bluuebunny/crossref_metadata_2025 huggingface dataset:
Sequential sampling of Volatile Organic Compounds (VOCs) and atmospheric oxidation products in the Sør Rondane Mountains, East-Antarctica.
Authors: Van Overmeiren Preben , Delcloo Andy , De Causmaecker Karen , Mangold Alexander , Demeestere Kristof , Van Langenhove Herman , Walgraeve Christophe
Published: March 2022

Antarctica is considered the most pristine environment on Earth. However, a detailed understanding of present-day atmospheric transport pathways of particles and volatile organic compounds (VOC) from source to deposition in Antarctica and the atmospheric reactions they undergo is essential to document biogeochemical cycles. Atmospheric composition plays an important role in present and near-future climate change. Airborne particles can serve as cloud condensation and ice nuclei and have therefore a strong influence on cloud formation and thus also on precipitation. This is of interest in Antarctica, since precipitation is the only source of mass gain to the Antarctic ice sheet which is expected to become the dominant contributor to global sea level rise in the 21st century. VOCs and their atmospheric oxidation products, secondary organic aerosols (SOA’s) can play an important role in this cloud formation process. However, current knowledge on VOCs and on the interaction between clouds, precipitation and aerosols in the Antarctic is still limited, both from direct observations and from regional climate models.

VOCs are traditionally sampled using axial thermal desorption sampling tubes containing a sorbent such as Tenax TA in a passive or active (pumped) fashion. While with passive sampling it is possible to sample over longer periods of time, up to a year in clean air conditions, the temporal information is lost. Because of uncertainties on the sample rate, which is driven by diffusion, obtaining precise air concentrations with passive sampling can be difficult. To sample VOC’s and oxidations products unsupervised and in a remote environment such as Antarctica a new active sequential sorbent tube autosampler was developed and deployed at the atmospheric observatory of the Princess Elisabeth Antarctic research station (71.95° S, 23.35° E, 1390 m asl). The autosampler collected samples from December 2019 to October 2020 and from January 2021 to June 2021. The obtained data is also used to complement and interpret atmospheric aerosol in-situ measurements conducted at the same location. Furthermore, to identify potential source regions, backward trajectory and dispersion modelling using FLEXTRA and FLEXPART will be applied. 
Demonstration of water vapor and Isotopes measurement from lidar using a multi-platform, multi-instrumental approach
Authors: Hamperl Jonas , Chazette Patrick , Totems Julien , Dherbecourt Jean-Baptiste , Melkonian Jean-Michel , Nicolas Philippe , Raybaut Myriam , Clémençon Aurélien , Geyskens Nicolas , Geneau Pascal , Flamant Cyrille , Zannoni Daniele , Sodemann Harald , Steen-Larsen Hans Christian , Monod Anne , Durand Amandine , Ravier Sylvain , Schwarzenboeck Alfons
Published: March 2022

The Lidar Emitter and Multispecies greenhouse gases Observation iNstrument (LEMON) objective is the development and test of a new Differential Absorption Lidar (DIAL) sensor concept for greenhouse gases and water vapor for spaceborne, airborne or ground-based measurements. The innovative instrument is based on a versatile transmitter. The concept of the measurement was recently preliminarily tested for water vapor in a co-dedicated field campaign from 13 to 24 September 2021 over the Aubenas airfield (France, 44° 32' N 4° 22' E). This campaign was also an opportunity to test different approaches for the measurement of the vertical water vapor profile using classical meteorological probes embarked on meteorological balloons and on an airplane, a vibrational Raman lidar WALI (Weather Atmospheric LIdar), a cavity ring-down spectrometer (CRDS) and of course a first version of the LEMON lidar named WaVIL (Water Vapor and Isotope Lidar). The field campaign involved an instrumented van with two lidars and three ULAs carrying various payloads: a backscatter Rayleigh-Mie lidar to identify atmospheric structures from the local to regional scales, a CRDS for water vapor isotope measurements and in situ samplers to characterize cloud-related forcing on atmospheric water vapor concentrations. The measurement strategy adopted made it possible to follow the evaporation of water vapor throughout the course of a thunderstorm and to sample an intrusion of dry air from high altitudes. It also provided initial answers as to the potential of the WaViL instrument for measuring the main isotope of water vapor and its secondary isotope HDO. The measurement campaign will be presented, as well as the first associated results.
Prediction of UT1-UTC by machine learning techniques
Authors: Dhar Sujata , Pathak Shashank , Heinkelmann Robert , Belda Santiago , Schuh Harald , Balasubramanian Nagarajan
Published: March 2022

