2011
DOI: 10.5194/acp-11-6607-2011
|View full text |Cite
|
Sign up to set email alerts
|

Abstract: Inverse modeling techniques used to quantify surface carbon fluxes commonly assume that the uncertainty of fossil fuel CO(2) (FFCO(2)) emissions is negligible and that intra-annual variations can be neglected. To investigate these assumptions, we analyzed the differences between four fossil fuel emission inventories with spatial and temporal differences over Europe and their impact on the model simulated CO(2) concentration. Large temporal flux variations characterize the hourly fields (similar to 40% and simi… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

6
112
0

Year Published

2014
2014
2018
2018

Publication Types

Select...
10

Relationship

1
9

Authors

Journals

citations
Cited by 87 publications
(118 citation statements)
references
References 24 publications
6
112
0
Order By: Relevance
“…Inverse model studies commonly utilize anthropogenic CO 2 emission inventories to estimate anthropogenic CO 2 and are then able to separate anthropogenic from biogenic or oceanic carbon sink and source influences. However, currently available emission inventories exhibit large discrepancies between each other of about 10-40 % at the country level (Peylin et al, 2011), and increase further with decreasing spatial scale (Gurney et al, 2005). These discrepancies suggest that biases may be on the order of about 70-100 % for highly resolved (0.1…”
Section: Introductionmentioning
confidence: 95%
“…Inverse model studies commonly utilize anthropogenic CO 2 emission inventories to estimate anthropogenic CO 2 and are then able to separate anthropogenic from biogenic or oceanic carbon sink and source influences. However, currently available emission inventories exhibit large discrepancies between each other of about 10-40 % at the country level (Peylin et al, 2011), and increase further with decreasing spatial scale (Gurney et al, 2005). These discrepancies suggest that biases may be on the order of about 70-100 % for highly resolved (0.1…”
Section: Introductionmentioning
confidence: 95%
“…There have been several studies using high-resolution mesoscale models for inversion analyses (e.g. Stroud et al, 2011;Valin et al, 2011;Klich and Fuelberg, 2014;Stock et al, 2014) with the lateral boundary conditions provided from global, coarse resolution models (e. g. Curci et al, 2010;Peylin et al, 2011). However, the consistency of boundary conditions becomes a critical issue in these regional analyses (e.g.…”
Section: Z Jiang Et Al: Regional Data Assimilation Of Multi-spectramentioning
confidence: 99%
“…In addition we performed a cross-validation of the KED MHs using the IGRA MHs and compare to independent UKMO radiosondes not part of IGRA. The second complication is more difficult to tackle, because uncertainties in prior fluxes were shown to have substantial impact on simulated CO 2 concentrations (Peylin et al, 2011). To isolate the effect of transport errors on the CO 2 concentrations we prescribed the same CO 2 fluxes for all simulations -more specifically we compare results of two model setups with different PBL parameterizations, the Yonsei University Scheme (YSU, K-diffusion, Hong et al, 2006) and the Mellor-Yamada-Janjic scheme (MYJ, Turbulent Kinetic Energy, Janjic, 2002), prescribing the same vegetation and anthropogenic CO 2 fluxes.…”
mentioning
confidence: 99%