Evaluation of the Warm Rain Formation Process in Global Models with Satellite Observations

Abstract

This study examines the warm rain formation process over the global ocean in global climate models. Methodologies developed to analyze CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations are employed to investigate the cloud-to-precipitation process of warm clouds and are applied to the model results to examine how the models represent the process for warm stratiform clouds. Despite a limitation of the present study that compares the statistics for stratiform clouds in climate models with those from satellite observations, including both stratiform and (shallow) convective clouds, the statistics constructed with the methodologies are compared between the models and satellite observations to expose their similarities and differences. A problem common to some models is that they tend to produce rain at a faster rate than is observed. These model characteristics are further examined in the context of cloud microphysics parameterizations using a simplified one-dimensional model of warm rain formation that isolates key microphysical processes from full interactions with other processes in global climate models. The one-dimensional model equivalent statistics reproduce key characteristics of the global model statistics when corresponding autoconversion schemes are assumed in the one-dimensional model. The global model characteristics depicted by the statistics are then interpreted as reflecting behaviors of the autoconversion parameterizations adopted in the models. Comparisons of the one-dimensional model with satellite observations hint at improvements to the formulation of the parameterization scheme, thus offering a novel way of constraining key parameters in autoconversion schemes of global models.

The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals

“…Current microphysical frameworks without such macrophysical aspects bring highly sensitive aerosol-cloud interactions, although they vary to some extent depending on the autoconversion scheme (Gettelman, 2015;Suzuki et al, 2015;Michibata and Takemura, 2015). Ghan et al (2016) also reported a wide diversity in the LWP response to N c among various GCMs, and concluded that their inconsistency could mainly be attributed to their different representations of the autoconversion process.…”

“…1b, MIROC overestimates S conv , particularly in the lower LWP range. This means that the model generates precipitation at a higher frequency, even at low LWPs, compared to observations, which is mainly because the autoconversion in the model is too rapid (Michibata and Takemura, 2015;Suzuki et al, 2015), as described above. Consequently, the PDF of the LWP is biased toward lower values because cloud water is depleted quickly by the rapid surface precipitation.…”

“…A commonly known problem in GCMs associated with low cloud precipitation microphysics is the timing of precipitation and its frequency and/or intensity (Suzuki et al, 2013b(Suzuki et al, , 2015. This issue is also related to the magnitudes of the aerosol indirect effect, i.e., dependency of precipitation on N c .…”

Assessing the impact of the Kuroshio Current on vertical cloud structure using CloudSat data

“…However, the general circulation models (GCMs) underestimated (overestimated) the cloud fraction (the radiative effect) in tropical low-level clouds (Nam et al, 2012) -so-called "too few, too bright" problem. Moreover, Suzuki et al (2015) reported that the warm cloud auto-conversion process in the GCMs was too rapid compared to that derived from satellite observation. It is necessary to understand the vertical structure of clouds to reduce the cloud bias in the GCMs.…”

The Importance of Considering Sub-grid Cloud Variability When Using Satellite Observations to Evaluate the Cloud and Precipitation Simulations in Climate Models

“…Here we employ the Super-122 parameterized Community Atmosphere Model Version 5 (SPCAM5, Wang et al, 2015) to 123 provide the sub-grid cloud and precipitation hydrometeor fields for a comparison study of the 124 simulated radar reflectivity and warm rain frequencies by COSP. Fundamentally different from 125 the convectional cloud parameterization schemes in GCMs, SPCAM5 consists of a two-126 dimensional cloud-resolving model (CRM) embedded into each grid of a conventional CAM5 127 Wang et al, 2015). The sub-grid cloud dynamical and 128 microphysical processes are explicitly resolved at a 4-km resolution in SPCAM5.…”