Machine Learning (ML) algorithms are used to learn from data and make data-driven predictions. These algorithms consider pattern recognition and computational learning on the data. Earth Orientation Parameters (EOP) are the monitoring parameters for the Earth’s rotation. UT1-UTC is an EOP that monitors the time required by the Earth to complete a rotation versus atomic time. This parameter is indispensable for many applications like precise satellite orbit determination, interplanetary space navigation, etc. In this study, we will use novel ML algorithms to predict the UT1-UTC (IERS C04) time series and investigate its performance against each other and also, w.r.t. the conventional prediction fitting methods, like Least Squares (LS), Auto-Regressive (AR), Multivariate Autoregressive (MAR) methods, etc. In this work, a diversity of advanced ML algorithms will be tested: Random Forest (RF), Generalized Linear Model (GLM), Gradient Boosted Model (GBM), K-means and prophet algorithms. We would like to optimize the UT1-UTC prediction technique to work well with the short-term prediction up to 10 days. Finally, these ML predictions will be compared against those from the last Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) from October 1, 2005 to February 28, 2008. This detailed study would be useful to understand the performance of ML techniques on the UT1-UTC time series and would lead to further development of better prediction models using ML algorithms. 

Key words: Machine Learning, Earth Orientation Parameters (EOP), UT1-UTC, predictions
Magma chamber imaged beneath an arc volcano
Authors: Chrapkiewicz Kajetan , Paulatto Michele , Morgan Joanna , Heath Benjamin , Hooft Emilie , Nomikou Paraskevi , Papazachos Constantinos , Schmid Florian , Warner Michael , Toomey Douglas
Published: March 2022

Arc volcanoes are underlain by complex systems of molten-rock reservoirs ranging from melt-poor mush zones to melt-rich magma chambers. Petrological and satellite data indicate that eruptible magma chambers form in the topmost few kilometres of the crust. However, no such a chamber has ever been imaged unambiguously, suggesting that large chambers responsible for caldera-forming eruptions are too short-lived to capture. Here we use a high-resolution imaging method based on finite-length seismic waveforms to detect a small, high-melt-fraction magma chamber embedded in a melt reservoir extending from ~2 to at least 4 km b.s.l. beneath Kolumbo – a submarine volcano near Santorini, Greece. The chamber coincides with the termination point of the recent earthquake swarms, and may be a missing link between a deeper melt reservoir and the high-temperature hydrothermal system venting at the crater floor. Though too small to be detected by standard seismic tomography, the chamber is large enough to threaten the nearby islands with tsunamigenic eruptions. Our results suggest that similar reservoirs (relatively small but high melt-fraction) may have gone undetected, and are yet to be discovered, at other active volcanoes.
 Satellite based Assessment of Groundwater Depletion by the Invasive Tree Species- Prosopis juliflora in a Semi-Arid Region of Gujarat, India  
Authors: Tundia Krutika , Rao Anand , Shastri Yogendra
Published: March 2022

Gando Bawal (Mad Tree) as it is called by the people of Kutch, Gujarat is the non-native species originally known as Prosopis juliflora which was introduced in this semi-arid region in the year 1960 for rehabilitation of sodic lands and to prevent the encroachment of Rann desert onto the Banni grassland. Studies by Pasha et al. 2014 have suggested that there was an increase of 42.9% of area under Prosopis cover in Kutch during 1977 to 2011. Due to its invasive nature it has spread over large areas and invaded the pastoral grasslands of Banni region of Kutch, Gujarat. There is an increase in frequency of droughts and the people of Banni are blaming Prosopis juliflora as the culprit. Prosopis juliflora has depleted the ground water sources by accessing it through its long roots. To evaluate this and to assess the rate of groundwater depletion in this region here we used terrestrial water storage-change observations from NASA's Gravity Recovery and Climate Experiment satellites (GRACE) and simulated soil-water variations from a data-integrating hydrological model to show that groundwater is being depleted. The data set was prepared by collecting the measured precipitation, remote sensing evaporation and ground water table from the period of 2002 to 2017. During this period, the other terrestrial water storage components i.e. soil moisture, surface waters and biomass did not contribute significantly to the observed decline in total water levels. The study provided valuable information in understanding the net groundwater depletion rate by the tree species. Although our observational record is brief, the available evidence suggests that the consumption of groundwater by the tree species Prosopis juliflora is the cause why the region is going through shortages of potable water, leading to extensive socio economic stresses.
Optical observations of thermospheric neutral temperature in aurora
Authors: Whiter Daniel , Price David
Published: March 2022

