Quantifying error in the radiative forcing of the first aerosol indirect effect

2008 ◽  
Vol 35 (2) ◽  
Author(s):  
Allison McComiskey ◽  
Graham Feingold
2017 ◽  
Vol 30 (17) ◽  
pp. 6959-6976 ◽  
Author(s):  
Yolanda L. Shea ◽  
Bruce A. Wielicki ◽  
Sunny Sun-Mack ◽  
Patrick Minnis

Cloud response to Earth’s changing climate is one of the largest sources of uncertainty among global climate model (GCM) projections. Two of the largest sources of uncertainty are the spread in equilibrium climate sensitivity (ECS) and uncertainty in radiative forcing due to uncertainty in the aerosol indirect effect. Satellite instruments with sufficient accuracy and on-orbit stability to detect climate change–scale trends in cloud properties will improve confidence in the understanding of the relationship between observed climate change and cloud property trends, thus providing information to better constrain ECS and radiative forcing. This study applies a climate change uncertainty framework to quantify the impact of measurement uncertainty on trend detection times for cloud fraction, effective temperature, optical thickness, and water cloud effective radius. Although GCMs generally agree that the total cloud feedback is positive, disagreement remains on its magnitude. With the climate uncertainty framework, it is demonstrated how stringent measurement uncertainty requirements for reflected solar and infrared satellite measurements enable improved constraint of SW and LW cloud feedbacks and the ECS by significantly reducing trend uncertainties for cloud fraction, optical thickness, and effective temperature. The authors also demonstrate improved constraint on uncertainty in the aerosol indirect effect by reducing water cloud effective radius trend uncertainty.


2006 ◽  
Vol 6 (1) ◽  
pp. 1579-1617 ◽  
Author(s):  
J. E. Penner ◽  
J. Quaas ◽  
T. Storelvmo ◽  
T. Takemura ◽  
O. Boucher ◽  
...  

Abstract. Modeled differences in predicted effects are increasingly used to help quantify the uncertainty of these effects. Here, we examine modeled differences in the aerosol indirect effect in a series of experiments that help to quantify how and why model-predicted aerosol indirect forcing varies between models. The experiments start with an experiment in which aerosol concentrations, the parameterization of droplet concentrations and the autoconversion scheme are all specified and end with an experiment that examines the predicted aerosol indirect forcing when only aerosol sources are specified. Although there are large differences in the predicted liquid water path among the models, the predicted aerosol indirect effect for the first experiment is rather similar. Changes to the autoconversion scheme can lead to large changes in the liquid water path of the models and to the response of the liquid water path to changes in aerosols. Nevertheless, these changes do not necessarily lead to large changes in the radiative forcing. The parameterization of cloud fraction within models is not sensitive to the aerosol concentration, and, therefore, the response of the modeled cloud fraction within the present models appears to be smaller than that which would be associated with model ''noise''. The prediction of aerosol concentrations, given a fixed set of sources, leads to some of the largest differences in the predicted aerosol indirect radiative forcing among the models. Thus, this aspect of modeling requires significant improvement in order to improve the prediction of aerosol indirect effects.


2006 ◽  
Vol 6 (4) ◽  
pp. 947-955 ◽  
Author(s):  
J. Quaas ◽  
O. Boucher ◽  
U. Lohmann

Abstract. Aerosol indirect effects are considered to be the most uncertain yet important anthropogenic forcing of climate change. The goal of the present study is to reduce this uncertainty by constraining two different general circulation models (LMDZ and ECHAM4) with satellite data. We build a statistical relationship between cloud droplet number concentration and the optical depth of the fine aerosol mode as a measure of the aerosol indirect effect using MODerate Resolution Imaging Spectroradiometer (MODIS) satellite data, and constrain the model parameterizations to match this relationship. We include here "empirical" formulations for the cloud albedo effect as well as parameterizations of the cloud lifetime effect. When fitting the model parameterizations to the satellite data, consistently in both models, the radiative forcing by the combined aerosol indirect effect is reduced considerably, down to −0.5 and −0.3 Wm−2, for LMDZ and ECHAM4, respectively.


2005 ◽  
Vol 5 (5) ◽  
pp. 9669-9690 ◽  
Author(s):  
J. Quaas ◽  
O. Boucher ◽  
U. Lohmann

Abstract. Aerosol indirect effects are considered to be the most uncertain yet important anthropogenic forcing of climate change. The goal of the present study is to reduce this uncertainty by constraining two different general circulation models (LMDZ and ECHAM4) with satellite data. We build a statistical relationship between cloud droplet number concentration and the optical depth of the fine aerosol mode as a measure of the aerosol indirect effect using MODerate Resolution Imaging Spectroradiometer (MODIS) satellite data, and constrain the model parameterizations to match this relationship. We include here ''empirical'' formulations for the cloud albedo effect as well as parameterizations of the cloud lifetime effect. When fitting the model parameterizations to the satellite data, consistently in both models, the radiative forcing by the combined aerosol indirect effect is reduced considerably, down to −0.5 and −0.3 Wm-2, for LMDZ and ECHAM4, respectively.


