bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Estimate of Global Ocean Climatology
Authors: Shahzadi Kanwal , Pinardi Nadia , Zavaterelli Marco , Simoncelli Simona
Published: March 2021

The estimation of climatology is a key element for improving our understanding of the ocean state. Historical data sets available today enables an almost complete reconstruction of global ocean fields. In this study, a new global ocean climatological estimate of basic physical parameters such as temperature, salinity, density, dissolved oxygen, and apparent oxygen utilization is computed using the World Ocean Database (WOD18). The reliability of estimate is closely tied to the quality assurance of the in-situ observations and statistical interpolation schemes of the mapping. Therefore, in this context, WOD18 used for this study has gone through a non-linear quality control procedure developed by Shahzadi (2020) on a global domain. The mapping of resulting data is carried out using Data Interpolating Variational Analysis (DIVA). Sensitivity experiments are carried out to choose the key parameters of DIVA, namely the horizontal correlation lengths, and the Noise to Signal ratio (N/S). Furthermore, two new indices such as roughness index, and root mean square of residuals are designed to show the impact of the correlation length, and N/S ratio choices. For temperature and salinity, two different versions of the climatological estimates are produced: (i) a long-term (1900 to 2017) climatology using multiple platforms in-situ data, and (ii) a shorter time estimate (2003-2017) using data from ocean drifting platforms such as profiling floats. The two versions are intercompared and differences are evaluated.  Similar procedures are applied for global mapping of Density, Oxygen, and Apparent Oxygen utilization. The new climatological estimates are compared with previous estimates such as World Ocean Atlas and World Argo Global Hydrographic climatological estimates, and thereby the differences are analysed.

Keywords: WOD18, temperature, salinity, apparent oxygen, DIVA, climatology, non-linear quality control.

Shahzadi, K., (2020): “A New Global Ocean Climatology”, Ph.D. Thesis (under evaluation), University of Bologna, Italy, pp. (19-35. of pages)
NOAA’s Contribution to the Geo-Ring: The New Geostationary Extended Observations (GeoXO) Atmospheric Composition Capability
Authors: Frost Gregory , Kondragunta Shobha , Kopacz Monika , Lindsey Daniel , Heidinger Andrew , Sullivan Pamela
Published: March 2021

NOAA’s Geostationary Extended Observations (GeoXO) satellite system is the ground-breaking mission that will advance Earth observations from geostationary orbit.  GeoXO will supply vital information to address major environmental challenges of the future in support of U.S. weather, ocean and climate operations. The GeoXO mission will continue and significantly expand observations provided by the GOES-R Series. GeoXO will bring new capabilities demonstrated by NASA, ESA, and KARI into an operational environment to address emerging environmental issues and challenges that threaten human health and the economy.

The recommended observations on GeoXO include hyperspectral observations in the ultraviolet, visible, and infrared, visible/infrared imaging during day and night time, and lightning mapping. The combination of these observing systems will provide an exciting new ability to continuously measure trace gases and aerosols over much of North America. Potential GeoXO atmospheric composition products offer new opportunities for understanding and predicting air quality, weather, climate, and their linkages.

This presentation will highlight GeoXO’s recommended atmospheric composition capabilities and describe NOAA’s efforts to engage the user community in planning for the applications of these future datasets.
Evaluation of climate model simulations in representing the precipitation non-stationarity by considering observational uncertainties
Authors: Wan Yongjing , Chen Jie , Xie Ping , Xu Chong-Yu , Li Daiyuan
Published: March 2021

The reliability of climate model simulations in representing the precipitation changes is one of the preconditions for climate-change impact studies. However, the observational uncertainties hinder the robust evaluation of these climate model simulations. The goal of the present study is to evaluate the capacities of climate model simulations in representing the precipitation non-stationarity in consideration of observational uncertainties. The mean of multiple observations (OBSE) from five observational precipitation datasets is used as a reference to quantify the uncertainty of observed precipitation and to evaluate the performance of climate model simulations. The non-stationarity of precipitation was represented using the mean and variance of annual total precipitation and annual maximum daily precipitation for the 1982–2015 period. The results show that the spatial distributions of annual and extreme precipitation are similar for various observational datasets, while there has less agreement in the variance changes of extreme precipitation. Climate models are capable of representing the spatial distributions of the annual and extreme precipitation amounts at the global scales. In terms of the non-stationarity, climate model simulations are capable of capturing the large-scale spatial pattern of the trend in mean for annual precipitation. On the contrary, the simulations are less reliable in reproducing the change of extreme precipitation, as well as the trend of variance for annual precipitation. Overall, climate models are more reliable in simulating the mean of precipitation than the variance, and they are more reliable in simulating annual precipitation than extreme. Besides, the uncertainties of precipitation for both observations and simulations are much larger in monsoon regions than in other regions. This study suggests that considering observational uncertainties is necessary when using observational datasets as the reference to project future climate change and assess the impact of climate change on environments.
The continued 2008-2010 subsidence of Dallol on the spreading Erta Ale ridge: InSAR observations and source models
Authors: Battaglia Maurizio , Pagli Carolina , Meuti Stefano
Published: March 2021