The aurora can have strong electric fields and currents associated with it, which deposit a significant amount of energy in the neutral upper atmosphere through heating. Such heating must be included in global atmospheric models used to study thermospheric dynamics, coupling between the atmospheric layers, climate, and drag on spacecraft and space debris in low Earth orbit. However, the heating rate is poorly quantified, and often spatial structure is not well represented. Heating is typically estimated by measuring the ionospheric electric field using radar, which is then combined with measurements or estimates of the neutral wind velocity and Pedersen conductivity to calculate a Joule heating rate. However, such measurements of the electric field necessarily neglect small scale spatial and temporal variability through their relatively coarse resolution and averaging. The Joule heating rate is proportional to the square of the electric field, and therefore the spatial and temporal averaging can lead to a significant underestimate of the Joule heating rate. As a step towards improving estimates of neutral heating, we have developed a technique to invert spectrographic measurements of aurora to observe the thermospheric neutral temperature altitude profile at high temporal resolution. Application of the technique to an auroral event shows substantial Joule heating adjacent to an arc where the E-field must be strong, as well as heating embedded within an auroral curl, which we associate with an intense field-aligned current.
The evaluation of soil organic carbon through VIS-NIR spectroscopy to support the soil health monitoring
Authors: Ezzy Haitham , Brook Anna , Bonfante Antonello , Ciavatta Claudio , Vignudelli Marco
Published: March 2022

Abstract

Increasing the organic matter content of the soil has been presented in the:”4per1000″ proposal as a significant climate mitigation measure able to support the achievement of Sustainable Development Goal 13 - Climate Action of United Nations.

At the same time, the report of the Mission Board for Soil health and Food, "Caring for soil is caring for life," indicates that one of the targets that must be reached by 2030 is the conservation and increase of soil organic carbon stock.  De facto, the panel clearly indicates the soil organic carbon as one of the indicators that can be used to monitor soil health, and at the same time, if the current soil use is sustainable or not.

Thus it is to be expected that the monitoring of SOC will become requested to check and monitor the sustainability of agricultural practices realized in the agricultural areas. For all the above reasons, the development of a reliable and fast indirect methods to evaluate the SOC is necessary to support different stakeholders (government, municipality, farmer) to monitor SOC at different spatial scales (national, regional, local).

Over the past two decades, data mining approaches in spatial modeling of soil organic carbon using machine learning techniques and artificial neural network (ANN) to investigate the amount of carbon in the soil using remote sensing data has been widely considered. Accordingly, this study aims to design an accurate and robust neural network model to estimate the soil organic carbon using the data-based field-portable spectrometer and laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The measurements will be on two sets of the same soil samples, the first by the standard protocol of requested laboratories for soil scanning, The second set of the soil samples without any cultivation to simulate the soil condition in the sampling field emphasizes the predictive capabilities to achieve fast, cheap and accurate soil status. Carbon soil parameter will determine using, multivariate regression method used for prediction with Least absolute shrinkage and selection operator regression (Lasso) in interval way (high, medium, and low). The results will increase accuracy, precision, and cost-effectiveness over traditional ex-situ methods.

 
Multi-Frequency and multi-Satellite Approaches for enhanced snow, ice and elevation in the polar oceans: updates from Polar+ and Cryosat+ ESA projects
Authors: Tsamados Michel ,   POLAR+ Snow on Sea Ice team
Published: March 2022

Multi-Frequency and multi-Satellite Approaches for enhanced snow, ice and elevation in the polar oceans: updates from Polar+ and Cryosat+ ESA projects

We propose new methods for multi-frequency snow, ice and sea surface retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI) and on the recent ESA projects: Polar+ Snow on Sea Ice and CryoSat+ Antarctic Ocean.  

The primary objective of the Polar+ Snow of Sea Ice ESA project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent "golden era in polar altimetry" with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent "operational" period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2).

The CryoSat+ Antarctic Ocean ESA project aims at exploring alternative methods to derive sea ice thickness and sea surface height measurements over the Antarctic Ocean. The potential of CryoSat-2 to retrieve information on mesoscale features over the area is also explored.  Exciting new results include (i) a detailed inter-comparison of all processing options along-track; (ii) novel optimal interpolation techniques; (iii) dual frequency approaches tested in the SO for snow retrieval; (iv) Lagrangian drift snow products for the SO. This work is supporting the progress of the gridded product development during. Complementing this project, a new ESA project looking at tides in the Southern Ocean (ALBATROSS) started and will offer a clear pathway to impact to the new algorithms developed as part of CSAO. 

We will present exciting methods explored to validate our results against in situ, airborne and other satellite data, including from NASA’s ICESat-2.
Nonlinear solver acceleration based on machine learning applied to multiphase porous media flow
Authors: Silva Vinicius , Salinas Pablo , Jackson Matthew , Pain Cristopher
Published: March 2022

We present a machine learning strategy to accelerate the nonlinear solver convergence for multiphase porous media flow problems. The presented approach dynamically controls an acceleration method based on numerical relaxation. The methodology is implemented and demonstrated in a Picard iterative solver; however, it can also be used with other types of nonlinear solvers. The goal of the machine learning acceleration is to reduce the number of iterations required by the nonlinear solver by adjusting the value of the relaxation factor to the complexity/physics of the system. A set of dimensionless parameters is used to train and control the machine learning. In this way, a simple two-dimensional layered reservoir can be used for training while still exploring a large portion of the dimensionless parameter space. As a result, the training process is simplified, and the machine learning model can be applied to any type of reservoir models.

