bluuebunny/crossref_metadata_2025 huggingface dataset:
Stratocumulus Clouds at the West Coast of South America: Observations of Diurnal and Seasonal Cycle
Authors: Schween Jan H. , Löhnert Ulrich , Westbrook Sarah
Published: March 2021

Marine stratocumulus clouds of the eastern Pacific play an essential role in the Earth's energy and radiation budget. Parts of these clouds off the west coast of South America form the major source of water for the Atacama, a hyper-arid area at the northern coast of Chile. Within the DFG collaborative research center 'Earth evolution at the dry limit', for the first time, a long-term study of the vertical structure of clouds and their environment governing the moisture supply to the coastal part of the Atacama is available.

Three state of the art ground based remote sensing instruments were installed for one year at the airport of Iquique/Chile (20.5°S, 70.2°W, 56m a.s.l.) in close cooperation with the Centro del Desierto de Atacama (Pontificia Universidad Católica de Chile). The instruments provide vertical profiles of wind, turbulence and temperature, as well as integrated values of water vapor and liquid water. The cloudnet algorithm is used to exploit instrument synergy and provides vertical cloud structure information.

We observe a land-sea circulation with a super-imposed southerly wind component. Highest wind speeds can be found during the afternoon. Clouds show a distinct seasonal pattern with a maximum of cloud occurrence during winter (JJA) and a minimum during summer (DJF). Clouds are higher and vertically less extended in winter than in summer. Liquid water path shows a diurnal cycle with highest values during night and morning hours and lowest values during noon. Furthermore, the clouds contain much more liquid water in summer. The turbulent structure of the boundary layer, together with the temperature profile, can be used to characterize the mechanism driving the cloud life cycle.
Optimizing the localization scale for a convective-scale ensemble radar data assimilation 
Authors: Taylor James , Honda Takumi , Amemiya Arata , Miyoshi Takemasa
Published: March 2021

For any ensemble-based data assimilation system sampling errors are introduced as a consequence of limited ensemble size, generating spurious backgound error covariances and leading to erroneous adjustments to the analysis. As a way to reduce the impact of these sampling errors, as well as improve rank deficiency, covariance localization is applied, which artifically reduces the weighting of error covariances beyond a defined physical distance between the background and observations deemed to be false.

In this study we perform sensitivity tests to find the appropriate horizontal localization scale for the SCALE-LETKF, a numerical weather prediction model that combines the SCALE regional model with the local ensemble transform Kalman filter. The system has been in development since 2013 to provide very high resolution modelling of convective weather systems and is unique in its ability to perform near real-time NWP operation at 500-m resolution refreshed every 30 seconds with observations from Phased Array Weather Radar (PAWR).  Here, we perform sensitivity tests at 500-m resolution with 30-second update cycling of PAWR data for several testcases of heavy convective rainfall over Tokyo metropolitan area from August/September 2019. Test scores showed horizontal localization scale of 2-km generally provided optimal forecast skill for lead times up to 30 minutes, although there were variations on this dependent upon lead time and case study. We show that by reducing localization scale, systematic errors leading to over-intensification of convective activity in forecasts were reduced, resulting in improved consistency with observations. This was a conseqence of generating more convectively stable, less dynamically active environment with smaller localization scale.
Quantification of methane emissions from offshore oil & gas platforms in the Norwegian Sea
Authors: Foulds Amy ,   North Sea Methane Team
Published: March 2021

Atmospheric methane (CH4) is an extremely potent greenhouse gas, with ever-increasing global emissions expected to have a significant influence on the Earth’s climate. The Oil and Gas sector is considered to be a significant source of CH4 to the atmosphere, estimated to make up approximately 22% of global emissions. Offshore facility emissions are poorly ground-truthed, with their quantification being heavily dependent on “bottom-up” scaling of inventory data. It is therefore important to devise reliable methods for locating these emissions and to pinpoint their sources, as this will aid emission quantification and validation against reported data.

As part of the United Nations Climate and Clean Air Coalition (UN CCAC) project, this study aims to characterise CH4 emissions from oil and gas infrastructure in the Norwegian Sea. The campaign comprised surveys of selected operational oil and gas platforms in this region and included targeted observations of CH4.  These surveys were conducted by the Facility of Airborne Atmospheric Measurements (FAAM) and Scientific Aviation Mooney research aircrafts in July and August 2019, with a total 14 flights. Fluxes are derived using a mass balance approach and aircraft sampling. The Lagrangian particle dispersion model “FLEXPART” is used to aid the attribution of the observed CH4 emissions to the platform(s). We will present results for derived fluxes and uncertainties for individual facilities in the Norwegian Sea.  These fluxes will be compared with emissions estimates from platform operators, as well as a global, gridded emission inventory.
Sea level response to Quartenary erosion and deposition in Scandinavia 
Authors: Pallisgaard-Olesen Gustav , Pedersen Vivi Kathrine , Gomez Natalya
Published: March 2021

The landscape in western Scandinavia has undergone dramatic changes through numerous glaciations during the Quaternary. These changes in topography and in the volumes of offshore sediment deposits, have caused significant isostatic adjustments and local sea level changes, owing to erosional unloading and depositional loading of the lithosphere. Mass redistribution from erosion and deposition also has the potential to cause significant pertubations of the geoid, resulting in additional sea-level changes. The combined sea-level response from these processes, is yet to be investigated in detail for Scandinavia.

