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Emergence of climate change in the tropical Pacific

Nature Climate Change volume 12, pages 356–364 (2022)Cite this article

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Abstract

Future changes in the mean climate of the tropical Pacific and characteristics of the El Niño/Southern Oscillation (ENSO) are established as being likely. Determining the time of emergence of climate change signals from the natural variability is critical for mitigation strategies and adaptation planning. Here, using a multimodel ensemble, we find that the annual-mean sea surface temperature (SST) signal has already emerged across much of the tropical Pacific, appearing last in the east. The signal of a wetter annual-mean rainfall in the east is expected to emerge by mid-century, with some sensitivity to emission scenario. However, the ENSO-related rainfall variability signal is projected to emerge by about 2040 regardless of emission scenario, about 30 years earlier than ENSO-related SST variability signal at about 2070. Our results are instructive for the detection of climate change signals and reinforce the rapidly emerging risks of ENSO-induced climate extremes regardless of mitigation actions.

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Fig. 1: MEM response and ToE of annual-mean SST and rainfall.
Fig. 2: ToE of annual-mean SST in the EEP.
Fig. 3: ToE of annual-mean rainfall in the EEP.
Fig. 4: MEM response and ToE of ENSO SST and rainfall.
Fig. 5: ToE of ENSO SST and rainfall in the EEP.

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Data availability

The CMIP6 model monthly outputs are archived at the Earth System Grid Federation server (https://esgf-node.llnl.gov/search/cmip6/). The HadISSTv1 dataset can be obtained from https://www.metoffice.gov.uk/hadobs/. The ERSSTv5 and COBEv2 datasets are both available from https://www.esrl.noaa.gov/psd/data/gridded/. All the outputs that support the findings of this study have been deposited in https://doi.org/10.5281/zenodo.588957564.

Code availability

All the codes are built on NCAR Command Language (NCL), available at https://doi.org/10.5281/zenodo.588576465.

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Acknowledgements

J.Y. was supported by the National Natural Science Foundation of China (grant numbers 41690121 and 41690120), the Scientific Research Fund of the Second Institute of Oceanography, Ministry of Natural Resources (grant number QNYC2001), the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (grant number 311021001) and the China Scholarship Council. M.C. was supported by a grant from the UK Natural Environment Research Council (NE/S004645/1). W.C. was supported by CSHOR, a joint research partnership between QNLM and CSIRO. A.T. and K.S. received funding from IBS under IBS-R028-D1. P.H. was supported by the National Natural Science Foundation of China (grant number 41975116) and the Youth Innovation Promotion Association of CAS (grant number 2016074). D.C. was supported by the National Natural Science Foundation of China (grant numbers 41690121 and 41690120).

Author information

Authors and Affiliations

  1. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China

    Jun Ying & Dake Chen

  2. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

    Jun Ying & Dake Chen

  3. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK

    Jun Ying & Matthew Collins

  4. Frontier Science Centre for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China

    Wenju Cai

  5. Qingdao National Laboratory for Marine Science and Technology (QNLM), Qingdao, China

    Wenju Cai

  6. Centre for Southern Hemisphere Oceans Research (CSHOR), CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

    Wenju Cai

  7. Center for Climate Physics, Institute for Basic Science (IBS), Busan, South Korea

    Axel Timmermann & Karl Stein

  8. Pusan National University, Busan, South Korea

    Axel Timmermann & Karl Stein

  9. Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Ping Huang

  10. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Ping Huang

  11. School of Oceanography, Shanghai Jiao Tong University, Shanghai, China

    Dake Chen

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  1. Jun Ying
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Contributions

J.Y. and M.C. conceived the study. J.Y. performed the analyses and designed the methods. J.Y., M.C. and W.C wrote the manuscript. A.T., P.H., D.C. and K.S. contributed to improving the manuscript. All of the authors discussed the results and reviewed the manuscript.

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Correspondence to Jun Ying.

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Nature Climate Change thanks Mohammad Hadi Bordbar, Ulla Heede and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Signal of EEP annual-mean SST and global-mean (60°S-60°N) SST change.

a, Signal of annual-mean SST in the EEP in MEM under historical and four future forcing scenarios and in observations. The shading denotes one intermodel standard deviation, with darker colours indicating higher emission scenarios; b and c, The same as a, but for global-mean SST change (b), and smoothed global-mean SST change by fitting a fourth-order polynomial (c).

Extended Data Fig. 2 Response of annual-mean SST to global warming.

a–c, Response of annual-mean SST to global warming for HadISSTv1, ERSSTv5 and COBEv2, respectively; d, MEM response of relative SST to global warming under historical and the SSP585 scenario; e–g, The same as a–c, but for relative SST. The green box in each plot denotes the eastern equatorial Pacific (2.5°S–2.5°N, 180°W–100°W). Stippling in a–c and e–g indicates that the regressions are significant above the 95% confidence level based on the Student’s t-test, while stippling in d denotes that more than 70% of models have the same sign.

Extended Data Fig. 3 EEP relative SST response to global warming calculated in increasing length of periods.

Shown is for response averaged over the EEP (2.5°S–2.5°N, 180°W–100°W) for 23 CMIP6 models under historical and the SSP585 scenario and in observations (y-axis, units: K K−1). The x-axis indicates the ending year of a period for calculation of the response, all starting from 1870; for example, when ending in 1949 (the first year shown), the response is calculated over the period from 1870–1949, and when ending in 2050 the response is calculated over the period from 1870–2050.

Extended Data Fig. 4 Intermodel relationship between dynamic rainfall response and annual-mean SST response.

a and b, Scatterplots of intermodel responses of dynamic rainfall with responses of relative SST (a) and tropical-mean SST (b) in the EEP under historical and the SSP585 scenario. The solid line in a denotes the linear regression. Digits on the upper-right corner of each plot are the intermodel correlations, with red (black) indicating statistically significant (insignificant) above the 95% confidence level based on a Student’s t-test. No regression line is plotted in (b) as the correlation is not significant.

Extended Data Fig. 5 Responses of ENSO SST and rainfall to global warming.

a–c, MEM Responses of ENSO SST under historical and the SSP126 (a), SSP245 (b), and SSP370 (c) scenarios. d–f, The same as a–c, but for HadISSTv1 (d), ERSSTv5 (e) and COBEv2 (f), respectively. Contours in d–f denote the respective ENSO SST pattern in the reference period (units: K, with an interval of 0.2 K, zero thickened and negative dashed). g–i, The same as a–c, but for ENSO rainfall. Stippling in a–c and g–i denotes that more than 70% of models have the same sign, while in d–f indicates that the regressions are significant above the 95% confidence level based on the Student’s t-test.

Extended Data Fig. 6 Signal of decomposed ENSO rainfall change and 30-year average relative SST change.

a, Signal of dynamic (red curve), thermodynamic (blue curve), and reconstructed (black curve) ENSO rainfall change in the EEP in MEM under historical and SSP585 scenario. The horizontal dashed lines denote the MEM threshold value for estimating the MEM detectable signal of dynamic (red), thermodynamic (blue), and reconstructed (black) ENSO rainfall. The vertical solid lines denote the ending year of a 30-year time window when the MEM signal of dynamic (red), thermodynamic (blue), and reconstructed (black) ENSO rainfall exceeds the defined threshold value. b, The same as Fig. 5b, but for the 30-year average relative SST change (see “Decomposition of 30-year average SST and ENSO rainfall change” in Methods).

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Ying, J., Collins, M., Cai, W. et al. Emergence of climate change in the tropical Pacific. Nat. Clim. Chang. 12, 356–364 (2022). https://doi.org/10.1038/s41558-022-01301-z

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