A joint data assimilation system (Tan-Tracker) to simultaneously estimate surface CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; fluxes and 3-D atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; concentrations from observations

X. Tian,H. Zhang,Y. Liu,Y. Fu,Z. Cai,Z. Xie,L. Feng

doi:10.5194/acp-14-13281-2014

Abstract

Abstract. We have developed a novel framework ("Tan-Tracker") for assimilating observations of atmospheric CO2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO2 concentrations and the surface CO2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO2 surface fluxes (CFs) and the atmospheric CO2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO2 surface fluxes and (2) the atmospheric CO2 transport is simulated by using the GEOS-Chem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO2 concentrations and CO2 fluxes, mainly due to the simultaneous estimation of CO2 concentrations and CFs in our Tan-Tracker data assimilation system. A experiment for assimilating the real dry-air column CO2 retrievals (XCO2) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.

Highlights

Carbon cycle data assimilation systems offer a promising new tool for CO2 surface flux (CF) inversion (e.g., Peters et al, 2005; Feng et al, 2009), which tends to yield CO2 surface flux estimates by optimally combining informationPublished by Copernicus Publications on behalf of the European Geosciences Union.X
Peters et al (2005) coupled the state-of-the-art atmospheric transport TM5 model to the ensemble square root filter (EnSRF), which forms the “CarbonTracker” data assimilation system, and its CF inversion results are fairly consistent with the majority of carbon inventories reported by the first North American State of the Carbon Cycle Report (SOCCR) (Peters et al, 2007)
The background simulations will inevitably deviate seriously from the “true” simulations due to the predetermined background CF series Fb (= 1.8FTrue). Since both the CO2 concentrations and CFs are simultaneously assimilated under the joint assimilation framework, it could largely eliminate the uncertainty of the initial CO2 concentrations on the CO2 evolution during the assimilation window and maximize the observations’ potential

Summary

Introduction

Carbon cycle data assimilation systems offer a promising new tool for CO2 surface flux (CF) inversion (e.g., Peters et al, 2005; Feng et al, 2009), which tends to yield CO2 surface flux estimates by optimally combining informationX. The ensemble Kalman filter (referred to as EnKF) has been widely adopted in carbon cycle data assimilation (e.g., Peters et al, 2007; Feng et al, 2009, 2011; Kang et al, 2012; Liu et al, 2012), largely due to its simple conceptual formulation and relative ease of implementation (Evesen, 2003). Kang et al (2011, 2012) presented a simultaneous data assimilation system of surface CO2 fluxes and atmospheric CO2 concentrations by means of the local ensemble transform Kalman filter (LETKF-CDAS). In LETKF-CDAS, the CFs were treated as part of the model states (as in Peters et al, 2005) and essentially a simple persistence dynamical model is adopted to describe the CFs’ integration. Feng et al (2009) developed an ensemble Kalman filter to estimate 8-day CO2 surface fluxes over geographical regions globally from satellite measurements of CO2

Methods

Results

Conclusion