Editing IPCC:AR6/WGI/Chapter-3 (section)

=== 3.7.4 Indian Ocean Basin and Dipole Modes ===

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The Indian Ocean Basin (IOB) and Dipole (IOD) modes are the two leading modes of interannual SST variability over the tropical Indian Ocean, featuring basin-wide warming/cooling and an east–west dipole of SST anomalies, respectively (Annex IV.2.4). The IOD mode is anchored to boreal summer to autumn by the air–sea feedback, and often develops in concert with ENSO. Driven by matured ENSO events, the IOB mode peaks in boreal spring and often persists into the subsequent summer. Similar patterns of Indian Ocean SST variability also dominate its decadal and longer time scale variability ( [[#Han--2014b|Han et al., 2014b]] ).

AR5 concluded that models show high and medium performance in reproducing the IOB and IOD modes, respectively ( ''medium confidence'' ), with difficulty in reproducing the persistence of the IOB and the pattern and magnitude of the IOD ( [[#Flato--2013|Flato et al., 2013]] ). There was ''low confidence'' that changes in the IOD were detectable or attributable to human influence ( [[#Bindoff--2013|Bindoff et al., 2013]] ).

Since AR5, CMIP5 model representation of these modes has been analysed in detail, finding that most of the models qualitatively reproduce the spatial and seasonal features of the IOB and IOD modes ( [[#Chu--2014|Chu et al., 2014]] ; [[#Liu--2014|Liu et al., 2014]] ; W. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ). Improvements in simulating the IOB mode since CMIP3 have been identified in reduced multi-model mean biases and inter-model spread (W. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ). CMIP5 models overall capture the transition from the IOD to IOB modes during an ENSO event (W. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ). The IOB mode is forced in part through a cross-equatorial wind–evaporation–SST feedback triggered by ENSO-forced anomalous ocean Rossby waves that propagate to the shallow climatological thermocline dome in the tropical south-western Indian Ocean ( [[#Du--2009|Du et al., 2009]] ). Consistently, models with a deeper climatological thermocline dome produce a weaker and less persistent IOB mode (G. [[#Li--2015|Li et al., 2015]] a; [[#Zheng--2016|Zheng et al., 2016]] ). The deep thermocline bias remains in the ensemble mean of CMIP5 models due to a common surface easterly wind bias over the equatorial Indian Ocean ( [[#Lee--2013|Lee et al., 2013]] ) associated with weaker South Asian summer monsoon circulation (G. [[#Li--2015|Li et al., 2015]] b). However, the influence of this systematic bias may be compensated by other biases, resulting in a realistic IOB magnitude (W. [[#Tao--2016|]] [[#Tao--2016|Tao et al., 2016]] ). [[#Halder--2021|Halder et al. (2021)]] found that CMIP6 models reproduce the IOB mode reasonably well, but did not evaluate the progress since CMIP5.

By contrast, the IOD magnitude is overestimated by CMIP5 models on average, though with noticeable improvements from CMIP3 models ( [[#Liu--2014|Liu et al., 2014]] ). The overestimation of the IOD magnitude remains in most of 34 CMIP6 models examined in [[#McKenna--2020|McKenna et al. (2020)]] with worsening on average in July and August. A too steep climatological thermocline slope along the equator due to the surface easterly wind bias in boreal summer and autumn contributes to this IOD magnitude bias through an excessively strong Bjerknes feedback in CMIP5 ( [[#Liu--2014|Liu et al., 2014]] ; G. [[#Li--2015|Li et al., 2015]] b; [[#Hirons--2018|Hirons and Turner, 2018]] ). The surface easterly bias and associated east–west SST gradient bias are not improved in CMIP6 ( [[#Long--2020|Long et al., 2020]] ; [[#3.5.1.2.3|Section 3.5.1.2.3]] ), suggesting that the thermocline bias also remains. [[#McKenna--2020|McKenna et al. (2020)]] additionally find degradation in the positive-negative asymmetry of the IOD but an improvement in IOD frequency in a subset of CMIP6 models compared to CMIP5. In terms of teleconnections, the equatorial surface easterly wind bias also affects the IOD-associated moisture transport anomalies toward tropical eastern Africa ( [[#Hirons--2018|Hirons and Turner, 2018]] ) where the IOD is associated with strong precipitation anomalies in boreal autumn (Annex IV.2.4). CMIP5 and CMIP6 models capture the IOD teleconnection to Southern and Central Australian precipitation although it is weaker on average than observed, with no clear improvements from CMIP5 to CMIP6 ( [[#Grose--2020|Grose et al., 2020]] ). Strong IOD events could also influence the Northern Hemisphere extratropical circulation in winter and in particular the NAM ( [[#3.7.1|Section 3.7.1]] ), based on interference between forced Rossby waves emerging from the Indian Ocean and climatological stationary waves ( [[#Fletcher--2015|Fletcher and Cassou, 2015]] ). The record positive phase of the NAO/NAM in winter 2019–2020 assessed over the instrumental era has been accordingly linked to the record IOD event of autumn 2019 ( [[#Hardiman--2020|Hardiman et al., 2020]] ), which has been associated with the devastating record fire season in Australia ( [[#Wang--2020|Wang and Cai, 2020]] ).

