A weekly cycle in aerosol pollution and some meteorological quantities is observed over Europe. In the present study we exploit this effect to analyse aerosol-cloudradiation interactions. A weekly cycle is imposed on anthropogenic emissions in two general circulation models that include parameterizations of aerosol processes and cloud microphysics. It is found that the simulated weekly cycles in sulfur dioxide, sulfate, and aerosol optical depth in both models agree reasonably well with those observed indicating model skill in simulating the aerosol cycle. A distinct weekly cycle in cloud droplet number concentration is demonstrated in both observations and models. For other variables, such as cloud liquid water path, cloud cover, top-of-the-atmosphere radiation fluxes, precipitation, and surface temperature, large variability and contradictory results between observations, model simulations, and model control simulations without a weekly cycle in emissions prevent us from reaching any firm conclusions about the potential aerosol impact on meteorology or the realism of the modelled second aerosol indirect effects.

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 first indirect effect for the first experiment is rather similar, about −0.6Wm−2 to −0.7Wm−2. 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. Adding an autoconversion scheme that depends on the droplet concentration caused a larger (negative) change in net outgoing shortwave radiation compared to the 1st indirect effect, and the increase varied from only 22% to more than a factor of three. The change in net shortwave forcing in the models due to varying the autoconversion scheme depends on the liquid water content of the clouds as well as their predicted droplet concentrations, and both increases and decreases in the net shortwave forcing can occur when autoconversion schemes are changed. 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, with values of cloud forcing ranging from −0.3Wm−2 to −1.4Wm−2. Thus, this aspect of modeling requires significant improvement in order to improve the prediction of aerosol indirect effects.

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 (tau a) and various cloud and radiation quantities in a manner that is consistent between the models and the satellite data. cloud droplet number concentration (N d) compares relatively well to the satellite data at least over the ocean. The relationship between (tau 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 tau 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–tau a relationship, our results indicate that none can be identified as a unique explanation. Relationships similar to the ones found in satellite data between tau 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 - tau a relationship show a strong positive correlation between tau a and fcld. The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of tau 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 clearand cloudy-sky forcings with estimates of anthropogenic tau a and satellite-retrieved Nd–tau a regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2Wm−2 and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5Wm−2, with a total estimate of −1.2±0.4Wm−2.

An investigation of the impact of the subgrid-scale variability of cloud liquid water on the autoconversion process as parameterized in a general circulation model is presented in this paper. For this purpose, a prognostic statistical probability density distribution (PDF) of the subgrid scale variability of cloud water is incorporated in a continuous autoconversion parameterization. Thus, the revised autoconversion rate is calculated by an integral of the autoconversion equation over the PDF of total water mixing ratio from the saturation vapor mixing ratio to the maximum of total water mixing ratio. An evaluation of the new autoconversion parameterization is carried out by means of one year simulations with the ECHAM5 climate model. The results indicate that the new autoconversion scheme causes an increase of the frequency of occurrence of high autoconversion rates and a decrease of low ones compared to the original scheme. This expected result is due to the emphasis on areas of high cloud liquid water in the new approach, and the non-linearity of the autoconversion with respect to liquid water mixing ratio. A similar trend as in the autoconversion is observed in the accretion process resulting from the coupling of both processes. As a consequence of the altered autoconversion, large-scale surface precipitation also shows a shift of occurrence from lower to higher rates. The vertically integrated cloud liquid water estimated by the model shows slight improvements compared to satellite data. Most importantly, the artificial tuning factor for autoconversion in the continuous parameterization could be reduced by almost an order of magnitude using the revised parameterization.

The distribution of model-based estimates of equilibrium climate sensitivity has not changed substantially in more than 30 years. Efforts to narrow this distribution by weighting projections according to measures of model fidelity have so far failed, largely because climate sensitivity is independent of current measures of skill in current ensembles of models. This work presents a cautionary example showing that measures of model fidelity that are effective at narrowing the distribution of future projections (because they are systematically related to climate sensitivity in an ensemble of models) may be poor measures of the likelihood that a model will provide an accurate estimate of climate sensitivity (and thus degrade distributions of projections if they are used as weights). Furthermore, it appears unlikely that statistical tests alone can identify robust measures of likelihood. The conclusions are drawn from two ensembles: one obtained by perturbing parameters in a single climate model and a second containing the majority of the world’s climate models. The simple ensemble reproduces many aspects of the multimodel ensemble, including the distributions of skill in reproducing the present-day climatology of clouds and radiation, the distribution of climate sensitivity, and the dependence of climate sensitivity on certain cloud regimes. Weighting by error measures targeted on those regimes permits the development of tighter relationships between climate sensitivity and model error and, hence, narrower distributions of climate sensitivity in the simple ensemble. These relationships, however, do not carry into the multimodel ensemble. This suggests that model weighting based on statistical relationships alone is unfounded and perhaps that climate model errors are still large enough that model weighting is not sensible.

