Hydrological variations in central China over the past millennium and their links to the Tropic Pacific and North Atlantic Oceans

Variations of precipitation, aka the Meiyu rain, in East Asian summer monsoon (EASM) domain during the last millennium could help enlighten the hydrological response to future global warming. Here we present a precisely dated and highly resolved stalagmite δ18O record from the Yongxing Cave, central China. Our new record, combined with a previously published one from the same cave, indicates that the Meiyu rain has changed dramatically in association with the global temperature change. In particular, our record shows that the Meiyu rain has been weakened during the Medieval Climate Anomaly (MCA), but intensified during the Little Ice Age (LIA). During the Current Warm Period (CWP), our record indicates a similar weakening of the Meiyu rain. Furthermore, during the MCA and CWP, our records show that the atmospheric precipitation is similarly wet in northern China and similarly dry in central China, but relatively wet during the CWP in southern China. This spatial discrepancy indicates a complicated localized response of the regional precipitation to the anthropogenic forcing. The weakened (intensified) Meiyu rain during the MCA (LIA) matches well with the warm (cold) phases of Northern Hemisphere surface air temperature. This Meiyu rain pattern also corresponds well with the climatic conditions over the Tropical Indo-Pacific warm pool. On the other hand, our record shows a strong association with the North Atlantic climate as well. The reduced (increased) Meiyu rain correlates well with positive (negative) phases of North Atlantic Oscillation. In addition, our record links well with the strong (weak) Atlantic meridional overturning circulation during the MCA (LIA) period. All above-mentioned localized correspondences and remote teleconnections on decadal to centennial timescales indicate that the Meiyu rain is coupled closely with oceanic processes in the Tropical Pacific and North Atlantic Oceans during the MCA and LIA

Forest biomass resources and utilization in China

Under the context of climate change, persistent high oil prices and rapidly growing dependence on imported oil prompt China to pay much more attention to biofuels that provide environmental benefits besides fuel. China has rich biodiversity with 30 thousand high plant species and 154 kinds of energy trees could produce seeds containing more than 40% of oil, with total production of the seeds totaling 5 million t, and 200 x109 t of biomass production per year, which is equal to 2 x 109 t of petroleum. There are over 2000 types of wild and cultivated firewood plants in the country. So far there is 4 million ha raising oil-bearing trees planted on some land in different regions. Another 57 million ha of waste land are available and suitable for planting trees for the production of forest bioenergy. On part of these lands, the central government plans to cultivate a total of 13 million ha of high-grade bioenergy forests by 2020. This will yield 6 million tons of diesel that would be enough to fuel power plants with a combined capacity of 11 GW each year. Moreover, forest biomass plantations potentially offer many direct and indirect environmental benefits. In view of climate change their globally significant environmental benefits may result from using forest biomass for energy rather than fossil fuels.Key words: Biomass energy, China, forest biomass resources

Variations in the East Asian summer monsoon over the past 1 millennium and their links to the Tropic Pacific and North 2 Atlantic oceans

Variations of East Asian summer monsoon (EASM) during the last millennium could help enlighten the monsoonal response to future global warming. Here we present a precisely dated and highly resolved stalagmite δ18O record from the Yongxing Cave, central China. Our new record, combined with a previously published one from the same cave, indicates that the EASM has changed dramatically in association with the global temperature rising. In particular, our record shows that the EASM has intensified during the Medieval Climate Anomaly (MCA) and the Current Warm Period (CWP) but weakened during the Little Ice Age (LIA). We find that the EASM intensity is similar during the MCA and CWP periods in both northern and central China, but relatively stronger during the CWP in southern China. This discrepancy indicates a complicated regional response of the EASM to the anthropogenic forcing. The intensified and weakened EASM during the MCA and LIA matches well with the warm and cold phases of Northern Hemisphere surface air temperature, respectively. This EASM pattern also corresponds well with the rainfall over the tropical Indo-Pacific warm pool. Surprisingly, our record shows a strong association with the North Atlantic climate as well. The intensified (weakened) EASM correlates well with positive (negative) phases of North Atlantic Oscillation. In addition, our record links well with the strong (weak) Atlantic meridional overturning circulation during the MCA (LIA) period. All above-mentioned correlations indicate that the EASM tightly couples with oceanic processes in the tropical Pacific and North Atlantic oceans during the MCA and LIA

YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional and Large Kernel Design for Antenna Interference Source Detection

In the era of 5G communication, removing interference sources that affect communication is a resource-intensive task. The rapid development of computer vision has enabled unmanned aerial vehicles to perform various high-altitude detection tasks. Because the field of object detection for antenna interference sources has not been fully explored, this industry lacks dedicated learning samples and detection models for this specific task. In this article, an antenna dataset is created to address important antenna interference source detection issues and serves as the basis for subsequent research. We introduce YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically designed for antenna interference source detection. Specifically, we initially formulated a lightweight design for the network depth and width, ensuring that subsequent investigations were conducted within a lightweight framework. Then, we propose a DSLK-Block module based on depthwise separable convolution and large convolution kernels to enhance the network's feature extraction ability, effectively improving small object detection. To address challenges such as complex backgrounds and large interclass differences in antenna detection, we construct DSLKVit-Block, a powerful feature extraction module that combines DSLK-Block and transformer structures. Considering both its lightweight design and accuracy, our method not only achieves optimal performance on the antenna dataset but also yields competitive results on public datasets