bluuebunny/crossref_metadata_2025 huggingface dataset:
Natural disturbances and protective effect: the role of biological legacies in protection forests
Authors: Costa Maximiliano , Marchi Niccolò , Trevisan Irene , Marangon Davide , Lingua Emanuele
Published: March 2021

Natural disturbance regimes are expected to be greatly altered in the next future byclimatechanges (e.g.increase in frequency and intensity, changing in seasonality). Among natural disturbances, windstorms represent one of the main large-scale factor that shape European landscape and that influence European forest structure. Moreover, windstorms may affect ecosystem services that are normally provided by mountain forests such as protection against natural hazards, conservation of biodiversity or erosion mitigation. However, after a disturbance event, structural biological legacies, like deadwood, may enhance or maintain some of these ecosystem services. After a stand-replacing event, the conservation or fast restoration of all these services should be the target of post disturbance management, but currently traditional practices (mainly salvage logging) are often leading to their depletion. The study of the impact of salvage logging (i.e. the removal of almost all the biological legacies) on the protective function of mountain stands has been poorly addressed. Structural biological legacies (i.e. snags, logs, stumps) may provide protection for the natural regeneration as well as they may increase the terrain roughness, providing a shielding effect against gravitative hazards like rockfall. The aim of the present study was to investigate how biological legacies affect the multifunctionality of mountain forests, focusing on the protective function. To observe the role of biological legacies we performed software simulations of rockfall activity on windthrown areas located in the Dolomites, region highly affected by the Vaia windstorm in October 2018.  Results showed the short-term important role of biological legacies in mitigating rockfall propagation, mainly as barrier effect rather than an energy reduction effect. After a natural disturbance, forest management should take into consideration the residual protective function of structural legacies. Salvage logging operations should be limited in areas where rockfall hazard is high, in order to take advantage on the multifunctionality of biological legacies during the recovery process.
Monitoring agroecological transformation processes induced by climate and agricultural innovations over time and space
Authors: Spiegel Heide , Miloczki Julia , Freyer Bernhard , Surböck Andreas , Friedel Jürgen K. , Kaul Hans-Peter , Wagentristl Helmut , Schaumberger Andreas , Mayer Renate , Bohner Andreas , Gaube Veronika , Sandén Taru
Published: March 2021

Sustainable agricultural production of food, feed, fibre and fuel with limited agricultural land to cover human demands and at the same time to secure natural resources is currently one of the biggest global challenges. Changes in agricultural management to ensure fertile soils, stable yields and product qualities and to avoid adverse environmental impacts, affect various soil and plant characteristics, agrobiodiversity and the micro-climate of agroecosystems.

Long-term field experiments (LTEs) are indispensable to detect and understand impacts of climate (drought, heat, floods, frost) and agricultural innovations on soils and plants. Amongst agricultural innovations are adaptions of crop rotations to climate change, efficient fertilisation systems with and without livestock, reduced soil tillage intensity, the conversion of a whole landscape section from conventional to organic farming and introducing landscape elements like flowering strips or hegdes that serve, e.g., as habitats for pollinators and beneficials.

For the evaluation of impacts of climate change and agricultural innovations, researchers of agricultural long-term ecological research (LTER) sites in Austria have developed indicators to enable the systematic comparison of long-term trials impact on soil-plant systems in different agroecological zones of Austria and Europe, respectively, including different agro-ecosystems, e.g., arable land and grassland. Examples for soil indicators include soil characteristics like organic carbon, nutrients and contaminants, biological and physical (e.g., porosity, structure) indicators that have already been measured since many years in various field experiments. Embedded in long-term socio-ecological regions (LTSER), which allow analyzing long-term socio-economic and biophysical drivers of change in agricultural management, these agricultural LTER sites contribute crucial insights into the interaction between nature and society.
Scattering of Oblique Electromagnetic Waves by a Thin Conducting Wire Parallel to the Ambient Magnetic Field in a Non-Gyrotropic Plasma in the Resonance Frequency Range
Authors: Shirokov Evgenii
Published: March 2021

A problem of scattering of oblique plane electromagnetic waves propagating in a cold non-gyrotropic plasma in the resonance frequency range by a thin finite-length conducting wire parallel to the ambient magnetic field is considered. The solution to the scattering theory integral equation for the current induced on the wire surface as well as the scattering field and cross section are found and analyzed. The approach is based on the perturbation theory that takes into account the thin wire approximation generalized to the case of the anisotropic plasma. Special attention is paid to the case of highly oblique quasi-electrostatic waves which scattering characteristics are quite unique. The results are important for analysis of (a) reception of electromagnetic waves in the space plasma using antennas onboard spacecraft and (b) diffraction of electromagnetic waves by long field-aligned plasma density irregularities in planetary magnetospheres under certain conditions.

