bluuebunny/crossref_metadata_2025 huggingface dataset:
From noise-to-signal: enhancing the sensitivity of long-term GNSS by explaining non-linear station motions
Authors: Hohensinn Roland , Bock Yehuda
Published: February 2023

Reducing uncertainties in long-term GNSS is crucial for providing the most stable realization of terrestrial reference frames. Besides the epoch-wise handling of errors in the GNSS processing of the raw observations, this includes the description of linear and non-linear station motions by using functional and stochastic time series models (trajectory models). While the state-of-the-art trajectory models explain common signals (such as station velocity, periodic motions and offsets), a main challenge is to explain the variety of signals that are still present in the residuals (i.e., transients and artifacts).We take the time series of almost 2000 stations from Europe and the Western U.S., and fit extended trajectory models (ETMs) using the Hector software. We show that correcting the vertical observations by geophysical loadings and common-mode errors (CME) enhances median GNSS sensitivity by a factor of two, which we demonstrate for important parameters of the ETM, for both, linear and non-linear motions. The analysis reveals a median sensitivity of 0.5 mm/year for station velocity, and 0.6 mm for annual seasonal motions. Supported by the comparison with the CME-corrected case, we highlight potential shortcomings of the non-tidal atmospheric and hydrological loading models (related to phase shifts and the presence of transients), and demonstrate biasing effects on uncertainty.Based on the post-fit residuals of the ETMs and unsupervised learning methods, yet unexplained geophysical transients can be classified and clustered, which is a significant step towards automatic data interpretation and quality control. In view of an optimal GNSS station deployment, this approach also helps to separate stable from unstable regions, for example regions of irregular subsidence.  We conclude, that this algorithmic framework cannot only be successfully used for GNSS data, but also for other geodetic time series data.
Calibrating the mass center offset of the GOCE gradiometer with star trackers and a global gravity field model
Authors: Pan Juanxia , Zou Xiancai , Zhao MinXing
Published: February 2023

Precise calibration of the GOCE gravity gradiometer is one of the premises to determine a state-of-the-art global gravity field model. Provided the center of the gradiometer coincides to the GOCE center of mass, the common-mode accelerometer observations can generally represent the satellite’s non-gravitational accelerations. However, during the satellite flight, it is necessary to consider the possible offset of the gradiometer center with the center of the satellite towards the end of the mission due to orbital maneuvers and other reasons. In this research, the gradiometer calibration model is established by a priori gravity field model with six accelerometer observations and three star trackers angular rates. Besides the inverse calibration matrices, it includes the mass center offset as new calibration parameters and the impact of the parameter drifts is discussed. The feasibility of this method to solve this parameter is verified using the nominal mode measured L1b data, and the impact of this offset on GOCE gravity gradient data quality are reduced to a large extent. In addition, the analysis of time series of the calibration parameters and the estimated PSD of the gravity gradient trace demonstrate that the accuracy of the calibrated gravity gradients is improved.
Detecting Landslide Affected Areas Using Deep Learning of Bi-Temporal Satellite Imagery Datasets
Authors: Tsai Fuan , Dippold Elisabeth , Huang Po-Jui , Lo Chi-Chuan
Published: February 2023

Landslide is one of the most frequently occurred and destructive natural hazards in Taiwan and many other places around the world. Using satellite images to help identify landslide affected regions can be an effective and economic alternative comparing to conventional ground-based measures. However, utilizing remotely sensed images for the investigation and analysis of landslides still faces challenges. In a long-term monitoring of landslide affected areas, it is common to observe landslides occur repeatedly at or around the same region, thus requiring change-detection analysis of multi-temporal image datasets to identify this type (repeatedly occurred) landslides, especially to monitor its expansion. In recent years, machine learning techniques are extensively adopted for image analysis, including satellite images. Therefore, integrating change-detection with machine learning algorithms should be helpful for identifying and mapping incremental landslides from multi-temporal satellite images. This research developed a systematic deep learning framework for detecting landslides with bi-temporal satellite image pairs as the training datasets. The training datasets are extracted and labelled from multi-temporal high-resolution multi-spectral satellite images covering two watershed regions where landslides occurred frequently. Experimental results indicate that the developed machine learning algorithms can achieve high accuracies and perform better than conventional methods for detecting landslide affected areas from time-series satellite images, especially in the places where landslides may occur repeatedly.
Response of soil fauna to land use intensification in a global meta-analysis
Authors: Betancur-Corredor Bibiana , Russell David
Published: February 2023