In this study we estimate the total sea level change from late-Pliocene- Quaternary glacial erosion and deposition in the Scandinavian region, using a gravitationally self-consistent global sea level model that includes the full viscoelastic response of the solid Earth to surface loading and unloading. In addition to the total late Pliocene-Quaternary mass redistribution, we also estimate transient sea level changes related specifically to the two latest glacial cycles.

We utilize existing observations of offshore sediment thicknesses of glacial origin, and combine these with estimates of onshore glacial erosion and estimates of erosion on the inner shelf. Based on these estimates, we can define mass redistribution and construct a preglacial landscape setting.

Our preliminary results show perturbations of the local sea level up to ∼ 200 m since the late-Pliocene in the Norwegian Sea, suggesting that erosion and deposition have influenced the local paleo sea level history in Scandinavia significantly.
Revision and preliminary excavation finds of the Early Islamic Plot-and-Berm groundwater harvesting agroecosystem by ancient Caesarea, Israel
Authors: Taxel Itamar , Roskin Joel
Published: March 2021

“Plot-and-Berm” (P&B) agroecosystems consist of sophisticated agricultural utilization of a high-water table within loose sand sheets, situated in agricultural hinterlands. Sunken agricultural plots between 3-6 m high sand berms coated with anthropogenic refuse/fines protect them from erosion. The plot level enables easy access to the groundwater for crop roots and shallow well construction, while refuse and organic material enrich the inert sand forming distinct grey sand-loam anthrosols. The agroecosystems require significant resources for construction and maintenance. The earliest recognized P&B agroecosystems are Early Islamic to early Crusader (9th-early 12th centuries a.d.) within several coastal sand bodies of Israel that were abandoned by unclear reasons. Similar agroecosystems in Iberia and Algeria and the southeastern Mediterranean (mawasi) coast historically date to the Middle Ages and early modern period.  

Here we reexamine the unpublished data of the only previous excavation (Porath, 1975) and present field and artifactual finds along with pulsed-photon portable luminescence (PPSL) profiling from our 2020 research excavation of the agroecosystem, at the southern outskirts of ancient Caesarea in order to refine understandings on construction materials, structure types and roles, and agroecosystem function, maintenance and timespan.

Berms are found to be constructed from over 3 m thick anthropogenic-mixed sand while their grey sand-loam anthrosol coat is differentially distributed. The ~30 cm thick grey sand-loam anthrosols of the plots still possess distinct contacts with the over- and under-lying sand. Probes in the eastern margins of the agroecosystem reveal sand with Roman sherds. Structural remains include short stand-alone walls, small storage/watching structures, and a unique structure on the western-most berm overlooking the beach with a 3 m deep stone wall and grey sand loam substrate. All of the structures and earthworks possess Early Islamic ceramics along with remains from Roman-Byzantine Caesarea.
ENSO asymmetry: the search for extreme El Niño events in HadGEM
Authors: Ineson Sarah , Dunstone Nick , Scaife Adam , Yamazaki Kuniko
Published: March 2021

Analysis of a long control run of the Hadley Centre coupled model shows that ENSO asymmetry is weak. We use the same model in our seasonal and decadal prediction systems, and while on seasonal timescales the initialised prediction realistically captures the amplitude of extreme El Niño events, on longer timescales the predictions revert to the control behaviour i.e. there are no very large El Niño events. This may impact on our ability evaluate the risk of extreme regional events. Here we show results exploring asymmetry in both the control model, and also from a number of perturbed parameter experiments, each a plausible realisation of the control.
Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset from a Chemical Transport Model
Authors: Dhomse Sandip , Chipperfield Martyn
Published: March 2021

High quality global ozone profile datasets are necessary to monitor changes in stratospheric ozone. Hence, various methodologies have been used to merge and homogenise different satellite datasets in order to create long-term observation-based ozone profile datasets with minimal data gaps. However, individual satellite instruments use different measurement methods and retrieval algorithms that complicate the merging of these different datasets. Furthermore, although atmospheric chemical models are able to simulate chemically consistent long-term datasets, they are prone to the deficiencies associated with the computationally expensive processes that are generally represented by simplified parameterisations or uncertain parameters.

