A Multimodel Study on Warm Precipitation Biases in Global Models Compared to Satellite Observations

Abstract

The cloud‐to‐precipitation transition process in warm clouds simulated by state‐of‐the‐art global climate models (GCMs), including both traditional climate models and a high‐resolution model, is evaluated against A‐Train satellite observations. The models and satellite observations are compared in the form of the statistics obtained from combined analysis of multiple‐satellite observables that probe signatures of the cloud‐to‐precipitation transition process. One common problem identified among these models is the too‐frequent occurrence of warm precipitation. The precipitation is found to form when the cloud particle size and the liquid water path (LWP) are both much smaller than those in observations. The too‐efficient formation of precipitation is found to be compensated for by errors of cloud microphysical properties, such as underestimated cloud particle size and LWP, to an extent that varies among the models. However, this does not completely cancel the precipitation formation bias. Robust errors are also found in the evolution of cloud microphysical properties from nonprecipitating to drizzling and then to raining clouds in some GCMs, implying unrealistic interaction between precipitation and cloud water. Nevertheless, auspicious information is found for future improvement of warm precipitation representations: the adoption of more realistic autoconversion scheme in the high‐resolution model improves the triggering of precipitation, and the introduction of a sophisticated subgrid variability scheme in a traditional model improves the simulated precipitation frequency over subtropical eastern ocean. However, deterioration in other warm precipitation characteristics is also found accompanying these improvements, implying the multisource nature of warm precipitation biases in GCMs.

Scale‐Aware and Definition‐Aware Evaluation of Modeled Near‐Surface Precipitation Frequency Using CloudSat Observations

“…Excessive drizzle keeps the shallow convective regions pristine, reduces cloud lifetime, and makes low clouds more susceptible to aerosol indirect effects (e.g., Twohy et al, ). The mappable diagnostics introduced here provide a simple companion to process‐oriented diagnostics developed by Suzuki et al () and Jing et al () who also use the CloudSat simulator framework to evaluate model light rain biases.…”

“…While strong radar beam attenuation limits the use of CPR reflectivities in heavy precipitation, CloudSat measures precipitation frequency everywhere, including both light and heavy precipitation frequency (Ellis et al, ). Previous studies have demonstrated the value of reflectivity‐based evaluation of model precipitation using CloudSat (e.g., Bodas‐Salcedo et al, ; Jing et al, ; Marchand et al, ; Nam et al, ; Stephens et al, ; Suzuki et al, ; Zhang et al, ), though uncertainties exist [e.g., owing to inconsistencies in the drop size distribution assumptions applied in the radar forward model, sensitivity to the definition of subgrid precipitation fraction (Di Michele et al, ), and in upscaling the CloudSat observations to coarser model resolutions (Stephens et al, )].…”

Incorporation of inline warm-rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation

“…where i ∈ {cloud, drizzle, rain}, and n slwc is the total sample number of the SLWCs detected by CloudSat and MODIS retrievals within the grid box at longitude λ and latitude φ. This metric provides information about where and how the warmrain occurrence frequency and intensity are biased in the model relative to the satellite observations (Jing et al, 2017; The second diagnostic is the probability density function (PDF) of radar reflectivity profiles scaled as a function of the vertically sliced in-cloud optical depth (ICOD), and is commonly referred to as the contoured frequency by optical depth diagram (CFODD), as proposed by Nakajima et al (2010) and Suzuki et al (2010). The diagnostic reveals how the vertical microphysical structures of SLWCs tends to transition from non-precipitating to precipitating regimes as a fairly monotonic function of the cloud-top particle size.…”

Data Assimilation for Climate Research: Model Parameter Estimation of Large‐Scale Condensation Scheme

“…Using a GCM, Golaz et al (2013) investigated the sensitivity of climate simulations to a tuning parameter of autoconversion, the critical radius from rain to cloud. In addition to their GCM, Jing et al (2017) reported large intermodel variability of the autoconversion process among several global-scale models. This suggested that the GCMs have flawed representations of autoconversion.…”

“…This suggested that the GCMs have flawed representations of autoconversion. In addition to their GCM, Jing et al (2017) reported large intermodel variability of the autoconversion process among several global-scale models. These studies suggested that the determination of reasonable parameters for autoconversion play an important role in climate ©2020.…”