The observed Indian Ocean basin-average SST increase on multi-decadal and centennial time scales is well represented by CMIP5 historical simulations, and has been attributed to the effects of greenhouse gases offset in part by the effects of anthropogenic aerosols mainly through aerosol-cloud interactions ( [[#Dong--2014|Dong and Zhou, 2014]] ; [[#Dong--2014b|Dong et al., 2014b]] ). The observed SST trend is larger in the western than eastern tropical Indian Ocean, which leads to an apparent upward trend of the IOD index, but this trend is statistically insignificant ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.3|Section 2.4.3]] ). CMIP5 models capture this warming pattern, which may be associated with Walker circulation weakening over the Indian Ocean due to greenhouse gas forcing ( [[#Dong--2014|Dong and Zhou, 2014]] ). However, strong internal decadal IOD-like variability and observational uncertainty preclude attribution ( [[#Cai--2013|Cai et al., 2013]] ; [[#Han--2014b|Han et al., 2014b]] ; [[#Gopika--2020|Gopika et al., 2020]] ). Such a positive IOD-like change in equatorial zonal SST gradient suggests an increase in the frequency of extreme positive events ( [[#Cai--2014|Cai et al., 2014]] ) and skewness ( [[#Cowan--2015|Cowan et al., 2015]] ) of the IOD mode. While there is some evidence of an increase in frequency of positive IOD events during the second half of the 20th century, the current level of IOD variability is not unprecedented in a proxy reconstruction for the last millennium ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.3|Section 2.4.3]] ; [[#Abram--2020|Abram et al., 2020]] ). Besides, the IOD magnitude in the late 20th century is not significantly different between CMIP5 simulations forced by historical and natural-only forcings, though this conclusion is based on only five selected ensemble members that realistically reproduce statistical features of the IOD ( [[#Blau--2020|Blau and Ha, 2020]] ). While selected CMIP5 models show weakening ( [[#Thielke--2019|Thielke and Mölg, 2019]] ) and seasonality changes ( [[#Blau--2020|Blau and Ha, 2020]] ) in IOD-induced rainfall anomalies in tropical eastern Africa, no comparison with observational records has been made. Likewise, while a strengthening tendency of the ENSO-IOB mode correlation and resultant intensification of the IOB mode are found in historical or future simulations in selected CMIP5 models ( [[#Hu--2014|Hu et al., 2014]] ; [[#Tao--2015|Tao et al., 2015]] ), such a change has not been detected in observational records.

After linear detrending, Pacific Decadal Variability (PDV; Annex IV.2.6; [[#3.7.6|Section 3.7.6]] ) has been suggested as a driver of decadal to multi-decadal variations in the IOB mode ( [[#Dong--2016|Dong et al., 2016]] ). However, correlation between the PDV and a decadal IOB index, defined from linearly detrended SST, changed from positive to negative during the 1980s ( [[#Han--2014a|Han et al., 2014a]] ). The increase in anthropogenic forcing and recovery from the eruptions of El Chichón in 1982 and Pinatubo in 1991 may have overwhelmed the PDV influence, and explain this change ( [[#Dong--2017|Dong and McPhaden, 2017]] ; L. [[#Zhang--2018a|]] [[#Zhang--2018|Zhang et al., 2018]] a ). However, the low statistical degrees of freedom hamper clear detection of human influence in this correlation change.

To summarize, there is ''medium confidence'' that changes in the interannual IOD variability in the late 20th century inferred from observations and proxy records are within the range of internal variability. There is no evidence of anthropogenic influence on the interannual IOB. On decadal- to multi-decadal time scales, there is ''low confidence'' that human influence has caused a reversal of the correlation between PDV and decadal variations in the IOB mode. The ''low confidence'' in this assessment is due to the short observational record, limited number of models used for the attribution, lack of model evaluation of the decadal IOB mode, and uncertainty in the contribution from volcanic aerosols. Nevertheless, CMIP5 models have medium overall performance in reproducing both the interannual IOB and IOD modes, with an apparently good performance in reproducing the IOB magnitude arising from compensation of biases in the formation process, and overly high IOD magnitude due to the mean state bias ( ''high confidence'' ). There is no clear improvement in the simulation of the IOD from CMIP5 to CMIP6 models, though there is only ''medium'' ''confidence'' in this assessment, since only a subset of CMIP6 models have been examined. There is no evidence for performance changes in simulating the IOB from CMIP5 to CMIP6 models.

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