Volcanoes commonly subside during eruptions as magma flows out of a chamber, but continued subsidence during non-eruptive episodes is not easy to explain. In this work, we use InSAR and source modelling to understand the causes of the continued subsidence of Dallol, a nascent volcano along the spreading Erta Ale ridge of Afar (Ethiopia). The Dallol volcano never erupted and no volcanic deposits originating from the volcano exists at the surface. Recent seismicity, diking and continuous deformation of a crustal magma chamber indicate the Dallol is a nascent central volcano with its own rift segment. An active magma plumbing exists and the injection of a dike beneath the volcano was imaged in 2004 from InSAR data. This unrest episode was followed by complete quiescence until subsidence started in 2008. We analysed InSAR data from 2004-2010 to create time-series of line-of-sight (LOS) surface deformation. Average velocity maps show that subsidence centred at Dallol initiated in October 2008 and continued as far as February 2010 at an approximately regular rate of up to 10 cm/yr. The inversion of InSAR average velocities found that a sill-like source, located a depth between 1.2 and 1.5 km under Dallol with a mean volume change of  -0.62 to -0.53 106 km3/yr and a radius of approximately 1.6 km, best fits the InSAR observations. The observed volume change could be explained by changes in pore fluid pressure in a confined hydrothermal aquifer or by thermoelastic deformation caused by changes in temperature in a volume of rock. Simple models of poro-elastic and thermo-elastic contraction indicates that the observed deformation would require either a decrease in pore fluid pressure of the order of 10-2G, where G is the rock shear modulus, or a decrease in temperature between 60 °C and 80 °C.  
Analysis of space-based observations of peroxyacetyl nitrate (PAN) and its relation to other atmospheric tracers
Authors: Franco Bruno , Clarisse Lieven , Clerbaux Cathy , Coheur Pierre-François
Published: March 2021

Peroxyacetyl nitrate (CH3C(O)O2NO2; abbreviated as PAN) is the main tropospheric reservoir of nitrogen oxide radicals (NOx) and contributes to redistributing NOx from source to remote regions. Recently, PAN total columns have been retrieved from the radiance spectra recorded by IASI (Infrared Atmospheric Sounding Interferometer) onboard the Metop satellite platforms, using a neural network-based retrieval approach. The daily global distributions obtained from these measurements provide a comprehensive picture of PAN through the troposphere.

Here we exploit as a climatology the 13-year time series of global PAN measurements derived from the IASI/Metop-A observations (October 2007 - December 2020) to characterize the spatial distribution and seasonal variability of PAN abundance worldwide. In particular, continental areas within the tropics appear to be source regions of PAN throughout the year, whereas PAN at North Hemisphere mid- and high latitudes exhibits a more pronounced seasonal cycle and peaks during the boreal summer. Strong outflows of PAN are captured over the oceans, downwind of continental source regions such as Eastern Asia and Eastern US. This dataset also allows us to investigate the recent trends of atmospheric PAN abundance over the last 13 years, over both source and remote areas.

To better understand what drives the spatial distribution and variability of PAN, we analyze alongside the regional time series of PAN those of carbon monoxide (CO) from IASI/Metop-A, and of formaldehyde (HCHO) and nitrogen dioxide (NO2) from OMI/Aura (Ozone Monitoring Instrument). Locally, we find simultaneous enhancements of PAN and CO abundances, which in this case indicates that most PAN originates from fire-derived precursors. This mainly occurs over the typical biomass burning regions in the tropics. Overall, strong correlations are observed over source areas between PAN and HCHO, which is used here as a tracer of tropospheric chemistry and of the presence of oxygenated volatile organic compounds (OVOCs), while there is no particular correlation with NO2. The preliminary results suggest that PAN distribution and seasonality is primarily driven by the availability in OVOCs, and hence in peroxyacetyl radical, and that a locally weak NO2 abundance does not prevent the formation of PAN.
Patchy bedrock explained: Tectonic fracture control on landscape evolution patterns in south-Central Chile 
Authors: Lodes Emma , Scherler Dirk , Wittmann Hella , Van Dongen Renee
Published: March 2021

Rock fracturing induced by tectonic deformation is thought to promote faster denudation in more highly fractured areas by lowering grain size and directing the flow of water. That the density and pattern of fractures in a landscape play a role in controlling erosion and landscape evolution has been known for over a century, but not until recently do we have tools, like cosmogenic nuclides, to quantify erosion rates in places with varying fracture densities. In the Nahuelbuta Range in south-central Chile, we observed that >30-m thick regolith exists next to patches of unweathered bedrock. We hypothesize that the density of fractures dictates the pace and patterns of chemical weathering, regolith conversion, and erosion in the Nahuelbuta Range. To test this, we used in situ cosmogenic 10Be to obtain denudation rates from amalgamated samples of bedrock, corestones and soils, and measured fracture density and orientation, as well as hillslope boulder size in several sites in the Nahuelbuta Range. We found that more highly fractured areas indeed have higher denudation rates than less fractured areas, and that bedrock denudation rates are ~10 m/Myr while soil denudation rates are ~30 m/Myr, suggesting that soil-covered areas may be sites of higher fracture density at depth. Fractures have orientations that match mapped faults across the Nahuelbuta range, and thus are considered to be tectonically-induced. In addition, both fracture and fault orientations match the orientation of streams incising the range, suggesting that fractures control stream channel orientation by weakening bedrock and thus directing flow.
"Look at the Sky and Tell the Weather"  The art and science of Eric Cloane
Authors: Passow Michael
Published: March 2021