This work was supported by the Russian Science Foundation under grant 20-12-00268.
Recent dust variability over South Asia controlled by North Atlantic SST anomalies
Authors: Banerjee Priyanka , Satheesh Sreedharan Krishnakumari , Krishna Moorthy Krishnaswamy
Published: March 2021

Several studies have associated high dust years over South Asia to warming of the central or eastern equatorial Pacific Ocean (El Nino conditions) and the resulting weakening of the summer monsoon. Using satellite aerosol data for 2001-2018, we show that there has been a departure from this relation since the second decade of the 21st century with the North Atlantic Ocean emerging as a major driver of interannual variability of dust over South Asia. This change in relation coincides with the end of the global warming hiatus and a shift towards persistent positive phase of the winter North Atlantic Oscillation (NAO). Positive phase of the NAO induces cold phase of the spring/summer North Atlantic sea surface temperature (SST) tripole pattern. We show here that high dust activity during 2011-2018 is associated with positive SST anomaly over the mid-latitude North Atlantic and negative SST anomaly over the sub-tropical North Atlantic: the two southern arms of the SST tripole pattern. Interestingly, the relation between NAO and these two southern arms of the SST tripole has undergone changes in recent years, which has impacted the South Asian monsoon. The result is general drying over South Asia and an increase in the strength of the dust-carrying northwesterlies. Simulations with the Community Earth System Model (CESM) shows that SST tripole-like anomalies recorded during 2011-2018 over the North Atlantic can generate mid-latitude wave train that weakens the South Asian monsoon circulation, leads to surface high pressure anomalies and increase in dust emission and transport over northwest India and Pakistan. Most of the increase in the dust load can be attributed to enhanced transport at 800 hPa pressure level during May-June, which can lead to ~40-50% increase in dust concentrations at this level.
Light availability and light demand of plants shape the arbuscular mycorrhizal fungal communities in their roots 
Authors: Neuenkamp Lena , Zobel Martin , Koorem Kadri , Jairus Teele , Davison John , Öpik Maarja , Vasar Martti , Moora Mari
Published: March 2021

Woody plant encroachment is influencing many open, grassy ecosystems across the globe, such as savanna, tundra and temperate grassland ecosystems. Drivers of woody plant encroachment are local land use change and global climate change, with shifts in grazing and mowing regimes as important local drivers and elevated CO2 levels, higher temperature and altered precipitation amounts as global drivers. Encroachment of woody species into open, herbaceous ecosystems comes along with substantial shifts in soil conditions, a reduction light availability and ultimately vegetation shifts in the understorey towards species better adapted to the ambient conditions. While vegetation shifts in response to woody plant encroachment in grassy ecosystems have been frequently investigated, e.g. regarding altered plant composition and functional traits related to resource acquisition and dispersal, consequences for biotic interactions have been less studied.

The symbiosis of plant roots with mycorrhizal fungi is one of the most relevant biotic interaction for plants species, with over 90% of all plants forming mycorrhizal symbiosis and arbuscular mycorrhizal symbiosis as the most prominent mycorrhizal type among herbaceous species. Plants involved in the arbuscular mycorrhizal (AM) symbiosis trade photosynthetically derived carbon for fungal-provided soil nutrients. However, little is known about how plant light demand and ambient light conditions influence root-associating AM fungal communities, and thus their response to prominent climate change processes like shrub encroachment.

We conducted a manipulative field experiment to test whether plants’ shade tolerance influences their root AM fungal communities in open and shaded grassland sites. We found that light-dependent shifts in AM fungal community structure were similar for experimental bait plant roots and the surrounding soil. Yet, lower AM fungal beta and gamma diversity for shade-intolerant plants in shade likely reflected preferential carbon allocation to specific AM fungi due to the limited plant carbon available to support symbiotic fungi. We conclude that favourable environmental conditions, including optimal light availability, widen the plant biotic niche, i.e. selectivity for specific AM fungi is reduced, and compatibility with a larger number of AM fungal taxa is promoted. With respect to predicted stronger woody plant encroachment predicted under current climate change scenarios, these results indicate that we might be losing AM fungal diversity and the functions associated with these fungal taxa. This calls for continous investment into conservation efforts and management practices to counteract this trend and keep savanna, tundra and grassland ecosystems open. 
The Influence of Ozone Feedbacks on Surface Climate following Spring Arctic Ozone Depletion
Authors: Friedel Marina , Chiodo Gabriel , Stenke Andrea , Domeisen Daniela , Muthers Stefan , Anet Julien , Peter Thomas
Published: March 2021

Links between springtime Arctic stratospheric ozone anomalies and anomalous surface weather in the Northern Hemisphere have been found recently. Stratospheric ozone thus provides valuable information which may help to improve seasonal predictability. However, the extent and causality of the ozone-surface climate coupling remain unclear and many state-of-the-art forecast models lack any representation of ozone feedbacks on planetary circulation.

