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

doi:10.17521/cjpe.2022.0116

Abstract

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

LI Bo-Xin, JIANG Chao, SUN Osbert Jianxin. Comprehensive assessment of vegetation carbon use efficiency in southwestern China simulated by CMIP6 models[J]. Chin J Plant Ecol, 2023, 47(9): 1211-1224.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0116

https://www.plant-ecology.com/EN/Y2023/V47/I9/1211

Figures/Tables 8

Table 1 General information of the 15 CMIP6 models used in this study

模式名称 Model name	所属机构 Institution	网格分辨率 Spatial resolution	陆面模式 Land surface model
ACCESS-ESM1-5	Commonwealth Scientific and Industrial Research Organisation, Australia	192 × 145	CABLE2.4
BCC-CSM2-MR	北京市气候中心 Beijing Climate Center, China	320 × 160	BCC_AVIM2
CanESM5	Canadian Centre for Climate Modelling and Analysis, Canada	128 × 64	CLASS3.6/CTEM1.2
CAS-ESM2-0	中国科学院 Chinese Academy of Sciences, China	256 × 128	CoLM
CESM2-WACCM	National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, USA	288 × 192	CLM5
CMCC-CM2-SR5	Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy	288 × 192	CLM4.5 (BGC mode)
CMCC-ESM2	Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy	288 × 192	CLM4.5 (BGC mode)
EC-Earth3-Veg	EC-Earth consortium, European Union	512 × 256	HTESSEL/LPJ-GUESS v4
EC-Earth3-Veg-LR	EC-Earth consortium, European Union	320 × 160	HTESSEL/LPJ-GUESS v4
INM-CM4-8	Institute for Numerical Mathematics, Russian Academy of Sciences, Russia	180 × 120	INM-LND1
INM-CM5-0	Institute for Numerical Mathematics, Russian Academy of Sciences, Russia	180 × 120	INM-LND1
IPSL-CM6A-LR	Institut Pierre Simon Laplace, France	144 × 143	ORCHIDEE (v2.0)
MPI-ESM1-2-HR	Max Planck Institute for Meteorology, Germany	384 × 192	JSBACH3.20
MPI-ESM1-2-LR	Max Planck Institute for Meteorology, Germany	192 × 96	JSBACH3.20
TaiESM	“中研院”环境变迁研究中心 Research Center for Environmental Changes, Academia Sinica, Taiwan, China	288 × 192	CLM4.0

Fig. 1 Inter-annual variations in regional mean vegetation carbon use efficiency (CUE) from MODIS observations (MOD17A2H) and simulations by CMIP6 models in southwestern China from 2001 to 2014. See Table 1 for general information on models.

Fig. 2 Taylor diagram for the spatial distribution of multi-year average vegetation carbon use efficiency (CUE) relative to the MODIS observation (MOD17A2H) field in southwestern China simulated by the CMIP6 models from 2001 to 2014. See Table 1 for general information on models. The radial line represents the correlation coefficient and the dashed line represents the root mean square error.

Fig. 3 Inter-annual variations in seasonal-scale regional mean vegetation carbon use efficiency (CUE) from MODIS observations (MOD17A2H) and simulations by CMIP6 models in southwestern China from 2001 to 2014. A, Winter. B, Spring. C, Summer. D, Fall. See Table 1 for general information on models.

Fig. 4 Seasonal-scale Taylor diagram for the spatial distribution of multi-year average vegetation carbon use efficiency (CUE) by the CMIP6 models from 2001 to 2014 relative to the MODIS observation (MOD17A2H) field in southwestern China simulated. A, Winter. B, Spring. C, Summer. D, Fall. See Table 1 for general information on models. The radial line represents the correlation coefficient and the dashed line represents the root mean square error.

Table 2 Integrative ranking of the CMIP6 models capability to simulate the annual and seasonal scale spatial distributions in multi-year average vegetation carbon use efficiency (CUE) in southwestern China from 2001 to 2014

模式名称 Model name	ANN	DJF	MAM	JJA	SON
ACCESS-ESM1-5	8	15	1	8	9
BCC-CSM2-MR	1	11	10	1	3
CanESM5	11	10	14	8	14
CAS-ESM2-0	5	14	4	5	4
CESM2-WACCM	11	5	3	15	7
CMCC-CM2-SR5	6	4	6	6	1
CMCC-ESM2	2	2	5	4	2
EC-Earth3-Veg	4	7	1	1	6
EC-Earth3-Veg-LR	7	13	6	7	5
INM-CM4-8	14	6	12	14	10
INM-CM5-0	15	3	13	13	10
IPSL-CM6A-LR	9	11	15	12	13
MPI-ESM1-2-HR	11	7	11	10	15
MPI-ESM1-2-LR	9	9	8	11	10
TaiESM	3	1	9	1	8
MME-S	1	1	1	1	1

Fig. 5 Annual and seasonal scale Taylor diagram for the spatial distribution of multi-year average vegetation carbon use efficiency (CUE) simulated by the MME-S from 2001 to 2014 relative to the MODIS observation (MOD17A2H) in southwestern China. MME-S-ANN, MME-S-DJF, MME-S-MAM, MME-S-JJA and MME-S-S-SON are collections of annual, winter, spring, summer and autumn multi-year mean-scale better models, respectively. The radial line represents the correlation coefficient and the dashed line represents the root mean square error.

Fig. 6 Spatial patterns in the differences (A、B) and correlations (C、D) between multi-year average and summer average vegetation carbon use efficiency (CUE) and MODIS observations in southwestern China from 2001 to 2014. A, C are annual average; B, D are summer average. For the spatial patterns of differences (A, B), the punctured region is the absolute value of differences > 0.2, and for the spatial patterns of correlations (C, D), the punctured region passes the 95% confidence test.

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