Here, we use chemically consistent output from a 3-D Chemical Transport Model (CTM, TOMCAT) and an ensemble of three machine learning (ML) algorithms (Adaboost, GradBoost, Random Forest), to create a 42-year (1979-2020) stratospheric ozone profile dataset. The ML algorithms are primarily trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset by selecting the UARS-MLS (1992-1998) and AURA-MLS (2005-2019) time periods. This ML-corrected version of monthly mean zonal mean TOMCAT (hereafter ML-TOMCAT) ozone profile data is available at both pressure (1000 hPa - 1 hPa) and geometric height (surface to 50 km) levels at about 2.5 degree horizontal resolution.

We will present a detailed evaluation of ML-TOMCAT profiles against range of merged satellite datasets (e.g. GOZCARDS, SAGE-CCI-OMPS, and BVertOzone) as well high quality solar occultation observations (e.g. SAGE-II v7.0 (1984-2005), HALOE v19 (1991-2005) and ACE v4.1 (2004-2020). Overall, ML-TOMCAT shows good agreement with the evaluation datasets but with poorer agreement at low latitudes. We also show that, as in different merged satellite data sets, ML-algorithms show larger spread in the tropical middle stratosphere. Finally, we will present a trend analysis from TOMCAT and ML-TOMCAT profiles for the post-1998 ozone recovery phase.
Hamiltonian Monte Carlo inversion of seismic reflection data in the acoustic approximation
Authors: Zunino Andrea , Mosegaard Klaus , Boehm Christian , Gebraad Lars , Fichtner Andreas
Published: March 2021

The Hamiltonian Monte Carlo method (HMC) is gaining popularity in the geophysical community to fully address nonlinear inverse problems and related uncertainty quantification. We present here an application of HMC to invert seismic data in the acoustic approximation in the context of reflection seismology. We address a 2-D problem, in the form of a vertical cross section where both source and receivers are located near the surface of the model. To solve the forward problem we utilise the finite-difference method with PML absorbing boundary conditions. The observed data are represented by a set of shotgathers.

The crucial aspect for a successful application of the HMC lies in the capability of performing gradient computations in an efficient manner. To this end, we use the adjont state method to compute the gradient of the misfit functional, which has a computational cost of only about twice that of the forward computation, a very efficient strategy. From the collection of samples characterising the posterior distribution obtained with the HMC, we can derive quantities of interest using statistical analysis and assess uncertainties.

We illustrate an application of this methodology on a synthetic test mimicking the setup encountered in exploration problems.
Solar wind propagation delay predictions between L1 and Earth based on machine learning
Authors: Baumann Carsten , McCloskey Aoife E.
Published: March 2021

GNSS positioning errors, spacecraft operations failures and power outages potentially originate from space weather in general and the solar wind interaction with the geomagnetic field in particular. Depending on the solar wind speed, information from L1 solar wind monitor spacecraft only give a lead time to take safety measures between 20 and 90 minutes.  This very short lead time requires end users to have the most reliable warnings when potential impacts will actually occur. In this study we present a machine learning algorithm that is suitable to predict the solar wind propagation delay between Lagrangian point L1 and the Earth.  This work introduces the proposed algorithm and investigates its operational applicability to a realtime scenario.

The propagation delay is measured from interplanetary shocks passing the Advanced Composition Explorer (ACE) first and their sudden commencements within the magnetosphere later, as recorded by ground-based magnetometers. Overall 380 interplanetary shocks with data ranging from 1998 to 2018 builds up the database that is used to train the machine learning model. We investigate two different feature sets. The training of one machine learning model DSCOVR real time solar wind (RTSW) like data which contains all three components of solar wind speed is used. For the other machine learning model ACE RTSW like data which only provide bulk solar wind speed will be used for training. Both feature sets also contain the position of the spacecrafts. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the different features have to be fed into the algorithms only and the evaluation of the propagation delay can be continuous.

Both machine learning models will be validated against a simple convective solar wind propagation delay model as it is also used in operational space weather centers. For this purpose time periods will be investigated where L1 spacecraft and Earth satellites just outside the magnetosphere probe the same features of the interplanetary magnetic field. This method allows a detailed validation of the solar wind propagation delay apart from the technique that relies on interplanetary shocks.
An application of automatic event detection based on neural network at St Gallen (Switzerland) deep geothermal field
Authors: Forlenza Paola , Scarpetta Silvia , Amoroso Ortensia , Capuano Paolo , Scarpa Roberto
Published: March 2021