We investigate the importance of the ozone-surface climate coupling with two Chemistry Climate Models, contrasting simulations with fully interactive ozone against prescribed zonally averaged climatological ozone under fixed present-day boundary conditions. We focus on springtime Arctic ozone minima and compare subsequent surface patterns in runs with and without interactive ozone, thus rendering a detailed and physically-based quantification of the stratospheric ozone impact on surface climate possible.  

All model simulations show a connection between Arctic ozone minima and a positive phase of the Arctic Oscillation in the month after the depletion in spring. Runs with interactive ozone chemistry show an amplified surface response and a 40% stronger Arctic Oscillation index after ozone depletion. This amplified Arctic Oscillation goes along with enhanced positive surface temperature anomalies over Eurasia. Moreover, composite surface patterns after spring ozone minima in model simulations with interactive ozone show a better agreement with composites in reanalysis data compared to runs with prescribed ozone.

Mechanisms whereby stratospheric ozone affects both the stratospheric and tropospheric circulation are explored. These include the reduction of short-wave heating over the pole due to ozone loss, thus amplifying stratospheric temperature anomalies and allowing for an intensification of the polar vortex with subsequent impacts on wave propagation and the stratospheric meridional circulation. This suggests that ozone is not only passively responding to stratospheric dynamics, but actively feeds back into the circulation. Following these results, stratospheric ozone anomalies actively contribute to anomalous surface weather in spring, emphasizing the potential importance of interactive ozone chemistry for seasonal predictions.
Shear banding in a micromechanics-based brittle damage model
Authors: Petit Léo , Olive Jean-Arthur , S. Bhat Harsha , Le Pourhiet Laetitia , Schubnel Alexandre
Published: March 2021

The Earth’s brittle upper crust is commonly modeled as a non-associated Mohr-Coulomb (MC) elasto-plastic continuum. This framework enables the localization of shear strain through a process referred to as structural softening: dilatancy related to the build-up of plastic strain inside a shear band can elastically unload the surrounding material as principal stresses rotate inside the band. The strains required to weaken the material and corresponding stress drops are compatible with experimental observations, and provide useful theoretical insights into strain softening parameterizations used in numerical geodynamics. This model however does not account for time-dependent behavior documented in rock deformation experiments, such as the loading rate dependence of the peak strength, and sample failure under a fixed applied stress in brittle creep tests. It also relies on macroscopic properties (e.g., dilatancy angle) which are not straightforwardly related to micro-mechanical and micro-structural rock properties. The MC model thus inherently carries an empirical parameterization which can be an obstacle to a deeper understanding of brittle inelastic deformation. 

On the other hand, models that account for time-dependent brittle behavior typically invoke the development of tensile microcracks around shear defects, and derive macroscopic constitutive laws from the micro-mechanics of fracture growth and interaction through a damage state variable. To investigate whether this class of models can account for the time-dependence of strain localization, we perform post-bifurcation analysis on the damage rheology constructed by Ashby & Sammis (1990), coupled with a stress corrosion law for crack growth kinetics. We calculate the co-evolution of stress and 2-D plane strain at a point located within an incipient damage shear band, and at a nearby point in the surrounding rock where damage cannot accumulate. We prescribe a constant shear strain rate within the band, enforce strain compatibility and stress continuity across the shear band boundary, and integrate the incremental constitutive relationships through time.

Dilatancy related to tensile crack growth in the band enables elastic unloading of the surrounding medium. In our simulations, this manifests as a sudden drop in shear stress coincident with a sharp increase in band damage. We characterize the localization phenomenon through the magnitude of both this stress drop and damage increase, and assess their sensitivity to macroscopic parameters such as shear strain rate, shear band orientation, confining pressure, as well as micro-mechanical parameters such as the orientation of shear defects, the stress exponent of the crack growth law, and the initial damage. This type of work may pave the way toward micromechanics-based parameterizations of brittle deformation in long-term tectonic models.
Features of the diffusive methane emission measurements on lakes by chamber method
Authors: Krivenok Liudmila , Kazantsev Vladimir
Published: March 2021

Methane is one of the main greenhouse gases in the atmosphere. Lakes are the third-largest natural source of methane on a global scale [Kirschke et al., 2013]. Currently, the chamber method is quite often applied in the measurements of diffusive GHG emissions from natural ecosystems, especially in remote areas, due to low cost and mobility. In lakes, methane can be transported to the atmosphere not only by diffusion but also by bubbling, so during measurements, it is important to divide these two pathways. We have complemented customary floating chambers with plastic shields located underside not blocking diffusive transfer but preventing gas bubbles from entering into the chamber.

