Climate processes

Abstract:

Human aerosol emissions change cloud properties by providing additional cloud condensation nuclei. This increases cloud droplet numbers, which in turn affects other cloud properties like liquid-water content and ultimately cloud albedo. These adjustments are poorly constrained, making aerosol effects the most uncertain part of anthropogenic climate forcing. Here we show that cloud droplet number and water content react differently to changing emission amounts in shipping exhausts. We use information about ship positions and modeled emission amounts together with reanalysis winds and satellite retrievals of cloud properties. The analysis reveals that cloud droplet numbers respond linearly to emission amount over a large range (1–10 kg h−1) before the response saturates. Liquid water increases in raining clouds, and the anomalies are constant over the emission ranges observed. There is evidence that this independence of emissions is due to compensating effects under drier and more humid conditions, consistent with suppression of rain by enhanced aerosol. This has implications for our understanding of cloud processes and may improve the way clouds are represented in climate models, in particular by changing parameterizations of liquid-water responses to aerosol.

Abstract:

The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 Wm^-2. Despite this, due to the opposite sign of these fluxes, the net average of anvil cloud radiative effect (CRE) over the tropics has been found to be neutral. Research into the response of anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact anvil CRE.

To study the connection between deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from Meteosat SEVIRI. Retrieved cloud properties and derived broadband radiative fluxes are provided by the CC4CL algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes.

We find that the distribution of anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs while larger, organised systems tend to have CRE closer to zero. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is indeed neutral within our range of uncertainty (0.86 ± 0.91 Wm^-2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks.

Abstract:

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60° N–60°S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R²=0.63) and longwave cloud radiative effects (CREs) (R²=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models.

Abstract:

Abstract. The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60∘ N–60∘ S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R2=0.63) and longwave cloud radiative effects (CREs) (R2=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models.