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

Abstract

Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) has been widely 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 COSP2, the latest version of the 5 simulator. The approach used here employs existing diagnostic methodologies that probe how the warm-rain processes occur using statistics constructed from simulators of multiple satellite instruments along with their subcolumn information. The new diagnostics are designed to produce statistics online 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. This inline tool also generates global maps of the 10 occurrence frequency of warm-rain regimes (i.e., non-precipitating, drizzling, and precipitating) classified according to Cloud-Sat radar reflectivity, putting the warm-rain process diagnostics into the context of geographical distributions of precipitation.The inline diagnostics are applied to the MIROC6 GCM to demonstrate how known biases common among multiple GCMs relative to satellite observations are revealed. The inline multisensor 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. 1 MotivationClouds play a critical role in the global climate system by controlling the hydrological cycle and radiation budget (Wood, 2012; L'Ecuyer et al., 2015;Matus and L'Ecuyer, 2017). However, general circulation models (GCMs) still contain large uncertainties related to cloud processes associated with subgrid-scale parameterizations, cloud feedbacks, and microphysics 20 (Bretherton, 2015; Gettelman and Sherwood, 2016;Mülmenstädt and Feingold, 2018). In particular, modeling aerosol-cloud interactions remains challenging (Boucher et al., 2013;Myhre et al., 2013) because warm-rain processes, which are central 1 https://doi.

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.…”

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

“…If the models overestimate the occurrence frequency of snow, then the potential phase change from snow to rain will also be over‐represented. The key point is that although snow has a large effect on the radiation budget through both the greenhouse and parasol effects (Li et al., 2019, 2020; Michibata, Suzuki, Sekiguchi, et al., 2019), the radiative effect of rain is negligible (Hill et al., 2018). Given the different radiative roles of rain and snow on in terms of climate, the precipitation phase change affects the climate sensitivity (Cesana et al., 2021).…”

“…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).…”

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

“…We have introduced two new diagnostics: (1) the occurrence frequencies of non-precipitating clouds (Z max < −15 dBZ e ), drizzling clouds (−15 dBZ e < Z max < 0 dBZ e ), and precipitating clouds (0 dBZ e < Z max ) and 2) the PDF distributions of radar reflectivity profiles normalized by ICOD, the so-called contoured frequency by optical depth diagram (CFODD). These diagnostics make synergistic use of the CloudSat and MODIS simulators (Michibata et al, 2019c).…”

“…The source code of COSP2 for the online diagnostics used in this study is available at https://doi.org/10.5281/zenodo.1442468 (Michibata et al, 2019b). Post-processing code and reference A-Train statistics can be found at https://doi.org/10.5281/zenodo.3370823 (Michibata et al, 2019c). The results of the MIROC6 simulation used to produce the figures are also included in the post-process package.…”