Land use intensification, particularly a shift from extensively to intensively managed agroecosystems is often seen as one of the main drivers of global biodiversity decline and is considered the main factor applying pressure on soil biodiversity. When confronted with future land use change, understanding the responses of soil biodiversity to different land use regimes is decisive for adequate land management. However, there is still substantial uncertainty about how consistently different taxonomic groups respond to land use intensification. Oftentimes, different taxa show divergent responses to more intense land use regimes, and the community composition is rarely correlated with land use intensity, which may suggest that the drivers of community composition may not be the same as drivers of diversity. The mechanisms that determine the response of different taxa to land use intensification may be regulated by changes in the plant community and abiotic environmental drivers. We systematically assessed and quantified through meta-analysis the effects of land use intensification on soil organisms in global agroecosystems and analyzed the dependence of these effects on abiotic factors such as soil properties (organic matter, pH, nutrient and water availability, texture) and climatic zone.
Governing Common Pool Resources in Fragile Political Systems: Modelling Behaviour, Institutions, and Social-Ecological Dynamics
Authors: Erfurth Sophie , Wildemeersch Matthias , Baggio Jacopo , Sahu Reetik Kumar , Garrick Dustin
Published: February 2023

Groundwater user groups in Tunisia face severe collective action problems. Aquifer depletion leads to empty wells and farmers’ unwillingness to pay water fees leads to bankrupt user groups – both disastrous for the many communities that rely on irrigation agriculture for their livelihoods. What conditions or combination of conditions drive water user behaviour in a system that is governed by institutional uncertainty and bounded rationality? What conditions or interventions are effective in avoiding or delaying system collapse? What is the role of social norms, particularly trust and leadership, in overcoming collective action problems? Based on and expanding on the theory of common pool resource governance, this paper ties institutional results to environmental outcomes. The complex common-pool resource system studied here is simulated by an Agent-Based Model (ABM) of groundwater user decision-making. This systematic coupling of social and biophysical data and models offers new insights into simulating dynamic interactions between human behaviour, social norms, and the underlying resource. The project aims to provide a guideline for alternative modes of policy-making and implementation to address the main water governance challenge in Tunisia, i.e. groundwater overexploitation. 
Hadean to Eoarchean stagnant lid tectonics recorded by the paleomagnetism of zircons
Authors: Tarduno John , Cottrell Rory , Bono Richard , Nimmo Francis , Watkeys Michael
Published: February 2023

Because Earth is the only known planet to host both plate tectonics and life it is sometimes concluded that the two phenomena are related. While life is thought to have originated by the Eoarchean (or earlier), the onset of plate tectonics remains unknown, with proposed initiation ages ranging as old as the Hadean. Paleomagnetism can be used to distinguish between mobile and fixed lithospheres, but studies have been impeded by the high-grade metamorphism and deformation that makes most rocks older than Paleoarchean in age unsuitable for analysis. However, select detrital zircons can preserve primary magnetizations, providing an opportunity to conduct direct tests. Here we examine the zircon paleomagnetic history recovered from Western Australia which provides evidence for near constant paleolatitudes between ca 3.9 and ca. 3.4 Ga. We further assess this record with select zircons bearing primary magnetic inclusions from South Africa, which yield magnetizations consistent with this history. The simultaneous recordings of the magnetic field by zircons from two continents with vastly different Phanerozoic geologic histories provide further support for the primary record of the zircon magnetizations, and for a pre-Paleoarchean stagnant lid regime of Earth. These data also indicate that life on Earth originated and was sustained without plate tectonic-driven geochemical cycling.
CO2 gradients and variability in Mexico City from in situ measurements and simulations
Authors: Porras Sandra , González del Castillo Eugenia , López Omar , Arredondo Thania , García-Reynoso José Agustín , Laurent Olivier , Ramonet Michel , Delmotte Marc , Gutter Michel
Published: February 2023

Emission inventories are the primary source of information regarding the spatial and temporal distribution of CO2 sources and sinks in urban environments. In most cities around the world, the impact of mitigation strategies is evaluated conventionally through an inventory methodology which -by nature- is subject to high uncertainties.  In this work, we report on the utility of deploying a small network of medium-cost, non-dispersive infrared (NDIR) CO2 microsensors in Mexico City, to directly quantify the gradients and variability of the molar fraction of atmospheric CO2. The measurements obtained were compared with simulations of a high-resolution 3D transport model (WRF-Chem), prescribed with city-scale, inventory-based CO2 emission maps. The focus of this presentation is on the description of the sensors construction, the characterization of each individual sensor’s performance evaluated against a reference instrument, and the ability of the network to represent the spatial and temporal variability of CO2 in a complex urban environment. Preliminary results of a multivariate calibration of the medium-cost microsensors using a Picarro G2401 as reference instrument, along with air temperature, relative humidity and pressure, results in RMSE ranging from 1 to 5 ppm of CO2. We discuss the potential that this network of sensors offer to evaluate whether the distribution of sources and sinks declared in an inventory can result in the variability of concentrations of CO2 measured in the atmosphere, and the possibility of being used as a tool to incorporate the contribution of out-of-city emissions or mobile sources emissions, currently not accounted for in the inventory.
A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting over North America
Authors: Arnal Louise , Clark Martyn P. , Fortin Vincent , Pietroniro Alain , Vionnet Vincent , Whitfield Paul H. , Wood Andy W.
Published: February 2023

