Submitted as: development and technical paper
21 Feb 2023
Submitted as: development and technical paper |  | 21 Feb 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

Assimilating the dynamic spatial gradient of a bottom-up carbon flux estimation as a unique observation in COLA (v2.0)

Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han

Abstract. Atmospheric inversion of high spatiotemporal surface CO2 flux without dynamic constraints and sufficient observations is an ill-posed problem, and a priori flux from a "bottom-up" estimation is commonly used in "top-down" inversion systems for regularization purposes. Ensemble Kalman filter-based inversion algorithms usually weigh a priori flux to the background or directly replace the background with the a priori flux. However, the "bottom-up" flux estimations, especially the simulated terrestrial-atmosphere CO2 exchange, are usually systematically biased at different spatiotemporal scales because of the deficiencies in understanding of some underlying processes. Here, we introduced a novel regularization algorithm into the Carbon in Ocean‒Land‒Atmosphere (COLA) data assimilation system, which assimilates a priori information as a unique observation (AAPO). The a priori information is not limited to "bottom-up" flux estimation. With the comprehensive assimilation regularization approach, COLA can apply the spatial gradient of the "bottom-up" flux estimation as a priori information to reduce the bias impact and enhance the dynamic information concerning the a priori "bottom-up" flux estimation. Benefiting from the enhanced signal-to-noise ratio in the spatial gradient, the global, regional, and grided flux estimations using the AAPO algorithm are significantly better than those obtained by the traditional regularization approach, especially over highly uncertain tropical regions in the context of observing simulation system experiments (OSSEs). We suggest that the AAPO algorithm can be applied to other greenhouse gas (e.g., CH4, NO2) and pollutant data assimilation studies.

Zhiqiang Liu et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-15', Anonymous Referee #1, 16 Apr 2023
    • AC1: 'Reply on RC1', Zhiqiang Liu, 19 Jul 2023
  • RC2: 'Comment on gmd-2023-15', Anonymous Referee #2, 28 Apr 2023
    • AC2: 'Reply on RC2', Zhiqiang Liu, 19 Jul 2023

Zhiqiang Liu et al.

Model code and software

Assimilating a priori as special observation (AAPO) algorithm Zhiqiang Liu, Ning Zeng, and Yun Liu

Zhiqiang Liu et al.


Total article views: 655 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
522 117 16 655 5 5
  • HTML: 522
  • PDF: 117
  • XML: 16
  • Total: 655
  • BibTeX: 5
  • EndNote: 5
Views and downloads (calculated since 21 Feb 2023)
Cumulative views and downloads (calculated since 21 Feb 2023)

Viewed (geographical distribution)

Total article views: 610 (including HTML, PDF, and XML) Thereof 610 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 21 Oct 2023
Short summary
We introduced a novel algorithm that assimilates a better a priori knowledge to improve the estimation of global surface carbon flux. The algorithm aims at separating the first-order systematic biases in the a priori "bottom-up" flux estimations out of the inversion framework from a comprehensive data assimilation perspective.