bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of stratospheric circulation on recent sea ice records in Antarctica.
Authors: Cordero Raul , Feron Sarah , Damiani Alessandro , Llanillo Pedro , Carrasco Jorge
Published: March 2024

The sea ice cover around Antarctica reached record highs in 2013 and 2014 before plummeting to record lows in 2017, 2022, and 2023. Recent anomalies in the Antarctic sea ice, both high and low, underscore the imperative for additional research into the underlying factors contributing to interannual sea ice changes. In this study, we utilized reanalysis data to investigate the relationship between the strength of the polar vortex and the sea ice extent around Antarctica. Our focus is on the last two decades, a period marked by significant shifts in stratospheric zonal flow, tropospheric westerly winds, and sea ice cover. Our findings suggest that polar vortex dynamics have played a role in this unprecedented interannual variability. The distinct signature of stratospheric circulation is evident in recent sea ice records, highlighting the impact of polar vortex strength on the sea ice extent around Antarctica.
Development of the signal-to-noise paradox in subseasonal forecasting models: After how long? Where? Why?
Authors: Schwartz Chen , Garfinkel Chaim I. , Knight Jeff , Taguchi Masakazu , Cohen Judah , Chen Wen , Butler Amy H. , Domeisen Daniela I.V. , Lawrence Zachary
Published: March 2024

Subseasonal forecast models are shown to suffer from the same  inconsistency found in climate models between the low strength of predictable signals and the relatively high level of agreement they exhibit with observed variability of the atmospheric circulation. That is, subseasonal forecast models show higher correlation with observed variability than with their own simulations, i.e., the signal-to-noise paradox. Also similar to climate models, this paradox is particularly evident in the North Atlantic sector.  The paradox is not evident in week 1 or week 2 forecasts, and hence is limited to subseasonal timescales. The paradox appears to be related to overly fast decay of Northern Annular  Mode regimes. Three possible causes of this overly fast decay and for the paradox in the Northern Hemisphere are identified:  a too-fast decay of polar stratospheric signals, overly weak downward coupling from the stratosphere to the surface (in some models),  and overly weak transient synoptic eddy feedbacks. While the paradox is clearly evident in the North Atlantic, things are qualitatively different in the Southern Hemisphere:  Southern Annular  Mode regimes persist realistically, the stratospheric signal is well maintained, and eddy feedback is, if anything, too strong and zonal.  
AI to Enhance the Capabilities of EUV-observing Satellites and Estimate Spectral Irradiance
Authors: Tremblay Benoit , Jarolim Robert , Jungbluth Anna , Munoz-Jaramillo Andrés , Bintsi Kyriaki-Margarita , Santos Miraflor , Mason James P. , Vourlidas Angelos , Sundaresan Sairam
Published: March 2024

Multiple satellites capture images of the Sun in Extreme Ultraviolet (EUV) light. However, only the Solar Dynamics Observatory (SDO) was equipped with instruments that measure the Sun's EUV spectral irradiance (i.e., MEGS-A and MEGS-B onboard the Extreme Ultraviolet Variability Experiment (EVE) suite). The MEGS-A instrument malfunctioned in 2014, making it impossible to measure the full irradiance spectrum ever since.  Using AI, we explore the translation of a set of EUV images of the Sun into spectral irradiance, effectively replacing the malfunctioning MEGS-A instrument onboard SDO. In other words, we generate a virtual irradiance instrument, MEGS-AI, for SDO. Using an Image-to-Image translation tool (ITI), this virtual instrument can also be trained and added on other EUV-observing satellites like STEREO, GOES, SolO, and the upcoming VIGIL satellite, enabling unprecedented irradiance estimates from additional satellite missions. In the case of the STEREO twin-satellites and VIGIL, this enables estimates of spectral irradiance prior to the Sun rotating into Earth’s view, which directly enables the forecast of enhanced irradiance. Additionally, we explore different combinations of images in different EUV channels and evaluate their contributions in estimating different irradiance channels. Finally, when combined with a neural radiance field model of the Sun (SuNeRFs), MEGS-AI can estimate spectral irradiance from any viewpoint in the solar system, enabling for the first time a complete 4pi spectral irradiance map of the Sun. This can be directly used to estimate the Sun’s impact on other planets in the solar system and to determine the total solar irradiance output in multiple EUV spectral bands.
An Automatic Approach for Grouping Sunspots and Calculating Relative Sunspot Number on SDO/HMI Continuum Images
Authors: Zhao Cui , Yang Shangbin
Published: March 2024

Relative Sunspot Number is one of the major parameters for the study of long-term solar activity. The automatic calculation of the Relative Sunspot Number is more stable and accurate as compared to manual methods. In this paper, we propose an algorithm that can detect sunspots and divide them into groups, to automatically calculate the Relative Sunspot Number. Mathematical Morphology was adopted to detect sunspots then group them. The dataset used were the continuum images from SDO/HMI. The process was carried out on the overall HMI data available on the timespan from January 2022 to May 2023 with a time cadence of one day. The experimental results indicated that the method achieved high accuracy of 85.3%. It was well fitted with the international Relative Sunspot Number provided by Solar Influences Data Analysis Center (SIDC) (CC=0.91). We calculated the conversion factor K value of SDO/HMI for calculating the Relative Sunspots Number(K=1.03).
After the Waters Receded: Destruction of Khakovka Dam Affects Ukraine’s Agricultural Production
Authors: Baber Sheila , Sadeh Yuval , Becker-Reshef Inbal
Published: March 2024

