bluuebunny/crossref_metadata_2025 huggingface dataset:
Indoor air quality assessment in a residential apartment in Budapest
Authors: Atfeh Bushra , Kristóf Erzsébet , Mészáros Róbert , Barcza Zoltán
Published: March 2021

This work focuses on indoor air quality measurements carried out in an apartment in the suburban region of Budapest. The measurements were made by an IQAir AirVisual node air quality monitor which is a so-called low-cost sensor capable to monitor PM2.5 and carbon dioxide concentration. In this study we analyze data measured during January 2017 that was characterized by an extreme air pollution episode in Budapest. The aim of the study was to calculate daily indoor PM2.5 concentrations that are comparable with the outdoor concentrations provided by the official Hungarian Air Quality Monitoring Network. Given the fact that AirVisual Pro provides data with irregular sampling frequency, data processing is expected to influence the calculated daily mean concentrations.  The results indicated that the uneven sampling frequency characteristic of AirVisual node indeed causes problems during data processing and has an effect on the calculated means. We propose a ‘best method’ for data processing for sensors with irregular sampling frequency.
Warm core mesoscale eddies along the boundary current and in the Sofia Deep north of Svalbard
Authors: Kolås Eivind H. , Kalhagen Kjersti , Koenig Zoe , Fer Ilker , Nilsen Frank
Published: March 2021

The Atlantic water boundary current north of Svalbard is a major heat and salt source to the Arctic Ocean. Yet, the mechanisms controlling the lateral transport of Atlantic water properties are not well understood. Model simulations suggest mesoscale eddies may be important for transporting heat away from the boundary current, but supporting observations are sparse.

Between September and November in 2018, a Seaglider was deployed north of Svalbard as part of the Nansen Legacy project to investigate intraseasonal variations in the boundary current and the transformation of Atlantic water. It made several transects across the boundary current and a transect across the Sofia deep. Warm core eddies originating from the boundary current were detected in the Sofia deep. Combining the Seaglider data with two year-long mooring arrays north of Svalbard, deployed in 2018 within the Nansen Legacy framework, we investigate mesoscale eddies using eddy recognition algorithms applied to glider transects and timeseries from moorings. Initial results indicate that mesoscale eddies frequently occur in the boundary current, with radius less than 10 km and velocity maxima as high as 0.35 m/s.
Life after recovery: new insights into post-drought compensatory growth and forest recovery dynamics
Authors: Ovenden Thomas , Perks Michael , Clarke Toni-Kim , Mencuccini Maurizio , Jump Alistair
Published: March 2021

Large scale losses in forest productivity linked to extreme drought are now being documented globally. With climate change set to increase the frequency, intensity and duration of future extreme events, understanding the impact of drought on forest productivity and the post-drought recovery dynamics of these systems is becoming increasingly important. However, current approaches to quantifying resilience limit our understanding of forest response dynamics, recovery trajectories and drought legacies by constraining and simplifying the temporal scale and resolution of assessment. To advance beyond one of the most commonly used approaches to estimating resilience, we first compared estimates of resilience for Pinus sylvestris trees following an extreme drought by comparing the same resilience index calculated over different pre- and post-drought time scales. We then developed an alternative approach using dynamic regression to capture each individual tree’s relationship between climate and growth, which was then used to forecast tree growth annually for the drought year and nine subsequent years, in a scenario where no drought had occurred. Here we present the results of this work, comparing observed tree growth with growth forecasted using dynamic regression at multiple stem heights and stand densities. This approach allowed us to increase the temporal scale and resolution of resilience assessment and follow tree and stand level growth relative to a no-drought scenario throughout recovery and into a post-recovery phase, where we find evidence for significant compensatory growth. The existence of compensatory growth post-recovery reduced estimates of drought induced losses of radial growth, indicating that current approaches risk underestimating tree and stand resilience to drought and overestimate losses in above-ground biomass. Similarly, we provide evidence for a temporal dependency in the stage during recovery at which pre-drought tree and stand attributes such as growth rates, basal area and stand densities were associated with growth resilience. Our results have wide reaching implications for both forest management targeted at increasing resilience, carbon budgeting and our understanding of drought legacy.
2019-2020 mechanisms of fresh water release from the Beaufort Gyre region of the Arctic Ocean
Authors: Proshutinsky Andrey , Krishfield Richard , Timmermans Mary-Louise , Le Bras Isabela , Toole John , Pickart Robert , Williams Bill , Zimmermann Sarah , Platov Gennady , Golubeva Elena , Dukhovskoy Dmitry , Rose Stine , Andersen Ole
Published: March 2021