We demonstrate that the presented technique dramatically reduces the number of nonlinear iterations without sacrificing the quality of the results, even for models that are far more complex than the training case. The average reduction in the number of nonlinear iterations obtained due to the presented method is 24% and the reduction in runtime is 37%. It is worth noting that the optimum value of the relaxation factor is not known a-priori and it is problem specific. Hence, having an acceleration that adapts itself to the complexity/physics of the system throughout the numerical simulation is extremely valuable and has driven several publications in multiple fields.

The method presented here provides an easy way to deal with nonlinear system of equations that does not necessitate as much effort as a custom nonlinear solver while producing outstanding results. We believe that the machine learning acceleration is not limited to the multiphase porous media flow but extendable to any other system that can be studied based on dimensionless numbers, and that a relaxation technique can be used to stabilize the nonlinear solver.
Implications of a realistic crustal rheology and intrusive magmatism on Venusian tectonics: a geodynamic perspective
Authors: Tian Jiacheng , Tackley Paul , Rozel Antoine
Published: March 2022

From the observations on ~1000 recognizable impact craters on Venus’ surface, the average surface age for Venus is comparable to the average surface age for Earth, and is significantly younger than the surface ages of other solar terrestrial planets. To explain Venus’ young surface without plate tectonics, the global tectonics of Venus have often been proposed to be in an episodic-lid regime with catastrophic global overturns. Previous episodic-lid geodynamic models often assume an olivine-diffusion-creep rheology for Venus’ crust, resulting in global overturns followed by stagnant-lid phases with near-zero surface mobilities. However, some tectonic units on Venus’ surfaces show substantial tectonic deformation, such as tesserae and coronae. Recent analyses of satellite images on Venus' surface also suggest possible widespread lithospheric mobilities in the lowland basins. And these observations can hardly be explained by the stagnant-lid phases between overturns in the episodic-lid models.

In this study, we test the influence of (1) a composite, experiment-based crustal rheology (including diffusion creep, dislocation creep, and plasticity), and (2) intrusive magmatism, on Venus’ surface tectonics, using the mantle convection code StagYY in a 2D spherical annulus geometry. Our results show that applying the experiment-based rheology and intrusive magmatism in the model results in (1) both global and regional overturns, (2) high and continuous surface mobilities that indicate substantial surface deformation between global overturns, and (3) a young and thinner crust that is consistent with current estimations.  As for volcanic activities, contrary to olivine-diffusion-creep models, there is no persistent mantle plume in our models when the realistic crustal rheology is applied. The basalt cumulated between the upper and lower mantle affects convective flows in the mantle and mantle upwellings from the core-mantle boundary. Also, there are short-term, randomly located volcanisms within crust between global overturns, which are consistent with recent observations of active magmatism on Venus’ surface and the short-term plumes suggested by coronae formation models. The surface tectonics in our models are dependent on the heat transfer efficiency in the upper mantle. And the tectonic regime is different from both episodic-lid regime and plutonic-squishy-lid regime that are proposed in previous literature, and can provide insights on the tectonic style for Venus and early Earth.
RoCliB - Bias corrected CORDEX RCM dataset over Romania
Authors: Dumitrescu Alexandru , Vlad Amihăesei Vlad , Cheval Sorin
Published: March 2022

Four climate parameters (i.e., maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO-CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971-2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP45 and RCP85).

The best method for bias-correction was selected following a 2-fold cross-validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N-dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between the inter-variables correlation of the adjusted datasets and the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC climate scenario dataset.

Based on the MBCn adjusted dataset, a climate change analysis over Romania was performed at the seasonal and annual scales. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount.

The adjusted RCMs are provided without any restrictions via an open-access repository in netCDF CF-1.4-compliant file format (https://doi.org/10.5281/zenodo.4642463). The BC climate models are archived at the 0.1° spatial resolution, in the WGS-84 coordinate system, at a daily temporal resolution. Based on bias-corrected dataset, relevant information about climate change over Romania’s territory is provided by using an interactive dashboard, implemented in an open-source web application (RoCliB data explorer - http://suscap.meteoromania.ro/roclib).

