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Intensification of El Niño-induced atmospheric anomalies under greenhouse warming

Nature Geoscience volume 14, pages 377–382 (2021)Cite this article

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Abstract

The El Niño/Southern Oscillation (ENSO) has a profound influence on global climate and ecosystems. Determining how the ENSO responds to greenhouse warming is a crucial issue in climate science. Despite recent progress in understanding, the responses of important ENSO characteristics, such as air temperature and atmospheric circulation, are still unknown. Here, we use a suite of global climate model projections to show that greenhouse warming drives a robust intensification of ENSO-driven variability in boreal winter tropical upper tropospheric temperature and geopotential height, tropical humidity, subtropical jets and tropical Pacific rainfall. These robust changes are primarily due to the Clausius–Clapeyron relationship, whereby saturation vapour pressure increases nearly exponentially with increasing temperature. Therefore, the vapour response to temperature variability is larger under a warmer climate. As a result, under global warming, even if the ENSO’s sea surface temperature remains unchanged, the response of tropical lower tropospheric humidity to the ENSO amplifies, which in turn results in major reorganization of atmospheric temperature, circulation and rainfall. These findings provide a novel theoretical constraint for ENSO changes and reduce uncertainty in the ENSO response to greenhouse warming.

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Fig. 1: Projected changes of ENSO humidity and air temperature.
Fig. 2: Projected changes in the tropical humidity response to air temperature under global warming.
Fig. 3: Vertical structure of the changes in ENSO humidity, temperature and circulation.
Fig. 4: Mechanism for the changes in ENSO atmospheric circulation and precipitation.

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Data availability

The CMIP6 data are available at https://pcmdi.llnl.gov/CMIP6/. The CMIP5 data are available at https://esgf-node.llnl.gov/search/cmip5/. The version 2 National Centers for Environmental Prediction/Department of Energy reanalysis and Global Precipitation Climatology Project precipitation data are from http://www.esrl.noaa.gov/psd/data/gridded/. Hadley Center SST data were provided by the Met Office Hadley Center (https://www.metoffice.gov.uk/hadobs/hadisst/).

Code availability

The code associated with this paper is available on request from K.H.

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Acknowledgements

The work was supported by the National Key Research and Development Program of China (fund number 2019YFA0606703), National Natural Science Foundation of China (41831175), Second Tibetan Plateau Scientific Expedition and Research (STEP) programme (grant number 2019QZKK0102), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20060500), National Natural Science Foundation of China (41775086, 41722504 and 91937302), Key Deployment Project of the Centre for Ocean Mega-Research of Science, Chinese Academy of Sciences (COMS2019Q03) and Youth Innovation Promotion Association of CAS. Y.K. was supported by the Japan Ministry of Education, Culture, Sports, Science and Technology (JPMXD0717935457) and Japan Society for the Promotion of Science (18H01278, 18H01281 and 19H05703). S.-P.X. was supported by the National Science Foundation (AGS 1934392).

Author information

Authors and Affiliations

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

    Kaiming Hu, Gang Huang & Ping Huang

  2. Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

    Kaiming Hu & Gang Huang

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

    Kaiming Hu & Ping Huang

  4. University of Chinese Academy of Sciences, Beijing, China

    Gang Huang

  5. Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan

    Yu Kosaka

  6. Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA

    Shang-Ping Xie

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  1. Kaiming Hu
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Contributions

K.H. and G.H. conceived the study, performed the analyses, built the mechanism and wrote the paper. P.H., Y.K. and S.-P.X. contributed to improving the paper and assisted in interpretation of the results.

Corresponding authors

Correspondence to Kaiming Hu or Gang Huang.

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The authors declare no competing interests.

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Peer review information Primary Handling Editor: Tom Richardson. Nature Geoscience thanks Tobias Bayr, Andrea Taschetto 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 Projected change in ENSO-driven variability in boreal winter tropical SST, humidity and air temperature.

The analysis is the same as that in Fig. 1 except for applying a “scaling” method (see Method) for detrending data prior to regressions. The result is almost the same as that in Fig. 1, indicating the result is not sensitive to the selection of method for detrending.

Extended Data Fig. 2 Projected change in ENSO SST, humidity and air temperature in 18 CMIP5 models (see Methods) from 1950-1999 in historical experiments to 2050-2099 in RCP8.5 experiments.

a, Changes in DJF climatological SST in the MME. b, Comparison of the standard deviation of DJF Niño 3.4 SST index over the two periods in each model and the MME. c, d, Comparison of DJF tropical mean (10°S-10°N) ENSO-driven surface specific humidity (\(q_{sfc}^\prime\); c) and 200-hPa air temperature (\(T_{200}^\prime\); d) between the two periods. Error bars in the MME mean correspond to the 95% confidence interval (see Methods). The MME \(q_{sfc}^\prime\) and \(T_{200}^\prime\) increases by 9.6±6.1% and 11.5±8.4% respectively for per 1 K background warming in the tropics.

