美国加州大学Jin, Yuming小组对南大洋三个纬度带的海气CO2交换进行了改善预估。2024年1月30日出版的《美国科学院院刊》杂志发表了这项成果。

研究人员称，通量估算显示了以往基于地表CO₂测量值反演的估算没有明确解决的两个特征：极地地区冬季除气作用弱，以及极地/亚极地和亚热带地区之间季节性通量周期的急剧相变。这些估算值表明，夏季极地/亚极地区域的吸收量高于通过神经网络插值分析浮点获得的PCO₂数据估计值，但还是比最近的研究结果的吸收量要弱一些，他们使用相同的空中数据和多大气传输模型(ATMs)来限制地表通量。

研究还使用了从再分析中得出的湿静态能量(MSE)预算，表明在南半球高纬度地区的夏季，大多数大气环流模式往往会有过度的非绝热混合(跨越潮湿等熵线、θe或Mθe表面的输送)，这引起了对海气CO₂交换的估计存在偏差。此外，该研究发现，基于潮湿静态能量的限制与基于空气和地面CO₂观测相结合的大气混合独立限制是一致的。

附：英文原文

Title: Improved atmospheric constraints on Southern Ocean CO₂ exchange

Author: Jin, Yuming, Keeling, Ralph F., Stephens, Britton B., Long, Matthew C., Patra, Prabir K., Rdenbeck, Christian, Morgan, Eric J., Kort, Eric A., Sweeney, Colm

Issue&Volume: 2024-1-30

Abstract: We present improved estimates of air–sea CO₂ exchange over three latitude bands of the Southern Ocean using atmospheric CO₂ measurements from global airborne campaigns and an atmospheric 4-box inverse model based on a mass-indexed isentropic coordinate (Mθe). These flux estimates show two features not clearly resolved in previous estimates based on inverting surface CO₂ measurements: a weak winter-time outgassing in the polar region and a sharp phase transition of the seasonal flux cycles between polar/subpolar and subtropical regions. The estimates suggest much stronger summer-time uptake in the polar/subpolar regions than estimates derived through neural-network interpolation of pCO₂ data obtained with profiling floats but somewhat weaker uptake than a recent study by Long et al. [Science 374, 1275–1280 (2021)], who used the same airborne data and multiple atmospheric transport models (ATMs) to constrain surface fluxes. Our study also uses moist static energy (MSE) budgets from reanalyses to show that most ATMs tend to have excessive diabatic mixing (transport across moist isentrope, θe, or Mθe surfaces) at high southern latitudes in the austral summer, which leads to biases in estimates of air–sea CO₂ exchange. Furthermore, we show that the MSE-based constraint is consistent with an independent constraint on atmospheric mixing based on combining airborne and surface CO₂ observations.

DOI: 10.1073/pnas.2309333121

Source: https://www.pnas.org/doi/abs/10.1073/pnas.2309333121