bluuebunny/crossref_metadata_2025 huggingface dataset:
Fire history and the relationship with late Holocene anthropogenic activity in Tăul Mare peat bog in the Lăpuș Mts (Eastern Carpathians). Romania
Authors: Petras Ancuta , Florescu Gabriela , Hutchinson Simon M. , Mindrescu Marcel
Published: March 2021

Predicted climate warming and increasing anthropogenic pressure on environmental resources are expected to increase wildfire risk in Central and Eastern Europe (including Romania) and thus may affect areas currently outside fire risk areas. Therefore, knowledge of the natural and anthropogenic variability of wildfire, as well as its long-term impacts on the environment can provide an important perspective and be used to enhance the environmental management of this region.

Our study aims to reconstruct fire history in relation to anthropogenic disturbances and climate variability, over the last 2000 years in a now forested but former mining area from Lăpuș Mts (Eastern Carpathians, Romania) based on an ombrotrophic peat sequence. To reconstruct past fire activity, we employed sedimentary macroscopic charcoal (counts and morphological characteristics), a widely used proxy for gaining insight into long-term fire history and vegetation burning. Ombrotrophic peat bogs are sensitive to local environmental changes and, given that the deposition of allochthonous material is exclusively atmospheric, they are ideal archives for recording charcoal fluxes resulted from vegetation burning. Past local soil/bedrock erosion and regional atmospheric pollution from (pre)historical mining were reconstructed on the basis of abiotic sediment properties such as elemental geochemistry, mineral magnetic characteristics, organic matter content and particle size. Published sources were used to extract information regarding regional climate variability and extra-local to regional vegetation history.

Results show that increases in macro-charcoal concentration, particularly the woody charcoal morphotype, were shortly followed by marked increases in heavy metal concentration and by enhanced soil and bedrock erosion, as inferred from geochemical, magnetic and grain-size proxies. This suggests increased local disturbance during intervals with mining activities and indicates the likelihood that humans used fire to clear the forests and open the access to the mining sites. Alternatively, humans could have deforested the landscape to obtain charcoal in kilns, for ore smelting. Such actions likely resulted in topsoil removal and exposed bedrock surfaces, which is supported by the increase in the concentration of detrital elements and small, topsoil-derived magnetic particles in our record. Over recent centuries, the recovery of the local environment is evident in the proxies, with low fire activity and decreased soil/bedrock erosion, which coincides with the abandonment of the mining sites. This multi-proxy study shows the impact of anthropogenic disturbances and the recovery of the local environment and can be used to predict future possible responses of the local environment to stressors.
The rain induced gamma-radiation dose rate enhancement at Järvselja SMEAR station
Authors: Hõrrak Urmas , Chen Xuemeng , Hõrrak Kristo , Rand Uko , Komsaare Kaupo , Vana Marko , Luts Aare , Junninen Heikki
Published: March 2021

The SMEAR Estonia station (58.277663 N, 27.308266 E, 36 m a.s.l.) was established in south-east of Estonia at the Järvselja Experimental Forestry in 2012 to investigate the atmosphere-biosphere interactions and atmospheric aerosol formation and growth.

In summer 2019, the gamma-radiation monitor GammaTRACER XL2-3 (Saphymo GmbH) was set up at Järvselja station and the rain sensor DRD11A (Vaisala Oyj) in autumn 2019. These devices enable to measure the gamma-radiation dose rate and precipitation intensity, which affect the ionization rate of atmospheric air close to ground, with high accuracy and time resolution, and complement our measurement system of atmospheric ions and aerosol particles.

The gamma-radiation dose rate measurements at about 1.2 m above the ground reveled on relatively steady background about 70 nSv/h occasional events with increase up to about 110 nSv/h, which correlated well with rainfall intensity. Commonly such events last 3-4 hours, but in specific meteorological situation with continuous long-lasting rain and air mass movement from southerly directions the effect can last 2-3 days, resulting in gradual increase in gamma-radiation dose rate level during about 24 h.

Such a phenomenon is known to occur due to wet deposition of radioactive aerosol particles during rain, namely due to the radon (222 Rn) short-lived daughter progeny products (Po-218, Pb-214, Bi-214) attached to atmospheric aerosol particles. The radon (222 Rn) daughter progeny involvement is confirmed by simultaneous gamma-spectrometric measurements with SARA AGS711F (Envinet GmbH) at Tõravere station (58° 15' 52,9" N, 26° 27' 42,1", 72 m), located about 50.3 km west from the Järvselja SMEAR station. The gamma dose rates showed very similar temporal behavior when both stations were affected by the same air mass with precipitation zone passing over the stations.

