bluuebunny/crossref_metadata_2025 huggingface dataset:
On the validation of the rotation procedure from HEE to MFA reference frame in presence of Alfvén waves in the interplanetary medium
Authors: Carnevale Giuseppina , Regi Mauro , Francia Patrizia , Lepidi Stefania
Published: February 2023

Alfvén waves play an important role in the stability, heating, and transport of magnetized plasmas. They are found to be ubiquitous in the solar wind, mainly propagating outward from the Sun, especially in high-speed streams emanating from coronal holes. When high-speed streams impinge on the Earth’s magnetosphere, the impact of Alfvénic fluctuations can cause magnetic reconnection between the intermittent southward IMF and the Geomagnetic field, leading to energy injection from solar wind into the Earth’s magnetosphere. In this work, we tested a rotation procedure from theHeliocentric Earth Ecliptic (HEE) to the Mean-Field Aligned (MFA) reference frame, identified by means of the Empirical Mode Decomposition (EMD), of both solar wind velocity and interplanetary magnetic field at 1 AU. Our aim is to check the reliability of the method and its limitations in identifying Alfvénic fluctuations through the spectral analysis of time series in the MFA reference frame. With this procedure, we studied the fluctuations in the main-field-aligned direction and those in the orthogonal plane to the main field. To highlight the peculiarities of each case of study and be able to better identify Alfvén waves when applying this procedure to real data, we reproduced the magnetic and velocity fields of a typical corotating high-speed stream. We tested the procedure in several cases, by adding the presence of Alfvén waves and noise. We performed the spectral analysis of the MFA component of both magnetic and velocity fields to define the power related to the two main directions: the one aligned to the ambient magnetic field and the one orthogonal to it. The efficiency of the procedure and the result’s reliability are supported by Monte Carlo tests. The method is as well applied to a real case represented by a selected corotating solar wind stream. The results are also compared with those obtained by using the Elsässer variables to analyze the Alfvénicity of fluctuations via the cross-helicity, which is related to the degree of correlation between the solar wind velocity and the magnetic field fluctuations.
Measuring Carbon: A Tool for Analysing Gridded, Continuous, Carbon Measurements at High Temporal and Spatial Resolution
Authors: Odhiambo Mitchell , Jain Raunaq , Gorana Yash , Kaushal Nikita , Mishra Abhilash
Published: February 2023

Measuring carbon emissions at high temporal and spatial resolution covering all parts of the globe is key to understanding the sources and sinks of carbon. These measurements are critical for informing both climate modeling and policy decisions to mitigate climate change. Fragmented data sources and the requirement of significant programming knowledge to retrieve, clean, and analyze data from existing data sources pose a significant barrier for climate researchers. As understanding of climate science becomes crucial for fields beyond geophysical sciences, it is especially urgent to build tools that can enable researchers from diverse academic backgrounds to analyze carbon emission data from satellites. In this presentation, we will present a novel, user-friendly platform which has pre-built functions and analysis pipelines allowing scientists to perform common data analysis tasks without the need to write code. The underlying data lake combines NASA’s Orbiting Carbon Observatory (OCO-2 and OCO-3) data with other data sources (e.g. MODIS-based fire data) that facilitate a more accurate and complete understanding of the dynamics of the carbon cycle and the factors that influence it. We highlight how our approach integrating data discovery, access, and analysis of climate data can help democratize climate research and inform policymaking.Potential research questions that can be addressed using this approach include: (i) studying the impacts of fires on the global carbon cycle with MODIS fire products providing information on the location, intensity, and types of fires, (ii) studying the photosynthetic activity of plants and the carbon cycle assimilating OCO-2 SIF data. OCO2-SIF data measures the fluorescence emitted by plants as a result of photosynthesis, which can be used as an indicator of plant health and productivity and (iii) AI-assisted audit of industrial emissions incorporating publicly available data on critical CO2 emitting sectors e.g. power plants, steel mills, cement plants, atmospheric “spillover” from agricultural and forest fires, traffic emissions, demographic and economic variables, etc
Evaluating RJ MCMC for parameter dimensionality adjustment in geological models
Authors: Chudalla Nils , Wellmann Florian , Bobe Christin , Brisson Sofia , Liang Zhouji
Published: February 2023

Uncertainty quantification is integral in geological modeling for mining, exploration and civil engineering projects. Workflows for uncertainty estimation for model input in implicit structural geological modeling and inversion are well-established when the parameter space is fixed. These approaches require a priori known or chosen number of parameters. In many cases, this may not be justified given the data, as the system’s number of layers or the number of phases is not known, resulting in overly complex or too simple models.

Trans-dimensional Markov chain Monte Carlo methods can be crucial to model selection in pursuing parsimonious models in complex geological settings with large uncertainty. Reversible jump Monte Carlo (RJ-MCMC) algorithms yield excellent solutions for inverse problems with varying dimensionality. In this study, we evaluate how far RJ-MCMC concepts can be applied to parameters of geological interpolation functions. Using the RJ-MCMC algorithm, we can automatically determine the optimal number of parameters for a given problem, reducing the need for subjective user input.

