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Opposite response of strong and moderate positive Indian Ocean Dipole to global warming

Nature Climate Change volume 11, pages 27–32 (2021)Cite this article

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Abstract

A strong positive Indian Ocean Dipole (pIOD) induces weather extremes such as the 2019 Australian bushfires and African floods. The impact is influenced by sea surface temperature (SST), yet models disagree on how pIOD SST may respond to greenhouse warming. Here we find increased SST variability of strong pIOD events, with strong equatorial eastern Indian Ocean cool anomalies, but decreased variability of moderate pIOD events, dominated by western warm anomalies. This opposite response is detected in the Coupled Model Inter-comparison Project (CMIP5 and CMIP6) climate models that simulate the two pIOD regimes. Under greenhouse warming, the lower troposphere warms faster than the surface, limiting Ekman pumping that drives the moderate pIOD warm anomalies; however, faster surface warming in the equatorial western region favours atmospheric convection in the west, strengthening equatorial nonlinear advection that forces the strong pIOD cool anomalies. Climate extremes seen in 2019 are therefore likely to occur more frequently under greenhouse warming.

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Fig. 1: Identification of observed strong and moderate pIOD events.
Fig. 2: Selection of models based on pIOD nonlinearity.
Fig. 3: Projected opposite response of moderate and strong pIOD SST.
Fig. 4: Mechanisms for the projected opposite change in moderate and strong pIOD.

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Data availability

Data relevant to the paper can be downloaded from websites listed below:

OISST v2 at https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html;

ORA-S5 at https://icdc.cen.uni-hamburg.de/daten/reanalysis-ocean/easy-init-ocean/ecmwf-oras5.html;

SODA3.3.1 at https://www2.atmos.umd.edu/~ocean/index_files/soda3.3.1_mn_download.htm;

GODAS at https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html;

CMIP5 database at http://www.ipcc-data.org/sim/gcm_monthly/AR5/;

CMIP6 database at https://esgf-node.llnl.gov/projects/cmip6/;

19 CMIP6 models are used in this study, including: MRI-ESM2–0 (refs. 40,41); CNRM-ESM2–1 (refs. 42,43); CNRM-CM6–1 (refs. 44,45); BCC-CSM2-MR (refs. 46,47); EC-Earth3-Veg (refs. 48,49); NorESM2-LM (refs. 50,51); MIROC6 (refs. 52,53); CESM2 (refs. 54,55); CAMS-CSM1-0 (refs. 56,57); CESM2-WACCM (refs. 58,59); MIROC-ES2L (refs. 60,61); IPSL-CM6A-LR (refs. 62,63); INM-CM4-8 (refs. 64,65); INM-CM5-0 (refs. 66,67); CanESM5 (refs. 68,69); MPI-ESM1-2-LR (refs. 70,71); UKESM1-0-LL (refs. 72,73); MCM-UA-1-0 (refs. 74,75); GFDL-CM4 (refs. 76,77).

Code availability

Codes for calculating EOF, the parameter |α| can be downloaded from https://drive.google.com/open?id=1d2R8wKpFNW-vMIfoJsbqIGPIBd9Z_8rj.

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Acknowledgements

This work is supported by National Key R&D Program of China (grant no. 2018YFA0605700). L.W. is supported by the National Natural Science Foundation of China (NSFC) projects (grant nos. 41490643, 41490640, U1606402 and 41521091). W.C., G.W., A.S. and B.N. are supported by CSHOR and the Earth System and Climate Change Hub of the Australian Government’s National Environment Science Program. CSHOR is a joint research Center for Southern Hemisphere Oceans Research between QNLM and CSIRO. G.H. is supported by NSFC (grant nos. 41831175 and 91937302), CAS XDA20060501 and COMS2019Q03. K.Y. is supported by China Postdoctoral Science Foundation (grant no. 2018M640168) and a scholarship from China Scholarship Council. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We are grateful to the reanalysis groups for making the datasets publicly available.