The ongoing war in Ukraine has drawn international attention to the impact of conflict on global agricultural production and food security. On June 6 2023, the Kakhovka Dam in Southeastern Ukraine collapsed under attack, draining the 2000 square km reservoir which served as a source of water and power for 20,000 people [1]. The loss of this critical agricultural and energy infrastructure has left farmers on the left bank of the Dnieper River without irrigation in the historically arid Kherson and Zaporizhzhia oblasts [2]. In this study, we use Earth Observation to measure the change in irrigation coverage in the occupied region resulting from the loss of the dam. Given the current conflict and the lack of ground-truth data from the occupied regions, we use human-labeled validation sets derived from the greenness, thermal, and wetness characteristics of irrigated fields [3], using pre-collapse (2020-2022) imagery from PlanetScope, Landsat-8 and 9, and Sentinel-2 as the baseline. Preliminary results from an unsupervised approach show a significant reduction in 2023 of the types of fields identified as ‘irrigated’ in the pre-collapse years. Given that much of the summer crops would have been planted by June, this change in 2023 is hypothesized to be due to lack of irrigation in already-planted fields, rather than farmers changing crop types in response to irrigation loss. 
References:
[1] Naddaf, M. (2023). Ukraine dam collapse: what scientists are watching. Nature, 618(7965), 440-441.
[2] Vyshnevskyi, V., Shevchuk, S., Komorin, V., Oleynik, Y., & Gleick, P. (2023). The destruction of the Kakhovka dam and its consequences. Water international, 48(5), 631-647.
[3] Deines, J. M., Kendall, A. D., Butler, J. J., & Hyndman, D. W. (2019). Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environmental Research Letters, 14(4), 044014.
Australia’s water quality trends over two decades
Authors: Guo Danlu , Zhang Qian , Minaudo Camille , Liu Shuci , Dupas Remi , Zhang Kefeng , Bende-Michl Ulrike , Duvert Clement , Lintern Anna
Published: March 2024

Water quality of rivers and streams can vary over time due to changing hydro-climatic conditions in the interaction with catchment physio-geographic conditions and local human activities. This study is the first exploration of long-term river water quality trends across Australian continent, consolidating 375 catchments with contrasting climate, hydrology, land use and land cover. We focused on five key water quality parameters and estimated their flow-normalized trends over 2000-2019 using the Weighted Regressions on Time, Discharge, and Season method (WRTDS). For each parameter, about half of nation’s catchments have significant trends, which are generally within ±10% per annum relative to the first year (2000). Except for TSS, there is no systematic non-linearity nor abrupt changes over time, while for TSS many catchments had a systematic shift from increasing to decreasing trends since around 2010. A random forest model was developed and found that catchment land characteristics, along with baseline water quality, can explain over a third of the spatial variation of the trends of EC, TN, TP and TSS (32-51% explained), but had limited explanatory power for DO (22% explained). These findings will provide critical information on the waterway health, thereby facilitating natural resources management for Australia.
A scale-dependent vertical structure for energy backscatter parameterizations
Authors: Zhang Wenda , Griffies Stephen , Yankovsky Elizabeth , Hallberg Robert , Adcroft Alistair
Published: March 2024

Ocean mesoscale eddies constitute a crucial component of ocean energy cascade, engaging in energy exchange with large-scale circulations, submesoscale eddies, and internal waves. State-of-the-art ocean climate models, which partially resolve mesoscale eddies (i.e., eddy-permitting), often exhibit weaker and more surface-intensified eddy kinetic energy (EKE) than in higher-resolution simulations and observations. The energy backscatter scheme has been employed in eddy-permitting simulations to enhance the energy of mesoscale eddies by compensating for the excessive dissipation caused by the viscosity closure. In this scheme, a proper vertical structure for the backscatter coefficient is necessary to simulate a more realistic vertical distribution of kinetic energy. Here we propose a parameterization for the vertical structure of subgrid EKE and implement it within the backscatter scheme in idealized eddy-permitting simulations of MOM6. The parameterization is grounded in the observation that the EKE is surface-intensified and decays faster with depth at smaller horizontal scales. The diagnosed vertical structure of EKE from eddy-resolving simulations is well-captured by surface quasi-geostrophic (SQG) modes, whose vertical structure depends on the eddy horizontal scale, Coriolis parameter, and stratification profile. Based on the SQG mode, we formulate a scale-aware parameterization of the vertical structure, accounting for the variation of subgrid eddy scale with the model horizontal grid spacing. This vertical structure is then applied to the energy backscatter coefficient used in 1/2° and 1/4° idealized simulations of basin-scale ocean circulations. The diagnostics of these eddy-permitting simulations are compared to those of a 1/32° reference simulation. The inclusion of the vertical structure in the backscatter improves the simulation of global kinetic energy distributions, large-scale circulation pathways, and isopycnal structures, compared with the eddy-permitting simulations without backscatter or with a depth-independent backscatter. Sensitivity tests show that a more surface-intensified backscatter tends to result in weaker total kinetic energy and more tilted isopycnals. This work provides insights into the parameterization of mesoscale energetics and its vertical variation in eddy-permitting simulations.
A glacier ice volume modeling framework based on generative adversarial networks and graph neural networks
Authors: Maffezzoli Niccolò , Lagnese Gianluca , Vascon Sebastiano , Petersen Troels , Barbante Carlo , Rignot Eric
Published: March 2024

