资源环境科技发展态势分析平台

The climatic relevance of aerosol-cloud interactions depends on the sensitivity of the radiative effect of clouds to cloud droplet number N, and liquid water path LWP. We derive the dependence of cloud fraction CF, cloud albedo A(C), and the relative cloud radiative effect rCRE = CF . A(C) on N and LWP from 159 large-eddy simulations of nocturnal stratocumulus. These simulations vary in their initial conditions for temperature, moisture, boundary-layer height, and aerosol concentration but share boundary conditions for surface fluxes and subsidence. Our approach is based on Gaussian-process emulation, a statistical technique related to machine learning. We succeed in building emulators that accurately predict simulated values of CF, A(C), and rCRE for given values of N and LWP. Emulator-derived susceptibilities partial derivative lnrCRE/partial derivative lnN and partial derivative lnrCRE/partial derivative lnLWP cover the nondrizzling, fully overcast regime as well as the drizzling regime with broken cloud cover. Theoretical results, which are limited to the nondrizzling regime, are reproduced. The susceptibility partial derivative lnrCRE/partial derivative lnN captures the strong sensitivity of the cloud radiative effect to cloud fraction, while the susceptibility partial derivative lnrCRE/partial derivative lnLWP describes the influence of cloud amount on cloud albedo irrespective of cloud fraction. Our emulation-based approach provides a powerful tool for summarizing complex data in a simple framework that captures the sensitivities of cloud-field properties over a wide range of states.