Published by Copernicus Publications on behalf of the European Geosciences Union. 8698 J. Quaas et al.: Aerosol indirect effects -general circulation model intercomparisonbetween τ a and f cld . The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of τ a , and parameterisation assumptions such as a lower bound on N d . 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±0.5 Wm −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 τ a and satellite-retrieved N d -τ a regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2 Wm −2 and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5 Wm −2 , with a total estimate of −1.2±0.4 Wm −2 .

A lack of reliable estimates of cloud condensation nuclei (CCN) aerosols over oceans has severely limited our ability to quantify their effects on cloud properties and extent of cooling by reflecting solar radiation—a key uncertainty in anthropogenic climate forcing. We introduce a methodology for ascribing cloud properties to CCN and isolating the aerosol effects from meteorological effects. Its application showed that for a given meteorology, CCN explains three-fourths of the variability in the radiative cooling effect of clouds, mainly through affecting shallow cloud cover and water path. This reveals a much greater sensitivity of cloud radiative forcing to CCN than previously reported, which means too much cooling if incorporated into present climate models. This suggests the existence of compensating aerosol warming effects yet to be discovered, possibly through deep clouds.

A prognostic equation for ice crystal number concentration together with an ice nucleation scheme are implemented in the National Center for Atmospheric Research (NCAR) Community Atmospheric Model version 3 (CAM3) with the aim of studying the indirect effect of aerosols on cold clouds. The effective radius of ice crystals, which is used in the radiation and gravitational settlement calculations, is now calculated from model-predicted mass and number of ice crystals rather than diagnosed as a function of temperature. A water vapor deposition scheme is added to replace the condensation and evaporation (C–E) in the standard CAM3 for ice clouds. The repartitioning of total water into liquid and ice in mixed-phase clouds as a function of temperature is removed, and ice supersaturation is allowed. The predicted ice water content in the modified CAM3 is in better agreement with the Aura Microwave Limb Sounder (MLS) data than that in the standard CAM3. The cirrus cloud fraction near the tropical tropopause, which is underestimated in the standard CAM3 as revealed through comparison with the Stratospheric Aerosol and Gas Experiment II (SAGE II) data, is increased by 20%–30%, and the cold temperature bias there is reduced by 1–2 K. However, an increase in the cloud fraction in polar regions makes the underestimation (by ∼20 W m−2) of downwelling shortwave radiation in the standard CAM3 even worse. A sensitivity test reducing the threshold relative humidity with respect to ice (RHi) for heterogeneous ice nucleation from 120% to 105% (representing nearly perfect ice nuclei) increases the global cloud cover by 1.4%, temperature near the tropical tropopause by 4–5 K, and water vapor in the stratosphere by 50%–80%.

[1] Aerosol indirect effects have remained the largest uncertainty in estimates of the radiative forcing of past and future climate change. Observational constraints on cloud lifetime effects are particularly challenging since it is difficult to separate aerosol effects from meteorological influences. Here we use three global climate models, including a multiscale aerosol-climate model PNNL-MMF, to show that the dependence of the probability of precipitation on aerosol loading, termed the precipitation frequency susceptibility (S pop ), is a good measure of the liquid water path response to aerosol perturbation (l), as both S pop and l strongly depend on the magnitude of autoconversion, a model representation of precipitation formation via collisions among cloud droplets. This provides a method to use satellite observations to constrain cloud lifetime effects in global climate models. S pop in marine clouds estimated from CloudSat, MODIS and AMSR-E observations is substantially lower than that from global climate models and suggests a liquid water path increase of less than 5% from doubled cloud condensation nuclei concentrations. This implies a substantially smaller impact on shortwave cloud radiative forcing over ocean due to aerosol indirect effects than simulated by current global climate models (a reduction by one-third for one of the conventional aerosolclimate models). Further work is needed to quantify the uncertainties in satellite-derived estimates of S pop and to examine S pop in high-resolution models. Citation: Wang, M., et al. (2012), Constraining cloud lifetime effects of aerosols using A-Train satellite observations, Geophys. Res. Lett., 39, L15709,

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There is ample evidence that anthropogenic aerosols have important effects on climate in the Northern Hemisphere but little such evidence in the Southern Hemisphere. Observations of Australian rainfall and cloudiness since 1950 show increases over much of the continent. We show that including anthropogenic aerosol changes in 20th century simulations of a global climate model gives increasing rainfall and cloudiness over Australia during 1951–1996, whereas omitting this forcing gives decreasing rainfall and cloudiness. The pattern of increasing rainfall when aerosols are included is strongest over northwestern Australia, in agreement with the observed trends. The strong impact of aerosols is primarily due to the massive Asian aerosol haze, as confirmed by a sensitivity test in which only Asian anthropogenic aerosols are included. The Asian haze alters the meridional temperature and pressure gradients over the tropical Indian Ocean, thereby increasing the tendency of monsoonal winds to flow toward Australia. Anthropogenic aerosols also make the simulated pattern of surface‐temperature change in the tropical Pacific more like La Niña, since they induce a cooling of the surface waters in the extratropical North Pacific, which are then transported to the tropical eastern Pacific via the deep ocean. Transient climate model simulations forced only by increased greenhouse gases have generally not reproduced the observed rainfall increase over northwestern and central Australia. Our results suggest that a possible reason for this failure was the omission of forcing by Asian aerosols. Further research is essential to more accurately quantify the role of Asian aerosols in forcing Australian climate change.