To our best knowledge, the details of rain-induced enhancement of gamma-radiation dose rate and atmospheric processes behind the phenomenon are not well known and are worth future investigations. The events of rain induced gamma-radiation dose rate enhancement at Järvselja SMEAR and Tõravere station are analyzed and discussed in more detail in the presentation and the spatial representativity of the phenomenon is estimated based on the gamma-radiation monitoring network data of Estonian Early Warning System.
Spatio-temporal ensemble predictions for wind and solar energy combining dispersion modelling methods and machine learning
Authors: Schicker Irene , Papazek Petrina , Perrone Elisa , Arnold Delia
Published: March 2021

With the increasing usage of renewable energy systems to meet the climate agreement aims accurate predictions of the possible amount of energy production stemming from renewable energy systems are needed. The need for such predictions and their uncertainty is manifold: to estimate the load on the power grid, to take measures in case of too much/not enough renewable energy with reduced nuclear energy availability, rescheduling/adjusting of energy production,  maintenance, trading, and more. Furthermore, TSOs and energy providers need the information as finegrained, spatially and temporarily, as possible, on third level hub or even on solar farm / wind turbine level for a comparatively large area.

These needs pose a challenge to numerical weather prediction (NWP) post-processing methods. Typically, one uses selected NWP fields aswell as observations, if available, as input in post-processing methods. Here, we combine two post-processing methods namely a neural network and random forest approach with the Flex_extract algorithm. Flex_extract is the pre-processing algorithm for the langrangian particle dispersion model FLEXPART and the trajectory model FLEXTRA. Flex_extract uses the three-dimensional wind fields of the NWP model and calculates additionally the instantaneous surfaces fluxes. Thus, coupling Flex_extract with a machine learning post-processing algorithm enables the usage of native NWP fields with a higher vertical accuracy than pressure levels. To generate an ensmeble in post-processing from deterministic sources different tools are available. Here, we will apply the Schaake Shuffle. 

In this study a neural network and random forest approach for probabilistic forecasting with a high horizontal grid resolution (1 km ) as well as a high temporal forecasting frequency of wind speed and global horizontal irradiance for Austria will be presented. Evaluation will be carried out against gridded analysis fields and observations.
Determining frequency dependence of carbon turnover in peat using spectral induced polarization
Authors: Katona Timea , Gilfedder Benjamin , Frei Sven , Aigner Lukas , Bücker Matthias , Flores Orozco Adrian
Published: March 2021

Our study discusses imaging results from a spectral induced polarization (SIP) survey to identify concurring processes (such as aerobic respiration, denitrification, or sulfate- and iron reduction) in a biogeochemically active peat in a wetland located in the Lehstenbach catchment in Southeastern Germany. Terrestrial wetland ecosystems such as peatlands are a critical element in the global carbon cycle. Due to their role as natural carbon sinks and ecological importance for an array of flora and fauna, there is a growing demand to conserve and restore degraded peatlands. Biogeochemical processes occur with non-uniform reaction rates within the peat, making the environment sensitive to physical disturbances. To investigate biogeochemical processes in-situ, it is important to avoid disturbing the redox-sensitive conditions in the subsurface by bringing oxygen into anoxic areas.  Our previous study demonstrated that the induced polarization (IP) was able to identify biogeochemically active and inactive areas of the peat. The IP response was sensitive to the presence of carbon turnover and P release in the absence of iron sulfide. These highly polarizable areas have high iron concentrations, but most likely in an oxidized form. As most iron oxides are poor conductors, the strong polarization response is unlikely related to an electrode polarization process.

Here we also analyzed the frequency dependence of the SIP data to investigate whether iron oxides and carbon-iron complexes, two possible mechanisms for the high polarization response, can be distinguished. SIP imaging data sets covered the frequency range between 0.06 and 225 Hz and were collected with varying electrode spacing (20 and 50 cm) at different locations within the Waldstein catchment characterized by different properties, e.g., saturated and non-saturated soils. Our imaging results reveal variations of the IP effect within the peat layer, indicating substantial heterogeneities in the peat composition and biogeochemical activity. The frequency dependence allowed us to resolve a sharper contrast between the different features of the peat. Geochemical analyses on a freeze core and pore water samples are used to validate our results and find correlations between the Cole-Cole parameters of the SIP response and the geochemical parameters.
The economic impacts of Climate Change to flood risk across the Central Asia Regional Economic Cooperation (CAREC)
Authors: Willis Iain , Shao Alex , Optiz-Stapleton Sarah
Published: March 2021

This case study documents the application of multi-RCM derived Intensity-Duration-Frequency (IDF) curves to assess the changing nature of probabilistic flood risk in the CAREC region at future time horizons.

In this study, multi-model precipitation extremes under RCP4.5 and RCP8.5 at future climate horizons (e.g. 2040s and 2070s) are used to derive alternative views of flood risk and damage potential across the eleven countries (Afghanistan, Azerbaijan, China (Inner Mongolia Autonomous Region; Xinjiang Uyghur Autonomous Region), Georgia, Kazakhstan, Kyrgyz Republic, Mongolia, Pakistan, Tajikistan, Turkmenistan and Uzbekistan) within the Central Asia Regional Economic Cooperation (CAREC).

