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CLIMATE CHANGE WILL UNLEASH INDIAN OCEAN EXTREMES

Posted on: May 7, 2020

IMAGE#1: CLIMATE VARIABILITY SYSTEMS OF THE TROPICS

IO-XXX

 

IMAGE#2: INDIAN OCEAN DIPOLE IO-5

IMAGE#3: CARLSBERG RIDGE: INDIAN OCEAN

 

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THIS POST IS A CRITICAL REVIEW OF “DiNezio etal, “Emergence of an equatorial mode of climate variability in the Indian Ocean”, Science Advances,  06 May 2020: Vol. 6, no. 19, eaay7684 DOI: 10.1126/sciadv.aay7684.  Abstract: Presently, the Indian Ocean (IO) resides in a climate state that prevents strong year-to-year climate variations. This may change under greenhouse warming, but the mechanisms remain uncertain, thus limiting our ability to predict future changes in climate extremes. Using climate model simulations, we uncover the emergence of a mode of climate variability capable of generating unprecedented sea surface temperature and rainfall fluctuations across the IO. This mode, which is inhibited under present-day conditions, becomes active in climate states with a shallow thermocline and vigorous upwelling, consistent with the predictions of continued greenhouse warming. These predictions are supported by modeling and proxy evidence of an active mode during glacial intervals that favored such a state. Because of its impact on hydrological variability, the emergence of such a mode would become a first-order source of climate-related risks for the densely populated IO rim. The full text of the article is provided below at the end of this post

 

 

CRITICAL COMMENTARY

  1. The researchers find that the Indian Ocean is remarkable in terms of its stability because of an absence of variability in sea surface temperature (SST) seen elsewhere either as short term monthly time scale “marine heat waves” [LINK] , or as cyclical decadal time scale changes changes in the SST pattern over large sections of ocean such as the ENSO cycle.
  2. In that context, the authors note that the paleo data show that during the most recent deglaciation into the Holocene, the Indian Ocean was not stable and had undergone intense “climate variability” in terms of SST and rainfall changes contemporaneous with vigorous upwelling and flattening of the thermocline. Since deglaciation is a time of rapid warming, it is postulated on that basis and verified with climate models that an increasing warming rate in AGW climate change may create similar conditions and cause the same kind of climate variability seen in deglaciation in the Indian Ocean but not seen at present.
  3. Here we present contrary evidence to contest the claim that the Indian Ocean is unusually stable in terms of SST and that SST changes necessarily have a climate change implication.  SST variability in the Indian Ocean at two different time scales is presented in paragraphs 5 and 6 below.
  4. It is true that compared with the Pacific, the Indian Ocean is relatively stable. That difference is generally attributed to the unique geography of the Indian Ocean in being “landlocked” in the north and thereby isolated from the Arctic Ocean, as seen in the map below. Although that may inhibit climate variability to some degree, we show below that climate variability is seen nevertheless both the short term and the long term.

THE INDIAN OCEAN IS LANDLOCKED IN THE NORTHERN HEMISPHERE

bandicam 2020-05-08 08-20-28-022

 

5. MARINE HEAT WAVES: The GIF image below is taken from a related post on marine heat waves [LINK] . It shows the location and intensity of marine heat waves (MHW) month by month from December 2018 to January 2020. It was created with data provided by marineheatwaves.org, an excellent keeper of MHW data. The intensity of MWH is denoted by the darkness of color with darker colors indicating higher temperatures in the SST anomaly that can be described as an MWH event. Here we find MHW events in the southwestern part of the Indian Ocean in most of the 14 months of data although at a relatively lower intensity than the intense dark events in the Arctic and the northern part of the Pacific. In terms of marine heat waves at brief time scales, it cannot be said that the Indian Ocean does not show SST variability.

