 |
| Titel |
Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations |
| VerfasserIn |
Y. Qian, C. N. Long, H. Wang, J. M. Comstock, S. A. McFarlane, S. Xie |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1680-7316
|
| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 12, no. 4 ; Nr. 12, no. 4 (2012-02-17), S.1785-1810 |
| Datensatznummer |
250010724
|
| Publikation (Nr.) |
 copernicus.org/acp-12-1785-2012.pdf |
|
|
|
|
|
| Zusammenfassung |
Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this
study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM long-term
ground-based measurements, with a focus on the vertical structure, total
amount of cloud and its effect on cloud shortwave transmissivity.
Comparisons are performed for three climate regimes as represented by the
Department of Energy Atmospheric Radiation Measurement (ARM) sites: Southern
Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA).
Our intercomparisons of three independent measurements of CF or sky-cover
reveal that the relative differences are usually less than 10% (5%)
for multi-year monthly (annual) mean values, while daily differences are
quite significant. The total sky imager (TSI) produces smaller total cloud
fraction (TCF) compared to a radar/lidar dataset for highly cloudy days
(CF > 0.8), but produces a larger TCF value than the radar/lidar for less
cloudy conditions (CF < 0.3). The compensating errors in lower and higher CF
days result in small biases of TCF between the vertically pointing
radar/lidar dataset and the hemispheric TSI measurements as multi-year data
is averaged. The unique radar/lidar CF measurements enable us to evaluate
seasonal variation of cloud vertical structures in the GCMs.
Both inter-model deviation and model bias against observation are
investigated in this study. Another unique aspect of this study is that we
use simultaneous measurements of CF and surface radiative fluxes to diagnose
potential discrepancies among the GCMs in representing other cloud optical
properties than TCF. The results show that the model-observation and
inter-model deviations have similar magnitudes for the TCF and the
normalized cloud effect, and these deviations are larger than those in
surface downward solar radiation and cloud transmissivity. This implies that
other dimensions of cloud in addition to cloud amount, such as cloud optical
thickness and/or cloud height, have a similar magnitude of disparity as TCF
within the GCMs, and suggests that the better agreement among GCMs in solar
radiative fluxes could be a result of compensating effects from errors in
cloud vertical structure, overlap assumption, cloud optical depth and/or
cloud fraction. The internal variability of CF simulated in ensemble runs
with the same model is minimal. Similar deviation patterns between
inter-model and model-measurement comparisons suggest that the climate
models tend to generate larger biases against observations for those
variables with larger inter-model deviation.
The GCM performance in simulating the probability distribution,
transmissivity and vertical profiles of cloud are comprehensively evaluated
over the three ARM sites. The GCMs perform better at SGP than at the other
two sites in simulating the seasonal variation and probability distribution
of TCF. However, the models remarkably underpredict the TCF at SGP and cloud
transmissivity is less susceptible to the change of TCF than observed. In
the tropics, most of the GCMs tend to underpredict CF and fail to capture
the seasonal variation of CF at middle and low levels. The high-level CF is
much larger in the GCMs than the observations and the inter-model
variability of CF also reaches a maximum at high levels in the tropics,
indicating discrepancies in the representation of ice cloud associated with
convection in the models. While the GCMs generally capture the maximum CF in
the boundary layer and vertical variability, the inter-model deviation is
largest near the surface over the Arctic. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|