A regime‐dependent parametrization of subgrid‐scale cloud water content variability

The subgrid‐scale spatial variability in cloud water content can be described by a parameter f called the fractional standard deviation. This is equal to the standard deviation of the cloud water content divided by the mean. This parameter is an input to schemes that calculate the impact of subgrid‐scale cloud inhomogeneity on gridbox‐mean radiative fluxes and microphysical process rates. A new regime‐dependent parametrization of the spatial variability of cloud water content is derived from CloudSat observations of ice clouds. In addition to the dependencies on horizontal and vertical resolution and cloud fraction included in previous parametrizations, the new parametrization includes an explicit dependence on cloud type. The new parametrization is then implemented in the Global Atmosphere 6 (GA6) configuration of the Met Office Unified Model and used to model the effects of subgrid variability of both ice and liquid water content on radiative fluxes and autoconversion and accretion rates in three 20‐year atmosphere‐only climate simulations. These simulations show the impact of the new regime‐dependent parametrization on diagnostic radiation calculations, interactive radiation calculations and both interactive radiation calculations and in a new warm microphysics scheme. The control simulation uses a globally constant f value of 0.75 to model the effect of cloud water content variability on radiative fluxes. The use of the new regime‐dependent parametrization in the model results in a global mean which is higher than the control's fixed value and a global distribution of f which is closer to CloudSat observations. When the new regime‐dependent parametrization is used in radiative transfer calculations only, the magnitudes of short‐wave and long‐wave top of atmosphere cloud radiative forcing are reduced, increasing the existing global mean biases in the control. When also applied in a new warm microphysics scheme, the short‐wave global mean bias is reduced. This article describes the development and impacts of a new cloud‐type dependent parametrization of the variability of cloud water content. This image shows a snapshot from the CloudSat observations (courtesy of the CloudSat Data Processing Center) used to derive the parametrization together with the corresponding MODIS view and highlights the clear difference in variability between convective clouds to the centre and right of the image and non‐convective clouds to the left.