MARINE HEAT WAVES DEC 2018 TO JAN 2020 (MONTHLY TIME SCALE)

mhw-gif

 

6. THE INDIAN OCEAN DIPOLE: At a longer multi decadal time scale, the Indian Ocean goes through an intense SST cycle similar to the ENSO called the Indian Ocean Dipole (IOD) that cycles through three SST states as (1) No SST anomaly, (2) A large area of high SST anomaly in the Southwest Indian Ocean and not anywhere else in the Indian Ocean, and  (3) A large area of high SST anomaly in the Southeast Indian Ocean and not anywhere else in the Indian Ocean. The IOD cycle is depicted graphically in the GIF image below. Much of the climatology in the region from Arabia to Australia is understood in terms of the IOD. In view of this intense climate variability, it cannot be said that the Indian Ocean does not show SST or climate variability.

INDIAN OCEAN DIPOLE DYNAMICS: DECADAL TIME SCALE

IO-2

7. THE CARLSBERG RIDGE AND THE CHAGOS-LACCADIVE RIDGE: It is known that the seafloor south and southwest of India (map below) is geologically active with significant geological features in terms of a series of ridges and their role in the Late Cretaceous Deccan Traps Volcanism recorded in the paleo data. Currently the Carlsberg Ridge, the Laccadive Ridge and the Chagos Ridge are all geologically active. The Chagos Ridge is not shown in the seafloor map below and can be thought of as a southern extension of the Laccadive Ridge.  Sporadically hydrothermal plumes and volcanism can release enormous amounts of heat and materials from the mantle into the Indian Ocean. It is noted that the reference paper reports that during the deglaciation SST anomalies the thermocline had flattened. We interpret that in this way. When the sun/atmosphere system is the heat source, SST is the hot part of the ocean in a vertical temperature profile in which the water is cooler the deeper you go. This is the thermocline. A flattening of the thermocline means there is no temperature gradient with the implication that there is a heat source in the bottom of the ocean too. The flattening of the thermocline reported by the authors suggests that the significant sources of geological heat in ridge system in the southwest Indian Ocean can create the the kind of temperature profile that is used in the study to propose that global warming will cause the Indian Ocean to become more climatically volatile. This conclusion does not take the geological features of the Indian Ocean into consideration. These features can transfer significant amounts of heat from the mantle to the ocean and can explain what the authors are trying to explain in terms of the atmosphere with climate models. It is a case of the atmosphere bias in climate research. It is noted that both in terms of MHW and the IOD, the high temperature events are found only in known geologically active areas. 

DECCAN TRAPS VOLCANISM

DECCAN1

 

INDIAN OCEAN GEOLOGY: THE CARLSBERG RIDGE

carlsberg-2

 

8. With regard to the climate model runs that led the authors to the conclusion that global warming will change the Indian Ocean from a region with no climate variability to a region with more and more climate variability, it is imperative that we consider the language used in the paper to present this conclusion. The paper states its findings as “Present-day conditions do not favor the coupled interactions required for the mode’s emergence, and indeed, historical observations do not show evidence of the occurrence of these extreme events. A potential activation under greenhouse warming, however, could lead to record-breaking SST and rainfall fluctuations, rendering the emergence of the mode a main factor determining future climate risks, including more frequent and devastating wildfires, flooding, and droughts“. The plain language translation of this conclusion is that “We think that global warming will make the Indian Ocean more changeable in terms of climate variables. We don’t see it in the Holocene data and we don’t see it in the model runs but the possibility exists that it could happen because we saw it during deglaciation.  This conclusion does not really say what the paper is supposed to say.

9. In this critical review, we find that the climate variability in the paleo data was assumed to be an atmospheric effect without the relevant geological data and it was thought that the effect could be re-produced in climate models with atmospheric forcing but that this exercise did not produce useful results. The conclusion that “it is still possible that global warming might increase climate variability in the Indian Ocean anyway” does not contain useful information and it is not a useful research finding. 