Multiple regional climate model (RCM) daily precipitation data from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are first bias corrected through non-parametric quantile mapping. Quantile mapping is an approach used to reduce systematic biases in RCM precipitation, particularly extremes, by adjusting the historical modeled precipitation distributions against observations and carrying the transformation forward to adjust future projections. The bias-corrected projections are used to derive sub-country level Intensity-Duration-Frequency (IDF) curves before being combined with a 10,000 year stochastic simulation of river and surface water flood event set to derive change factors in baseline hydrology for river gauges and gridded precipitation points across Central Asia. These change factors have been used to create a series of alternative stochastic flood event sets for the various time horizons and emission scenarios, which in turn, are then analysed against the GED4ALL economic exposure data and a detailed taxonomy of fragility curves to assess the economic impact of climate change in all CAREC countries. The study captures the complex and non-linear relationship between climate change and flood risk across a diverse continent. In turn, focus is given to how these findings may affect key global planning horizons with regard to disaster risk financing and sustainable development. 
Imprint of the ocean mesoscale activity on air-sea-ice interactions in a regional coupled model of the Adélie Land sector
Authors: Huot Pierre-Vincent , Kittel Christoph , Fichefet Thierry , Jourdain Nicolas , Fettweis Xavier
Published: March 2021

The mesoscale activity of the ocean – eddies and fronts with dimensions ranging from 1 to 100 km which populate the Southern Ocean – is thought to modulate air-sea interactions due to its imprint on the sea surface conditions. However, very little is known about the effects of the mesoscale activity on the exchanges between the ocean and the atmosphere of polar regions. The smaller deformation radius and the seasonal sea ice coverage suggest that air-sea interactions at the mesoscale could be very different at high latitude. In this study, we examine how mesoscale ocean eddies affect the state of the atmosphere and the air-sea interactions in polar regions. We use a regional, eddy resolving ocean-sea ice-atmosphere coupled model (NEMO-LIM 1/24° and MAR at 10 km) of the Southern Ocean off the Adélie Land sector, in East Antarctica. We describe the imprint of the eddies on the near surface atmosphere with specific attention to the effect of the sea ice. The role of feedbacks between the air, sea and ice is further investigated. A series of experiments is carried out where the signature of the mesoscale variability on the sea surface is filtered out before the exchange with the atmosphere model. We use these experiments to explore the role of the modulation of air-sea-ice interactions by the ocean mesoscale activity in the evolution of the ocean, sea ice and atmosphere near the Marginal Ice Zone on daily to seasonal time scales. This study advances our understanding of the poorly explored role of the eddies on air-sea interactions in the polar regions.
Timing, thresholds and processes for complete future Greenland deglaciation
Authors: Vizcaino Miren , Petrini Michele , Sellevold Raymond , Georgiou Sotiria , Muntjewerf Laura , Lipscomb William , Leguy Gunter
Published: March 2021

The Greenland ice sheet is currently losing mass at a rate of 0.8 mm of global mean sea level rise (SLR) per year. Here, we simulate its future evolution under an idealized scenario of high greenhouse gas forcing (1% increase per year until four times pre-industrial CO2).  To this end, we use the newly, bi-directionally coupled Community Earth System Model version 2 and Community Ice Sheet Model version 2 (CESM2-CISM2, Muntjewerf et al, GRL, 2020), that includes an advanced calculation of the surface mass balance in the land component with elevation classes downscaling to CISM. Deglaciation rates increase from 2 mm SLR per year by simulation year 140 (or time of CO2 stabilization) to 7 mm SLR per year two centuries later as the ablation areas expand and net solar radiation and turbulent (latent, sensible) heat fluxes become the dominant energy sources for melt. The ice sheet retreats to an ice cap in the interior of the northern half of Greenland, that melts completely by simulation year 1,700. We compare the Greenland climate evolution with a CESM2 simulation with fixed topography, and evaluate the role of vegetation, clouds, precipitation, and surface energy fluxes on the relatively fast decay of the ice sheet. In addition, we use a set of CISM2 simulations forced with CESM2 SMB to estimate the global warming/forcing threshold for complete deglaciation.
Coda-attenuation imaging of the North Anatolian Fault Zone, northern Turkey
Authors: Sketsiou Panayiota , De Siena Luca , Cornwell David G.
Published: March 2021

