bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Modeling of Sorption on Novel Sorbent Materials 
Authors: Koš Adna , Kuráž Michal
Published: March 2021

The emission of metal ions in the environment has increased in recent times and since metal ions are not biodegradable, they belong to the cumulative toxins. Contamination of the environment with metal ions poses a serious danger to the entire ecosystem, agricultural production, quality of food and water, as well as to the health of humans and animals. This study investigates sorption as one of the processes which can be used for pollutants removal and efficiency of certain sorbent materials. Specifically, we focus on development and validation of non-linear Langmuir model and non-linear Freundlich model. Their application in sorption experiments is examined by applying different error functions and statistical methods which are employed to calculate the error divergence between observed data and predicted data of sorbate-sorbent system. Presented non-linear sorption models are developed by using programming language Fortran, and the data analysis is obtained by using different tools and packages in programming language R. Many authors are using linear sorption models in the way that they would linearize non-linear sorption models. It is evident that linear sorption models are used due to their simplicity in parameters estimation. We use approach of trying different algorithms and tools in programming language R in order to find the best objective function. This study shows that both non-linear Langmuir model and non-linear Freundlich model can be used for experimental data representation. The results also denote that better estimation and the better fit is given by Langmuir model due to divergence in error functions and graphical representation itself. The choice of sorption model has a great influence on the prediction of solute transfer and great care should be taken in selection of convenient approach.
Unravelling the opposing effects of network clustering on electricity system models with high shares of renewables
Authors: Frysztacki Martha , Hörsch Jonas , Hagenmeyer Veit , Brown Tom
Published: March 2021

Energy systems are typically modeled with a low spatial resolution that is based on administrative boundaries such as countries, which eases data collection and reduces computation times. However, a low spatial resolution can lead to sub-optimal investment decisions for renewable generation, transmission expansion or both. Ignoring power grid bottlenecks within regions tends to underestimate system costs, while combining locations with different renewable capacity factors tends to overestimate costs. We investigate these two competing effects in a capacity expansion model for Europe’s future power system that reduces carbon emissions by 95% compared to 1990s levels, taking advantage of newly-available high-resolution data sets and computational advances. We vary the model resolution by changing the number of substations, interpolating between a 37-node model where every country and synchronous zone is modeled with one node respectively, and a 512-node model based on the location of electricity substations. If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with higher capacity factors and thus reduces system costs by up to 10.5% compared to a low resolution model. This results in a big swing from offshore to onshore wind investment. However, if we introduce grid bottlenecks by raising the network resolution, costs increase by up to 19% as generation has to be sourced more locally where demand is high, typically at sites with worse capacity factors. These effects are most pronounced in scenarios where transmission expansion is limited, for example, by low social acceptance.
Recalibrating Extremes for Decadal Predictions
Authors: Magro France-Audrey , Pasternack Alexander , Rust Henning W.
Published: March 2021

Decadal predictions have become essential for near-term decision making and adaptation strategies. In parallel, interest in weather and climate extremes has increased strongly in the past. Thus, a combination of decadal predictions and extreme value theory is reasonable and necessary. Since decadal predictions suffer from typical discrepancies, such as start- and lead-year dependent conditional and unconditional biases, many ways for their recalibration have been proposed (Eade et al., 2014; Fučkar et al.,2014; Fyfe et al., 2011; Kharin et al., 2012; Kruschke et al., 2016; Raftery et al., 2005; Sansom et al., 2016; Sloughter et al., 2007). However, in previous studies, extremes have not been considered. Therefore, the aim of this study is to investigate how extremes from decadal predictions can be adequately recalibrated and how this affects forecasting skill. Pasternack et al. (2018) introduced a parametric Decadal Climate Forecast Recalibration Strategy (DeFoReSt 1.0), based on estimating polynomial adjustment terms (Gangstø et al., 2013). DeFoReSt assumes normality for the probability distribution (PDF) to be recalibrated and optimizes the cross-validated continuous ranked probability score (CRPS) with this assumption build in Gneiting et al. (2005). For a proof of concept, Pasternack et al. (2018) introduced a toy model for generating pseudo decadal forecast-observation pairs. For toy model data and surface temperatures from MiKlip hindcasts, improvement of forecast quality over a simple calibration from Kruschke et al. (2016) has been found. We extend these methods to extreme values with two modifications: (1) Follow DeFoReSt, but assume general extreme value (GEV) distributed forecasts. Again the CRPS is optimized but with the GEV build into the score (Friederichs and Thorarinsdottir, 2012). Both DeFoReSt strategies (DeFoReSt-normaland DeFoReSt-GEV) and the calibration from Kruschke et al. (2016) are compared to a forecast based on climatology. (2) The toy model is modified to generate pseudo decadal forecast-observation pairs with GEV distributed observations. For validation, a bootstrapping scheme is applied to temperature maxima hindcasts from MiKlip verified with HadEX2 observations. After recalibration, both DeFoReSt strategies perform similar for the toy model and MiKlip hindcasts, none significantly outperforms the other. However, they consistently show considerable improvements over the climatological forecast for the lower and upper quartiles in the toy model data. For the recalibrated MiKlip hindcasts, the findings are in accordance, but not as considerable, presumably due to their very small ensemble size (Sienz et al., 2016). This suggests that extremes may be directly recalibrated with the assumption of a Normal distribution, as long as this represents the characteristics of the decadal forecast ensemble. Thus, the forecasting skill of recalibrations appears to be unaffected by the underlying distribution of the observations.
Predicting the Optical Properties of Arbitrarily Shaped Black Carbon Aerosols with Graph Neural Networks
Authors: Lamb Kara D. , Gentine Pierre
Published: March 2021