Seasonal streamflow forecasts are critical for many different sectors - e.g., water supply management, hydropower generation, and irrigation scheduling. Initial hydrological conditions (e.g., snow storage and soil moisture) are an important source of hydrological predictability on seasonal timescales. Snowmelt is the main source of runoff generation in high-latitude and/or high-altitude headwaters basins across North America, and the basins downstream. As a result, data-driven forecasting from snow observations is a well-established approach for operational seasonal streamflow forecasting in the USA and Canada.The aim of this work is to benchmark the skill of probabilistic seasonal streamflow forecasts across North America. To this end, we developed a reproducible data-driven workflow and implemented it for basins with a nival regime across North America. The workflow uses snow water equivalent measurements from the Canadian historical Snow Water Equivalent dataset (CanSWE), the Natural Resources Conservation Service (NRCS) manual snow surveys, and the SNOTEL automatic snow pillow in the USA. These datasets are gap filled using quantile mapping based on neighboring snow and precipitation stations. Principal Component Analysis is then used to define a small set of orthogonal predictor variables. These principal components are used as predictors in a regression model to generate ensemble hindcasts of streamflow volumes for basins across North America. Preliminary results for 93 nival basins and 17 glacial basins across Canada suggest that this forecasting method has the ability to provide skilful hindcasts (i.e., better than streamflow climatology) during the snowmelt season with up to 2-3 months lead. The results of this study provide a reference against which alternative forecasting methods (e.g., process-based forecasting models or machine learning approaches) can be assessed in the future.This work is a contribution of the recently launched Cooperative Institute for Research to Operations in Hydrology (CIROH) initiative that aims to develop next-generation water prediction capabilities. The CIROH program and the Global Water Futures (GWF) program are advancing capabilities for probabilistic streamflow forecasting over North America.
Groundwater mixing and pollution induced by the geothermal system operations in shallow groundwater
Authors: Kim Jaeyeon , Baek Jiyoung , Ko Hye-na , Kaown Dugin , Oh Haerim , Lee Kang-Kun
Published: February 2023

Groundwater, one of the environmental tools to achieve carbon neutrality, can be a sustainable thermal resource for a geothermal system. This study aims to investigate the groundwater system characteristics related to the thermal use of groundwater from the view of groundwater mixing and pollution for sustainable water resource management. Investigations have been conducted using hydrogeochemistry, multiple isotopes (O, H, Sr, and Rn), and microbial community structure data, around an open loop groundwater heat pump (GWHP) system. Continuous data of groundwater level and temperature showed the thermal plume propagation characteristics depending on the system operations. Multiple isotopes also revealed the specific characteristics accompanying the thermal use of groundwater. Especially, radon tracer quantitatively showed that the horizontal and vertical mixing occurred along main groundwater flow direction by mixing ratio calculations. In contrast, the clogging effects were observed in the wells located near the main flow direction by PHREEQC geochemical modeling and microbial diversity data, suggesting intensive management in these wells. A lot of time for recovery is needed in these wells. Overall results confirmed that combined analysis of hydrogeochemistry, multiple isotopes, and microbial community structure data can be effectively used to identify the impacts of geothermal system on shallow groundwater and to suggest effective management plan.
A Hybrid Hydrological Modelling Approach Combining Physically-Based and Deep-Learning-Based Models to Consider Dam Operations
Authors: Kim Yongchan , Kim Dongkyun
Published: February 2023

Physically-based hydrologic models can accurately simulate streamflow in natural environment, but they cannot precisely consider the anthropogenic disturbance caused by the operation of large-scale dams. We tried to overcome this issue by developing a hybrid modeling framework, consisting of physically-based models for simulating upstream natural watersheds and deep-learning-based models for simulating dam operation. The model was developed for the Paldang Dam watershed, a major water source for Seoul metropolitan area, where the importance of stable water supply has increased due to the increase of population and water use per capita. The prediction performance of the hybrid model was compared with that of models built based only on the physically-based hydrologic model, namely the Variable Infiltration Capacity model, with single and cascaded structure. For the validation period, Nash-Sutcliffe Efficiency from the developed hybrid model, the single model, and the cascaded model were 0.6410, -0.1054, and 0.2564, respectively, suggesting that the consideration of dam operation aided by the machine learning algorithm is essential for accurate assessment of streamflow.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).
The 2022 Hunga Tonga-Hunga Ha'apai Volcanic Earthquake’s Source Mechanism Revealed Through a Hierarchical Bayesian Treatment of Moment Tensor and Single-Force
Authors: Tkalčić Hrvoje , Hu Jinyin , Pham Thanh Son
Published: February 2023

Inferring the seismic source mechanisms of small-to-medium-size earthquakes from the observed waveforms via inverse methods remains challenging. Firstly, a more generalized source representation is required to include a broader range of seismic sources. A seismic moment tensor (MT) is widely used to parameterize a seismic point source by assuming no net torque. However, there are well-documented seismic sources for which net torques are significant, and single force (SF) components are necessary to describe the physics of the problem, e.g., landslides and volcanic and glacier earthquakes. Secondly, the inter-parameter correlation, e.g., the tradeoffs between the MT’s isotropic and compensated-linear-vector-dipole components for shallow explosive events and the MT and SF components at all depths, can be significant. Therefore, there is imperative for advanced sampling algorithms to explore the parameter space thoroughly and effectively. Thirdly, a complete uncertainty treatment should consider theory error primarily due to the imperfection of Earth's structure (referred to as structural error) apart from data noise. To date, the uncertainty of the 1D Earth model (1D structural error) has been investigated and proven indispensable in source studies. A rigorous uncertainty estimate can improve the resolvability of source parameters, but its implementation has been challenging.We propose a joint point-source MT and SF inversion within the hierarchical Bayesian framework to address the abovementioned set of challenges in treating the 2022 Hunga Tonga-Hunga Ha'apai event. MT and SF are combined to represent a broader range of sources in the waveform inversion. Our approach takes advantage of affine-invariant ensemble samplers to explore the parameter space thoroughly and effectively. Furthermore, we invert for station-specific time shifts to treat the structural errors along specific source-station paths (2D structural errors). After comprehensive synthetic experiments to demonstrate the feasibility of our approach, we focus on physics-based scenarios for the 2022 Hunga Tonga-Hunga Ha'apai volcanic earthquake. More specifically, we analyze the non-double-couple character and the role of SF in the source mechanism. Our approach provides further insights into this particular earthquake and a platform for future studies of seismic events in various geological environments.
Large Sample Basin Attribute Generation and Interpretation
Authors: Kovacek Dan , Weijs Steven
Published: February 2023

In recent years several large-sample hydrometeorological datasets have been developed and used as inputs in both process-based and machine learning hydrological models, often for runoff prediction in ungauged basins.  Large sample hydrology datasets take information from a rapidly evolving array of geospatial data sources to create indices describing basin attributes associated with runoff-generating processes.  In this study we discuss nuances of computational representation of basins, attribute interpretation with respect to physical processes, attributes vs. applications, the rate of change of spatial information sources, and the rapid growth and use of open source software tools.  Preliminary findings from generating a large sample dataset of ungauged basin attributes (~1M basins) are presented to support convergence towards standardized computational methods for basin attribute selection and calculation.
The Southern Ocean supergyre: a unifyingdynamical framework identified by machinelearning
Authors: Sonnewald Maike , Reeve Krissy A , Lguensat Redouane
Published: February 2023

The Southern Ocean closes the global overturning circulation and is key to the regulation of carbon and heat, biological production, and sea level. However, the dynamics of the general circulation and upwelling pathways remain poorly understood. Here, a unifying framework is proposed invoking a semi-circumpolar `supergyre' south of the Antarctic circumpolar current: a massive series of  ‘leaking’ sub-gyres spanning the Weddell and Ross seas that are connected and maintained via rough topography that acts as scaffolding. The supergyre framework challenges the conventional view of having separate circulation structures in the Weddell and Ross seas and suggests a limited utility for climate applications of idealized models and conventional zonal averaged frameworks. Machine learning was used to reveal areas of coherent driving forces within a vorticity-based analysis. Predictions from the supergyre framework are supported by available observations and could aid observational and modelling efforts of the climatically key region undergoing rapid change.
Science Communication: Deeper engagement with industry and government stakeholders through documentary film, art, and graphical newsletters
Authors: Hall Chelsie , Risk David
Published: February 2023

Canada’s academic science funding system requires substantial collaboration and partnership with stakeholders in government and industry. Meaningful and robust science, whether it is applied or fundamental, often depends on the ability to clearly communicate the nature and benefits of research. But the vocabulary of academic scientists and researchers usually differs from those in government and industry. Visual art and media, including artistic documentaries, creative writing, and animation, has potential to illustrate commonalities among stakeholder groups, and reach new audiences. Our research group, FluxLab, has generated effective visual media using a variety of strategies, including an informal artist-in-residence program, photography training for graduate students, and by using a graphic designer for print and web materials. In this presentation we share films, drawings, animations, newsletter layouts, and other materials used in stakeholder communication, along with context, aims, and success of each initiative.
Development of the century-long homogenized daily temperature data set in China
Authors: zheng xiang
Published: February 2023