Eric Sloane was a 20th century artist who created a unique style and provided interesting, idiosyncratic, and informative images of clouds. He was the author of many books, but also created massive cloud murals, including one on an entire wall at the Smithsonian Air & Space Museum in Washington, DC. Sloane became interested in clouds when he flew as a passenger with some of the pioneers of aviation, including Amelia Earhart and Wiley Post. He also became weatherwise through his early employment as a painter of advertisements on yje sides of  barns, allowing him to observe many aspects of weather. Sloane received many awards for his contributions to science, including a special award from the American Meteorological Society in 185 for  “for pioneering contributions to public awareness of clouds, their beauty, complexity, and scientific importnace; for an artistic legacy to all who feel a sense of wonder when they look at a cloud filled sky.”
Temporal b-value variation before and after ML ≥ 6.0 Taiwan earthquakes from 2012 to 2019
Authors: Chen Po-Yuan , Chen Sean Kuanhsiang , Wu Yih-Min
Published: March 2021

Recent studies show that earthquake b values gradually decrease before large earthquakes at the epicenters and then immediately increase after the earthquakes. Temporal b-value variations may result from crustal stress changes associated with a large earthquake. However, the physical process is rarely observed and remains unclear. Taiwan island is a young orogeny leading to frequent earthquakes with magnitudes greater than ML 6.0, which provides an excellent laboratory to examine the physical process. We calculated b-value variation before and after ML ≥ 6.0 Taiwan earthquakes at the epicenters from 2012 to 2019. The time period is based on an enhancement of earthquake detection capability from the Central Weather Bureau Seismic Network in Taiwan, which allows the magnitude of completeness (Mc) down to 1.5 in the inland region. We used a relocated earthquake catalog to precisely estimate b value and Mc by the maximum likelihood method and maximum curvature method, respectively. We designed three steps in our research. First, we calculated the b value and Mc at the epicenters of the ML ≥ 6.0 earthquakes in overall 8 years to know the background seismic activity. Based on this, second, we calculated b values and Mc per half year to test the sensitivity between the radius from epicenters (r) and the number of earthquakes with magnitudes greater than Mc (n). Finally, we will apply moving window approach with specific criteria to continuously calculate temporal b-value variations. Our results showed that spatial b values in Taiwan in overall 8 years have an average of 1.0. The b values are systematically lower in the epicenters of ML ≥ 6.0 earthquakes from 2012 to 2019. We have determined suitable r and n values for each earthquake at the epicenters and some epicenters share similar r and n values. We preliminarily observed temporal b-value decreases before the 2018 Mw 6.4 Hualien earthquake. Considering temporal b-value variation by moving windows, we aim to realize whether temporal b-value variation by a large earthquake can be frequently observed in Taiwan.
Machine learning-based uncertainty quantification for data assimilation: a simple model experiment
Authors: Ruiz Juan , Sacco Maximiliano , Zhen Yicun , Tandeo Pierre , Pulido Manuel
Published: March 2021

Quantifying forecast uncertainty is a key aspect of state-of-the-art data assimilation systems which has a large impact on the quality of the analysis and then the following forecast. In recent years, most operational data assimilation systems incorporate state-dependent uncertainty quantification approaches based on 4-dimensional variational approaches, ensemble-based approaches, or their combination. However, these quantifications of state-dependent uncertainties have a large computational cost. Machine learning techniques consist of trainable statistical models that can represent complex functional dependencies among different groups of variables. In this work, we use a fully connected two hidden layer neural network for the state-dependent quantification of forecast uncertainty in the context of data assimilation. The input to the network is a set of three consecutive forecasted states centered at the desired lead time and the network’s output is a corrected forecasted state and an estimation of its uncertainty. We train the network using a loss function based on the observation likelihood and a large database of forecasts and their corresponding analysis. We perform observing system simulation experiments using the Lorenz 96 model as a proof-of-concept and for an evaluation of the technique in comparison with classic ensemble-based approaches.

 Results show that our approach can produce state-dependent estimations of the forecast uncertainty without the need for an ensemble of states (at a much lower computational cost),  particularly in the presence of model errors. This opens opportunities for the development of a new type of hybrid data assimilation system combining the capabilities of machine learning and ensembles.
Investigation of reservoir age variations in Lake Druksiai caused by anthropogenic factors
Authors: Butkus Laurynas , Barisevičiūtė Rūta , Šapolaitė Justina , Ežerinskis Žilvinas , Maceika Evaldas , Pabedinskas Algirdas , Garbaras Andrius , Remeikis Vidmantas
Published: March 2021

The reservoir effect (RE) is defined as the difference between the radiocarbon isotope ratio (14C/12C) in the terrestrial and aquatic samples. Both natural processes and anthropogenic activities affecting carbon cycle in the water ecosystem can lead to changes in the RE. Therefore, reservoir effect studies can help to assess the impact of external factors on a hydrological system [1].

