Month: April 2024
Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases
Abstract
Assessment of past-climate simulations of regional climate models (RCMs) is important for understanding the reliability of RCMs when used to project future regional climate. Here, we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km, named WRFG, from the North American Regional Climate Change Assessment Program (NARCCAP) in simulating wet season precipitation over the Central United States for a period when observational data are available. The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer, although it tends to underestimate the magnitude of precipitation. This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation. Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor. The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between, leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains. The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence, for the development of moist convection as well. Therefore, a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.
Persistent Variations in the East Asian Trough from March to April and the Possible Mechanism
Abstract
The East Asian trough (EAT) profoundly influences the East Asian spring climate. In this study, the relationship of the EATs among the three spring months is investigated. Correlation analysis shows that the variation in March EAT is closely related to that of April EAT. Extended empirical orthogonal function (EEOF) analysis also confirms the co-variation of the March and April EATs. The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April. Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature (SST) pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean. The dipole SST pattern over the North Atlantic, with one center east of Newfoundland Island and another east of Bermuda, could trigger a Rossby wave train to influence the EAT in March–April. The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific, subsequently impacting the southern part of the EAT in March–April. Besides the SST factors, the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April. These three impact factors are generally independent of each other, jointly explaining large variations in the EAT EEOF1. Moreover, the signals of the three factors could be traced back to February, consequently providing a potential prediction source for the EAT variation in March and April.
Climate–Vegetation Coverage Interactions in the Hengduan Mountains Area, Southeastern Tibetan Plateau, and Their Downstream Effects
Abstract
Little is known about the mechanism of climate–vegetation coverage coupled changes in the Tibetan Plateau (TP) region, which is the most climatically sensitive and ecologically fragile region with the highest terrain in the world. This study, using multisource datasets (including satellite data and meteorological observations and reanalysis data) revealed the mutual feedback mechanisms between changes in climate (temperature and precipitation) and vegetation coverage in recent decades in the Hengduan Mountains Area (HMA) of the southeastern TP and their influences on climate in the downstream region, the Sichuan Basin (SCB). There is mutual facilitation between rising air temperature and increasing vegetation coverage in the HMA, which is most significant during winter, and then during spring, but insignificant during summer and autumn. Rising temperature significantly enhances local vegetation coverage, and vegetation greening in turn heats the atmosphere via enhancing net heat flux from the surface to the atmosphere. The atmospheric heating anomaly over the HMA thickens the atmospheric column and increases upper air pressure. The high pressure anomaly disperses downstream via the westerly flow, expands across the SCB, and eventually increases the SCB temperature. This effect lasts from winter to the following spring, which may cause the maximum increasing trend of the SCB temperature and vegetation coverage in spring. These results are helpful for estimating future trends in climate and eco-environmental variations in the HMA and SCB under warming scenarios, as well as seasonal forecasting based on the connection between the HMA eco-environment and SCB climate.
Spatial Patterns of Subjective Well-Being on the Aquitaine Coastline, France
Abstract
Well-Being, as a mutidimensional concept, has progressively become an essential measure to categorize territories and to evaluate and communicate on their economic, social and environmental performance. Especially, subjective measures are gaining increasing attention for policy makers. In this context, the spatial dimension of the concept of Subjective Well-Being (SWB) has not been sufficiently explored and remains a major challenge for researchers. This paper is an attempt to detect and explain spatial differentiations in the assessment of SWB. To this end, we propose an innovative clustering method (ClustGeo) adapted to the multidimensional analysis of SWB. The approach enables us to establish classes of perceived quality of life, and to highlight spatial patterns of the linkages between the structuring dimensions of SWB. It is applied empirically, using data on specific life domains, as perceived by the inhabitants of coastal municipalities in south-western France. The results highlight the spatial heterogeneity among individuals in relation to their perception of quality of life. Involving the spatial component in the measurement enables us to detect and identify local trends. The clustering builds a clear distinction of classes even on similar perimeters while identifying local particularities, thus showing that the variability of data is not necessarily and/or solely driven by geographic location. The possibilities offered by ClustGeo pave the way for further statistical developments in the combination of different types of data.
A systematic review for assessing the impact of climate change on landslides: research gaps and directions for future research
Abstract
The magnitude and intensity of landslides due to changing climate have created environmental and socio-economic implications for society. Through an in-depth analysis of the existing research on landslides in a changing climate from 1996 to 2021, this paper aims to carry out bibliometric and thematic analyses, identify the research gaps in the existing literature, and suggest a future framework for climate change-induced landslide risk assessment and mitigation. The data for review was collected from the Web of Science and Scopus platforms using a set of relevant keywords. After meeting the exclusion and inclusion criteria, 200 studies were finally selected to analyze the current state of research. The findings revealed that most of the reviewed studies focused on economic vulnerability to landslides, while social and ecological aspects of vulnerability at the micro-scale were scant in the past literature. Uncertainty in landslide-climate modeling, lack of advanced models for predicting landslide risk, and lack of early warning systems were identified as the major research gaps. A holistic methodological approach is proposed for assessing landslide risk and devising landslide mitigation strategies. The identified research gaps and the proposed framework may help in the future progression of climate change-induced landslide research in spatial information science.
