 |
| Titel |
Greenhouse gas network design using backward Lagrangian particle dispersion modelling - Part 1: Methodology and Australian test case |
| VerfasserIn |
T. Ziehn, A. Nickless, P. J. Rayner, R. M. Law, G. Roff, P. Fraser |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 14, no. 17 ; Nr. 14, no. 17 (2014-09-10), S.9363-9378 |
| Datensatznummer |
250119018
|
| Publikation (Nr.) |
 copernicus.org/acp-14-9363-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| This paper describes the generation of optimal atmospheric measurement networks for determining
carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model
is used in reverse mode together with a Bayesian inverse modelling framework to calculate the
relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil
fuel combustion, and hourly concentration observations for the
Australian continent. Meteorological driving fields are provided by the regional version of the
Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly
timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil
fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange
(CABLE) model and the Fossil Fuel Data Assimilation
System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but
proves to be negligible for the network design. Existing ground-based measurement stations in
Australia are assessed in terms of their ability to constrain local flux estimates from the
land. We find that the six stations that are currently operational are already able to reduce the
uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is
generated based on logistic constraints and an incremental optimisation scheme is used to extend
the network of existing stations. In order to achieve an uncertainty reduction of about
50%, we need to double the number of measurement stations in Australia. Assuming equal data
uncertainties for all sites, new stations would be mainly located in the northern and eastern part
of the continent. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|