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Authors and Affiliations

  1. Key Laboratory of Physical Oceanography–Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

    Wenju Cai, Lixin Wu & Guojian Wang

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

    Wenju Cai, Agus Santoso, Benjamin Ng & Guojian Wang

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

    Kai Yang & Gang Huang

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

    Gang Huang

  5. Australian Research Council (ARC) Centre of Excellence for Climate Extremes, The University of New South Wales, Sydney, New South Wales, Australia

    Agus Santoso

  6. Application Laboratory, JAMSTEC, Yokohama, Japan

    Toshio Yamagata

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  1. Wenju Cai
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  2. Kai Yang
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Contributions

W.C. conceived the study and wrote the initial manuscript in discussion with K.Y. K.Y. performed all analysis and generated final figures. All authors contributed to interpreting results, discussion of the associated dynamics and improvement of this paper.

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Correspondence to Wenju Cai or Lixin Wu.

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Extended data

Extended Data Fig. 1 Characteristics of strong pIOD events.

a–c, SON SST anomalies (°C, color shading) and wind stress anomalies (N m−2, vectors) for the 1994, 1997, and 2006 strong pIOD event, respectively. The pattern is dominated by strong cooling in the equatorial eastern Indian Ocean. Strong equatorial easterly anomalies extend to the central-western Indian Ocean. The anomalies are referenced to the average over the 1982–2015 period. d–f, SON SST anomalies (in °C) relative to the SST threshold for tropical convection, defined as the SON SST average over 20°S to 20°N, for the 1994, 1997, and 2006 strong pIOD event, respectively. Areas with SST lower than the convection threshold have been masked; there is a large area of suppressed convection in the equatorial eastern Indian Ocean. g–i, Equatorial vertical potential temperature anomalies (in °C) over SON of 1994, 1997, and 2006, respectively. The equatorial vertical potential temperature is calculated as the average between 2.5°S to 2.5°N. Strong equatorial cooling is seen in the eastern Indian Ocean. Vertical potential temperature and wind stress data are from ORA-S5.

Extended Data Fig. 2 Characteristics of moderate pIOD events.

a–c, SON SST anomalies (°C, color shading) and wind stress anomalies (N m−2, vectors) for the 1982, 1987, and 2015 moderate pIOD event, respectively. The pattern is dominated by a broad-scale warming over the western Indian Ocean. Easterly anomalies are confined to the equatorial central-eastern Indian Ocean. The anomalies are referenced to the average over the 1982–2015 period. d–f, SON SST anomalies (in °C) relative to SST threshold for tropical convection, defined as SON SST average over 20°S to 20°N for 1982, 1987, and 2015 moderate pIOD event, respectively. Areas with SST lower than the convection threshold have been masked; there is a small area of suppressed convection in the equatorial southeastern Indian Ocean. g–i, Equatorial vertical potential temperature anomalies (in °C) over SON of 1982, 1987, and 2015, respectively. The equatorial vertical potential temperature is calculated as the average between 2.5°S to 2.5°N. There is a general lack of cooling in the equatorial eastern Indian Ocean as cooling occurs only off Sumatra-Java. Vertical potential temperature and wind stress data are from ORA-S5.

Extended Data Fig. 3 Heat budget analysis based on observations.

