Long‐Lived Mesoscale Convective Systems of Superparameterized CAM and the Response of CAM

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.

Mesoscale Convective Systems in a Superparameterized E3SM Simulation at High Resolution

“…Another approach recently employed is a feature‐tracking approach to detect features of MCSs within the embedded CRMs of the host‐GCM. For example, Chen and Kirtman (2018) applied the criteria of Yuan and Houze (2010) to detect MCSs in 2D CRMs within the 1‐degree SP‐CAM and used a clustering algorithm to group MCS candidate columns into larger coherent structures for tracking. Because our SP simulations are performed at the unprecedented host‐GCM resolution of 0.25°, we are able to apply another tracking approach that has been developed based on observations and applied to both observations and GCMs at multiple resolutions (Feng et al., 2021).…”

Estimates of Decadal Climate Predictability From an Interactive Ensemble Model

“…For example, decadal variability associated with ocean eddy activities and coupled air-sea feedbacks could be weak or even absent in low-resolution models, high-resolution CCSM4 with eddy-resolved ocean component model produces more realistic decadal variability (Chen & Kirtman, 2018;Laurindo et al, 2018;Siqueira & Kirtman, 2016). We speculate this underestimate (at least in CCSM4) could be possibly due to underresolved ocean mesoscale features and underestimated coupled feedbacks.…”

“…We speculate this underestimate (at least in CCSM4) could be possibly due to underresolved ocean mesoscale features and underestimated coupled feedbacks. For example, decadal variability associated with ocean eddy activities and coupled air-sea feedbacks could be weak or even absent in low-resolution models, high-resolution CCSM4 with eddy-resolved ocean component model produces more realistic decadal variability (Chen & Kirtman, 2018;Laurindo et al, 2018;Siqueira & Kirtman, 2016).…”

Toward Form-Function Relationships for Mesoscale Structure in Convection

“…SP models can simulate surprisingly subtle phenomena such as the nocturnal precipitation peak in central US climatology ascribed to propagating mesoscale storms (Carbone et al 2002), a version of which somehow emerges from SP-permitted scales alone (Pritchard et al 2011, Kooperman et al 2013, Elliott et al 2016. Other emergent effects in SP-CAM are described by Chen and Kirtman (2018). A new frontier of SP dubbed ultra-parameterization (Grabowski 2016) has only 4km CCPM domains, with a 250m grid (Parishani et al 2018)!…”