The meteorological observation data of the early period (1840-1950) in China were  manually corrected the input and clerical errors, and then according to the length or coverage of time, the main series was determined. The observation time system of unknown sites is determined by the difference method introduced. After these operations, the data of all sites are unified into the same format. Then, through the ridge regressions established by data from modern reference stations, the missing maximum temperature (T-max) and minimum temperature (T-min) are interpolated, and then combined with modern data to form the heterogeneous extreme temperature data set of 1840-2020 in China. RHtest software was used to adjust the homogenate problem in  data set. Finally, the century-long homogenized daily temperature data set including 45 key city stations in China was obtained. There are 20 stations with observation record more than one hundred year. The length of temperature observation series of 17 stations is between 80 and 100 years. The series length of the remaining 7 sites is between 68 and 80 years. The data integrity of 5 stations is 50 - 60%, 9 stations is 60 - 70%, 8 stations is 70 - 75%, 5 stations is 75 - 80% and 18 stations is 85 - 100%. Finally, the angular distance weighting (ADW) method is used to interpolate the dataset into grid products, and the grid accuracy is 2.5 ° * 2.5 °. Climatic research unit dataset (CRU data set) was compared to verify the new dataset. The correlation between the four longest sequences in the newly developed data set  and the corresponding sites in the CRU data set is very good, and the correlation coefficient is very high, and the correlation between the remaining sites of the two data sets is also very good so the quality of CUG dataset is still trustworthy.
Foreshocks preceding moderate earthquakes in Western Yunnan, China
Authors: Zhu Gaohua , Yang Hongfeng
Published: February 2023

Although the physical mechanism of earthquake nucleation processes and the link with foreshocks are under debate, foreshocks are still considered as the most reliable earthquake precursors. Investigating the temporal and spatial evolution of foreshock sequences with high resolution and monitoring b-values in real time may shed light on these key issues. Many foreshock and aftershock sequences accompanying moderate mainshocks have been reported in the west of Yunnan Province, China, such as the 2016 Yunlong M 5.1 and 2021 Yangbi Ms 6.4 earthquake sequences. The recently improved coverage of seismic network in western Yunnan provides the opportunity to investigate how the foreshock sequence evolved and establish the temporal transient in b values. To find missing earthquakes and built more comprehensive earthquake catalogs, we carried out earthquake detection using the matched-filter detector. We used events in the standard catalog of China Earthquake Networks Center as templates to scan through continuous waveforms 3-6 months before and after the main shock. We then estimated the b-value and its temporal changes based on the newly developed catalogs. An obvious reduction in b-values before the major earthquake is observed in both the 2016 Yunlong and 2021 Yangbi sequences. We also found that the scattered spatial pattern of foreshocks exhibits a cascading manner and does not support the hypothesis of slow slip driving nucleation of mainshocks.
A convolutional LSTM model with high accuracy to predict extreme precipitation space-time fields
Authors: Choi Hyojeong , Kim Dongkyun
Published: February 2023

Precipitation forecast models based on meteorological radar data using machine learning architectures accurately predict spatio-temporal progress of precipitation. However, these data-driven forecasting models tend to underestimate magnitude of extreme precipitation events because the training of them is based on the observed precipitation data in which the normal precipitation events are included significantly more than the rare extreme events. This study proposes a ConvLSTM-based precipitation nowcasting model that can accurately predict space-time field of extreme precipitation. First, precipitation events were classified into 5 subsets using the k-means clustering algorithm based their statistical properties such as mean, standard deviation, skewness, duration, and the calendar month at which the precipitation event occurred. Then, a ConvLSTM-based neural network was trained based on the subset containing extreme precipitation events (events with large mean, variance, and duration occurred in summer months). The model was trained and tested based on the 4km-10minute resolution radar-gauge composite precipitation field of central part of South Korea (200km x 200km) for the period of 2009-2015 and 2016-2020, respectively. The NSE of the model that was trained based on the whole precipitation data was 0.55 while the one trained based on the subset of extreme precipitation was 0.78 showing a significant improvement.This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A2C2003471). 
Sensitivity analysis and discontinuity removal of 6SV LUT-based surface reflectance for each channel: based on GEO-KOMPSAT-2A
Authors: Jung Daeseong , Han Kyung-soo , Seong Noh-hun , Sim Suyoung , Woo Jongho , Kim Nayeon , Choi Sungwon
Published: February 2023

