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Abstract We investigate a scaling relationship between global tropical cyclone (TC) frequency and the latitude of the intertropical convergence zone (ITCZ) in simulations performed with a 50‐km‐resolution aquaplanet version of the Geophysical Fluid Dynamics Laboratory Atmosphere Model 4.0. The simulations use fixed, zonally symmetric sea surface temperature distributions, including some with uniform warming and cooling perturbations. We find that TC frequency per unit area is proportional to the Coriolis parameter at the ITCZ, following the same scaling introduced in a previous study. We hypothesize that TCs in these simulations originate as precursor disturbances at the ITCZ and intensify into TCs upon reaching sufficiently warm SSTs. We test this interpretation by tracking TC precursors, with different methods based on precipitation and vorticity, and comparing TC precursor frequency with TC frequency and ITCZ latitude. Both tracking methods show that precursors predominantly originate around the poleward edge of the ITCZ, consistent with our hypothesized TC genesis pathway. We also verify that most TC genesis events are immediately preceded by the occurrence of a precursor in the same area. However, precursor frequency is only weakly correlated with the Coriolis parameter at the ITCZ and precursor frequency. The correlation is stronger for vorticity‐based precursors than for precipitation‐based precursors. These mixed results provide partial, but not complete, support for our hypothesized interpretation. They also illustrate how results can depend on the choice of precursor tracking scheme, underlining a need for improved understanding of how best to define and track TC precursors. 

Abstract The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework.