bluuebunny/crossref_metadata_2025 huggingface dataset:
Hysteresis in water content of ultrafine glassy organic aerosols: Evidence from laboratory and modelling study
Authors: Cheng Manqiu , Kuwata Mikinori , Li Ying
Published: March 2024

Water content of aerosol particles is important for atmospheric impacts, such as radiative effects and chemical reactivity. Traditionally, crystalline inorganic aerosol particles such as NaCl were known to experience hysteresis in water content, meaning that hygroscopic growth depends on exposure history to water vapor. On the contrary, past laboratory studies for organic aerosol reported absence of hysteresis, especially for ultrafine size range. Here, we show that water contents for ultrafine organic aerosol particles have hysteresis at sub-0 °C. Hygroscopic growth of monodisperse ultrafine particles (diameter = 40, 100, and 200 nm) of sucrose and glucose were investigated for the temperature range of -21 °C to +23 °C. Hygroscopic growth of these particles did not exhibit any hysteresis process at +23 °C, consistent with literature. However, hygroscopic growth of these particles was different for hydration and dehydration experiments at sub-0 °C, demonstrating the occurrence of hysteresis. The lowest relative humidity (RH), at which the two modes of experiments provided the same water content, was defined as merge RH. Merge RH was approximately the same as that for the glass transition point, demonstrating that water diffusion in a highly viscous matrix of organic aerosols is the key for the occurrence of hysteresis. Employment of a kinetic multilayer model provided quantitative prediction of merge RH as a function of temperature, particle size, and residence time. Considering the temperature and RH range of Earth’s atmosphere, we hypothesize that hysteresis in organic aerosol ubiquitously occur in the upper troposphere.
Clear-sky temperature and water vapor retrievals utilizing collocated microwave and infrared hyperspectrometers
Authors: Liu Lei , Bliankinshtein Natalia , Huang Yi , Gyakum John , Gabriel Philip , Xu Shiqi , Wolde Mengistu
Published: March 2024

Accurately monitoring atmospheric temperature and water vapor profiles with high spatial and temporal resolutions is crucial for weather forecasting and climate research. Hyperspectral radiance measurements offer a promising opportunity to retrieve these profiles due to the distinct absorption features of various atmospheric compositions.Three clear-sky field campaigns were conducted in Ottawa to collect collocated hyperspectral measurements from two sophisticated instruments: the High Spectral Resolution Airborne Microwave Sounder (HiSRAMS) and the Atmospheric Emitted Radiance Interferometer (AERI). HiSRAMS operates within the microwave oxygen band (49.6-58.3 GHz) and microwave water vapor band (175.9-184.6 GHz), while AERI operates in the infrared spectral range (520-1800 cm-1). Both instruments possess high spectral resolution, enabling the detection of subtle changes in temperature and water vapor profiles. Radiosonde measurements were simultaneously taken during each field campaign to serve as the truth for this study.Initially, we performed a simultaneous assessment of the radiometric accuracy of HiSRAMS and AERI through radiative closure tests. A persistent warm radiance bias was detected in AERI observations in the window band. Correcting this bias improved radiative closure within the same band. HiSRAMS observations, when directed towards the nadir, displayed a smaller brightness temperature bias compared to zenith observations. We diagnosed and compared the radiometric accuracy of both instruments based on the relationship between radiometric bias and optical depth. HiSRAMS exhibited similar radiometric accuracy to AERI in nadir-pointing measurements but demonstrated comparatively poorer accuracy in zenith-pointing measurements, necessitating further characterization.Subsequently, clear-sky temperature and water vapor concentration profiles were successfully retrieved from collocated HiSRAMS flight measurements and AERI ground measurements. These retrieved profiles were validated against radiosonde measurements, demonstrating good agreement. When both instruments were positioned on the ground for zenith-pointing measurements, infrared hyperspectral measurements provided higher information content and better vertical resolution for temperature and water vapor retrievals compared to microwave hyperspectral measurements. Combining airborne nadir-pointing microwave measurements and ground-based zenith-pointing infrared measurements, termed the “sandwich” sounding approach, exhibited increased information content and reduced retrieval uncertainty for temperature and water vapor concentrations across all retrieval levels.
Quantitative Assessment of Affected Population Risk by Tropical Cyclones Using the Hybrid Modeling Combining GAM and XGBoost: A Case Study of Hainan Province
Authors: Meng Chenna , Xu Wei
Published: March 2024

Global climate change is expected to increase the proportion of intense tropical cyclones in the Northwest Pacific. This study focuses on how factors, especially extreme events, may affect disaster losses. To address this issue, an event-based multivariate tropical cyclone risk assessment model, which employs Copula, generalized additive model, and undersampling extreme gradient boosting decision tree techniques, is developed to enhance the accuracy of disaster loss prediction. The results suggest that on Hainan Island, the rate of the affected population is positively correlated with maximum wind speed and maximum daily rainfall but negatively correlated with gross domestic product and elevation. The study also shows that the tropical cyclone risk in the cities in Hainan increases as the return periods expand, and each return period scenario shows a unique geospatial distribution of the tropical cyclone risk on Hainan Island, with higher risks in coastal and eastern regions. These results emphasize the importance of implementing effective disaster management strategies to mitigate the impact of severe tropical cyclones in the region.
A purely data-driven transformer model for real-time predictions of the 2023-24 climate condition in the tropical Pacific
Authors: Zhang Ronghua
Published: March 2024

The tropical Pacific experienced triple La Nina conditions during 2020-22, and the future evolution of the climate condition in the region has received extensive attention. Recent observations and studies indicate that an El Nino condition is developing with its peak stage in late 2023, but large uncertainties still exist. Here, a transformer-based deep learning model is adopted to make predictions of the 2023-24 climate condition in the tropical Pacific. This purely data driven model is configured in such a way that upper-ocean temperature at seven depths and zonal and meridional wind stress fields are used as input predictors and output predictands, representing ocean-atmosphere interactions that participate in the form of the Bjerknes feedback and providing physical basis for predictability. In the same way as dynamical models, the prediction procedure is executed in a rolling manner; multi-month 3D temperature fields as well as surface winds are simultaneously preconditioned as input predictors in the prediction. This transformer model has been demonstrated to outperform other state-of-the-art dynamical models in retrospective prediction cases. Real-time predictions indicate that El Nino conditions in the tropical Pacific peak in late 2023. The underlying processes are further analyzed by conducting sensitivity experiments using this transformer model, in which initial fields of surface winds and upper-ocean temperature fields can be purposely adjusted to illustrate the changes to prediction skills. A comparison with other dynamical coupled model is also made.
Role of arbuscular mycorrhizal fungi behind the plant ameliorated tolerance against cadmium stress: A global meta-analysis
Authors: Tan Qiyu , Wei Rongfei , Hu Huiying , Guo Qingjun
Published: March 2024

Increasing industrial activity has led to a growing risk of cadmium (Cd) accumulations and biomagnifications in plants and humans. Arbuscular mycorrhizal fungi (AMF) have been extensively studied as a soil amendment technique due to their capability to reduce the accumulation of Cd in plant tissues. However, a quantitative and data-based consensus has yet to be reached on the effect of AMF on host plant growth, Cd uptake, and tolerance. Here, a meta-analysis was conducted to quantitatively evaluate the impact of AMF using 2079 individual observations from 157 articles. The research showed that adding AMF to the plants stopped the accumulation of Cd in the shoots and roots and increased biomass, phosphorus (P), and catalase (CAT) in the leaves compared to the control. Yet these effects varied with different mycorrhizal colonization rates, AMF species, plant families and functional types, and soil Cd contents. Mycorrhizal colonization rates positively correlate with changes in biomass and P content in shoots and roots, and CAT and proline in leaves, while showing no significant correlation with Cd concentration in plant tissues. Plants inoculated with Funneliformis caledonium exhibited greater biomass accumulation, while those inoculated with Rhizophagus irregularis showed higher P uptake. Mycorrhizal Legumes demonstrated the most significant reduction in Cd concentration among the plant families, whereas Compositae exhibited the highest increase in biomass, P content, and CAT. In addition, soils with intermediate and high Cd levels were more favorable for AMF to promote plant biomass accumulation. This study shows that AMF can help plants become more resistant to environments with excessive Cd. It also talks about how to manage and use them as bio-inoculants for farming and environmental restoration.  AcknowledgmentsThis research received funding from the National Key Research and Development Program of China (No. 2022YFC3701303) and the National Natural Science Foundation of China (Grant Nos. U2344228). 
Imprints of climate and neotectonic activity in lake sediments developed upon a reverse drag (Lake Ifrah, NW Africa): Integration of detailed sedimentological and sequence stratigraphic approaches.
Authors: Azennoud Khalil , Baali Abdennasser , Ait Brahim Yassine
Published: March 2024

Lacustrine settings constitute a unique environment that preserves detailed expressions of allocyclic signals such as those of climate and tectonics. Possible decryption of these signatures may stem from careful scrutiny of the sedimentation dynamics (temporary base-level variations), lake-level fluctuations (accommodation), and resulting strata bounding surfaces that are used to build a conventional sequence stratigraphic framework. The present work deals with this challenge and provides insights from a case study where lacustrine sedimentation occurs onto a tectonically active reverse drag associated with normal faulting within a key climate region (Lake Ifrah, Northwest Africa). Moreover, we investigated soft-sediment deformation structures (SSDSs) within Lake Ifrah sediments, which can be good proxies for neotectonic and paleoseismic activities. We analyzed both brittle and liquidization-induced SSDSs, including load structures, water-escape structures, shrinkage cracks, subsidiary grabens, and bed-parallel slips. Up to five facies models (accounting for lithological domination, wind-driven energy, and lake-level state) and three lowest rank T-R sequences, deposited since the Marine Isotope Stage-3 (MIS-3), have been identified. Periods with sustained high lake levels appear to be mainly precession-paced (as during MIS-3 and the Early Holocene). Furthermore, sedimentation dynamics are shown to probably respond to millennial timescale climate variability associated with North Atlantic cooling events (Dansgaard-Oeschger stadials, Heinrich events) and, interestingly, to enhanced Saharan winds during the deglacial period. On the other hand, tectonism had a rather instantaneous effect on lake level and sedimentation. Two tectonic pulses marking instantaneous differential hanging-wall subsidence have triggered a sharp drop in relative lake level, hence conditioning a forced regression. Facies analysis and deformation-trigger assessment helped to discern endogenic from exogenic triggers, with seismic activity inferred as a primary cause for liquidization-induced deformations.
A millennial record of precipitation volatility along the Lower Missouri River, Central USA
Authors: Lombardi Ray , Kwon Youngsang , Burnette Dorian
Published: March 2024

The Missouri River Basin drains one-sixth of the surface area in the contiguous United States of America (USA) and is prone to both water deficit and surplus, leading to catastrophic and potentially compounding hydrologic hazards. Rising global temperatures amplify evaporation and moisture flux in the hydrologic cycle, resulting in volatile precipitation shifts between extremely dry and wet conditions (precipitation whiplash events). Observed and projected precipitation trends in the central interior of the USA, like many other mid-latitude world regions, indicate precipitation whiplash events are characteristic of modern climate change. Historically, managing river basins with precipitation extremes leading to drought or flood includes distinct and opposing strategies such that a measure for one hazard can negatively impact the effectiveness of measures for the other. There is an urgent need to understand the regional drivers and frequency of precipitation whiplash events to make climate-informed water management decisions and improve resilience in river basins dealing with droughts and floods in quick succession. This study leverages the long-term and detailed interannual perspective afforded by tree ring records to address the following objectives: 1) reconstruct a chronology of precipitation whiplash events in the Missouri River Basin during the last millennium, and 2) examine the climate drivers of pre-historic precipitation volatility. We synthesized a single standardized and detrended chronology for the lower Missouri River Basin from tree rings in the International Tree Ring Database (ITRB) that are well-calibrated with total precipitation from the Global Precipitation Climatology Center dataset. We present trend analysis findings to determine whether precipitation volatility in the Missouri River basin is increasing monotonically or has demonstrated long-term non-linear or periodic trends in response to climate change. Next, we used self-organizing maps (SOMs), a type of artificial neural network, to discern spatio-temporal precipitation patterns during precipitation whiplash events. Finally, we compare SOM in paired years to ocean and atmospheric variables from the Community Earth System Model–Last Millennium Ensemble (CESM-LME) to test the correlation between local precipitation volatility and large-scale ocean-atmospheric patterns. Results from this study include the first precipitation whiplash chronology in the central USA and provide timely insight into the drivers of non-linear precipitation extremes in an important socio-economic river basin.
Can neural networks outperform quantile mapping for post-processing seasonal weather forecast variables over the Alpine region?
Authors: Uttarwar Sameer Balaji , Lerch Sebastian , Avesani Diego , Majone Bruno
Published: March 2024

The possibility to use seasonal weather forecasts is of paramount importance in hydrological and socio-economical applications. However, current seasonal weather forecasts from global numerical weather prediction (NWP) models inherit systematic biases resulting from inaccurate representation and parameterization of local to global scale environmental processes. Therefore, the hydrological community frequently uses the quantile mapping (QM) statistical postprocessing for bias correction and downscaling of the meteorological inputs (i.e., daily precipitation and temperature) to hydrological models. The QM often assumes a linear and static relationship between quantiles of observed and simulated data over time. These limitations can be relaxed by employing a Neural Network (NN) based postprocessing method. In this context, the objective of this study is to compare the accuracy of QM and NN statistical postprocessing of ensemble seasonal weather forecasts over the Trentino-South Tyrol region (north-eastern Italian Alps), characterised by complex topography. The study uses the latest fifth-generation seasonal weather forecast system (SEAS5) total precipitation and 2m-temperature dataset produced by European Centre for Medium-Range Weather Forecast (ECMWF), available at a horizontal grid resolution of 0.125° x 0.125° with 25 ensemble members in a re-forecast period from 1981 to 2016. The respective reference dataset is a high-resolution gridded observation (250 m x 250 m) over the region of interest. The QM method derives a functional relationship between the variable of interest and the corresponding predictor, whereas the NN based methods can be used with a set of predictors to learn the linear and non-linear relationships in a data-driven way.The analysis is divided into training (1981 – 2010, 30 years) and testing (2011 – 2016, 6 years) period to compare the cumulative ranked probability scores (CRPS) of both the statistical postprocessing methods. The statistical postprocessing is implemented univariately on the daily dataset (2m temperature and precipitation) over a month for each lead time. The raw forecasts and postprocessed forecasts are compared with particular focus on the effects of the forecast lead time and location, as well as diurnal and seasonal cycles in forecast performance. The postprocessed forecasts revealed a significant improvements compared to the raw forecasts.
Early results of OSEs conducted for the SynObs international multi-system OSE effort using an Japanese operational system
Authors: Fujii Yosuke , Ishikawa Ichiro , Hirahara Shoji
Published: March 2024

“Synergistic Observing Network for Ocean Prediction (SynObs)” is a project of the United Nations Decade of Ocean Science for Sustainable Development. SynObs aims to find the way to extract maximum benefits from the combination among various ocean observation platforms, including satellite and in situ observations. A major ongoing effort led by SynObs is the international multi-system OSEs/OSSEs. In this activity, various operational centers and research institutes participating will conduct Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs) using a variety of ocean or coupled ocean-atmosphere prediction systems with the common setting to evaluate ocean observation impacts which are robust for most ocean prediction systems. More than 10 ocean prediction systems with various model resolutions and diverse data assimilation methods are used in this activity, and impacts of various observation data, including satellite sea surface temperature and height, Argo floats, and tropical mooring buoys, will be evaluated.The activity is divided into two parts. The first part is the ocean prediction OSEs. In this part, we run several ocean reanalysis runs assimilating different observation datasets at least for 2020 (preferably extended to 2022), and conduct 10-day ocean predictions from the reanalysis fields of every 5 days. Three-dimensional oceanic temperature, salinity, and velocity fields with the 1/10-degree resolution, and several two-dimensional diagnostics with the 1/4-degree resolution will be analyzed. The second part is the subseasonal-to-seasonal (S2S) OSEs. Here, we run several ocean reanalysis runs for 2003-2022, and conduct 1-month (4-month) coupled predictions from the reanalysis fields of every month (twice a year). We will evaluate the impacts of ocean observation data on the long-term reanalysis and S2S predictions using the coupled prediction systems. We also plan to conduct OSSEs using multiple ocean prediction systems in order to assess newly emerging or future observing systems, such as SWOT, ocean gliders, etc. We are currently conducting the S2S OSEs using a Japanese operational global ocean data assimilation and coupled prediction system for S2S forecasts. We are now conducting OSEs assimilating no in situ observations and withholding temperature and salinity profiles observed by Argo floats. In the presentation, we will introduce the results and the perspective of the collaborative activities.
JointNet: A multimodal deep-learning-based approach for joint inversion of Rayleigh wave dispersion and ellipticity
Authors: Huang Xiang , Yu Ziye , Wang Weitao , Wang Fang
Published: March 2024

Joint inversion of multiple datasets is an effective approach for high-precision imaging of the crustal and upper mantle velocity structures. In this study, we propose a novel deep learning-based method called JointNet for jointly inverting Rayleigh wave phase velocity and ellipticity data to obtain high-precision shear wave velocity models. JointNet, a multimodal deep neural network, is designed to analyze these independent physical parameters and generate outputs that include a velocity model and layer thicknesses. The network is trained using a large dataset of randomly generated 1D models along with their corresponding calculated phase velocities and ellipticities. Our tests using synthetic and observed data demonstrate that JointNet produces inversion results that are highly comparable to those obtained through a Markov Chain Monte Carlo-based method. This indicates that the network effectively captures the nonlinear relationship between phase velocity, ellipticity data, and the 1D Vs model. In addition, JointNet eliminates the need for prior information input and significantly reduces the computational time for inversion compared to traditional nonlinear methods. Training using synthetic data based on a global model ensures its wide applicability in various regions with different velocity structures. Furthermore, JointNet can be readily adapted to incorporate additional datasets, such as receiver functions, to further enhance imaging resolution. Essentially, JointNet can also function as a novel inversion framework for more extensive model inversion studies.
Advancing Corporate Governance through SBSC: Navigating Compliance with IFRS S2 in Port Logistics
Authors: Wu HeLing
Published: March 2024

In today's global economy, the importance of transparent and quantitative sustainability reporting is escalating, reshaping corporate disclosure standards. This evolving landscape presents challenges to traditional business models and management tools, necessitating innovative approaches for effective adaptation. The development of standards such as IFRS S2, a set of global standards for climate-related disclosures that mandates companies to report on their environmental impact and climate risks, further complicates the reporting and compliance environment. This study explores the utility of the Sustainable Balanced Scorecard (SBSC) as a strategic instrument to enhance environmental, social, and governance (ESG) performance in light of heightened compliance demands. Utilizing the SBSC framework, the research begins with the development of a sustainability strategy map for a Taiwanese port logistics company, outlining its sustainability objectives and providing a foundation for analyzing the impact of IFRS S2. The research also employ the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze the causations among strategy goals, enriching the understanding of their interconnections and influence. The study then delves into the specifics of IFRS S2, assessing how these standards affect the company's financial disclosures, strategic planning, and governance framework. This dual approach highlights the intricate relationship between corporate strategy, sustainability integration, and IFRS S2 requirements. It identifies key areas where these elements intersect, offering insights into potential improvement areas and gaps. This research is particularly relevant for entities in the port logistics sector and related industries, emphasizing the critical role of innovative management tools like the SBSC in aligning business strategies with global sustainability goals and managing climate risks effectively.
Microphysical complexity of black carbon particles restricts their warming potential
Authors: Peng Jianfei , Huang Xiao-Feng , Peng Yan , Wei Jing , Lin Xiao-Yu , Tang Meng-Xue , Cheng Yong , Men Zhengyu , Fang Tiange , Zhang Jinsheng , He Ling-Yan , Liu Chao , Cao Li-Ming , Mao Hongjun , Seinfeld John H. , Wang Yuan
Published: March 2024

