Regional CO2 Inversion Through Ensemble-Based Simultaneous State and Parameter Estimation : TRACE Framework and Controlled Experiments
(2023) In Journal of Advances in Modeling Earth Systems 15(3).- Abstract
Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next-generation CO2-observing satellites, providing both an opportunity and a challenge for inversions. This study introduces the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, which employ an ensemble-based simultaneous state and parameter estimation (ESSPE) approach to enable the assimilation of large volumes of observations for constraining CO2 flux parameters. TRACE uses an online... (More)
Atmospheric inversions provide estimates of carbon dioxide (CO2) fluxes between the surface and atmosphere based on atmospheric CO2 concentration observations. The number of CO2 observations is projected to increase severalfold in the next decades from expanding in situ networks and next-generation CO2-observing satellites, providing both an opportunity and a challenge for inversions. This study introduces the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, which employ an ensemble-based simultaneous state and parameter estimation (ESSPE) approach to enable the assimilation of large volumes of observations for constraining CO2 flux parameters. TRACE uses an online full-physics mesoscale atmospheric model and assimilates observations serially in a coupled atmosphere–carbon ensemble Kalman filter. The data assimilation system was tested in a series of observing system simulation experiments using in situ observations for a regional domain over North America in summer. Under ideal conditions with known prior flux parameter error covariances, TRACE reduced the error in domain-integrated monthly CO2 fluxes by about 97% relative to the prior flux errors. In a more realistic scenario with unknown prior flux error statistics, the corresponding relative error reductions ranged from 80.6% to 88.5% depending on the specification of prior flux parameter error correlations. For regionally integrated fluxes on a spatial scale of 106 km2, the sum of absolute errors was reduced by 34.5%–50.9% relative to the prior flux errors. Moreover, TRACE produced posterior uncertainty estimates that were consistent with the true errors. These initial experiments show that the ESSPE approach in TRACE provides a promising method for advancing CO2 inversion techniques.
(Less)
- author
- Chen, Hans W. LU ; Zhang, Fuqing ; Lauvaux, Thomas ; Scholze, Marko LU ; Davis, Kenneth J. and Alley, Richard B.
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- CO, data assimilation, ensemble methods, flux estimation, inverse modeling, Kalman filter
- in
- Journal of Advances in Modeling Earth Systems
- volume
- 15
- issue
- 3
- article number
- e2022MS003208
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85151520344
- ISSN
- 1942-2466
- DOI
- 10.1029/2022MS003208
- language
- English
- LU publication?
- yes
- id
- f6d62d9c-f498-475c-acdc-a8c2d8139ca0
- date added to LUP
- 2023-05-23 11:31:53
- date last changed
- 2023-10-11 09:22:38
@article{f6d62d9c-f498-475c-acdc-a8c2d8139ca0,
abstract = {{<p>Atmospheric inversions provide estimates of carbon dioxide (CO<sub>2</sub>) fluxes between the surface and atmosphere based on atmospheric CO<sub>2</sub> concentration observations. The number of CO<sub>2</sub> observations is projected to increase severalfold in the next decades from expanding in situ networks and next-generation CO<sub>2</sub>-observing satellites, providing both an opportunity and a challenge for inversions. This study introduces the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, which employ an ensemble-based simultaneous state and parameter estimation (ESSPE) approach to enable the assimilation of large volumes of observations for constraining CO<sub>2</sub> flux parameters. TRACE uses an online full-physics mesoscale atmospheric model and assimilates observations serially in a coupled atmosphere–carbon ensemble Kalman filter. The data assimilation system was tested in a series of observing system simulation experiments using in situ observations for a regional domain over North America in summer. Under ideal conditions with known prior flux parameter error covariances, TRACE reduced the error in domain-integrated monthly CO<sub>2</sub> fluxes by about 97% relative to the prior flux errors. In a more realistic scenario with unknown prior flux error statistics, the corresponding relative error reductions ranged from 80.6% to 88.5% depending on the specification of prior flux parameter error correlations. For regionally integrated fluxes on a spatial scale of 10<sup>6</sup> km<sup>2</sup>, the sum of absolute errors was reduced by 34.5%–50.9% relative to the prior flux errors. Moreover, TRACE produced posterior uncertainty estimates that were consistent with the true errors. These initial experiments show that the ESSPE approach in TRACE provides a promising method for advancing CO<sub>2</sub> inversion techniques.</p>}},
author = {{Chen, Hans W. and Zhang, Fuqing and Lauvaux, Thomas and Scholze, Marko and Davis, Kenneth J. and Alley, Richard B.}},
issn = {{1942-2466}},
keywords = {{CO; data assimilation; ensemble methods; flux estimation; inverse modeling; Kalman filter}},
language = {{eng}},
number = {{3}},
publisher = {{Wiley-Blackwell}},
series = {{Journal of Advances in Modeling Earth Systems}},
title = {{Regional CO<sub>2</sub> Inversion Through Ensemble-Based Simultaneous State and Parameter Estimation : TRACE Framework and Controlled Experiments}},
url = {{http://dx.doi.org/10.1029/2022MS003208}},
doi = {{10.1029/2022MS003208}},
volume = {{15}},
year = {{2023}},
}