气候变化领域集成服务门户(CCPortal): Terrestrial carbon cycle model-data fusion: Progress and challenges

CCPortal

DOI	10.1007/s11430-020-9800-3
	Terrestrial carbon cycle model-data fusion: Progress and challenges
	Li, Xin; Ma, Hanqing; Ran, Youhua; Wang, Xufeng; Zhu, Gaofeng; Liu, Feng; He, Honglin; Zhang, Zhen; Huang, Chunlin
通讯作者	Li, X (通讯作者)，Chinese Acad Sci, State Key Lab Tibetan Plateau Earth Syst Resource, Natl Tibetan Plateau Sci Data Ctr, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China. ; Li, X (通讯作者)，Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China.
发表日期	2021
ISSN	1674-7313
EISSN	1869-1897
起始页码	1645
结束页码	1657
卷号	64 期号:10
英文摘要	The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations. Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the equifinality of models, the identifiability of model parameters, the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and long-term time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality.
关键词	DATA-ASSIMILATION SYSTEM GLOBAL VEGETATION MODELS LAND-SURFACE MODEL ECOSYSTEM MODEL SENSITIVITY-ANALYSIS LEAF SCALE UNCERTAINTY PRODUCTIVITY PERSPECTIVE PARAMETERS
英文关键词	Carbon cycle; Model-data fusion; Data assimilation; Parameter estimation; Remote sensing; Uncertainty
语种	英语
WOS研究方向	Geology
WOS类目	Geosciences, Multidisciplinary
WOS记录号	WOS:000682649500002
来源期刊	SCIENCE CHINA-EARTH SCIENCES
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/255163
作者单位	[Li, Xin] Chinese Acad Sci, State Key Lab Tibetan Plateau Earth Syst Resource, Natl Tibetan Plateau Sci Data Ctr, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China; [Li, Xin] Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China; [Ma, Hanqing; Ran, Youhua; Wang, Xufeng; Liu, Feng; Zhang, Zhen; Huang, Chunlin] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Remote Sensing Gansu Prov, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China; [Zhu, Gaofeng] Lanzhou Univ, Coll Resources & Environm, Lanzhou 730000, Peoples R China; [He, Honglin] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Li, Xin,Ma, Hanqing,Ran, Youhua,et al. Terrestrial carbon cycle model-data fusion: Progress and challenges[J]. 中国科学院西北生态环境资源研究院,2021,64(10).
APA	Li, Xin.,Ma, Hanqing.,Ran, Youhua.,Wang, Xufeng.,Zhu, Gaofeng.,...&Huang, Chunlin.(2021).Terrestrial carbon cycle model-data fusion: Progress and challenges.SCIENCE CHINA-EARTH SCIENCES,64(10).
MLA	Li, Xin,et al."Terrestrial carbon cycle model-data fusion: Progress and challenges".SCIENCE CHINA-EARTH SCIENCES 64.10(2021).