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碳循环信心与不确定性:探究土壤生物地球化学模型的变化。

Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models.

机构信息

Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO, USA.

Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA.

出版信息

Glob Chang Biol. 2018 Apr;24(4):1563-1579. doi: 10.1111/gcb.13979. Epub 2017 Nov 27.

Abstract

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20 century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

摘要

人们对导致土壤有机质稳定和分解的因素的新认识正在被应用于各种领域,但需要新的工具来促进对土壤生物地球化学理论和模型的理解、评估和改进,从区域到全球范围都是如此。为了分离模型结构不确定性对土壤碳储量和周转率全球分布的影响,我们开发了一个土壤生物地球化学测试床,该测试床强制使用三种不同的土壤模型,这些模型具有一致的气候和植物生产力输入。这里测试的模型包括一个一阶、微生物隐含方法(CASA-CNP),以及两个最近开发的可以在全球范围内运行的微生物显式模型(MIMICS 和 CORPSE)。当用常见的环境驱动因素来强制模拟时,土壤模型产生了相似的初始土壤碳储量估计值(全球约 1400Pg C,0-100cm),但每个模型都显示出了平均年温度与推断周转率之间的不同功能关系。随后,这些模型对这些土壤碳储量在 20 世纪的命运做出了不同的预测,在 1901 年至 2010 年间,全球有 20 多个 Pg C 的模型要么增加,要么减少。改变输入、温度和湿度的单一强制实验表明,与模型之间的直接温度不确定性相比,与冻融过程相关的不确定性以及土壤质地对土壤碳稳定的影响更大。最后,模型对季节异养呼吸速率的时间和幅度产生了不同的预测,这再次反映了与环境敏感性和土壤有机质物理化学稳定相关的结构不确定性。通过提供一个可计算的、数值一致的框架来评估模型,我们旨在更好地理解模型之间的不确定性,并深入了解调节土壤有机质周转的因素。

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