From September 2019 to September 2020, the sea-level atmospheric pressure over the Beaufort Gyre region (BGR) was reduced relative to climatology and a well pronounced cyclonic circulation forcing of sea ice and ocean lasted more than eight months. This resulted in the following: increased sea ice area in 2020 relative to 2019; periodic reversals of sea ice drift from anticyclonic to cyclonic; the formation of an unusual donut-shaped sea ice cover pattern (in August-September 2020); upwelling in the central BGR with a reduction of freshwater content by ~1000 km3; downwelling along the periphery of the BGR; changes in the intensity and trajectories of freshwater fluxes from the Mackenzie river and Bering Strait and fresh water contributions to the BGR freshwater content; unusual warming of the Pacific water layer in the northern BGR; and biogeochemical changes driven by ocean circulation and water mass redistribution. Numerical modeling is used to better understand the causes and consequences of the observed changes. Sea-level atmospheric pressure from NCAR/NCEP reanalysis, sea ice concentration and ice motion from NSIDC, altimetry based sea surface heights from Technical University of Denmark, and hydrographic data from the Beaufort Gyre project and USCGC Healy expeditions are used in the study.
Shallow landslide occurrence and propagation in tropical mountainous terrain with open source models. A case study in the Colombian Andes.
Authors: Palacio Johnnatan , Aristizábal Edier , Mergili Martin , Echeverri Oscar
Published: March 2021

Colombia is located in a tropical environment with mostly warm and humid climatic conditions and complex mountainous terrain, where landslides triggered by intense rainfall are very common. Therefore, determining the occurrence and propagation of these events is of great interest in risk management and territorial planning programs.

Landslide propagation is difficult to predict due to uncertainties of rheological properties as well as initiation and dynamics of rock or sediment mobilization.  In Colombia, methodologies and models for landslide propagation have been less addressed than those corresponding to the occurrence, even though the consequences on people and infrastructure are generally are strongly related to travel distances and impact areas. Most propagation models are based on empirical methods to establish the travel distance of the sliding material, employing geometric approximations or geomorphological interpretation. In the last decades, physically based dynamic landslide propagation models have been proposed. These models use digital elevation models in combination with flow parameters. Their application represents a complex task because of the difficulty in constraining – depending on the model – sometimes the large number of relevant flow parameters.

In this study, we apply two open source models that work as extensions to the GRASS GIS software: (i) the r.slope.stability model for slope stability assessment using a limit equilibrium model for different sliding surface geometries, together with a probabilistic analysis applied to a range of geotechnical parameters (cohesion, internal friction); and (ii) r.avaflow for landslide propagation, which employs a multi-phase model considering solids and fluids. The models are implemented in the catchment area known as La Arenosa (9.9 km2), located in the municipality of San Carlos (Antioquia, Colombia). On September 21, 1990, an event of rainfall of short duration and high intensity precipitated on La Arenosa catchment. approx. 200 mm of precipitation fell within the study area in less than 3 hours, triggering approx. 700 landslides many of which have converted into hillslope debris flows. The zones categorized with a high probability of failure through r.slope.stability are defined as source areas and propagated down with r.avaflow. The results are evaluated against the landslide inventory in order to evaluate the potential of the proposed model combination for predictive simulations.
Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project
Authors: Engström Erik , Azorin-Molina Cesar , Wern Lennart , Hellström Sverker , Sturm Christophe , Joelsson Magnus , Zhang Gangfeng , Minola Lorenzo , Deng Kaiqiang , Chen Deliang
Published: March 2021

Here we present the progress of the first work package (WP1) of the project “Assessing centennial wind speed variability from a historical weather data rescue project in Sweden” (WINDGUST), funded by FORMAS – A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the “stilling” phenomenon and the recent “recovery” since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).

The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.

During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the “Guidelines on Best Practices for Climate Data Rescue” of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.
A generalized approach to generate synthetic short-to-medium range hydro-meteorological forecasts
Authors: Brodeur Zachary , Steinschneider Scott
Published: March 2021

Forecast informed operations hold great promise as a soft pathway to improve water resources system performance. Generating synthetic forecasts of hydro-meteorological variables is crucial for robust validation of this approach, as advanced numerical weather prediction hindcasts have a limited timespan (10-40 years) that is insufficient for assessing risk related to forecast-informed operations during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the flexible Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables and forecast lead times and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from operational CONUS hydrologic and meteorological forecast models. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for flood management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for the design and testing of forecast informed policy or characterization of forecast uncertainty for water resources risk analysis.
oKat-SIM – An Interdisciplinary Research Project to Optimize Natural Disaster Management Using Augmented Reality
Authors: Ehses Julia , Zeilinger Gerold , Hefner Marcel , Uhde Stefanie , Belli Francesco , König Florian , Schornsheim David
Published: March 2021

