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DOI10.5194/acp-22-9215-2022
Estimated regional CO2 flux and uncertainty based on an ensemale of atmospheric CO2 inversions
Chandra, Naveen; Patra, Prabir K.; Niwa, Yousuke; Ito, Akihiko; Iida, Yosuke; Goto, Daisuke; Morimoto, Shinji; Kondo, Masayuki; Takigawa, Masayuki; Hajima, Tomohiro; Watanabe, Michio
发表日期2022
ISSN1680-7316
EISSN1680-7324
起始页码9215
结束页码9243
卷号22期号:14页码:29
英文摘要Global and regional sources and sinks of carbon across the earth's surface have been studied extensively using atmospheric carbon dioxide (CO2) observations and atmospheric chemistry-transport model (ACTM) simulations (top-down/inversion method). However, the uncertainties in the regional flux distributions remain unconstrained due to the lack of high-quality measurements, uncertainties in model simulations, and representation of data and flux errors in the inversion systems. Here, we assess the representation of data and flux errors using a suite of 16 inversion cases derived from a single transport model (MIROC4-ACTM) but different sets of a priori (bottom-up) terrestrial biosphere and oceanic fluxes, as well as prior flux and observational data uncertainties (50 sites) to estimate CO2 fluxes for 84 regions over the period 2000-2020. The inversion ensembles provide a mean flux field that is consistent with the global CO2 growth rate, land and ocean sink partitioning of -2.9 +/- 0.3 (+/- 1 sigma uncertainty on the ensemble mean) and -1.6 +/- 0.2 PgC yr(-1), respectively, for the period 2011-2020 (without riverine export correction), offsetting about 22%-33% and 16%-18% of global fossil fuel CO2 emissions. The rivers carry about 0.6 PgC yr(-1) of land sink into the deep ocean, and thus the effective land and ocean partitioning is -2.3 +/- 0.3 and -2.2 +/- 0.3, respectively. Aggregated fluxes for 15 land regions compare reasonably well with the best estimations for the 2000s (similar to 2000-2009), given by the REgional Carbon Cycle Assessment and Processes (RECCAP), and all regions appeared as a carbon sink over 2011-2020. Interannual variability and seasonal cycle in CO2 fluxes are more consistently derived for two distinct prior fluxes when a greater degree of freedom (increased prior flux uncertainty) is given to the inversion system. We have further evaluated the inversion fluxes using meridional CO2 distributions from independent (not used in the inversions) aircraft and surface measurements, suggesting that the ensemble mean flux (model-observation mean +/- 1 sigma standard deviation = -0.3 +/- 3 ppm) is best suited for global and regional CO2 flux budgets than an individual inversion (model-observation 1 sigma standard deviation = -0.35 +/- 3.3 ppm). Using the ensemble mean fluxes and uncertainties for 15 land and 11 ocean regions at 5-year intervals, we show promise in the capability to track flux changes toward supporting the ongoing and future CO2 emission mitigation policies.
学科领域Environmental Sciences; Meteorology & Atmospheric Sciences
语种英语
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号WOS:000826518800001
来源期刊ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/273332
作者单位Japan Agency for Marine-Earth Science & Technology (JAMSTEC); Chiba University; Tohoku University; National Institute for Environmental Studies - Japan; Japan Meteorological Agency; Research Organization of Information & Systems (ROIS); National Institute of Polar Research (NIPR) - Japan; Nagoya University; University of Tokyo
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Chandra, Naveen,Patra, Prabir K.,Niwa, Yousuke,et al. Estimated regional CO2 flux and uncertainty based on an ensemale of atmospheric CO2 inversions[J],2022,22(14):29.
APA Chandra, Naveen.,Patra, Prabir K..,Niwa, Yousuke.,Ito, Akihiko.,Iida, Yosuke.,...&Watanabe, Michio.(2022).Estimated regional CO2 flux and uncertainty based on an ensemale of atmospheric CO2 inversions.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(14),29.
MLA Chandra, Naveen,et al."Estimated regional CO2 flux and uncertainty based on an ensemale of atmospheric CO2 inversions".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.14(2022):29.
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