Quantifying the Zonal‐Mean Structure of Tropical Precipitation

Abstract

The tropical zonal‐mean precipitation in climate models is well known to have substantial biases such as an erroneous double intertropical convergence zone in the Pacific, but a comprehensive quantification of these biases is currently missing. Therefore, we introduce a set of nine indicators that fully characterize the position and magnitude of the tropical extrema in zonal‐mean precipitation. An analysis of the Coupled Model Intercomparison Project (CMIP) historical and Atmospheric Model Intercomparison Project (AMIP) simulations reveals large biases in the position and, especially, in the magnitude of the zonal‐mean precipitation extrema in both sets of simulations relative to observations. We find some of the nine indicators to be correlated, and that the structure of tropical precipitation can be well represented using four indicators, though these indicators are different in AMIP and CMIP. Previously defined indicators can only partly explain the biases, and so the more comprehensive terminology introduced here is a useful tool for characterizing tropical precipitation.

Stronger zonal convective clustering associated with a wider tropical rain belt

“…The substantial impact of the zonal clustering of convection on the tropical rainfall distribution suggests that the ability of models to represent the zonal convective clustering along the equator is of primary importance in the quest for more realistic predictions of the tropical rain belt. In particular, our study suggests that potential model biases in the zonal convective clustering could contribute to known biases in the width 15 and the double peak structure 56,57 of the ITCZ.…”

“…P E denotes the equatorial mean precipitation, W P the precipitation-inferred and W ω the dynamically inferred width of the intertropical convergence zone (ITCZ), ϕ S denotes the distance between the two peaks in zonal-mean precipitation following ref. 56 , T 2m denotes the average 2m temperature from 6S to 6N, Δ λ T 2m the zonal standard deviation in 2m temperature, Δ ϕ T 2m the difference in average 2m temperature between the zonal band from 6 S to 6 N and the two zonal bands from 10 to 16 degrees latitude, ∇ λ Á qu the zonal standard deviation of zonal moisture convergence at the equator normalized by the equatorial precipitation and ∇ ϕ Á qv the mean meridional moisture convergence. H rad denotes the vertically integrated atmospheric radiative cooling between 6 S and 6 N calculated from the CERES-EBAF data set and H ACRE its cloud-radiative contribution (only from March 2000 to December 2016).…”

Controls on the Width of Tropical Precipitation and Its Contraction Under Global Warming

“…Previous work has demonstrated that WA ITCZ over the Pacific has contracted in recent decades (Wodzicki & Rapp, ) and robustly contracts in climate models under increased atmospheric CO 2 concentrations (Byrne & Schneider, ; Lau & Kim, ) although the proposed mechanisms involve competing effects from transient‐eddy energy fluxes (Byrne & Schneider, ), gross moist stability of the tropics (Byrne & Schneider, ; Chou et al, ), and cloud radiative effects (Su et al, ). We note that while other studies have defined the ITCZ width as the tropical region of upwelling atmospheric motion (Byrne & Schneider, ; Lau & Kim, ), we focus on the meridional distance between the tropical precipitation peaks in each hemisphere that was previously identified as the “distance between the two ITCZs” (Popp & Lutsko, ).…”

Control of ITCZ Width by Low‐Level Radiative Heating From Upper‐Level Clouds in Aquaplanet Simulations

“…Their spatiotemporal variability affects the global climate on variety of time scales. The processes controlling the latitudinal extent (hereafter referred to as the width, see section 3) of the ITCZ have been explored extensively, since most climate models struggle in capturing even the spatiotemporally averaged mean width accurately (Byrne & Schneider, 2016a;Hwang & Frierson, 2013;Lin, 2007;Popp & Lutsko, 2017;Xiang et al, 2017;Zhang & Wang, 2006). Understanding the processes controlling the ITCZ width is important not only to improve the climate models (G. Li & Xie, 2014) but also to understand the response of hydrological cycle under climate change scenarios (e.g., DeAngelis et al, 2015;Held & Soden, 2006;Lambert & Webb, 2008;Su et al, 2017).…”