In seismology, when dealing with low signal-to-noise recordings, traditional event detection methods are often unable to recognise all the weak events hidden within the seismic noise. We are interested in investigating how machine learning techniques can be a useful tool to improve automatic event detection by recognising the similarity between events. We are  interested in studying areas where anthropogenic activity, related to the exploitation of subsoil resources, can generate induced seismicity. Therefore, it is essential to increase the detection of weak events to improve knowledge about the seismicity of the area and its related consequences.
The SOM (Self-Organizing Map) is an unsupervised machine learning approach that is widely used for clustering, visualization and data-exploration tasks in various applications. The SOM carries out a nonlinear mapping of data onto a two-dimensional map, preserving the most important topological and metric relationships of the data. One of the reasons for using SOM for clustering indeed is to benefit from its topological structure when interpreting the data clusters. 
In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components.
The SOM was trained on dataset, recorded at the St Gallen geothermal site, composed of 388 records of seismic noise and 347 earthquakes with magnitude (MLcorr) between -1.2 and 3.5 collected by the Swiss Seismological Service in 2013 while realizing well control measures after drilling and acidizing the GT-1 well.
We obtained promising first results as SOM strategy correctly discriminates all known earthquakes events, clustering them into different nodes, distant from the group of nodes where noise falls. We also jointly tested synthetic traces in which we have hidden events traces within seismic noise or noise artificially generated. We studied the signals of each cluster individually, assessing the similarities of the waveform and spectral characteristics for the three components. In addition, the results are also evaluated in terms of events location, hypocentral distance, magnitude, and origin time.
This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by the Italian Ministry of Education and Research.
Easing and promoting the application of ML and AI in earth system sciences - introducing the KI:STE platform
Authors: Seidler Thomas , Schultz Norbert , Quade Dr. Markus , Autermann Christian , Gräler Dr. Benedikt , Abel PD Dr. Markus
Published: March 2021

Earth system modeling is virtually impossible without dedicated data analysis. Typically, data are big and due to the complexity of the system, adequate tools for the analysis lie in the domain of machine learning or artificial intelligence. However, earth system specialists have other expertise than developing and deploying state-of-the art programming code which is needed to efficiently use modern software frameworks and computing resources. In addition, Cloud and HPC infrastructure are frequently needed to run analyses with data beyond Tera- or even Petascale volume, and corresponding requirements on available RAM, GPU and CPU sizes. 

Inside the KI:STE project (www.kiste-project.de), we extend the concepts of an existing project, the Mantik-platform (www.mantik.ai), such that handling of data and algorithms is facilitated for earth system analyses while abstracting technical challenges such as scheduling and monitoring of training jobs and platform specific configurations away from the user.

The principles for design are collaboration and reproducibility of algorithms from the first data load to the deployment of a model to a cluster infrastructure. In addition to the executive part where code is developed and deployed, the KI:STE project develops a learning platform where dedicated topics in relation to earth system science are systematically and pedagogically presented.

In this presentation, we show the architecture and interfaces of the KI:STE platform together with a simple example.
Observationally constrained multi-model atmospheric response to future Arctic sea ice loss 
Authors: Smith Doug ,   PAMIP
Published: March 2021

The possibility that Arctic sea ice loss could weaken mid-latitude westerlies and promote more severe cold winters has sparked more than a decade of scientific debate, with support from observations but inconclusive modelling evidence. Here we analyse a large multi-model ensemble of coordinated experiments from the Polar Amplification Model Intercomparison Project and find that the modelled response is proportional to the simulated eddy momentum feedback, and that this is underestimated in all models. Hence, we derive an observationally constrained model response showing a modest weakening of mid-latitude tropospheric and stratospheric winds, an equatorward shift of the Atlantic and Pacific storm tracks, and a negative North Atlantic Oscillation. Although our constrained response is consistent with observed relationships which have weakened recently, we caution that emergent constraints may only provide a lower bound.
Ocean radiocarbon response to circulation changes
Authors: Pookkandy Byju , Graven Heather
Published: March 2021

This study investigates the impact of circulation changes on the distribution of radiocarbon in the ocean interior using simulations of the NEMO model forced by JRA-55 atmospheric reanalysis data. We performed four simulations from 1850 to 2017 with transient or fixed boundary conditions for atmospheric ∆14C and CO2, forced with inter-annually varying or fixed neutral year JRA-55 reanalysis data. The difference between variable and steady-state ocean dynamics revealed the areas in the world ocean where the radiocarbon distribution is most affected by variable ocean circulation: the midlatitude North Pacific and North Atlantic, the midlatitude South Pacific and South Indian, and the Weddell Sea. The difference between fixed and transient atmospheric boundary conditions over the past few decades shows the impact on natural vs bomb 14C. We investigate the potential drivers of variability from gas exchange, mixing in the thermocline, and interior transport. In general, the impact of circulation changes on bomb and natural 14C shows similar patterns but the effect is larger for bomb 14C. Compared to observed decadal changes in ∆14C between the 1990s and 2010s, the model underestimates the changes in ∆14C and potential density suggesting that the model response to circulation change is rapid.
Rock glacier sediment, kinematics and geomorphometry: a study case from the Eastern Italian Alps
Authors: Minotti Federica , Kofler Christian , Gems Bernhard , Mair Volkmar , Comiti Francesco
Published: March 2021

