Abstract

Abstract. Stratocumulus clouds in the marine boundary layer cover a large fraction of ocean surface and play an important role in the radiative energy balance of the Earth system. Simulating these clouds in Earth system models (ESMs) has proven to be extremely challenging, in part because cloud microphysical processes such as the autoconversion of cloud water into precipitation occur at scales much smaller than typical ESM grid sizes. An accurate autoconversion parameterization needs to account for not only the local microphysical process (e.g., the dependence on cloud water content qc and cloud droplet number concentration Nc) but also the subgrid-scale variability of the cloud properties that determine the process rate. Accounting for subgrid-scale variability is often achieved by the introduction of a so-called enhancement factor E. Previous studies of E for autoconversion have focused more on its dependence on cloud regime and ESM grid size, but they have largely overlooked the vertical dependence of E within the cloud. In this study, we use a large-eddy simulation (LES) model, initialized and constrained with in situ and surface-based measurements from a recent airborne field campaign, to characterize the vertical dependence of the horizontal variation of qc in stratocumulus clouds and the implications for E. Similar to our recent observational study (Zhang et al., 2021), we found that the inverse relative variance of qc, an index of horizontal homogeneity, generally increases from cloud base upward through the lower two-thirds of the cloud and then decreases in the uppermost one-third of the cloud. As a result, E decreases from cloud base upward and then increases towards the cloud top. We apply a decomposition analysis to the LES cloud water field to understand the relative roles of the mean and variances of qc in determining the vertical dependence of E. Our analysis reveals that the vertical dependence of the horizontal qc variability and enhancement factor E is a combined result of condensational growth throughout the lower portion of the cloud and entrainment mixing at cloud top. The findings of this study indicate that a vertically dependent E should be used in ESM autoconversion parameterizations.

Highlights

  • Marine boundary layer (MBL) clouds play an important role in Earth’s climate system

  • The probability density function (PDF) of cloud-base heights in Fig. 4e shows the prevalent stratocumulus cloud base and a secondary peak corresponding to shallow cumulus, most of which rise into the stratocumulus deck

  • One of the major uncertainties in warm-rain simulations within Earth system models (ESMs) is accounting for microphysical processes occurring at the subgrid scale

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Summary

Introduction

Marine boundary layer (MBL) clouds play an important role in Earth’s climate system. Stratocumulus clouds are one of the predominant types of MBL cloud systems, covering more area in annual mean (∼ 20 % of the Earth’s surface) than other MBL clouds (Warren et al, 1986; Wood, 2012). As a result of this vertical structure, the enhancement factor E for the autoconversion parameterization due to qc variation tends to decrease from cloud base toward cloud top, with a minimum value below cloud top, and increases slightly toward cloud top This observation-based study shed important light on the vertical dependence of the subgrid variation of qc and the corresponding impacts on E and autoconversion process rates. Tives of this paper are (1) to use LES to achieve a processlevel understanding of the vertical dependence of the horizontal variation of cloud water in stratocumulus clouds, in particular the connections with key microphysical processes such as condensational growth and entrainment, and (2) to better understand how these subgrid-scale variations of cloud water affect the enhancement factor associated with warmrain processes in ESMs. Z21 identified the importance of variability in both qc and Nc, as well as covariability between them, but here we first address only variability in qc, with Nc being a fixed parameter.

The 18 July 2017 case study from the ACE-ENA campaign
LES model description and configuration
LES base state and mean turbulent fluxes
Vertical profiles of cloud horizontal variability
Vertical profiles of enhancement factor
Dependence on droplet concentration
Discussion and conclusions
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