2025-05-09 中国科学院(CAS)
<関連情報>
- https://english.cas.cn/newsroom/research_news/earth/202505/t20250512_1042954.shtml
- https://onlinelibrary.wiley.com/doi/10.1111/gcb.70126
気候変動による地球緑化の主要因は、21世紀の歴史的期間に対して2倍以上の割合で寄与している Global Greening Major Contributed by Climate Change With More Than Two Times Rate Against the History Period During the 21th Century
Hao Zhang, Zengyun Hu, Xi Chen, Jianfeng Li, Qianqian Zhang, Xiaowei Zheng
Global Change Biology Published: 11 March 2025
DOI:https://doi.org/10.1111/gcb.70126
ABSTRACT
Future variations of global vegetation are of paramount importance for the socio-ecological systems. However, up to now, it is still difficult to develop an approach to project the global vegetation considering the spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid-by-grid; multi-algorithms; optimal combination) to construct an optimal model using six algorithms (i.e., LR: linear regression; SVR: support vector regression; RF: random forest; CNN: convolutional neural network; and LSTM: long short-term memory; transformer) based on five climatic factors (i.e., Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, and CO2). The optimal model is employed to project the future changes in leaf area index (LAI) for the global and four sub-regions: the high-latitude northern hemisphere (NH), the mid-latitude NH, the tropics, and the mid-latitude southern hemisphere. Our results indicate that global LAI will continue to increase, with the greening rate expanding to 2.25 times in high-latitude NH by 2100 against the 1982–2014 period. Moreover, RF shows strong applicability in the global and NH models. In this study, we introduce an innovative model GGMAOC, which provides a new optimal model scheme for environmental and geoscientific research.
