Characterizing CMIP5 model spread in simulated rainfall in the Pacific Intertropical Convergence and South Pacific Convergence Zones

Abstract

Current‐generation climate models exhibit various errors or biases in both the spatial distribution and intensity of precipitation relative to observations. In this study, empirical orthogonal function analysis is applied to the space‐model index domain of precipitation over the Pacific from Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations to explore systematic spread of simulated precipitation characteristics across the ensemble. Two significant modes of spread, generically termed principal uncertainty patterns (PUPs), are identified in the December‐January‐February precipitation climatology: the leading PUP is associated with the meridional width of deep convection, while the second is associated with tradeoffs in precipitation intensity along the South Pacific Convergence Zone, the Intertropical Convergence Zone (ITCZ), and the spurious Southern Hemisphere ITCZ. An important factor distinguishing PUPs from the analogy to time series analysis is that the modes can reflect either true systematic intermodel variance patterns or internal variability. In order to establish that the PUPS reflect the former, three complementary tests are performed by using preindustrial control simulations: a bootstrap significance test for reproducibility of the intermodel spatial patterns, a check for robustness over very long climatological averages, and a test on the loadings of these patterns relative to interdecadal sampling. Composite analysis based on these PUPs demonstrates physically plausible relationships to CMIP5 ensemble spread in simulated sea surface temperatures (SSTs), circulation, and moisture. Further analysis of atmosphere‐only, prescribed SST simulations demonstrates decreased spread in the spatial distribution of precipitation, while substantial spread in intensity remains.

Quantifying the Zonal‐Mean Structure of Tropical Precipitation

“…In AMIP simulations there is less spread in the precipitation maxima and so different indicators can be used to reproduce the profile of tropical precipitation. This picture is consistent with the results obtained by Li and Xie () and Lintner et al () who find that in the tropical Pacific there is larger spread in the position and width of the ITCZ in CMIP than in AMIP simulations, but that substantial biases in the amount of precipitation remain in AMIP. In particular, Li and Xie () also find that more than half the variance between models in precipitation in the tropical Pacific is associated with the double‐ITCZ bias in the CMIP simulations.…”

“…Previous studies have characterized aspects of the tropical precipitation in models, observations, or reanalysis using empirical orthogonal functions (Li & Xie, 2014;Lintner et al, 2016), the "tropical precipitation asymmetry index" (Adam, Schneider, et al, 2016;Hwang & Frierson, 2013;Xiang et al, 2017), the "equatorial precipitation index" (Adam et al, 2016), spatial correlations in precipitation between models and observations (Zhang et al, 2015), or simply by determining the position (e.g., Gruber, 1972;Mechoso et al, 1995;Lin, 2007) and the width (Dias & Pauluis, 2011;Byrne & Schneider, 2016b) of the Intertropical Convergence Zone (ITCZ). We define here the ITCZ as the region of the maxima in zonal-mean precipitation.…”

Using Atmospheric Energy Transport to Quantitatively Constrain South Pacific Convergence Zone Shifts during ENSO

“…We suggest that the 2D energetics framework may ultimately provide a useful tool for multimodel analysis, particularly in terms of interpreting some of the problematic tropical precipitation biases models in currentgeneration climate models. Among these, the double ITCZ in the eastern Pacific is arguably the best known, but the SPCZ also manifests biases; for example, some current generation models simulate a too zonal SPCZ and/or SPCZ rainfall extending too far to the east, particularly in coupled models (Lintner et al 2016). Given that model biases likely involve the details of the parameterizations involved, the 2D energetics framework is unlikely to identify directly the bias sources.…”

“…Enhanced low-level MSE during El Niño may be expected to support precipitating deep convection in the otherwise climatologically unfavorable region for deep convection (in a thermodynamic sense) located to the northeast of the SPCZ. Takahashi and Battisti (2007) and Lintner and Neelin (2008) also highlight the potential role of southeasterly trade wind variability, particularly in determining the position of the eastern margin of the SPCZ: with the slackened trades during El Niño, horizontal advection of relatively low MSE (cool and dry) air into the SPCZ from the southeastern tropical Pacific is suppressed, which supports convection occurring to the east of its mean position. Other studies, particularly at high frequencies, have emphasized upper-level forcing associated with extratropical-tropical interactions, Rossby wave dynamics, and equatorial waves (Matthews et al 1996;Widlansky et al 2011;Matthews 2012) While past work has no doubt provided much insight into potential physical mechanisms underlying the SPCZ spatial displacements, many of the studies have been largely qualitative.…”

Multiobjective constraints for climate model parameter choices: PragmaticPareto fronts in CESM1

“…This study focuses on the tropical Pacific climate at the seasonal time scale, where uncertainty is largely due to under-constrained moist processes. An inexhaustive list of GCM issues in this region includes excessive precipitation in the Southern Hemisphere and the double intertropical convergence zone (ITCZ) [Dai, 2006;Lin, 2007], issues with dynamics related to the El Niño-Southern Oscillation (ENSO) [Latif et al, 2001] and the South Pacific Convergence Zone (SPCZ) [Brown et al, 2010;Lintner et al, 2016], sea surface temperature biases leading to the excessive equatorial cold tongue [Li and Xie, 2014], issues in simulating the three-dimensional structure of moisture and temperature in the atmosphere [Tian et al, 2013], persistent errors representing clouds and microphysics [Bony and Dufresne, 2005], and uncertainty related to land-sea contrasts and representation of topography, particularly over the Amazon [Yin et al, 2013].…”