Black carbon (BC) strongly absorbs solar radiation, but its warming effect on climate is poorly quantified. A key challenge is to accurately assess BC light absorption after BC is mixed with non-BC components. However, there has consistently been a large observation-modeling gap in BC light absorption estimation, reflecting the insufficient understanding of realistic BC complexity. Here, we conduct comprehensive in situ measurements of BC single-particle microphysics, e.g., size, coating amounts, density, and shape, along with optical closure calculation. Specifically, the observed particle-to-particle heterogeneities in size and coating and the non-spherical BC shape only explain the lower observed BC absorption by ∼20% and ∼30%, respectively. A remaining gap for fully aged spherical BC-containing particles is related to the off-center BC-core position. The global climate model assessment shows that fully accounting for the observed BC complexity in the aerosol microphysical representation reduces the global BC direct radiative forcing by up to 23%.
Carbon Stocks and Greenhouse Gas Emissions (CO2 and CH4) in Mangrove Forests and Aquaculture Ponds in East Kalimantan, Indonesia  
Authors: Novita Nisa , Asyhari Adibtya , Putra Chandra Agung Septiadi , Gangga Adi , Ritonga Rasis , Anggoro Aji , Hidayat Topik , Yang Yiwei , Lewin Allison , Ilman Muhammad
Published: March 2024

Mangroves, as part of the blue carbon ecosystem, are considered a cost-effective nature-based solution pathway to help mitigate climate change and achieve the Paris Agreement’s aim to limit warming to 1.5˚C. The accurate quantification of greenhouse gas (GHG) emissions and carbon accounting has become a key challenge for policymakers and scientists addressing climate change.  Globally, Indonesia emits the highest potential CO2 emissions from soils in the mangrove ecosystems because of its high rates of mangrove losses in recent decades. Unfortunately, there are limited studies on carbon and GHG emissions from Indonesian mangroves. This study aims to quantify carbon loss due to mangrove conversion due to aquaculture development by combining carbon stocks and GHG emissions data located in Tabalar Muara Village, Berau, East Kalimantan, Indonesia. We collected data from aboveground (vegetation, downwood) and belowground (roots and soil) carbon stocks in five and three transects of mangrove forests and aquaculture ponds, respectively. Soil bulk density and carbon concentration in various soil depth intervals were also analyzed. In addition, we conducted three consecutive days of regular monthly monitoring of CO2 and CH4 fluxes associated with soil physicochemical properties in mangrove forests and aquaculture ponds from January – December 2023. Total ecosystem carbon stocks in mangrove forests and aquaculture ponds were 926 ± 20 and 658 ± 45 Mg C ha−1, respectively. Thus, it implies 984 Mg CO2 ha−1 of potential carbon loss during mangrove forest conversion to aquaculture ponds. Soil carbon stocks between 0 and 300 cm depth varied significantly, where carbon stock in aquaculture ponds (658 Mg C ha−1) was 18% lower than in mangrove forests (777 Mg C ha−1). Soil carbon dominates total ecosystem carbon stocks by up to 88% in mangrove forests.  For GHG fluxes, mangrove forests have six times higher heterotrophic CO2 emissions (79.44 ± 4.47 Mg CO2 ha-1 yr-1) compared to that from the aquaculture ponds (13.88 ± 0.88 Mg CO2 ha-1 yr-1). The annual total CH4 flux was 17 times higher in mangrove forests (7.72 ± 0.50 Mg CO2e ha-1 yr-1) than in aquaculture ponds  (0.46 ± 0.04 Mg CO2e ha-1 yr-1). The results of this research are useful to refine GHG emissions accounting on mangroves by providing higher Tier of emission factors to fulfill Indonesia’s Enhanced Nationally Determined Contributions.
A Critical Look at Machine Learning Algorithms in River/Stream Water Temperature Modeling 
Authors: Corona Claudia , Hogue Terri
Published: March 2024

As climate change continues to impact stream systems worldwide, water temperature is an increasingly important indicator of distribution patterns and mortality rates among fish, amphibians, and macroinvertebrates. Technological advances tracing back to the 1960s have improved our ability to measure stream water temperature (SWT) at varying spatial and temporal resolutions, for the fundamental goal of better understanding stream function and ensuring ecosystem health. Despite significant advances, there continue to be a large number of stream reaches, stream segments and entire catchments that are difficult to access for a myriad of reasons, including but not limited to physical limitations. Moreover, there are noted access issues, financial constraints, and temporal and spatial inconsistencies or failures within situ instrumentation.Over the last few decades and in response to these limitations, statistical methods and physically-based computer models have been steadily employed to examine SWT dynamics and controls. Most recently, the use of artificial intelligence, specifically machine learning (M.L.) algorithms, has garnered significant attention and utility in hydrologic sciences, specifically as a novel tool to learn yet-to-be-discovered patterns from complex data and attempt to fill data streams and knowledge gaps. Our review of publications focusing on stream water temperature modeling and prediction identified a similar number (~26) of publications utilizing M.L. in the previous four years (2020-2023), as were published in the previous 19 years, (2000-2019).  The objective of this work is three-fold: first, to provide a concise review of the utilization of M.L. algorithms in stream water temperature modeling and prediction. Second, to review M.L. performance evaluation metrics as it pertains to SWT modeling and prediction and identify the commonly-used metrics as well as suggest guidelines for easier comparison of M.L. performance across SWT studies. Finally, we examine where progress has been made in our understanding of the physical system from the use of M.L. in SWT modeling and prediction, and identify where progress is still needed to advance our understanding of spatial and temporal patterns of stream water temperature.
Fractional Integral Statistical Model: A new way for climate prediction and projection from the perspective of scaling
Authors: Yuan Naiming , Franzke Christian , Nian Da , Fu Zuntao , Ying Kairan , Xiong Feilin , Dong Wenjie
Published: March 2024