 

 
Soil moisture: coauthor of the soil mosaic patterns
Authors: Kocum Jiří , Šefrna Luděk , Vlček Lukáš
Published: March 2022

Soil coverage of Czechia has been well-mapped thanks to Complex Soil Survey (CSS) during the 1970s. Despite its considerable details, it is not capturing many of the non-negligible phenomena, and it is not as accurate as it could be as if it were performed today with today technology. This study deals with soil mosaics visible from aerial photographs on agricultural lands, which are not affected in the CSS maps, in terms of determination and classification based on the processes that create these mosaics. For this purpose, fifty localities in Czechia were selected. Attention is paid to the way of development, places of occurrence and the shape of the mosaic itself. One of the main goals is to use only freely available data such as DMR, aerial photographs and soil maps. That is because we want this analysis to be easily reusable by other pedologists in other places. In this study, we propose a classification of different soil mosaics.

The proposed classification consists of three groups, containing seven categories defined by the primary causes of occurrence. Not all sites are formed only by one degradation process. The mosaic is often composed of several degradation processes, and the resulting shape is its sum. The main factors are changes in the soil properties, soil organic matter content, and different ability in water retention (differences in soil moisture). The resulting classification can be used for a) further continuation of soil mapping (where it could serve as an aid in selecting suitable sites for soil probes), b) soil sample gathering (to decide where to gather soil samples to get the most representative soil sample of the area), c) precise determination of polygons with similar soil properties, d) better planning in precision agriculture, e) more realistic estimation of the K factor in the RUSLE equation, to raise the accuracy of estimation of soil erosion.

Furthermore, the relationship between the shape of the mosaic and the processes that create the mosaic was analysed. Because it's evident that similar processes cause similar patterns, we choose a Box-Counting method to calculate its fractal dimension. Results of this analysis didn't show any correlation, and it wasn't possible to determine the type of mosaic only according to its value of the fractal dimension. The machine learning approach would be probably more suitable for this problem. Database of shapes, divided into the categories according to the classification above. Automatical analysis of pictures of the mosaics and their sorting according to their highest similarity with one of the categories. The possibility of using more detailed satellite images for soil reflectance values in the case of soil mosaic classification could also be very beneficial in the future. For capturing the soil mosaic by satellite images, high-quality images are necessary. Such images are not freely available yet, and that's why we didn't use them in this study.
Bayesian updating despite model errors? A sliding time-window approach to rescue 
Authors: Guthke Anneli , Hsueh Han-Fang , Wöhling Thomas , Nowak Wolfgang
Published: March 2022

Bayesian mechanistic modeling often suffers from overconfident and biased posterior distributions for parameters and predictions. This phenomenon arises because the fundamental assumption of Bayesian Model Analysis is violated: the underlying model is assumed to be true, but in fact, it is a simplification of reality with structural errors that show at least during some periods of the modeled time span. As a result, a compromise solution in parameter space is identified that can formally fit the full data set best, but this parameter set will not be representative of the true system state. Neither will it be representative of the “compensation mode” in which the model is whenever structural error kicks in. As a logical consequence, predictions will be biased and their intervals too narrow. The longer the data set used for calibration, the stronger the misleading effect. Typical sources of severe structural deficits that produce dynamically occurring errors are missing or misspecified processes in the model. 

We propose a formal time-windowed Bayesian analysis to overcome this general problem. When performing Bayesian updating on shorter time windows, the assumption of a (quasi-) true model becomes more plausible, and by sliding this window through the calibration time series, we let the model adjust its posterior parameter distributions according to the current strength of error. These time-shifting parameter distributions allow us to (1) identify periods of statistically significant model error occurrence via measuring time-varying Bayesian model evidence, (2) diagnose potential sources of model error by understanding the time-varying parameter compensation mechanisms, and (3) predict with more realistic uncertainty intervals by distribution averaging. 

We demonstrate the proposed method on a set of synthetic and real-world scenarios of soil moisture modeling. With this example, we also highlight its usefulness to analyze dynamic systems in a wide range of disciplines, such as water quality modeling, decision support, and risk assessment. Results show that the time sequence of posterior parameter distributions (and dependent model mechanisms such as water retention curves and unsaturated hydraulic conductivity functions) provides valuable insights into the model’s weaknesses, and it also provides guidance for model improvement.
Climate at various global warming levels: importance of scenario differences 
Authors: Dittus Andrea , Hawkins Ed , Collins Matthew , Sexton David
Published: March 2022

In recent years, there has been increasing interest in how possible future climates at different stabilised, policy relevant global warming levels above pre-industrial might look like. Modelling groups are designing novel climate model simulations to investigate these questions and help answer important questions on the linearity of future climate change across warming levels, tipping points, and climate extremes, among others. A key question is how these projected changes are dependent on scenario choices, particularly the role of future anthropogenic aerosol emissions.

Here, we present the results of new “quasi-stable” climate model simulations with UKESM1.0. Six multi-century simulations have been run under fixed forcings, branching-off from ScenarioMIP simulations of the same model. These simulations explore a range of global warming levels, from approximately 1.5 to 5°C above pre-industrial. In addition, they also explore the role of different balances of forcings for achieving the same target warming level, in particular different combinations of greenhouse gas concentrations and anthropogenic aerosols. In this presentation, we describe how the climate evolves in each of these simulations. We focus on two key aspects:  1) differences between a more stable climate vs. transient climate change at the same warming level and 2) importance of scenario differences, in particular differences in anthropogenic aerosol emissions at the same warming level.