In the event of a natural disaster, local authorities often have to rely on limited experience and sporadic training to make important and lifesaving decisions. This increases the stress levels of the workforce involved in the response effort and can result in an inaccurate assessment of the situation with potentially catastrophic consequences. oKat-SIM aims to support local administrative offices in natural disaster situations by providing augmented reality (AR)-based training to public authorities in order to increase geohazard awareness and improve associated responses. Our initial focus is on possible flood and landslide scenarios in three different regions of Germany: the lowlands of Görlitz, urbanized Leverkusen, and the mountainous Garmisch-Partenkirchen region. These scenarios are based on state-of-the-art modelling of realistic, cascading natural disaster events and incorporate environmental parameters such as precipitation, high-resolution topography, and examples from past events. Together with local partners, we are developing training simulations adapted to the threats posed by natural disasters in each of the study areas. We use the Unity game engine to translate GIS-based data and modeling results into the AR simulation environment. AR training immerses the participants in realistic states of emergency while maintaining direct communications, which results in safer and more rapid decision making that will ultimately protect communities from natural disasters. The success of the training will be evaluated by cognitive science methods including measuring the learning effect under different stress levels. These measurements will be used to modify the training environment to achieve optimal learning results.
Waveform quality checks at German networks
Authors: Stammler Klaus
Published: March 2021

Recently a set of quality control procedures have been implemented at the data center of the BGR (Seismic Survey of Germany). Goal is to identify unusual deviations in amplitude, timing and waveform caused by data and metadata errors. One of the strategies applied is to evaluate long term observations of seismic noise at specific frequencies at many stations. Particularly at lower frequencies this analysis is quite sensitive to amplitude changes. Also useful is the characterization of station sites by looking at anthropogenic noise patterns in a frequency range of 4-14 Hz. The sites show fundamental differences when looking at daily and weekly noise patterns and some also have specific responses to local wind. Changes in the noise patterns indicate changes in the environment or uncompensated hardware or metadata changes. Furthermore, correlations of teleseismic signals reveal  possible inconsistencies in waveform shape, travel time residuals and amplitudes within the station set. When applied systematically a statistical  analysis of the correlation parameters indicates long term deviations in these three observables. Finally, a formal check of the transfer function given in the metadata is implemented to identify wrong settings in the normalization and illegal specifications in the poles and zeros (conjugate complex pairs and negative real part at poles). These implemented measures help us to keep our data at a high quality level and to react quickly on the occurrence of  hardware and metadata errors.

 
Joint probability analysis of drought and hot extremes in Australia
Authors: Páscoa Patrícia , Gouveia Célia , Russo Ana , Ribeiro Andreia
Published: March 2021

The simultaneous or sequential occurrence of extreme climate events, often designated as compound events, has recently received further attention, due to the higher impacts they cause, when compared to individual extreme events, and also due to the expected increase in their frequency within a warming climate context. The occurrence of compound dry and hot extremes has been observed in several regions throughout the world. The recent extreme bushfire season of 2019-2020 in Australia was probably driven by the sequential occurrence of spring drought and severe summer heatwaves.

Previous works have used correlation analysis to study these extreme dry and hot compound events, but it has been shown that, although necessary, antecedent drought is not a sufficient condition for the occurrence of hot extremes. For this reason, in this work we used copula functions to study the joint probability of occurrence of these extremes. This method, already applied for this type of compound events in other regions of the globe, allows to study dependences between variables, even if they are non-linear.

The drought conditions were assessed using the Standardized Precipitation Evaporation Index (SPEI) at time scales of 1, 3, and 6 months, using data from the CRU TS 4.04 dataset. The Number of Hot Days (NHD) and Number of Hot Nights (NHN) were used to quantify the hot extremes in the summer months in Australia and were computed with temperature data from the ERA5 dataset. The probability of occurrence of hot extremes given drought/non-drought conditions were estimated over the different regions of Australia. Differences in these probabilities further suggest the effect on hot summer extremes by droughts occurring on the concurrent and on previous months.

 

Acknowledgements: This work was partially supported by projects FireCast (PCIF/GRF/0204/2017), and IMPECAF (PTDC/CTA-CLI/28902/2017).
Erodibility of fine sediment deposits in gravel bed rivers: investigation of the spatial variability
Authors: Haddad Hanna , Jodeau Magali , Antoine Germain , Legoût Cédric
Published: March 2021

Fine sediments exhibit various stages of deposition and erosion during their transport from hillslopes to the ocean. In mountainous environments, high fine sediment load during runoff or dam flushing events can lead to important amounts of deposits in gravel bed rivers. Massive deposits may lead to bar elevation, riparian vegetation growth and consequently to bar stabilization, which can increase flood risks. High amount of fine sediment deposits alters also aquatic life and habitat.