Aerosols sourced from combustion such as black carbon (BC) are important short-lived climate forcers whose direct radiative forcing and atmospheric lifetime depend on their morphology. These aerosols are typically fractal aggregates consisting of ~20-80 nm spheres. This complex morphology makes modeling their optical properties difficult, contributing to uncertainty in both their direct and indirect climate effects. Accurate and fast calculations of BC optical properties are needed for remote sensing inversions and for radiative forcing calculations in atmospheric models, but current methods to accurately calculate the optical properties of these aerosols such as the multi-sphere T-matrix method or generalized multiple-particle Mie Theory are computationally expensive and must be compiled in extensive data-bases off-line and then used as a look-up table. Recent advances in machine learning approaches have applied the graph convolutional neural network (GCN) to various physical science applications, demonstrating skill in generalizing beyond initial training data by exploiting and learning internal properties and interactions inherent to the larger system. Here we demonstrate for the first time that a GCN trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped aerosol particles, even over much larger aggregates than in the training dataset, providing a fast and accurate method to calculate aerosol optical properties in atmospheric models and for observational retrievals. This approach could be integrated into atmospheric models or remote sensing inversions to more realistically predict the physical properties of arbitrarily-shaped aerosol and cloud particles. In addition, GCN’s can be used to gain physical intuition on the relationship between large-scale properties (here of the radiative properties of aerosols) and small-scale interactions (here of the spheres’ positions and their interactions).
Drainage basins and glacier catchments for the Greenland Ice Sheet
Authors: Krieger Lukas , Floricioiu Dana
Published: March 2021

The drainage divides of ice sheets separate the overall glaciated area into multiple sectors and outlet glaciers. These catchments represent essential input data for partitioning glaciological measurements or modelling results to the individual glacier level. They specify the area over which basin specific measurements need to be integrated.

The delineation of drainage basins on ice sheets is challenging due to their gentle slopes accompanied by local terrain disturbances and complex patterns of ice movement. Therefore, in Greenland the basins have been mostly delineated along the major ice divides, which results in large drainage sectors containing multiple outlet glaciers. In [1] we developed a methodology for delineating individual glaciers that was applied to the Northeast Greenland sector and proposed slightly changed separations between 79N and Zachariae basins driven by the ice flow lines. In the present study the method is extended to the entire Greenland Ice Sheet.

We present a fully traceable approach that combines ice sheet wide velocity measurements by Sentinel-1 SAR and the 90 m TanDEM-X global DEM to derive individual glacier drainage basins for the entire Greenland Ice Sheet with a modified watershed algorithm. We delineate a total of 335 individual glacier catchments, a result triggered by the number and location of the selected seed points.

The resulting dataset will be made publicly available online and is extensible by even more granular delineations of individual tributaries upon request. The proposed approach has the potential to produce catchment areas also for the entirety of the Antarctic Ice Sheet.

 

[1] Krieger, L., D. Floricioiu, and N. Neckel (Feb. 1, 2020). “Drainage Basin Delineation for Outlet Glaciers of Northeast Greenland Based on Sentinel-1 Ice Velocities and TanDEM-X Elevations”.  In:Remote  Sensing  of  Environment 237,  p.  111483.
Flood damage assessment to economic activities: implementation and transferability of French methodology
Authors: Grelot Frédéric , Galliani Marta , Bremond Pauline , Molinari Daniela , Pugnet Lilian , Richert Claire , Ballio Francesco
Published: March 2021

Since 2010, a national method is available in France for multi-criteria analysis of flood prevention projects. The method uses national damage functions to estimate losses to the different exposed items, including economic activities. Despite the business sector suffers significant losses in case of flood, flood damage modelling to businesses is less advanced than for other exposed sectors, as e.g. residential buildings. Reasons are many and include: the high variability of activities types composing this sector and then the difficulty of standardisation (above all when contents are considered), and the lack of data to understand and quantify damage and validate existing modelling tools. The collection of damage data in two case studies, in France and in Italy, and the collaboration between two research groups in the two countries allowed to study the applicability, the validity, and the transferability of the French damage functions for economic activities to Italy. Firstly, the functions were tested and validated in a French case study, i.e. the flood that affected the Île-de-France Region in 2016. This validation exercise faced the problem of working with few information about the identity of the activities, and propose a solution; moreover, it allowed to verify the actual availability of input data to implement the functions in France and pointed out the paucity of information to validate the methodology. Testing the functions in a foreign case study, i.e. the flood occurred in 2002 in Italy in the city of Lodi, allowed instead to verify the transferability of the method.
Analyzing trends of savannah degradation in Tanzania using Google Earth Engine and INLA 
Authors: Wiethase Joris , Critchlow Rob , Beale Colin
Published: March 2021