The aim of this research was to evaluate the impact of anthropogenic 14C contamination from Ignalina nuclear power plant (Ignalina NPP, INPP) on the Lake Drūkšiai system. The lake water was used to cool the reactors of the INPP. The lake sediment and fish (both benthic and pelagic) scale samples were collected from the Drūkšiai lake. ABA (acid-base-acid) chemical pretreatment procedure was used to extract humin (HM) and humic acid (HA) fractions from the sediments. Radiocarbon measurements in these samples were performed using the accelerator mass spectrometer (AMS).

In 1963, increased concentrations of radiocarbon due to the testing of nuclear weapons showed that atmosphere-lake CO2 exchange accounted for about 22% carbon in bottom sediments. During the first 15 years of operation of the Ignalina Nuclear Power Plant, 14C-enriched dissolved inorganic carbon (DIC) was continuously released into Lake Druksiai. During that period, an average of about 0.24 GBq of radiocarbon was released per year. Measurements of radiocarbon concentrations in fish confirm that the 14C contamination was in dissolved inorganic form.

Around 2000, 14C-enriched DIC (2.3 GBq radiocarbon) was released into Lake Druksiai from Ignalina NPP. In addition, organic compounds were additionally released in the same year. These compounds were not 14C-enriched but affected the interaction between humic and humic acids. Almost a decade after the end of operation of the Ignalina NPP, there is still some 14C pollution (from INPP) remaining in Lake Druksiai. The concentration of radiocarbon in the bottom sediments is still higher than in the atmosphere. 

 

[1] R. Barisevičiūtė et al., Tracing Carbon Isotope Variations in Lake Sediments Caused by Environmental Factors During the Past Century: A Case Study of Lake Tapeliai, Lithuania, Radiocarbon 61(4), 885–903, (2019).
Mapping land use on Irish raised bogs using Sentinel-2 imagery and Google Earth Engine
Authors: Habib Wahaj , Connolly John , McGuiness Kevin
Published: March 2021

Peatlands are one of the most space-efficient terrestrial carbon stores. They cover approximately 3 % of the terrestrial land surface and account for about one-third of the total soil organic carbon stock. Peatlands have been under severe strain for centuries all over the world due to management related activities. In Ireland, peatlands span over approximately 14600 km2, and 85 % of that has already been degraded to some extent. To achieve temperature goals agreed in the Paris agreement and fulfil the EU’s commitment to quantifying the Carbon/Green House Gases (C/GHG) emissions from land use, land use change forestry, accurate mapping and identification of management related activities (land use) on peatlands is important.

High-resolution multispectral satellite imagery by European Space Agency (ESA) i.e., Sentinel-2 provides a good prospect for mapping peatland land use in Ireland. However, due to persistent cloud cover over Ireland, and the inability of optical sensors to penetrate the clouds makes the acquisition of clear sky imagery a challenge and hence hampers the analysis of the landscape. Google Earth Engine (a cloud-based planetary-scale satellite image platform) was used to create a cloud-free image mosaic from sentinel-2 data was created for raised bogs in Ireland (images collected for the time period between 2017-2020). A preliminary analysis was conducted to identify peatland land use classes, i.e., grassland/pasture, crop/tillage, built-up, cutover, cutaway and coniferous, broadleaf forests using this mosaicked image. The land-use classification results may be used as a baseline dataset since currently, no high-resolution peatland land use dataset exists for Ireland. It can also be used for quantification of land-use change on peatlands. Moreover, since Ireland will now be voluntarily accounting the GHG emissions from managed wetlands (including bogs), this data could also be useful for such type of assessment.
Microbiological research program in deep HRW repository «Yeniseisky»: plans and first results
Authors: Safonov Alexey , Abramova Elena , Popova Nadezhda , Artemiev Grigory , Boldyrev Kirill
Published: March 2021

The concept of engineered geologic disposal has been developed for the safe long-term management of long-lived high-level radioactive waste (HRW) by many countries. Russian Federation evaluates the “Yeniseiskiy” Nizhnekansky granite-gneiss crystalline formation, in Krasnoyarsk region. To this date microbiological studies became an integral part of safety assessment in Russia like other countries.

A multi-barrier concept was used to create super-container (SC). SC contains a primary vitrified radioactive waste package, placed in the carbon steel casing, bentonite and aluminate concrete layers placed between the casing and inner IC surface. All selected barrier materials are potential areas for biogenic processes.

