Bibliographic Details
| Title: |
Simulating spatiotemporal variations of atmospheric CO2 using a nested hemispheric model |
| Authors: |
Geels, C.1,2 cag@dmu.dk, Christensen, J.H.2, Frohn, L.M.2, Brandt, J.2 |
| Source: |
Physics & Chemistry of the Earth - Parts A/B/C. 2002, Vol. 27 Issue 35, p1495. 11p. |
| Subjects: |
Eulerian graphs, Carbon dioxide |
| Abstract: |
A three-dimensional Eulerian nested model for transport, diffusion, and surface fluxes of CO2 has been developed. The model is used to study the spatial and temporal variations of atmospheric CO2 concentrations as a step towards a better understanding of the regional source pattern over Europe. The influence from the national surface characteristics, as e.g. fossil CO2 emissions or ecosystem fluxes, can be treated on a continental or national level in the model. In the future the model can be used as a mean to corroborate national and EU-wide controls or reductions of carbon emissions. One of the major questions in the estimation of e.g. national CO2 budgets is the quantification of surface ecosystem fluxes. In order to test the sensitivity of atmospheric CO2 concentrations to ecosystem fluxes, two different parameterizations [J. Geophys. Res. 92 (1987) 2999] and [Global Biogeochem. Cycles 10 (1996) 269] have been implemented in the model. The model both with and without nest is validated by comparison with measurements and the different model results will be discussed. The results show that including a nested domain with higher spatial resolution significantly improves the model performance in some areas. In spite of regional deviations in the two sets of ecosystems fluxes no major differences are seen over Europe as a whole when either of the two parameterizations are included in the model and the results compared. [Copyright &y& Elsevier] |
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Copyright of Physics & Chemistry of the Earth - Parts A/B/C is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