Semiarid rangelands have been identified as at high risk of degradation as a result of changing socio-ecological conditions. Tanzanian savannahs are typical and some areas have become degraded in recent years, while other areas maintain resilience. To track pathways to degradation, we developed a workflow to create annual maps of degradation for all of Tanzania, at a high spatial (30m) and temporal (30+ years) resolution, as a function of bare ground and invasive plant cover. Making use of the freely available Google Earth Engine (GEE) computing platform, we created annual composites of Landsat remote sensing data. Using GEE machine learning algorithms, trained with data from extensive field surveys conducted in 2016, we predicted degradation scores for all of Tanzania from the Landsat composites. Our models produced significant correlations at the pixel level between test predictions and observations, rather better for the bare ground component of degradation than the invasive plants cover (bare ground r = 0.7, invasive plant cover r = 0.44). The resulting map provides an unprecedented data source for degradation in terms of extent and spatial resolution for the region. Through a novel data analysis approach using Integrated Nested Laplace Approximations (INLA), we show that degradation correlates with rainfall, human population and livestock density, as well as different management strategies. This study showcases the potential of GEE for analysing savannah degradation over large geographical areas, whilst highlighting the usefulness of INLA for this type of analysis.
Relationships between meteorological data and soil properties: quantifying precipitation and aridity in the Middle Danube Basin through geophysical proxies
Authors: Zeeden Christian , Vinnepand Mathias , Ryzner Kamila , Rolf Christian , Laag Christian , Sardar Abadi Mehrdad , Radaković Milica G. , Gavrilov Milivoj B. , Marković Slobodan B.
Published: March 2021

Spatial patterns of precipitation and aridity across Europe are most likely to vary in response to changing temperature. Our knowledge about such responses is, however, still limited as most geoscientific studies provide point data rather than cover wider areas. Such an approach would require reliable proxies, which can be determined in high spatial resolution along with detailed knowledge about how these reflect climate. Classically, we derive (paleo-) precipitation and -temperature for terrestrial areas from climofunctions, which base on magnetic susceptibility (χ) and its frequency dependence (χfd). These parameters reflect the quantity and modification state of magnetic minerals like magnetite, which are dependent on the combined influence of temperature and precipitation. Recently, also the maghemite contribution to the high-temperature dependent susceptibility (χtd) has been used to create climofunctions, which are mainly constrained to reconstruction of (palaeo-) precipitation. Yet, such relationships have mostly been reported from Asia.

In this study, we test if we can qualify and quantify (palaeo-) precipitation, temperature and aridity by room- and high-temperature rock magnetic and colorimetric data of recent topsoils in a narrow precipitation range between ~535 mm/a and 585 mm/a. The data are derived from geographically evenly distributed bulk-samples from the Backa Loess Plateau (Middle Danube Basin). Our results show that we can quantify precipitation by rock magnetic properties (χ, χfd, as well as χtd), but colorimetric methods are more challenging to interpret. While we can also reconstruct aridity, temperature is difficult to determine in a meaningful way.

In this contribution, we show the results of statistical analysis performed on a multivariate rock magnetic and colorimetric dataset, and their relation to geographical differences in prevailing climatic regimes of the Middle Danube Basin. While care needs to be taken not to overfit the data (due to more colorimetric variables than spatial data points), we regard our multivariate approach at least as relevant as trying to fit individual magnetic or colorimetric proxies to climate variables.
Effects of episodic sediment supply on experimental step-pool channel morphology, bedload transport and channel stability
Authors: Wang Jiamei , Hassan Marwan A. , Saletti Matteo , Chen Xingyu , Fu Xudong , Zhou Hongwei , Yang Xingguo
Published: March 2021

Steep step-pool streams are often coupled to adjacent hillslope, directly receiving episodic sediment supply from mass movement processes such as landslides and debris flows. The response of step-pool channels to the variations in sediment supply remains largely unexplored. We conducted flume experiments with a poorly sorted grain-size distribution in an 8%-steep, 5-m long flume with variable width at the University of British Columbia, to study the effects of episodic sediment supply on channel evolution. After a conditioning phase with no feed, the channel was subjected to sediment pulses of different magnitude and frequency under constant flow discharge. High-resolution data of hydraulics, bedload transport, bed surface grain size, and channel morphology were collected every 10-20 minutes and an additional time at the end of each pulse.

