EGU2020-4312
https://doi.org/10.5194/egusphere-egu2020-4312
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparison of Regional Simulation of Biospheric CO2 Flux from the Updated Version of CarbonTracker Asia with FLUXCOM and Other Inversions over Asia

Samuel Takele Kenea, Lev Labzovskii, Tae‐Young Goo, Shanlan Li, Young‐Suk Oh, and Young‐Hwa Byun
Samuel Takele Kenea et al.
  • National Institute of Meteorological Sciences (NIMS), Jeju-do, Korea, Climate Research Division, Korea, Republic of (samueltake@yahoo.ca)

There are still large uncertainties in the estimates of net ecosystem exchange of CO2
(NEE) with atmosphere in Asia, particularly in the boreal and eastern part of temperate Asia. To
understand these uncertainties, we assessed the CarbonTracker Asia (CTA2017) estimates of the
spatial and temporal distributions of NEE through a comparison with FLUXCOM and the global
inversion models from the Copernicus Atmospheric Monitoring Service (CAMS), Monitoring
Atmospheric Composition and Climate (MACC), and Jena CarboScope in Asia, as well as
examining the impact of the nesting approach on the optimized NEE flux during the 2001–2013
period. The long‐term mean carbon uptake is reduced in Asia, which is −0.32 ± 0.22 PgC yr‐1,
whereas –0.58 ± 0.26 PgC yr‐1 is shown from CT2017 (CarbonTracker global). The domain
aggregated mean carbon uptake from CTA2017 is found to be lower by 23.8%, 44.8%, and 60.5%
than CAMS, MACC, and Jena CarboScope, respectively. For example, both CTA2017 and CT2017
models captured the interannual variability (IAV) of the NEE flux with a different magnitude and
this leads to divergent annual aggregated results. Differences in the estimated interannual
variability of NEE in response to El Niño–Southern Oscillation (ENSO) may result from
differences in the transport model resolutions. These inverse models’ results have a substantial
difference compared to FLUXCOM, which was found to be –5.54 PgC yr‐1. On the one hand, we
showed that the large NEE discrepancies between both inversion models and FLUXCOM stem
mostly from the tropical forests. On the other hand, CTA2017 exhibits a slightly better correlation
with FLUXCOM over grass/shrub, fields/woods/savanna, and mixed forest than CT2017. The land
cover inconsistency between CTA2017 and FLUXCOM is therefore one driver of the discrepancy in
the NEE estimates. The diurnal averaged NEE flux between CTA2017 and FLUXCOM exhibits
better agreement during the carbon uptake period than the carbon release period. Both CTA2017
and CT2017 revealed that the overall spatial patterns of the carbon sink and source are similar, but
the magnitude varied with seasons and ecosystem types, which is mainly attributed to differences
in the transport model resolutions. Our findings indicate that substantial inconsistencies in the
inversions and FLUXCOM mainly emerge during the carbon uptake period and over tropical
forests. The main problems are underrepresentation of FLUXCOM NEE estimates by limited eddy
covariance flux measurements, the role of CO2 emissions from land use change not accounted for
by FLUXCOM, sparseness of surface observations of CO2 concentrations used by the assimilation
systems, and land cover inconsistency. This suggested that further scrutiny on the FLUXCOM and
inverse estimates is most likely required. Such efforts will reduce inconsistencies across various
NEE estimates over Asia, thus mitigating ecosystem‐driven errors that propagate the global
carbon budget. Moreover, this work also recommends further investigation on how the
changes/updates made in CarbonTracker affect the interannual variability of the aggregate and
spatial pattern of NEE flux in response to the ENSO effect over the region of interest.

How to cite: Takele Kenea, S., Labzovskii, L., Goo, T., Li, S., Oh, Y., and Byun, Y.: Comparison of Regional Simulation of Biospheric CO2 Flux from the Updated Version of CarbonTracker Asia with FLUXCOM and Other Inversions over Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4312, https://doi.org/10.5194/egusphere-egu2020-4312, 2020