We simulate geophysical measurements for simple structural geological models built with GemPy to create a ground truth. In the second step, we create an ensemble of prior structural models, which are updated using geophysical inversion. We can address models’ likelihood in this probabilistic framework and enable further probabilistic inference given new data. We evaluate the performance of the trans-dimensional RJ-MCMC compared to standard, fixed-dimensional methods.
EarthCARE algorithms for retrieval of clouds, aerosols and vertical air motions
Authors: Okamoto Hajime , Sato Kaori , Nishizawa Tomoaki
Published: February 2023

Clouds, aerosols and vertical air motions are essential components to control climate systems. We need better Earth observing systems that can provide aerosol-, cloud- and precipitation microphysics, their radiation properties and in-cloud air motions to reduce uncertainties in climate change predictions. The Earth Clouds Aerosol and Radiation Explorer (EarthCARE) is a joint mission by Japan Aerospace Exploration Agency (JAXA) and the European Space Agency (ESA). It will carry Doppler cloud profiling radar (CPR), atmospheric high spectral resolution lidar (ATLID), multi-le spectral imager (MSI) and broad band radiometer (BBR). JAXA EarthCARE algorithms have been developed. Doppler capability of CPR is expected to provide terminal velocity of cloud and precipitation particles and vertical air motion inside clouds. High spectral resolution and polarization functions of ATLID provides true backscattering coefficient, extinction coefficient and depolarization ratio. Theoretical studies indicated that the two-dimensional diagram of lidar ratio and depolarization ratio is essential for discriminating among cloud particle shapes and orientations (Okamoto et al., 2019, Okamoto et al., 2020) in addition to aerosol types. Recently developed physical model (Sato et al., 2018) and vectorized physical model (Sato et al., 2019) can be used to take lidar multiple scattering into account for the interpretation of active sensor signals of optically thick clouds. Synergy of new observations with the theoretical improvements enables better cloud microphysics retrievals, leading to better estimation of radiative properties of cloud and precipitation particles and aerosol-cloud interactions. Synergetic ground-based observation systems are developed to evaluate the EarthCARE algorithms for space-borne active sensors. The systems consist of Multi-Field-of-view Multiple Scattering Polarization Lidar (Okamoto et al., 2016, Nishizawa et al., 2021), high spectral resolution lidar using a scanning Michelson interferometer (Jin et al., 2020), Direct detection Doppler lidar (Ishii et al., 2022) and 94 GHz cloud radar. The systems are designed to capture the characteristics of CloudSat, CALIPSO, and EarthCARE, leading to bridge the gap of these satellite data sets.  
Elevation bias due to penetration of spaceborne radar signal on Grosser Aletschgletscher, Switzerland
Authors: Bannwart Jacqueline , Piermattei Livia , Dussaillant Inés , Krieger Lukas , Floricioiu Dana , Berthier Etienne , Roeoesli Claudia , Machguth Horst , Zemp Michael
Published: February 2023

Digital elevation models (DEMs) from the spaceborne interferometric radar mission TanDEM-X hold a large potential for glacier elevation change assessments and monitoring. However, a bias is potentially introduced through the penetration of the X-band signal into snow and firn that can be substantial. The magnitude of this bias has been analysed in some glaciarized regions of the world; still, the knowledge about X-band penetration of TanDEM-X in the European Alps is limited.In this study, we investigated the unique situation of almost synchronous acquisition of TanDEM-X and Pléiades DEMs over the Grosser Aletschgletscher, complemented with in-situ observations (ground penetrating radar, snow cores, snow pits), all within a four-day period in late winter 2021. The comparison of the TanDEM-X and Pléiades DEM revealed an elevation bias due to radar penetration of up to 8 m above 3400 m. Further, the concurrent in-situ measurements reveal that the signal is not obstructed by the last summer horizon but reaches into perennial firn.Our study improves our understanding about the magnitude of X-band penetration of TanDEM-X in the Alps and the underlying process with a relevance for glaciology, snow science, remote sensing and the wider geoscience community.
Crustal Structures Across The Northern Scandinavia Along The SENJA OBS SURVEY Profile
Authors: Alemdar Rafet Ender , Kahraman Metin , Shulgin Alexey , Mjelde Rolf , Artemieva Irina , Thybo Hans
Published: February 2023