The talk presented a plan of experimental research of biogenic processes and first step results. Microbiological studies at the first stage are carried out in laboratory conditions, at the second stage they will be carried out under the conditions of URL and include:

- changes of host rock sorption and diffusion properties due to biofilms formation,

- assessment of the possibility biocolloidal particles formation and determination clay and ferruginous colloids stability during biogenic processes;

To date, the study of the phylogenetic and functional diversity of the microflora of the granite-gneiss massif, near-surface waters, and clay materials that will be used in disposal has been carried out. It has been established that microorganisms, isolated from samples, collected nearby the zone of the future repository are capable of participating in a number of undesirable processes, including steel corrosion acceleration, the formation of biogenic gases, and changes in the properties of clay materials. Our studies also show that microbial communities of clays are activated by hydrogen, which is a product of steel corrosion and water radiolysis. At the first stage, we found an increase in the corrosion of carbon steel in the presence of microorganisms sampled from the study area.

An acceleration of the corrosion rate of carbon steel (from 1.3 to 1.9 μm/year) was observed with an increase in temperature from 20 to 500С, the calculated value of the activation energy was 22 kJ/mol * K. Based on the data obtained, a kinetic model of carbon steel corrosion in the presence of microorganisms was created, including both the inclusion of the activation energy and the inhibition of corrosion by the formed corrosion products.

 
VirES for Swarm & Virtual Research Environment: Software, guides & infrastructure to boost accessibility of Swarm
Authors: Smith Ashley , Pačes Martin
Published: March 2021

ESA's Swarm mission continues to deliver excellent data providing insight into a wide range of geophysical phenomena. The mission is an important asset whose data are used within a number of critical resources, from geomagnetic field models to space weather services. As the product portfolio grows to better deliver on the mission's scientific goals, we face increasing complexity in accessing, processing, and visualising the data and models. ESA provides “VirES for Swarm” [1] (developed by EOX IT Services) to help solve this problem. VirES is a web-based data retrieval and visualisation tool where the majority of Swarm products are available. VirES has a graphical interface but also a machine-to-machine interface (API) for programmable use (a Python client is provided). The VirES API also provides access to geomagnetic ground observatory data, as well as forwards evaluation of geomagnetic field models to give data-model residuals. The "Virtual Research Environment" (VRE) adds utility to VirES with a free cloud-based JupyterLab interface allowing scientists to immediately program their own analysis of Swarm products using the Python ecosystem. We are augmenting this with a suite of Jupyter notebooks and dashboards, each targeting a specific use case, and seek community involvement to grow this resource.

[1] https://vires.services
Evaluation of agricultural drought changes due to implementation of preventive drought management measures. Study case Torola basin
Authors: paez ana , Corzo Gerald
Published: March 2021

Agricultural droughts are becoming more frequent and severe, triggering a range of pervasive effects on society, environment, and economy. In drought-prone areas, multiple interventions aimed at efficient water use and protecting water resources have been used as preventive drought management measures. However, many of these solutions are colloquial or implemented inconsistently, and the actual contribution to drought preparation and response is limited or unclear. This study evaluates the applicability and effectiveness of preventive drought management measures (Hydrological-based measures). To achieve this goal, we divided the work into two stages. First, a quantitative analysis consisted of a review, classification, and mathematical representation of potential preventive drought management measures. Second, a modelling-based analysis compared droughts characteristics before and after implementing three selected measures from the first stage (rainwater harvesting reservoirs, afforestation, and intercropping). The study was developed in the Torola basin, a drought-prone area located in Honduras northeast. We applied the threshold level method to detect and analyse drought characteristics and the Soil Water Assessment Tool (SWAT) for hydrological modelling and representing the selected measures. We defined three scenarios for evaluating the effects of each measure. Results showed that selected measures increase infiltration and soil moisture content alleviating the severity and duration of drought events locally, but enhance the drought situation in surrounding areas.

Keywords: Agricultural droughts, preventive drought management measures, SWAT model.
Tracking COVID-19 in Real-time: Challenges Faced and Lessons Learned
Authors: Gardner Lauren
Published: March 2021

In response to the COVID-19 public health emergency, we developed an , first released publicly on January 22, 2020, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The dashboard visualizes and tracks the number of reported confirmed cases, deaths and recoveries for all countries affected by COVID-19. Further, all the data collected and displayed on the dashboard is made freely available in a GitHub repository, along with the live feature layers of the dashboard. The motivation behind the development of the dashboard was to provide researchers, public health authorities and the general public with a user-friendly tool to track the outbreak situation as it unfolds, critically, with access to the data underlying it. The demand for such a service became evident in the first weeks the dashboard was online, and by the end of February we were receiving over one billion requests for the dashboard feature layers every day, which since increased to between three and 4.5 billion requests every day. The dashboard has been featured on most major national and international media outlets (NYT, Washington Post, CNN, NPR, etc), and is either directly embedded in their websites, or used as the data source for in-house mapping efforts. Further, members of the public health community, including local and national governmental organizations, emergency response teams, public health agencies, and infectious disease researchers around the world rely on the dashboard and its data for informing and planning COVID-19 response. In this talk I will give a brief overview of the evolution of the dashboard, discuss some of the challenges we faced along the way, and suggest some methods by which disease tracking could be done better in the future.
Modeling methane from the North Sea region with ICON-ART
Authors: Scharun Christian , Ruhnke Roland , Weimer Michael , Braesicke Peter
Published: March 2021

Methane (CH4) is the second most important greenhouse gas after CO2 affecting global warming. Various sources (e.g. fossil fuel production, agriculture and waste, biomass burning and natural wetlands) and sinks (the reaction with the OH-radical as the main sink contributes to tropospheric ozone production) determine the methane budget. Due to its long lifetime in the atmosphere methane can be transported over long distances.

