Existing, state-of-the-art vegetation models disagree by a factor of four on the seasonal amplitude of the global, terrestrial carbon cycle. This seasonal amplitude is likely increasing over time due to climate change, and disagreements among vegetation models therefore complicate efforts to quantify how climate change is impacting the carbon cycle. We evaluate the seasonal cycle of terrestrial CO fluxes from an ensemble of vegetation models using CO observations from the Orbiting Carbon Observatory-2 (OCO-2), in situ CO observations, and inverse models. We find that vegetation models with a larger seasonal amplitude are also more sensitive to climate change, in that they exhibit a larger increase in amplitude during the past century. Furthermore, ten of the 17 models analyzed have a seasonal amplitude smaller than an ensemble of inverse CO flux estimates based on OCO-2 observations; these discrepancies are largest across the Eastern US, boreal Asia, the Congo, and the Amazon. Vegetation models with larger seasonal amplitudes, when run through an atmospheric transport model (i.e. GEOS-Chem), typically exhibit a better fit compared to atmospheric CO observations. We also find that vegetation models produce similar seasonal amplitudes of net CO fluxes using very different combinations of gross primary production and respiration, making these model disagreements challenging to resolve.

中文翻译：

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轨道碳观测站 2 (OCO-2) 和原位二氧化碳数据表明，与许多动态全球植被模型相比，陆地碳循环的季节性幅度更大


现有的最先进的植被模型与全球陆地碳循环的季节性幅度的差异是四倍。由于气候变化，这种季节性幅度可能会随着时间的推移而增加，因此植被模型之间的分歧使量化气候变化如何影响碳循环的工作变得更加复杂。我们使用轨道碳观测站 2 (OCO-2) 的 CO 观测、原位 CO 观测和反演模型，评估了植被模型集合中陆地 CO 通量的季节性循环。我们发现，具有较大季节性幅度的植被模型对气候变化也更加敏感，因为它们在过去一个世纪中表现出较大的幅度增加。此外，分析的 17 个模型中有 10 个的季节性幅度小于基于 OCO-2 观测的逆 CO 通量估计值的集合；这些差异在美国东部、北亚、刚果和亚马逊地区最为严重。当通过大气传输模型（即 GEOS-Chem）运行时，具有较大季节性幅度的植被模型通常比大气 CO 观测表现出更好的拟合效果。我们还发现，植被模型使用非常不同的总初级生产和呼吸组合产生相似的净二氧化碳通量季节性幅度，这使得这些模型分歧难以解决。