In response to sediment pulses, we recorded an increase in bedload transport rates, channel aggradation, bed surface fining, and continuous step formation and collapse. In between pulses, bedload rates dropped by several orders of magnitude, net erosion occurred, the bed surface gradually coarsened, and steps became more stable. The small-magnitude high-frequency pulses caused smaller but more frequent spikes in bedload transport, bed surface evolution, and thus step stability. Instead, the large-magnitude low-frequency pulses cause larger changes but provided a longer time for the channel to recover. This suggests that in step-pool channels pulse magnitude is a key control on channel rearrangement, while pulse frequency controls how fast and strong the recovery is.

The frequency and stability of steps varied as a function of local channel width, showing that channel geometry is a primary control on step formation and stability even under episodic sediment supply conditions. Instead, the effect of sediment pulses is less important because the total number and average survival time of steps were similar among runs with different pulses. The critical Shields stress decreased following sediment pulses, then increased immediately after, and fluctuated until the next pulse. The variations in sediment supply caused cycles in bedload transport rate, surface and bedload texture, thus controlling the variability in the threshold for motion.

Our results indicate that episodic sediment supply is a primary control on the evolution of step-pool channels, with sediment feed magnitude affecting mostly morphological changes, and sediment feed frequency controlling channel stability.
An 11 year record of GOSAT XCO2 measurements from NASA's ACOS version 9 retrieval algorithms: comparisons to models, TCCON, and OCO-2
Authors: Taylor Thomas , O'Dell Christopher , Eldering Annmarie , Crisp David , Gunson Michael , Fisher Brendan , Nelson Robert , Payne Vivienne , Lindqvist Hannakaisa , Rigel Kivi , Griffith David , Osterman Greg , Wunch Debra , Kuze Akihiko
Published: March 2021

The GOSAT TANSO-FTS sensor has been collecting high spectral resolution measurements of reflected solar radiation in the Oxygen A-band (0.76 microns) and two shortwave-infrared carbon dioxide (CO2) absorption bands (1.6 and 2.0 microns) since April, 2009. The measured radiances allow for estimates of the total column carbon dioxide (XCO2) via retrieval inversion. An eleven year long record of XCO2 retrieved via NASA’s Atmospheric Carbon Observations from Space (ACOS) build 9 software suite is analyzed and discussed. The v9 XCO2 data has been publicly available on the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) since the spring of 2020.

The ACOS GOSAT v9 XCO2  is evaluated against CO2 flux inversion models, observations from the Total Carbon Column Observation Network (TCCON), as well as against collocated measurements from NASA’s OCO-2 satellite. The results indicate a product that agrees with OCO-2 and models within approximately 0.25 ppm with less than 1 ppm standard deviation (σ). Agreement with TCCON is within approximately 0.1 ppm with approximately 1 ppm σ for daily overpass mean aggregated data. The ACOS GOSAT v9 XCO2 product will allow CO2 flux inversion modelers and terrestrial ecologists to address questions about long term (decadal) carbon cycle dynamics related to net and gross carbon fluxes.
Stability of solitary waves on deep water with constant vorticity
Authors: Dosaev Alexander , Shishina Maria , Troitskaya Yuliya
Published: March 2021

Waves on deep water with constant vorticity propagating in the direction of the shear are known to be weakly dispersive in the long wave limit. Weakly-nonlinear evolution of such waves can be described by the Benjamin-Ono equation, which is integrable and has stable soliton solutions. In the present study we investigate behaviour of finite-amplitude counterparts of Benjamin-Ono solitons by modelling their dynamics within exact equations of motion (Euler equations). Due to the solitons having a near-Lorentzian shape with slowly decaying tails, we need to approach them by examining periodic waves, whose crests, indeed, become more and more localised as the period increases. We perform a parameter space study and analyse how stability of very long waves depends on their amplitude and period. We show that large-amplitude solitary waves are unstable.
This research was supported by RFBR (grant No. 16-05-00839) and by the President of Russian Federation (grant No. MK-2041.2017.5). Numerical experiments were supported by RSF grant No. 14-17-00667, data processing was supported by RSF grant No. 15-17-20009.
Recurrent Neural Networks for Ionospheric Time Delays Prediction Using Global Navigation Satellite System Observables
Authors: Kaselimi Maria , Doulamis Nikolaos , Delikaraoglou Demitris
Published: March 2021

Total Electron Content (TEC) is the integral of the location-dependent electron density along the signal path and is a crucial parameter that is often used to describe ionospheric variability, as it is strongly affected by solar activity. TEC is highly depended on local time, latitude, longitude, season, solar and geomagnetic conditions. The propagation of the signals from GNSS (Global Navigation Satellite System) throughout the ionosphere is strongly influenced by short- and long-term changes and ionospheric regular or irregular variations. 
Long short-term memory network (LSTM) is a specific recurrent neural network architecture and is capable of learning time dependence in sequential problems and can successfully model ionosphere variability. As LSTM networks “memorize” long term correlations in a sequence, they can model complex sequences with various features, where solar radio flux at 10.7 cm and magnetic activity indices are taken into consideration to provide more accurate results. 
Here, we propose a deep learning architecture to create regional TEC models around a station. The proposed model allows different solar and geomagnetic parameters to be inserted into the model as features. Our model has been evaluated under different solar and geomagnetic conditions. Also, the proposed model is tested for different time periods and seasonal variations and for varying geographic latitudes. 
A reflexion on the environmental effect on the transmission of COVID-19
Authors: Barradas Victor L , Ballinas Monica
Published: March 2021