The Senja onshore-offshore seismic profile is located in the north-western part of Europe across the Norwegian coast into the North Atlantic ocean. A number of terranes and microcontinents collided to form this region from the Archean to the Paleoproterozoic. The Sveconorwegian (Grenvillian) and Caledonian orogenies significantly affected this region and created the major Caledonian mountain belt. Despite being far from any active plate boundaries, the Baltic Shield contains a mountain range called the Scandes that reaches heights of up to 2500 meters. This mountain range is oriented northeast-southwest and mainly correlates with the deformed Caledonian and Sveconorwegian part of the western North Atlantic coastal region.We present a crustal scale seismic profile along the northwest-to-southeast-directed Senja OBS Survey Profile in northern Scandinavia between 12°E and 20°E. This profile extends offshore and onshore for a total of ~300 kilometres across the Norwegian shelf in the North Atlantic Ocean, the Senja Island and into mainland Norway. The seismic sources were airgun shots from the vessel Hakon Mosby along the offshore profile. The seismic data set was collected by 68 onshore stations located at 1.3 kilometer distance and 5 ocean bottom seismometers located on the shelf, slope, and within the oceanic environment. The results of this investigation will provide new data for interpretation of the cause of the unusual onshore topography and offshore bathymetry at the North Atlantic Ocean's edge. We present the results from ray tracing modelling of a seismic P-wave velocity section  along the profile. 
Establishment of Rural Living Area Boundary for Sustainable Agri-Food System
Authors: Han Jeongwoo , Song Kihwan , Chon Jinhyung
Published: February 2023

In general, rural areas are declining due to urbanization and climate change, which affects the agri-food system centered on rural residents. For agri-food systems, the size and connectivity of regional systems are important, and the boundaries of people living in rural areas must be clearly defined. To effectively respond to these problems, the Republic of Korea proposed a policy plan for rural areas. However, there are issues with this approach since it is based on a legal spatial unit—meaning rural residents are not receiving the full breadth of intended benefits. It is necessary to readjust the spatial boundaries by aligning them more closely with the extent of the rural residents' living radius and the standards for services. This study aimed to establish the concept of a living area as it relates to rural areas, present criteria for setting the range and dimensions of a living area, and to then apply it to case studies. The target area was Muju-gun, Korea. It contains rural areas that face various problems such as population decline and aging. First, the concept of the rural living area was established based on insights gained from relevant literature reviews. The rural living area concept was defined as “a unit or range of spaces where rural residents can receive services to live and to support economic activities.” Second, building on the concept of the rural living area, the Muju-gun population, living service facilities, road networks, and watershed items were established, while relevant maps were collected. These materials were leveraged to conduct a network analysis. The closest facility analysis was performed and a network map was developed by overlaying the population and living service facilities with 12 key sectors (childcare, education, welfare, culture, physical education, health, medical care, commerce, finance, administration, transportation, and rest) and using the road network connecting them. Third, the range of living zones classified in order of size (small/medium/large) and by key sector in Muju-gun was derived. Excluding any missing values, a total of 30 Muju-gun living area ranges were drawn and presented. It was asserted that this was due to the fact that life service facilities in Muju-gun vary based on sector and size. The results of this study are particularly meaningful in that they presented a range based on the information that rural residents live in reality, and not an administrative district superimposed by the Republic of Korea—which reflects the existing legal standard unit. Since the derived range was based on the actual living range of rural residents, we expect efficient policy utilization in the planning and management of the agri-food system.This work was carried out with the support of “Cooperative Research Program for Agriculture Science and Technology Development(Project No. PJ0171102022)” Rural Development Administration, Republic of Korea.
Microsite amelioration by post-fire deadwood in a Pinus nigra planted forest in central Italy
Authors: Taccaliti Flavio , Marangon Davide , Vitali Alessandro , Urbinati Carlo , Marzano Raffaella , Lingua Emanuele
Published: February 2023

The widespread role of fire in shaping ecosystem composition and distribution, as well as its evolutionary importance, are nowadays fully recognized by scientists. In contemporary ecosystems, forest fires can induce different kinds of effects, depending on species characteristics, with the presence of fire-related traits often allowing full ecoystems recovery also after stand replacing or high severity events.By modifying growing space conditions (e.g. exposed bare soil, reduced competition), forest fires may trigger ecological processes such as forest regeneration. In the absence of specific fire adaptations, biological legacies persisting after the event have been proven crucial to drive regeneration dynamics. Indeed, deadwood can create safe sites and favourable microsite conditions for seedling establishment and survival. Despite the potential increase in coarse dead fuel load resulting from post-disturbance deadwood, its removal (e.g. through salvage logging activities) can cause long lasting negative effects on the ecosystems and the services they provide, often delaying or altering forest recovery.Studies are needed to assess the importance of facilitation mechanisms in different post-fire conditions, in order to provide suitable information on deadwood management and Assisted Natural Regeneration  approaches to forest planners and managers.In a xeric Pinus nigra planted forest in central Italy (Marche Region) affected by a large crown fire in 2017, soil temperature and moisture were measured in the topmost  5 cm of soil at various distances (n = 5) from downed logs (n = 14). Values were recorded in nine time steps during the summer of 2022.A significative positive effect of downed logs on microsite conditions (lower summer temperature, higher soil moisture) was observed. Based on these first results, a long-term monitoring project has been set up to evaluate if regeneration has actually a better performance in the identified microsites. An extensive natural regeneration sampling was performed in the burnt area, and artificial regeneration, both from seeds and trasplanted seedlings, was placed at different positions around the deadwood, trying to mimic the patterns of natural regeneration.The preliminary otuputs of this study will be integrated with other case studies and further field sampling campaigns, but they suggest that post-fire deadwood can facilitate natural regeneration  and should not be completely removed from burnt areas.
Seasonal changes in phytoplankton community composition and primary production in the southern Baltic Sea based on monitoring, ocean glider and satellite data
Authors: Paczkowska Joanna , Queste Bastien Y. , Monforte Chiara , Biddle Louise C.
Published: February 2023

