The Double‐ITCZ Bias in CMIP3, CMIP5, and CMIP6 Models Based on Annual Mean Precipitation

Abstract

The double-intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual double-ITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multi-model ensemble mean precipitation errors in the tropics. The notorious double-ITCZ bias and its big inter-model spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its inter-model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6.Plain Language Summary The double-intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models that may reduce the reliability of future climate prediction based on these models. The models have two ITCZs (i.e., zonally elongated narrow belt of high precipitation) in both hemispheres, over the equatorial central and eastern Pacific and Atlantic, instead of one ITCZ over the northern hemisphere in observations except for a short period in March and April. Here, we examine such bias in the latest models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) based on long-term annual mean precipitations in observations and models and compare the biases among the recent three generations of CMIP models (CMIP3, CMIP5, and CMIP6). We find that the double-ITCZ bias with a big inter-model spread persists in all CMIP models and still remains a serious problem in the latest CMIP6 models. However, the bias is slightly reduced in CMIP6 models from CMIP3 or CMIP5 models based on several precipitation bias indices. In addition, the annual equatorial Pacific cold tongue bias also persists in all CMIP models, but its inter-model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6.

Comparison of past and future simulations of ENSO in CMIP5/PMIP3 and CMIP6/PMIP4 models

“…More sophisticated feedback analysis revealed that the reduction of ENSO variability is due to either the increase of the negative feedback by the mean current thermal advec-tion (An and Bong, 2018) or the reduction of the major positive feedback processes (thermocline, zonal advection and Ekman feedbacks) (Chen et al, 2019a;Tian et al, 2017). The negative feedback due to the thermal advection by the mean current was intensified by the stronger cross-equatorial winds associated with the northward migration of the ITCZ (e.g.…”

“…Such distinct changes in ENSO characteristics through the 20th and 21st centuries may be related to low-frequency modulation by natural variability or the global warming trend due to increasing greenhouse gas concentrations, or a combination of natural and anthropogenic factors (e.g. An et al, 2008;Cai et al, 2015a;Collins, 2000;Gergis and Fowler, 2009;Timmermann et al, 1999;Trenberth and Hoar, 1997;Yang et al, 2018;Yeh et al, 2014;Yeh and Kirtman, 2007).…”

Evaluating the Performance of Climate Models Based on Wasserstein Distance

“…We evaluate next the problems still encountered by CMIP5 models in reproducing key aspects of tropical dynamics (Tian & Dong, 2020). Averaging the data over the Tropics, we obtain the ranking on the vertical axis in Figure 1.…”

Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Boreal Summer Land Precipitation Simulation With the NCAR CESM1.2

“…Precipitation simulation and projection in global climate models (GCMs) are of great scientific and societal interest. Although some impressive improvements have been made in recent decades (e.g., Wang et al., 2016; Xie et al., 2019; G. J. Zhang & Wang, 2006), GCMs, to some extent, are still suffering from many deficiencies (e.g., weak synoptic and intraseasonal variabilities, the double Intertropical Convergence Zone, and underestimated precipitation extremes) in various aspects of precipitation simulation (Flato et al., 2013; Lin et al., 2006; Tian & Dong, 2020; Zhang et al., 2013), among which the biases over some land areas (e.g., southern China, central India, and the US Great Plains) are stubborn (Harding et al., 2013; Huang et al., 2013; Koutroulis et al., 2016; Kumar et al., 2014; Mehran et al., 2014; Phillips & Gleckler, 2006).…”