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Suppressed Atlantic Niño/Niña variability under greenhouse warming

Nature Climate Change volume 12, pages 814–821 (2022)Cite this article

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Abstract

The Atlantic Niño/Niña is a dominant mode of interannual variability peaking in boreal summer with substantial climate impacts. How the Atlantic Niño/Niña sea surface temperature (SST) variability may change under greenhouse warming remains unclear. Here we find a robust suppression in future Atlantic Niño/Niña variability in models that simulate a reasonable mean climatology of the equatorial Atlantic. Under greenhouse warming, the equatorial Atlantic atmosphere becomes more stable, reducing sensitivity of the equatorial zonal winds to the zonal SST gradient; further, weakened trade winds lead to a deepened thermocline in the east, reducing SST sensitivity to thermocline anomalies. These changes feed into Bjerknes feedback to cause suppression in Atlantic Niño/Niña SST variability. These findings are in stark contrast to the Pacific and the Indian Ocean where El Niño/La Niña SST variability and strong Indian Ocean Dipole variability are projected to increase.

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Fig. 1: Equatorial Atlantic climatology in observations and coupled ocean–atmosphere general circulation models.
Fig. 2: Projected decrease in Atlantic Niño/Niña variability.
Fig. 3: Deepened thermocline and more stable atmosphere weaken Bjerknes feedback.
Fig. 4: Weakened Bjerknes feedback suppresses Atlantic Niño/Niña variability.
Fig. 5: Weakened Bjerknes feedback leading to suppression of ATL3 variability in other scenarios.

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Data availability

All datasets related to this paper are publicly available and can be downloaded from the following websites: HadISSTv1.1 (http://hadobs.metoffice.com/hadisst/data/download.html), SODA 2.2.4 (http://apdrc.soest.hawaii.edu/las/v6/dataset?catitem=4782), NCEP/NCAR Reanalysis 1 (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html), CMIP6 (Supplementary Tables 1 and 2) (https://pcmdi.llnl.gov/CMIP6/) and CESM-HR transient simulations from iHESP (https://ihesp.github.io/archive/products/ihesp-products/data-release/DataRelease_Phase2.html).

Code availability

The code for conducting the bootstrap test is publicly available via Zenodo at https://doi.org/10.5281/zenodo.6791870 (ref. 47). All other codes are available from the corresponding author on request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) project (grant nos. 41976005, 41876008, 41730534), Strategic Priority Research Program of Chinese Academy of Sciences (XDB40000000). W.C., B.N. and G.W. are supported by the Centre for Southern Hemisphere Oceans Research, a joint research centre between QNLM and CSIRO. G.W. is also supported by the Australian government under the National Environmental Science Program. F.J. is supported by the National Key Research and Development Program of China (2020YFA0608801) and Youth Innovation Promotion Association of Chinese Academy of Sciences (2021205). Y.Y. is supported by the Fundamental Research Funds for the Central Universities. Computation is supported by the Center for High Performance Computing and System Simulation, Pilot National Laboratory for Marine Science and Technology (Qingdao).

Author information

Authors and Affiliations

  1. College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

    Yun Yang

  2. Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China

    Lixin Wu, Wenju Cai, Guojian Wang & Tao Geng

  3. Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

    Lixin Wu, Wenju Cai, Guojian Wang & Tao Geng

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

    Wenju Cai, Benjamin Ng & Guojian Wang

  5. CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China

    Fan Jia

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Contributions

Y.Y. conceived and wrote the initial manuscript in discussion with L.W. and W.C.; Y.Y., F.J., G.W., B.N. and T.G. conducted the analysis. All authors contributed to improving the manuscript.

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Correspondence to Lixin Wu or Wenju Cai.

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Extended data

Extended Data Fig. 1 Climatological equatorial Atlantic temperatures.

a, Climatological JJA temperatures (°C) averaged over the 3°S-3°N region during the period of 1950 to 1999 in an ocean reanalysis, referred to as Simple Ocean Data Assimilation (SODA)44. b, West-minus-east equatorial Atlantic Z20 gradient (m), defined as difference in Z20 between averages over the western (3°S-3°N, 30°W-50°W) and eastern (3°S-3°N, 15°W-5°E) equatorial Atlantic in SODA (red-filled bar) and all climate models. The model (HadGEM3-GC31-MM) that fails to reproduce Bjerknes feedback is greyed out and excluded from the selected model group. The selected models are indicated by blue-filled-non-greyed-out bars. Models to the right of the vertical line are not selected. The red dashed lines represent the multi-model ensemble means of the selected and non-selected models, respectively. c, d, Same as (a) but for ensemble mean of selected (c) and non-selected models (d). The black thick lines in (a, c, d) represent Z20. e, Same as (b) but for Z20 in the eastern (3°S-3°N, 15°W-5°E) equatorial Atlantic. Only models with available outputs are shown.

Extended Data Fig. 2 Observed and modeled anomaly pattern of Atlantic Niño.

a, Total response of SST to ATL3 (°C) in HadISST42. b, Standard variability of ATL3 (°C) in HadISST (red-filled bar) and all climate models. The selected models are indicated by blue-filled bars. Models to the right of the vertical line are not selected. The red dashed lines represent the multi-model ensemble means of the selected and non-selected models, respectively. c, d, Same as (a) but for ensemble means of selected (c) and non-selected models (d).

