What Limits Predictive Certainty of Long‐Term Carbon Uptake?
What Limits Predictive Certainty of Long‐Term Carbon Uptake?
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DOI：
10.1029/2018jg004504
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
B. Raczka;M. Dietze;S. Serbin;K. Davis
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文献类型：
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作者：
B. Raczka;M. Dietze;S. Serbin;K. Davis
陆地生物圈模型可以帮助识别控制碳动态的物理过程，包括陆地大气二氧化碳通量，并有可能预测陆地生态系统对气候变化的响应。重要的是要确定对模型预测不确定性负有最大责任的生态系统过程，并设计改进的模型表示和观测系统研究以减少这种不确定性。在这里，我们确定了对机械陆地生物圈模型（生态系统人口学，版本 2.1）ED2 内的净生态系统交换、净初级生产和地上生物量的长期（约 100 年）预测贡献最大不确定性的模型参数。不确定性分析确定了代表光到光合作用转换的量子效率、叶片呼吸和土壤-植物水转移的参数是模型不确定性的最大贡献者，无论时间范围（每年、十年和百年）和产出（例如净生态系统交换、净初级生产、地上生物量）。与预期相反，与繁殖、竞争和死亡相关的演替过程的贡献并没有随着时间尺度的增加而增加。这些发现表明，控制短期生态系统过程的参数的不确定性仍然是减少预测不确定性的最重要瓶颈。减少参数不确定性的关键行动包括跨多个位点进行更多叶级性状测量，以测量量子效率和叶呼吸速率。此外，土壤-植物水传输的经验表示应替换为水流的机械、水力表示，这可以通过直接测量来约束。该分析重点关注地上生态系统过程。未来的研究应解决地下碳循环、初始条件和气象强迫的影响。
Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land‐atmosphere CO2 fluxes, and have the potential to project the terrestrial ecosystem response to changing climate. It is important to identify ecosystem processes most responsible for model predictive uncertainty and design improved model representation and observational system studies to reduce that uncertainty. Here we identified model parameters that contribute the most uncertainty to long‐term (~100 years) projections of net ecosystem exchange, net primary production, and aboveground biomass within a mechanistic terrestrial biosphere model (Ecosystem Demography, version 2.1) ED2. An uncertainty analysis identified parameters that represent the quantum efficiency of light to photosynthetic conversion, leaf respiration and soil‐plant water transfer as the highest contributors to model uncertainty regardless of time frame (annual, decadal, and centennial) and output (e.g., net ecosystem exchange, net primary production, aboveground biomass). Contrary to expectations, the contribution of successional processes related to reproduction, competition, and mortality did not increase as the time scale increased. These findings suggest that uncertainty in the parameters governing short‐term ecosystem processes remains the most significant bottleneck to reducing predictive uncertainty. Key actions to reduce parameter uncertainty include more leaf‐level trait measurements across multiple sites for quantum efficiency and leaf respiration rate. Further, the empirical representation of soil‐plant water transfer should be replaced with a mechanistic, hydraulic representation of water flow, which can be constrained with direct measurements. This analysis focused on aboveground ecosystem processes. The impact of belowground carbon cycling, initial conditions, and meteorological forcing should be addressed in future studies.