The study was conducted on August 19–20, 2019 in the vicinity of Vaskiny Dachi field station (68.8663° N, 70.3040° E, Central Yamal, Western Siberia, Russia). Measurements were carried out in the central part of the thermokarst lake with a depth of 1.6 m. To compare results of customary and modified chambers, samples were taken in parallel from chambers with and without shields (two chambers of each type) every two hours during the day. Sampling and flux calculations were conducted according to [Bastviken et al., 2010]. The methane concentrations in samples were determined in the laboratory by a Crystal 5000.2 gas chromatograph with a flame ionization detector.

According to the sign test for the 0.05 p-level, methane fluxes measured using chambers with and without shields differ statistically significant considering their diurnal dynamics. At the same time, within the group of fluxes measured by the same type of chambers, no statistically significant differences were found, and mean and median flux values are higher for chambers without shields. It appears that observed differences are not due to natural variability, but due to the contribution of bubble component to the fluxes measured by chambers without shields.

 

References

Bastviken D., Santoro A.L., Marotta H. et al. 2010. Methane emissions from Pantanal, South America, during the low water season: toward more comprehensive sampling. – Environmental Science & Technology, vol. 44, No. 14, pp. 5450–5455.

Kirschke S., Bousquet P., Ciais P. et al. 2013. Three decades of global methane sources and sinks. – Nature Geoscience, vol. 6, No. 10, pp. 813–823.
SPAGETTA Weather Generator Linked with Climate Models May Produce Weather Series for Future Climate 
Authors: Dubrovsky Martin , Lhotka Ondrej , Miksovsky Jiri , Stepanek Petr , Meitner Jan
Published: March 2021

Stochastic weather generators (WGs) are tools for producing weather series, which are statistically similar to the real world weather series. The synthetic series may represent both present and changed (not only the future) climate. In the latter case, WG parameters derived from the observed weather series are modified with climate change scenario, which is typically based on RCM or GCM simulations. As the GCM/RCM simulations are very demanding on computer resources, the numbers of simulations made for individual possible emission scenarios are limited, especially for some (mostly the less probable ones) emission scenarios (e.g. RCP 2.6). Still, many climate change impact studies try to give projections of the CC impacts assuming uncertainties coming from all possible sources, including the modeling uncertainty and  uncertainties in emissions & climate sensitivity. To allow generation of weather series fitting the projection of any GCM forced by any emission scenario, we use a pattern scaling approach, in which the standardized climate change scenario (consisting of changes in climatic characteristic related to 1ºC change in global mean temperature) derived from a given GCM is multiplied by a change in global mean temperature (dTg) projected (for a selected emission scenario and climate sensitivity) by a simple climate model MAGICC.

In our contribution, we will demonstrate the use of the generator (using SPAGETTA WG, which is our multi-site multi-variate parametric daily WG) in probabilistic projection of future changes in selected climatic characteristics of temperature (T) and precipitation (P); we will focus on spatial hot/cold/dry/wet/hot-dry/hot-wet/cold-dry/cold-wet spells). Standardized climate change scenarios will be derived from multiple GCMs (taken from CMIP5 database) and scaled by dTg projected by MAGICC. Effects of the three above-named sources of uncertainty, as well as the effects of changes in individual statistical characteristics (the means & the site-specific variabilities & the characteristics of the temporal and spatial variability of both T and P) will be assessed.

Acknowledgements: Projects GRIMASA (Czech Science Foundation, project no. 18-15958S) and SustES (European Structural and Investment Funds, project no. CZ.02.1.01/0.0/0.0/16_019/0000797).
Performance Assessment of GNSS-R Polarimetric Observations for Sea Level Monitoring
Authors: Rajabi Mahmoud , Hoseini Mstafa , Nahavandchi Hossein , Semmling Maximilian , Ramatschi Markus , Goli Mehdi , Haas Rüdiger , Wickert Jens
Published: March 2021

Determination and monitoring of the mean sea level especially in the coastal areas are essential, environmentally, and as a vertical datum. Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) is an innovative way which is becoming a reliable alternative for coastal sea-level altimetry. Comparing to traditional tide gauges, GNSS-R can offer different parameters of sea surface, one of which is the sea level. The measurements derived from this technique can cover wider areas of the sea surface in contrast to point-wise observations of a tide gauge.  

We use long-term ground-based GNSS-R observations to estimate sea level. The dataset includes one-year data from January to December 2016. The data was collected by a coastal GNSS-R experiment at the Onsala space observatory in Sweden. The experiment utilizes three antennas with different polarization designs and orientations. The setup has one up-looking, and two sea-looking antennas at about 3 meters above the sea surface level. The up-looking antenna is Right-Handed Circular Polarization (RHCP). The sea-looking antennas with RHCP and Left-Handed Circular Polarization (LHCP) are used for capturing sea reflected Global Positioning System (GPS) signals. A dedicated reflectometry receiver (GORS type) provides In-phase and Quadrature (I/Q) correlation sums for each antenna based on the captured interferometric signal. The generated time series of I/Q samples from different satellites are analyzed using the Least Squares Harmonic Estimation (LSHE) method. This method is a multivariate analysis tool which can flexibly retrieve the frequencies of a time series regardless of possible gaps or unevenly spaced sampling. The interferometric frequency, which is related to the reflection geometry and sea level, is obtained by LSHE with a temporal resolution of 15 minutes. The sea level is calculated based on this frequency in six modes from the three antennas in GPS L1 and L2 signals.