This research is a general reflection of the possible transmission not only of COVID-19 but of any influenza disease depending on environmental parameters such as solar radiation, air humidity and air temperature (vapor pressure deficit), evoking the Penman-Monteith model regarding the evaporation of the water that constitutes the small water droplets (aerosols) that carry the virus. In this case the evapotranspiration demand of the atmosphere with which it can be deduced that the spread of the disease will be higher in those places with less evaporative demand, that is, high air humidity and / or low temperatures, and / or low radiation intensities, and vice versa. It can also be deduced that the hours of greatest potential contagion are the night hours, while those with the lowest risk are between 2:00 p.m. and 4:00 p.m. On the other hand, in those rooms with low temperatures the contagion would be more effective. So, considering that the drops produced by a sneeze, by speaking or breathing can go beyond two meters away, it is roughly explained that the use of face masks and keeping a safe minimum distance of two meters can limit transmission of viruses and / or infections. However, this practice is not entirely safe as the environment can play an important role. What is recommended to reduce the spread of these pathogens is to produce high evaporative demands: increasing solar radiation, and increasing air temperature and reducing air humidity, which is practice that can be effective in closed rooms.
Impact of Antarctic Sudden Stratospheric Warming on Mid-Latitude Thermosphere and Ionosphere over USA and Europe
Authors: Goncharenko Larisa , Harvey V Lynn , Greer Katelynn , Zhang Shun-Rong , Coster Anthea
Published: March 2021

Limited observational evidence indicates that ionospheric changes caused by Arctic SSWs propagate to at least the middle latitudes in the Southern Hemisphere. However, it is not known if similar ionospheric anomalies are produced by Antarctic SSWs, mostly because Antarctic SSWs occur less often than the Arctic events. The sudden stratospheric warming of September 2019 has provided a perfect opportunity to investigate whether SSW are linked to upper atmospheric anomalies at middle latitudes of the opposite hemisphere. In this study we provide an overview of thermospheric and ionospheric anomalies observed in September 2019 at middle latitudes in the Northern Hemisphere. Our results indicate persistent and strong positive anomalies in total electron content and thermospheric O/N2 ratio observed in the western region of USA. Central and eastern regions of USA do not experience similar positive perturbations and show mostly moderate suppression of TEC reaching 20-40% of the baseline. Both positive and negative anomalies are observed over the central Europe. We discuss potential mechanisms that could be responsible for the observed features and suggest that regional differences in TEC response could be related to modulation of thermospheric winds by SSW and large declination angle over Western US.
Water monitoring with Very High Resolution satellite imagery
Authors: Kalaitzis Freddie , Mateo Garcia Gonzalo , Marchisio Giovanni
Published: March 2021

The water volume on Earth's surface constantly varies with precipitation: an excess of water might lead to flooding, while its absence indicates upcoming droughts. We cannot afford in-situ monitoring devices on all rivers and streams worldwide, and free satellite imagery lacks the spatial and temporal resolution for continuous monitoring.

 

This talk will provide several examples of water monitoring using PlanetScope daily imagery. The global daily coverage of Planet's data presents new opportunities for developing robust models of flood hazard, providing timely mapping in support of relief operations, and applying near real time predictive models for river flow estimation based on simultaneous measurements over entire river basins.

 

First, we will describe how satellite data enable quantitative urban flood risk analysis by intersecting building segmentation maps with high risk flood zones. Rapid urbanization in developing countries is often unplanned and carries substantial risk for critical infrastructures. More frequent and severe flooding caused by climate change is exacerbating this. We capture rapid urbanization trends in African cities from high cadence imagery, and use flood risk data to quantify the humanitarian risk from flooding.

 

Second, we will show hurricane Harvey risk areas and demonstrate flood mapping. Flood mapping through high-cadence data provides vital information to first respondents on the ground on the damage of road networks and infrastructure.

 

Third, we will present Pix2Streams: a methodology to estimate water occurrence at the stream level developed in partnership with Frontier Development Lab and the USGS.

 

Pix2Streams is a pipeline that consists of

i) a water segmentation model that fuses several days of 3m PlanetScope imagery with 1m LiDAR data that is able to detect streams 5-7m wide,

ii) integration of the output of this model with a DEM-derived flow-line map to estimate water % coverage at the stream level.

 

Applying Pix2Streams across 2 years of daily PlanetScope imagery produces the first high-resolution dynamic map of stream flow frequency.

