植物生态学报 ›› 2023, Vol. 47 ›› Issue (9): 1211-1224.DOI: 10.17521/cjpe.2022.0116

所属专题: 生态遥感及应用

• 研究论文 • 上一篇    下一篇

CMIP6模式对中国西南部地区植被碳利用率模拟能力综合评估

李伯新, 姜超*(), 孙建新   

  1. 北京林业大学生态与自然保护学院, 北京 100083
  • 收稿日期:2022-04-02 接受日期:2022-12-03 出版日期:2023-09-20 发布日期:2023-09-28
  • 通讯作者: * 姜超(jiangchao@bjfu.edu.cn)
  • 基金资助:
    国家自然科学基金(42175170)

Comprehensive assessment of vegetation carbon use efficiency in southwestern China simulated by CMIP6 models

LI Bo-Xin, JIANG Chao*(), SUN Osbert Jianxin   

  1. School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
  • Received:2022-04-02 Accepted:2022-12-03 Online:2023-09-20 Published:2023-09-28
  • Contact: * JIANG Chao(jiangchao@bjfu.edu.cn)
  • Supported by:
    National Natural Science Foundation of China(42175170)

摘要:

中国西南部地区地形复杂, 生态系统和植被类型丰富多样, 是重要的生态资源区。受气候变化和人类活动影响, 其生态屏障作用不断被减弱, 准确评估该地区的植被碳利用率(CUE)对揭示碳平衡机理和预测陆地碳收支具有重要意义。该研究利用2001-2014年MODIS遥感观测数据和参加第六次国际耦合模式比较计划(CMIP6)的15个模式模拟数据, 分别从年和季节尺度综合分析了新一代模式对中国西南部地区植被CUE的模拟能力, 并基于综合评级指标(MR)对模式的模拟能力进行排名, 以寻求模拟能力较好的模式, 旨在有效降低未来预估结果的不确定性。结果表明: (1)大多数模式对年尺度区域平均植被CUE的模拟存在低估情况, 且对植被CUE空间变化趋势的模拟能力相对较差, 但部分模式可以较好地模拟出多年平均植被CUE空间分布, 其中位于前1/3的较优模式依次为BCC-CSM2-MR、CMCC-ESM2、TaiESM、EC-Earth3-Veg、CAS-ESM2-0; (2)四个季节中, 各模式对夏季多年平均植被CUE空间分布模拟能力最优, 其中位于前1/3的较优模式依次为BCC-CSM2-MR、EC-Earth3-Veg、TaiESM、CMCC-ESM2、CAS-ESM2-0, 各模式对冬季的模拟能力仅次于夏季, 而春季和秋季则相对较差; (3)相较于单一模式而言, 较优模式的集合在一定程度上可以削弱单一模式带来的不确定性, 且在各时间尺度都表现出了较强的模拟能力, 尤其可以合理再现四川盆地等局部地区植被CUE空间分布特点, 但是对青藏高原以及横断山区等地形复杂区域植被CUE空间分布的模拟能力仍存在不足。总体来说, 在使用CMIP6模式进行区域植被CUE模拟前, 从多角度展开多模式的综合评估以挑选出模拟性能较好的模式是十分必要的。

关键词: 碳利用率, CMIP6模式, MODIS, 评估, 中国西南部地区

Abstract:

Aims The southwestern China is a region with complex topography and diverse ecosystem and vegetation types. However, its role as an ecological barrier is being weakened by the effects of climate change and increasing pressure of human activities. This study examines the temporal dynamics of vegetation carbon use efficiency (CUE) in this region using the CMIP6 models, aiming to effectively reducing the uncertainties in prognostic results of future predictions.
Methods We used MODIS remote sensing data for the period 2001-2014 and simulations from 15 models in the Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to determine the capability of the new generation models in simulating the seasonal and annual vegetation CUE in the southwestern China. The performance of the models was ranked based on the composite rating index (MR).
Important findings Most of the models used in this study underestimated the annual vegetation CUE, and their ability to simulate the spatial patterns in the trends of vegetation CUE is generally poor. However, some models performed relatively well in simulating the spatial distribution of multi-year average vegetation CUE; the top 1/3 tier included BCC-CSM2-MR, CMCC-ESM2, TaiESM, EC-Earth3-Veg and CAS-ESM2-0 in the order of performance. Among the seasons, the models best simulated the spatial distribution of vegetation CUE in summer, with better results from BCC-CSM2-MR, EC-Earth3-Veg, TaiESM, CMCC-ESM2 and CAS-ESM2-0. The simulation capability of the models for winter is second only to that for summer, and relatively poor for spring and autumn. Compared to the simulations by individual models, the multi-model ensemble mean (MME-S) reduced the uncertainties and exhibited a strong simulation capability, especially in the spatial distribution of vegetation CUE in local areas such as the Sichuan Basin. There was a lack of good simulation capability for the spatial distribution of vegetation CUE in Qingzang Plateau, Hengduan Mountains and other topographically complex areas. In general, before applying the CMIP6 models for regional vegetation CUE simulation, it is necessary to comprehensively evaluate the specific models from multiple perspectives to select the models with better simulation performance.

Key words: carbon use efficiency, CMIP6 models, MODIS, evaluation, southwestern China