Evaluation of autoconversion and accretion enhancement factors in general circulation model warm-rain parameterizations using ground-based measurements over the Azores
-
Published:2018-12-07
Issue:23
Volume:18
Page:17405-17420
-
ISSN:1680-7324
-
Container-title:Atmospheric Chemistry and Physics
-
language:en
-
Short-container-title:Atmos. Chem. Phys.
Author:
Wu PengORCID, Xi BaikeORCID, Dong Xiquan, Zhang ZhiboORCID
Abstract
Abstract. A great challenge in climate modeling is how to parameterize
subgrid cloud processes, such as autoconversion and accretion in warm-rain
formation. In this study, we use ground-based observations and retrievals
over the Azores to investigate the so-called enhancement factors,
Eauto and Eaccr, which are often used in climate models
to account for the influence of subgrid variance of cloud and precipitation
water on the autoconversion and accretion processes. Eauto and
Eaccr are computed for different equivalent model grid sizes. The
calculated Eauto values increase from 1.96 (30 km) to 3.2
(180 km), and the calculated Eaccr values increase from 1.53
(30 km) to 1.76 (180 km). Comparing the prescribed enhancement factors in
Morrison and Gettleman (2008, MG08) to the observed ones, we found that a
higher Eauto (3.2) at small grids and lower Eaccr (1.07)
are used in MG08, which might explain why most of the general circulation models (GCMs) produce
too-frequent precipitation events but with too-light precipitation intensity. The
ratios of the rain to cloud water mixing ratio (qr/qc) at Eaccr=1.07 and
Eaccr=2.0 are 0.063 and 0.142, respectively, from observations,
further suggesting that the prescribed value of Eaccr=1.07 used in
MG08 is too small to simulate precipitation intensity correctly. Both
Eauto and Eaccr increase when the boundary layer becomes
less stable, and the values are larger in precipitating clouds (CLWP>75 gm−2) than those in non-precipitating clouds (CLWP<75 gm−2). Therefore, the selection of Eauto and
Eaccr values in GCMs should be regime- and resolution-dependent.
Funder
Office of Science Division of Atmospheric and Geospace Sciences
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference77 articles.
1. Ahlgrimm, M. and Forbes, R.: Improving the Representation of Low Clouds and
Drizzle in the ECMWF Model Based on ARM Observations from the Azores, J.
Climate, 142, 668–685, https://doi.org/10.1175/MWR-D-13-00153.1, 2014. 2. Albrecht, B., Fairall, C., Thomson, D., White, A., Snider, J., and Schubert,
W.: Surface-based remote-sensing of the observed and the adiabatic liquid
water-content of stratocumulus clouds, Geophys. Res. Lett., 17, 89–92,
https://doi.org/10.1029/Gl017i001p00089, 1990. 3. Austin, P., Wang, Y., Kujala, V., and Pincus, R.: Precipitation in
Stratocumulus Clouds: Observational and Modeling Results, J. Atmos. Sci.,
52, 2329–2352, https://doi.org/10.1175/1520-0469(1995)052<2329:PISCOA>2.0.CO;2, 1995. 4. Bai, H., Gong, C., Wang, M., Zhang, Z., and L'Ecuyer, T.: Estimating
precipitation susceptibility in warm marine clouds using multi-sensor
aerosol and cloud products from A-Train satellites, Atmos. Chem. Phys., 18,
1763–1783, https://doi.org/10.5194/acp-18-1763-2018, 2018. 5. Barker, H. W., Wiellicki, B. A., and Parker, L.: A parameterization for computing
grid-averaged solar fluxes for inhomogeneous marine boundary layer clouds.
Part II: Validation using satellite data, J. Atmos. Sci., 53, 2304–2316,
1996.
Cited by
23 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|