[1] Natural modes of variability on many timescales influence aerosol particle distributions and cloud properties such that isolating statistically significant differences in cloud radiative forcing due to anthropogenic aerosol perturbations (indirect effects) typically requires integrating over long simulations. For state-of-the-art global climate models (GCM), especially those in which embedded cloud-resolving models replace conventional statistical parameterizations (i.e., multiscale modeling framework, MMF), the required long integrations can be prohibitively expensive. Here an alternative approach is explored, which implements Newtonian relaxation (nudging) to constrain simulations with both pre-industrial and present-day aerosol emissions toward identical meteorological conditions, thus reducing differences in natural variability and dampening feedback responses in order to isolate radiative forcing. Ten-year GCM simulations with nudging provide a more stable estimate of the global-annual mean net aerosol indirect radiative forcing than do conventional free-running simulations. The estimates have mean values and 95% confidence intervals of À1.19 AE 0.02 W/m 2 and À1.37 AE 0.13 W/m 2 for nudged and free-running simulations, respectively. Nudging also substantially increases the fraction of the world's area in which a statistically significant aerosol indirect effect can be detected (66% and 28% of the Earth's surface for nudged and free-running simulations, respectively). One-year MMF simulations with and without nudging provide global-annual mean net aerosol indirect radiative forcing estimates of À0.81 W/m 2 and À0.82 W/m 2 , respectively. These results compare well with previous estimates from three-year free-running MMF simulations (À0.83 W/m 2 ), which showed the aerosol-cloud relationship to be in better agreement with observations and high-resolution models than in the results obtained with conventional cloud parameterizations.

Satellite-based estimates of the aerosol indirect effect (AIE) are consistently smaller than the estimates from global aerosol models, and, partly as a result of these differences, the assessment of this climate forcing includes large uncertainties. Satellite estimates typically use the present-day (PD) relationship between observed cloud drop number concentrations (N c ) and aerosol optical depths (AODs) to determine the preindustrial (PI) values of N c . These values are then used to determine the PD and PI cloud albedos and, thus, the effect of anthropogenic aerosols on top of the atmosphere radiative fluxes. Here, we use a model with realistic aerosol and cloud processes to show that empirical relationships for lnðN c Þ versus lnðAODÞ derived from PD results do not represent the atmospheric perturbation caused by the addition of anthropogenic aerosols to the preindustrial atmosphere. As a result, the model estimates based on satellite methods of the AIE are between a factor of 3 to more than a factor of 6 smaller than model estimates based on actual PD and PI values for N c . Using lnðN c Þ versus lnðAIÞ (Aerosol Index, or the optical depth times angstrom exponent) to estimate preindustrial values for N c provides estimates for N c and forcing that are closer to the values predicted by the model. Nevertheless, the AIE using lnðN c Þ versus lnðAIÞ may be substantially incorrect on a regional basis and may underestimate or overestimate the global average forcing by 25 to 35%.A n increase in aerosol concentrations leads to an increase in cloud droplet number concentration (N c ) for a cloud with a constant cloud liquid water content. The increase in N c implies a decrease in the cloud droplet effective radius, which leads to an increase in cloud optical depth and an increase in cloud reflectivity (1); this climate forcing is known as the first aerosol indirect effect (AIE).Satellite measurements of N c versus aerosol optical depth (AOD) have been used to estimate the effect of changes in clouds due to anthropogenic aerosols. The estimated changes to clouds from preindustrial (PI) to present-day (PD) conditions gives an AIE that ranges from −0.2 Wm −2 to −0.5 Wm −2 at the top of the atmosphere (TOA) (2, 3). In contrast, model results that rely on mechanistic descriptions of the relationship between aerosols and cloud drop number concentrations give an AIE that ranges from −0.5 to −2.03 Wm −2 (4, 5).Satellite-based estimates rely on a linear fit to the spatial variation of lnðN c Þ versus lnðAODÞ in the PD to determine PI values for N c , rather than temporal variations induced by actual changes between PD and PI aerosol concentrations. Improved estimates from satellites are expected if measurements of N c versus AI (where AI is the Aerosol Index, or the angstrom exponent times AOD) are used rather than N c versus AOD, because AI is a better measure of aerosol number concentration (6), but can still give values smaller than process-based models. Previous studies have attempted to combine satellite data with model...