Our investigation shows that the sea-looking antennas perform better compared to the up-looking antenna. The highest accuracy is achieved using the sea-looking LHCP antenna and GPS L1 signal. The annual Root Mean Square Error (RMSE) of 15-min GNSS-R water level time series compared to tide gauge observations is 3.7 (L1) and 5.2 (L2) cm for sea-looking LHCP, 5.8 (L1) and 9.1 (L2) cm for sea-looking RHCP, 6.2 (L1) and 8.5 (L2) cm for up-looking RHCP. It is worth noting that the GPS IIR block satellites show lower accuracy due to the lack of L2C code. Therefore, the L2 observations from this block are eliminated.
Microplastic regulation should be more precise to incentivize both innovation and environmental safety
Authors: Mitrano Denise , Wohlleben Wendel
Published: March 2021

Numerous studies have made the ubiquitous presence of plastic in the environment undeniable, and thus it no longer comes as a surprise when scientists monitor the accumulation of macroplastic litter and microplastic fragments in both urban and remote sites. The presence of plastic in the environment has sparked considerable discussion amongst scientists, regulators and the general public as to how industrialization and consumerism is shaping our world. Restrictions on the intentional use of primary microplastics, small solid polymer particles in applications ranging from agriculture to cosmetics, are under discussion globally, despite uncertain microplastic hazards and prioritization amongst options for action. In some instances, replacements are technically simple and easily justified, but in others substitutions may come with more uncertainty such as significant performance questions and monetary costs. Scientific impact assessment of primary microplastics compared to their alternatives relies on a number of factors including, but not limited to, microplastic harm, existence of replacement materials, and the quality, cost and hazards of alternate materials. Here we assess the scope, effectiveness and utility of microplastic regulations with specific emphasis on the new definitions proposed by ECHA for restriction of primary microplastics under REACH. To this end, we aim to 1) provide a systematic orientation of the polymer universe, to appreciate which (micro)plastic characteristics are relevant, measurable and enforceable, 2) cluster specific uses of solid plastic to highlight how primary microplastic can add to issues of environmental pollution and human health, 3) evaluate drivers leading to regulations and their potential for enforceability and impact and 4) suggest priority cases where regulations should be focused and precision increased to incentivize innovation of sustainable materials and promote environmental health and safety. Regulations need a precise focus and must be enforceable by measurements. Policy must carefully evaluate under which contexts microplastic use may be warranted and where incentives to replace certain microplastics can stimulate innovation of new, more competitive and environmentally conscious materials. 
Trends in snowmelt rates over Europe inferred from historical snow depth observations converted to SWE
Authors: Fontrodona-Bach Adrià , Larsen Josh , Woods Ross , Schaefli Bettina , Teuling Ryan
Published: March 2021

There is strong evidence that rising temperatures mostly lead to less snow accumulation and to an earlier melt onset. However, changes in the frequency and intensity of snowmelt events remain unclear. While higher temperatures should intuitively lead to faster snowmelt, some studies find that melt rates are slower because the melt onset occurs earlier in the year when less energy is available for melt. Modelling of these snow dynamics is challenged by a lack of continuous observations on water content of the snowpack, the highly sought after SWE. However, high quality observations of snow depth can be more available in both space and time, even at higher altitudes. Therefore, an increasing number of models try to estimate SWE from snow depth and other variables. Here we first investigate if these models accurately reproduce the snow accumulation and melt dynamics, and to what extent they can be used for hydrological studies. We then convert a long-term pan-European snow depth dataset to SWE by making use of these models and we assess model performance. Historical trends of snowmelt rates, melt onset, and frequency and intensity of melt events are shown for several seasonal snow locations across Europe. Trends across a variety of timescales are generally weak and spatially inhomogeneous, suggesting local conditions dominate over regional climate trends. However, it seems that under the current climate change conditions, the decrease of snowpack depth over most of Europe causes snowpacks to melt faster (i.e. in less days) than before. 
Predicting post-fire vegetation recovery patterns in three different forest types
Authors: Maria Floriana Spatola , Marco Borghetti , Angelo Rita , Angelo Nolè
Published: March 2021