We present year-long, vertically-resolved, high-resolution observations of phytoplankton community and primary productivity in the Bornholm Basin of the Baltic Sea. Sustained high-resolution monitoring reveals how the small-scale environmental dynamics regulate the phytoplankton community composition and primary productivity in the Bornholm Basin. We combine data from the Swedish National Monitoring Program, collecting phytoplankton (abundance, biomass and productivity) and environmental  (e.g. nutrients concentration) samples with remote sensing data and sustained observations from an Ocean Glider Observatory from March 2021 to 2022.The Ocean Glider Observatory continuously collected high-resolution physical (e.g.temperature, salinity) and biochemical (chlorophyll a, dissolved oxygen) data (vertical resolutions at 10-centimetre scale and vertical profiles with a mean temporal resolution of ~37 min). Glider data are calibrated against remote sensing data, while in situ measurements of photosynthetic yield and community composition allow us to model community composition and primary productivity across the dataset.Accurate estimation of primary productivity reveals the importance of short term and rapid changes in near-surface stratification and the deep chlorophyll maximum for the Bornholm ecosystem. Vertically resolved glider estimates also highlight limitations of remote sensing methods in this cloud covered region. As phytoplankton plays a crucial role in the transfer of energy through the food web via zooplankton up to fish, the importance of sustained, continuous high-resolution observations is required to fully capture the magnitude and variability of ecosystem processes and consequences to productivity.
Differentiation of saffron geographic origin by stable isotope (C, H, O, N) and 87Sr/86Sr analysis
Authors: Horacek Micha
Published: February 2023

Saffron is regarded as the most expensive spice, due to its laborious production and the required environmental conditions. Iran accounts for ca. 90% of the global saffron production. Very often, saffron of a production other than Iran is significantly more expensive than Iranian saffron. As fraught and mislabeling with respect to saffron is common, besides tools for the control oft he authenticity of saffron (detection of fake-saffron), also techniques are required to control the declared origin of saffron, to prevent consumer deception.In the present study I compare C-, H-, O-, N- isotope patterns of saffron samples of various geographic origin. Furthermore, 87Sr/86Sr of saffron samples are investigated and compared.The results show notable variations of the isotope patterns of saffron samples of different geographic origin, but also of different vintages for samples of same geographic origin. 87Sr/86Sr results indicate a good differentiation for certain countries of origin (e.g. Morocco and India), but other geographic origins cannot satisfyingly differentiated. The discrimination of Iranian saffron from saffron of other geographic origin remains a challenging task.
Architecture of seepage zones combined with their residence time to constrain hydrogeological models
Authors: Abhervé Ronan , Roques Clément , Chatton Eliot , Servière Laurent , de Dreuzy Jean-Raynald , Aquilina Luc
Published: February 2023

Hydrological predictions for ungauged basins at catchment and regional scales are still challenged by the lack of available data. Under the assumption that the perennial stream network is mostly fed by groundwaters, its spatial extent is controlled by the magnitude of the subsurface hydraulic conductivity (K) with respect to the actual recharge rate (R). In addition, the residence time of groundwater is directly controlled by the storage capacity of the aquifer system, i.e. the porosity (Ө). Here we propose a new inversion approach that jointly considers the spatial organization of observed hydrographic network and the residence times of groundwater measured at springs to infer the geometry of the aquifer system and its hydraulic properties.We used a dataset gathered in an alpine catchment observatory (Natural conservation area of the Massif of Saint-Barthélemy, Pyrenees, France). The extent of the stream network has been mapped using field observation. Residence times have been obtained from concentrations of dissolved CFCs and SF6 gases measured at 6 spring locations distributed over the catchment. The average transit time is about 30 years for perennial springs with a significant variability across the watershed. The relatively high residence time is also confirmed by high Helium concentrations.In our inversion scheme, we evaluate the accuracy of an ensemble of 3D hydrogeological models with different aquifer geometries and hydraulic properties. We found that topography and aquifer compartmentalization, through the decreasing trend in hydraulic conductivity, are key parameters in setting the spatial pattern of seepage areas and the distribution of transit times across the catchment. In addition, by running transient simulations of the model ensemble we further explore the accuracy of the models by comparing results with measurements of stream discharge and the intermittency of the hydrographic network. We found that intermittence seems to be connected to high transmissive shallow flow structures with low storage capacities (mostly organized within shallow soils and rockslides). However, perennial springs are sustained by deep groundwater flow paths within the bedrock. In perspective, we discuss the potential evolution of the extent, discharge magnitude and the transit time of seeping groundwater under changing recharge scenarios.
An updated sea surface pCO2 data-product for the global coastal ocean
Authors: Roobaert Alizee , Laruelle Goulven Gildas , Landschützer Peter , Regnier Pierre
Published: February 2023