Estimating the ice volume of Earth's glaciers is a key challenge in Earth System science, crucial for understanding their evolution, quantifying future global sea level rise and freshwater resources in climate-sensitive regions. Given current global warming that causes glaciers mass loss, precise ice volume estimates become a top priority in face of future climate scenarios. Here we present the SKYNET project, which aims to develop a general modeling framework for estimating ice volumes of Earth’s glaciers using generative deep learning. The modeling framework comprises two main networks. The first network, a generative adversarial network, reconstructs glacier bedrocks using elevation maps of surrounding ice-denuded regions. Trained on over 1 million Digital Elevation Maps, the model learns key geometrical features and patterns of Earth’s mountain regions. The challenge is addressed as an image inpainting problem, in which the objective is to reconstruct the bedrock altitude in a missing portion of the image, using surrounding information. The second network leverages existing ice thickness measurements. Such a network is trained on local features such as ice velocity, slope, distance from glacier boundaries, and other glacier statistics to predict local ice thickness. We use the Glacier Ice Thickness Dataset (GlaThiDa) and other input products as training dataset. We employ a graph neural network (GNN) with an architecture that explicitly accounts for the connectivity of the data. The GNN’s local ice thickness estimates serve as a prior to refine the inpainting network’s generated ice thickness maps. We introduce the model, discuss its concept, advantages, limitations, current challenges and present preliminary results and tests on diverse continental glaciers across the globe. 
Evolution and Disappearance of the paleo-West Pacific Anomaly: implications to the South Atlantic Anomaly
Authors: Yue Yaochen , Gao Jiawei , Wei Yong , He Fei , Cai Shuhui , Wang Huapei , Wang Yuqi , Rong Zhaojin
Published: March 2024

Paleomagnetism and modern geomagnetic measurements indicate that SAA is undergoing rapid changes, with rapid expansion of area and rapid decreasing of intensity. Even in recent years SAA has expanded the minor minima seen in southern Africa. This has led to speculation that the expanding SAA may be an indicator of an upcoming geomagnetic reversal. However, how to know the future evolution direction of SAA is still debatable.Apparently, the study of the changes of other SAA-like structure anomaly regions in history has important implications for the study of SAA evolution. Here, we focus on the evolution and disappearance of the paleo West Pacific Anomaly (WPA). Utilizing the gufm1 model, it is found that WPA occurred between 1620 and 1810 CE. Over its duration, WPA underwent phases of rapid expansion, drift, and split, eventually the primary part vanished, while a new segment persisted and expanded. The striking similarity in evolutionary behavior between WPA and the rapid expansion, drift, and recent splitting in the SAA over the past century is noteworthy. This similarity can be attributed to the fact that WPA, like the SAA, is controlled by the reversal flux patch in the CMB on the northern hemisphere. Therefore, we posit WPA as a significant magnetic anomaly in the Northern Hemisphere, and propose that its evolutionary patterns can serve as predictive indicators for the future evolution of the SAA. Studying the evolution of WPA is an important means to understand the global magnetic field and the evolution of Southeast Asia magnetic field. It is of great significance for us to understand the nature of SAA and predict the evolution trend of SAA.
Geochemistry and Melt Inclusion study of Peridotite Xenoliths from Sinsanri, Jeju, South Korea
Authors: Khant Naing Aung , Ryu Han-Sun , Moon Jinah , Kim* Heejung
Published: March 2024