2013 ◽  
Vol 13 (2) ◽  
pp. 917-931 ◽  
Author(s):  
D. Painemal ◽  
P. Zuidema

Abstract. The first aerosol indirect effect (1AIE) is investigated using a combination of in situ and remotely-sensed aircraft (NCAR C-130) observations acquired during VOCALS-REx over the southeast Pacific stratocumulus cloud regime. Satellite analyses have previously identified a high albedo susceptibitility to changes in cloud microphysics and aerosols over this region. The 1AIE was broken down into the product of two independently-estimated terms: the cloud aerosol interaction metric ACIτ =dlnτ/dlnNa|LWP , and the relative albedo (A) susceptibility SR-τ =dA/3dlnτ|LWP, with τ and Na denoting retrieved cloud optical thickness and in situ aerosol concentration respectively and calculated for fixed intervals of liquid water path (LWP). ACIτ was estimated by combining in situ Na sampled below the cloud, with τ and LWP derived from, respectively, simultaneous upward-looking broadband irradiance and narrow field-of-view millimeter-wave radiometer measurements, collected at 1 Hz during four eight-hour daytime flights by the C-130 aircraft. ACIτ values were typically large, close to the physical upper limit (0.33), with a modest increase with LWP. The high ACIτ values slightly exceed values reported from many previous in situ airborne studies in pristine marine stratocumulus and reflect the imposition of a LWP constraint and simultaneity of aerosol and cloud measurements. SR-τ increased with LWP and τ, reached a maximum SR-τ (0.086) for LWP (τ) of 58 g m−2 (~14), and decreased slightly thereafter. The 1AIE thus increased with LWP and is comparable to a radiative forcing of −3.2– −3.8 W m−2 for a 10% increase in Na, exceeding previously-reported global-range values. The aircraft-derived values are consistent with satellite estimates derived from instantaneous, collocated Clouds and the Earth's Radiant Energy System (CERES) albedo and MOderate resolution Imaging Spectroradiometer (MODIS)-retrieved droplet number concentrations at 50 km resolution. The consistency of the airborne and satellite estimates, despite their independent approaches, differences in observational scales, and retrieval assumptions, is hypothesized to reflect the ideal remote sensing conditions for these homogeneous clouds. We recommend the southeast Pacific for regional model assessments of the first aerosol indirect effect on this basis. This airborne remotely-sensed approach towards quantifying 1AIE should in theory be more robust than in situ calculations because of increased sampling. However, although the technique does not explicitly depend on a remotely-derived cloud droplet number concentration (Nd), the at-times unrealistically-high Nd values suggest more emphasis on accurate airborne radiometric measurements is needed to refine this approach.


2015 ◽  
Vol 15 (2) ◽  
pp. 703-714 ◽  
Author(s):  
J. Tonttila ◽  
H. Järvinen ◽  
P. Räisänen

Abstract. The impacts of representing cloud microphysical processes in a stochastic subcolumn framework are investigated, with emphasis on estimating the aerosol indirect effect. It is shown that subgrid treatment of cloud activation and autoconversion of cloud water to rain reduce the impact of anthropogenic aerosols on cloud properties and thus reduce the global mean aerosol indirect effect by 19%, from −1.59 to −1.28 W m−2. This difference is partly related to differences in the model basic state; in particular, the liquid water path (LWP) is smaller and the shortwave cloud radiative forcing weaker when autoconversion is computed separately for each subcolumn. However, when the model is retuned so that the differences in the basic state LWP and radiation balance are largely eliminated, the global-mean aerosol indirect effect is still 14% smaller (i.e. −1.37 W m−2) than for the model version without subgrid treatment of cloud activation and autoconversion. The results show the importance of considering subgrid variability in the treatment of autoconversion. Representation of several processes in a self-consistent subgrid framework is emphasized. This paper provides evidence that omitting subgrid variability in cloud microphysics contributes to the apparently chronic overestimation of the aerosol indirect effect by climate models, as compared to satellite-based estimates.


Author(s):  
Graham Feingold ◽  
Reinhard Furrer ◽  
Peter Pilewskie ◽  
Lorraine A. Remer ◽  
Qilong Min ◽  
...  

2012 ◽  
Vol 60 ◽  
pp. 153-163 ◽  
Author(s):  
M.G. Manoj ◽  
P.C.S. Devara ◽  
Susmitha Joseph ◽  
A.K. Sahai

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