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Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming

Abstract

The Indian Ocean dipole is a prominent mode of coupled ocean–atmosphere variability1,2,3,4, affecting the lives of millions of people in Indian Ocean rim countries5,6,7,8,9,10,11,12,13,14,15. In its positive phase, sea surface temperatures are lower than normal off the Sumatra–Java coast, but higher in the western tropical Indian Ocean. During the extreme positive-IOD (pIOD) events of 1961, 1994 and 1997, the eastern cooling strengthened and extended westward along the equatorial Indian Ocean through strong reversal of both the mean westerly winds and the associated eastward-flowing upper ocean currents1,2. This created anomalously dry conditions from the eastern to the central Indian Ocean along the Equator and atmospheric convergence farther west, leading to catastrophic floods in eastern tropical African countries13,14 but devastating droughts in eastern Indian Ocean rim countries8,9,10,16,17. Despite these serious consequences, the response of pIOD events to greenhouse warming is unknown. Here, using an ensemble of climate models forced by a scenario of high greenhouse gas emissions (Representative Concentration Pathway 8.5), we project that the frequency of extreme pIOD events will increase by almost a factor of three, from one event every 17.3 years over the twentieth century to one event every 6.3 years over the twenty-first century. We find that a mean state change—with weakening of both equatorial westerly winds and eastward oceanic currents in association with a faster warming in the western than the eastern equatorial Indian Ocean—facilitates more frequent occurrences of wind and oceanic current reversal. This leads to more frequent extreme pIOD events, suggesting an increasing frequency of extreme climate and weather events in regions affected by the pIOD.

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Figure 1: Comparison of moderate and extreme pIOD and identification of extreme pIOD events.
Figure 2: Multi-model ensemble average of the principal variability patterns of austral spring season rainfall and their nonlinear relationship.
Figure 3: Multi-model statistics associated with the increase in frequency of extreme pIOD events.
Figure 4: Multi-model ensemble average of rainfall anomalies (referenced to the ‘control’ period) during extreme pIOD events and associated statistics in affected regions.

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Acknowledgements

W.C. and E.W. are supported by the Australian Climate Change Science Program, and the Goyder Institute. W.C. is also supported by a CSIRO Office of Chief Executive Science Leader award. A.S. is supported by the Australian Research Council.

Author information

Authors and Affiliations

Authors

Contributions

W.C. conceived the study and directed the analysis. G.W. and E.W. performed the model output analysis. A.S. conducted the heat budget analysis. W.C. wrote the initial draft of the paper. All authors contributed to interpreting results, discussion of the associated dynamics and improvement of this paper.

Corresponding author

Correspondence to Wenju Cai.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Heat budget analysis of the extreme pIOD events based on an ocean reanalysis34.

a, Temperature anomalies averaged over 5° S–5° N and 60° E–100° E, over the top 50 m, and over September–November. The filled blue and red circles indicate negative and positive DMI, with the size of the markers indicating the relative strength of the DMI. b, The rate of change of the temperature anomalies as a function of calendar month for all positive DMI values, with that of 1961 shown in green, 1994 in light red and 1997 in dark red, and all others in grey. c, The heat budget components averaged over July–October of Indian Ocean dipole development phase, for the 1961, 1994 and 1997 extreme events, and a composite of moderate pIOD events in the satellite era (1982, 1987, 2002 and 2006). The uncertainty bar on each composite represents the range of values over the four moderate pIOD events. The nonlinear zonal advection term () (dark red in c) is particularly large during the 1961, 1994 and 1997 events (see Methods for more details).

Source data

Extended Data Figure 2 Nonlinear zonal advection term over the growth phase of pIOD events.