We discuss various aspects of how climate changes in each of the above simulations, including climate extremes, which arguably are one of the most important aspects to consider when assessing the socio-economic impacts of possible future climate conditions at different warming levels and under different scenarios.
Statistical survey of Arase satellite data sets in conjunction with the Finnish Riometer Network
Authors: Thomas Neethal , Kero Antti , Miyoshi Yoshizumi , Shiokawa Kazuo , Hyotyla Miikka , Raita Tero ,   Arase satellite Instrument PIs and members
Published: March 2022

During disturbed geomagnetic conditions, the energetic particles in the inner magnetosphere are known to undergo precipitation loss due to interaction with various plasma waves. In this study, we investigate the energetic particle precipitation events statistically using coordinate observations from ground riometer network and inner-magnetospheric satellite mission, Arase. We have compared cosmic noise absorption (CNA) data obtained from Finnish ground riometer network located in the auroral/sub-auroral latitudes with the comprehensive dataset of omnidirectional electron/proton flux and plasma waves in ELF/VLF frequency range from Arase satellite during the overpass intervals. The study period includes one and a half years of data between March 2017 to September 2018 covering Arase conjunctions with the riometer stations from all magnetic local time sectors. The relation between the plasma flux/waves observed at the satellite with the riometer absorptions are investigated statistically for CNA (absorption>0.5 dB) and non-CNA (absorption<0.5 dB) cases separately. During CNA events, Arase observed elevated electron flux in the medium energy range (2 - 100 keV), and plasma wave activity in the whistler-mode frequency range (0.5-3 kHz) of the spectra. Our study provides an estimate of the statistical dependence of the electron flux and plasma wave observations at Arase with the ground reality of actual precipitation.
Exploring the North Atlantic response to anthropogenic aerosols through idealised single-forcing experiments in a model at two different resolutions
Authors: Lai Michael , Robson Jon , Wilcox Laura , Sutton Rowan , Dunstone Nick
Published: March 2022

Anthropogenic aerosols have been implicated as an important driver of North Atlantic variability. However, the exact mechanism of how aerosol affect the North Atlantic is not well understood and questions remain about the relative importance of aerosols compared to other forcings or internal variability. Therefore, to better understand how aerosols can drive the North Atlantic, we performed idealised experiments using a state-of-the-art coupled climate model (HadGEM3-GC3.1) by applying varying levels of North American and European anthropogenic sulphate aerosol emissions. Medium and (0.25° ocean, ~60km atmosphere) and low-resolution (1° , ~135km) versions of the model were used to assess how model differences may impact on the forced response. We show that the aerosol increases initially cool the North Atlantic SST by a combination of decreased surface shortwave radiation and increased turbulent heat loss. This surface cooling induces surface density anomalies and strengthening of the Atlantic Meridional Overturning Circulation (AMOC), leading to a lagged warming of the Subpolar North Atlantic. However, the AMOC response and subsequent warming is much stronger in the medium-resolution model, despite an overall stronger radiative forcing in the low-resolution model. We show evidence that this AMOC difference is consistent with differences in the sea ice response in a key region of the Subpolar North Atlantic. These results indicate that while surface temperature, sea ice and the AMOC are all sensitive to aerosol forcing in the HadGEM3-GC3.1 models, small regional differences between the model climatologies can significantly alter the pattern and magnitude of the large-scale response.  
Analysis of the impact of urbanization on the ecological state of soils and woody vegetation in the forest parks of New Moscow
Authors: Demina Sofiya , Makhiniya Ksenia , Vasenev Viacheslav
Published: March 2022

Green spaces and recreational soils play a crucial role in ensuring the sustainable development and functioning of cities. They help to reduce the content of volatile organic substances and fine dust in the air, create a microclimate, optimize water balance and preserve biodiversity, and provide cultural, aesthetic, and educational functions and services. The interaction of soil and plant components has a more significant impact on the sustainable development of green infrastructure in urbanized areas. This is especially true for new urbanized territories, where the history of land use has an additional impact on the properties of soil and green spaces.