In order to better understand the dynamics of re-suspension of these deposits, and to accurately predict it with numerical modelling, field monitoring campaigns were performed to assess both the spatial variability and the controlling factors of the erodibility of fine deposits. The cohesive strength-meter (CSM) device, a pocket penetrometer and a pocket shear vane were used to evaluate the erodibility of fine sediments deposited in two rivers in the French Alps: The Isère and Galabre.

The results highlight the specificity of gravel bed rivers with an abundance of areas of deposition of fine sediments, which are discontinuous compared to estuaries and lowland rivers. A high spatial variability of the erodibility was observed and related to the spatial organization of the deposits. The location of the deposit and its elevation, the moisture and the grain sizes are inter-related and have important correlations with the erodibility. Measurements show that high altitude dry deposits and low altitude humid deposits are more easily eroded than intermediate deposits with medium moisture. The measured variables explain part of the variability of the erodibility but other processes such as the history or the origin of the deposit might also be important factors to consider.
Air-traffic restrictions at the Madeira International Airport due to adverse winds: links to synoptic-scale patterns and mountain wave phenomena
Authors: Belo-Pereira Margarida , Santos João
Published: March 2021

The Madeira International Airport (MIA) lies on the island south-eastern coast and it is known to be exposed to wind hazards. A link between these adverse winds at MIA and the synoptic-scale circulation is established using a weather type (WT) classification. From April to September (summer period), five WTs prevail, cumulatively representing nearly 70% of days. These WTs reflect the presence of well-established Azores high, with some variations on location and strength. Although with a low frequency of occurrence (<5%), this anticyclone occasionally strengthens and extends towards Iberia, inducing anomalously strong NNE/NE up to 3-5 km over Madeira. The most severe and longer-lasting wind conditions at the MIA, with a higher frequency of gusts above 35 kt, are driven by this synoptic-scale pattern and are more common in summer. An episode of adverse winds at the MIA is analyzed, illustrating the occurrence of upstream stagnation, flow splitting, and lee wake formation. The upstream conditions include a low-level inversion, strong NNE/NE winds near and above the inversion and a Froude number less than 1. AROME model predicted the occurrence of downslope winds, in association with a large-amplitude mountain wave. At this time, the strongest wind gusts were registered and a missed approach occurred. The wind regime in different places of the island suggests that these conditions are relatively frequent, mostly in summer. Lastly, this study provides an objective verification of the AROME wind forecasts, for a 3-year period and from June to August.
Role of the heliospheric current sheet in high energy proton transport through modelling of historic GLE events 
Authors: Waterfall Charlotte , Dalla Silvia
Published: March 2021

The influence of the heliospheric current sheet (HCS) on the propagation of high energy solar protons is explored using 3D test particle modelling. The test particle model, which includes drift effects, is used to simulate specific past ground level enhancement (GLE) events which cover a range of HCS configurations. For example, the effects of a source location close to and far from the HCS for events both poorly and well-connected to Earth are examined. Similarly, the effect of the Earth’s location relative to the HCS is explored. The modelling is performed for high energy (300-1200 MeV) protons to represent the energetic conditions under which GLEs occur. The derived intensity profiles at 1AU are compared to observations from HEPAD onboard GOES, as well as STEREO (at locations away from Earth) and neutron monitor data. 
Improving heavy precipitation forecasting over the western Mediterranean: Benefits of stochastic techniques for model error sampling
Authors: Hermoso Alejandro , Homar Victor , Plant Robert
Published: March 2021

The western Mediterranean region is frequently disrupted by heavy precipitation and flash flood episodes. Designing convection-permitting ensembles capable of accurately forecasting socially relevant aspects of these natural hazards such as timing, location, and intensity at basin scales of the order of a few hundred of squared kilometers is an extremely challenging effort. The usual forecast underdispersion prevailing at these scales motivates the research of sampling methodologies which are able to provide an adequate representation of the uncertainties in the initial atmospheric state and its time-integration by means of numerical models. This work investigates the skill of multiple techniques to sample model uncertainty in the context of heavy precipitation in the Mediterranean. The performance of multiple stochastic schemes is analyzed for a singular event occurred on 12 and 13 September 2019 in València, Murcia, and Almería (eastern Spain). This remarkable and enlightening episode caused seven casualties, the flooding of hundreds of homes and economic exceeding 425 million EUR.