The nonlinear zonal advection term () averaged over July to October for: a, a composite of moderate pIOD events, b, the 1961 pIOD event, c, the 1994 pIOD event and d, the 1997 pIOD event. The moderate pIOD events taken for the composite in a are the those in the satellite era: the 1982, 1987, 2002 and 2006 events. Stippled locations in a indicate composite values that are significant above the 95% confidence level (P-value <0.05) according to a Student’s t-test. e, The approximation of the nonlinear advection term, , averaged over the equatorial boxed region (5° S–5° N and 60° E–100° E; as shown in a) using the product between the corresponding zonal wind stress and the DMI (see Methods). The DMI is a measure of zonal gradient of temperature anomalies averaged over the western and eastern boxed regions in a. f, The total zonal current versus total zonal wind stress averaged over the equatorial box region in a. A particularly strong zonal current reversal is seen during the 1961, 1994 and 1997 pIOD events (large red dots in f, see Extended Data Fig. 1a).

Source data

Extended Data Figure 3 Circulation anomalies associated with the principal variability patterns of austral spring (September–November) rainfall.

ac, Vertical velocity ω at 500 mb (Pa s−1) from reanalysis data33 (positive indicating descending motion) (a), SST (°C) (ref. 32) (b) and thermocline depth (m) (ref. 34) (c) anomalies associated with the first principal variability pattern (Fig. 1c). The patterns are obtained through linear regression of the corresponding variables onto the principal component time series of EOF1. df, The same as for ac, but for the second principal variability pattern (Fig. 1d).

Source data

Extended Data Figure 4 Reconstruction of an extreme pIOD and a moderate pIOD event using the first two principal rainfall variability patterns.

ad, Composite of anomalies associated with the 1994 and 1997 extreme pIOD events, showing the observed rainfall and wind stress anomalies, and anomalies reconstructed from the first principal, the second principal, and the first and second principal components combined, using satellite-era rainfall anomaly data from the Global Precipitation Climatology Project version 2 (ref. 31) and reanalysis wind stress33. Note the different vector scales shown in the top right corner for each panel. eh, The same as ad, but for composites of anomalies associated with the 1982, 1987, 2002 and 2006 moderate pIOD events. The exercise highlights that the difference between a moderate and an extreme pIOD depends on the role of the second principal component, and can only be realized with the use of both of the two principal components.

Source data

Extended Data Figure 5 Principal variability patterns of vertical velocity at 500 mb (ω), their nonlinear relationship, and the associated wind stress vectors during austral spring (September–November), based on a reanalysis33.

A positive vertical velocity indicates descending, while a negative ω indicates ascending motion. a, b, Spatial patterns obtained by applying a statistical and signal processing method—EOF analysis—to the vertical velocity anomalies in the equatorial region (10° S–10° N, 40° E–100° E) for data since 1979. The associated pattern and wind stress vectors from reanalysis data are obtained by linear regression onto the principal component time series of the EOFs. The first and second principal spatial pattern accounts for 32.6% and 16.8% of the total variance. The colour scale indicates vertical velocity in Pa s−1 per 1 s.d. change; blue or red contours indicate increased or decreased convection. Note the different vector scales shown in the top right corner in a and b. c, A nonlinear relationship between the associated principal component time series. An extreme pIOD event (red dots) is defined as when the first principal component is greater than 1 s.d., and the second principal component is greater than 0.5 s.d. A moderate pIOD event (green dots) is determined from a detrended DMI when its amplitude is greater than 0.75 s.d., except for the 1994 and 1997 extreme pIOD events. Negative IOD and neutral years are indicated with blue dots. d, Relationship between the second principal component time series and rainfall over the eastern equatorial Pacific (Niño3) region (5° S–5° N, 150° W–90° W). While the 1997 extreme pIOD was associated with a large rainfall in the Niño3 region, the 1961 and 1994 extreme pIODs were not.

Source data

Extended Data Figure 6 Multi-model ensemble average of the principal variability patterns of vertical velocity at 500 mb (ω), their nonlinear relationship, and the associated wind stress vectors during austral spring (September–November).

