Abstract
Mesoscale organized convection is generally misrepresented in the large‐scale convective parameterizations used in contemporary climate models. This impacts extreme weather events (e.g., Madden‐Jullian Oscillation) and the general circulation driven by the significant amount of latent heat released from mesoscale organized convection. Studies show that the missing processes could be partially recovered by embedding a 2‐D cloud‐resolving model in each general circulation model columns, that is, superparameterization. To enable analysis of mesoscale convective systems (MCSs) in the multiscale modeling framework, we apply a detection and hierarchical clustering algorithm on the 3‐hourly 2‐D cloud‐resolving model embedded in the superparameterized Community Atmosphere Model (SPCAM) 5.2. We then examine the fields of a long‐lived and large MCS cluster at the central Pacific. The MCS cluster shows a squall line‐like circulation throughout the life cycle in SPCAM. We simultaneously obtain the 3‐hourly CAM parameterized convection outputs based on the time step‐wise perfect initial conditions given by SPCAM. This allows pure model physics comparison without introducing initial condition errors. The results show that CAM has a systematically biased stratiform cooling and moistening response below 3 km to the given SPCAM deep convection favoring conditions. We show that this bias is mainly due to the CAM's stratiform microphysics scheme. The mesoscale organization in SPCAM thus provides a baseline for improvements of convective parameterization of CAM.
Plain Language Summary
There remain uncertainties in climate models that impact the regional prediction of floods and droughts. One of the major uncertainties impacting the rainfall pattern lies in the accurate representation of clouds, which are better captured by day‐to‐day weather models. As technology advances, the cost of high‐resolution global simulations has been declining but remains high for long‐term climate forecasts. Therefore, a compromised approach has been developed to embed high‐resolution models in a climate model in a unique fashion to retain realistic weather events. This nested model has shown improvements in simulating subseasonal extreme weather events and gain popularity over traditional climate models. We performed a comparison of the traditional versus the nested climate model and found drastically different results of a major physical cloud process. This finding allows us to propose new approaches to improve the traditional climate model on a cost‐effective basis.
Key Points
A rigorous detection and hierarchical clustering algorithm is implemented to track MCS clusters in SPCAM
A long‐lived large MCS cluster shows realistic squall‐like circulation resembling convective organization by upgradient momentum transport
CAM shows biased stratiform heating and moisture mode given deep convection conditions, presumably caused by stratiform microphysics scheme