Disused and active offshore platforms can emit methane, the amount being difficult to quantify. In addition, explorations of the sea floor in the North Sea showed a release of methane near the boreholes of both, oil and gas producing platforms. The basis of this study is the established emission data base EDGAR (Emission Database for Global Atmospheric Research), an inventory that includes methane emission fluxes in the North Sea region. While methane emission fluxes in the EDGAR inventory and platform locations are matching for most of the oil platforms almost all of the gas platform sources are missing in the database. We develop a method for estimating the missing emission sources based on the EDGAR inventory and the known locations of gas platforms as additional point sources will be inserted in the model.

In this study the global model ICON-ART (ICOsahedral Nonhydrostatic model - Aerosols and Reactive Trace gases) is used. ART is an online-coupled model extension for ICON that includes chemical gases and aerosols. One aim of the model is the simulation of interactions between the trace substances and the state of the atmosphere by coupling the spatiotemporal evolution of tracers with atmospheric processes. ICON-ART sensitivity simulations are performed with inserted and adjusted sources to access their influence on the methane and OH-radical distribution on regional (North Sea) and global scales.
The impact of non-ideality on reconstructing spatial and temporal variations of aerosol acidity with multiphase buffer theory
Authors: Cheng Yafang , Zheng Guangjie , Su Hang , Wang Siwen , Pozzer Andrea
Published: March 2021

Aerosol acidity is a key parameter in atmospheric aqueous chemistry and strongly influence the interactions of air pollutants and ecosystem. The recently proposed multiphase buffer theory provides a framework to reconstruct long-term trends and spatial variations of aerosol pH based on the effective acid dissociation constant of ammonia (Ka,NH3*). However, non-ideality in aerosol droplets is a major challenge limiting its broad applications. Here, we introduced a non-ideality correction factor (cni) and investigated its governing factors. We found that besides relative humidity (RH) and temperature, cni is mainly determined by the molar fraction of NO3- in aqueous-phase anions, due to different NH4+ activity coefficients between (NH4)2SO4- and NH4NO3-dominated aerosols. A parameterization method is thus proposed to estimate cni at given RH, temperature and NO3- fraction, and is validated against long-term observations and global simulations. In the ammonia-buffered regime, with cni correction the buffer theory can well reproduce the Ka,NH3* predicted by comprehensive thermodynamic models, with root-mean-square deviation ~0.1 and correlation coefficient ~1. Note that, while cni is needed to predict Ka,NH3* levels, it is usually not the dominant contributor to its variations, as ~90% of the temporal or spatial variations in Ka,NH3* is due to variations in aerosol water and temperature.
Volume drop of Pyrenean Glaciers from 2011 to 2020 observed with airborne techniques; LiDAR and SfM
Authors: Revuelto Jesús , Vidaller-Gayán Ixeia , Izagirre Eñaut , Rojas-Heredia Francisco Eduardo , Alonso-González Esteban , Rico Ibai , Gascoin Simon , Berthier Etienne , Rene Pierre , López-Moreno Juan Ignacio
Published: March 2021

Pyrenean glaciers are one of the southernmost glaciers in Europe. These ice bodies have suffered a fast retreat in the last decades mainly caused by the temperature increase of the last century. Here, we use state of the art airborne techniques to present the most complete evaluation of glacier volume change from 2011 to 2020.

In 2011 the Spanish Geographical Institute covered the entire country with airborne LiDAR. The glacier topography on the Spanish side of the Pyrenees (and also several hundreds of meters beyond the French border) was retrieved between September and November, when snow cover was minimal. In autumn 2020, we used different Unmanned Aerial Vehicles to survey 17 out of the 19 Pyrenean glaciers. The images acquired in these flights were processed with Structure from Motion algorithms to reconstruct the Digital Surface Model (DSM) in 3D of the glacier surfaces and nearby terrain.

Differencing of the DSM in 2011 and 2020 reveals a drastic retreat and volume loss. The mean elevation drop is 7 m, some glaciers had losses of more than 12 m in average with a surface lowering of more than 20 m locally. The mean annual mass balance observed when considering the 2D projection of glaciers surface was -1.83 m w.e./yr. Taking into account the true glaciers extent from the 3D surface retrieved from the UAV observations, the annual mass balance decreases to -1.30 m w.e./yr. The difference between these mass balances highlights the impact that utilising close range remote sensing observations have, when compared to satellite acquisitions, to accurately observe glaciers evolution in steep mountain areas.
Strongly coupled ensemble transform Kalman filter estimation of ocean optical parameters in a coupled GCM
Authors: Kitsios Vassili , Sandery Paul , O'Kane Terence , Fiedler Russell
Published: March 2021

