Inverse modelling method named Maximum likelihood Ensemble Filter (MLEF) was used to estimate gridded surface CO fluxes using continuous, flask and Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) data for the years 2009-2011. Here, MLEF coupled with Parametric Chemistry Transport Model (PCTM) driven by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) weather data has been used. Flux estimation was done by solving separate multiplicative biases for photosynthesis, respiration, and air-sea gas exchange fluxes. Hourly land fluxes derived from Simple Biosphere-version 3 (SiB3) model, Takahashi ocean fluxes and Brenkert fossil fuel emissions were used as the prior fluxes. The inversion was carried out by assimilating hourly CO observations, According to this study, North America showed about 60-80% uncertainty reduction while the Asian and European regions showed moderate results with 50-60% uncertainty reduction. Most other land and oceanic regions showed less than 30% uncertainty reduction. The results were mainly compared with well-known CarbonTracker and some parallel inversion studies by considering long-term averages of the estimated fluxes for the TransCom regions. Boreal North America, Temperate North America and Australia showed similar annual averages in each case. Tropical Asia and Europe showed comparable results with all other studies except for the CarbonTracker. The biases were poorly constrained in the regions having few measurement sites like South America, Africa and Eurasian Temperate which showed completely different result with other studies.

Terrestrial biosphere models (TBMs) play a key role in detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon-climate system. TBM evaluation commonly involves direct comparison to eddy-covariance flux measurements. This study uses atmospheric CO2 mole fraction ([CO2]) measured in situ from aircraft and tower, in addition to flux-measurements from summer 2016 to evaluate the CASA TBM. WRF-Chem is used to simulate [CO2] using biogenic CO2 fluxes from a CASA parameter-based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO2 boundary condition ensembles. The resulting “super ensemble” of modeled [CO2] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO2] data show that the CASA ensemble net ecosystem exchange (NEE) of CO2 is biased high (NEE too positive) and identify the maximum light use efficiency Emax a key parameter that drives the spread of the CASA ensemble. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements indicates that modeled [CO2] biases are mainly due to missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilates observed [CO2]. Flux tower analyses, however, reveal unrealistic overestimation of nighttime respiration in CT2017.