海-气CO₂通量估算是定量评估海洋碳收支及其源汇格局时空变化的重要环节。通常，海-气CO₂通量估算是基于海表CO₂分压差（pCO₂，即表层海水CO₂分压（pCO₂sw）与大气CO₂分压（pCO₂air）之差）的测定。由于大气pCO₂相对均一恒定，因此海表pCO₂的准确测定则成为海-气CO₂通量估算的决定性因素。一般计算中的海表pCO₂数据是基于现场走航观测数据的网格化处理。其不确定性通常来自于三个方面：（1）pCO₂本身的仪器测定误差（analytical error, E_m）；（2）由于pCO₂在海表的不均一分布产生的空间变异性（spatial variance, σ²_s）；（3）由于走航采样空间覆盖率不足，以及采样站位并非均匀分布，由采样不足产生的偏差（bias resulted from under sampling, σ²_u）。

 基于遥感与现场观测数据，王桂芝等研究人员成功地建立了定量估算海表pCO₂网格化数据三种不确定性的方法。并将上述方法应用于2009年8月夏季航次东海段走航pCO₂数据。结果显示，pCO₂仪器测定误差（E_m）在0.1 – 2.2 μatm之间，仅占总不确定性的1%。空间变异性（σ²_s）在3.2 – 77.8 μatm之间，约占总不确定性的95%，其变化则从近岸向外海递减，最大值出现在长江口外附近，而最小值则位于东海外陆架区域。采样不足造成的偏差（σ²_u）与σ²_s变化趋势相同，最大值10.2 μatm，位于长江口外，最小值0.3 μatm，位于东海外陆架区域，其约占总不确定性的4%。 简言之，利用这一方法，本研究组已成功地厘清了海表pCO₂网格化数据的三个不确定性来源并进行了定量估算，由pCO₂在海表的不均一分布产生的空间变异性是产生不确定性的最主要因素。该方法对于准确估算海-气CO₂通量，进而评估海洋碳收支及其源汇格局的时空变化具有十分重要的意义。

本成果发表在Journal of Geophysical research上。Quantifying uncertainty sources in the gridded data of sea surface CO₂ partial pressure. Wang, GZ; Dai, MH; Shen, SSP; Bai, Y; Xu, Y. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2014. 119: 5181-5189.  

Abstract: The bulk uncertainty in the gridded sea surface pCO₂ data is crucial in assessing the reliability of the CO₂ flux estimated from measurements of air-sea pCO₂ difference, because atmospheric pCO₂ are relatively homogeneous and well defined. The bulk uncertainty results from three different sources: analytical error (E_m), spatial variance ( ), and the bias from undersampling ( ). Common uncertainty quantification by standard deviation may mix up the different sources of uncertainty. We have established a simple procedure to determine these three sources of uncertainty using remote sensing-derived and field-measured pCO₂ data. E_m is constrained by the analytical method and data reduction procedures.  is derived from the remotely sensed pCO₂ field.  is determined by spatial variance and the effective number of observations, considering, for the first time, the geometric bias introduced by pCO₂ sampling. This approach is applied to 1° × 1° gridded pCO₂ data collected from the East China Sea. We demonstrate that the spatial distribution of these biases is uneven and that none of them follow the same spatial trend as the standard deviation.  contributes the most to the uncertainty in gridded pCO₂ data over those grid boxes with good sampling coverage, while   dominates the total uncertainty in the grid boxes with poor sampling coverage. Application of this procedure to other parts of the global ocean will help to better define the inherent spatial variability of the pCO₂ field and thus better interpolate and/or extrapolate pCO₂ data, and eventually better constrain air-sea CO2 fluxes.

Link: http://onlinelibrary.wiley.com/doi/10.1002/2013JC009577/abstract.