Over the past decade, the number of high-quality measurements of the sea surface partial pressure of CO2 (pCO2) has rapidly increased and large-scale community efforts have led to the compilation of these measurements into uniform quality-controlled databases. Moreover, the development of different robust interpolation techniques allowed one to circumvent the limitation of these datasets that remain discontinuous in time and space to create continuous spatiotemporal pCO2 maps. While significant progress has been made regarding the development of several global data-products for the global ocean, most of these products omit the coastal ocean and/or their spatial resolution is too coarse to fully capture the highly heterogeneous spatiotemporal pCO2 dynamics that occurs in these regions. As a result, the evaluation of the interannual variability and the long-term trends of the coastal air-sea CO2 exchange using a continuous CO2 flux (FCO2) product dedicated to the shallow portion of the global ocean has not yet been attempted and, hence, remains poorly understood. To address these limitations, this study updates the global coastal data-product of Laruelle et al. (2017) based on the coastal version of the Self Organizing Map and Feed Forward Network method and uses ~ 32 million observations to cover the longest period available for the coastal ocean (1982-2020). The good performance in space and time of this new data-product using several evaluation methods allows us to reconstruct the temporal evolution of the global coastal FCO2 sink based on observations. Our results indicate that today’s coastal ocean acts as a CO2 sink and that it has been a CO2 sink since the beginning of our study period (1982). This CO2 sink has however increased over time from a value of -0.25 Pg C yr-1 (for a total shelf surface area of 77 million km2) in the early 1980s to a current value of -0.6 Pg C yr-1. Our new product provides a new constraint for closing the global carbon cycle and its temporal evolution as well as for establishing regional carbon budgets requiring high resolution coastal flux estimates.
Coffee Agrosystems and Climate Change
Authors: Della Peruta Raniero , Mereu Valentina , Spano Donatella , Marras Serena , Trabucco Antonio
Published: February 2023

Coffee is one of the most important agri-food systems from a global economic point of view. Most of the production takes place on small and medium-sized farms and is the main source of income for many rural families in several developing countries. Areas suitable for coffee production are very biodiverse and ecologically important, thus negative impacts should be minimized.Coffee production requires special environmental and climatic conditions. Current and future climate changes could cause problems for a sustainable production and result in lower yields. To overcome these problems, it is necessary to investigate the effectiveness of possible adaptation measures, such as intercropping with other tree species that can provide more shade to coffee plants and favour environmental sustainability. In order to study how such modifications could improve the resilience and sustainability of coffee production, the use of process-based models can be very useful. The DynACof model was developed specifically to simulate coffee farming systems, including phenological development, physiological processes related to flower and fruit production, carbon allocation, the effect of water availability, light and temperature, as well as management. We tested the DynACof model on some study areas in Mexico, Brasil and Rwanda and verified that the yield predictions were in line with the observations. We then developed a modelling tool where the model can be applied to entire geographical areas in a spatially explicit manner, using global climatic and soil datasets.We used this tool to simulate yields in Latin America and Africa, both for the period 1985-2014 and for the period 2036-2065 using climate projections. Comparing the two periods, the model predicts a decrease in yields of about 28% in Latin America and about 12% in Africa. We then simulated specific management options (e.g. agroforestry shading vs intensive monocropping) to assess their efficacy in enhancing environmental sustainability and resilience to climate risks. These impact analyses will be crossed with socio-economic indicators for a more comprehensive climate risk assessment to support adaptation recommendations.
Semi-supervised feature-based learning for prediction of Mass Accumulation Rate of sediments
Authors: Parameswaran Naveenkumar , Gonzalez Everardo , Bur­wicz-Ga­ler­ne Ewa , Greenberg David , Wallmann Klaus , Braack Malte
Published: February 2023

