Changes in net ecosystem productivity (NEP) in terrestrial ecosystems in response to climate warming and land cover changes have been of great concern. In this study, we applied the normalized difference vegetation index (NDVI), average temperature, and sunshine hours to drive the C-FIX model and to simulate the regional NEP in China from 2000 to 2019. We also analyzed the spatial patterns and the spatiotemporal variation characteristics of the NEP of terrestrial ecosystems and discussed their main influencing factors. The results showed that (1) the annual average NEP of terrestrial ecosystems in China from 2000 to 2019 was 1.08 PgC, exhibiting a highly significant increasing trend with a rate of change of 0.83 PgC/10 y. The terrestrial ecosystems in China remained as carbon sinks from 2000 to 2019, and the carbon sink capacity increased significantly. The NEP of the terrestrial ecosystem increased by 65% during 2015–2019 compared to 2000–2004 (2) There was spatial differences in the NEP distribution of the terrestrial ecosystems in China from 2000–2019. Taking the line along the Daxinganling-Yin Mountains-Helan Mountains-Transverse Range as the boundary, the NEP was significantly higher in the eastern part than in the western part. Among them, the NEP was positive (carbon sink) in northeastern, central, and southern China, and negative (carbon source) in parts of northwestern China and the Tibet Autonomous Region. The spatial variation of NEP in terrestrial ecosystems increased from 2000 to 2009. The areas with a significant increase accounted for 45.85% and were mainly located in the central and southwestern regions. (3) The simulation results revealed that vegetation changes and CO2 concentration changes both contributed to the increase in the NEP in China, contributing 85.96% and 36.84%, respectively. The vegetation changes were the main factor causing the increase in the NEP. The main contribution of this study is to further quantify the NEP of terrestrial ecosystems in China and identify the influencing factors that caused these changes.

In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.

The accurate delineation of the spatial extent of cold regions provides the basis for the study of global environmental change. However, attention has been lacking on the temperature-sensitive spatial changes in the cold regions of the Earth in the context of climate warming. In this study, the mean temperature in the coldest month lower than − 3 °C, no more than 5 months over 10 °C, and an annual mean temperature no higher than 5 °C were selected to define cold regions. Based on the Climate Research Unit land surface air temperature (CRUTEM) of monthly mean surface climate elements, the spatiotemporal distribution and variation characteristics of the Northern Hemisphere (NH) continental cold regions from 1901 to 2019 are analyzed in this study, by adopting time trend and correlation analyses. The results show: (1) In the past 119 years, the cold regions of the NH covered on average about 4.074 × 107 km2, accounting for 37.82% of the total land area of the NH. The cold regions can be divided into the Mid-to-High latitude cold regions and the Qinghai-Tibetan Plateau cold regions, with spatial extents of 3.755 × 107 km2 and 3.127 × 106 km2, respectively. The Mid-to-High latitude cold regions in the NH are mainly distributed in northern North America, most of Iceland, the Alps, northern Eurasia, and the Great Caucasus with a mean southern boundary of 49.48° N. Except for the southwest, the entire region of the Qinghai-Tibetan Plateau, northern Pakistan, and most of Kyrgyzstan are cold regions. (2) In the past 119 years, the rates of change in the spatial extent of the cold regions in the NH, the Mid-to-High latitude, and the Qinghai-Tibetan Plateau were − 0.030 × 107 km2/10 a, − 0.028 × 107 km2/10 a, and − 0.013 × 106 km2/10 a, respectively, showing an extremely significant decreasing trend. In the past 119 years, the mean southern boundary of the Mid-to-High latitude cold regions has been retreating northward at all longitudes. For instance, the mean southern boundary of the Eurasian cold regions moved 1.82° to the north and that of North America moved 0.98° to the north. The main contribution of the study lies in the accurate definition of the cold regions and documentation of the spatial variation of the cold regions in the NH, revealing the response trends of the cold regions to climate warming, and deepening the study of global change from a new perspective.

No abstract

This paper presents a system, namely, the abnormal-weather monitoring and curation service (AWMC), which provides people with a better understanding of abnormal weather conditions. The service can analyze a set of multivariate weather datasets (i.e., 7 meteorological datasets from 18 cities in Korea) and show (i) which dates are mostly abnormal in a certain city, and (ii) which cities are mostly abnormal on a certain date. In particular, the dynamic graph-embedding-based anomaly detection method was employed to measure anomaly scores. We implemented the service and conducted evaluations. Regarding the results of monitoring abnormal weather, AWMC shows that the average precision was approximately 90.9%, recall was 93.2%, and F1 score was 92.1% for all the cities.