The North Anatolian Fault (NAF), a right-lateral strike-slip fault spanning 1500 km in length, stretches across northern Turkey and it marks the boundary between the Eurasian and Anatolian plates. Nucleating in the east, at the Karliova triple junction and reaching the Aegean Sea at the west, it is a particularly active fault zone with a series of migrating high-magnitude earthquakes. Using 6445 Z-component waveforms from a temporary seismic network in the area (Dense Array for North Anatolia – DANA), this study aims to investigate the western part of the NAF, which splays into a northern and southern branch. Coda attenuation imaging is utilised for imaging the absorption characteristics of the area, as it can be used as a marker for source and dynamic Earth processes due to its higher sensitivity to small variations of lithospheric properties compared to seismic velocity. The absorption structure is recovered by inverting for the coda attenuation quality factor, Qc, at frequencies between 3-18 Hz, using sensitivity kernels. The extensive seismicity in the area, as well as the density of the seismic stations, provide high-resolution models of 0.04-0.05 degrees in spacing. The scattering structure of the region is imaged using peak-delay time, which is used as a direct measure for multiple forward scattering. Preliminary results show a clear change in scattering between the Istanbul and Sakarya zones, north and south of the fault respectively, with the scattering increasing from north to south at lower frequencies and decreasing at higher frequencies. At a smaller scale, absorption and scattering anomalies appear to outline contrasting geological units beneath the DANA network.
The trace elements in the snow cover of the 2018-2019 season in the Novy Urengoy region (Arctic part of Western Siberia)
Authors: Belyanin Dmitriy , Vosel Yuliya , Mezina Kseniya , Melgunov Mikhail , Dobretsov Vladimir
Published: March 2021

Revealing background concentrations of chemical elements and radionuclides in the surface components of the environment exposed to constant atmospheric processes is the first step towards detecting areas with their abnormally high concentrations of natural and man-made character. In this work, we present the results of studying the content of trace elements and microparticles in the snow cover accumulated during the 2018-2019 winter season in the Novy Urengoy region. Samples were taken along the roads using a rare sampling grid over the entire depth of the snow cover. In laboratory conditions, after the snow melted, the solution was filtered. The results of mass spectrometric measurements of the trace element concentrations in the filtrates show that their composition is homogeneous and does not vary slightly at the sampling points. Evaluation of the prevailing directions of backward air-mass trajectories was computed using the HYSPLIT model.

This work was supported by the Russian Science Foundation grant (project No 18-77-10039). Analytical studies were carried out at the Center for multi-elemental and isotope research SB RAS.
Fault evolution, scaling relationships and hazard
Authors: Faure Walker Joanna , Iezzi Francesco , Roberts Gerald
Published: March 2021

Changes in fault geometry, throw-rates and slip-rates along the length of a fault are crucial for understanding fault evolution and interaction and need to be incorporated in interpretation of fault scaling relationships and earthquake hazard assessments. Normal fault examples from Iceland and Italy provide examples of soft linkage, breach faults, and bends in faults that can be used to investigate fault growth at different stages of fault linkage. We find that at all stages of fault linkage studied, bends in strike along a fault affect throw-rate profiles along the fault. Crucially, for fault-based seismic hazard assessment, we need to consider how we interpret throw-rate and slip-rate profiles along a fault because how we interpret slip-rate profiles will impact moment release calculations and hence recurrence intervals. We therefore need detailed data regarding fault geometry and slip-rates to inform fault-based seismic hazard assessments, uncertainties and where further study is needed.
Spectral reflectivity variations of Brassica napus depending on degree of soil moisture
Authors: Davidovich Yury
Published: March 2021

Studying of the optical properties of agricultural vegetation is one of the methods for plants condition estimation, prediction of their development and changes influenced by natural and anthropogenic factors.

The work is dedicated to the investigation of spectral reflectance function of agricultural Brassica napus taking into account the degree of soil moisture. When most of the agricultural lands in Belarus are covered with vegetation in summer, employing the optical properties of agricultural vegetation for deciphering the soil depends on the degree of soil moisture. Insufficient numbers of days in year when the soil is not covered by vegetation or is in a plowed state requires in-situ optical measurements, because there are more than 50 % cloudy conditions in the year, especially in spring and autumn time.

The study has been carried out near the Minsk 11.06.2020 (53.837004º N, 27.487597º E) in clear, cloudless day. The relief for investigated field is hilly-ridge, characterized by a predominance of elevation marks from 250 to 300 m and it is actively sown field. During the spectrometric measurements, the field has been sown with Brassica napus in the phenological phase of pod formation.

When studying the spectral reflectance of Brassica napus, in-situ spectrometric measurements and analysis of a multispectral image have been carried out. Spectrometric measurements have been carried out by FSR-02 spectrometer (spectral range 400-900 nm, spectral resolution 4.3 nm) aiming to retrieve spectral reflectance function.