Mass accumulation rates of sediments[g/cm2/yr] or sedimentation rates[cm/yr] on the seafloor are important to understand various benthic properties, like the rate of carbon sequestration in the seafloor and seafloor geomechanical stability. Several machine learning models, such as random forests, and k-Nearest Neighbours have been proposed for the prediction of geospatial data in marine geosciences, but face significant challenges such as the limited amount of labels for training purposes, skewed data distribution, a large number of features etc. Previous model predictions show deviation in the global sediment budget, a parameter used to determine a model's predicitve validity, revealing the lack of accurate representation of sedimentation rate by the state of the art models. Here we present a semi-supervised deep learning methodology to improve the prediction of sedimentation rates, making use of around 9x106  unlabelled data points. The semi-supervised neural network implementation has two parts: an unsupervised pretraining using an encoder-decoder network. The encoder with the optimized weights from the unsupervised training is then taken out and fitted with layers that lead to the target dimension. This network is then fine-tuned with 2782 labelled data points, which are observed sedimentation rates from peer-reviewed sources. The fine-tuned model then predicts the rate and quantity of sediment accumulating on the ocean floor, globally.The developed semi-supervised neural network provide better predictions than supervised models trained only on labelled data. The predictions from the semi-supervised neural network are compared with that of the supervised neural network with and without dimensionality reduction(using Principle Component Analysis).
Elucidating climate change adaptation potential of improved maize (Zea mays L.) varieties with crop modelling
Authors: Chemura Abel , Arumugum Ponraj , Awori Eresi Kutesa , Gornott Christoph
Published: February 2023

The threats to crop yields are projected to increase under climate change and one of the most promising adaptation measures is for farmers to adjust their crop varieties over time to minimize climate risk. An improved or modern variety is a new variety of a plant species which produces higher yields, higher quality or provides better resistance to plant pests and diseases while minimizing the pressure on the natural environment. Selecting best maize varieties for various sites is also a good agricultural practice that can increase current yields in many low-productivity areas.  In this study, we aimed at identifying the climate change buffering potential of improved maize varieties using a spatialized DSSAT model using a case study across Uganda. We calibrate the model with observed weather data and then replace the weather files with climate projections from the ISIMIP3b. Model evaluation showed that the model performance was satisfactory with a correlation  coefficient (r) of 0.89, coefficient of determination (R2) of 0.79, index agreement (d) of 0.83 with observed yields. The impact of climate change on maize yield show spatial and temporal disparities with general trends showing that they worsen with time (2030 to 2090) and scenario (SSP1-RCP2.6 to SSP3-RCP7.0). At the national level, we project a yield loss of 6.2% (SSP1-RCP2.6) and 4.4% (SSP3-RCP7.0), by around 2030, 8.6% (SSP1-RCP2.6) and 14.3% (SSP3-RCP7.0) by around 2050, and 8.8% (SSP1-RCP2.6) and 26.8% (SSP1-RCP7.0) by around 2090. Switching to an improved variety results in at least double the maize yield under current climatic conditions (113.2%) compared to the current varieties, with maize yield exceeding 10 t/ha in the south-western, western and eastern parts of the country.  This positive yield effect was realized across all grids but substantially varied from around 10% to 500% yield change. Comparing the effect of climate change with an improved variety versus with a conventional variety shows it is always better to use an improved variety under climate change (positive effect), especially under worse case climatic conditions(2.9% and  8% yield buffering by 2090 under  SSP1:RCP2.6 and SSP3:RCP7.0 respectively) at national level. We therefore conclude that improved maize varieties offer a more durable solution to adapt to climate change and seed systems should therefore be strengthened to increase access to improved maize varieties for farmers.
Adaptive Surrogate Likelihood Function for Blended Hydrologic Models
Authors: Arabzadeh Rezgar , Romero-Cuellar Jonathan , Chlumsky Robert , Craig James , Tolson Bryan
Published: February 2023

This abstract introduces a recipe for an adaptive general likelihood function and its application in the Bayesian epistemology of model parameters and structure uncertainty. The proposed methodology focuses on a special class of likelihood function, hereinafter mentioned as adaptive general likelihood function (AGL), which require a minimum priori assumptions/knowledge about the model residuals. The goal of the AGL is to characterize the model residuals independently from the inference framework in order to avoid incorrectly posterior estimation as a result of jointly inferencing of model and error model parameters. Mathematically, AGL is structured with a mixture of gaussian distributions joined with a first order autoregressive model, account for error model shape and autocorrelation respectively. To assess the AGL application, it is benchmarked with a formal likelihood function formulated by Schoups and Vrugt (2010) and evaluated for 24 Camels basins where the blended model has been deterministically applied with success (Chlumsky et al. 2022). Both approaches are compared with the residual’s empirical distributions using various statistical tests. The model used here is a blended hydrologic model introduced by Mai et al., (2021) which is a class of hydrologic models constructed by averaging (blending) various process options at the process flux level. This blending means calibration of the model functions to identify traditionally calibrated model process parameters as well as the weights utilized to average multiple process options. The model is deployed in the Raven hydrologic framework (Craig et al., 2020) and simultaneously both processes weights and parameters were calibrated deterministically for both high flows and low flows using PADDS algorithm (Asadzadeh and Tolson, 2013). This multi-objective calibration yields a suite of sample of calibrated blended models which is then utilized for error model development and testing. The tests results indicated a statistically comparable performance for both methods for t-distributed residuals highly skewed and long-tailed residual errors which are apparent in many hydrologic model residuals. Finally, to disjoin the epistemic Bayesian inference framework from the error model parameters, an epsilon-support vector regression (eps-SVR) is deterministically trained as a surrogate model to map the structural/parametric variability to residual error model parameters. The eps-SVR calibration performance metrics indicated high quality of surrogate for training set indicating promising performance.
On the Assimilation of Remote Sensing Data for Soil Moisture Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in an Heterogeneous Ecosystem under water-limited conditions
Authors: Montaldo Nicola , Corona Roberto , Gaspa Andrea
Published: February 2023