 

A very different interpretation and review of the DiNezio paper is found at Science Alert Magazine [LINK] where the authors refer to the deglaciation variability identified by DiNezio as “an ancient El Nino system of the Indian Ocean” and promote the paper’s findings as a dangerous and alarming climate change impact in the form of re-activating an ancient El Nino system in the Indian Ocean. The author is David Nield of the Guardian.

nield

 

 

 

FULL TEXT OF THE RESEARCH PAPER:

  1. INTRODUCTION: Predicting changes in the pattern and magnitude of sea surface temperature (SST) fluctuations over the tropical oceans is critical for attributing changing climate variability and extreme weather over large parts of the world (1). Observations show that the Indian Ocean (IO)—a tropical ocean long considered a minor driver of climate variability relative to the Pacific or the Atlantic oceans (2)—is experiencing changes in its mean state that could favor stronger SST variations (3–5). These long-term changes appear to be forced by increasing greenhouse gas (GHG) concentrations (5–7); however, models are inconclusive on whether SST variability will increase or not (8–12). Paleoclimate records show that SST variability in the eastern IO has increased since the 1850s (13), a trend that, if continued, could exacerbate the already sizable climatic impacts of subtle variations in IO temperatures over surrounding land masses (11, 14–17). While the changes in mean state—particularly a shoaling thermocline in the eastern IO—are likely to strengthen the coupled feedbacks governing SST variability (9–12), the lack of model consensus limits our ability to attribute the observed trends and predict future changes. The tropical IO exhibits much weaker SST variability than the tropical Pacific and Atlantic oceans (Fig. 1A). Unlike these oceans, where the El Niño–Southern Oscillation (ENSO) phenomenon and the Atlantic Niño drive pronounced basin-wide SST anomalies (SSTAs), variability in the IO is restricted to the western side of the basin and along the coast of Sumatra and Java (18). Large SSTAs spanning the equatorial IO are extremely rare because the uniformly deep thermocline (Fig. 1B, shading) and a lack of equatorial upwelling (not shown) hinder coupled ocean-atmosphere interactions in this ocean (19). Depending on the season, the dominant mode of SST variability in the IO has a uniform warming pattern over the entire basin. This IO Basin (IOB) mode is not generated via ocean dynamical processes and instead is forced by El Niño events via changes in evaporation and cloud cover (20–23). The IO Dipole (IOD) is the second mode of variability in terms of explained SST variance and has SSTAs restricted to the western IO and the off-equatorial region along the coast of Sumatra and Java (18). SSTAs driven by IOD events do not reach the equatorial IO, which only responds during very rare extreme cold events, such as during 1997 (11). (A) SST variability, (B) annual mean subsurface ocean temperature along the equator (5°S to 5°N), and (C) annual mean SST (shading) and surface wind stress (vectors). SST variability is computed as the SD of monthly anomalies relative to the monthly mean seasonal cycle. In the tropical oceans, a metric of variability that is dominated by variations occurring on interannual time scales. SST and surface wind stress are from TropFlux (46) and subsurface ocean temperature data are from ORAS-S4 (37). The intensity and spatial pattern of SST variations in the IO are thus determined by the direction of the prevailing winds along the equator, which are weakly westerly (Fig. 1C, vectors), and by the subtle east-west SST gradient underlying them (Fig. 1C, shading). Model simulations show that continued greenhouse warming could alter these features, and the IO could evolve into a mean state similar to the Pacific or Atlantic oceans (5, 7, 10). Historical observations support this prediction, showing a tendency for easterly winds along the equator, an eastward shoaling thermocline, and a reversal of the east-west SST gradient since the 1950s (3–6). These changes should be accompanied by increased SST variability along the equatorial IO (19); however, model predictions are not consistent with this theoretical expectation (8–11). Furthermore, the possibility that the IO could harbor stronger modes of climate variability has remained largely unexplored. Here, we address these questions using numerical simulations of past and future climate changes in which the mean state of the IO could favor stronger variability. Our goal is to assess physical processes that could cause new modes of variability to emerge in the IO under continued greenhouse warming as well as the potential existence of these modes during past