This is a new map that - if applied over entire watersheds - could fundamentally improve how we manage our water resources around the world, and may also evolve into an early warning system for floods or droughts. In particular, calibration and validation of measurements from space against USGS gage measurements downstream and the associated time lag could be a topic of future research.
Quantifying the impact of submesoscale dynamics on the evolution of Arctic freshwater fronts
Authors: Alberty Marion , Legg Sonya , Hallberg Robert , MacKinnon Jennifer , Sprintall Janet , Alford Matthew , Mickett John , Fine Elizabeth
Published: March 2021

The dramatic decrease in Arctic sea ice has resulted in a corresponding increase in the seasonal freshwater flux due to melt water in the Canada Basin. This source of freshwater can be quite patchy as sea ice breaks aparts and melts, resulting in freshwater fronts that are strained and stirred by the mesoscale eddy field. We would like to understand the relevant processes that determine the evolution of these freshwater fronts and how heat and salt are exchanged between the fresh melt water and the background water masses. In particular we investigate the importance of submesoscale processes for the lateral and vertical exchange of heat and salt, using high resolution observations of a freshwater front in the Arctic to initialise idealised simulations of frontal evolution. We isolate the effect of submesoscale dynamics by comparing high resolution submesoscale-resolving simulations with lower resolution simulations permitting only larger-scale eddies. Comparisons with observed temperature wavenumber spectra will be presented to investigate whether the simulated dynamics are representative of observations. Heat and salt budgets are presented for the simulations and the impact of submesoscale dynamics on the balance between across-front ageostrophic and geostrophic transports will be discussed. We will also discuss the implications of these results on the seasonal redistribution of heat over the upper ocean, specifically do submesoscale dynamics lead to an increase in the vertical transport of heat across the base of the summer mixed layer, therefore increasing the heat content within the winter mixed layer and delaying the formation of sea ice in the fall?
Tracking carbon flows through the biosphere: a new capability for the simple climate model Hector
Authors: Gering Skylar , Bond-Lamberty Benjamin , Woodard Dawn
Published: March 2021

Simple climate models focusing on the global climate and carbon cycle are valuable tools for large-ensemble sensitivity studies, model coupling experiments, and policy analyses. One example is Hector, an open-source model with multiple biomes, ocean chemistry, and a novel permafrost implementation. However, Hector does not currently have the capability to reconstruct the flow of carbon from one carbon pool (e.g., atmosphere and ocean) to another or report, at the end of a model run, the origin of the carbon within each pool. We developed a novel ‘trackedval’ C++ class and integrated it into Hector’s codebase. In addition to keeping track of a pool’s total carbon, the trackedval class also records the origin pools of the carbon, determined at the start of a run. If carbon tracking is enabled, this record is updated every timestep to reflect carbon fluxes (pool-to-pool transfers). To demonstrate this capability, we reconstruct and visualize the movement of carbon for several example model runs. Hector is the only simple climate model that we are aware of with the ability to reconstruct the carbon-cycle in detail through carbon tracking. The addition of the trackedval class to Hector opens up opportunities for deeper exploration of the effects of climate change on the global carbon cycle and can be used to track carbon isotopes or other elements in the future.
An approach to Storm Filomena severe snowfall and precipitation in Spain: preliminary results
Authors: Rodríguez-Sánchez Alejandro , Granda-Maestre Roberto , Calvo-Sancho Carlos , Oliver-García Álvaro
Published: March 2021

Between 7-10 January 2020, severe snowfall and precipitation event swept over south, center and eastern Spain, with a total amount of precipitation of more than 200 mm on the south, snowfall accumulations of 50 cm or more on widespread areas of center Spain and 25 cm on Zaragoza and Ebro valley.

The low, called Filomena, was an unusual event with excessive social impact. In this study we will present the synoptic framework, characterized by the presence of three different air masses: cold air mass on low levels, more humid Mediterranean air mass on low-mid levels, at around 2-3 kilometres from surface; and a wet and warm subtropical air mass from the south. The interaction of these three air masses lead to the exceptional precipitation and snow accumulations. For this end, ERA-5 reanalysis and satellite images will be used. For mesoscale analysis, WRF-ARW will be used with both GFS and ERA-5 reanalysis. This extreme event, although it was generally predictable, had key points of low predictability in some parts with high social impact, including very populated areas.
Assessing the rate of post-depositional change within the 2004 Indian Ocean tsunami deposit: implications for long-term records of paleotsunamis
Authors: Pearson Lillian , Pilarczyk Jessica , Hawkes Andrea , Gouramanis Chris , Majewski Jędrzej , Ismail Nazli , Afrizal Tomi , Horton Benjamin
Published: March 2021

Foraminifera are commonly used to examine patterns of tsunami inundation occurring over centennial to millennial timescales. However, the impacts of post-depositional change on geologic reconstructions is unknown. In tropical environments, the taphonomic character (i.e. test surface condition) of a foraminifer can deteriorate, rendering them unidentifiable, and in the worst case, dissolve them entirely. Here, we investigate the rates and extent of post-depositional change associated with the foraminiferal assemblages found within the 2004 Indian Ocean Tsunami (IOT) deposit over a 15-year time interval in Aceh, Indonesia from 2007 to 2019. 