The normalized vegetation index NDVI has been used for analyzing the multispectral image from Landsat 8 OLI system with a spatial resolution of 30 m. The results of a study of the correlation between the reflection coefficient of Brassica napus and the area of observed soils will be presented. In addition, the results of the analysis of quasi-synchronous values of the NDVI index and in-situ measurements of the spectral reflectance of Brassica napus will be discussed.
Using Gradient Boosting Regressors to forecast the ambient solar wind from coronal magnetic models
Authors: Bailey Rachel , Reiss Martin A. , Möstl Christian , Arge C. Nick , Henney Carl , Owens Matt , Amerstorfer Ute , Amerstorfer Tanja , Weiss Andreas , Hinterreiter Jürgen
Published: March 2021

In this study we present a method for forecasting the ambient solar wind at L1 from coronal magnetic models. Ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and accurately modelling and forecasting the ambient solar wind flow is therefore imperative to space weather awareness. We describe a novel machine learning approach in which solutions from models of the solar corona based on 12 different ADAPT magnetic maps are used to output the solar wind conditions some days later at the Earth. A feature analysis is carried out to determine which input variables are most important. The results of the forecasting model are compared to observations and existing models for one whole solar cycle in a comprehensive validation analysis. We find that the new model outperforms existing models and 27-day persistence in almost all metrics. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth, and is specifically well-suited for ensemble modelling or for application with other coronal models.
Effects of improved interpolation in the wet-scavenging scheme of FLEXPART
Authors: Tipka Anne , Seibert Petra
Published: March 2021

The Lagrangian dispersion model FLEXPART v10.4 uses cloud water content, temperature, and precipitation rates to calculate wet scavenging. Currently, only precipitation fields are interpolated spatially to the particle positions. A simple nearest-neighbour approach is used for cloud parameters and temperature. This is made worse by the fact that precipitation fields from the European Centre for Medium Range Weather Forecasts (ECMWF) are temporal integrals whereas all the other parameters refer to a specific time. The pre-processor flex_extract disaggregates the precipitation fields to construct point values that can preserve the integral quantity when interpolated in FLEXPART. However, this method does not preserve precipitation in each time interval, leading to smoothing, or even shifting precipitation into dry periods.

We have implemented interpolation of all fields relevant for wet scavenging in FLEXPART v10.4 as well as the option to use our improved precipitation disaggregation scheme (https://doi.org/10.5194/gmd-11-2503-2018). It introduces two additional subgrid points within one original time interval. This secures consistency, continuity and mass conservation of precipitation within each time interval.

These updates lead to a massive improvement of the wet deposition fields in a specific test case where we applied a high-resolution outgrid that makes the effects of interpolation issues more visible. Originally, a kind of checkerboard pattern was visible, as well as a banded structure due to the finite time interval between meteorological input fields. Both features are mostly eliminated now. Additionally, the influence of varying the temporal and spatial resolution of the ECMWF input fields was investigated, and the benefit of using the ECMWF cloud water content instead of parametrised values. We also look at the impact of the new version on other, previously used test cases, for example, a lifetime analysis of aerosol particles as well as transport of mineral dust and black carbon.
Classifying Copper-Molybdenum-Gold Porphyry Deposit Alteration using Magnetic and Spectral data
Authors: Dixon Oliver , McCarthy William , Madani Nasser , Petronis Michael , McRobbie Steve , Cloutier Jonathan
Published: March 2021

Copper is one of the most important critical metal resources needed to achieve carbon neutrality with a projected increase in demand of >300% over the next half century from electronics and renewables.  Porphyry deposits account for most of the global copper production, but the discovery of new reserves is ever more challenging. Machine learning presents an opportunity to cross reference new and traditionally under-utilised data sets with a view to developing quantitative predictive models of hydrothermal alteration zones to guide new, ambitious exploration programs.

The aim of this study is to demonstrate a new alteration classification scheme driven by quantitative magnetic and spectral data to feed a machine learning algorithm. The benefits of an alteration model based on quantitative data rather than subjective observations by geologists, are that there is no bias in the data collected, the arising model is quantifiable and therefore easy to model and the process be fully automated. Ultimately, this approach aids more detailed exploration and mine modelling, in turn, reducing the extraction process carbon footprint and more effectively identifying new deposits.

Presented here are magnetic susceptibility and shortwave infrared (SWIR) data collected from the KazMinerals plc. owned Aktogay Cu-Mo giant porphyry deposit, eastern Kazakhstan, which has a throughput of 30Mtpa of ore. These data are cross referenced using a newly developed machine learning algorithm. Generated autonomously, our results reveal twelve statistically and geologically significant clusters that define a new alteration classification for porphyry style mineralisation. Results are entirely non-subjective, reproducible, quantitative and modellable.

