Climate Change Data Portal
| DOI | 10.1016/j.accre.2022.01.007 |
| Deep learning projects future warming-induced vegetation growth changes under SSP scenarios | |
| Chen Z.-T.; Liu H.-Y.; Xu C.-Y.; Wu X.-C.; Liang B.-Y.; Cao J.; Chen D. | |
| 发表日期 | 2022 |
| ISSN | 1674-9278 |
| 起始页码 | 251 |
| 结束页码 | 257 |
| 卷号 | 13期号:2 |
| 英文摘要 | Climate warming has been projected to enhance vegetation growth more strongly in higher latitudes than in lower latitudes, but different projections show distinct regional differences. By employing big data analysis (deep learning), we established gridded, global-scale, climate-driven vegetation growth models to project future changes in vegetation growth under SSP scenarios. We projected no substantial trends of vegetation growth change under the sustainable development scenario (SSP1-1.9) by the end of the 21st century. However, the increase of vegetation growth driven by climate warming shows distinct regional variability under the scenario representing high carbon emissions and severe warming (SSP5-8.5), especially in Northeast Asia where growth could increase by (6.00% ± 4.21%). This may be attributed to the high temperature sensitivities of the deciduous needleleaf forests and permanent wetlands in these regions. When the temperature sensitivity that is defined as permutation importance in deep learning is greater than 0.05, the increase in vegetation growth will be more prominent. In addition, an extreme temperature increase across grasslands, as well as changing land-use management in northern China may also influence the vegetation growth in the future. The results suggest that the sustainable development scenario can maintain stable vegetation growth, and it may be a reliable way to mitigate global warming due to potential climate feedbacks driven by vegetation changes in boreal regions. Deciduous needleleaf forests will be a centre of greening in the future, and it should become the focus of future vegetation dynamics modelling studies and projections. © 2022 The Authors |
| 英文关键词 | Climate change; Climate sensitivity; Deep learning; Future projection; Vegetation growth |
| 语种 | 英语 |
| 来源期刊 | Advances in Climate Change Research
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/262080 |
| 作者单位 | College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, 100871, China; Faculty of Geographical Sciences, Beijing Normal University, Beijing, 100875, China; Department of Earth Sciences, University of Gothenburg, Gothenburg, 40530, Sweden |
| 推荐引用方式 GB/T 7714 | Chen Z.-T.； Liu H.-Y.； Xu C.-Y.； Wu X.-C.； Liang B.-Y.； Cao J.； Chen D.. Deep learning projects future warming-induced vegetation growth changes under SSP scenarios[J],2022,13(2). |
| APA | Chen Z.-T.； Liu H.-Y.； Xu C.-Y.； Wu X.-C.； Liang B.-Y.； Cao J.； Chen D..(2022).Deep learning projects future warming-induced vegetation growth changes under SSP scenarios.Advances in Climate Change Research,13(2). |
| MLA | Chen Z.-T.； Liu H.-Y.； Xu C.-Y.； Wu X.-C.； Liang B.-Y.； Cao J.； Chen D.."Deep learning projects future warming-induced vegetation growth changes under SSP scenarios".Advances in Climate Change Research 13.2(2022). |
| 条目包含的文件 | 条目无相关文件。 | |||||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
