Intermodel uncertainty in response of the Pacific Walker circulation to global warming

Abstract

The Pacific Walker circulation (PWC) is one of the major atmospheric circulations that plays an essential role in ocean-atmosphere interactions and global climate. The response of the PWC to greenhouse warming remains a mystery and model results are inconsistent. Based on multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6), this study explores the intermodel uncertainty of the change in the PWC under global warming. The combination of the El Niño-like warming pattern and the interbasin warming contrast between the Indian and Pacific Oceans strengthens (weakens) the west (east) branch of the Pacific trade winds, resulting in a structural shifting of the PWC. By conducting a set of Atmosphere General Circulation Model (AGCM) experiments, we demonstrate that the western Pacific warming plays a critical role in driving the PWC shift. An intensified western Pacific warming counteracts the effect of Indian Ocean warming on the PWC, leading to a uniformly weakened PWC in the tropical Pacific due to the SST gradient. In contrast, a decreased warming in the western Pacific strengthens the west branch of the PWC, shifting the turning longitude of zonal wind changes eastward. Our finding highlights that the relative warming pattern in the Indian and Pacific Oceans is coupled with the PWC change in a warmer climate.

Appendices

Appendix A

Design of the CAM5 experiments.

Previous studies have demonstrated that the enhanced tropical Indian Ocean warming plays an important role in modulating the Pacific trade winds (Luo et al. 2012; Zhang et al. 2019). We firstly conduct two experiments to verify the effect of Indian Ocean warming on the PWC. In addition to the control run (CTRL), which is forced by the observed monthly climatological SST and sea ice concentration, an experiment named WARMIO impose 1-K warming in the tropical Indian Ocean (25°S-25°N, 30°E-130°E) to mimic the Indian Ocean warming (Fig. 14a). As a cosine function, the imposed warming is largest in the center of the basin (80°E, equator) and gradually decreases in the surrounding areas.

Most CMIP6 models project an El Niño-like warming over the Pacific Ocean. To investigate the combined effect of the Indian Ocean warming and the El Niño-like warming on the PWC, we then design another series of experiments, imposing both 1-K Indian Ocean warming and 2-K equatorial Pacific (5°S-5°N, 130°E-90°W) warming. The imposed warming is largest in the eastern Pacific cold tongue (100°W, equator) and gradually decreases westward as a sine function. Considering the large intermodel diversity of the western Pacific warming, we set three different drop rates to simulate the various patterns of the SST warming in the tropical Pacific, with the imposed warming decreasing to 0 K, 0.5 K, and 1 K at 160°E, named WP0K, WP0.5 K, and WP1K, respectively (Fig. 14b, c, d). The SST warming is constant throughout the year. Each experiment runs for 30 years. The climatological mean of the last 20-year of each experiment is analyzed.

Appendix B

Correction method for the Pacific trade winds projections.

The 36-year (1979–2014) mean precipitation with the IPO signal linearly removed in the eWCP (2.5°S-2.5°N, 140°E-170°W) from CMIP6 historical simulation and GPCP dataset are called $P\left(m\right)$ and $P\left(obs\right)$, respectively. The IPO signal is represented by the IPO tripole index (TPI, Henley et al. 2015) in this study. The model bias of eWCP precipitation is calculated as:

$$\begin{array}{c}{\text{P}}^{{\prime }}\left(m\right)=P\left(\text{m}\right)-P\left(\text{o}\text{b}\text{s}\right)\ \left(\text{A}1\right)\end{array}$$

where obs and m donate observation and individual model, respectively.

By conducting a linear regression analysis, we get the regression patterns “$a\left(s\right)$” and “$b\left(s\right)$” which express the present-future relationship between the climatologically mean eWCP precipitation “$P\left(m\right)$” and the surface zonal winds change “$C(s,m)$” in CMIP6 models:

$$\begin{array}{c}C\left(s,m\right)=a\left(s\right)\times P\left(m\right)+b\left(s\right)\ \left(\text{A}2\right)\end{array}$$

where s denotes space (longitude $\times$ latitude).

Given the significant correlation between present simulations and future projections, the error changes in surface zonal winds “${C}^{{\prime }}\left(s,m\right)$” of an individual model can be estimated by the regression coefficient “$a\left(s\right)$” and the precipitation bias “${\text{P}}^{{\prime }}\left(m\right)$”, which represent the magnitudes of the present-future relationship and the bias, respectively.

$$\begin{array}{c}{C}^{{\prime }}\left(s,m\right)=a\left(s\right)\times {\text{P}}^{{\prime }}\left(m\right)\ \left(\text{A}3\right)\end{array}$$

Then we get the corrected result for each model:

$$\begin{array}{c}{C}^{*}\left(s,m\right)=C\left(s,m\right)-{C}^{{\prime }}\left(s,m\right)\ \left(\text{A}4\right)\end{array}$$