a, Composite of heat budget terms over the equatorial Indian Ocean (10°S–10°N, 40°E–100°E) in August-September-October (ASO). The uncertainty bar on each composite represents the range over the three strong or three moderate pIOD events. b, c, Composite of ASO temperature tendency during strong and moderate pIOD events. The green box marks the area where the main warm temperature tendency is located over the southwestern Indian Ocean (10°S–0°, 50°E–80°E). d, Relationship between normalized temperature tendency and the Ekman pumping term in ASO averaged over the main warming area in the southwestern Indian ocean (10°S–0°, 50°E–80°E, green box in c), with statistical properties shown. e, Map of ASO correlation coefficients between the Ekman pumping term averaged over 10°S–0°, 50°E–80°E and zonal wind stress (TauX) anomalies. Correlation coefficients of ±0.27, ±0.32, ±0.41 are statistically significant at the 90%, 95%, and 99% confidence levels, respectively. The area indicated by the red box is used to calculate the average zonal wind stress anomalies. f, Time series of normalized ASO Ekman pumping term averaged over the western Indian Ocean (10°S–0°, 50°E–80°E) (red curve) and zonal wind stress (TauX) anomalies averaged over the equatorial Indian Ocean (5°S–5°N, 60°E–100°E, red box in e) (green curve). The Ekman pumping term has been multiplied by −1 for plotting. The data used in this figure are from ORA-S5 for the period of 1979–2018.

Extended Data Fig. 4 Anomaly patterns of strong and moderate pIOD in some selected models.

A composite of SON SST anomalies (°C) for strong pIOD with an S-index>1.5 s.d. and moderate pIOD with an M-index>1.25 s.d. in a CESM1-BGC, b CNRM-CM5, c GFDL-ESM2M, d MPI-ESM-LR. Left panels show strong pIOD and right panels show moderate pIOD. These show that anomaly patterns can be rather different from one model to another.

Extended Data Fig. 5 Uncertainty in projected pIOD SST using the DMI.

a, Comparison of the standard deviation of the DMI in the present-day (1900–1999) and future (2000–2099) climate in 20 models that simulate a nonlinear coefficient α greater than 50% of the observed in the quadratic fit between the first two principal components (PC1 and PC2) from EOF analysis on SON SST anomalies, PC2(t) = α [PC1(t)]2+β PC1(t) + γ. Although a total of 15 out of the 20 selected models simulate lower DMI variability in the future period (red bars) than in the present-day climate (blue bars), the multi-model mean decrease is not statistically significant. The error bar represents the 95% confidence level. Models that simulate an increase in variability are indicated by green circles. Models from CMIP6 are shown in purple. b, Same as a, but for 34 models that simulate an α smaller than 50% of the observed value. A total of 17 out of the 34 models (50%) simulate smaller variability in the DMI in the future period than in the present-day period. There is no inter-model consensus, and the multi-model mean change is not statistically significant.

Extended Data Fig. 6 Projected change in pIOD frequency.

a, Moderate and b, strong pIOD using a threshold of M-index>1.25 s.d. and S-index>1.5 s.d., respectively. Shown is the comparison of frequency (events per 100 years) in the present-day (1900–1999) and future (2000–2099) climate in the 20 selected models, that is, with an α greater than 50% of the observed value. A total of 17 out of the 20 models (85%) simulate a decreased frequency in moderate pIOD events, with a statistically significant decrease of 52% in the multi-model mean, from 9.60 events per 100 years in the present-day (blue bars) to 4.65 events per 100 years (red bars) in the future climate. By contrast, a total of 16 out of the 20 models (80%) simulate an increased frequency in strong pIOD events, with a statistically significant increase of 66% in the multi-model average, from 7.5 events per 100 years in the present-day (blue bars) to 12.45 events per 100 years (red bars) in the future climate. Models that simulate an opposite change to the multi-model mean are indicated by green circles. c, Same as a, but using a threshold of M-index>1.5 s.d. A total of 18 out of the 20 selected models (90%) simulate a decreased frequency in moderate pIOD events, with a statistically significant decrease of 60% in the multi-model ensemble mean. d, Same as b, but using a threshold of S-index>1 s.d. A total of 18 out of the 20 models (90%) simulate an increased frequency in strong pIOD, with a statistically significant increase of 44% in the multi-model ensemble mean. Models from CMIP6 are indicated in purple.