Stochastic methods are compared to the popular multiphysics strategy in terms of both diversity and skill. The considered techniques include stochastic parameterization perturbation tendencies of state variables and perturbations to specific and influential parameters within the microphysics scheme (cloud condensation nuclei, fall speed factors, saturation percentage for cloud formation). The introduction of stochastic perturbations to the microphysics parameters results in an increased ensemble spread throughout the entire simulation. A conclusion of special relevance for the western Mediterranean, where local topography and deep moist convection play an essential role, is that stochastic methods significantly outperform the multiphysics-based ensemble, indicating a clear potential of stochastic parameterizations for the short-range forecast of high-impact events in the region.
Reconciling Juno and past mission datasets to improve large-scale models of radiation-belt electron and radio emission distributions at Jupiter
Authors: Santos-Costa Daniel , Allegrini Frederic , Wilson Rob , Kollmann Peter , Clark George , Mauk Barry , Connerney Jack , Jorgensen John L. , Gulkis Samuel , Janssen Michael A. , Oyafuso Fabiano , Brown Shannon T. , Levin Steven M. , Becker Heidi N. , Bolton Scott J.
Published: March 2021

We present our latest model of electron radiation belts developed for a large region of Jupiter's magnetosphere (1-50 Rj). For the region inward of Io, electron distributions are computed from a computational code that solves the governing three-dimensional Fokker-Planck equation. This physics-based model accounts for different mechanisms to discuss the energy and spatial distributions of electrons for L values between 1 and 5. The model for the innermost magnetospheric region is expanded to the middle magnetosphere using an empirical approach. In this paper, we first show how our large-scale model of Jupiter's electron radiation belts agrees with data sets from past missions (Pioneer 10 and 11 GTT, Galileo EPD and EPI measurements). We then focus on our effort to combine Juno (JEDI, JADE Electron Ambient Background Counts) and Galileo EPD (> 1.5, 11.5 MeV) datasets to improve our model for both the region beyond Io and the inner edge of the Jovian electron radiation belts. Finally, simulations of Jupiter's synchrotron emission are presented to gauge the contribution of ultra-energetic electrons trapped beyond L ~ 3 at different latitudes to radio emission observed by Juno MWR.
Сlimate-active aerosol components in the Siberian Arctic, by data from new-developed research aerosol station on island Bely 
Authors: Popovicheva Olga , Kobelev Vasilii , Chichaeva Marina , Kasimov Nikolai , Hansen Antony
Published: March 2021

Black carbon is a short - living climate forcer, it plays a significant role especially in the Arctic environment due to heating the atmosphere and changing the radiation balance while depositing on snow and ice. Analysis of black carbon (BC) in the Arctic atmosphere shows a contribution of anthropogenic combustion of fossil fuels and natural wildfires to the Arctic atmosphere chemistry as well as of the main characteristics of Arctic aerosol pollution. Presently, assessments of the environment and climate change in the Siberian Arctic are strongly complicated by an existing lack of knowledge about emission sources, quantity, and composition of the aerosol pollution defining the impacts on an Arctic ecosystem.

Research aerosol station is firstly installed on island Bely located in Kara sea, Siberian Arctic. It takes place on the pathway of air mass from the Northern Siberia region of high anthropogenic and gas flaring activity to the Arctic. Presently, assessments of the environment and climate change in this region are strongly complicated by an existing lack of knowledge about emission sources, quantity and composition of the aerosol pollution defining the impacts on an Arctic ecosystem. Aethalometer and aerosol sampling system is continuously operated on the aerosol station in order to analyze black carbon and chemical characteristics including ionic and elemental composition. Annual BC trend obtained from august 2019 to September 2020 shows the typical Arctic aerosol tendency of a seasonal variability, disturbed by episodes of large-scale emission transportation.

Unprecedented high BC is observed in September 2020 at the research aerosol station on the island Bely. The BC concentrations early in September were exceeded 20 times the arctic background. They are found to be even higher than the highest arctic haze concentrations observed in December 2019.   Monthly averaged black carbon concentration in September 2020 exceeded 3 times that one in previous summer months. Such strong event is a result of large-scale air mass transportation from Eurasian continent in the period of strong wildfires in western Siberia, namely in Krasnoyarsk Kray and Yakutia, where around one million hectares of forest were burned out in August 2020. 

Basic researches of aerosol characteristics as a tracer of anthropogenic emissions are supported by Russian Fond for Basic Research, project №18-60084.