Wildfire disturbances severely modifies the ecosystem structure and natural regeneration processes. Predicting mid- to long-term post-fire vegetation recovery patterns, is pivotal to improve post-fire management and restoration of burned areas forest ecosystems management. Currently, many research efforts have been conducted, in order to monitor and predict wildfires, using Machine Learning and Remote sensing techniques. Instead, the method proposed in this study combines Satellite images and Data Mining algorithm to process data collected by time series and regional forest dataset to predict post-fire vegetation recovery patterns. For this reason, we analysed Normalized Burn Ratio (NBR) patterns from Landsat Time series (LTS), to assess post-fire vegetation recovery for several wildfires that occurred in three different forest Corine Land Cover classes (311, 312, 313) in the Basilicata region during the period 2005-2012. Random Forest model, was used to classify the observed recovery patterns and investigate the influence of burn severity, topographic variables, climate and spectral vegetation indices on post-fire recovery. Image acquisition and Random Forest classifier was undertaken in Google Earth Engine (GEE). Results of bootstrapping classification, across forest type, show high percentage for high recovered (HR) classes and moderate recovered (MR) classes and moderate-low percentage for low (LR) and unrecovered (UR) classes. Specifically, in the holm- and cork-oak and oak forests show medium to high recovery rates, while Mediterranean pine and conifer-oak forests show the slowest recovery rates. Different post-fire recovery patterns are related to fire severity, vegetation type and post-fire environmental conditions. Our methodology shows that post-fire recovery classification, using RF classifier provides a robust method for both local and broad scale monitoring for mid- to long-term recovery response.

Keywords: Wildfires, Post-fire recovery, Landsat Time Series (LTS), Google Earth Engine, Wildfires, Machine Learning, Random Forest.
In terms of radiative forcing, not all BC emissions are equal
Authors: Räisänen Petri , Partanen Antti-Ilari , Makkonen Risto , Merikanto Joonas , Savolahti Mikko , Kirkevåg Alf , Sand Maria , Seland Øyvind
Published: March 2021

The development of robust emission metrics to guide climate policy is more complicated for short-lived climate forcers like black carbon (BC) than for long-lived greenhouse gases like CO2. The challenge is that for short-lived climate forcers, the atmospheric concentrations, the radiative forcing (RF), and ultimately, effects on climate, depend on the location and timing of the emissions.  In the present work, the impact of emission location and season on the RF resulting from emissions of BC is studied using the NorESM1 climate model. NorESM1 is run in a configuration in which the distribution of aerosols is simulated using a state-of-the-art aerosol scheme, but the interactive aerosols are not allowed to influence the simulated meteorological conditions. Consequently, the patterns of weather are repeated identically irrespective of the assumed aerosol emissions. This allows for an essentially noise-free evaluation of the radiative forcing associated with changes in aerosol emissions, irrespective of the magnitude and spatiotemporal extent of the emission changes.

We employ the model to systematically evaluate the radiative forcing efficiency (i.e., global-mean RF divided by the emissions) of BC emissions, for various assumptions about the latitude, longitude and season of the emissions. The BC direct effect and the effect of BC on snow albedo are considered. Preliminary results from tests focusing on BC emissions in the subarctic region (60-70°N) indicate the RF efficiency depends strongly both on the timing and longitude of the emissions. The RF efficiency of emissions in spring and summer is much larger than that of emissions in fall and winter, mainly due to the stronger insolation. Furthermore, emissions in the Siberian and North American sectors have higher RF efficiency than emissions in the Atlantic and European sectors. This is largely because emissions from subarctic Siberia and North America preferentially increase the atmospheric BC burden and BC deposition in regions with seasonal snow cover persisting into late spring / early summer. This acts to increase both the BC direct RF and the RF due to BC in snow. Furthermore, long atmospheric residence times act to increase the direct RF associated with Siberian BC emissions in summer.

An implication is that the use of large-scale mean (e.g., subarctic average) emission metrics may mispresent the role of BC emissions from smaller regions like individual countries.
Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach
Authors: van der Kooij Eva , Schleiss Marc , Taormina Riccardo , Fioranelli Francesco , Lugt Dorien , van Hoek Mattijn , Leijnse Hidde , Overeem Aart
Published: March 2021

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.

We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.

We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.

To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.
Hudson valley floods an unlikely trigger for Inter-Allerød Cold Period (IACP) cooling 13,300 years ago
Authors: Pendleton Simon , Condron Alan , Donnelly Jeffrey
Published: March 2021

It has long been hypothesized that periodic meltwater input from a retreating Laurentide Ice Sheet (LIS) inhibited North Atlantic deep water (NADW) formation, weakened the strength of the Atlantic Meridional Overturning Circulation (AMOC), and triggered several cold periods in the North Atlantic region during the last deglaciation (21-8ka yrs BP). Since the establishment of this hypothesis more than thirty years ago, geomorphic and chronologic evidence of meltwater flows from the LIS have been shown to roughly coincide with centennial-to-millennial scale cool periods (e.g., Younger Dryas, the 8.2 ka event).