climate intervals. We analyze an ensemble of simulations of 21st-century climate performed by 36 models participating in the Coupled Model Intercomparison Project 5 (CMIP5). These simulations were run under increasing GHG concentrations following a “business as usual” high-emission scenario (see “Data” and “Methods” sections). These models accurately reproduce the observed patterns of variability in the southeastern IO (fig. S1) as well as long-term changes in the east-west gradient over the 1900–2017 period (fig. S2), lending credibility to their predictions of an altered mean state under continued greenhouse warming throughout the second half of the 21st century (see Supplementary Text 1 for additional model evaluation). We also analyze simulations of the climate at the Last Glacial Maximum (LGM)—a past climatic interval ∼21,000 years before present when the IO exhibited a similarly altered mean state featuring stronger upwelling and an eastward shoaling thermocline (24, 25). The LGM simulations were performed with the Community Earth System Model version 1.2 (CESM1) (26), a model that simulates changes in IO mean state supported by multiproxy syntheses from this climate interval (24) and consistent changes in variability (27). To our knowledge, CESM1 is one of the very few climate models capable of simulating physical processes in the IO amplifying regional climate changes during the LGM (24), justifying the use of a single model for this part of our study. Despite being triggered by exposure of continental shelves due to lower glacial sea level (28), these changes result in changes in IO mean state and variability (24, 25, 27) analogous to those simulated under greenhouse warming, albeit in a globally colder climate. Additional CESM1 LGM simulations are used to isolate changes associated with both emergent and existing modes (Materials and Methods).
  2. RESULTS: Our simulations indicate that under greenhouse warming and LGM conditions, the IO can exhibit increased SST variability in the eastern equatorial IO (EEIO) (Fig. 2, A and B). This pattern of intensification resembles modern variability in the other tropical oceans and represents a pronounced departure from current variability in the IO, which is minimal along the equator (Fig. 1A). The increase in gSST variability occurs during late boreal summer (August-September-October) following changes in the mean state favoring stronger coupled interactions during the preceding months. Increased equatorial upwelling and an eastward shoaling thermocline during July-August-September (JAS) (Fig. 2, C and D) favor the development of SSTAs in the EEIO. The changes in mean state are also part of a coupled ocean-atmosphere response. Equatorial winds become more easterly under greenhouse warming and glacial conditions (Fig. 2, E and F, vectors), a response that is reinforced by the changes in the underlying SST gradient (Fig. 2, E and F, shading) via the cooling effect of a shallower thermocline. These coupled responses are initiated by different atmospheric processes: a reversal of westerly winds over the eastern IO driven by a weaker Walker circulation, for greenhouse warming (7); and an atmospheric response to shelf exposure, for the LGM (28). Despite the different triggering mechanisms, the same coupled feedbacks amplify the changes in both cases, generating an oceanic mean state reminiscent of the eastern equatorial Pacific and Atlantic oceans, with a shallow equatorial thermocline and vigorous upwelling favoring stronger air-sea interactions and SST variability (19). Changes in (A and B) SST variability, (C and D) subsurface ocean temperature (shading, m), vertical velocity (contours, m/day), and (E and F) SST (shading) and surface wind stress (vectors). Glacial changes (left) are computed from a simulation of LGM relative to a simulation of preindustrial (PI) climate, both performed with the CESM1. Changes under greenhouse warming are computed for the 2050–2100 interval in high-emission scenario [Representative Concentration Pathway 8.5 (RCP8.5)] simulations performed by 36 CMIP5 models relative to the 1850–1950 interval from historical simulations. The changes in variability are computed as the difference in SD of SSTAs during the August-September-October (ASO) season. Changes in mean state are computed for the JAS season. The changes under greenhouse warming are the average among the changes simulated among all 36 CMIP5 models. Dashed and solid red curves in (C) and (D) indicate the depth of thermocline in the reference (PI and historical) and altered (LGM and RCP8.5) climate states, respectively. (G) Relationship between changes in SD of SST anomalies in the EEIO (70°E to 95°E, 2.5°S to 2.5°N) during the ASO season and zonal wind stress in the