The Sinsanri region of Jeju Island, South Korea, is renowned for its abundant xenoliths, making it one of the prime locations for their occurrence. Jeju Island itself is a Cenozoic intraplate volcano situated in East Asia. A variety of xenolith types such as Spinel Lherzolite, Spinel Harzburgite to Wehrlite are entrapped in Quaternary intraplate alkali basalts on Sinsanri, Jeju Island, South Korea, during the first and second eruption episodes of the Quaternary Volcanic Activity.  Here we report the major element composition of the xenoliths, including the first Raman spectroscopy analysis of the melt inclusion inside olivine to constrain the post-entrapment depth and the latest recrystallizing depth of the melt inclusion itself.  The prominent xenoliths are spinel lherzolite, showing transitions between proto-granular textures and porphyroclastic textures. Mg# ranges from 73.4 to 93.8 in the Sinsanri xenolith’s samples. The spinel lherzolite xenoliths originated at pressure of 15kb with an equilibrium temperature ranging from 922°C.  The size of the CO2 rich melt inclusion entrapped inside the mantle peridotite xenoliths ranges from 20 to 40 µm.  The densities of CO2 melt inclusion were measured to be between 302.8516 kg/m³ and 972.4025 kg/m³.  Most of the melt inclusions inside the xenoliths may have formed while they are ascending to the surface together with the host basalt.  The post-entrapment depth is calculated using the equilibrium temperature of the olivine mineral, which caused the most melt inclusion.  It can be estimated that the post-entrapment depth of the CO2 melt inclusion of the Sinsanri is 86 MPa. The Sinsanri xenoliths are predominantly formed at relatively lower temperatures and pressures.Acknowledgement This research was funded by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant numbers 2019R1I1A2A01057002 and 2019R1A6A1A03033167); and Korea Ministry of Environment as “The SS (Surface Soil conservation and management) projects; 2019002820004”.
UAV lidar: from volcanoes to forests
Authors: Rodgers Mel , Van Alphen Robert , Bakowski Rachel , Berkey Troy , Sadeghi Chorsi Taha , Malservisi Rocco , Connor Charles. B. , Dixon Timothy. H.
Published: March 2024

Improvements in miniaturization and affordability of lidar technology, mainly due to innovation in self-driving cars, means that UAV lidar is now an accessible option for geoscience research. We present applications in which UAV lidar contributes to data collection in ways that would otherwise not be possible in the time frame, budget, and/or with the resolution required.Volcanoes: Lava flow surface texture can provide information on lava flow dynamics and emplacement. The transition between pahoehoe and a’a flow textures can indicate changes in flow rates and flow thickness, and the morphology of ripples in ropey pahoehoe flows can indicate flow direction. Hell’s Half Acre, Idaho, USA, is a basaltic lava flow that was erupted ~5000 y.a. Analysis of UAV lidar data at this lava field shows lava flow surface texture in sufficient resolution to define cm-scale pahoehoe ripples. In addition, larger scale lava features such as channels and inflation/deflation ridges can be mapped which allows us to understand the dynamics of the lava flow emplacement.Vegetation: UAV Lidar can be useful for analysis of vegetation canopy, both in stripping canopy (lidar last return) and in using it for tree height (lidar first return). By combining UAV lidar with other airborne data, e.g. multispectral imaging, we can identify and map tree species at Ft de Soto Park, in Florida, USA.Permafrost: Permafrost thermokarst features can develop rapidly and climate change will cause an increase in these rapid thaw events. With UAV lidar we can strip the vegetation to reveal the underlying ground surface which can then be used to assess and model permafrost processes. UAV surveys are quick and relatively inexpensive (as compared to crewed aviation) and data can be collected in response to a thaw event. We present data from Alaska, USA, at known sites of rapid thermokarst thaw.UAV lidar, both as a stand-alone dataset, and when integrated with other data streams e.g. multispectral and visible imagery, can provide high-resolution data (both spatial and temporal) on a platform that is relatively low-cost and logistically straightforward to deploy.
COS, CO2 and H2O eddy covariance flux measurements over Agave sisalana 
Authors: Kohonen Kukka-Maaria , Skogberg Mikko , Kübert Angelika , Räsänen Matti , Merbold Lutz , Buchmann Nina , Mammarella Ivan , Pellikka Petri , Vesala Timo
Published: March 2024

Carbonyl sulfide (COS) has gained attention as a proxy for stomatal conductance and photosynthesis. It is taken up by the plants through their stomata, similar to carbon dioxide (CO2), and destroyed at the chloroplast surface by the enzyme carbonic anhydrase in a hydrolysis reaction. The main limiting factor of COS uptake in the leaves has been found to be the stomatal control. Thus, COS flux measurements have been linked to stomata-controlled carbon and water fluxes. However, existing studies, both in the field and in the laboratory, have predominantly focused on C3 and C4 plants, leaving a gap in understanding COS exchange in crassulacean acid metabolism (CAM) plants.CAM plants, such as sisal (Agave sisalana), aim to minimize water loss by closing stomata during the day and opening them at night. During nighttime, they take up CO2 but also other gases, including COS, from the atmosphere. CO2 is stored as malic acid until light becomes available during daytime and it can be utilized in photosynthesis. This allows the plants to avoid water loss in harsh environments and reach high water-use efficiencies.In this study, we measured COS fluxes with the eddy covariance (EC) technique over a sisal plantation for the first time. The measurement period covers three weeks during the rainy season in Kenya in November and December 2019. We show that COS and CO2 fluxes followed a similar diurnal pattern, with uptake observed during nighttime, while water (H2O) fluxes showed an opposite cycle with highest evaporation observed during daytime. We also show that the soil COS fluxes, measured with soil chambers, were positive under radiation (i.e., indicating COS emission) and negative (i.e., indicating COS uptake) in the dark, and soil COS emissions increased with increasing soil temperature. Our aim is to quantify the canopy conductance of Agave sisalana using COS together with H2O and CO2 flux measurements at the ecosystem scale. Our study provides valuable insights into the intricate interplay of COS with water and carbon fluxes in ecosystems dominated by CAM plants. 
Mysterious emissions of Halon-2402 in eastern Asia drive the global trend
Authors: Choi Haklim , M. Western Luke , Kim Jooil , Mühle Jens , Thompson Rona , Lee Gawon , K. Salameh Peter , M. Harth Christina , F. Weiss Ray , Rigby Matthew , Park Sunyoung
Published: March 2024