            Here, we use realizations of the MITgcm ocean model to investigate the possibility that meltwater discharge from the Hudson River, New York City, USA ~13,300 yrs BP triggered the Inter-Allerød Cold Period (IACP). Using estimates of flood volumes, we assess the sensitivity of AMOC to both short duration (1 month), and long duration (1 year) flood events. We also evaluate the impact of successive flood events on AMOC to determine if sequential floods impact AMOC differently than a single flood event. Finally, we also assess whether the continuous background flux of meltwater from the paleo-Hudson River played role in ‘pre-conditioning’ the AMOC to weaken in response to short duration outburst floods.

We find that in all of our experiments, regardless of flood magnitude, duration, reoccurrence interval (frequency) or background meltwater flux, there is no significant weakening of the AMOC. This limited impact suggests that although the Hudson Valley floods occurred near the beginning of the IACP, they are unlikely the sole driver. Additional modeling experiments are needed to determine if the combination of multiple drainage pathways from the LIS could have been a trigger and/or contributor to the IACP.

Our results have significant implications for determining whether other isolated deglacial flood events triggered periods of climatic cooling: for example, the millennial-length Younger Dryas cooling is thought to have been triggered by a flood only 3-times larger than the one from the Hudson River, which again questions the role short-lived outburst floods played in triggering centennial to millennial scale climate cooling.
The potential of monitoring traffic conditions up to 15 times a day using sub-meter resolution EO images
Authors: Duro Refiz , Neubauer Georg , Bojor Alexandra-Ioana
Published: March 2021

Urbanization and the trend of people moving to cities often leads to problematic traffic conditions, which can be very challenging for traffic management. It can hamper the flow of people and goods, negatively affecting businesses through delays and the inability to estimate travel times and thus plan, as well as the environment and health of population due to increased fuel consumption and subsequent air pollution. Many cities have a policy and rules to manage traffic, ranging from standard traffic lights to more dynamic and adaptable solutions involving in-road sensors or cameras to actively modify the duration of traffic lights, or even more sophisticated IoT solutions to monitor and manage the conditions on a city-wide scale. The core to these technologies and to decision making processes is the availability of reliable data on traffic conditions, and better yet real-time data. Thus, a lot of cities are still coping with the lack of good spatial and temporal data coverage, as many of these solutions are requiring not only changes to the infrastructure, but also large investments.

One approach is to exploit the current and the forthcoming advancements made available by Earth Observation (EO) satellite technologies. The biggest advantage is EOs great spatial coverage ranging from a few km² to 100 km² per image on a spatial resolution down to 0.3m, thus allowing for a quick, city-spanning data collection. Furthermore, the availability of imaging sensors covering specific bands allows the constituent information within an image to be separated and the information to be leveraged.

In this respect, we present the findings of our work on multispectral image sets collected on three occasions in 2019 using very high resolution WorldView-3 satellite. We apply a combination of machine learning and PCA methods to detect vehicles and devise their kinematic properties (e.g., movement, direction, speed), only possible with satellites with a specific design allowing for short time lags between imaging in different spectral bands. As these data basically constitute a time-series, we will discuss how the results presented fully apply to the forthcoming WorldView-Legion constellation of satellites providing up to 15 revisits per day, and thus near-real time traffic monitoring and its impact on the environment.
A Regional Climate Model Laboratory for Understanding Coastal Climate
Authors: O'Brien Travis , Burkle Thomas , Krauter Michael , Trapp Thomas
Published: March 2021

Midlatitude western coastal regions are recognized as being important for the global energy cycle, marine and terrestrial biodiversity, and regional economies.  These coastal regions exhibit a rich range of weather and climate phenomena, including persistent stratocumulus clouds, sea-breeze circulations, coastally-trapped Kelvin waves, and wind-driven upwelling. During the summer season, when impacts from transient synoptic systems are relatively reduced, the local climate is governed by a complex set of interactions among the atmosphere, land, and ocean.  This complexity has so far inhibited basic understanding of the drivers of western coastal climate, climate variability, and climate change.

As a way of simplifying the system, we have developed a hierarchical regional climate model experimental framework focused on the western United States. We modify the International Centre for Theoretical Physics RegCM4 to use steady-state initial, lateral, and top-of-model boundary conditions: average July insolation (no diurnal cycle) and average meteorological state (winds, temperature, humidity, surface pressure).  This July Base State simulation rapidly reaches a steady state solution that closely resembles the observed mean climate and the mean climate achieved using RegCM4 in a standard reanalysis-driven configuration.  It is particularly notable that the near-coastal stratocumulus field is spatially similar to the satellite-observed stratocumulus field during arbitrary July days: including gaps in stratocumulus coverage downwind of capes. We run similar Base State simulations for the other calendar months and find that these simulations mimic the annual cycle.  This suggests that the summer coastal stratocumulus field results from the steady-state response of the marine boundary layer to summertime climatological forcing; if true for the real world, this would imply that stratocumulus cloud fraction, within a given month, is temporally modulated by deviations from the summer base state (e.g., transient synoptic disturbances that interrupt the cloud field).  We describe modifications to this simplified experimental framework aimed at understanding the factors that govern stratocumulus cloud fraction and its variability.
An improved representation of ephemeral pool hydrology in a semi-distributed hydrologic model
Authors: Bizhanimanzar Mohammad , Larocque Marie , Roux Marjolaine
Published: March 2021