Importantly, magnetic susceptibility measurements improve the algorithm’s ability to identify clusters by between 29-36%; enhancing the sophistication of the included magnetic data promises to yield substantially better statistical results. Magnetic remanence data are therefore being complied on representative samples from each of the twelve identified clusters, including hysteresis, isothermal remanent magnetisation (IRM) acquisition, FORC measurements, natural remanent magnetisation (NRM) and anhysteretic remanent magnetisation (ARM). Through collaboration with industry partners, we aim to develop an automated means of collecting these magnetic remanence data to accompany the machine learning algorithm.
The Amazonian Low-Level Jet and its effect on Ozone concentrations above the rain forest
Authors: Wolff Stefan , Walter David , Tsokankunku Anywhere , Brondani Daiane , Rossato Fernando , Jones Sam , Brill Sebastian , Pfannerstill Eva , Edtbauer Achim , Souza Rodrigo , de Oliveira Sá Marta , de Araújo Alessandro C. , Dias-Júnior Cléo Q. , Pöhlker Christopher , Sörgel Matthias
Published: March 2021

The pristine Amazon rainforest is a unique place to study ozone (O3) deposition rates and tropospheric transport, due to the absence of nearby sources of anthropogenic pollution. Parts of the low background O3 are considered to be transported from the stratosphere into the troposphere. This occurs due to general entrainment of stratospheric air at the tropopause. Within the troposphere, downdrafts provide effective vertical mixing and are known to increase surface O3 values. Low-level jets can also enhance O3 concentrations due to long range transport and locally induced mixing in the nocturnal boundary layer. Therefore, we study these phenomena based on long term datasets from 2012 to present from tall measurements towers (80 m and 325 m).

Ozone mixing ratios were measured at the ATTO site (Amazon Tall Tower Observatory) in the Central Amazon (02°08’38.8’’S, 58°59’59.5’’W) since 2012 at 8 different heights between 5 cm and 80 meters and additional measurements from 80 m up to 325 meters are running since 2017. From 2015 to 2017, 3-dimensional wind measurements have been performed in 150 meters height in 10 Hz sampling rate, showing evidences for the formation of a nocturnal low-level jet (LLJ), which leads to higher turbulent mixing inside the residual layer/ stable nocturnal layer. We were comparing the nocturnal LLJ with downdrafts of air due to strong thunderstorms which led to increases of O3 as well. We are analyzing these events regarding their in-canopy air exchange, their frequency and seasonality and comparing them with the effects of the nocturnal LLJ. As the data series comprises more than eight years of data we are also analyzing the interannual variability.
Shear banding in elasto-plastic slumping process on a GPU: mechanical and hydromechanical MPM solver
Authors: Jaboyedoff Michel , Wyser Emmanuel , Podladchikov Yury
Published: March 2021

Strain localization plays an important role in the mechanical response of a slumping mass and defines the overall behaviour of such process. We study strain localization with the help of the Material Point Method (MPM), which is well-suited to simulate large deformation problem.

We implemented both mechanical and hydromechanical (i.e., we assume fully saturated conditions of the material) MPM-based solvers within a rate-dependent formulation framework under a GPU architecture. We selected an explicit MPM formulation enriched with the Generalized Interpolation Material Point (GIMP) variant, which fixes a major flaw of MPM, i.e., the cell-crossing error. To avoid spurious oscillation of the pressure field (due to the use of low-order elements) for both solid and liquid phase, we used an element-based averaging technique. This minimizes volumetric locking problems. This numerical framework allows to study high-resolution two-dimensional elasto-plastic (i.e., Mohr-Coulomb plasticity) problems in an affordable amount of time. The solvers were written in a CUDA C environment on a single Nvidia GPU. We report a speed-up factor of 500 compared to a similar MATLAB implementation.

Our results showcase a contribution of pore water pressures over shear banding. In particular, we report a significant influence of the liquid phase over the steady thickness of the shear bands and their location. Pore pressures add a viscous contribution to the elasto-plastic rheological model we choose, i.e., Mohr-Coulomb.

As a future perspective, even high resolution could be achieved considering the extension of the actual implementation toward a multi-GPU solver using MPI.
Observation of the cryosphere by altimetry: past, present and future contributions
Authors: Fleury Sara , Shepherd Andrew , Humbert Angelika , Helm Veit
Published: March 2021

Thanks to the relatively high inclination (81.5°N/S) of the ERS2, Envisat, CryoSat-2, Saral and S3 space altimeters, the Polar Regions have been observed continuously by radar altimetry since the 1990s. We thus have time series over nearly 30 years of the topography of the polar ice caps and the thickness of the ice pack.  However, these measurements took a qualitative leap forward with the launch of CryoSat-2 in 2010, thanks to the advent of SAR/SARIN altimetry and a near-polar inclination of 88°N/S.

SAR/SARIN altimetry has led to considerable improvements in measurement accuracy thanks to better focusing (reducing the footprint by a factor of about 100) and better resolution (by a factor of about 2). The inclination of 88°N/S provides us with almost complete coverage of the Polar Regions, enabling us to carry out 10-year assessments of polar caps and sea-ice volume variations.

During this presentation, we will first show the many scientific advances made possible by polar altimetry and its various evolutions, including the high-precision lidar solution on board NASA's IceSat-2 satellite.

We will then present the HPCM CRISTAL mission, the only new polar altimetry mission planned to date.  We will see the technical advances proposed by this mission and its importance in monitoring the Polar Regions in the context of global warming.
Advancements in the mesoscale Hydrologic Model at the global scale
Authors: Rakovec Oldrich , Kaluza Maren , Kumar Rohini , Schweppe Robert , Shrestha Pallav , Thober Stephan , Mueller Sebastian , Attinger Sabine , Samaniego Luis
Published: March 2021

This study synthesizes the advancements made in the setup of the mesoscale Hydrologic Model (mHM; [1,2,3]) at the global scale. Underlying vegetation and geophysical characteristics are provided at ≈200m, while the mHM simulates water fluxes and states between 10 km and 100 km spatial resolution. The meteorologic forcing data are derived from the readily available, near-real time ERA-5 dataset [4]. The total of 50 global parameters of the Multiscale Parameter Regionalization (MPR) are constrained in two modes: (1) streamflow only across 3054 gauges, and (2) streamflow across 3054 gauges and simultaneously with FLUXNET ET and GRACE TWSA across 258 domains consisting of ≈10° x 10° blocks. Model performance is finally evaluated against a range of observed and reference data since 1985. 

The single best parameter set evaluated across 3054 GRDC global streamflow station yield median performance of 0.47 daily KGE (0.55 monthly KGE). This performance varies strongly between continents. For example, median daily KGE across Europe is around 0.55 (N basins=972) and across northern America around 0.5 (N basins=1264). So far, the worst model performance is observed across Africa, with median KGE of 0 (N basins=202), using the same globally constrained parameter set. The deterioration of model performance based on seamless parameterization can be explained by the quality of the underlying data, which corresponds to areas, where water balance closure error is the biggest. Additionally, missed model processes play an important role as well. Finally, there remains a large gap between the onsite calibrations and global calibrations and ongoing research is being done to narrow down these differences. This work is the fundament for building skillful global seasonal forecasting system ULYSSES [6], which provides hindcasts and operational seasonal forecasts of hydrologic variables using four state of the art hydrologic/land surface models with lead time of 6 months.

[1] https://www.ufz.de/mhm

[2] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008WR007327

[3] https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012WR012195

[4] https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803

[5] https://www.ufz.de/ulysses
Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry
Authors: Johnson Thomas , Tsamados Michel , Muller Jan-Peter , Stroeve Julienne
Published: March 2021

Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident roughness is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using nested cross-validation, and product performance is assessed with independent validation. We present a derived sea ice roughness product at 1.1km resolution over a two-decade timespan (1999 – 2020) and a corresponding time series analysis by region. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.
Impact of higher spatial resolution on the representation of Canary upwelling system
Authors: Sylla Adama , Gomez Emilia Sanchez , Parages Jorge Lopes , Mignot Juliette
Published: March 2021

The oceanic region located off the of the Iberian Peninsula at 43°N to south of Senegal at about 10°N, coasts is one of the most productive in the world in terms of marine ecosystems. This is due to the presence of the Canary Upwelling System (CUS). This upwelling region is separated into two distinct areas: the Iberian coast and the Northwest African coast. Improving our knowledge of the functioning and long term changes in the CUS is of crucial importance, since the much of the food resources and economy of neighboring countries greatly depends on its characteristics. Most of research efforts aimed at the understanding of the functioning of the CUS and its seasonal to long term variations, are based on observations and regional models operating at very high resolution. However, observational datasets based on satellite products, which are suitable to study upwelling systems, cover short periods of time, which does not allow for a robust estimate of long-term variations (i.e. climate change) of the upwellings and the associated mechanisms. The use of very high-resolution regional ocean models leads to a correct representation of the physical mechanisms associated to the upwellings, but the numerical experiments entail an important computational cost, which also limits the study of long-term changes. Standard coupled ocean-atmosphere models, such as those used in the international exercises like Coupled Model Experiment Phase (CMIP), provide an interesting alternative to study decadal to long-term changes in the upwellings. Recently, studies based on coupled models, focusing on the response of the upwellings to climate change, have received increasing attention. However, these studies show contradictory results on the question whether coastal upwelling will be more intense or weak in the next decades. One of the reasons for this uncertainty is the low resolution of climate models, making it difficult to properly resolve coastal zone processes.

The main goal of this study is to evaluate the ability of an ensemble of global coupled models in simulating the properties of the CUS (seasonal cycle, intensity and thermal signatures). The numerical experiments used here were performed within the H2020 PRIMAVERA European project, which is part of the HighResMIP initiative at European level. We will use pairs of models operating at diverse nominal resolutions under present-day climate conditions. Our objective will be to study the impact of model resolution in the representation of the CUS.