Micro and nanoarchitectonics of ZrC filler with size effects on densification and ablative mechanism of C/C-SiC-ZrC composites
Abstract
To shorten the manufacturing process and enhance the ZrC content, C/C-SiC-ZrC composites were prepared using the nano-filler slurry infiltration method in combination with precursor infiltration and pyrolysis method. The effect of ZrC filler sizes on densification, microstructure, and properties of composites was studied. Results revealed that the ZrC content and density of the C/C-SiC-ZrC composites are inversely correlated to the filler size. The composites with nano-fillers reach the highest density of 2.67 g cm−3 as well as the lowest open porosity of 1.21%. After being ablated under oxyacetylene flame for 60 s, the composites with nano-fillers owns the lowest linear ablation rate and surface temperature, with 10 μms−1 and 2700 °C. Due to the highest ZrC content and best film-forming ability of nano-fillers, ZrO2 outer layer with the densest structure can provide an enhanced protective effect, and the thickness of the oxide layer decreases from 509 to 264 μm as compared with micron fillers. Therefore, the oxidation damage of the composites is reduced, and the thickness of the SiC depletion layer decreases simultaneously. The above results further demonstrate that introducing ZrC nano-filler to the C/C-SiC-ZrC composites can considerably improve their ablative properties at 2700 °C.
Protein kinase D2-Aurora kinase A-ERK1/2 signalling axis drives neuroendocrine differentiation of epithelial ovarian cancer
Abstract
Epithelial ovarian cancer (EOC) is deadliest gynecological malignancy with poor prognosis and patient survival. Despite development of several therapeutic interventions such as poly-ADP ribose polymerase (PARP) inhibitors, EOC remains unmanageable and discovery of novel early detection biomarkers and treatment targets are highly warranted. Although neuroendocrine differentiation (NED) is implicated in different human cancers including prostate adenocarcinoma and lung cancer, mechanistic studies concerning NED of epithelial ovarian cancer are lacking. We report that Aurora kinase A drives NED of epithelial ovarian cancer in an ERK1/2-dependent manner and pharmacological and genetic inhibition of Aurora kinase A suppress NED of ovarian cancer. Moreover, we demonstrate that protein kinase D2 positively regulated Aurora kinase A to drive NED. Overexpression of catalytically active PKD2 drives NED and collectively, PKD2 cross talks with Aurora kinase A/ERK1/2 signalling axis to positively regulate NED of EOC. PKD2/Aurora kinase A/ERK1/2 signalling axis is a novel therapeutic target against neuroendocrine differentiated EOC.
Non-Newtonian rheology of blood in elliptical cross-section artery affected by several stenosis: Prandtl fluid model
Abstract
A variety of circulatory disorders can be brought on by stenosis, which restricts a bodily passage artery or orifice. It may cause of brain disorders, heart diseases and legs disabilities. With the assistance of the Prandtl fluid model, the present investigation proposes an analytical strategy for monitoring blood flow through a stenosed artery. The artery is depicted as a tube with a cross-sectional view of an ellipse form. The flow-regulating equations are derived in the dimensionless form under the assumption of mild stenosis. The solution of mathematical equations is obtained by employing the perturbation technique via polynomial of degree four. It is analyzed graphically how flow-related parameters such as artery length L, stenosis height \(\delta _l\) , fluid parameters \(\alpha \) , and K influence the velocity distribution, wall shear stress, and flow resistance. It is observed that the non-Newtonian effects dominate in the surrounding stenosed wall of the artery along the minor axis. The wall shear stress is found to attain its maximum value at the peak of stenosis. The height of stenosis considerably impacts the flow resistance, and as it increases, so does the opposition, which develops the disorder in the confined area. The streamlines are also plotted to analyze the flow behavior, and it is observed that the contours are produced in the constricted zone.
Special perceptual parsing for Chinese landscape painting scene understanding: a semantic segmentation approach
Abstract
The automatic and precise perceptual parsing of Chinese landscape paintings (CLP) significantly aids in the digitization and recreation of artworks. Manual extraction and analysis of objects in CLPs is challenging, even for expert painters with professional knowledge and sharp discernment. Two main key reasons restricted the development of CLP parsing: (1) a lack of pixel-level labeled data used to supervise model training, and (2) the inherent complexity of CLP images compared to real scenes, characterized by varied scales, diverse textures, and intricate empty spaces. To address these challenges, we first construct a pixel-level annotated CLP segmentation datasets to advance perceptual parsing. Then, a novel CLP Perceptual Parsing (CLPPP) model is designed to fully utilize the intrinsic features of CLP images. To dynamically and adaptively capture context information, we introduced a set of learnable kernels into the CLPPP model based on the multiscale features of objects within CLPs. This enabled the model to learn an appropriate receptive field for context information extraction. Additionally, a positional attention head is devised to effectively eliminate noise from the intergroup and help the kernel gain inter-object position information. This iterative optimization process is helpful to learn powerful feature representations for different textures in CLPs. The experiment results demonstrate that the proposed CLPPP model outperforms state-of-the-art methods with mIoU, aAcc, and mAcc scores of 55.45, 75.08, and 71.15, respectively, achieving a large margin on the CLP dataset under consistent conditions.