 |
| Titel |
Statistical variability of top of atmosphere cloud-free shortwave aerosol radiative effect |
| VerfasserIn |
T. A. Jones, S. A. Christopher |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 7, no. 11 ; Nr. 7, no. 11 (2007-06-12), S.2937-2948 |
| Datensatznummer |
250005040
|
| Publikation (Nr.) |
 copernicus.org/acp-7-2937-2007.pdf |
|
|
|
|
|
| Zusammenfassung |
| The statistical variability of globally averaged MODIS
aerosol optical thickness at 0.55 μm (AOT) and top of atmosphere CERES
cloud-free shortwave radiative effect (SWRE) is presented. Statistical
variability is defined as the robustness of globally averaged statistics
relative to data distribution. At the CERES footprint level, which we label
"raw data", both the AOT and SWRE data derived from clear-sky CERES-SSF
products show significant deviations from a normal distribution as evidenced
by high skewness values. The spatial and temporal distribution of the data
is also not uniform, with a greater concentration of data occurring in aerosol
heavy-regions. As a result, globally averaged AOT and SWRE are overestimated
when derived from raw data alone. To compensate, raw data are gridded into 2×2
degree grid-cells (called "gridded" data) to reduce the effect of
spatial non-uniformity. However, the underlying non-normal distribution
remains and manifests itself by increasing the uncertainty of grid-cell
values. Globally averaged AOT and SWRE derived from a gridded dataset are
substantially lower than those derived from raw data alone. The range of
globally averaged AOT and SWRE values suggests that up to a 50%
statistical variability exists, much of which is directly tied to how the
data are manipulated prior to averaging. This variability increases when
analyzing aerosol components (e.g. anthropogenic) since component AOT (and
SWRE) may not exist at all locations were AOT is present. As a result,
regions where a particular component AOT does not exist must either not be
included in the global average or have data within these regions set to null
values. However, each method produces significantly different results. The
results of this work indicate simple mean and standard deviation statistics
do not adequately describe global aerosol climate forcing data sets like the
one used here. We demonstrate that placing raw observations on to a uniform
grid is a necessary step before calculating global statistics. However, this
by no means eliminates uncertainty in globally averaged AOT and SWRE values,
while adding its own set of assumptions. When reporting any globally
averaged statistic, it is important to report corresponding distribution and
coverage information, in the form of skewness values, probability density
functions, and spatial distribution plots, to help quantify its usefulness
and robustness. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|