To monitor the surface based on Earth observation optical satellites, accurate atmospheric correction of satellite images is required. Surface reflectance is calculated using a look-up table (LUT) based on a radiative transfer model. In addition, atmospheric gas components and geometric information of solar and satellite observations used in LUT construction are applied to each channel at equal intervals. However, the atmospheric gas components are sensitive to the atmospheric effect in a specific wavelength range of the satellite sensor. The higher the geometric information appears in the satellite observation area, the greater the variability of the atmospheric effect occurs because the moving distance of light increases. Because of this, LUT-based atmospheric correction at equal intervals generates discontinuities in surface reflectance in satellite images. In this study, to improve the quality of the surface reflectance applied with atmospheric correction, a Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) radiation transfer model was used to analyze the sensitivity of the surface reflectance for each channel according to the GEO-KOMPSAT-2A-based atmospheric gas component and the geometric information of the solar and satellite observations. After figuring out the variability of surface reflectance for each channel according to the intervals of variables used in LUT construction, an error analysis of surface reflectance was performed for the optimal LUT interval considering the interpolation technique. In the future, it is considered that the results of this study can be used to identify LUT-based surface reflectance characteristics for removing discontinuities in surface reflectance, including increasing the utilization of geostationary satellite images.
Aboveground biomass production and litter accumulation in coastal grassland ecosystems: the basis of a global study
Authors: O'Halloran Lydia
Published: February 2023

Herbaceous dominated ecosystems are found throughout the world representing a wide variety of locations that differ in their mean annual precipitation, annual temperature ranges, light availability, and soil nutrient concentrations. This is reflected in a wide range of annual above- and belowground biomass production, plant diversity and canopy density between these ecosystems worldwide. Our study aims to understand how nutrient availability and disturbance impact species relative abundance, diversity, and annual aboveground production. Here, we present background data on herbaceous dominated ecosystems found in coastal South Carolina, USA that are part of a global network (DragNet) of herbaceous dominated ecosystem study sites distributed across the world. Dominated by both grass species and forbs, we found that within a site, there is variation in the relationship between species diversity and biomass production. Aboveground biomass and accumulated litter are not correlated suggesting that the drivers for carbon assimilation and decomposition are different. This research will serve as the foundation for future research on how nutrients and soil disturbance interact with the amount of carbon assimilation and if some species show trait variation in response to these treatments in different regions of the world.
Analysis on magnetic field gradients in turbulent magnetosheath by using MMS data
Authors: Ji Yong , Shen Chao , Ma Lan , Ren Nian , Ahmad Nisar
Published: February 2023

Magnetic field gradients determine magnetic topological structure and current density in space plasma turbulence. This study uses multi-point method analyze high-quality field and plasma data measured by the Magnetospheric Multiscale (MMS) mission in the turbulent magnetosheath. The statistical properties of the curvature of the magnetic field line and the geometric invariant of the magnetic field gradient tensor are further investigated. The results show that the probability distribution function of curvature has two scaling laws. There is a correlation between large curvatures and pressure anisotropy, indicating the acceleration due to curvature drifts. During strong magnetic field, flux ropes and tubes are the most possible magnetic structures. Statistics in the plane formed by geometrical invariants show that about 23% are force free structures consist of 20.5% flux tubes and 79.5% flux ropes. The remaining actively evolved structures are comprised of 30% flux tubes and 70% flux ropes. Moreover, the conditional average of current density and Lorentz force decomposition in geometrical invariants plane are conducted. Results show that flux ropes carried more current density than flux tubes for same geometrical invariants, and flux ropes tend to associate with magnetic pressure force and flux tubes tend to associate with magnetic tension.
Using Brutsaert’s Equation to Understand the Spatiotemporal Variations of Downwelling Longwave Radiation
Authors: Tian Yinglin , Kleidon Axel , Alam Ghausi Sarosh , Zhong Deyu , Wang Guangqian
Published: February 2023

Downwelling longwave radiation (Rld) is a dominant term in the surface energy balance and is central to global warming. It is influenced by the radiative properties in the whole atmospheric column, particularly greenhouse gases, water vapor, clouds, and atmospheric heat storage. To reveal the leading terms responsible for the spatiotemporal climatological variations in Rld, we use the semi-empirical equation derived by Brutsaert (1975, “B75”), which only needs near-surface observations of air temperature and humidity. We first evaluated B75 and its extension by Crawford and Duchon (1999, "C&D99") with FLUXNET observations, NASA-CERES satellite data, and ERA5 reanalysis. We found a strong agreement, with R2 being 0.87, 0.97, and 0.99, respectively. We then used the equations to show that diurnal and seasonal variations in Rld are predominantly controlled by changes in atmospheric heat storage. Variations in atmospheric emissivity form a secondary contribution to the variation of Rld, and are mostly controlled by anomalies in cloud cover. We also found that with increased aridity, the contributions by changes in atmospheric heat storage and emissivity acted to compensate each other (20~30 W/m2 and ~-40 W/m2, respectively), thus explaining the relatively little variation in Rld with aridity (-20~-10 W/m2). The equations further indicate that under global warming, the amplification of water vapor is stronger in arid regions because clear-sky conditions are more sensitive to an increase in greenhouse gases. These equations thus provide a firm, physical basis to understand the spatiotemporal variability of downwelling longwave radiation at the surface. This should be helpful to better understand and interpret climatological changes, for instance those associated with global warming and extreme events.
Laboratory model of Martian gullies
Authors: Czechowski Leszek
Published: February 2023