 
High Energy Particle Spectrometer for ESA Lagrange mission
Authors: Hajdas Wojtek , Marcinkowski Radoslaw , Xiao Hualin , Kramert Ronny
Published: March 2021

The LGR High Energy Particle Spectrometer HEPS for the ESA Lagrange mission belongs to the satellite in-situ instrument suite. The satellite will be placed at the Lagrange point L5 for space weather measurements and real-time observations and alerts. The HEPS instrument with its six detector subsystems will enable the detecting of electrons, protons, and heavy ions at high flux conditions during Solar Energetic Particle Events. The electron and proton detection systems rely on standard telescope techniques covering energy ranges from 100 keV to 15 MeV and 3 MeV to 1 GeV respectively. Two sets of telescopes will be installed facing opposite directions along the Parker spiral. Additional detector with a wide angular range will enable measurements of angular distributions of particles traveling towards the satellite from the Sun. The HEPS heavy-ion telescope HIT represents a new design utilizing a set of scintillators and SiPM light converters. HIT electronics is equipped with a dedicated radiation-tolerant ASIC optimized for low power use and fast signal detections. The first model of HIT was developed and verified for spectroscopic measurements and ion identification. We report on test measurements as well as Monte Carlo simulations of the whole instrument. Results will be discussed and implications on the final design of the instrument provided.
Improving global land subsidence analysis
Authors: Ezquerro Pablo , Herrera-García Gerardo , Tomás Roberto , Béjar-Pizarro Marta , López-Vinielles Juan , Rossi Mauro , Mateos Rosa M. , Carreón-Freyre Dora , Lambert John , Teatini Pietro , Cabral-Cano Enrique , Erkens Gilles , Galloway Devin , Hung Wei-Chia , Kakar Najeebullah , Sneed Michelle , Tosi Luigi , Wang Hanmei , Ye Shujun
Published: March 2021

Land subsidence associated with groundwater withdrawal is often an underestimated geological hazard that may produce important damage to buildings and infrastructure, change flood risk in some areas, and cause loss of groundwater storage capacity. In the current framework of global climate change, the increasing agricultural and urban use of groundwater resources is a growing problem, especially in arid and semiarid areas. Because monitoring subsidence in these areas is important for management, but early detection is difficult due to slow displacement rates, we developed global groundwater induced land subsidence probability maps.  Global land subsidence probability was calculated by applying statistical methods to a set of susceptible geographical, environmental and geological properties based on known, documented subsidence affected areas. Highest values of subsidence probability are concentrated over flat areas composed of unconsolidated sediments, and in agricultural or urban areas subject to prolonged dry periods. Including water scarcity and groundwater use data resulted in an estimation of a proxy land subsidence hazard. Calculated probability does not imply that all the high value areas are currently incurring land subsidence, but it can alert policymakers and groundwater managers to areas that have potential exposure to subsidence hazards and warrant monitoring. The complete results of this work are published in Science Policy Forum section under the title “Mapping the global threat of land subsidence” DOI: 10.1126/science.abb8549
Generation of a high-resolution snow cover dataset from Sentinel-2 images for snow model calibration 
Authors: Hofmeister Florentin , Arias-Rodriguez Leonardo F. , Borga Marco , Premier Valentina , Marin Carlo , Notarnicola Claudia , Disse Markus , Chiogna Gabriele
Published: March 2021

Modeling the runoff generation of high elevation Alpine catchments requires fundamental knowledge of the snow storage and the spatial distribution of snow cover. Since in-situ snow observations are often very scarce and represent only a point information, spatial snow information from satellite data is used since decades. However, the accuracy of snow cover maps through remote sensing products depends strongly on the cloudiness. In order to generate a spatial and temporal highly resolved dataset of snow cover maps, we applied the pixel identification processor (IdePix available in SNAP v7.0) to retrieve diverse cloud layers from Sentinel-2 Level-1C products. This makes it possible to use also high-clouded images for the snow detection, which increases significantly the data availability for the later performed snow model calibration. Cloudy areas, for which snow detection by the NDSI calculation is not possible, are set to no data. Sentinel-2 images that do not have cloud information require an extra correction based on the assumption that the snow cover has a pronounced elevation gradient. The entire NDSI dataset is subdivided into 200 m elevation zones and statistically analyzed. Thereby, the cloud-influenced images clearly stand out as outliers in the elevation zones >3000 m. If an elevation zone is detected as an outlier, the corresponding elevation zone is set to no data as well. After the comprehensive cloud detection, a pixel wise comparison with in-situ snow depth observation of four different sites allows us a first validation of the snow detection quality. In a second step, the generated snow maps are compared with the snow and cloud detection algorithm developed by Eurac Research. The final snow cover maps are used together with the in-situ snow depth observations to calibrate two different snowmelt approaches of the hydrological model WaSiM - the T-index and the energy balance-based approach (including gravitational snow redistribution) - over a mountainous basin in the Eastern Italian Alps.