Philip Stier

Abstract:

Aerosols have a potentially large effect on climate, particularly through their interactions with clouds, but the magnitude of this effect is highly uncertain. Large volcanic eruptions produce sulfur dioxide, which in turn produces aerosols; these eruptions thus represent a natural experiment through which to quantify aerosol–cloud interactions. Here we show that the massive 2014–2015 fissure eruption in Holuhraun, Iceland, reduced the size of liquid cloud droplets—consistent with expectations—but had no discernible effect on other cloud properties. The reduction in droplet size led to cloud brightening and global-mean radiative forcing of around −0.2 watts per square metre for September to October 2014. Changes in cloud amount or cloud liquid water path, however, were undetectable, indicating that these indirect effects, and cloud systems in general, are well buffered against aerosol changes. This result will reduce uncertainties in future climate projections, because we are now able to reject results from climate models with an excessive liquid-water-path response.

Abstract:

<jats:p>Abstract. The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide high-time-resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument's field of view and discuss these results in relation to retrieval biases. We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Results show a mean bias of +0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. Analysis of the spatio-temporal distribution of these errors shows that absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night- and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset. Keeping these biases in mind, we quantify the seasonal, diurnal, and spatial variation of cloud top temperature across SEVIRI's field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias, we find that diurnal variations in the retrieval bias can be small but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be significantly impacted by diurnal variability in the accuracy of the retrieval. We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures. This is the first study to relate the diurnal variations in SEVIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection. </jats:p>

Abstract:

Much of the uncertainty in estimates of the anthropogenic forcing of climate change comes from uncertainties in the instantaneous effect of aerosols on cloud albedo, known as the Twomey effect or the radiative forcing from aerosol–cloud interactions (RFaci), a component of the total or effective radiative forcing. Because aerosols serving as cloud condensation nuclei can have a strong influence on the cloud droplet number concentration (Nd), previous studies have used the sensitivity of the Nd to aerosol properties as a constraint on the strength of the RFaci. However, recent studies have suggested that relationships between aerosol and cloud properties in the present-day climate may not be suitable for determining the sensitivity of the Nd to anthropogenic aerosol perturbations. Using an ensemble of global aerosol–climate models, this study demonstrates how joint histograms between Nd and aerosol properties can account for many of the issues raised by previous studies. It shows that if the anthropogenic contribution to the aerosol is known, the RFaci can be diagnosed to within 20% of its actual value. The accuracy of different aerosol proxies for diagnosing the RFaci is investigated, confirming that using the aerosol optical depth significantly underestimates the strength of the aerosol–cloud interactions in satellite data.