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Improving Summer Precipitation Prediction in China Using Deep Learning
Authors: Jin Weixin , Luo Yong
Published: March 2021

Summer precipitation in China exhibits considerable spatial-temporal variation with direct social and economic impact. Yet seasonal prediction remains a long-standing challenge. The dynamical models even with a 1-month lead still shows limited forecast skill over China in summer. The present study focuses on applying deep learning to summer precipitation prediction in China. We train a convolutional neural network (CNN) on seasonal retrospective forecast from forecast centres in several European countries, and subsequently use transfer learning on reanalysis and observational data of 160 stations over China. The Pearson’s correlation coefficient (PCC) and the root mean square error (RMSE) are used to evaluate the performance of precipitation forecasts. The results demonstrate that deep learning approach produces skillful forecast better than those of current state-of-the-art dynamical forecast systems and traditional statistical methods in downscaling, with PCC increasing by 0.1–0.3, at 1–3 months leads. Moreover, experiments show that the data-driven model is capable to learn the complex relationship of input atmospheric state variables from reanalysis data and precipitation from station observations, with PCC of about 0.69. Image-Occlusion technique are also performed to determine variables and  spatial features of the general circulation in the Northern Hemisphere which contribute maximally to the spatial distribution of summer precipitation in China through the automatic feature representation learning, and help evaluate the weakness of dynamic models, in order to gain a better understanding of the factors that limit the capability to seasonal prediction. It suggests that deep learning is a powerful tool suitable for both seasonal prediction and for dynamical model assessment.
Problem ot the energy transfer for radio paths near single-hop limiting distance for low solar activity
Authors: Atamaniuk Barbara , Krasheninnikov Igor V. , Popov Alexei , Matyjasiak Barbara
Published: March 2021

Formation the feature, in a form of deep trough, in frequency dependence of the wave field strength for single-hop paths with distances near classical limiting distance 3000 km at low level of solar activity was considered. Model calculations within the framework of the extended global ionospheric IRI model show high probability for appearing such a situation in the local daytime with a developed regular E-layer of the ionosphere. Some experimental results in multifrequency radio sounding of the ionosphere with a registration of the deep trough in frequency dependence of signal-to-noise ratio (SNR) were analyzed. It is shown that the IRI model, in principle, makes it possible to reproduce this peculiarity in the wave field energy parameters, but in some cases of experimental data, to a large extent, is able to provide only a qualitative description of this effect. Possible reasons for the quantitative discrepancy between experimental and model results are discussed.
Intensifying fire regimes in the arctic-boreal zone: recent changes, global implications, and possible solutions
Authors: Rogers Brendan , Elder Molly , Frumhoff Peter , Gasser Thomas , Kukavskaya Elena , MacDonald Erin , Mack Michelle , Natali Susan , Phillips Carly , Scholten Rebecca , Treharne Rachael , Veraverbeke Sander , Walker Xanthe
Published: March 2021

Across much of the high latitudes, wildfires have been increasing in frequency, area burned, and severity in response to longer fire seasons, more severe fire weather, and increased ignitions. These fires not only affect the tundra and boreal forests they burn, but also global climate due to the high levels of carbon emitted during combustion that take decades to re-aggrade. Carbon emissions from high latitude fires are generally not included in global models that inform policy nor emissions reductions commitments from relevant countries. In this presentation we describe recent progress and critical unknowns related to intensifying fire regimes in high latitude ecosystems, with a particular focus on (i) trends in burned area and large fire years; (ii) changing ignitions sources including lightning, human, and overwintering fires; (iii) patterns and drivers of carbon emissions, including interactions with permafrost; (iv) implications for global carbon budgets; and (v) potential climate mitigation through increased resources for carbon-focused fire management.
The conductive dusty ionosphere of Saturn
Authors: Shebanits Oleg , Hadid Lina , Cao Hao , Morooka Michiko , Dougherty Michele , Wahlund Jan-Erik , Hunt Gregory , Waite Hunter , Müller-Wodarg Ingo
Published: March 2021

Cassini’s Grand Finale orbits brought us historical first in-situ measurements of Saturn’s ionosphere, showing that it contains dusty plasma in the equatorial region. We present the Pedersen and Hall conductivities of the top ionosphere (10:50 – 12:17 Saturn Local Time, 10N – 20S planetocentric latitude), derived from particle and magnetometer data. We constrain the Pedersen conductivities to be at least 10-5 – 10-4 S/m at ionospheric peak, a factor 10-100 higher than estimated previously by remote measurements, while the Hall conductivities are very close to 0 or in fact negative. We show that this is an effect of dusty plasma. Another effect is that ionospheric dynamo region thickness is increased to 300-800 km. Furthermore, our results suggest a temporal variation (decrease) of the plasma densities, mean ion masses and consequently the conductivities over the period of one month.
Machine-learning the parcel-model simulations of the paths from aerosols to raindrops: activation, condensation, and collision-coalescence
Authors: Takeishi Azusa , Wang Chien
Published: March 2021