Extended Data Fig. 3 Projected change in variability of SST gradient and SSH sensitivity to wind.

a, Comparison of normalized ∇SST variability (s.d.) over the 20th (blue-edged bars) and 21st (red-edged bars) century for the 25 selected models. The multi-model ensemble means over each period are shown in blue-filled and red-filled bars, respectively; error bars are calculated as 1.0 s.d. of a total of 10,000 inter-realizations of a bootstrap method (see Method section ‘Bootstrap test’); models that simulate an increase are greyed out. Vertical line separates the CMIP6-HighResMIP (right) from the other (left) models. b, Same as (a), but for SSHCT sensitivity to UWAtl (s). Only models with available outputs are shown.

Extended Data Fig. 4 Projected decrease in occurrences of extreme Atlantic Niño/Niña events.

a, Comparison of the number of extreme events, defined as normalized |ATL3| > 1.5 s.d., over the 20th (blue-edged bars) and 21st (red-edged bars) century for the 25 selected models. The multi-model ensemble means over each period are shown in blue-filled and red-filled bars, respectively; error bars are calculated as 1.0 s.d. of a total of 10,000 inter-realizations of a bootstrap method (see Method section ‘Bootstrap test’); models that simulate an increase are greyed out. Vertical line separates the CMIP6-HighResMIP (right) from the other (left) models. b, Same as (a), but for a threshold of |ATL3| > 1.75 s.d..

Extended Data Fig. 5 Projected flattening of equatorial Atlantic thermocline in the selected models.

a, Ensemble mean of scaled difference in climatological Z20 (m per °C of global SST warming) in JJA. We calculate Z20 difference between the 21st and 20th century for each model. We then scale this difference by the increase of global mean SST simulated in each individual model before taking ensemble mean of the scaled Z20 and average over the selected models. b, Comparison of Z20 gradient (m) over the 20th (blue-edged bars) and the 21st (red-edged bars) century for the selected models. The multi-model ensemble means over the 20th and 21st century are shown in blue-filled and red-filled bars, respectively; error bars are calculated as 1.0 s.d. of a total of 10,000 inter-realizations of a bootstrap method; models that simulate an increase are greyed out. Vertical line separates the CMIP6-HighResMIP (right) from the other (left) models. c, Histograms of 10,000 realizations of the Bootstrap method for Z20 gradient (m) in the 20th (blue) and 21st (red) century climate. The blue and red lines indicate the mean values of the 10,000 realizations for each period. The grey shaded areas refer to the respective 1.0 s.d. of the 10,000 realizations (see Method section ‘Bootstrap test’). Only models with available outputs are shown.

Extended Data Fig. 6 Impacts of MAM wind anomalies on JJA mean climatology in the selected models.

a Inter-model relationship between changes in MAM equatorial zonal wind (m s−1 per °C of global warming, 3°S-3°N, 40°W-10°E, black box in b) and Z20 gradient (m per °C of global warming) in JJA (a). A linear fit (black solid line) is shown together with the correlation coefficient R, slope, and P value from the regression. To enhance inter-model comparability, we scale the changes by increase in global mean SST of each model. The four models from CMIP6-HighResMIP are not included due to short integration time. b, Ensemble mean of scaled difference in climatological SST (°C per °C of global SST warming, color shading) and wind (m s−1 per °C of global SST warming, vector) in MAM. c, Same as (a), but for changes in MAM equatorial zonal wind (m s−1 per °C of global warming) and in JJA ∇SST (°C per °C of global warming). Please note that we only show available model outputs.

Extended Data Fig. 7 Projected weakening of the Atlantic cold tongue in the selected models.

Same as Extended Data Fig. 5, but for scaled difference of JJA SST (°C per °C of global SST warming, color shading) and wind (m s−1 per °C of global SST warming, vector) (a), and ∇SST (°C) (b, c). The green and black boxes in (a) represent western (3°S-3°N, 25°W-45°W) and eastern (3°S-3°N, 20°W-0°E) equatorial Atlantic, respectively.

Extended Data Fig. 8 Processes of stabilized atmosphere in affecting thermocline variability.

a, Inter-model relationship between changes in total response of UWAtl to ∇SST (m s−1 per °C of global warming) and in UWAtl variability (m s−1 per °C of global warming). A linear fit (black solid line) is shown together with the correlation coefficient R, slope, and P value from the regression. To enhance inter-model comparability, we scale the changes by increase in global mean SST of each model. The four models from CMIP6-HighResMIP are not included due to short length of integration. b, Same as a, but for changes in total response of SSHCT to UWAtl (m per °C of global warming) and in SSHCT variability (m per °C of global warming).

Extended Data Fig. 9 Changes in variability of three sensitivities involved in Bjerknes feedback under other emission scenarios.

(a-c) Comparisons of ATL3 sensitivity to SSHCT (°C m−1) over the 20th (x-axis) and 21st (y-axis) century for the selected models in SSP1-2.6 (left), SSP2-4.5 (middle), and SSP-3.70 (right) scenarios. Number of models that project an increase/decrease is shown in red/blue colors. (d-f) and (g-i), same as (a-c) but for UWAtl sensitivity to ∇SST (m s−1°C−1) (d-f) and SSHCT sensitivity to UWAtl (s) (g-i). Only models with available outputs are shown.

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Yang, Y., Wu, L., Cai, W. et al. Suppressed Atlantic Niño/Niña variability under greenhouse warming. Nat. Clim. Chang. 12, 814–821 (2022). https://doi.org/10.1038/s41558-022-01444-z

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