Global mean cloud feedbacks in ten atmosphere-only climate models are estimated in perturbed sea surface temperature (SST) experiments and the results compared to doubled CO2 experiments using mixed-layer ocean versions of these same models. The cloud feedbacks in any given model are generally not consistent: the sign of the net cloud radiative feedback may vary according to the experimental design. However, both sets of experiments indicate that the variation of the total climate feedback across the models depends primarily on the variation of the net cloud feedback. Changes in different cloud types show much greater consistency between the two experiments for any individual model and amongst the set of models analyzed here. This suggests that the SST perturbation experiments may provide useful information on the processes associated with cloud changes which is not evident when analysis is restricted to feedbacks defined in terms of the change in cloud radiative forcing.

In this study, we examine the time evolution of the relative contribution of sulfate aerosols and greenhouse gases to anthropogenic climate change. We use the new IPSL-CM4 coupled climate model for which the first indirect effect of sulfate aerosols has been calibrated using POLDER satellite data. For the recent historical period the sulfate aerosols play a key role on the temperature increase with a cooling effect of 0.5 K, to be compared to the 1.4 K warming due to greenhouse gas increase. In contrast, the projected temperature change for the 21st century is remarkably independent of the effects of anthropogenic sulfate aerosols for the SRES-A2 scenario. Those results are interpreted comparing the different radiative forcings, and can be extended to other scenarios. We also highlight that the first indirect effect of aerosol strongly depends on the land surface model by changing the cloud cover.

The indirect effects of anthropogenic aerosols are expected to cause a significant radiative forcing of the Earth’s climate whose magnitude, however, is still uncertain. Most climate models use parameterizations for the aerosol indirect effects based on so-called ‘‘empirical relationships’’ which link the cloud droplet number concentration to the aerosol concentration. New satellite datasets such as those from the POLDER and MODIS instruments are well suited to evaluate and improve such parameterizations at a global scale. We derive statistical relationships of cloud-top droplet radius and aerosol index (or aerosol optical depth) from satellite retrievals and fit an empirical parameterization in a general circulation model to match the relationships. When applying the fitted parameterizations in the model, the simulated radiative forcing by the first aerosol indirect effect is reduced by 50% as compared to our baseline simulation (down to -0.3 and -0.4 Wm-2 when using MODIS and POLDER satellite data, respectively).

In their contribution to PNAS, Penner et al. (1) used a climate model to estimate the radiative forcing by the aerosol first indirect effect (cloud albedo effect) in two different ways: first, by deriving a statistical relationship between the logarithm of cloud droplet number concentration, ln Nc, and the logarithm of aerosol optical depth, ln AOD (or the logarithm of the aerosol index, ln AI) for present-day and preindustrial aerosol fields, a method that was applied earlier to satellite data (2), and, second, by computing the radiative flux perturbation between two simulations with and without anthropogenic aerosol sources. They find a radiative forcing that is a factor of 3 lower in the former approach than in the latter [as Penner et al. (1) correctly noted, only their “inline” results are useful for the comparison].

Realistic simulations of clouds are of uppermost importance for climate modelling using general circulation models. Satellite data are well suited to evaluate model parametrizations. In this study we use the Laboratoire de Me´te´orologie Dynamique general circulation model (LMDZ). We evaluate the current LMDZ cloud phase parametrization, in which the repartition of condensed cloud water between liquid and ice is a function of the local temperature. Three parameters are used to derive a relation between liquid cloud water content and temperature, two of which are not physically based. We use the POLDER-1 satellite data to infer more realistic parameters by establishing statistical relationships between cloud top thermodynamical phase and cloud top temperature, consistently in both satellite data and model results. We then perform a multitude of short model integrations and derive a best estimate for the lowest local temperature where liquid water can exist in a cloud (Tice = -32°C in our parametrization). The other parameter which describes the shape of the transition between ice and liquid water is also estimated. A longer simulation has then been performed with the new parameters, resulting in an improvement in the representation of the shortwave cloud radiative forcing.