Convective Transition Statistics over Tropical Oceans for Climate Model Diagnostics: GCM Evaluation

Abstract

To assess deep convective parameterizations in a variety of GCMs and examine the fast-time-scale convective transition, a set of statistics characterizing the pickup of precipitation as a function of column water vapor (CWV), PDFs and joint PDFs of CWV and precipitation, and the dependence of the moisture–precipitation relation on tropospheric temperature is evaluated using the hourly output of two versions of the GFDL Atmospheric Model, version 4 (AM4), NCAR CAM5 and superparameterized CAM (SPCAM). The 6-hourly output from the MJO Task Force (MJOTF)/GEWEX Atmospheric System Study (GASS) project is also analyzed. Contrasting statistics produced from individual models that primarily differ in representations of moist convection suggest that convective transition statistics can substantially distinguish differences in convective representation and its interaction with the large-scale flow, while models that differ only in spatial–temporal resolution, microphysics, or ocean–atmosphere coupling result in similar statistics. Most of the models simulate some version of the observed sharp increase in precipitation as CWV exceeds a critical value, as well as that convective onset occurs at higher CWV but at lower column RH as temperature increases. While some models quantitatively capture these observed features and associated probability distributions, considerable intermodel spread and departures from observations in various aspects of the precipitation–CWV relationship are noted. For instance, in many of the models, the transition from the low-CWV, nonprecipitating regime to the moist regime for CWV around and above critical is less abrupt than in observations. Additionally, some models overproduce drizzle at low CWV, and some require CWV higher than observed for strong precipitation. For many of the models, it is particularly challenging to simulate the probability distributions of CWV at high temperature.

Cascading Toward a Kilometer‐Scale GCM: Impacts of a Scale‐Aware Convection Parameterization in the Goddard Earth Observing System GCM

“…A critical variable related to the temporal evolution and intensity of deep convective precipitation particularly in the tropics is column, or precipitable, water vapor (CWV) (e.g., Adams et al (2013, 2017), Ahmed & Schumacher (2015), Bretherton et al (2004), Holloway & Neelin (2009), Kuo et al (2020), Lintner et al (2017), Stevens et al (2019)). We combined the August 2016 GPM daily mean precipitation rate at 0.1° with the daily mean CWV from ERA‐5 global reanalysis after regridding onto the GPM grid resolution.…”

Fifty Years of Research on the Madden‐Julian Oscillation: Recent Progress, Challenges, and Perspectives

“…In a dry environment, a rising convective parcel can lose its buoyancy quickly due to dilution by turbulent entrainment and resulting evaporative cooling within the parcel, limiting the depth of convective penetration, and favoring shallow cumuli. As a result, heavy area‐averaged rainfall associated with oceanic deep convection mostly occurs in moist environments as shown in Figure 3 (Adames, 2017; Bretherton et al, 2004; Kuo et al, 2019; O. Peters & Neelin, 2006; Rushley et al, 2018; Thayer‐Calder & Randall, 2009). A particularly strong coupling between moisture and convection is observed for MJO wavenumbers and frequencies (Yasunaga & Mapes, 2012), illuminating the crucial role of the convection‐moisture feedbacks for the MJO.…”

Multivariate Process-Based Evaluation of CMIP6 Historical Simulations Over the Tropical Oceans

“…The second was the difference in Ni34 index, corresponding to the well-known ENSO modulation of tropical precipitation (Trenberth, 1997;van Oldenborgh et al 2005;Nogueira, 2019c). The third was the difference in atmospheric water vapor content variability, reflecting an emergent relation between P and W over the tropical oceans previously found in models and observations (Bretherton et al, 2004;Peters and Neelin, 2006;Rushley et al, 2018;Kuo et al, 2020). The fourth was the difference in DLR variability, in agreement with the longwave energy constraints of precipitation at global scale (Allen & Ingram, 2002;Stephens & Ellis, 2008;Nogueira,2019b) and at regional scale over the tropical oceans (Nogueira, 2019c).…”