The study is devoted to studying the recreational areas of New Moscow, formed on the site of a natural forest. The conducted studies make it possible to assess in a complex the interaction of plant and soil components and the impact of land use on the functions and properties of soils and green spaces in 4 parks located in New Moscow. Until July 1, 2012, which included all research objects in Moscow's forest park protective belt, and after 2012 it transferred them to Moscow. All of them have been sampled at 39 points to a depth of 50 cm and 10 points to a depth of 1 m. It carried out a visual assessment for 4117 trees located at each point within a radius of 20 m from the soil sampling. An analysis of species diversity and an analysis of the state of woody vegetation did not reveal significant differences. Thus, the best condition of the trees was found in the Rasskazovka park - 1.61 (on a 6-point scale by V. Alekseev), and the worst in the Ulyanovsk forest park - 1.82. The lowest average density of surface horizons (0–10 cm) was noted for the park of the 3rd microdistrict of the Moscow and Ulyanovsk Forest Park – 0.8 ± 0.1 g/cm3 and 0.8 ± 0.3 g/cm3. The pH value in the Ulyanovsk forest park is 5.1 ± 0.4. Also, there is an excess of approximately permissible concentrations (APC) in the parks for nickel, zinc, arsenic, and cadmium. For example, in the soil in the Troitskaya Grove park, excesses for all four metals are shown for some points. The data obtained indicate that forest parks are subject to anthropogenic impact, which negatively affects soil quality. The analysis of microbiological activity and carbon and the calculation of integral pollution indices will help more accurately determine the differences and factors that have a negative impact on forest ecosystems.
The 2021 Nyiragongo (DR Congo) eruptive crisis monitored by multi-sensor satellite remote sensing data
Authors: Balagizi Charles , Ganci Gaetana , Trasatti Elisa , Tolomei Cristiano , Beccaro Lisa
Published: March 2022

The 2021 Nyiragongo (DR Congo) eruption started on 22 May 2021, nineteen years after the last effusive eruption of 2002. The lava flows erupted from three vents, one East of the summit area and two along the southern slope, and produced two lava flows, the western of which inundated part of the Goma city, causing serious damages to population, buildings and infrastructures and stopped only at ~1 km from the Goma international Airport. Here we process a variety of satellite imagery, including visible, infrared and radar data, mapping the pre-eruptive phase, the evolution of the eruption and the post-eruptive phase. Most of the remote sensing data were acquired in the framework of Virunga Geohazards Supersite, which is part of the GEO-GSNL (Geohazard Supersite and National Laboratories) initiative. In particular we analysed: (i) Sentinel 1 (European Space Agency, ESA) and (ii) COSMOSkymed, CSK (Italian Space Agency, ASI) data providing displacement time-series and eruptive source model; (iii) Visible Infrared Imaging Radiometer Suite, VIIRS ( NASA/NOAA Suomi National Polar-orbiting Partnership) data at 375 m spatial resolution to provide thermal maps; (iv) different Pleiades (AIRBUS) triplets, at 0.5 m spatial resolution, to update the topography of the volcano.

Pre-eruptive 2020-2021 InSAR (Interferometric Synthetic Aperture Radar) analysis from Sentinel-1 and high resolution CSK data show a deflation of the summit area of Nyiragongo and Nyamuragira volcanoes amounting to few cm/yr Line of Sight (LOS). The syn-eruptive InSAR data evidence surface deformation of 70 cm LOS located South of Nyiragongo, in a wide area including the city of Goma and Lake Kivu. Modelling of the InSAR syn-eruptive data show a sub-vertical dike located from South Nyiragongo reaching Lake Kivu. The top depth is 1.5 km from the surface, and the volume variation is slightly less than 0.2 km3. Post-eruptive Sentinel-1 and CSK data showed deflation of the summit area of Nyiragongo, negative LOS surface deformation at Goma and lava cooling.

VIIRS data allowed us to see an increase in the size and temperature of the lava lake a few months before the eruption, and provided a first image of the erupted lava flow on 22 May 2021 at 22:47 GMT. Thanks to Pleiades imagery we could retrieve the lava flow area and by using a pre-eruptive topography we also provided an estimation of the erupted volumes.

Results highlight how the synergic use of multi-source, multi-temporal satellite imagery, along with innovative and automatic processing techniques, may be adopted for real-time hazard estimates in an operational environment especially in remote volcanoes with limited terrestrial networks.

This contribution is supported by the GEO-GSNL initiative and the H2020 Reliance project (grant agreement 101017501).
A Climate Research Intensive Undergraduate Program Pivot During a Pandemic
Authors: Sparrow Elena B. , Spellman Katie , Fochesatto Gilberto J. , Castillo Crystal , Coyne Claire , Cutkomp Jeff , Ornelas Cristina , Ramirez Abigail
Published: March 2022