Rock glaciers are important geomorphological structures of high mountain environments and fundamental indicators for permafrost. They consist of unconsolidated rock debris – generally derived from talus or till - held together by ice, moving slowly downslope due to the gravitation in combination with uncountable freeze-thaw-cycles in the active layer. The downslope movement of rock glaciers leads to lobate structures with depressed areas as well as ridges where the sediments tend to accumulate, creating a typical surface morphology defined as "ridges and furrows". This study focuses on the analysis of one rock glacier system located in the Pfitsch/Vizze valley (South Tyrol), in the Eastern Italian Alps.  The debris in this area comprises exclusively the granitic Central Gneiss of the Tauern window. Rock glacier sediment derives from talus, consisting essentially of more or less foliated to planar angular material, which was essentially formed by frost weathering. The size and shape of sediments present at the surface of the rock glacier system were analyzed in correlation with displacement and geomorphometry, with the hypothesis that sediments shape and size at different sites across the rock glacier might relate to its past and present dynamics. The displacement analyses were carried out to quantify rock glaciers movements during the last 20 years, and the geomorphometrical characteristics were investigated to identify specific geometrical attributes that may be linked to internal ice changes.
Clasts analysis showed how rock glacier sediments are very heterogeneous, with dimensions being mainly determined by transport distance, and sphericity and roundness by lithology. A role of sediments characteristics on displacement rate did not turn out evident. Convexities and concavities observed on the study site are apparently created respectively by the accumulation of sediments and the collapse of the structure due to the internal ice melting. Indeed, the recent, marked increase in air temperature observed in the last decades in the Alps has likely caused an accelerated ice melting in the less protected – in terms of solar radiation – rock glaciers, as is the case for our study area. Sediments here are no longer bound by ice and have become rather unstable. Therefore, the monitoring of rock glaciers is fundamental to anticipate future changes in the type and magnitude of natural hazards originating at high elevations, as thicker layers of sediments are becoming increasingly unstable.
Soil pollution indexes through the assessment of a compositional baseline - the Langreo case study, Spain
Authors: Albuquerque Teresa , Boente Carlos , R. Gallego José Luís , Egozcue Juan José , Pawlowsky Vera
Published: March 2021

When considering complex scenarios, such as in environmental characterization, where a multiset of attributes must be considered, a dimensional reduction of the problem is mandatory for a clear apprehension of the reality. Maps, broadly mentioned in the literature, are great for spatial pattern visualization of pollutant’s concentration distribution, or to assign areas of contamination enrichment, either if natural or triggered by anthropogenic activities. In the present study, a set of 15 Potentially Toxic Elements - PTEs - (As, Ba, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Se, Tl, V, and Zn) were analyzed in soils gathered in the Langreo area (80 Km2) —Asturias, Spain— and used (a) to analyze how high concentrations in PTEs are spatially distributed and (b) to explore dissemination trends and the definition of clusters of relative enrichment. To quantify soil pollution, it is important to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Given that the concentration of chemical elements is compositional, as the attributes vary together, our approach is based on compositional principles. Finding a balance of pollutant (numerator) over non-pollutant (denominator) elements, aiming sparsity and simplicity as properties, is the key issue for the construction of a Compositional Pollution Index, and two approaches have been explored: (1) taking into account the whole observed composition, and (2) taking into account only a subcomposition, based on expert knowledge, in which the reported elements are: Na, K, Ca, Al, Mg, Fe, Cu, Pb, Zn, As, Sb, Hg. In all tested cases, Sb, Pb, and Hg appear in the numerator of the balance, and K, Al, and Zn in the denominator. A conclusion could be that the overall pollution in the Langreo region is dominated by the content of Sb, Pb, and Hg, relative to the content of K, Al, and Zn. Finally, both indexes went through a stochastic sequential Gaussian simulation. The spatial characterization allowed a broad discussion about not only the concentrations’ spatial distribution and associated uncertainty, but also a better understanding of the possibility of trends of relative enrichment and insight in PTEs fate.
Machine Learning Classification of Cohen's Class Time-Frequency Representations of Non-Stationary Signals: Effects on Earthquake Detection
Authors: Njirjak Marko , Otović Erik , Jozinović Dario , Lerga Jonatan , Mauša Goran , Michelini Alberto , Štajduhar Ivan
Published: March 2021

