Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability
Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability
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DOI：
10.1029/2020gb006788
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Takano, Yohei
中科院分区：
文献类型：
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作者：
Gloege, Lucas;McKinley, Galen A.;Takano, Yohei
减少全球碳收支的不确定性需要更好地量化海洋二氧化碳吸收及其时间变率。根据pCO(2)观测重建海气CO2交换的几种方法表明，年代际变化比利用海洋模式估计的要大。我们开发了多个大集合地球系统模型的新应用，以评估这些重建估计时空变化的能力。在我们的大型集合试验台上，来自四个独立地球系统模型中的25个集合成员的pCO(2)场被作为观测结果进行了次采样，并像与真实世界的观测一样进行了重建。试验台的强大之处在于，每个原始模型域的完美重建都是已知的；因此，可以全面评估重建技能。我们发现，当有足够的数据训练时，神经网络方法可以熟练地重建空气-海洋CO2通量。全球平均和北半球的通量偏倚较低，但南半球的区域偏倚可能较高。在热带以外的地区，季节周期的相位和幅度可以精确地重建，但较长期的变化只能用一般的技巧重建。对于南大洋的年代际变率，采样不足导致幅度高估了31%（15%:58%，四分位间距），而相位与已知真实值仅适度相关（r = 0.54[0.46:0.63]）。在全球范围内，年代际变化幅度被高估了21%（3%:34%）。当提供足够的数据时，机器学习可以熟练地重建海洋属性。然而，数据稀疏仍然是海洋碳汇年代际变化量化的一个基本限制。
Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from pCO(2) observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions' ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO(2) fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real-world observations. The power of a testbed is that the perfect reconstruction is known for each of the original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a neural-network approach can skillfully reconstruct air-sea CO2 fluxes when it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 31% (15%:58%, interquartile range) overestimation of amplitude, and phasing is only moderately correlated with known truth (r = 0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% (3%:34%). Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.