equatorial IO (50°E to 80°E, 2.5°S to 2.5°N) during the JAS season for each model simulated response to greenhouse warming (blue circles) and LGM boundary conditions (red circle). Models with mode activation are outlined in red. The CMIP5 models show a direct link between the changes in mean climate and the increase in variability under greenhouse warming. The magnitude of the increase in SST variability, measured by the change in SD of SSTAs averaged over the EEIO, is strongly anticorrelated with the changes in zonal wind stress along the equator (r = −0.73, P <0.001; Fig. 2G). This indicates that greater easterly wind stress leads to a larger increase in variability, a relationship that reflects the influence of zonal winds on seasonal upwelling and thermocline depth over the EEIO. Most CMIP5 models predict increases in variability and more easterly winds for the second half of the century (Fig. 2G); however, the magnitude of these responses differs by an order of magnitude. Some CMIP5 models predict pronounced changes in equatorial winds accompanied by increases in SST variability of up to 100%. In these models, the magnitude of the changes represents a reversal of the climatological winds, i.e., absolute easterlies develop across the equatorial IO along with seasonally colder SSTs over the EEIO. A similar wind reversal and seasonal “cold tongue” is simulated under LGM conditions (not shown), resulting in the largest changes in variability among all simulations (Fig. 2G, red circle). These seasonal variations are similar to those occurring in the modern Pacific and Atlantic oceans, which sustain the ENSO and Atlantic Niño modes. Likewise, the simulated changes in the IO give rise to its own El Niño–like variability. Under the altered mean states of the LGM and high-emission scenarios, climate variability in the IO manifests as warm and cold events that are physically different from those associated with the IOD—currently a dominant mode of IO climate variability (18, 29)—although superficially similar to very extreme IOD events. To isolate the dynamics of the emergent mode, we use our subset of LGM simulations in which ENSO and IOD modes are disabled (see “Methods” section). Results from these simulations show that events associated with the emergent mode are independent from the IOD (fig. S3) and, more importantly, that they are triggered by a distinct atmospheric precursor on the western IO (fig. S3A). Because additional simulations cannot be run with CMIP5 models, we isolate the events associated with the emergent mode using a methodology based on this wind precursor (fig. S4; also Materials and Methods). The CMIP5 simulations also show that these events are driven by a coupled mode that has not been observed in historical observations and could become active under continued greenhouse warming. Unlike IOD events, which are triggered by wind fluctuations in the southeastern IO along the coast of Java and Sumatra (16, 30), events associated with the emergent mode are initiated remotely by an atmospheric circulation anomaly over the western IO and Arabian Sea (Fig. 3, left; vectors). This atmospheric precursor develops during late boreal spring and influences the EEIO via propagation of downwelling oceanic Kelvin waves along the equator (Fig. 3, left; contours). For warm events, the atmospheric precursor has a westerly wind stress anomaly along the equator that drives a Kelvin wave response characterized by a thermocline deepening toward the East (Fig. 3, A and C, contours). This response suppresses climatological cooling over the EEIO during late boreal summer, when the thermocline is seasonally shallower and upwelling is strong (Fig. 2, C and D), driving an initial warming in the EEIO. An anomalous zonal SST gradient is established along the equatorial IO, further weakening surface winds. These wind changes drive oceanic responses, thermocline deepening, and reduced upwelling that continue the warming of the EEIO until its peak during late boreal summer (Fig. 3, B and D, shading). Such coupled responses are akin to the positive feedback loop proposed by Bjerknes (31) for the growth of El Niño events in the Pacific Ocean. First column: SST (shading), surface wind stress (vectors), and thermocline depth (contours) anomalies associated with the atmospheric precursor of warm equatorial mode (A) in a subset of CMIP5 greenhouse warming simulations, (C) a LGM simulation, and (E) historical observations. Second column: Same as first column but for the peak of the event, 3 months after the occurrence of the precursor. Warm equatorial events are triggered by the atmospheric precursor under (B) greenhouse warming and (D) LGM conditions, but not (F) under historical conditions because the mean state is not conducive