The analysis of non-stationary signals is often performed on raw waveform data or on Fourier transformations of those data, i.e., spectrograms. However, the possibility of alternative time-frequency representations being more informative than spectrograms or the original data remains unstudied. In this study, we tested if alternative time-frequency representations could be more informative for machine learning classification of seismic signals. This hypothesis was assessed by training three well-established convolutional neural networks, using nine different time-frequency representations, to classify seismic waveforms as earthquake or noise. The results were compared to the base model, which was trained on the raw waveform data. The signals used in the experiment were seismogram instances from the LEN-DB seismological dataset (Magrini et al. 2020). The results demonstrate that Pseudo Wigner-Ville and Wigner-Ville time-frequency representations yield significantly better results than the base model, while Margenau-Hill performs significantly worse (P < .01). Interestingly, the spectrogram, which is often used in non-stationary signal analysis, did not yield statistically significant improvements. This research could have a notable impact in the field of seismology because the data that were previously hidden in the seismic noise are now classified more accurately. Moreover, the results might suggest that alternative time-frequency representations could be used in other fields which use non-stationary time series to extract more valuable information from the original data. The potential fields encompass different fields of geophysics, speech recognition, EEG and ECG signals, gravitational waves and so on. This, however, requires further research.
Solar Radiation Modification: a multi-century commitment
Authors: Baur Susanne , Nauels Alexander , Schleussner Carl-Friedrich
Published: March 2021

A growing body of literature investigates the effects of Solar Radiation Modification (SRM) on global and regional climates. Previous studies on SRM have mainly focused on potentials and side effects of deployment without addressing plausible avenues of a subsequent phase-out. This would require large-scale carbon dioxide removal (CDR). Here, we look at SRM deployment lengths to keep global temperature increase to 1.5°C under three emissions scenarios that follow current climate policies until 2100 and are continued with varying assumptions about the magnitude of net-negative CDR (-11.5, -10 and -5 GtCO2yr-1). Our results show that there would be a lock-in of around 245 - 315 years of continuous SRM engagement. During peak deployment in 2125 around 2.80 Wm-2 would have to be compensated by SRM, a number at the upper end of currently estimated maximum SRM potential in climate model environments. In total, around 976 - 1344 GtCO2would need to be removed from the atmosphere via CDR. We find only minor effects of SRM on carbon fluxes a few decades after cessation. Our study shows that even if SRM is combined with high CDR, SRM would come with very long legacies of deployment, implying centuries of costs, cumulative risks and all negative side effects of SRM and CDR combined. 
Study of turbulent mixing processes at a mesoscale confluence through aerial drone imagery and eddy-resolved modelling
Authors: Duguay Jason , Biron Pascale , Buffin-Bélanger Thomas
Published: March 2021

The large-scale turbulent structures that develop at confluences fall into three main categories: vertically orientated (Kelvin-Helmholtz) vortices, large-scale secondary flow helical cells and smaller strongly coherent streamwise orientated vortices. The causal mechanisms of each class, how they interact with one another and their respective contributions to mixing is still unclear. Our investigation emphasises the role played by the instantaneous flow field in mixing at a mesoscale confluence (Mitis-Neigette, Quebec, Canada) by complementing aerial drone observations of turbulent suspended sediment mixing processes with results from a high-resolution eddy-resolved numerical simulation. The high velocity near-surface flow of the main channel (Mitis) separates at the crest of the scour hole before downwelling upon collision with the slower tributary (Neigette). Fed by incursions of lateral momentum of the Mitis, shear generated Kelvin-Helmholtz instabilities expand as they advect along the mixing-interface. As the instabilities shed, water from the deeper Neigette passes underneath the fast, over-topping Mitis, causing a large portion of the Neigette’s discharge to cross under the mixing-interface in a short distance. The remaining flow of the tributary crosses over inside large-scale lateral incursions farther downstream. The downwelling Mitis, upwelling Neigette and recirculatory cell interact to generate coherent streamwise vortical structures which assist in rapidly mixing the waters of the two rivers in the vicinity of the mixing-interface. Evidence of large-scale helical cells were not observed in the flow field. Results suggest that flow interaction with bathymetry, and both vertical and streamwise orientated coherent turbulent structures play important roles in mixing at confluences. Our findings strongly suggest that investigating mixing at confluences cannot be based solely on mean flow field variables as this approach can be misleading. Visualization of a confluence’s mixing processes as revealed by suspended sediment gradients captured in aerial drone imagery complemented with eddy-resolved numerical modelling of the underlying flow is a promising means to gain insights on the role of large-scale turbulent structures on mixing at confluences.
Towards model-data comparison of the deglacial temperature evolution in space and time
Authors: Weitzel Nils , Andres Heather , Baudouin Jean-Philippe , Bothe Oliver , Dolman Andrew , Jonkers Lukas , Kapsch Marie , Kleinen Thomas , May Maximilian , Mikolajewicz Uwe , Paul Andre , Rehfeld Kira
Published: March 2021

The increasing number of Earth system model simulations that try to simulate the climate during the last deglaciation (ca 20 to 10 thousand years ago) creates a demand for benchmarking against environmental proxy records synthesized for the same time period. Comparing these two data sources over a period with changing background conditions requires new methods for model-data comparison that incorporate multiple types and sources of uncertainty.