Increasing temperatures in the Arctic are thawing permafrost, changing vegetation, and increasing wildfires at unprecedented rates. Climate change is also affecting other regions in the United States and in the world. This presentation describes a climate change research immersive program born from a partnership between an international climate research center and a science, technology engineering and mathematics (STEM) program in a Southern California community college. For four years, the Santa Ana College Mathematics, Engineering, Science Achievement (MESA) program and the University of Alaska Fairbanks (UAF) International Arctic Research Center brought groups of science and engineering students North to witness climate change in the Arctic and conduct original research on an aspect of the changes. The COVID 19 pandemic posed a challenge and halted this collaborative work; however in 2021 gave us the impetus to creatively continue in providing a research intensive experience to first generation college students from groups underrepresented in STEM fields, through a blended on-line and in-person Summer Climate Research Intensive Course that examines the ecological impacts of climate change through hands-on field research. Student groups in Santa Ana, California and Fairbanks, Alaska met for instruction, training and collaborative work online through video conference, and completed comparative field work in their two different forest ecosystems with the help of local scientist mentors at location. Students gained science skills and experience in research question framing, ecological fieldwork, laboratory procedures, collaboration, data analysis and communication, as they designed and conducted their research projects alongside professional climate change scientists. All students completed a research project and developed a project poster which they presented at a blended science symposium and plan to present at other professional conferences. The Course was jointly sponsored by the Arctic and Earth STEM Integrating GLOBE and NASA program (UAF International Arctic Research Center), the Santa Ana College Math, Engineering and Science Achievement (MESA) Program, and the UAF Climate Scholars Program (UAF Honors College). Lessons learned and evaluation results will also be shared.
Geodesy: a sensor for hydrology 
Authors: Chanard Kristel
Published: March 2022

Understanding how the Earth’s shape, gravity field and rotation change in response to shifting hydrological, atmospherical and oceanic mass loads at its surface has great potential for monitoring the evolving climate. Recent advances in the field, namely hydrogeodesy, have required hand-in-hand development and improvement of the observing techniques and of our understanding of the solid Earth-climate interactions. 

In particular, measurement of the spatial and temporal variations of the Earth's gravity field by the GRACE and GRACE-Follow On satellite missions offer an unprecedented measurement of the evolution of water mass redistribution, at timescales ranging from months to decades. However, the use of GRACE and GRACE-FO data for hydrological applications presents two major difficulties. First, the mission design and data processing lead to measurement noise and errors that limit GRACE missions to large-scale applications and complicates geophysical interpretation. Moreover, temporal observational gaps, including the 11 month-long gap between missions, prevent the interpretation of long-term mass variations. Secondly, disentangling sources of signals from the solid Earth and continental hydrology is challenging and requires to develop methods benefiting from multiple geodetic techniques. 

To reduce noise and enhance geophysical signals in the data, we develop a method based on a spectral analysis by Multiple Singular Spectrum Analysis (M-SSA) which, using the spatio-temporal correlations of the GRACE-GRACE-FO time series, can fill observational gaps and remove a significant portion of the distinctive noise pattern while maintaining the best possible spatial resolution. This processing reveals hydrological signals that are less well or not resolved by other processing strategies. We discuss regional hydrological mass balance, including lakes, aquifers and ice caps regions, derived from the GRACE-GRACE-FO M-SSA solution. Furthermore, we discuss methods to separate sources of gravity variations using additional in-situ hydrological data or geodetic measurements of the Earth’s deformation. 
Assessment of Land Subsidence Hazard, Vulnerability and Risk: A case study for National Capital Region in India
Authors: Garg Shagun , Motagh Mahdi , Jayaluxmi Indu , Karanam Vamshi , Selvakumaran Sivasakthy , Marinoni Andrea
Published: March 2022

Risk assessment and zoning are very important to risk management as it indicates how severe the hazard can be, and who would be most affected. It plays a crucial role in risk management, especially for densely populated areas. 

Delhi- the capital of India, is the fifth most populous city in the world, with a population density of nearly 30,000 people per square mile. Like other global megacities, Delhi is also facing a looming water crisis due to urbanization and rapid population expansion. The increasing demand for water has translated into the extraction of larger quantities of groundwater in the region. One of the many consequences of groundwater over-extraction is land subsidence. Amongst all other ways to monitor land subsidence, Interferometric Synthetic Aperture Radar (InSAR) is considered to be the most effective and widely used technique.  We used the InSAR technique and analyzed Sentinel-1 data acquired during 2014 - 2020 and identified some localized subsidence zones in the region. In addition to that,  a risk assessment was also performed by considering hazards and vulnerability approach.

In this study, a land subsidence risk assessment index was proposed based on the Disaster Risk Index. The cumulative subsidence volume, the land subsidence velocity, subsidence gradient, and the groundwater exploitation intensity were collected, analyzed, and put together to create a land subsidence hazard evaluation map in the National capital region India. The population density, land cover, and population estimates were adopted as indexes to create the vulnerability map. Finally, the land subsidence risk map was created by combining the hazard and vulnerability maps using the matrix multiplication approach. Specifically, the final risk map was classified into three levels, i.e., high, medium, and low. The analysis highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of land subsidence, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR.