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Abstract. One of the challenges inrepresenting warm rain processes in global climate models (GCMs) is relatedto the representation of the subgrid variability of cloud properties, such ascloud water and cloud droplet number concentration (CDNC), and the effectthereof on individual precipitation processes such as autoconversion. Thiseffect is conventionally treated by multiplying the resolved-scale warm rainprocess rates by an enhancement factor (E_q) which is derived fromintegrating over an assumed subgrid cloud water distribution. The assumedsubgrid cloud distribution remains highly uncertain. In this study, we derivethe subgrid variations of liquid-phase cloud properties over the tropicalocean using the satellite remote sensing products from Moderate ResolutionImaging Spectroradiometer (MODIS) and investigate the correspondingenhancement factors for the GCM parameterization of autoconversion rate. Wefind that the conventional approach of using only subgrid variability ofcloud water is insufficient and that the subgrid variability of CDNC, as wellas the correlation between the two, is also important for correctlysimulating the autoconversion process in GCMs. Using the MODIS data whichhave near-global data coverage, we find that E_q shows a strongdependence on cloud regimes due to the fact that the subgrid variability ofcloud water and CDNC is regime dependent. Our analysis shows a significantincrease of E_q from the stratocumulus (Sc) to cumulus (Cu) regions.Furthermore, the enhancement factor E_N due to the subgrid variation ofCDNC is derived from satellite observation for the first time, and resultsreveal several regions downwind of biomass burning aerosols (e.g., Gulf ofGuinea, east coast of South Africa), air pollution (i.e., East China Sea),and active volcanos (e.g., Kilauea, Hawaii, and Ambae, Vanuatu), where theE_N is comparable to or even larger than E_q, suggesting an importantrole of aerosol in influencing the E_N. MODIS observations suggest thatthe subgrid variations of cloud liquid water path (LWP) and CDNC aregenerally positively correlated. As a result, the combined enhancementfactor, including the effect of LWP and CDNC correlation, is significantlysmaller than the simple product of E_q⋅E_N. Given the importanceof warm rain processes in understanding the Earth's system dynamics and watercycle, we conclude that more observational studies are needed to provide abetter constraint on the warm rain processes in GCMs. 

Numerous studies have indicated that El Niño and the Southern Oscillation (ENSO) could have determinant impacts on remote weather and climate using the conventional correlation-based methods, which however cannot identify cause-and-effect of such linkage and ultimately determine a direction of causality. This study employs the Vector Auto-Regressive (VAR) model estimation method with the long-term observational data and reanalysis data to demonstrate that ENSO is the modulating factor that can result in abnormal surface temperature, pressure, precipitation and wind circulation remotely. We also carry out the sensitivity simulations using the Community Atmospheric Model (CAM) to further support the causality relations between ENSO and abnormal climate events in remote regions. 

Abstract. Satellite cloud observations have become an indispensable tool for evaluatinggeneral circulation models (GCMs). To facilitate the satellite and GCMcomparisons, the CFMIP (Cloud Feedback Model Inter-comparison Project)Observation Simulator Package (COSP) has been developed and is nowincreasingly used in GCM evaluations. Real-world clouds and precipitation canhave significant sub-grid variations, which, however, are often ignored oroversimplified in the COSP simulation. In this study, we use COSP cloudsimulations from the Super-Parameterized Community Atmosphere Model (SPCAM5)and satellite observations from the Moderate Resolution ImagingSpectroradiometer (MODIS) and CloudSat to demonstrate the importance ofconsidering the sub-grid variability of cloud and precipitation when usingthe COSP to evaluate GCM simulations. We carry out two sensitivity tests:SPCAM5 COSP and SPCAM5-Homogeneous COSP. In the SPCAM5 COSP run, the sub-gridcloud and precipitation properties from the embeddedcloud-resolving model (CRM) of SPCAM5 are used to drive the COSP simulation, while inthe SPCAM5-Homogeneous COSP run only grid-mean cloud and precipitationproperties (i.e., no sub-grid variations) are given to the COSP. We find thatthe warm rain signatures in the SPCAM5 COSP run agree with the MODIS andCloudSat observations quite well. In contrast, the SPCAM5-Homogeneous COSPrun which ignores the sub-grid cloud variations substantially overestimatesthe radar reflectivity and probability of precipitation compared to thesatellite observations, as well as the results from the SPCAM5 COSP run. Thesignificant differences between the two COSP runs demonstrate that it isimportant to take into account the sub-grid variations of cloud andprecipitation when using COSP to evaluate the GCM to avoid confusing andmisleading results.