The IOT deposit consisted of a 13-18cm thick, medium-fine sand unit that sharply overlays a muddy sand contact. During the 15-year time series analysis, the IOT deposit remained a consistent thickness and maintained easily recognizable stratigraphical contacts between the overlying soil layer and the underlying mud layer. The overlying soil layer increased in thickness from 2cm in 2007 to 6cm in 2019 and resulted in roots bioturbating the IOT deposit. Calcareous taxa dominated the IOT deposit assemblage, where hyaline taxa accounted for 62% of the assemblage, porcelaneous taxa for 34% of the assemblage and agglutinated taxa for 4% of the assemblage. The concentration of calcareous foraminifera within the tsunami deposit decreased by 5% from 2007 to 2019. This trend is attributable to the high abundance of delicate porcelaneous tests, which are more susceptible to post-depositional processes than the more robust hyaline tests. The taphonomic character of the foraminiferal assemblage became more corraded (dissolved, abraded and/or pitted) over the 15-year period. The relative abundance of corraded individuals within the foraminiferal assemblage increased by 4% in the IOT deposit, to reach a relative abundance of 50% by 2019 compared to 46% in 2007. Our results indicate that there is minimal change occurring within the deposit and presents good evidence that microfossils can be used as reliable indictors of tsunami origin and to identify characteristics of a tsunami deposit. While it is minimal, we recommend that post-depositional change should still be considered, especially with regards to the more delicate porcelaneous tests and over longer taphonomic timescales.
Long-term evolution of directional spectra of wind waves modelled by DNS and kinetic equations, and comparison with airborne measurements
Authors: Annenkov Sergei , Shrira Victor , Romero Leonel , Melville Ken
Published: March 2021

We consider the evolution of directional spectra of waves generated by constant and changing wind, modelling it by direct numerical simulation (DNS), based on the Zakharov equation. Results are compared with numerical simulations performed with the Hasselmann kinetic equation and the generalised kinetic equation, and with airborne measurements of waves generated by offshore wind, collected during the GOTEX experiment off the coast of Mexico. Modelling is performed with wind measured during the experiment, and the initial conditions are taken as the observed spectrum at the moment when wind waves prevail over swell after the initial part of the evolution.

Directional spreading is characterised by the second moment of the normalised angular distribution function, taken at selected wavenumbers relative to the spectral peak. We show that for scales longer than the spectral peak the angular spread predicted by the DNS is close to that predicted by both kinetic equations, but it underestimates the corresponding measured value, apparently due to the presence of swell. For the spectral peak and shorter waves, the DNS shows good agreement with the data. A notable feature is the steady growth of angular width at the spectral peak with time/fetch, in contrast to nearly constant width in the kinetic equations modelling. Dependence of angular width on wavenumber is shown to be much weaker than predicted by the kinetic equations. A more detailed consideration of the angular structure at the spectral peak at large fetches shows that the kinetic equations predict an angular distribution with a well-defined peak at the central angle, while the DNS reproduces the observed angular structure, with a flat peak over a range of angles.

In order to study in detail the differences between the predictions of the DNS and the kinetic equations modelling under idealised conditions, we also perform numerical simulations for the case of constant wind forcing. As in the previous case of forcing by real wind, the most striking difference between the kinetic equations and the DNS is the steady growth with time of angular width at the spectral peak, which is demonstrated by the DNS, but is not present in the modelling with the kinetic equations. We show that while the kinetic theory, both in the case of the Hasselmann equation and the generalised kinetic equation, predicts a relatively simple shape of the spectral peak, the DNS shows a more complicated structure, with a flat top and dependence of the peak position on angle. We discuss the approximations employed in the derivation of the kinetic theory and the possible causes of the found differences of directional structure.
The human and climate drivers of Holocene grassland fires in the South Caucasus: A macro-charcoal, brGDGTs, and pollen reconstruction
Authors: Cromartie Amy , Barhoumi Chéïma , Dugerdil Lucas , Ménot Guillemette , Peyron Odile , Messager Erwan , Etienne David , Khatchadourian Lori , Badalyan Ruben , Smith Adam T. , Meliksetian Khachatur , Joannin Sébastien
Published: March 2021

