 |
| Titel |
Turbulent transport, emissions and the role of compensating errors in chemical transport models |
| VerfasserIn |
P. A. Makar, R. Nissen, A. Teakles, J. Zhang, Q. Zheng, M. D. Moran, H. Yau, C. diCenzo |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1991-959X
|
| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 7, no. 3 ; Nr. 7, no. 3 (2014-05-27), S.1001-1024 |
| Datensatznummer |
250115626
|
| Publikation (Nr.) |
 copernicus.org/gmd-7-1001-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| The balance between turbulent transport and emissions is a key issue in
understanding the formation of O3 and particulate matter with diameters less than 2.5 μm (PM2.5). Discrepancies between
observed and simulated concentrations for these species have, in the past,
been ascribed to insufficient turbulent mixing, particularly for
atmospherically stable environments. This assumption may be simplistic –
turbulent mixing deficiencies may explain only part of these discrepancies,
and as turbulence parameterizations are improved, the timing of primary
PM2.5 emissions may play a much more significant role in the further
reduction of model error. In a study of these issues, two regional
air-quality models, the Community Multi-scale Air Quality model (CMAQ, version 4.6) and A Unified Regional Air-quality Modelling System (AURAMS, version 1.4.2), were
compared to observations for a domain in north-western North America.
The air-quality models made use of the same emissions inventory, emissions
processing system, meteorological driving model, and model domain, map
projection and horizontal grid, eliminating these factors as potential
sources of discrepancies between model predictions. The initial statistical
comparison between the models and monitoring network data showed that
AURAMS' O3 simulations outperformed those of this version of CMAQ4.6,
while CMAQ4.6 outperformed AURAMS for most PM2.5 statistical measures. A
process analysis of the models revealed that many of the differences between
the models' results could be attributed to the strength of turbulent
diffusion, via the choice of an a priori lower limit in the magnitude of
vertical diffusion coefficients, with AURAMS using
0.1 m2 s−1 and
CMAQ4.6 using 1.0 m2 s−1. The use of the larger CMAQ4.6 value for
the lower limit of vertical diffusivity within AURAMS resulted in a similar
performance for the two models (with AURAMS also showing improved PM2.5,
yet degraded O3, and a similar time series as CMAQ4.6). The differences
between model results were most noticeable at night, when the higher minimum
turbulent diffusivity resulted in an erroneous secondary peak in predicted
night-time O3. A spatially invariant and relatively high lower limit in
diffusivity could not reduce errors in both O3 and PM2.5 fields,
implying that other factors aside from the strength of turbulence might be
responsible for the PM2.5 over-predictions. Further investigation showed
that the magnitude, timing and spatial allocation of area source emissions
could result in improvements to PM2.5 performance with minimal O3
performance degradation. AURAMS was then used to investigate a
land-use-dependant lower limit in diffusivity of 1.0 m2 s−1 in
urban regions, linearly scaling to 0.01 m2s−1 in rural areas, as
employed in CMAQ5.0.1. This strategy was found to significantly improve mean
statistics for PM2.5 throughout the day and mean O3 statistics at
night, while significantly degrading (halving) midday PM2.5 correlation
coefficients and slope of observed to model simulations. Time series of
domain-wide model error statistics aggregated by local hour were shown to be
a useful tool for performance analysis, with significant variations in
performance occurring at different hours of the day. The use of the
land-use-dependant lower limit in diffusivity was also shown to reduce the
model's sensitivity to the temporal allocation of its emissions inputs. The
modelling scenarios suggest that while turbulence plays a key role in O3
and PM2.5 formation in urban regions, and in their downwind transport,
the spatial and temporal allocation of primary PM2.5 emissions also has
a potentially significant impact on PM2.5 concentration levels. The
results show the complex nature of the interactions between turbulence and
emissions, and the potential of the strength of the former to mask the impact
of changes in the latter. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|