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Future greening of the Earth may not be as large as previously predicted
Agricultural and Forest Meteorology ( IF 6.2 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.agrformet.2020.108111
Qian Zhao , Zaichun Zhu , Hui Zeng , Weiqing Zhao , Ranga B. Myneni

Abstract Changes in global vegetation growth and its drivers during recent decades have been well studied with satellite data, ecosystem models and field experiments. However, a systematic understanding of how global vegetation will respond to projected changes in climate and atmospheric composition is still lacking. Here, we analyze changes in projected global leaf area index (LAI) from 16 Coupled Model Intercomparison Project phase 5 (CMIP5) and 17 phase 6 (CMIP6) Earth System Models (ESMs) during the 21st century under future scenarios RCP2.6, RCP4.5 and RCP8.5 and future scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5, respectively. In addition to the widely-used multi-model ensemble mean (MME) method, we employed the Reliability Ensemble Averaging (REA) strategy to integrate the modeled results. The REA integration weights were determined by combining the performance of the ESMs in simulating present-day LAI changes, which was based on a comparison with three long-term remote sensing LAI data sets, with their convergence of future predictions. The results suggest that global LAI will increase under all seven future scenarios with both integration methods. The magnitude of LAI growth is expected to increase with the forcing levels of the scenarios. The ESMs integrated with the REA weights predicted significantly smaller magnitudes and lower uncertainties in global LAI growth by the end of the 21st century than those integrated with the MME method. Both REA and MME results suggest that the growth in atmospheric CO2 concentration is the main positive driver of the projected increment of global LAI, which is partly offset by the negative effects of global warming. Our improved comprehensive prediction based on CMIP5 and CMIP6 ESMs using the REA integration strategy provides a more robust estimation of global vegetation change during the 21st century, and is expected to help better understand the state of the planet in the coming decades.

中文翻译:

未来地球的绿化可能没有之前预测的那么大

摘要 近几十年来,全球植被生长的变化及其驱动因素已通过卫星数据、生态系统模型和实地试验得到了很好的研究。然而,仍然缺乏对全球植被将如何响应气候和大气成分的预测变化的系统理解。在这里,我们分析了 21 世纪 16 个耦合模型比对项目第 5 阶段(CMIP5)和 17 个第 6 阶段(CMIP6)地球系统模型(ESM)在未来情景 RCP2.6、RCP4 下预计全球叶面积指数(LAI)的变化.5 和 RCP8.5 以及未来情景分别为 SSP1-2.6、SSP2-4.5、SSP3-7.0 和 SSP5-8.5。除了广泛使用的多模型集成平均 (MME) 方法之外,我们还采用了可靠性集成平均 (REA) 策略来整合建模结果。REA 集成权重是通过结合 ESM 在模拟当前 LAI 变化中的性能来确定的,该性能基于与三个长期遥感 LAI 数据集的比较,以及它们对未来预测的收敛。结果表明,在使用这两种集成方法的所有七种未来情景下,全球 LAI 都将增加。LAI 增长的幅度预计会随着情景的强迫水平而增加。与 REA 权重相结合的 ESM 预测到 21 世纪末全球 LAI 增长的幅度和不确定性显着低于与 MME 方法相结合的 ESM。REA 和 MME 结果均表明,大气 CO2 浓度的增长是全球 LAI 预计增量的主要积极驱动因素,这部分被全球变暖的负面影响所抵消。我们使用 REA 整合策略基于 CMIP5 和 CMIP6 ESM 的改进综合预测提供了对 21 世纪全球植被变化的更可靠估计,并有望帮助更好地了解未来几十年的地球状况。
更新日期:2020-10-01
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