Natural archives of past reality are distributed sparsely and non-uniformly in space and time. Signals that can be obtained are in addition perturbed by uncertainties related to dating, the relationship between the proxy sensor and environmental fields, the archive build-up, and measurement. On the other hand, paleoclimate simulations are four-dimensional, complete, and physically consistent representations of the climate. However, they are subject to errors due to model inadequacies and sensitivity to the forcing protocol, and will not reproduce any particular history of unforced variability. 

We present a method for probabilistic, multivariate quantification of the deviation between paleo-data and paleoclimate simulations that draws on the strengths of both sources of information and accounts for the aforementioned uncertainties. We compare the shape and magnitude of orbital- and millennial-scale temperature fluctuations during the last deglaciation and compute metrics of regional and global model-data mismatches. We test our algorithm with an ensemble of published simulations of the deglaciation and simulations from the ongoing PalMod project, which aims at the simulation of the last glacial cycle with comprehensive Earth system models. These are evaluated against a compilation of temperature reconstructions from multiple archives. Our work aims for a standardized model-data comparison workflow that will be used in PalMod. This workflow can be extended subsequently with additional proxy data, new simulations, and improved representations of proxy uncertainties. 
High-resolution soil moisture mapping through the use of Cosmic-Ray Neutron Sensor and Sentinel-1 data for temperate and semi-arid environments
Authors: Said Hami , Mbaye Modou , Heng Lee Kheng , Fulajtar Emil , Weltin Georg , Franz Trenton , Dercon Gerd , Strauss Peter , Rab Gerhard , Saud Al-Menaia Habiba , Ndiaye Mapathe
Published: March 2021

Global climate change has a major impact on the availability of water in agriculture. Sustainable agricultural productivity to ensure food security requires good agricultural water management.

Soil moisture is one of the important variables in irrigation management, and there are many different techniques for estimating it at different scales, from point to landscape scales.

Cosmic-Ray Neutron Sensor (CRNS) technology has the capability to estimate field-scale soil moisture (SM) in large areas of up to 20 to 30 ha and has demonstrated its ability to support agricultural water management and hydrology studies. However, measurement of soil moisture on a global or regional scale can only be achieved from satellite remote sensing.

Recently, active microwave remote sensing Synthetic Aperture Radar (SAR) imaging from Sentinel-1 shows great potential for high spatial resolution soil moisture monitoring and can be the basis for producing soil moisture maps. However, these maps can be only used after calibration. Such calibration can be done through traditional, point soil moisture sampling or measurement, which is time-consuming and costly. CRNS technology can be used for calibration and validation remote sensing imagery predictions at field and area-wide level.

In this study a conversion model to retrieve soil moisture from Sentinel-1 (SAR) was developed using the VV (vertical-vertical) polarization, which is highly sensitive to soil moisture, and then calibrated and validated using CRNS data from temperate (Austria) and semi-arid (Kuwait) Environments. This study is a major step in the monitoring of soil moisture at high spatial and temporal resolution by combining remote sensing and the CRNS based nuclear technology. The preliminary results show the great potential of using nuclear technology such as CRNS for remote sensing calibration of Sentinel-1 (SAR).
Spaceborne GNSS Reflectometry for soil moisture using data from CYGNSS mission: results in the Contiguous United States
Authors: Setti Junior Paulo de Tarso , van Dam Tonie
Published: March 2021

Soil moisture is an essential climate variable, influencing geophysical and hydrological processes such as vegetation and agriculture, land-atmosphere circulation, and drought development. It is possible to remotely sense soil moisture based on the dielectric constant of soil at microwave frequencies. Low earth orbit (LEO) satellites are capable of receiving Global Navigation Satellite Systems (GNSS) signals reflected off the surface of the Earth to infer properties of the reflecting surface itself, in a technique known as GNSS-Reflectometry (GNSS-R). However, converting surface reflectivity derived from GNSS-R into soil moisture is not straightforward. Reflectivity is influenced by other factors such as the vegetation optical depth and the soil roughness around the specular reflection. The Cyclone Global Navigation Satellite System (CYGNSS) is a mission from the National Aeronautics and Space Administration (NASA) consisting of eight small GNSS-R satellites with the primary objective of measuring wind speed in hurricanes and tropical cyclones. The satellites were launched in December 2016 in a 35° inclination orbit, and the measurements are made of reflected Global Positioning System (GPS) L1 (1.575 GHz) navigation signals. Reflections over land can be used to estimate soil moisture in the upper 5 cm of soil surface if they are correctly treated and modelled. In this work, we use three years of observations from CYGNSS mission (March 2017 - March 2020) to compute surface reflectivity over land assuming coherent reflections. Using linear regression models and ancillary information from Soil Moisture Active Passive (SMAP) mission (soil moisture, vegetation optical depth, and roughness coefficient), these reflectivity observations are then used to estimate soil moisture. Retrievals are compared with observations from 44 in-situ soil moisture stations from the International Soil Moisture Network (ISMN) in the Contiguous United States (CONUS), presenting in most of the cases a good agreement. Results are also correlated with vegetation optical depth, surface roughness, and topographic relief around the in-situ stations. In addition, some challenges regarding soil moisture estimation using spaceborne GNSS-R data are presented and discussed.
Mending and extending observational temperature series by linear and nonlinear regression
Authors: Mikšovský Jiří , Štěpánek Petr
Published: March 2021

