Estimation of Global Scale Carbon Fluxes Using Maximum Likelihood Ensemble Filter Article Swipe
Manjula Perera
,
R. S. Lokupitiya
,
Scott Denning
,
Prabir K. Patra
,
D. Zupanski
,
Milija Županski
,
Gayan Meegama
,
Erandi Y. Lokupitiya
,
Ian Baker
,
D. F. Baker
,
Toshinobu Machida
,
Hidekazu Matsueda
,
Yousuke Sawa
,
Yosuke Niwa
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/essoar.10503686.1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/essoar.10503686.1
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.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1002/essoar.10503686.1
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3101441088
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https://openalex.org/W3101441088Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/essoar.10503686.1Digital Object Identifier
- Title
-
Estimation of Global Scale Carbon Fluxes Using Maximum Likelihood Ensemble FilterWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-07-28Full publication date if available
- Authors
-
Manjula Perera, R. S. Lokupitiya, Scott Denning, Prabir K. Patra, D. Zupanski, Milija Županski, Gayan Meegama, Erandi Y. Lokupitiya, Ian Baker, D. F. Baker, Toshinobu Machida, Hidekazu Matsueda, Yousuke Sawa, Yosuke NiwaList of authors in order
- Landing page
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https://doi.org/10.1002/essoar.10503686.1Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1002/essoar.10503686.1Direct OA link when available
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Biosphere, Environmental science, Boreal, Temperate climate, Biosphere model, Climatology, Inversion (geology), Atmospheric sciences, Data assimilation, Meteorology, Multiplicative function, Geography, Mathematics, Structural basin, Geology, Ecology, Archaeology, Paleontology, Mathematical analysis, BiologyTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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