Estimation of Global Scale Carbon Fluxes Using Maximum Likelihood
Ensemble Filter
Abstract
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.