Convective Momentum Transport and Its Impact on the Madden‐Julian Oscillation in E3SM‐MMF

Abstract

Convective momentum transport (CMT) is the process of vertical redistribution of horizontal momentum by small‐scale turbulent flows from moist convection. Traditional general circulation models (GCMs) and their multiscale modeling framework (MMF) versions poorly represent CMT due to insufficient information of subgrid‐scale flows at each GCM grid. Here the explicit scalar momentum transport (ESMT) scheme for representing CMT is implemented in the Energy Exascale Earth System Model‐Multiscale Modeling Framework (E3SM‐MMF) with embedded 2‐D cloud‐resolving models (CRMs), and verified against E3SM‐MMF simulations with 3‐D CRMs and observations. The results show that representing CMT by ESMT helps reduce climatological mean precipitation model bias over the western Pacific and the ITCZ regions, which is attributed to the weakened mean easterlies over the Pacific. Also, CMT from simulations with 2‐D and 3‐D CRMs impose a similar impact on Kelvin waves by reducing their variability and slowing down their phase speed, but opposite impacts on the Madden‐Julian Oscillation (MJO) variability. The ESMT scheme readily captures the climatological mean spatial patterns of the zonal and meridional components of CMT and their variability across multiple time scales, but shows some differences in estimating its magnitude. CMT mainly affects the MJO by decelerating its winds in the free troposphere, but accelerates its near‐surface winds. This study serves as a prototype for implementing CMT scheme in the MMF simulations, highlighting its crucial role in reducing model bias in mean state and spatiotemporal variability.

Understanding Precipitation Bias Sensitivities in E3SM‐Multi‐Scale Modeling Framework From a Dilution Framework

“…This wet bias over the northwestern tropical Pacific is largely removed with the use of a 3D CRM domain (Figures S1d and S1e in Supporting Information ), consistent with the result of Yang et al. (2022). They also suggested that 3D produced a weaker MJO compared to the 2D case, which can be partly attributed to the different structure of background zonal flow.…”

“…The difference in precipitation between 2D and 3D is consistent with the findings of Yang et al. (2022), who also suggested that 3D produced a weaker MJO than 2D, partly due to difference in the zonal flow. However, in comparison to the striking effect of dimensionality, impacts of CMT are minor in reducing this precipitation bias.…”

Convective Self‐Aggregation Occurs Without Radiative Feedbacks in Warm Climates

“…(2021b) found that the role of radiative feedbacks in TC genesis diminished with surface warming. Therefore, both TCs and the MJO can emerge without radiative feedbacks, as demonstrated by studies using CRMs (Muller & Romps, 2018; Reyes & Yang, 2020; Wing et al., 2016), as well as high‐resolution GCMs (Hsieh et al., 2023; Zhang et al., 2021b) and super‐parameterized GCMs (Arnold & Randall, 2015; Yang & Hannah, 2022). In this study, we show that CSA can emerge under similar conditions, suggesting that the underlying physics of the three distinct phenomena may share a common foundation.…”

“…A detailed discussion of the LMSE analysis and its comparison to the conventional column MSE analysis can be found in Yao et al (2022). The framework has also been applied to study TCs and the MJO recently (Yang & Hannah, 2022;Zhang et al, 2022).…”

“…Note Equation 1 is weighted by 𝐴𝐴 𝐴𝐴0 instead of 𝐴𝐴 𝐴𝐴 2 0 as in Yao et al (2022). Then if we divide Equation 1 by the total column mass, we get a quantity with a unit of energy variance, consistent with Yang and Hannah (2022). The budget equation for the LMSE variance in the troposphere is given by…”