A positive vertical velocity indicates descending, while a negative ω indicates ascending motion. a, b, Spatial patterns obtained by applying a statistical and signal processing method—EOF analysis—to the vertical velocity anomalies in the equatorial region (10° S–10° N, 40° E–100° E). The associated pattern and wind stress vectors are obtained by linear regression onto the principal component time series. The colour scale below gives vertical velocity in m s−1 per 1 s.d. change; blue or red contours indicate increased or decreased convection. Note the different vector scales shown in the top right corner in a and b. c and d, A nonlinear relationship between the two principal component time series for the ‘control’ (1900–1999) and ‘climate change’ (2000–2099) periods. An extreme pIOD event (red dots) is defined as when the first principal component is greater than 1 s.d. and the second principal component is greater than 0.5 s.d. A moderate pIOD event (green dots) is determined from a detrended DMI when its amplitude is greater than 0.75 s.d. but is not an extreme pIOD event. Negative IOD and neutral years are indicated with blue dots. Number of extreme pIOD and moderate pIOD events is indicated in c and d.

Source data

Extended Data Figure 7 Multi-model statistics between El Niño and pIOD in selected CGCMs.

a, Changes (‘climate change’ minus ‘control’ period) in the number of occurrences of extreme pIOD events versus changes in the number of El Niño events defined as when the amplitude of the detrended Niño3 (5° S–5° N, 150° W–90° W) SST index is greater than 0.5 s.d. b, Changes in the number of extreme pIOD events versus changes in the number of El Niño events defined as when the Niño3 total rainfall is greater than 5 mm day−1 as in ref. 38. c, The same as b, except an extreme El Niño is determined from a detrended Niño3 (5° S–5° N, 150° W–90° W) SST index when its amplitude is greater than 1.5 s.d. d, Correlation between a detrended Niño3 index and a detrended DMI index1 for the ‘climate change’ (y axis) and the ‘control’ periods (x axis). e, Changes in the number of occurrences of extreme pIOD events versus changes in the number of Modoki El Niño events defined as when the amplitude of a detrended index39 (see Methods) is greater than 0.5 s.d. f, Correlation between a detrended El Niño index and a detrended DMI index for the ‘climate change’ (y axis) and the ‘control’ periods (x axis). The inter-model correlation and its statistical significance or otherwise are indicated in the bottom right corner of each panel, with a P-value less than 0.05, indicating significance above the 95% confidence level, a condition not met in a, b, c and e. Models with a stronger relationship between ENSO and the Indian Ocean dipole in the ‘control’ period tend to have a stronger such relationship in the ‘climate change’ period, and the tendency is statistically significant, although the relationship weakens slightly in the ‘climate change’ period. The same is true for the Modoki relationship between ENSO and the Indian Ocean dipole.

Source data

Extended Data Figure 8 Multi-model ensemble average of mean state changes for selected CGCMs.

The changes (‘climate change’ minus ‘control’ period) of the ensemble average mean state for: a, rainfall (mm day−1), b, SST (°C), c, wind stress vectors (N m−2) and d, thermocline depth (m). The result shows that rainfall off Sumatra is decreasing, the southern eastern Indian Ocean is warming at a slower rate than the west, there is a trend of easterlies over the equatorial Indian Ocean, and the thermocline is shallowing in the eastern equatorial Indian Ocean. Areas where changes are statistically significant at the 95% confidence level are indicated with stipples, in a, b, and d. In c, vectors in bold indicate statistical significance at the 95% confidence level.

Source data

Extended Data Figure 9 Schematic of extreme pIOD in response to greenhouse warming.

a, pIOD events are characterized by westward-flowing wind anomalies (blue arrow at the surface) and the associated westward-flowing current anomalies (blue arrow at depth) acting against the prevailing background eastward circulations (black arrows), in association with the anomalous positive west-minus-east SST gradient. These result in generally weaker-than-normal eastward atmosphere and ocean circulations (grey arrows), with anomalously wet condition in the west and dry in the east. b, During extreme pIOD events, these anomalies are amplified, with occurrences of strong reversals of the mean eastward winds and currents (grey arrows). As the mean Walker circulation and the associated eastward-flowing ocean current weaken (red arrows), wind and current reversals (orange arrows) can occur more easily in association with pIOD anomalies. Greenhouse warming thus induces more frequent occurrences of extreme pIOD events.

Extended Data Table 1 Performance of 23 selected CMIP5 CGCMs forced under climate change emission scenario RCP8.5

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Cai, W., Santoso, A., Wang, G. et al. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature 510, 254–258 (2014). https://doi.org/10.1038/nature13327

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