Coupled general circulation models (GCM) of the atmosphere, ocean, land and sea-ice have many parameters. Some of which govern the numerics of the dynamical core, whilst others represent the influence of unresolved subgrid process based on our current fundamental physical understanding. The spatio-temporal structure of many of these parameters are known with little precision, which contributes to the inherent model biases in the underlying GCM. To address this problem we use the CSIRO Climate re-Analysis and Forecast Ensemble (CAFE) system to estimate both the climate state (atmosphere, ocean, sea-ice) and also spatio-temporally varying parameter maps of the ocean surface albedo and shortwave radiation e-folding length scale in a coupled climate GCM of CMIP resolution and complexity. The CAFE system adopts a 96 member ensemble transform Kalman filter within a strongly coupled data assimilation (DA) framework. The parameters (and states) are determined by minimising the error between short term DA cycle forecasts of the climate model and a network of real world atmospheric, oceanic, and sea-ice observations.  Several DA cycle lengths are tested between 3 to 28 days. The DA system has an improved fit to observations over the period from 2010 to 2012, when estimating both of the ocean optical parameters either individually or simultaneously. However, only individually estimated maps of shortwave e-folding length scale attain systematically reduced bias in multi-year climate forecasts during the out-of-sample period from 2012 to 2020. Parameter maps determined from longer DA cycle lengths also have further reduced multi-year forecast bias. Such improved climate forecasts would potentially enable policy makers to make better informed decisions on water, energy and agricultural infrastructure and planning.
Dynamic temperature effects on diffusive transport behaviors of VOC vapors in unsaturated soils
Authors: Parlin Asma , Watanabe Noriaki , Yamada Mizuki , Nakamura Kengo , Komai Takeshi
Published: March 2021

Investigation of the transport behaviors of volatile organic compounds (VOCs) in contaminated soils has previously been conducted in various environments. Accordingly, the present study focuses on specific phenomena in the near-surface soil environment where dynamic temperature affects the diffusive flux of VOC vapor phase as previous studies have suggested that temperature variations significantly influence such transport behaviors near-surface soils, but the nature of those influences and their mechanisms have remained unclear because of unexpected correlation of flux with the temperature that impacts on VOC vapor transport. More specifically, current practices report on a set of experiments into the vertical and upward vapor phase diffusive transport of benzene and trichloroethylene (TCE) in sandy soils that were conducted in soil column with water content conditions of up to 10 wt% and sinusoidal temperature conditions ranging from 20 to 30°C. This studies experimentally investigated that in all conditions tested, the top (outlet) flux change correlated positively with temperature change, while the bottom (inlet) flux change showed negative correlations. These results are consistent with previous observations showing that, at relatively deeper locations, there is little correlation between near-surface vertical VOC flux and soil temperature levels, and that VOC concentrations can be independent of the soil temperature at those locations. The present study's results highlighted for the first time that the negative correlation impact of temperature on VOC transport may occur frequently at deeper locations of subsurface soil. This occurs because the VOC concentration gradient is reduced by VOC desorption and the evaporation of water containing VOCs that accompany increasing temperature levels. However, our results also show that such mechanisms have a positive impact on VOC emissions from the upper part of subsurface soils to the atmosphere that can act as a low concertation boundary.
Aftershock signature of the M7.5 Palu 2018 supershear rupture from a rapidly deployed nodal array 
Authors: Lythgoe Karen , Muzli Muzi , Oo Win , Zeng Hongyu , Triyono Rahmat , Maung Maung Phyo , Karnawati Dwikorita , Wei Shengji
Published: March 2021

Supershear earthquakes have significant implications for seismic hazard, in terms of  ground shaking and aftershock pattern. It has been suggested that supershear ruptures are associated with fewer aftershocks on the supershear rupture segment, however this needs to be tested using high resolution event locations. Current aftershock catalogues for the M7.5 Palu 2018 supershear rupture are not of sufficient resolution to identify any characteristic aftershock pattern. Additionally it is unclear whether the supershear rupture speed occurred from the time of earthquake initiation, or at a later time on a certain segment of the fault.

We deployed a nodal array to record aftershocks following the main event. The array comprised of twenty short-period nodes, which can be deployed rapidly, making them ideal for post-rupture investigations in areas of sparse coverage. We expand the earthquake catalogue by applying template matching to the nodal array data. We then relocate seismicity recorded by the array using a double difference method. We also relocate seismicity that occurred before the array was active, using a relative relocation method. To do this, we calibrate the more distant permanent stations using events well-located by the nodal array. We further derive moment tensors for the largest events by waveform modelling using short-period and broadband records.