It is well recognized that climate memory is one the origins for climate predictability, but how to include the concept of climate memory into the climate prediction, is still an open question. Here in this work, we suggest the Fractional Integral Statistical Model (FISM), a generalized stochastic climate model, as a new way for this purpose. With FISM, one can extract the “forcing-induced direct component ε(t)” and the “memory-induced indirect component M(t)” from a given variable x(t). By predicting ε(t), one can further obtain the predicted x(t) using FISM. Different from traditional prediction approaches which normally focus on x(t), here this new strategy based on FISM clarifies the climate memory impacts. From this new perspective, we have quantified the climate memory induced predictability, and developed a temperature response model that can project the future warming trend. Compared to CMIP6 simulations, our approach projects lower global warming levels over the next few decades. A further examination indicates that many CMIP6 models overestimated the climate memory, which might contribute to the overestimated future warming trend.
The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data
Authors: Tang Zhaojun , Jiang Zhe
Published: March 2024

The adjoint of the GEOS-Chem (Goddard Earth Observing System with Chemistry) model has been widely used to constrain the sources of atmospheric compositions. Here, we designed a new framework to facilitate emission inventory updates in the adjoint of the GEOS-Chem model. The major advantage of this new framework is good readability and extensibility, which allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) meteorological data, which allows us to perform long-term analyses with consistent meteorological data for the period 1979–present. The performances of the developed capabilities were evaluated with the following steps: (1) diagnostic outputs of carbon monoxide (CO) sources and sinks to ensure the correct reading and use of emission inventories, (2) forward simulations to compare the modeled surface and column CO concentrations among various model versions, (3) backward simulations to compare adjoint gradients of global CO concentrations to CO emissions with finite-difference gradients, and (4) observing system simulation experiments (OSSEs) to evaluate the model performance in 4D variational (4D-Var) assimilations. Finally, an example application of 4D-Var assimilation was presented to constrain anthropogenic CO emissions in 2015 by assimilating Measurement of Pollution in the Troposphere (MOPITT) CO observations. The capabilities developed in this work are important for better applications of the adjoint of the GEOS-Chem model in the future. These capabilities will be submitted to the standard GEOS-Chem adjoint code base for better development of the community of the adjoint of the GEOS-Chem model.
Integrated Monitoring and Multi-Hazard Early Warning System for Himalayan Region: Insights from the Chamoli Disaster of 202
Authors: Tiwari Anil , Sain Kalachand , Kumar Amit
Published: March 2024

The material/rock failure is not a sudden progression but is preceded by multiple progressive nucleation phases during which relaxation or rearrangement of material leads to creep and accelerates with time before any major rupture. The monitoring of Himalayan surficial dynamics is challenging and expensive to access for scientific research purposes. The unfelt destructions produced by the surficial mass movement activities can only be recognized by satellite images if other monitoring is not possible. We focused on the Chamoli region, which is the most vulnerable or hazard-prone region in the NW Himalaya. Recently, on 7th February 2021, a huge rock-ice mass detached from the Raunthi peak at a height of 5600 m in the Chamoli district of Uttarakhand Himalaya. We found several pre-collapse and unfelt activities,in a post-mortem study, which were recorded at nearby highly sensitive broad-band seismic stations and radon detector instruments. The integrated study of the recorded signatures allows us to reconstruct the complete dynamic time-dependent nucleation phases, which intensify as time gets closer to the main detachment. Continuous monitoring of vulnerable regions, coupled with the identification and characterization of precursory signals, holds the fundamental clue for hazard mitigation. After the Chamoli disaster, we are more focused on monitoring unfelt activities and anomalies linked to hazards in the proximity of potentially endangered zones and also planning to deploy multi-parametric instruments such as automatic weather stations (AWS), broad-band seismometers (BBS), automatic water level recorders (AWLR) and infrasound array for real-time monitoring and integrated analysis with a view to forewarn against the hazards in the Himalayan terrain. The dense network of sensors will allow us to collect high-quality data and crucial information as a way forward for disaster mitigation and societal benefit.
Nature of temperature intermittency in the solar wind turbulence
Authors: Wang Xin , Wang Yuxin , Yuan Haochen
Published: March 2024

