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

Abstract

Abstract. The Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) is used to diagnose model performance and physical processes via an apple-to-apple comparison to satellite measurements. Although the COSP provides useful information about clouds and their climatic impact, outputs that have a subcolumn dimension require large amounts of data. This can cause a bottleneck when conducting sets of sensitivity experiments or multiple model intercomparisons. Here, we incorporate two diagnostics for warm rain microphysical processes into the latest version of the simulator (COSP2). The first one is the occurrence frequency of warm rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to CloudSat radar reflectivity, putting the warm rain process diagnostics into the context of the geographical distributions of precipitation. The second diagnostic is the probability density function of radar reflectivity profiles normalized by the in-cloud optical depth, the so-called contoured frequency by optical depth diagram (CFODD), which illustrates how the warm rain processes occur in the vertical dimension using statistics constructed from CloudSat and MODIS simulators. The new diagnostics are designed to produce statistics online along with subcolumn information during the COSP execution, eliminating the need to output subcolumn variables. Users can also readily conduct regional analysis tailored to their particular research interest (e.g., land–ocean differences) using an auxiliary post-process package after the COSP calculation. The inline diagnostics are applied to the MIROC6 general circulation model (GCM) to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multi-sensor diagnostics are intended to serve as a tool that facilitates process-oriented model evaluations in a manner that reduces the burden on modelers for their diagnostics effort.

Reconciling Compensating Errors Between Precipitation Constraints and the Energy Budget in a Climate Model

“…This method, referred to as the contoured frequency by optical depth diagram (CFODD; Nakajima et al, 2010), is particularly useful in evaluations of how the vertical microphysical structures of warm clouds differs between nonprecipitating and precipitating regimes depending on the cloud‐top effective radius R e (Suzuki et al, 2010). These diagnostics have also been implemented in COSP2 as an inline warm‐rain diagnostic tool (Michibata, Suzuki, Ogura, et al, 2019), which is employed in this study.…”

Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions

“…The WRDs take as inputs gridbox-mean simulated MODIS retrievals of LWP, IWP, COT and Re, as well as subcolumn CloudSat reflectivity profiles. Deviations from the original WRDs implemented in COSPv2.0 (Michibata et al, 2019b) include the application of the simulated CloudSat ground-clutter filter (available in COSPv2.0, but not applied to the WRDs previously) for better comparison with CloudSat retrievals, and the elimination of the "fracout" input used in the SLWC detection scheme from SCOPS. "Fracout" is the subcolumn-level cloud classification by vertical level from SCOPS, where each level of each subcolumn is designated as large-scale stratiform, convective, or clear-sky.…”

“…Autoconversion perturbations affect base cloud state (e.g., LWP, cloud fraction) and could, for example, cause stronger ERFaci by increasing cloud amount rather than increasing the impact of ACI on SW radiative forcing. Jing et al ( 2019) evaluated different autoconversion parameterization schemes in an ESM using the CFODD analysis described in Michibata et al (2019b) and found that the autoconversion scheme that yielded the best warm rain representation predicted a significantly stronger ERFaci that exceeded the uncertainty range of the IPCC AR5 and canceled out much of the warming trend of the last century. The conflict between process representation and ERFaci predictions in Jing et al ( 2019) underscore a challenge with process-based constraints: improving the representation of a process can result in adverse outcomes to climate prediction due to compensating biases in the model.…”

Too Frequent and Too Light Arctic Snowfall With Incorrect Precipitation Phase Partitioning in the MIROC6 GCM

“…Instrument simulators replicate synthetic retrievals of individual observations using ISCCP (Klein & Jakob, 1999;Webb et al, 2001), MISR (Marchand & Ackerman, 2010), PARASOL (Konsta et al, 2016), MODIS (Pincus et al, 2012), CALIPSO (Chepfer et al, 2008), and CloudSat (Haynes et al, 2007) simulators. COSP summarizes statistics based on individual simulators into output histograms and diagnostics (e.g., Michibata, Suzuki, Ogura, & Jing, 2019).…”

“…For simplicity, we extracted the single-layer warm clouds (SLWCs) from the COSP output and satellite retrievals following Michibata, Suzuki, Ogura, et al (2019). Following Michibata et al (2016), the cloud layer was defined as being where the CPR cloud mask is >30, CPR radar reflectivity is higher than −30 dBZe, MODIS cloud optical depth is >0.1 (within an uncertainty of <5), and the MODIS cloud effective radius is 5-35 μm (within an uncertainty of <1 μm).…”