But not all of the observed variability of lower stratospheric water vapor in the tropics can be explained by changes in average tropopause temperature. Other zonally asymmetric processes may contribute  (Fueglistaler and Haynes, 2005). Climatologically in boreal wintertime, temperatures over the Indo-Pacific warm pool (IPWP) region are colder relative to the zonal mean near the tropopause, due to enhanced convection and diabatic heating here (Highwood and Hoskins, 1998). The cold temperatures over the IPWP region in boreal winter govern the amount of water vapor that can reach higher in the stratosphere (Mote et al., 1996). Previous studies found a correlation between the cold-point temperature and SSTs in the IPWP region (Rosenlof and Reid, 2008; Garfinkel et al., 2013). \citet{Zhou_2017} defined the warm/cold phase of the IPWP (the so-called IPWP Niño/Niña) according to the regional mean SST anomalies. They found that the IPWP can significantly affect stratospheric temperature, circulations, and the stratosphere-troposphere exchange. \citet{Xie_2018} showed that IPWP Niño dries the stratospheric water vapor by causing a broad cooling of the tropopause, and vice versa for IPWP Niña.
However,  the above studies have considered “well-mixed” tropics, and thus focused primarily on the variations in the tropical-wide average (20sheshiduS–20N) of the stratospheric water vapor. Recently, \citet{Stolarski_2014} showed significant differences in the observed seasonality of ozone between the northern and southern tropics (NT and ST, respectively), in contrast to usual assumption that the tropics can be treated as a horizontally homogeneous region. This implies that the paradigm of well-mixed tropics needs to be revised to consider latitudinal variations within the tropics.
In this study, we further examine the NT-ST difference in the effect of the IPWP Niño/Niña on the lower stratospheric water vapor. We first quantify the hemispheric difference of the lower stratospheric water vapor response to IPWP Niño/Niña events. We then examine the relative role of different transport processes and chemical production and loss in producing the lower stratospheric water vapor in the ST and NT. This analysis involves a more detailed Transformed Eulerian Mean (TEM) analysis of WACCM4 simulations.
The models used and method of analysis are described in the next section. In section 3 we quantify the hemispheric difference of the lower stratospheric water vapor response to IPWP Niño/Niña events, while in section 4 we discuss the dynamical processes that lead to the hemispheric contrast.

2 Model, Methods and Data  

2.1 WACCM4

We employ the Whole Atmosphere Community Climate Model, version 4 (WACCM4) (Marsh et al. 2013), which is the atmospheric component of the coupled climate system model Community Earth System Model (CESM) (Garcia et al. 2007). The standard version has 66 vertical levels extending from the ground to 4.5 × 10−6 hPa (160 km geometric altitude), with a vertical resolution of 1.1–1.4 km in the tropical tropopause layer and the lower stratosphere (< 30 km). All simulations use a horizontal resolution of 1.9× 2.5 (latitude × longitude) and do not include interactive chemistry (Garcia et al. 2007). Fixed greenhouse gas (GHG) values used in the model radiation scheme are based on emissions scenario A2 of the Intergovernmental Panel on Climate Change (IPCC) (WMO, 2003) over the period 1980–2015. And the prescribed ozone forcing, with a 12-month seasonal cycle averaged over the period 1980–2015 from CMIP5 ensemble mean ozone output, is used in our simulations. The Quasi Biennial Oscillation (QBO) forcing time series is determined using a 28-month fixed cycle.
A group of model integrations are used to isolate the impact of IPWP Niño and IPWP Niña on the SWV. Briefly, we examined three 30-year time-slice simulations forced by repeating annual cycles of SSTs that represent IPWP Niño and IPWP Niña. Composite SST anomalies for IPWP Niño and IPWP Niña (see Figs. 1c, and 1d in \citet{Zhou_2017}) are used to force the simulations. The key point is that these model integrations provide many samples of the atmospheric response to identical SSTa and are long enough to achieve statistical robustness  (Garfinkel et al. 2013b).