,2-Dibromotetrafluoroethane (C2Br2F4, Halon-2402, H-2402) is used as a fire suppressant due to its stability because it maintains a liquid state at room temperature with a relatively high boiling point. However, H-2402, containing bromine (Br), was identified as a potent ozone-depleting substance, with a destructive capacity six times higher than that of CFC-11. Therefore, under the Montreal Protocol, its production and consumption were phased out globally in 2010, with developed countries starting their phase out in 1994. Russia, the primary producer of H-2402, reportedly ceased production after 2000. For essential uses where no alternatives are available (e.g., military fire extinguishers, oil and gas pipelines), existing or recycled supplies of H-2402 are permitted to be used. Despite H-2402 being under strict international regulation, accurate reporting and statistical information on essential production and consumption by countries remain limited.This study analyzes the atmospheric mole fractions records of H-2402 measured from 2008 to 2020 at Gosan station, South Korea. While the background mole fractions of H-2402 at Gosan station are gradually decreasing at -0.01 ppt/yr, similar to the global decreasing trend, high pollution cases were continuously observed throughout the entire period. Also, the frequency of occurrence also increased by more than three times in 2020 compared to 2008. This increase in pollution signals, not observed at major Northern Hemisphere background monitoring stations (such as Mace Head, Trinidad Head, and Jungfraujoch), suggests potential regional emissions in eastern Asia. Based on long-term atmospheric observations, and a combined analysis using the Lagrangian particle dispersion model (FLEXPART) and the Bayesian inverse framework (FLEXINVERT+), we have estimated the annual regional emissions in eastern Asia. We present observation-based results on the long-term, regional-scale variability of H-2402 emissions over eastern Asia and their significant contributions from a global perspective.
Characterizing uncertainty in sea level rise from 900 future groundwater depletion scenarios
Authors: Wessel Jacob , Lamontagne Jonathan , Kemp Andrew
Published: March 2024

Sea level change is driven by numerous processes which vary across scales both spatially and temporally. Data-driven model abstractions for the natural and anthropogenic processes governing sea level are used to make future sea level projections through the remainder of the 21st century and beyond, both globally and by region. In addition to the primary drivers of sea level rise (mass loss of terrestrial ice and thermal expansion of seawater), changes in terrestrial water storage, namely groundwater extraction and reservoir impoundment, have also been identified as contributors. Uncertainty surrounding future projections of groundwater depletion and its implications on global and relative sea level have thus far been inadequately characterized and underexplored, and rely on a small number of studies based on simple methods with little or no scenario dependence or spatial variability. We use a 900-member large ensemble of basin-level groundwater depletion simulations through 2100 from a global integrated assessment model to explore a wide outcome space of groundwater futures and characterize their critical drivers from among six systematically varied inputs: socioeconomics (SSPs), climate forcing (RCPs), climate model (GCM), groundwater availability, surface water storage, and hydrological model. The large ensemble of simulations enables a more robust discussion of uncertainty and scenario dependence than previously available. We find the median global mean sea level (GMSL) rise by 2100 due to groundwater depletion in our model ensemble to be 83 (17-205) mm. We also find that the greatest concentrations of groundwater depletions take place in the Western US, the Nile Basin, the Middle East, and Central and South Asia, though the correlation between basins can vary widely. This basin-level dataset also enables sea level fingerprinting to assess the spatially variable effects of groundwater depletion on relative sea level (RSL). Uncertainty in this fingerprinting can then be compared with the uncertainty bounds traced by the large ensemble of model runs.
Generating dense time-series of spatially continuous 30m fractional vegetation cover for the Qinghai-Tibetan Plateau based on Google Earth Engine
Authors: Tao Guofeng , Jia Kun
Published: March 2024

