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| Titel |
Multi-model ensemble simulations of tropospheric NO2 compared with GOME retrievals for the year 2000 |
| VerfasserIn |
T. P. C. Noije, H. J. Eskes, F. J. Dentener, D. S. Stevenson, K. Ellingsen, M. G. Schultz, O. Wild, M. Amann, C. S. Atherton, D. J. Bergmann, I. Bey, K. F. Boersma, T. Butler, J. Cofala, J. Drevet, A. M. Fiore, M. Gauss, D. A. Hauglustaine, L. W. Horowitz, I. S. A. Isaksen, M. C. Krol, J.-F. Lamarque, M. G. Lawrence, R. V. Martin, V. Montanaro, J.-F. Müller, G. Pitari, M. J. Prather, J. A. Pyle, A. Richter, J. M. Rodriguez, N. H. Savage, S. E. Strahan, K. Sudo, S. Szopa, M. Roozendael |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 6, no. 10 ; Nr. 6, no. 10 (2006-07-17), S.2943-2979 |
| Datensatznummer |
250004009
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| Publikation (Nr.) |
 copernicus.org/acp-6-2943-2006.pdf |
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| Zusammenfassung |
| We present a systematic comparison of tropospheric NO2 from 17 global
atmospheric chemistry models with three state-of-the-art retrievals from the
Global Ozone Monitoring Experiment (GOME) for the year 2000. The models used
constant anthropogenic emissions from IIASA/EDGAR3.2 and monthly emissions
from biomass burning based on the 1997–2002 average carbon emissions from
the Global Fire Emissions Database (GFED). Model output is analyzed at 10:30
local time, close to the overpass time of the ERS-2 satellite, and
collocated with the measurements to account for sampling biases due to
incomplete spatiotemporal coverage of the instrument. We assessed the
importance of different contributions to the sampling bias: correlations on
seasonal time scale give rise to a positive bias of 30–50% in the
retrieved annual means over regions dominated by emissions from biomass
burning. Over the industrial regions of the eastern United States, Europe
and eastern China the retrieved annual means have a negative bias with
significant contributions (between –25% and +10% of the NO2
column) resulting from correlations on time scales from a day to a month. We
present global maps of modeled and retrieved annual mean NO2 column
densities, together with the corresponding ensemble means and standard
deviations for models and retrievals. The spatial correlation between the
individual models and retrievals are high, typically in the range 0.81–0.93
after smoothing the data to a common resolution. On average the models
underestimate the retrievals in industrial regions, especially over eastern
China and over the Highveld region of South Africa, and overestimate the
retrievals in regions dominated by biomass burning during the dry season.
The discrepancy over South America south of the Amazon disappears when we
use the GFED emissions specific to the year 2000. The seasonal cycle is
analyzed in detail for eight different continental regions. Over regions
dominated by biomass burning, the timing of the seasonal cycle is generally
well reproduced by the models. However, over Central Africa south of the
Equator the models peak one to two months earlier than the retrievals. We
further evaluate a recent proposal to reduce the NOx emission factors
for savanna fires by 40% and find that this leads to an improvement of
the amplitude of the seasonal cycle over the biomass burning regions of
Northern and Central Africa. In these regions the models tend to
underestimate the retrievals during the wet season, suggesting that the soil
emissions are higher than assumed in the models. In general, the
discrepancies between models and retrievals cannot be explained by a priori profile
assumptions made in the retrievals, neither by diurnal variations in
anthropogenic emissions, which lead to a marginal reduction of the NO2
abundance at 10:30 local time (by 2.5–4.1% over Europe). Overall, there
are significant differences among the various models and, in particular,
among the three retrievals. The discrepancies among the retrievals
(10–50% in the annual mean over polluted regions) indicate that the
previously estimated retrieval uncertainties have a large systematic
component. Our findings imply that top-down estimations of NOx
emissions from satellite retrievals of tropospheric NO2 are strongly
dependent on the choice of model and retrieval. |
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