In the solar wind turbulence, proton temperature fluctuations are highly intermittent, especially at small scales in the inertial range. This phenomenon may contain information about solar wind intermittent heating. However, the physical nature of the temperature intermittency is not yet clear. Based on the measurements from Solar Orbiter between 2020 and 2023, we identify temperature intermittent structures in the fast and slow solar wind, respectively. We compare the nature and kinetic effects of them. According to the variations of proton temperature and magnetic field when the temperature intermittency occurs, we classify the temperature intermittency in the fast wind into the following five categories: (1) 20% of the cases are identified as linear magnetic holes with local temperature enhancement, and a majority of them are unstable to mirror-mode (MM) instability. (2) 18% are related to tangential current sheets also with local temperature enhancement. (3) 9% are tangential discontinuities with a temperature interface, which could separate two different parcels of plasma. (4) 15% of the cases are accompanies by local temperature decrease that may be also due to MM instability. (5) 25% of the cases show a chain of magnetic field variations probably related to Alfven vortices. In the slow wind, the situation is different. The temperature intermittent structures are mainly associated with firehose instability. These results will help to further understand the intermittent dissipation process in the solar wind turbulence.
Computer Simulations and the Need for Nuclear Weapon Testing
Authors: Vargas Celso
Published: March 2024

At least since 1998, we know that the main nuclear weapons states are using computer simulations aimed at having the capability of designing new nuclear weapons without conducting any nuclear test, among other relevant jobs in which computer simulations are used. Currently, three important developments have increased the power of computer simulations: a) the development of new supercomputers with a tremendous capacity for processing and realising complex jobs; b) the accumulation of billions of data useful for simulations considering the number of nuclear tests conducted in the past and the results of computer simulations, and c) the recent developments of powerful AI foundation models with billions of parameters that can be used in nuclear weapon design very successful. The dominant thesis, since 2002, is that experiments “using simulants to replace nuclear materials are permitted under the Comprehensive Test Ban Treaty (CTBT)”. What this means is that states can develop new nuclear weapons complying with the CTBT, but this, I consider, is contrary to the goal of the CTBT and CTBTO. Nuclear proliferation was expected during the first two decades of the 21st century.  However, in the current global geopolitical situation, where other forms of nuclear proliferation are observed, these computer simulations pose challenges to the CTBT and CTBTO.
The influence of Ontong Java Plateau volcanism on oceanic deoxygenation during Oceanic Anoxic Event 1a: evidence from Mo isotope
Authors: Wu Sifan , Li Congying , Huang Jing , Sun Weidong
Published: March 2024

Oceanic Anoxic Event 1a (OAE 1a, ~120Ma) during the early Aptian represents a dramatic perturbation of the global carbon cycle and is associated to widely deposition of black shale [1]. The emplacement of the Ontong Java Plateau is considered to be the trigger of OAE 1a, which led to a series of environmental perturbations including marine anoxia [2]. However, whether Ontong Java Plateau volcanism is the direct driving factor of oceanic deoxygenation is still under debate. Most research suggested that the eruption of Ontong Java Plateau injected enormous CO2 into the atmosphere-ocean system and accelerated continental weathering, which eventually resulted in ocean anoxic/euxinic conditions [1]. Few argued that oceanic deoxygenation was an immediate response to the Ontong Java Plateau volcanism [3].Here, we select DSDP Site 463 and ODP Site 866A in the Pacific Ocean near Ontong Java Plateau as study sections. We present high-resolution δ98Mo records to reflect changes in ocean redox conditions and use Mn and Fe abundances and Eu/Eu* ratios to resolve volcanic phases. Combined with other redox, biological productivity, and weathering proxies, we reconstruct the history of Ontong Java Plateau eruption and ocean redox environment across OAE 1a. Our data reveal that oceanic deoxygenation started before OAE 1a and was driven by Ontong Java Plateau volcanism instead of continental weathering. Volcanically sourced nutrients fluxed into the ocean and stimulated local organic productivity, resulting in ocean deoxygenation. During OAE 1a, ocean maintained anoxic/euxinic conditions. The coeval global seawater δ98Mo was probably around or greater than 2.1‰. [1] Jenkyns, 2010, Geochemistry Geophysics Geosystems 11.[2] Percival et al., 2021, Global and Planetary Change 200.[3] Bauer et al., 2021, Geology 49, 1452-1456.
Multi-GNSS double/triple-frequency PPP-AR/RTK performance evaluation in the European region
Authors: Cui Bobin , Du Shi , Jiang Xinyuan , Wang Le , Huang Guanwen , Ge Maorong , Schuh Harald
Published: March 2024