The Qinghai-Tibetan Plateau (QTP) is one of the most sensitive and vulnerable regions under global climate change. Vegetation is the key component of the QTP ecosystem and is closely related to the ecological vulnerability. Fractional vegetation cover (FVC) is an important parameter to characterize vegetation conditions in the horizontal direction. Therefore, dense time-series of spatially continuous FVC at high spatial resolution is essential for understanding the detailed spatiotemporal dynamic changes in vegetation across QTP. Landsat is an ideal remote sensing data source for high spatial resolution FVC monitoring. However, frequent cloud cover during the growing season on QTP makes it challenging to observe FVC constantly only using Landsat. Spatiotemporal fusion methods integrating the advantages of Landsat and MODIS have been widely developed. Currently, most spatiotemporal fusion methods assume that the relationship between Landsat and MODIS is fixed over the prediction period. For regions with strong heterogeneity and large temporal variations, the relationship between Landsat and MODIS is variable along time. In addition, most methods fuse the reflectance bands separately without considering the interrelationship between bands. Therefore, a method blending Landsat and MODIS reflectance to generate FVC with 30m spatial resolution and 8-day interval based on Google Earth Engine (GEE) is proposed in this study. This method considers the dynamic relationship between MODIS and Landsat by analyzing the time-series data collected from multiple years. And a novel two-band simultaneous smoothing strategy is developed in this method, which can generate smoothed and consistent time-series of red and near-infrared bands simultaneously. Compared with three previous typical methods in two challenging QTP regions with rapid vegetation change, it can be found that the synthesized 30m reflectance data generated by the proposed method can get more accurate FVC. The validation results compared with the field-measured FVC further confirm the validity of the proposed method. The generated FVC products across QTP exhibit spatial continuity and reasonable time-series profiles. The proposed method is thus expected to provide high-quality FVC time-series with high spatiotemporal resolution over multiple years for QTP and other regions with frequent data missing based on GEE.
Memory and Management: Competing Controls on Long-Term Nitrate Trajectories in U.S. Rivers
Authors: Van Meter Kimberly , Basu Nandita , Byrnes Danyka
Published: March 2024

Excess nitrogen from intensive agricultural production, atmospheric N deposition, and urban point sources elevates stream nitrate concentrations, leading to problems of eutrophication and ecosystem degradation in coastal waters. A major emphasis of current US-scale analysis of water quality is to better our understanding of the relationship between changes in anthropogenic N inputs within watersheds and subsequent changes in riverine N loads. While most water quality modeling assumes a positive linear correlation between watershed N inputs and riverine N, many efforts to reduce riverine N through improved nutrient management practices result in little or no short-term improvements in water quality. Here, we use nitrate concentration and load data from 478 US watersheds, along with developed N input trajectories for these watersheds, to quantify time-varying relationships between N inputs and riverine N export. Our results show substantial variations in watershed N import-export relationships over time, with quantifiable hysteresis effects. Our results show that more population-dense urban watersheds in the northeastern U.S. more frequently show clockwise hysteresis relationships between N imports and riverine N export, with accelerated improvements in water quality being achieved through the implementation of point-source controls. In contrast, counterclockwise hysteresis dynamics are more common in agricultural watersheds, where time lags occur between the implementation of nutrient management practices and water-quality improvements. Finally, we find higher tile-drainage densities to be associated with more linear relationships between N inputs and riverine N. The empirical analysis in this study is bolstered by modeled simulations to reproduce and further explain drivers behind the hysteretic relationships commonly observed in the monitored watersheds.
 Greenhouse gas emissions and mitigation in the global solar photovoltaic industry chain
Authors: Chen Shi , Lu Xi
Published: March 2024

Against the backdrop of the rapid global rise of solar photovoltaic (PV) energy, its supply chain from manufacturing to installation has gradually exhibited dynamic spatial evolution, yet the spatiotemporal distribution of greenhouse gas emissions and mitigation throughout the entire PV industry chain has not received sufficient attention. The pathways to enhance the net mitigation benefits of PV through international collaboration across the entire industrial chain remains in its initial stage. This presentation will outline the author's systematic accounting of global supply chain carbon emissions and mitigation, combining spatiotemporal dynamic lifecycle assessments and scenario analyses. The analysis explored the spatiotemporal evolution of net greenhouse gas mitigation from 2009 to 2060 within the global PV industry. The study reveals that optimized collaboration between manufacturing and installation globally could increase the net mitigation effects of the entire industrial chain by 97.5 Gt carbon dioxide equivalent, equivalent to 1.9 times the global GHG emissions in 2020. This finding provides theoretical and empirical support for enhancing international strategic cooperation to enhance global greenhouse gas mitigation.
Improving gross primary production estimation accuracy on the Qinghai-Tibet Plateau considering the effect of atmospheric CO2 fertilization
Authors: Li Jie , Jia Kun
Published: March 2024

Involving the effect of atmospheric CO2 fertilization is effective for improving gross primary production (GPP) estimation accuracy using light use efficiency (LUE) model. However, the widely used LUE model, the remote sensing-driven Carnegie-Ames-Stanford Approach (CASA) model, is scarcely considering the effects of atmospheric CO2 fertilization, which cause GPP estimation uncertainties. Therefore, this study proposed an improved GPP estimation method based on CASA model integrating the atmospheric CO2 concentration and generated a long time series GPP dataset with high precision for the Tibetan Plateau. The CASA model was improved by considering the atmospheric CO2 effect on vegetation productivity and distinguishing the CO2 gradients differences within the canopy and leaves brought by the influence of leaf stomatal conductance and leaf saprophyte activity. A 500m monthly GPP dataset for the Tibetan Plateau from 2003 to 2020 were generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the CASA model (R2 = 0.67, RMSE = 499.32 g C/m2/year), and MODIS GPP products. The GPP on Qinghai-Tibet Plateau increased significantly with the increase of atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved GPP estimation method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on Qinghai-Tibet Plateau.
The formation mechanism and enrichment patterns of H2S in Es4 of Dawangzhuang area, Chezhen Sag
Authors: Han Dai , Dong Chunmei , Ma Pengjie
Published: March 2024