Processes that convert small cloud droplets, on the order of tens of micrometers, into raindrops, on the order of millimeters, consist of condensational growth and collision-coalescence: the former is efficient for small droplets, whereas the latter becomes predominant later in the growth stage when droplets are larger than about 30 micrometers. Thus, how droplets can quickly grow to 30 micrometers solely by inefficient condensation has been a topic of discussion for a long time. As a result, many parameterizations used in current models that cannot directly resolve these processes are actually based on empirical estimates. Recently, some studies have shown the impact of turbulences that can enhance collision-coalescence for droplets smaller than 30 micrometers, explaining the fast growth of cloud droplets into raindrops as observed. We have implemented these new equations of collision-coalescence in a parcel model where the activation of aerosol particles and their condensational growth are also explicitly calculated based on physical equations across numerous size bins. After the successful implementation of these processes, we have then applied machine-learning algorithms of training a machine to mimic the behavior of the explicit physical model to model-simulated mass and number of raindrops alongside ten dynamical and microphysical variables as input features. The machine-learned results are also compared with those from existing parameterizations frequently used in regional and climate models. Furthermore, the use of this new machine-learning-based parameterization, covering processes from aerosol activation to the formation of raindrops, in a regional model will be discussed.
Stochastic differential equations for modeling of nonlinear wave-particle interaction
Authors: Lukin Alexander , Artemyev Anton , Petrukovich Anatoly
Published: March 2021

The charged particle resonant interaction with electromagnetic waves propagating in an inhomogeneous plasma determines the dynamics of plasma populations in various space plasma systems, such as shock waves, radiation belts, and plasma injection regions. For systems with small wave amplitudes and a broad wave spectrum, such resonant interaction is well described within a framework of the quasi-linear theory, which is based on the Fokker-Planck diffusion equation. However, in systems with intense waves, this approach is inapplicable, because nonlinear resonant effects (such as phase bunching and phase trapping) and non-diffusive processes play an essential role in the acceleration and scattering of charged particles. In this work we consider a generalized approach for modelling of wave-particle resonant interaction for intense coherent waves. This approach is based on application of stochastic differential equations for simulation resonant scattering and trapping. To test and verify an applicability of this approach, we use a simple model system with high-amplitude electrostatic whistler waves and energetic electrons propagating in the Earth radiation belts. We show that the proper determination of the model parameters allows us to describe the dynamics of the electron distribution function evolutions dominated by nonlinear resonant effects. Moreover, the proposed approach significantly reduces the calculation time in comparison with test particles methods generally used for simulations of nonlinear wave-particle interactions.
Machine learning-based allocation of renewable power production
Authors: Zeyringer Marianne , Sirotko-Sibirskaya Natalia , Benth Fred Espen
Published: March 2021

The integration of renewable energy sources into the power grid is of the utmost importance for achieving the goal of zero carbon emission. Although there are feasibility studies showing that renewable energy might be able to cover 2050 global energy demand using less than 1 % of the world's land for footprint and spacing, see Jacobson and Delucchi (2011), nowadays renewable energy production is known to be highly intermittent due to substantial uncertainties in the weather conditions. One possibility to reduce such uncertainty (besides storage and employing hydrogen technologies) is spatiotemporally diversified allocation of renewable power capacities which (alongside with the transmission infrastructure) should guarantee that the power demand is met at any given time with a certain (high) probability. We treat the question of spatiotemporal diversification of renewable capacities as a Markowitz portfolio problem with the difference that instead of n = 1, …, N stocks we have geographical locations each with a certain expected level of renewable power production (instead of expected returns for stocks) and the corresponding variance. Another difference to a classical Markowitz portfolio problem is that we require additionally that at each given time point t = 1, …, T, we can reach a predetermined level of renewable power production with a certain probability, i. e. we solve so called chance-constrained problem. Finally, instead of solving one-step problem as it is the case with a Markowitz portfolio we reformulate our problem in the optimal control framework in continuous time and solve it with a reinforcement learning algorithm as suggested in Lillicrap et al. (2019). The advantage of this approach is that the optimal capacities (control) are updated continuously as a response to changing weather conditions (state). We exemplify our approach with the data from ERA5 data, see Hersbach et al. (2020), and suggest possible allocation of renewable energy sources across the European Union.
Root Exudates Alters Nutrient Transport in Soil
Authors: Paporisch Amit , Harel Bavli Harel , Strickman Rachel , Neuman Rebecca , Schwartz Nimrod
Published: March 2021