Extended Data Fig. 7 Sensitivity of projected changes in pIOD variability to model selections.

a, b, Comparison of variability of the moderate pIOD (M-index) and strong pIOD (S-index), respectively, in the present-day (1900–1999) and future (2000–2099) climate in 30 models that simulate a nonlinear coefficient α greater than 33.3% of the observed value. In a, a total of 27 out of the 30 models (90%) simulate a decrease in M-index variability in the future period (red bars) from that in the present-day period (blue bars). This leads to a multi-model mean decrease of 17%, statistically significant above the 95% confidence level, as indicated by the error bars. In b, a total of 23 out of the 30 selected models (77%) simulate an increase in S-index variability in the future climate, with a multi-model mean increase of 19%, statistically significant above the 95% confidence level. Models from CMIP6 are indicated in purple. Models that simulate an opposite change to the multi-model mean are indicated by green circles.

Extended Data Fig. 8 Sensitivity of projected changes in pIOD variability to emission scenario.

Shown are results for emission scenario RCP 4.5. a, b, Comparison of variability of the moderate pIOD (M-index) and strong pIOD (S-index), respectively, in the present-day (1900–1999) and future (2000–2099) climate in 15 out of 33 CMIP5 models that simulate a nonlinear coefficient α greater than 50% of the observed value. In a, a total of 14 out of the 15 models (93%) simulate a decrease in M-index variability in the future period (red bars) from that in the present-day period (blue bars). This leads to a multi-model mean decrease of 13%, statistically significant above the 95% confidence level, as indicated by the error bars. In b, a total of 13 out of the 15 selected models (87%) simulate an increase in S-index variability in the future climate, with a multi-model mean increase of 16%, statistically significant above the 95% confidence level. Models that simulate an opposite change to the multi-model mean are indicated by green circles. A total of 33 CMIP5 models are used here as outputs from CMCC-CESM and FGOALS-g2 are unavailable.

Extended Data Fig. 9 Relationship between projected changes in ENSO and in pIOD.

a, Scatter plot of changes in September, October, and November (SON) S-index variability versus changes in SON Ni\({\tilde{\mathrm n}}\)o3.4 index variability. The change is defined as the difference between the present-day (1900–1999) and future (2000–2099) period scaled by the corresponding increase in global mean SST in each model. b, Same as a, but between changes in SON M-index variability and in SON Ni\({\tilde{\mathrm n}}\)o3.4 index variability. c, d, Same as a, b, but for changes in strong pIOD (S-index>1.5 s.d.) or moderate pIOD (M-index>1.25 s.d.) frequency (events per 100 years) vs frequency of El Ni\({\tilde{\mathrm n}}\)o (Ni\({\tilde{\mathrm n}}\)o3.4>1.0 s.d.). The associated correlation coefficient and P-value are plotted. There is no relationship between changes in El Niño and S-index in a and c. Although there is a positive correlation in b and d, the changes in M-index and in El Niño are in the opposite direction, contravening the relationship between El Ni\({\tilde{\mathrm n}}\)o and M-index.

Extended Data Fig. 10 Projected mean state change in the tropical Indian Ocean.

Shown are averages over the 20 models that simulate a nonlinear coefficient α greater than 50% of the observed value. a, Multi-model ensemble averaged mean state changes of SON SST (in °C) between the future (2000–2099) and the present-day (1900–1999) climate. b, Same as a, but for the mean state change in surface wind stress (in N m−2); changes in wind stress magnitude (in N m−2) are indicated by colors. c, Same as a, but for the mean state change in latent heat flux (in W m−2). d, Same as a, but for the mean state change in total heat flux (in W m−2); positive values indicate an upward transfer of energy. Stippled areas indicate where changes are statistically significant above the 95% confidence level according to a two-tailed Student’s t-tests.

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Cai, W., Yang, K., Wu, L. et al. Opposite response of strong and moderate positive Indian Ocean Dipole to global warming. Nat. Clim. Chang. 11, 27–32 (2021). https://doi.org/10.1038/s41558-020-00943-1

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