TY - JOUR
T1 - Evaluating clouds, aerosols, and their interactions in three global climate models using satellite simulators and observations
JF - Journal of Geophysical Research: Atmospheres
Y1 - 2014/09//
SP - 10,876
EP - 10,901
A1 - George A. Ban-Weiss
A1 - Ling Jin
A1 - Susanne E. Bauer
A1 - Ralf Bennartz
A1 - Xiaohong Liu
A1 - Kai Zhang
A1 - Yi Ming
A1 - Huan Guo
A1 - Jonathan H. Jiang
KW - aerosol indirect effect
KW - aerosol-cloud interactions
KW - cloud droplet number concentration
KW - global climate model
KW - model evaluation
KW - satellite simulator COSP
AB - Accurately representing aerosol-cloud interactions in global climate models is challenging. As parameterizations evolve, it is important to evaluate their performance with appropriate use of observations. In this investigation we compare aerosols, clouds, and their interactions in three global climate models (GFDL-AM3, NCAR-CAM5, GISS-ModelE2) to MODIS satellite observations. Modeled cloud properties are diagnosed using a MODIS simulator. Cloud droplet number concentrations (N) are computed identically from satellite-simulated and MODIS-observed values of liquid cloud optical depth and droplet effective radius. We find that aerosol optical depth (τa) simulated by models is similar to observations in many regions around the globe. For N, AM3 and CAM5 capture the observed spatial pattern of higher values in coastal marine stratocumulus versus remote ocean regions, though modeled values in general are higher than observed. Aerosol-cloud interactions were computed as the sensitivity of ln(N) to ln(τa) for coastal marine liquid clouds near South Africa (SAF) and Southeast Asia (SEA) where τa varies in time. AM3 and CAM5 are more sensitive than observations, while the sensitivity for ModelE2 is statistically insignificant. This widely used sensitivity could be subject to misinterpretation due to the confounding influence of meteorology on both aerosols and clouds. A simple framework for assessing the sensitivity of ln(N) to ln(τa) at constant meteorology illustrates that observed sensitivity can change from positive to statistically insignificant when including the confounding influence of relative humidity. Satellite-simulated versus standard model values of N from CAM5 are compared in SAF; standard model values are significantly lower with a bias of 83 cm−3.
VL - 119
IS - 18
JO - J. Geophys. Res. Atmos.
ER -
TY - JOUR
T1 - Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data
JF - Atmospheric Chemistry and Physics
Y1 - 2009/11//
SP - 8697
EP - 8717
A1 - Johannes Quaas
A1 - Yi Ming
A1 - Surabi Menon
A1 - Toshihiko Takemura
A1 - Minghuai Wang
A1 - Joyce E. Penner
A1 - Andrew Gettelman
A1 - Ulrike Lohmann
A1 - Nicolas Bellouin
A1 - Olivier Boucher
A1 - Andrew M. Sayer
A1 - Gareth E. Thomas
A1 - Allison McComiskey
A1 - Graham Feingold
A1 - Corinna Hoose
A1 - Jon E. Kristjánsson
A1 - Xiaohong Liu
A1 - Yves Balkanski
A1 - Leo J. Donner
A1 - Paul A. Ginoux
A1 - Philip Stier
A1 - Benjamin Grandey
A1 - Johann Feichter
A1 - Igor Sednev
A1 - Susanne E. Bauer
A1 - Dorothy M. Koch
A1 - Roy G. Grainger
A1 - Alf Kirkevåg
A1 - Trond Iversen
A1 - Øyvind Seland
A1 - Richard C. Easter
A1 - Steven J. Ghan
A1 - Philip J. Rasch
A1 - Hugh Morrison
A1 - Jean-Francois Lamarque
A1 - Michael J. Iacono
A1 - Stefan Kinne
A1 - Michael Schulz
AB - Aerosol indirect effects continue to constitute one of the most important uncertainties for anthropogenic climate perturbations. Within the international AEROCOM initiative, the representation of aerosol-cloud-radiation interactions in ten different general circulation models (GCMs) is evaluated using three satellite datasets. The focus is on stratiform liquid water clouds since most GCMs do not include ice nucleation effects, and none of the model explicitly parameterises aerosol effects on convective clouds. We compute statistical relationships between aerosol optical depth (a) and various cloud and radiation quantities in a manner that is consistent between the models and the satellite data. It is found that the model-simulated influence of aerosols on cloud droplet number concentration (Nd ) compares relatively well to the satellite data at least over the ocean. The relationship between a and liquid water path is simulated much too strongly by the models. This suggests that the implementation of the second aerosol indirect effect mainly in terms of an autoconversion parameterisation has to be revisited in the GCMs. A positive relationship between total cloud fraction (fcld) and a as found in the satellite data is simulated by the majority of the models, albeit less strongly than that in the satellite data in most of them. In a discussion of the hypotheses proposed in the literature to explain the satellite-derived strong fcld–a relationship, our results indicate that none can be identified as a unique explanation. Relationships similar to the ones found in satellite data between a and cloud top temperature or outgoing long-wave radiation (OLR) are simulated by only a few GCMs. The GCMs that simulate a negative OLR - a relationship show a strong positive correlation between a and fcld. The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of a, and parameterisation assumptions such as a lower bound on Nd . Nevertheless, the strengths of the statistical relationships are good predictors for the aerosol forcings in the models. An estimate of the total short-wave aerosol forcing inferred from the combination of these predictors for the modelled forcings with the satellite-derived statistical relationships yields a global annual mean value of −1.5±0.5Wm−2. In an alternative approach, the radiative flux perturbation due to anthropogenic aerosols can be broken down into a component over the cloud-free portion of the globe (approximately the aerosol direct effect) and a component over the cloudy portion of the globe (approximately the aerosol indirect effect). An estimate obtained by scaling these simulated clear and cloudy-sky forcings with estimates of anthropogenic a and satellite-retrieved Nd–a regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2Wm−2and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5Wm−2, with a total estimate of −1.2±0.4Wm−2.
VL - 9
IS - 22
ER -