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基于观测的全球土壤异养呼吸表明,陆地生态系统模型低估了土壤碳的周转和固存。

Observation-based global soil heterotrophic respiration indicates underestimated turnover and sequestration of soil carbon by terrestrial ecosystem models.

机构信息

Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.

Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.

出版信息

Glob Chang Biol. 2022 Sep;28(18):5547-5559. doi: 10.1111/gcb.16286. Epub 2022 Jun 14.

Abstract

Soil heterotrophic respiration (R ) refers to the flux of CO released from soil to atmosphere as a result of organic matter decomposition by soil microbes and fauna. As one of the major fluxes in the global carbon cycle, large uncertainties still exist in the estimation of global R , which further limits our current understanding of carbon accumulation in soils. Here, we applied a Random Forest algorithm to create a global data set of soil R , by linking 761 field observations with both abiotic and biotic predictors. We estimated that global R was 48.8 ± 0.9 Pg C year for 1982-2018, which was 16% less than the ensemble mean (58.6 ± 9.9 Pg C year ) of 16 terrestrial ecosystem models. By integrating our observational R with independent soil carbon stock data sets, we obtained a global mean soil carbon turnover time of 38.3 ± 11 year. Using observation-based turnover times as a constraint, we found that terrestrial ecosystem models simulated faster carbon turnovers, leading to a 30% (74 Pg C) underestimation of terrestrial ecosystem carbon accumulation for the past century, which was especially pronounced at high latitudes. This underestimation is equivalent to 45% of the total carbon emissions (164 Pg C) caused by global land-use change at the same time. Our analyses highlight the need to constrain ecosystem models using observation-based and locally adapted R values to obtain reliable projections of the carbon sink capacity of terrestrial ecosystems.

摘要

土壤异养呼吸(R)是指土壤微生物和动物分解有机物导致 CO 释放到大气中的通量。作为全球碳循环的主要通量之一,全球 R 的估算仍然存在很大的不确定性,这进一步限制了我们对土壤碳积累的现有理解。在这里,我们应用随机森林算法通过将 761 个野外观测结果与非生物和生物预测因子联系起来,创建了一个全球土壤 R 数据集。我们估计,1982-2018 年全球 R 为 48.8 ± 0.9 Pg C 年,比 16 个陆地生态系统模型的集合平均值(58.6 ± 9.9 Pg C 年)低 16%。通过将我们的观测 R 与独立的土壤碳储量数据集相结合,我们获得了全球平均土壤碳周转时间为 38.3 ± 11 年。利用基于观测的周转时间作为约束,我们发现陆地生态系统模型模拟的碳周转更快,导致过去一个世纪陆地生态系统碳积累低估了 30%(74 Pg C),在高纬度地区尤其明显。这种低估相当于同期全球土地利用变化导致的总碳排放量(164 Pg C)的 45%。我们的分析强调需要利用基于观测和本地化的 R 值来约束生态系统模型,以获得陆地生态系统碳汇能力的可靠预测。

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