Liquid water is currently not stable on the surface of Mars. Therefore, we should be careful if we assume the hypothesis of the formation of young geological formations on Mars, if it requires the presence of liquid water. Note also that p-T conditions on the surface of Mars are not suitable for liquid CO2. Therefore, the geological young structures on the surface of Mars are not probably the result of processes requiring the liquid. Here we present the results of our laboratory experiments on the processes that may be responsible for the formation of gullies on the surface of Mars.Gullies are usually found on steep slopes. A gully forms usually a single incised channel. There is a dendritic alcove at the upper end. A fan-shaped deposited material ('apron') is at the lower end. Gullies are estimated to be relatively young. Some of gullies could be recurrent seasonal structures. Terrestrial gullies are often the result of stream of liquid water. Initially, such an origin was also attributed to Martian gullies.  This would imply that in the recent (geological) past there were water streams on the surface of Mars capable of carving out a significant channel (a 'river bed'). An alternative hypothesis assumes that the gullies are the result of the movement of debris. However, many gullies are on slopes with a low inclination, too low for flows of  debris. Moreover, the sinuous shape of some gullies suggests flow of liquid. We performed experiments on a scale 0.5-2 m using dust, sand, gravel, pebbles and other granular matter (as the model of regolith). With the appropriate geometry of the model, it was possible to obtain the basic qualitative features of gullies, including the channel and the apron (in the lower end). Adequate quantitative scaling remains a problem (e.g. channel sizes depending on the properties of the material used, model dimensions and geometry, etc.). Of course, ourresults do not meanthat gullies cannot be the result of temporalbrinesoccurring on Mars, but theyindicate the possibility that some gullies could have formed without liquid water. We will continue our research to improve our models of gullies.
Interpretable Unsupervised Classification of River Catchments with Network Science
Authors: Ciulla Fabio , Varadharajan Charuleka
Published: February 2023

The classification of river catchments has been an active field of study for decades and the recent surge in hydrological and environmental datasets promotes the formulation of new approaches to this endeavor. We present a novel method for catchment classification based on physical traits similarity using network science, where the relationship among the catchments is represented by the edges of a network. Under this framework we leverage the capability of networks to capture collective behaviors to find clusters of catchments with similar physical traits. The use of networks allows the adoption of similarity metrics other than the common euclidean distance, which is subjected to quality degradation in high dimensions but is still required in many traditional clustering algorithms. Also, a network of traits is built to investigate their similarity patterns and condense this information into a small number of interpretable traits categories. Such categories are used to provide a characterization of each cluster of catchments. The method has been tested on over 9000 river catchments across the contiguous United States, each one accompanied by traits such as climate or vegetation coverage, and anthropogenic features such as land use or proximity to developed areas. The resulting classification shows a remarkable geographical coherence supported by the characteristic traits categories. Additionally, we find that when hydrological indices (like statistics on streamflow or water temperature) are aggregated according to the clusters of catchments, different clusters show different hydrologic behaviors. This, along with the information from cluster characterization, allows us to establish a connection between hydrological behaviors and physical traits. Finally, this framework can be applied at multiple scales, from continental to regional. When tested on a regional scale, the method automatically modifies the network topology to reflect the traits patterns relevant to the area under investigation.
Colder eastern equatorial Pacific and stronger Walker circulation in the early 21st century: an Indo-Pacific ocean thermostat versus natural variability
Authors: Fedorov Alexey , Heede Ulla
Published: February 2023