Our results show that the aftershocks cluster at the northern and southern extents of rupture. There is a relative dearth of aftershocks in the middle part of the rupture, particularly in the Palu valley, where rupture terminated to the surface. The fault here is a long and straight distinctive geomorphic feature. Many secondary faults were triggered, particularly in the southern Sapu valley fault system. An earthquake swarm was triggered 1 month after the main event on a strike-slip fault 200km away.
A Mechanistic Dissolved Oxygen Modeling for Riverine Fish Kill Prevention
Authors: Dai Jia-Yin , Chen You-Jia , Hwang Gwo-Wen , Cheng Su-Ting
Published: March 2021

Dissolved oxygen (DO) is a critical factor that controls the health and survival of the aquatic life. In the lower Danshuei River of Taiwan, DO was occasionally lower than 2 mg/L leading to several fish kill events. Since 2018, the Taipei city government started to continuously monitor hourly DO and other water quality factors at sites of Cheng-Mei Bridge and Cheng-De Bridge. However, at most sites, the monitoring has been conducted once a month. To provide sufficient DO predictions for preventing the occurrence of fish kills, a mechanistic DO modeling is required. As a result, in this study, we developed a system dynamic DO modeling considering oxygen exchange between the air-water and up/downstream interfaces with instream interactions of reaeration, photosynthesis, sediment oxygen demand (SOD), biochemical oxygen demand (BOD), respiration, and deoxygenation using the STELLA Architect software. In the model, we used meteorological data, water quality data, and hydrological data (flow rate, cross-section area, and hydraulic depth) simulated by HEC-RAS as input data to simulate daily DO at Cheng-Mei Bridge. Field measurements ranging from 0.21 to 10.34 mg/L were used to calibrate and validate the simulation results during Jan. to Aug. 2018, and Sep. to Dec. 2018, respectively. Our simulation results appeared reasonably good accuracy, in which the root mean square error (RMSE) ranging from 0.5 to 1.5 mg/L, and the percentage root mean square error (PRMSE) ranging from 5 to 15%. Moreover, results showed that DO was most sensitive to hydrological data, deoxygenation coefficient, and reaeration coefficient such that the meteorological conditions, like temperature and wind speed, were also important variables triggering hypoxia or anoxia that caused fish kills. Consequently, to better avoid or mitigate the occurrence of fish kills, we believe this physically-based DO modeling coupled with meteorological variables will offer useful information in predicting the condition of DO along the lower Danshuei River for managers to take preventative actions.
Leakage detection in water pipe networks using machine learning
Authors: Li Yu , Huang Jinhui Jeanne , Yan Ran
Published: March 2021

Leakage in the water supply system is a world problem that happens everywhere, not only in China but also in Japan, the US, and Europe. It not only results in the waste of water resources but also raises safety issues in drinking water. The traditional solution is the Minimum Night Flow method with manual leak detectors. This solution could only find leakage at night. The engineers have to search the leaking point randomly using leak detectors. It not only highly relies on domain knowledge and expertise but is also labor-consuming. The response time is quite long, might be a couple of days to several days. Here, time series analysis based on a dynamic time warping algorithm is used to detect anomalies in time series of pressure stations and flow stations, and the risk coefficient of each pipe network is determined by using a neural network combined with existing data. The water treatment plants don't even have to install new sensors if the budget is limited.
Submarine landslides in the west continental slope of the South China Sea and their tsunamigenic potential
Authors: Pan Xiaoyi , Li Linlin , Nguyen Hong Phuong , Wang Dawei
Published: March 2021

The 109 meridian fault is located in the west of the South China Sea (SCS) connecting to the offshore Red River Shear Zone. The evolution processes of the 109 meridian fault: striking-uplifting-subsidence of adjacent basin led to a nearly 1000m sharp bathymetric difference in the offshore region of central Vietnam. Combined with the high sediment input from numerous montane rivers in the rising hinterland, the continental slope near central Vietnam possesses the ideal condition for developing submarine landslides. Seismic data indicates many submarine landslides were developed along the steep continental slope. In this study, we analyze the possible trigger mechanisms of these landslides based on the local geological background and sedimentary environment, and assess their tsunamigenic potential along the coast of the Southern Central Vietnam (SCV). We point out that the landslide failures in this region could be triggered by several mechanisms, including seismic activities in the offshore SCV, volcanic activities, gas seep on the slope and the relative sea-level changes. The seismic and volcanic activities are related directly to the late middle Miocene volcanism generated by the change from left- to right-lateral motion on the Red River Shear Zone, showing that tectonism play a significant role in the generation of submarine landslide in the western continental slope of the SCS. To estimate the impact of tsunami waves on SCV coastline, we use two numerical models—NHWAVE and FUNWAVE-TVD to model 4 representative landslides with volume ranging between 1-4km3 and water depth of 300-1000m. The submarine landslides were treated as rigid slump and deformable slide corresponding to two different sedimentary environments. Our results show that the tsunami waves generated by rigid slump can reach up to 20m height in the landslide source area and arrive earlier to the coast of SCV than waves generated by deformable slide. Among these simulated scenarios, tsunami waves generated by the worst-case scenario arrive at the populated cities including Quy Nhơn (109.3°E,13.77°N), Tuy Hòa (109.37°E ,13.08°N) and Vung Ro Bay (109.43°E，12.86°N) in less than 25mins with maximum height of 5m. It is worth mentioning that the Vung Ro Bay will be affected by tsunami waves in all simulated scenarios. We quantify the influence of landslide characteristics (volume, water depth and material) and highlight the local effect of coastal bathymetry on the tsunami generation and propagation which lead to different hazard level of SCV coast.