Data assimilation techniques allow for optimally merging remote sensing observations in ecohydrological models, guiding them for improving land surface flux predictions. Nowadays freely available remote sensing products, like those of Sentinel 1 radar, Landsat 8, and Sentinel 2 sensors, allow for monitoring land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index, NDVI, for leaf area index, LAI) at unprecedented high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semi-arid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. An assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic-land surface model is proposed. It is based on the Ensemble Kalman filter (EnKF), and it is not limited to assimilate remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), the saturated hydraulic conductivity, and the grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture in an heterogeneous ecosystem in Sardinia, for 5 years period with contrasting hydrometeorological (dry vs wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared to the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors less than 4% and 15%, respectively, although the initial model conditions were extremely biased.
Improved Kinematics in a Weak Interface Model for Stratified Snow Packs
Authors: Rheinschmidt Florian , Weißgraeber Philipp , Rosendahl Philipp L.
Published: February 2023

The danger of dry snow slab avalanches is dependent on the conditions of the snow cover in alpine regions. Whether an avalanche is triggered from its own weight, wind or additional loads as backcountry skiers depends strongly on the conditions of the so-called weak layer. These porous and faceted layers grow as surface and depth hoar and are buried by densified snow layers, the so-called slab. In terms of mechanical properties, the slab has a relative high stiffness and tensile strength, while the weak layers with their low densities are more compliant and prone to collapse. These so-called anti-cracks nucleate in the weak layer and propagate afterwards through the slope until the slab ruptures and the avalanche is released.Providing an efficient stability assessment of stratified snowpacks demands for a mechanical model that can capture both the anti-crack nucleation and propagation. We present a highly efficient and accurate model based on the weak interface models from fracture mechanics, which is able to render stresses and energy release rates in snow packs in real time. The improved kinematics of the weak layer in combination with an improved derivation of the energy release rate enable one to substitute finite element computations in avalanche mechanics. In particular, the model covers the effect of the layering order on both the extensional and bending stiffness of the slab. It can be used for externally-loaded slopes and for stability tests such as the propagation saw test.
The NOAA Stratospheric Aerosol processes, Budget and Radiative Effects (SABRE) Project
Authors: Thornberry Troy , Jensen Eric
Published: February 2023

Stratospheric aerosols are an important component of Earth’s albedo, and therefore energy balance, and provide surface area for heterogeneous chemistry, which can lead to stratospheric ozone loss. Acquiring a comprehensive database of stratospheric aerosol, trace gas and dynamical observations to establish the baseline state and background variability of the stratosphere is essential to (1) developing a complete understanding of stratospheric dynamical and chemical processes that determine aerosol microphysics, radiative properties and heterogeneous chemistry, (2) evaluating the stratospheric response to natural and anthropogenic perturbations including climate change, volcanic eruptions, and potential climate intervention activities, and (3) strengthening the scientific foundation to inform policy decisions related to regulating global emissions that impact the stratosphere.The NOAA Stratospheric Aerosol processes, Budget and Radiative Effects (SABRE) project is a multi-year, multi-deployment UTLS airborne science measurement program to study the microphysics, transport, chemistry and radiative properties of aerosols in the upper troposphere and lower stratosphere (UTLS).  The SABRE instrument payload and flight campaigns are designed to provide extensive, detailed measurements of aerosol size distributions, composition and radiative properties along with relevant trace gas species in different regions and seasons, which are critical for improving the ability of global models to accurately simulate the radiative, dynamical and chemical impacts of changes in stratospheric aerosol loading.The first deployment of the SABRE project took place in February 2022, consisting of six flights from Ellington Field, Houston, TX. The flights provided an opportunity to field test several newly developed instruments that will be important for addressing SABRE science questions in subsequent deployments and sampled both the subtropical tropopause layer and midlatitude upper troposphere and lower stratosphere. The Realtime Air Quality Modeling System (RAQMS) was used for flight planning to target atmospheric dynamical features and species gradients. Complex structure was observed in trace gases and aerosol in the midlatitude UTLS, indicating significant stratosphere-troposphere exchange. Highlights from the deployment will be presented.Subsequent SABRE deployments will target aspects of the stratospheric aerosol budget including high latitude processes with a deployment to Alaska in February-March 2023 and to the tropics to study Tropical Tropopause Layer (TTL) processes in 2024. Each deployment will also include flights from Houston, TX to investigate seasonal and interannual variability in the subtropical and midlatitude stratosphere.
On the numerical dependence of balance state in geophysical flows
Authors: Chouksey Manita , Eden Carsten , Masur Gökce Tuba , Oliver Marcel
Published: February 2023