Ephemeral pools are seasonally flooded geographically isolated wetlands with distinct hydrology i.e., they are filled in winter and spring with inflow from snowmelt, and precipitation and dry out during summer. Ephemeral pools offer a variety of biodiversity benefits notably providing breeding habitat for several amphibian and invertebrate species. The quality of their ecosystem services is mainly controlled by their hydroperiod which is regulated by hydrology i.e., inflow /outflow of the pools. The classic water budget modeling approach with a simplified representation of the flow exchange between the pool and surface-subsurface zones may not adequately reveal their sensitivity to anthropogenic interventions and climatic changes. On the other hand, the generic volume-area-depth relationship of isolated wetlands in deterministic hydrologic models may not adequately reveal their dynamic water level fluctuations. The objective of this study, in the first place, is to improve the representation of ephemeral pools in the semi-distributed SWAT hydrological model, notably in the pothole module which is used for modeling isolated wetlands. The developed model will then be used to analyze the impact of land use and climate changes on dynamics of hydroperiods of ephemeral pools of the Saumon River watershed (68 km2) in the Canadian Shield of the Outaouais region (Quebec, Canada). A detailed bathymetry survey along with a long series (one to five years) of daily water level measurements available at ten pools allowed to replace the simplified linear volume-area relationship with the measured rating curve for the ephemeral pools in this region. The calibration process of the revised model is performed using the standard SWAT calibration code (SWAT-CUP) coupled to a Particle Swarm Optimization (PSO) algorithm adjusting evaporation and seepage coefficients of the revised module for all isolated wetlands of the region. This double calibration ensures representation of both the watershed hydrology (10 years of river flow rates) and the water level fluctuations in the pools. The simulation results show that the revised SWAT version can adequately reproduce the dynamic water level behavior of the monitored pools as well as streamflow discharges. The model is currently used with scenarios of human and climatic disturbances to understand their impact on the filling-drying cycle of ephemeral pools and on the integrated hydrologic system at the watershed scale.
Quantifying Uncertainty through 3D Geological Modeling for Carbon Capture Utilization and Storage in the Unayzah Formation in Saudi Arabia
Authors: Mantilla Salas Sofia , Corrales Miguel , Hoteit Hussein , Alafifi Abdulkader , Tasianas Alexandros
Published: March 2021

The development of Carbon Capture Utilization and Storage (CCUS) technology paired with existing energy systems will facilitate a successful transition to a carbon-neutral economy that offers efficient and sustainable energy. It will also enable the survival of multiple and vital economic sectors of high-energy industries that possess few other options to decarbonize. Nowadays, just about one-ten-thousandth of the global annual emissions are being captured and geologically-stored, and therefore with today’s emission panorama, CCS large-scale deployment is more pressing than ever. In this study, a 3D model that represents the key reservoir uncertainties for a CCUS pilot was constructed to investigate the feasibility of CO2 storage in the Unayzah Formation in Saudi Arabia. The study site covers the area of the city of Riyadh and the Hawtah and Nuayyim Trends, which contain one of the most prolific petroleum-producing systems in the country. The Unayzah reservoir is highly stratified and it is subdivided into three compartments: the Unayzah C (Ghazal Member), the Unayzah B (Jawb Member), and the Unayzah A (Wudayhi and Tinat Members). This formation was deposited under a variety of environments, such as glaciofluvial, fluvial, eolian, and coastal plain. Facies probability trend maps and well log data were used to generate a facies model that accounted for the architecture, facies distribution, and lateral and vertical heterogeneity of this high complexity reservoir. Porosity and predicted permeability logs were used with Sequential Gaussian Simulation and co-kriging methods to construct the porosity and permeability models. The static model was then used for CO2 injection simulation purposes to understand the impact of the flow conduits, barriers, and baffles in CO2 flow in all dimensions. Similarly, the CO2 simulations allowed us to better understand the CO2 entrapment process and to estimate a more realistic and reliable CO2 storage capacity of the Unayzah reservoir in the area. To test the robustness of the model predictions, geological uncertainty quantification and a sensitivity analysis were run. Parameters such as porosity, permeability, pay thickness, anisotropy, and connectivity were analyzed as well as how various combinations between them affected the CO2 storage capacity, injectivity, and containment. This approach could improve the storage efficiency of CO2 exceeding 60%. The analyzed reservoir was found to be a promising storage site. The proposed workflow and findings of the static and dynamic modeling described in this publication could serve as a guideline methodology to test the feasibility of the imminent upcoming pilots and facilitate the large-scale deployment of this very promising technology.