The mountainous area of Armenia has been a steppe throughout the Holocene with a rich history of fire events throughout this period. Previous research has found that changes in fire are linked to shifts between Poaceae grasslands and semi-arid Chenopodiaceae steppes. However, the climate and human drivers of these fires has yet to be fully explored in an area where agriculture has been practiced for almost 8,000 years. To elucidate these changes, we performed and compiled macro-charcoal analysis on four wetland sediment cores from the Kasakh Valley, Armenia. We aimed to understand fire frequency, intensity, size, and drivers of these events. In addition, we utilize a paleotemperature molecular biomarker branched glycerol dialkyl glycerol tetraethers (brGDGTs), a pollen climate reconstruction for temperature and precipitation, and the vast amount of archaeological data to help us untangle these changes. Early results suggest fires increase as temperature rose during the early Holocene and continue to increase with temperature during the Mid-Holocene despite an increase in agriculture during the Early Bronze Age. Between 4000 - 2000 cal. BP fires are small and almost disappear from the record. During this period these declines appear to be driven both by temperature fluctuations and an increase in regional mobile pastoralism resulting in declining biomass. Over the last 2000 years, humans appear to be the primary driver of fires with an increase in large intense events that are local to the watershed.
Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning
Authors: Konapala Goutam , Kumar Sujay
Published: March 2021

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. But MS sensors may not penetrate cloud cover, whereas SAR is plagued by operational errors such as noise-like speckle challenging their viability to global flood mapping applications. An attractive alternative is to effectively combine MS data and SAR, i.e., two aspects that can be considered complementary with respect to flood mapping tasks. Therefore, in this study, we explore the diverse bands of Sentinel 2 (S2) derived water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to access their capability in generating accurate flood inundation maps. For this purpose, a fully connected deep convolutional neural network known as U-Net is applied to combinations of S1 and S2 bands to 446 (training: 313, validating: 44, testing: 89) hand labeled flood inundation extents derived from Sen1Floods11 dataset spanning across 11 flood events. The trained U-net was able to achieve a median F1 score of 0.74 when using DEM and S1 bands as input in comparison to 0.63 when using only S1 bands highlighting the active positive role of DEM in mapping floods. Among the, S2 bands, HSV (Hue, Saturation, Value) transformation of Sentinel 2 data has achieved a median F1 score of 0.94 outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Also, when combined with Sentinel 1 SAR imagery too, HSV achieves a median F1 score 0.95 outperforming all the well-established water indices in detecting floods in majority of test images.
Med-BGC MIP: A Mediterranean Biogeochemical models comparison.
Authors: Palmieri Julien , Mignot Alexandre , Dutay Jean-Claude , Richon Camille , Macias Moy Diego , d’Ortenzio Fabrizio , Schmechtig Catherine , Uitz Julia , Houpert Loic , Lamouroux Julien , Baklouti Melika , Pages Remi , Cosimo Solidoro , Teruzzi Anna , Lazzari Paolo , Ciavatta Stefano , Kay Susan , Triantafyllou George , Tsiaras Kostas , Somot Samuel ,   BGC-Med team
Published: March 2021

The Mediterranean Sea has been identified as a hotspot for climate change. Furthermore, its very diverse trophic regimes, in such a little area, make it an extremely interesting region from a biogeochemical perspective. Numerous studies aim at better understanding and representing the Mediterrenean dynamics and biogeochemistry through modeling. This is a crucial step in order to predict the future anthropogenic impacts on the Mediterranean Sea and their possible effects on its biogeochemistry,  and all what depends on it. The number of models that simulate the Mediterranean biogeochemistry, and the data available to compare with are now sufficient to draw an overall picture of the Mediterranean Sea biogeochemical models state of the art.

In this study, we gathered 10 biogeochemical simulations of the Mediterranean Sea, including 8 regional and 2 high-resolution global configurations. The simulations are compared with surface chlorophyll estimates derived from satellite observations; chlorophyll, nitrate, oxygen, and particulate organic carbon concentrations derived from BGC-Argo floats, and  phytoplankton group-specific  primary production estimated from ocean color satellite observations. 

Our first aim is to describe and compare all known Mediterranean biogeochemical models, and to highlight their specificity. This should give an insight into the current achievements, and expose what biogeochemical model products are hence available for further ecological analysis. 

Furthermore, a specific attention is given to how well each model performs in selected regions of the Mediterranean Sea, in order to understand which specific process is needed to adequately represent the different trophic regimes of the Mediterranean Sea.

 
Large influence of dust on the Precambrian climate 
Authors: Liu Peng , Liu Yonggang , Peng Yiran , Lamarque Jean-Francois , Wang Mingxing , Hu Yongyun
Published: March 2021

On present-day Earth, dust emissions are restricted only to a few desert regions mainly due to the distribution of land vegetation. The atmospheric dust loading is thus relatively small and has a slight cooling effect on the surface climate. For the Precambrian (before ~540 Ma), however, dust emission might be much more widespread since land vegetation was absent. Here, our simulations using an Earth system model (CESM1.2.2) demonstrate that the global dust emission during that time might be an order of magnitude larger than that of the present day, and could have cooled the global climate by ~10 °C. Similarly, the dust deposition in the ocean, an important source of nutrition for the marine ecosystem, was also increased by a factor of ~10. Therefore, dust was a critical component of the early Earth system, and should always be considered when studying the climate and biogeochemistry of the Precambrian.