Balance flows dictate the evolution and dynamics of geophysical flows, such as the atmosphere and ocean, that are central to the Earth's climate. Here, balance geophysical flows are balanced using two different methods and compared in simulations of the single-layer shallow water model with two different numerical model codes and two different initial conditions over a range of different parameters. Both methods: nonlinear higher order balancing and optimal balance, add to the linear geostrophic mode, the linear wave mode contributions. The resulting approximately balanced states are characterized by very small residual wave emission during time evolution of the flow. Overall, the performance of both methods is comparable. Cross-balancing suggests that both methods find approximately the same balanced states. The results contradict previous studies claiming significant spontaneous wave emission from balanced flow. Further, the results clearly show that the notion of balance in numerical models of geophysical flows is ultimately related to the particular discretization.
Solar irradiance estimation for planetary studies
Authors: de Oliveira Isabela , Sowmya Krishnamurthy , Nèmec Nina-Elizabeth , Gizon Laurent
Published: February 2023

Solar irradiance is the main source of energy input to the planets of the Solar System. The solar rotation and the evolution of active regions on the surface of the Sun are two of the sources of solar irradiance variability. Nèmec et al. (2020) showed that the variability of solar irradiance is dominated by one of these two sources depending on the timescale of interest. The solar rotation dominates the variability for periods between 4-5 days and the synodic solar rotation period (27.3 days), while the evolution of active regions dominate for the remaining timescales.Usually, the irradiance measurements at Earth are extrapolated to estimate the irradiance at other planets and study the effect of solar irradiance on other planets' atmospheres (Thiemann et al., 2017). In this "lighthouse model", the irradiance source regions on the surface of the Sun are assumed to simply rotate with a Carrington sidereal period of 25.38 days. This means that the solar rotation is the only cause of variability of the irradiance in this model, and the evolution of active regions is neglected.In this work, we develop a model to calculate the irradiance at other planets by accounting for the evolution of magnetic features. Our method follows the Spectral And Total Irradiance REconstruction (SATIRE; Fligge et al. 2000; Krivova et al. 2003) approach and works by Nèmec et al. (2020) and Sowmya et al. (2021). First, the Surface Flux Transport Model (SFTM; Cameron et al. 2010) is used to obtain the time-dependent surface distribution of magnetic features (faculae and spots). Then, the solar irradiance is calculated as the sum of the contributions from the quiet Sun (i.e., regions with no magnetic activity), faculae, and spots. Our method allows calculating the solar irradiance directly at a given position within the ecliptic, regardless of the position of the Earth. We compare our irradiance calculations with those of the extrapolation method. We find that taking the evolution of active regions into account improves the estimation of solar irradiance significantly, especially when it comes to wavelengths in the visible and infrared ranges. Therefore, we suggest that our method provides more accurate estimates of solar irradiance to be used as input in studies of planetary atmospheres.We would like to note that our method of irradiance calculation is currently only statistical. We use the SFTM as we do not have information of the areas of the far-side of the Sun, which are needed in order to get the correct rotational variability. To determine real daily values of irradiance, we need to combine our calculations with methods of helioseismology, which is still a work in progress.
Mapping Himalayan glacial lake outburst flood hazard through time and space
Authors: McCoy Scott , Jacquet Jonathan , McGrath Daniel , Ghuffar Sajid
Published: February 2023

When glacial dams fail catastrophically, the ensuing glacial lake outburst floods (GLOFs) can cause devastating impacts to downstream environments and infrastructure. Large-impact GLOFs imprint distinct geomorphic features in the landscape that can remain diagnostic for hundreds of years, particularly for GLOFs sourced from moraine-dammed lakes. In this work, we used multi-temporal very-high-resolution-satellite imagery to systematically map the occurrence of impactful GLOFs from moraine-dammed lakes along the Himalayan arc between the Indus and the Salween rivers. Additionally, we binned mapped events by approximate date of occurrence to quantify changes in GLOF frequency through time. This new data set adds over 200 newly mapped GLOFs from ~200 lakes to the 108 events documented in published compilations. We find notable spatial heterogeneity in GLOF hazard along the Himalayan arc. Furthermore, we find that GLOF frequency from moraine-dammed lakes in the last 20 years is markedly lower than earlier time periods from 1970-2000 or from the end of the Little Ice Age to 1970. This decrease in GLOF frequency in recent time is despite continued growth of glacial lakes, likely increases in the frequency of mass movements that commonly trigger GLOFs from moraine-dammed lakes, and mapping bias that likely underestimates GLOF occurrence from earlier time periods.