With the continuous development and updates of GNSS systems, an increasing number of satellites now emit triple-frequency signals. Currently, research on triple-frequency positioning is predominantly focused on the Asian region, with limited attention given to the multi-frequency positioning performance in Europe. This study utilizes triple-frequency signals from BDS-3/GPS/Galileo satellites and employs observations from EUREF Permanent GNSS Network (EPN) to evaluate the performance of Precise Point Positioning Ambiguity Resolution (PPP-AR) and Atmosphere-augmented Real-Time Kinematics (PPP-RTK) modes in the European region. We calculate Uncalibrated Phase Delay (UPD) using 46 EPN stations and perform PPP-AR on all 138 stations to derive ionospheric and tropospheric delays. The fixing residuals of EWL/WL/NL UPD achieve 99.9%/98.2%/84.9% for BDS, 100.0%/97.1%/89.9% for Galileo, and 99.9%/94.9%/88.7% for GPS satellites within 0.15 cycles, respectively. Double and triple-frequency PPP-AR 68th percentile results achieve 7.0/3.5 and 7.0/6.0 minutes for horizontal and vertical components using GPS/Galileo/BDS constellations. Additionally, the ionospheric delays derived from double and triple frequencies show only slight differences, measured at the centimeter-level among GPS/Galileo/BDS constellations. Relying on atmospheric delay augmentation, i.e., PPP-RTK, we further analyze the positioning performance under varying inter-station distances from 100 km to 400 km. The triple-frequency brings about a 5% improvement in convergence for BDS and Galileo satellites with respect to double-frequency solutions, while only slightly enhancing GPS satellites. Combining GPS/BDS/Galileo achieves nearly instantaneous convergence even at distances up to 400 km. Overall, the European region using GPS/Galileo/BDS constellations can achieve rapid positioning by triple-frequency signals, and instantaneous convergence can be achieved for double and triple-frequency solutions when atmosphere delays are implemented.
AI-Empowered Near-realtime Operational Prediction System of Landslides in Lao Cai province, Vietnam
Authors: Bui Du Duong , Nguyen Quoc Phi , Du Le Thuy Tien , Bui Khanh Linh , Tran Thi Tra Giang , Hoang Van Hung , Vu Van Lan , Vu Minh Cat
Published: March 2024

Vietnam faces heightened vulnerability to severe climate change impacts, notably sea level rise, flooding, and landslides. In recent years, the northwest mountainous regions have experienced recurrent and widespread landslides during the rainy season (May to October), resulting in significant economic losses. This study focuses on the Lao Cai province, employing various data mining techniques—Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF)—to spatially predict landslide hazards. Initially, a comprehensive landslide inventory map was constructed from diverse sources, pinpointing past landslide occurrences. Subsequently, multiple factors influencing landslides were considered, including slope angle, slope aspect, profile curvature, wetness index, lithology, Normalized Difference Vegetation Index (NDVI), soil type, soil moisture, road density, house density, and rainfall. Utilizing these factors, landslide susceptibility indexes were computed through the respective models. Validation, using landslide locations not utilized in the training phase, revealed that models employing Random Forest (RF) exhibited the highest prediction capability. The trained model was then applied to generate real-time forecasts of landslide susceptibility maps for up to 16 days, using bias-corrected Global Forecast System (GFS) precipitation data. This WebGIS operational prediction system enhances preparedness and awareness, facilitating improved mitigation strategies to mitigate the impact of landslides.
Shallow velocity structure of the Tainan frontal thrust based on Eikonal tomography
Authors: Yang Tzu-Cheng , Chen Ying-Nien , Rau Ruey-Juin
Published: March 2024

    We deployed a dense array of 173 seismometers covering a 30 by 40 km2 area in the Tainan frontal thrust of southwestern Taiwan from February to June 2021. The seismic array with an inter-station distance of about 2 km, spanned from west to east, across four major tectonic regimes: Anping Plain, Tainan Tableland, Dawan Lowland, and Chungchou Tableland. Structurally, the Houchiali Fault separates the Tainan Tableland and Dawan Lowland, while the right-lateral Hsinhua Fault is located 5 kilometers northeast of the Houchiali Fault. For the Eikonal tomography analysis, we included data from ten BATS (Broadband Array in Taiwan for Seismology) stations, five stations from CWASN (Central Weather Administration Seismographic Network), and one station from TSMIP (Taiwan Strong Motion Instrumentation Program). These stations were strategically positioned at distances ranging from 40 to 80 kilometers away from the dense array, with azimuths between 45° to 140° and 270° to 360°. We then calculated the cross-correlation function (CCF) between 173 seismometers and these stations. These results were subsequently used by beamforming to measure the relative surface wave arrival times. To perform Eikonal tomography, we calculated the surface wave propagation of the ambient noise and the shallow velocity structure for each period between 4 and 10 seconds. Our result shows that the velocity on Tainan Tableland is almost uniform, which is probably due to the gently folded character of the underneath Tainan anticline. Meanwhile, a low-velocity zone of approximately 2.5 by 2.5 km2 with a 4s period was revealed northeast of the Houchiali Fault and southwest of the Hsinhua Fault, with a shear-wave velocity of approximately 0.5 km/s. Upon reaching the Hsinhua Fault to the northeast, the velocity increases five times to 2.5 km/s. For the 4s period, the average velocity in this region is approximately 1.5 km/s, however, the velocity distribution does not conform with the regional velocity models. This suggests the presence of potentially unexamined small-scale structures in this area. Furthermore, this area encountered strong shaking from three local or regional moderate earthquakes that occurred in 1946, 2010, and 2016 respectively. Coincidentally, the low-velocity zone aligns roughly with the soil liquefaction sites caused by these three events.