While time series of meteorological measurements from land-based weather stations still represent one of the basic types of data employed in climate research, it not uncommon for these records to be incomplete, interrupted by periods of missing or otherwise compromised values. Such gaps typically need to be filled before a subsequent analysis can be performed, and records from other nearby measuring sites are frequently used for this purpose. In this presentation, results of central European daily temperatures estimation from other concurrent measurements by various statistical methods are showcased, with a particular emphasis on assessing potential benefits of application of nonlinear regression techniques. Using multi-decadal daily temperature series originating from a dense network of weather stations covering the territory of the Czech Republic, we show that while nonlinear regression does not always outperform its linear counterpart, it can substantially improve accuracy of temperature estimates for some target locations. The gain is shown to be especially prominent for sites exhibiting atypical behavior compared to their local geographic neighborhood, such as isolated mountain-based stations. In addition to regression-based restoration of compromised segments in the temperature records, use of this methodology for extending the temperature records beyond their original period of measurements is also discussed, as well as its potential for homogeneity testing.
An affordable and reliable device for direct bed shear stress measurements
Authors: Niewerth Stephan , Núñez-González Francisco , Llull Toni
Published: March 2021

The entrainment and transport of sediment by hydrodynamic mechanisms is strongly related to bed shear stress exerted by flow. Therefore, to quantify sediment transport and to determine sediment incipient motion conditions, accurate estimations of bed shear stress are required. Most of the existing methods used in hydraulics and river engineering to determine bed shear stress are indirect, and are mostly restricted to limited flow conditions or contain a large degree of uncertainty. Although devices to perform direct measurements of boundary shear stress exist, they are normally based on expensive technology. We developed a shear plate for direct shear stress measurements, using relatively low cost components. In this work we present preliminary results of measurements performed with the new shear plate, to characterize the bottom shear stress generated by a ship propeller. The data result in the expected quadratic relation between bed shear stress and jet velocities, and also give evidence of a good reproducibility. We show that the new shear plate appears to be a promising device for reliable measurements of submerged boundary shear stress under a wide range of environments and flow conditions.
Interaction of ions of heavy metals and organic toxicants with humic substances in system ‘atmospheric precipitation - lysimetric waters - lake waters’ of the Reserve of the European Territory of Russia: climatic, technogenic, geochemical fact
Authors: Dinu Marina
Published: March 2021

The reactions of toxicants with organic substances of a humic nature are complex and depend on many geochemical factors. Differences in the mechanisms of the selected toxicants binding with organic natural substances of various natural waters - atmospheric precipitation, lake waters (acidic and alkaline), lysimetric waters are especially interesting. Due to significant concentration differences, features of functional groups and size distribution of components, the inactivation features of humic substances are selective and highly variable. We studied the waters of an acid lake near the city of Valday (Valday National Park, conditionally a background lake) and alkaline lake Valday (city of Valday, local technogenic influence). Near each lake there was a sediment collector (a container for collecting atmospheric precipitation) and a lysimeter (a container under the soil for collecting soil moisture) under the humus horizon (about 20 cm). Particular attention was paid to soil (lysimetric) waters with varying degrees of anthropogenic impact. We considered the behavior of a large group of heavy metals, as well as benzopyrene. To assess the composition and qualitative features of organic substances, gas chromatography-mass spectrometric methods of analysis were used. Chromatographic methods were used to assess the molecular weight distribution of the components. Possible reaction mechanisms were studied by IR spectral methods. Evaluation of the reactivity of organic substances was carried out by the methods of dynamic light scattering (zeta potential, MM, size) using the "Zeta-sizer nano". In addition to humic substances in the waters, the contents of autochthonous organic matter were estimated, especially in an alkaline lake, which in some periods prevailed over humic ones. In addition to humic substances in the waters, the contents of autochthonous organic matter were estimated, especially in an alkaline lake, which in some periods prevailed over humic ones. For separation, exchange technique and fluorometric evaluations were used. We conducted research in the period 2015-2020, sampling was carried out in spring, summer, autumn. Thus, we studied the circulation (in miniature) of changes in the protective properties of humic substances, depending on a large number of factors.