 |
| Titel |
Variability and budget of CO2 in Europe: analysis of the CAATER airborne campaigns – Part 2: Comparison of CO2 vertical variability and fluxes between observations and a modeling framework |
| VerfasserIn |
I. Xueref-Remy, P. Bousquet, C. Carouge, L. Rivier, P. Ciais |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 11, no. 12 ; Nr. 11, no. 12 (2011-06-20), S.5673-5684 |
| Datensatznummer |
250009852
|
| Publikation (Nr.) |
 copernicus.org/acp-11-5673-2011.pdf |
|
|
|
|
|
| Zusammenfassung |
Our ability to predict future climate change relies on our understanding of
current and future CO2 fluxes, particularly on a regional scale
(100–1000 km). CO2 regional sources and sinks are still poorly
understood. Inverse transport modeling, a method often used to quantify
these fluxes, relies on atmospheric CO2 measurements. One of the main
challenges for the transport models used in the inversions is to properly
reproduce CO2 vertical gradients between the boundary layer and the
free troposphere, as these gradients impact on the partitioning of the
calculated fluxes between the different model regions. Vertical CO2
profiles are very well suited to assess the performances of the models. In
this paper, we conduct a comparison between observed and modeled CO2
profiles recorded during two CAATER campaigns that occurred in May 2001 and
October 2002 over Western Europe, as described in a companion paper. We test
different combinations between a global transport model (LMDZt), a mesoscale
transport model (CHIMERE), and different sets of biospheric fluxes, all
chosen with a diurnal cycle (CASA, SiB2 and ORCHIDEE). The vertical profile
comparison shows that: 1) in most cases the influence of the biospheric flux
is small but sometimes not negligible, ORCHIDEE giving the best results in
the present study; 2) LMDZt is most of the time too diffuse, as it
simulates a too high boundary layer height; 3) CHIMERE better reproduces
the observed gradients between the boundary layer and the free troposphere,
but is sometimes too variable and gives rise to incoherent structures. We
conclude there is a need for more vertical profiles to conduct further
studies to improve the parameterization of vertical transport in the models
used for CO2 flux inversions.
Furthermore, we use a modeling method to quantify CO2 fluxes at the
regional scale from a chosen observing point, coupling influence functions
from the transport model LMDZt (that works quite well at the synoptic scale)
with information on the space-time distribution of fluxes. This modeling
method is compared to a dual tracer method (the so-called Radon method) for
a case study on 25 May 2001 during which simultaneous well-correlated in situ
CO2 and Radon 222 measurements have been collected. Both methods give a
similar result: a flux within the Radon 222 method uncertainty (35%),
that is an atmospheric CO2 sink of −4.2 to −4.4 gC m−2 day−1.
We have estimated the uncertainty of the modeling method to be at least
33% on average, and even more for specific individual events. This method
allows the determination of the area that contributed to the CO2
observed concentration. In our case, the observation point located at
1700 m a.s.l. in the north of France, is influenced by an area of
1500×700 km2 that covers the Benelux region, part of Germany and western Poland.
Furthermore, this method allows deconvolution between the different
contributing fluxes. In this case study, the biospheric sink contributes
73% of the total flux, fossil fuel emissions for 27%, the oceanic flux
being negligible. However, the uncertainties of the influence function
method need to be better assessed. This could be possible by applying it to
other cases where the calculated fluxes can be checked independently, for
example at tall towers where simultaneous CO2 and Radon 222
measurements can be conducted. The use of optimized fluxes (from atmospheric
inversions) and of mesoscale models for atmospheric transport may also
significantly reduce the uncertainties. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|