Since the early 1990s the Pacific Walker circulation has strengthened, while SSTs in the eastern equatorial Pacific became colder, which is opposite to future model projections. Whether these trends, evident in many climate indices especially before the 2015 El Niño, reflect the coupled ocean-atmosphere response to global warming or the negative phase of the Pacific Decadal Oscillation (PDO) remains debated. Here we show that sea surface temperature (SST) trends during 1980-2020 are dominated by three signals: a spatially uniform warming trend, a negative PDO pattern, and a Northern Hemisphere/Indo-West Pacific warming pattern. The latter pattern, which closely resembles the transient ocean thermostat-like response to global warming emerging in a subset of CMIP6 models, shows cooling in the central-eastern equatorial Pacific but warming in the western Pacific and tropical Indian ocean. Together with the PDO, this pattern drives the Walker circulation strengthening. CMIP6 historical simulations appear to underestimate this pattern, contributing to the models’ inability to replicate the Walker cell strengthening. We further discuss how such changes in the Walker circulation can effect ENSO.Reference:  Heede, U. and A.V. Fedorov, 2023: Colder eastern equatorial Pacific and stronger Walker circulation in the early 21st century: separating the forced response to global warming from natural variability. In press, GRL
Deep learning of systematic sea ice model errors from data assimilation increments
Authors: Gregory William , Bushuk Mitchell , Adcroft Alistair , Zhang Yongfei
Published: February 2023

Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, closely mirror the systematic bias patterns of the dynamical model. In this work, we show that Convolutional Neural Networks (CNNs) can be used to learn a mapping from model state variables to analysis increments, thus promoting the feasibility of a data-driven model parameterization which predicts state-dependent model errors. We showcase this problem using an ice-ocean data assimilation system within the fully coupled Seamless system for Prediction and EArth system Research (SPEAR) model at the Geophysical Fluid Dynamics Laboratory (GFDL), which assimilates satellite observations of sea ice concentration. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, and sea-surface salinity. We show that the CNN is able to make skilful predictions of the increments, particularly between December and February in both the Arctic and Antarctic, with average daily spatial pattern correlations of 0.72 and 0.79, respectively. Initial investigation of implementation of the CNN into the fully coupled SPEAR model shows that the CNN can reduce biases in retrospective seasonal sea ice forecasts by emulating a data assimilation system, further suggesting that systematic sea ice biases could be reduced in a free-running climate simulation.
Floral analysis of a ~140 yr sediment record using sedaDNA and diatoms, Summit Lake Nevada, USA
Authors: Noble Paula , Crump Sarah , Muscott Amelia , Larsen Darren , Ruiz Fernandez Ana Carolina , Chandra Sudeep , Simmons James , Fifo Ayowole , Csank Adam
Published: February 2023

The sediment fill at Summit Lake shows strong potential for reconstructing past changes (Holocene to present) in the paleoecology and regional paleoclimate for the subalpine region of the Black Rock Range, northwestern Nevada, USA. Summit Lake is a high desert terminal lake with a surface elevation of 1780m.a.s.l. and is a eutrophic, alkaline (~8.4), dimictic lake with a maximum depth of ~10 m and a small surface area (2.8 km2). There is abundant growth of macrophytes, including Polygonium (smartweed), Myriophyllum (milfoil), and Ceratophyllum (coon tail), and growth progresses throughout the summer months. The lake falls within the domain of the Summit Lake Paiute Tribe, who has an inextricable cultural connection to the lake via the native Lahontan cutthroat fishery. The tribe’s original name, Agai Panina Ticutta, translates as the Summit Lake Fish Eaters.In 2021 and 2022 C.E., we collected a series of surface and long sediment cores from multiple sites in the lake. This study focuses on sediments collected from the lake’s central depocenter. Preliminary age control of Summit Lake sediments is determined by 210Pb-dating (corroborated by the stratigraphic profiles of the man-made radionuclides 137Cs, 241Am) for the upper ~40 cm, and the Mount Mazama tephra (~7.6 ka) at 410cm depth. The age model suggests high and increasing sedimentation rates between early 1900 C.E. and 2021 C.E. (from 0.05 to 1.87 cm/yr). In comparison, the mean sedimentation rate between the Mazama tephra and ~1900 C.E. is low (0.05 cm/yr). We present preliminary sedaDNA and diatom data for the last ca. 140 years. Plant sedaDNA shows good preservation, with a stable alpha diversity of ~40 ASVs. The plant record is dominated by the aquatic plants Myriophyllum and Potamogeton (pondweed) in the upper 40cm, and terrestrial plants are also represented, including sagebrush, willow, aspen, and a variety of herbaceous plants, including aster, rose, primrose, buckwheat, borage, lupin, and saxifrage. The diatom flora of the upper 40cm is dominated by the benthic epiphyte Cocconeis placentula, which is consistent with a large macrophyte community seen in the modern system and indicated by the plant sedaDNA. Modern sampling shows the epiphytic relationship between C. placentula and milfoil. Future work will include pairing and harmonizing the diatom record derived from traditional morphotaxonomy and eDNA, contextualizing the plant eDNA with the modern plant community, and refining the age model to better discern Holocene climate events that may be driving the changes in sediment flux and the floral community. These data will be extended down-core to reconstruct the past climate and lake levels, informing the Tribe’s management efforts for a resilient watershed and fishery in the future.