Extended Data Fig. 3 Projected change in ENSO SST, humidity and air temperature in 30 CMIP6 models (see Methods) from 1950-1999 in historical experiments to 2050-2099 in SSP585 experiments.

a, Changes in DJF climatological SST in the MME. b, Comparison of the standard deviation of DJF Niño 3.4 SST index over the two periods in each model and the MME. c, d, Comparison of DJF tropical mean (10°S-10°N) ENSO-driven surface specific humidity (\(q_{sfc}^\prime\); c) and 200-hPa air temperature (\(T_{200}^\prime\); d) between the two periods. Error bars in the MME mean correspond to the 95% confidence interval (see Methods). The MME \(q_{sfc}^\prime\) and \(T_{200}^\prime\) increases by 10.2±6.0% and 12.2±8.5% respectively for per 1 K background warming in the tropics.

Extended Data Fig. 4 Vertical structure of ENSO-driven anomalies of boreal winter air humidity, temperature and circulation.

a, DJF zonally mean ENSO-driven specific humidity anomalies (q′; contours; at interval of 0.05 g kg–1 K–1) and air temperature anomalies (T′; colors) during 1979-2018 in the observations. b, DJF zonally mean ENSO-driven zonal wind anomalies (U′; black contours; at interval of 0.2 m s–1 K–1) and geopotential height anomalies (Z′; colors) during 1979-2018 in the observations. c-d, similar to a and b but for the MME during 400-449 model years in the PI control runs. Stippling in a and b denotes passing 95% confidence level based on a two-tailed Student’s t test, and in c and d indicates that more than 85% of models agree on the sign of the MME.

Extended Data Fig. 5 ENSO-driven anomalies of boreal winter tropospheric air temperature, 200hPa geopotential height and circulation.

a and c, Vertically averaged (850-200 hPa) ENSO-driven DJF air temperature anomalies (T′). b and d, 200-hPa ENSO-driven DJF geopotential height (Z200′; colors) and wind (UV200′; vectors) anomalies. Anomalies in a and b are derived from the observations during 1979-2018, while in c and d are from the MME during 400-449 model years in the PI control runs. Stippling in a and b denotes passing 95% confidence level based on a two-tailed Student’s t test, and in c and d indicates that more than 85% of models agree on the sign of the MME.

Extended Data Fig. 6 ENSO-driven DJF anomalies in boreal winter specific humidity and rainfall.

a and c, ENSO-driven DJF low-level 1000-500hPa vertically averaged specific humidity anomalies (q′). b and d, ENSO-driven DJF rainfall anomalies (Pr′). Anomalies in a and b are derived from the observations during 1979-2018, while in c and d are from the MME during 400-449 model years in the PI control runs. Stippling in a and b denotes passing 95% confidence level based on a two-tailed Student’s t test, and in c and d indicates that more than 85% of models agree on the sign of the MME.

Extended Data Fig. 7 Changes in the response of specific humidity to tropical Indian Ocean SST variability under global warming.

a and b, The regression of June-to-August (JJA) mean SST (a) and 1000-500hPa vertically averaged specific humidity (b) onto a tropical Indian Ocean SST index in the period of P_ctrl. The tropical Indian Ocean SST index is defined as averaging JJA SST anomalies in the domain of 10°S-10°N, 40°E-100°E. c and d, Similar to a and b but for the period of P_warm. e and f, Differences between the two periods (P_warm- P_ctrl). Stippling indicates that more than 85% of models agree on the sign of the MME. The result shows that the response of specific humidity to tropical Indian Ocean SST variability strengthens under global warming.

Extended Data Fig. 8 Changes in the response of specific humidity to tropical Atlantic SST variability under global warming.

a and b, The regression of JJA SST (a) and 1000-500hPa vertically averaged specific humidity (b) onto a tropical Atlantic SST index in the period of P_ctrl. The tropical Atlantic SST index is defined as averaging JJA SST anomalies in the domain of 10°S-10°N, 60°W-0°. c and d, Similar to a and b but for the period of P_warm. c and d, Differences between the two periods (P_warm- P_ctrl). Stippling indicates that more than 85% of models agree on the sign of the MME. The result shows that the response of specific humidity to tropical Atlantic SST variability strengthens under global warming.

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Hu, K., Huang, G., Huang, P. et al. Intensification of El Niño-induced atmospheric anomalies under greenhouse warming. Nat. Geosci. 14, 377–382 (2021). https://doi.org/10.1038/s41561-021-00730-3

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