Hydrogen sulfide (H2S) is a harmful gas associated with oil and gas, which seriously affects the safety and effective exploitation of oil and gas, so it is important to evaluate the origin and distribution of hydrogen sulfide effctively. H2S rich oil and gas reservoirs were found in lacustrine carbonate rocks of Es4, Dawangzhuang area, Chezhen Sag. In order to clarify the formation mechanism and enrichment law of H2S in this area, the geochemical characteristics of natural gas, formation water, elemental characteristics of reservoir solid bitumen, and sulfur isotope characteristics of kerogen, salt-rock and crude oil are analyzed comprehensively. It is concluded that H2S in Dawangzhuang area is caused by thermochemical sulfate reduction (TSR). H2S in Dawangzhuang area is generated from the northern deep depression zone and migrated to the upper submember of Es4 carbonate reservoir in the study area through the successor fault activities during and after Guantao accumulation period. This understanding will provide reference for the future development of sulfur-rich oil and gas reservoirs in Dawangzhuang area and the avoidance of high-yield H2S risk.
THE HYDROMETEOR IDENTIFICATION FOR THE GPM DPR: Version 8 Updates
Authors: Chandrasekar Chandra V , Le Minda , Harri Ari-Matti
Published: March 2024

Since May 2018, the Dual-frequency Precipitation Radar (DPR) on board the GPM core observatory satellite has operated in full scan mode. Dual-frequency full swath data provides us a valuable chance to improve our knowledge of precipitation processes by providing greater dynamic range, more detailed information on microphysics, and better accuracies in rainfall and liquid water content retrievals [1]. The DPR Level-2 algorithms consist of several modules including the classification (CSF) module, where precipitation type is classified into three major types: stratiform, convective, and other.  Besides that, estimates of the melting layer top and bottom are provided in the classification module with product name as “binDFRmMLTop”, “binDFRmMLBottom” and the quality metric of “flagMLquality”. Three flags namely, identifiers of falling snow on the ground, graupel or hail along vertical profile termed, “flagSurfaceSnowfall”, “flagGraupelHail” and “flagHail” are recently developed in the DPR level-2 algorithm using a concept of precipitation type index (PTI). All these are currently developed products (version 7) in classification module of GPM DPR level-2 algorithm based on full-swath dual-frequency observations [2][3][4]. 

 

In near future, a new feature will be added to the version 8 of the GPM DPR level-2 algorithm to provide vertical profile of hydrometeors for full swath data.  A conceptual flow  for initial implementation will be presented. The judgements are made mainly on the DPR products omly to provide an independednt assessment. Mixed phase hydrometeors are judged with melting layer top and bottom together with the 0° isotherm. Flag of surface snowfall is used to identify snow only profile, while flags for detecting graupel and hail help identify range bins with those hydrometeor types. The whole judgement  a robust detection system to not only combine the products but enforce meteorologically meaningful. In the initial phase, five hydrometeor types will be introduced. They are dry snow/ice crystal (DS/ICE), wet snow (WS), graupel (GPL), hail (Hail) and rain (Rain). DS/ICE, GPL and Hail represent low-density, medium-density, high-density particles respectively.  
Assessment of Land Surface Model's Evapotranspiration Response
Authors: Cai Yaoting , Lu Xingjie , Wei Zhongwang , Wei Nan
Published: March 2024

Biotic factors have been identified as one of the most important controls on evapotranspiration (ET) variation in the scenario of future climate change. Land surface models have developed sophisticated canopy processes to emphasize the importance of vegetation. However, as the vegetation processes become more and more complicated, the relative importance of biotic impact in comparison with abiotic impact on ET has not been well quantified. Failing to understand the relative importance between abiotic and biotic impact may result model bias in water cycle prediction. We collected satellite-basedET dataset (GLEAM, CRv1, P-LSH), climate data, biotic factor estimates, and apply the variance decomposition analysis to quantify the relative importance between biotic and abiotic impacts. Then, we compared with the model counterpart, i.e. the ensemble means of LS3MIP and CMIP6. Variance decomposition analysis on ET dataset show that about 70% of the ET inter-annual variation is contributed from abiotic factors, such as vapor pressure deficit (VPD), net radiation, and precipitation, whereas only 30% of ET variance is explained by biotic factors, such as stomata conductance and leaf area index (LAI). The abiotic contributions of the models show great uncertainties, which range from 36% to 60%. Overall, the abiotic factor contributions of most models are significantly higher than satellite-based ET dataset. ET variation of grassland is mostly explained by abiotic factors, which is consistent between models and ET dataset. VPD and precipitation explained most of the ET variation in ET dataset, especially in high latitude, whereas stomata conductance and LAI explained most of the ET variation in LS3MIP and CMIP6 models in boreal forest. The model overestimates of abiotic contribution indicate more complicated canopy processes require better constraints. Climate change leading to increase in VPD and more frequent extreme precipitation potentially play more important role in future ET changes. More efforts, such as model parameterization, calibration, new process development, still need to be made by modelers to improve model meteorological feedback.
Compound events and hydro-climate extremes – how they are impacting Australia, now and in the future
Authors: Sharples Wendy , Sharmila Sur , Ulrike Bende-Michl , Ghajarnia Navid , Bharamian Katayoon , Hou Jiawei , Pickett-Heaps Christopher , Carrara Elisabetta
Published: March 2024

