基于CMIP6模式优化集合平均预估21世纪全球陆地生态系统总初级生产力变化

doi:10.12006/j.issn.1673-1719.2020.221

摘要/Abstract

摘要：

利用国际耦合模式比较计划第六阶段(CMIP6)中18个地球系统模式总初级生产力(GPP)模拟数据,基于传统的多模式集合平均(MME)和可靠集合平均方法(REA),在4个未来情景(SSP1-2.6,SSP2-4.5,SSP3-7.0和SSP5-8.5)下预估了21世纪全球陆地生态系统GPP的变化量,并分析了GPP变化的驱动因子。研究结果表明：在4个未来情景下,基于REA方法预估的全球陆地生态系统年GPP在未来时期(2068—2100年)比历史时期(1982—2014年)分别增长了(14.85±3.32)、(28.43±4.97)、(37.66±7.61)和(45.89±9.21)Pg C,其增量大小和不确定性都明显低于MME方法。在4个情景下,大气CO₂浓度增长对GPP变化的贡献最大,基于REA方法计算的贡献占比分别为140%、137%、115%和75%;除SSP5-8.5(24%)外,其他情景下升温均导致全球陆地生态系统GPP降低(-42%、-37%、-16%),部分抵消了CO₂施肥效应的正面贡献。温度的影响存在纬度差异：升温在低纬度地区对GPP有负向贡献,在中高纬度地区为正向贡献。降水和辐射变化对GPP变化的贡献相对较小。

关键词: 总初级生产力(GPP), 地球系统模式, 国际耦合模式比较计划第六阶段(CMIP6), 可靠集合平均(REA)

Abstract:

Using data set of 18 Earth system models in CMIP6, the global terrestrial annual gross primary productivity (GPP) changes in the 21st century were projected, and its driving factors were analyzed under four future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) based on traditional Multi-Model Ensemble Mean (MME) and Reliability Ensemble Averaging (REA) methods. The results show that under the four scenarios, the global terrestrial GPP predicted by the REA method in the future period (2068-2100) would increase by (14.85±3.32), (28.43±4.97), (37.66±7.61), and (45.89±9.21) Pg C compared with that in historical period (1982-2014), where the increment magnitude and uncertainty are significantly lower than those based on MME method. Attribution analysis shows that under the four scenarios, atmospheric CO₂ concentration increasing contributes the most to the changes of GPP, whose proportion calculated based on REA method are 140%, 137%, 115% and 75%. With the exception of SSP5-8.5 (24%), warming would lead to global GPP decreasing under other scenarios (-42%, -37%, -16%), which partially offsets the positive contribution of CO₂ fertilization effects. There are different latitudinal patterns of the effect of temperature: warming in low latitudes contributes negatively to GPP changes, while it has positive contribution in middle and high latitudes. Precipitation and radiation changes contribute relatively little to GPP changes.

Key words: Gross primary productivity (GPP), Earth system models, CMIP6, Reliability ensemble averaging (REA)

黄禄丰, 朱再春, 黄萌田, 赵茜, 马伟蕊, 曾辉. 基于CMIP6模式优化集合平均预估21世纪全球陆地生态系统总初级生产力变化[J]. 气候变化研究进展, 2021, 17(5): 514-524.

HUANG Lu-Feng, ZHU Zai-Chun, HUANG Meng-Tian, ZHAO Qian, MA Wei-Rui, ZENG Hui. Projection of gross primary productivity change of global terrestrial ecosystem in the 21st century based on optimal ensemble averaging of CMIP6 models[J]. Climate Change Research, 2021, 17(5): 514-524.

图/表 7

表1 CMIP6中18个地球系统模式基本信息

Table 1 The characteristics of 18 CMIP6 models

图1 SSP2-4.5情景下基于REA和MME方法的历史时期GPP与GPPNIRv的趋势差(a)与年均值差(b)的概率密度分布

Fig. 1 Probability distributions of the difference between trends (a) and mean value (b) of GPPNIRv and REA & MME GPP under SSP2-4.5

图2 1982—2100年不同SSP情景下REA和MME方法预估的全球GPP变化

Fig. 2 Inter-annual changes of global GPP simulated by CMIP6 models integrated with REA and MME methods during 1982-2100 under 4 SPPs

图3 4种情景下未来时期(2068—2100年)相比于历史时期(1982—2014年)GPP变化量的空间分布

Fig. 3 Spatial and latitudinal patterns of changes in GPP between historical (1982-2014) and future (2068-2100) periods

图4 像元尺度下多元回归方程拟合效果R的概率密度曲线

Fig. 4 Probability distributions of the goodness of fitting R in pixels

图5 不同情景下环境因子对全球GPP变化的贡献占比

Fig. 5 Proportion of environmental factors’ contribution to global GPP changes under 4 SSPs

图6 基于REA方法不同情景下环境因子对全球GPP变化贡献的空间格局

Fig. 6 Spatial patterns of the environmental factors’ contribution to global GPP changes under 4 SSPs based on REA method

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