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Estimation of Global Scale Carbon Fluxes Using Maximum Likelihood Ensemble Filter
  • +11
  • Manjula Perera,
  • Ravindra Lokupitiya,
  • Scott Denning,
  • Prabir K. Patra,
  • Dusanka Zupanski,
  • Milija Zupanski,
  • Gayan Meegama,
  • Erandi Y. Lokupitiya,
  • Ian Baker,
  • David Baker,
  • T. Machida,
  • Hidekazu Matsueda,
  • Yousuke Sawa,
  • Yosuke Niwa
Manjula Perera
University of Sri Jayewardenepura

Corresponding Author:[email protected]

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Ravindra Lokupitiya
University of Sri Jayewardenepura
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Scott Denning
Colorado State University
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Prabir K. Patra
JAMSTEC
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Dusanka Zupanski
Spire Global, Inc.
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Milija Zupanski
Colorado State University
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Gayan Meegama
University of Sri Jayewardenepura
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Erandi Y. Lokupitiya
University of Colombo
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Ian Baker
Colorado State University
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David Baker
Colorado State University
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T. Machida
National Institute for Environmental St.
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Hidekazu Matsueda
Meteorological Research Institute
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Yousuke Sawa
Japan Meteorological Agency
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Yosuke Niwa
National Institute for Environmental Studies
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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.