Scale‐aware parameterization of liquid cloud inhomogeneity and its impact on simulated climate in CESM

Abstract

Using long-term radar-based ground measurements from the Atmospheric Radiation Measurement Program, we derive the inhomogeneity of cloud liquid water as represented by the shape parameter of a gamma distribution. The relationship between the inhomogeneity and the model grid size as well as atmospheric condition is presented. A larger grid scale and more unstable atmosphere are associated with larger inhomogeneity that is described by a smaller shape parameter. This relationship is implemented as a scale-aware parameterization of the liquid cloud inhomogeneity in the Community Earth System Model (CESM) in which the shape parameter impacts the cloud microphysical processes. When used in the default CESM1 with the finite-volume dynamic core where a constant liquid inhomogeneity parameter was assumed, it reduces the cloud inhomogeneity in high latitudes and increases it in low latitudes. This is due to both the smaller (larger) grid size in high (low) latitudes in the longitude-latitude grid setting of CESM and the more stable (unstable) atmosphere. The single-column model and general circulation model sensitivity experiments show that the new parameterization increases the cloud liquid water path in polar regions and decreases it in low latitudes. An advantage of the parameterization is that it can recognize the spatial resolutions of the CESM without special tuning of the cloud water inhomogeneity parameter.

Exploring a Variable‐Resolution Approach for Simulating Regional Climate in the Rocky Mountain Region Using the VR‐CESM

“…For example, the underestimation of precipitation in the late spring is present in both UNIF and VR‐CESM simulations (Figure ), which was also found in three of six RCMs (Wang et al, ). Such model biases may be reduced by using scale‐aware parameterizations (e.g., Xie & Zhang, ). Despite this, our results show that the current version of VR‐CESM has the capability to simulate the key aspects of regional climate in the Rocky Mountain region.…”

Reply

“…(27) from the MODIS retrieval of COT and CER. Several previous studies have shown that the subpixellevel surface contamination, subpixel cloud inhomogeneity, and three-dimensional radiative transfer effects can cause significant errors in the MODIS CER retrievals, especially over broken cloud regions (Zhang and Platnick, 2011;Zhang et al, 2012Zhang et al, , 2016. Given the fact that the CDNC retrieval is highly sensitive to CER error as a result of N d ∼ r − 5 2 e , the influence of retrieval uncertainty on subgrid CDNC variation cannot be ruled out.…”

Regime dependence of ice cloud heterogeneity – a convective life‐cycle effect?

“…A number of predictors for the FSD were investigated and cloud depth above the melting level was found to be a more successful discriminant between high and low FSD samples for ice cloud than either cloud-top height, height above ground, atmospheric stability (in the form of a moist static energy difference between the mid-troposphere and surface, as suggested in Xie and Zhang, 2015) or variables scaling directly with height and latitude (such as e.g. temperature or humidity).…”

“…Observations of cloud properties can provide guidance on this topic, and conclusions from previous studies include characterization of cloud heterogeneity by a single global parameter (Shonk et al, 2010), or from a climatology of cloud heterogeneity from satellite observations (Oreopoulos and Cahalan, 2005). However, observations also show that condensate heterogeneity varies systematically with cloud regime (Oreopoulos and Cahalan, 2005;Boutle et al, 2014;Huang and Liu, 2014;Xie and Zhang, 2015;Ahlgrimm and Forbes, 2016), and thus it is desirable to understand and capture this regime dependence for a more accurate representation of subgrid-scale process rates.…”