soil erosion under climate change. The case study of Mera watershed.
Authors: Bocchiola Daniele , Casale Francesca , Stucchi Leonardo
Published: March 2022

The Mera River watershed in the Rhaetian Alps, between Italy and Switzerland, is subject to distributed erosion, and soil degradation, affecting slope stability, and sediment transport in the river. In the future under climate change, erosion is projected to increase especially in winter, as due to larger rainfall share, and smaller snow accumulation. It is therefore necessary to develop best practices for the maintenance of slopes, such as terracing, to reduce erosion and soil loss in the area. We present the results of the recent GE.RI.KO Mera Interreg, and IPCC MOUPA projects.

We first calibrate a hydrological model Poli-Hydro in the study area during 2010-2019, against discharge data, and snow cover area from satellite. Then a Dynamic-RUSLE (D-RUSLE) model is used to simulate spatially distributed soil erosion. The model considers snow melt/accumulation, and the year round dynamics of vegetation. Potential soil erosion is validated against sediment transport data taken in a sample station in the Mera River.

The dynamics of snow cover is simulated using Poli-Hydro, while the C-factor of land cover is corrected using NDVI (Normalized Difference Vegetation Index) from satellite images, accounting for variable vegetation stages, and larger leaf cover (LAI) in summer. The C-factor is further corrected for pasture areas, using productivity data as calculated using the Poli-Pasture model, mimicking pasture growth and biomass productivity. We considered two index species for high/low altitudes, and inter-specific competition.

We then project future scenarios of climate change, and impacts thereby. Six GCMs and four SSPs of the IPCC AR6 are used, to develop 24 climate change scenarios for precipitation and temperature. We also consider changes in CO2 concentration, and temperature increase, upon land cover, through variation of timberline and growing season. Based upon our results, conservative practices may be devised, to help improvement of pasture productivity, and reduce soil erosion.
Atmospheric variability in the Northern Hemisphere winter in a warm past and a future climate
Authors: Oldeman Arthur , Baatsen Michiel , von der Heydt Anna , van Delden Aarnout , Dijkstra Henk
Published: March 2022

The Northern Annular Mode (NAM) is the leading mode of atmospheric climate variability in the middle and high Northern latitudes in the present-day climate. Its most prominent regional expression is the North Atlantic Oscillation (NAO), a mode of variability that is well-known and has a strong influence on North Atlantic weather patterns. According to the IPCC AR6 WGI report, the current generation of climate models are ‘skillful’ in simulating the spatial features and variance of the historical and present-day NAM/NAO. However, what kind of NAM or NAO patterns can we expect in a warm future climate?

To answer this question, we have performed equilibrium climate simulations of a warm ‘future’ as well as a warm past climate. Specifically, we have simulated the mid-Pliocene climate, a warm (~400 ppm CO2) geological period approximately 3Ma ago, using a global coupled climate model (CESM1.0.5). Our simulations compare well to higher latitude sea-surface temperature reconstructions. We have performed sensitivity studies using a pre-industrial and a mid-Pliocene geography, as well as two levels of radiative forcing, as a part of intercomparison project PlioMIP2. But the question remains, to what extent can we treat the mid-Pliocene as an ‘analog’ for a future warm climate?

Looking at Northern hemisphere winter (DJF) sea-level pressure data, we find that the annular ‘belts of action’ move poleward partially due to increase in CO2, but mainly due to the mid-Pliocene boundary conditions. Over the North Pacific Ocean, sea-level pressure variability slightly increases with CO2, but greatly reduces due to the mid-Pliocene geography. The NAM seems to behave more ‘annular’ and less ‘sectoral’ or regional due to the mid-Pliocene climate boundary conditions. We will focus on the mechanisms that explain the differences between the past and future simulations.
A Database of Aircraft Carbon Monoxide (CO) Measurements with High Temporal and Spatial Resolution during 2011 – 2021
Authors: Catoire Valéry , Xue Chaoyang , Krysztofiak Gisèle , Brocchi Vanessa , Chevrier Stéphane , Chartier Michel , Jacquet Patrick , Robert Claude
Published: March 2022

To understand tropospheric air pollution at a regional/global scale, the SPIRIT airborne instrument (SPectromètre Infra-Rouge In situ Toute altitude) was developed in 2011 and used on aircraft to measure CO, an important indicator of air pollution, during the last decade. SPIRIT could provide high-quality CO measurements with 1σ precision of 0.3 ppbv at a time resolution of 1.6 s. It can be operated on different aircraft from DLR (Germany) and SAFIRE (CNRS-CNES-Météo France) such as Falcon-20 and ATR-42. With support from various projects, more than 200 flight hours measurements were conducted over three continents (Europe, Asia, Africa), including two inter-continental transect measurements (Europe-Asia and Europe-Africa). Levels of CO and its horizontal and vertical distribution are briefly discussed and compared between different regions/continents. A 3D trajectory mapped by CO level was plotted for each flight and presented in this study. The database containing all the raw data will be archived on the AERIS database (www.aeris-data.fr), the French national center for Earth observation dedicated to the atmosphere. The database can help to understand the horizontal and vertical distribution of CO over different regions and continents. Besides, it can help to validate model performance and satellite measurements. For instance, the database covers measurements at high-latitude regions (i.e., Kiruna, Sweden, 68˚N) where satellite measurements are still a challenge, and at low-latitude regions (West Africa and South-East Asia) where in situ data are scarce and satellites need more validation by airborne measurements.
Biophysical Measurements and Soil Loss Rate Assessment in Field Studies with Improved Cropping Techniques
Authors: Tsanis Ioannis , Sarchani Sofia , Panagea Ioanna , Koutroulis Aristeidis
Published: March 2022

Conventional cropping practices in soils that are under imminent threat of desertification, as those on Crete, often lead to soil erosion. An experiment under the framework of the SoilCare H2020 EU project was set up in three field sites in Western Crete, Greece, to evaluate the impacts of diverse cultivation techniques on soil loss. The targeted crops were olive orchards, vineyards and fruit orchards, in which a control versus treatment (soil-improving cropping system, SICS) experimental design was applied. Different tillage practices were compared in olive orchards (normally tilled to no-tilled), vetch cover crop to no vetch application was tested in a vineyard, whereas the conversion of an orange grove to an avocado farm was implemented. Soil loss (erosion/deposition) rate and soil properties as well were monitored between 2018 and 2021 in the field studies, comparing the results from control areas and SICS areas. The soil loss rate monitoring occurred with measurements through cross sections on the olive orchards and vineyards, or soil pins on the fruit orchards. The biophysical measurements concerned soil texture, saturated hydraulic conductivity, water stable aggregates, bulk density, mineral nitrogen, available phosphorous, exchangeable potassium, sodium and magnesium, soil organic carbon, soil pH, soil electrical conductivity, and earthworm count. The results [1] indicate that for the olive orchards, no-tillage practice reduced average erosion/deposition by 14%. The application of vetch treatment reduced mean soil loss by 13% and for the fruit orchards, the rotation of orange trees to avocado trees reduced mean soil erosion/deposition by over 34%. The biological health and condition of the SICS plots of the olive orchards and vineyards were improved compared to the control ones. Water and solute movement as well as soil aeration were appropriate for no-tillage and avocado trees treatments, and slightly improved in the case of vetch cover application. The experimental results demonstrate the critical footprint of improved cropping techniques to soil loss mitigation and sustainable land management.

[1] Tsanis, I.K., Seiradakis, K.D., Sarchani, S., Panagea, I.S., Alexakis, D.D., Koutroulis, A.G.: The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment, Land 2021, 10(9): 964. https://doi.org/10.3390/land10090964 