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

doi:10.17521/cjpe.2022.0116

摘要/Abstract

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

李伯新, 姜超, 孙建新. 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. 植物生态学报, 2023, 47(9): 1211-1224. DOI: 10.17521/cjpe.2022.0116

LI Bo-Xin, JIANG Chao, SUN Osbert Jianxin. Comprehensive assessment of vegetation carbon use efficiency in southwestern China simulated by CMIP6 models. Chinese Journal of Plant Ecology, 2023, 47(9): 1211-1224. DOI: 10.17521/cjpe.2022.0116

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0116

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

图/表 8

表1 本研究使用的15个CMIP6模式基本信息

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

图1 MODIS观测(MOD17A2H)和CMIP6模式模拟的2001-2014年中国西南部地区区域平均植被碳利用率(CUE)的多年变化。各模式信息见表1。

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.

图2 CMIP6模式模拟的2001-2014年中国西南部地区多年平均植被碳利用率(CUE)空间分布相对于MODIS观测(MOD17A2H)场的泰勒图。各模式信息见表1。图中辐射线代表相关系数, 虚线代表均方根误差。

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.

图3 MODIS观测(MOD17A2H)和CMIP6模式模拟的2001-2014年中国西南部地区季节尺度区域平均植被碳利用率(CUE)的多年变化。A, 冬季。B, 春季。C, 夏季。D, 秋季。各模式信息见表1。

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.

图4 CMIP6模式模拟的2001-2014年中国西南部地区季节尺度多年平均植被碳利用率(CUE)空间分布相对于MODIS (MOD17A2H)观测场的泰勒图。A, 冬季。B, 春季。C, 夏季。D, 秋季。各模式信息见表1。图中辐射线代表相关系数, 虚线代表均方根误差。

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.

表2 CMIP6模式对2001-2014年中国西南部地区年和季节尺度多年平均植被碳利用率(CUE)空间分布模拟能力的综合排名

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

图5 较优模式集合模拟的2001-2014年中国西南部地区年和季节尺度多年平均植被碳利用率(CUE)空间分布相对于MODIS (MOD17A2H)观测场的泰勒图。MME-S-ANN、MME-S-DJF、MME-S-MAM、MME-S-JJA、MME-S-SON分别为年、冬季、春季、夏季、秋季尺度多年平均较优模式的集合。图中辐射线代表相关系数, 虚线代表均方根误差。

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.

图6 较优模式集合模拟的2001-2014年中国西南部地区年平均及夏季多年平均植被碳利用率(CUE)与MODIS观测的差值空间分布(A、B)及相关关系(C、D)空间分布。A、C为年尺度; B、D为夏季。对于差值空间分布(A、B)打点区域为差值绝对值>0.2, 对于相关关系空间分布(C、D)打点区域为通过95%置信度检验。

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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