Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA; Center for Earth System Science, Tsinghua University, Beijing, 100084, China.
Glob Chang Biol. 2015 May;21(5):1737-51. doi: 10.1111/gcb.12766. Epub 2014 Dec 3.
Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool-dominant transfers, and the first-order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.
陆地生态系统大约封存了人为碳排放的 30%。然而,这一估计并不是直接从陆地生态系统本身的研究中推断出来的,而是从大气和海洋数据中推断出来的。这就提出了一个问题:陆地碳循环在多大程度上具有内在可预测性?本文研究了陆地碳循环的基本特性,检验了其内在可预测性,并提出了一系列未来的研究方向,以提高对经验的理解和模型的预测能力。具体来说,我们将陆地碳循环的内源性内部过程与外源性强迫变量隔离开来。内部过程在地球上所有类型的生态系统中都具有五个基本特性(即隔室化、光合作用碳输入、库间分配、供体库主导转移和一阶衰减)。这五个特性共同导致了对各种碳循环组分对外源性强迫五类的响应的可预测性的一个突现约束。未来的观测和实验研究应该集中在那些预测性较低的成分上,而模型研究则需要提高对那些预测性较高的成分的模型预测能力。我们认为,对可预测性的理解应该为未来的观测、实验和建模研究提供指导。