Many natural disasters in Australia are the result of compound events, where the assessment of single climate system drivers in isolation do not fully capture hydro-climate extremes. Multivariate compound events such as ‘hot and dry’ and ‘wet and windy’ events, portend a multitude of hazards from heatwaves and bushfires through to coastal inundation and floods. The multiple drivers compounding together in these events, lead to extreme conditions ripe for natural disasters to occur. Presently, compound events are negatively impacting Australia’s ability to protect its population and environmental and economic assets, as Australia tries to adjust to the greenhouse gas driven climatic shifts, with potential projected increases in hazard severity. We aim to understand the change in frequency, duration and intensity of ‘hot and dry’ and ‘wet and windy’ compound events, at current and increased global warming levels. The ‘hot and dry’ compound event is defined as the co-occurrence of SPI drought conditions, and at least 3 consecutive days of hot temperatures. The ‘wet and windy’ compound event is defined as the co-occurrence of both extreme wind and precipitation. These two compound events were chosen to begin with due to the historic severity of their associated impacts. However further research is planned to understand all types of compound events including preconditioned, and, spatially and temporally compounding, in order to fully gauge Australia’s potential vulnerability to natural disasters now and in the future.
The Emergence of Low-Frequency Variability: Comparison of Historical Data and Simulations
Authors: Hébert Raphaël , Skiba Vanessa , Laepple Thomas
Published: March 2024

Regional climate change projections over the course of the 21st century require the accurate simulation of both anthropogenic and natural variability. The spatial patterns of natural variability are relatively well constrained on sub-decadal timescales based on instrumental data evidence, and climate models can simulate them. For longer (supra-decadal) timescales, however, the spatial patterns of natural (temperature) variability are poorly constrained because of the shortness of the instrumental record and the overlap with anthropogenic influences. Insights gained from paleoclimate data over land in mid-latitudes suggest that oceanic influence was the main driver of increased low-frequency natural variability, in contrast to its stabilizing role on sub-decadal timescales. Here, by studying the spatial imprint of multi-decadal climate variability, we show that the instrumental data is consistent with this hypothesis. While the pattern is also observed in climate models, it is much weaker and seems to rely solely on forced variability. Therefore, while climate models can simulate anthropogenic warming, our evidence indicates, particularly over the northern land mid-latitudes, that they are not simulating supra-decadal natural variability (forced and internal) consistent with instrumental observations in terms of the spatial pattern and its amplitude.  
Surface factors controlling the volume of accumulated Labrador Sea Water
Authors: Kostov Yavor , Messias Marie-José , Mercier Herlé , Marshall David P. , Johnson Helen L.
Published: March 2024

We explore historical variability in the volume of Labrador Sea Water (LSW) using ECCO, an ocean state estimate configuration of the Massachusetts Institute of Technology general circulation model (MITgcm). The model’s adjoint, a linearization of the MITgcm, is set up to output the lagged sensitivity of the watermass volume to surface boundary conditions. This allows us to reconstruct the evolution of LSW volume over recent decades using historical surface wind stress, heat, and freshwater fluxes. Each of these boundary conditions contributes significantly to the LSW variability that we recover, but these impacts are associated with different geographical fingerprints and arise over a range of time lags. We show that the volume of LSW accumulated in the Labrador Sea exhibits a delayed response to surface wind stress and buoyancy forcing outside the convective interior of the Labrador Sea, at important locations in the North Atlantic Ocean. In particular, patterns of wind and surface density anomalies can act as a “traffic controller” and regulate the North Atlantic Current’s (NAC) transport of warm and saline subtropical water masses that are precursors for the formation of LSW. This propensity for a delayed response of LSW to remote forcing allows us to predict a limited yet substantial and significant fraction of LSW variability at least a year into the future.  Our analysis also enables us to attribute LSW variability to different boundary conditions and to gain insight into the major mechanisms that drive volume anomalies in this deep watermass. We point out the important role of key processes that promote the formation of LSW both in the Irminger and Labrador Seas: buoyancy loss and preconditioning along the NAC pathway, in the Iceland Basin, the Irminger Sea, and the Nordic Seas.