Root exudates affect the physical properties of the rhizosphere, but how these changes affect its solute transport properties is unknown. Understanding how exudates affect the rhizosphere’s transport properties could advance the knowledge on nutrient dynamics in soil and its availability to plants. In the current study, we tested the effects of two exudates (chia mucilage and wheat root exudates) on the transport of iodide and potassium in soil. Solute breakthrough experiments, conducted in saturated loamy sand or coarser textured quartz sand, revealed that increasing the exudate concentration in soil results in increasingly non-equilibrium transport of both solutes. This was demonstrated by an initial solute breakthrough at a lower pore volume, followed by the arrival of the peak solute concentration at a higher pore volume. These patterns were more pronounced in soil mixed with mucilage, and in the quartz sand. An equilibrium or a physical non-equilibrium mobile-immobile transport model, fitted to the measured results, indicated an increase in the fraction of immobile water when increasing the exudates’ concentration in soil. For example, the estimated fraction of immobile water was up from 0 in quartz sand without exudates to 0.75 at a mucilage concentration of 0.2% in quartz sand. The solutes’ breakthrough under variably saturated conditions was also altered by the exudates, demonstrated by higher amounts of the solutes measured per volume of water extracted from soil mixed with exudates, compared to soil without exudates. The results indicate that exudates have a major effect on the rhizosphere’s transport properties, most likely since in its presence low-conducting flow paths are formed, resulting in a physical non-equilibrium during solute transport.
Can Saharan dust deposition impact snow stability in the French Alps?
Authors: Dick Oscar , Viallon-Galinier Léo , Hagenmuller Pascal , Fructus Mathieu , Lafaysse Matthieu , Dumont Marie
Published: March 2021

Mineral dust and black carbon are potent drivers of the snow cover evolution. After their deposition on the snow surface, they can impact snow albedo and thus the snowpack evolution including the timing of snow-melt. While BC deposition is rather constant along the winter season, mineral dust deposition is more sporadic in the French Alps, subject to large dust outbreak events coming from Sahara. The dust deposition drastically changes the snow color, its absorption of solar energy and, as a consequence, modifies the internal temperature of the snow layers and their metamorphism. While mountain practitioners often report higher avalanche activities after dust deposition events, there is, up to now, no clear evidence neither from observations nor modelling that dust deposition enhances avalanche activity. Here, we investigate, using ensemble detailed snowpack simulations, the impact of dust outbreak on snow metamorphism, snow stratigraphy and mechanical stability by comparing simulations with and without dust deposition under several meteorological conditions. The results show that the dust deposition can impact the spatial and temporal distribution of the unstable slopes. The effect of the deposition largely depends on the timing of dust deposition with respect to subsequent snowfalls. It also depends on the elevation, the aspect and the time since deposition event. By using multiphysics simulations, we were able to assess the robustness of our conclusions with respect to snowpack modelling errors.
Numerical investigation of fracture aperture heterogeneity on performance of geothermal reservoir in EGS
Authors: Zhou Dejian , Tatomir Alexandru , Sauter Martin
Published: March 2021

In the attempt to reduce the CO2 emissions and dependence on fossil fuels geothermal energy started to receive increased scientific interest. With the development of the Enhanced Geothermal System (EGS) technology, extensive geothermal energy applications have become feasible. However, the geothermal reservoirs are usually situated several kilometers below the ground whichmeans the experiments within the geothermal reservoir are difficult to be implemented. Therefore, the models capable of simulating thermohydraulic (TH) effects were the common approaches to analyzing geothermal reservoir efficiency. To simulate fluid migration and heat propagation within the fractured geothermal reservoir in EGS, discrete fracture models (DFMs) of the TH processes  were widely used. However, the heterogeneity of the fracture apertures is most of the times ignored in these models. In this work, considering the aperture heterogeneity, a DFM of the TH processes was established. It is assumed the apertures follow a normal distribution. The outlet temperature and energy production rate are employed to evaluate the efficiency of the geothermal reservoir. The results of the simulation show that the heterogeneity of the aperture strongly affects the performance of the geothermal reservoir. At the end of simulation, the variation in outlet temperature decreased by approximately 20% and the average produced energy had a reduction of over 26%. Furthermore, the average produced energy has an inversely proportional relationship with the aperture heterogeneity. Finally, several statistical realizations of the fracture network were generated to test and verify if the influence from aperture heterogeneity are generally valid. 