for coupled interactions. Events are identified and composited, when the April-May-June standardized zonal wind stress anomalies averaged over the western IO (40°E to 60°E, 2.5°S to 2.5°N) exceeds 0.5. The precursor phase is during May and the peak in August. The LGM simulation has modes of variability disabled as described in the “Methods” section. At their peak, westerly wind anomalies coupled to the underlying SST gradients span most the basin (Fig. 3, B and D, vectors). These anomalous winds keep the thermocline anomalously deep in the EEIO (Fig. 3, B and D, contours) and suppress equatorial upwelling (not shown) sustaining the positive SSTAs in the EEIO. The pronounced equatorial signature of these events is consistent with the pattern of SST variability increase (Fig. 2, A and B), and their activation occurs under large changes in mean state, such as under LGM conditions (Fig. 2G, red circle), and in the subset of simulations of future climate with the largest increases in variability (Fig. 2G, blue circles with red outline). The changes in mean state also favor the emergence of equatorial cold events with negative SSTAs exhibiting similar magnitude, spatial patterns, and underlying dynamics as the warm events (fig. S5; see Supplementary Text 2). Observations do not show an active mode under current conditions (Fig. 3, E and F, and fig. S5, E and F) because the mean state is not favorable for large-scale coupled interactions. The emergence of the equatorial mode could drive rainfall variability with stronger amplitude and altered patterns over the IO and surrounding land masses relative to currently experienced. Warm events, with their positive SSTAs spanning much of the equatorial IO, could drive rainfall deficits over the Horn of Africa as well as over Southern India, in addition to increased rainfall over Indonesia and Northern Australia (Fig. 4C). Rainfall anomalies with such patterns and magnitudes have not been observed during the historical period because warm IOD events are extremely weak and their rainfall impacts are restricted to the southeastern IO (Fig. 4A). On the other hand, cold events associated with the equatorial mode could drive rainfall anomalies with a similar spatial pattern and magnitude as the warm events, but with opposite polarity and subtle, yet important differences for terrestrial precipitation (Fig. 4D). For example, cold equatorial events are associated with increased rainfall over peninsular India and thus drive a response opposite to the impacts of a typical cold IOD event (Fig. 4B). These high-amplitude rainfall impacts have only been observed in 1997, during the strongest, cold IOD event on record (11)—the only observed event with SSTAs reaching the EEIO. The emergence of the equatorial mode could make these high-amplitude SSTAs a common occurrence by the second half of the 21st century when CMIP5 models predict two to four events (warm or cold) per decade (range was estimated from the subset of models with mode activation). Over Sumatra and Java, the associated rainfall fluctuations could represent a surplus (or deficit) of 30 to 50% of current seasonal rainfall during the JAS season. Thus, predicting and attributing changing distributions of future extremes in a warming climate must consider these dynamical changes in rainfall variability alongside with thermodynamic effects (32). Composite rainfall anomalies (shading) during (A, B) observed Dipole Mode events and (C, D) simulated Equatorial Mode events active in the IO under greenhouse warming. In both cases, warm (A, C) and cold (B, D) events are, respectively, characterized by positive or negative SST anomalies (contours) over the eastern IO. SST contour interval is 0.25 K. Equatorial Mode events show rainfall and SST anomalies spanning much of the equatorial IO. Anomalies correspond to the peak season of each mode, September-October-November (SON) for the Dipole Mode and August-September-October for the Equatorial Mode. Observed Dipole Mode events are selected and composited on the basis of SON values of the Dipole Mode Index (18) with a 0.5σ threshold. Equatorial Mode events are selected and composited on the basis of indices of the western IO atmospheric precursor and the peak SSTA in the EEIO during the ASO season with a 0.5σ threshold (see “Data” and “Methods” sections). Both criteria combined isolate events that evolve into large-scale SST anomalies. Dipole Mode composites are based on the Global Precipitation Climatology Project (42) and TropFlux (36) observational datasets over the 1980–2017 period. Equatorial Mode composites are based on output from CMIP5 rcp85 simulations over the 2050–2100 period composited for each model run and then averaged across the 10 models with mode activation.
  3. DISCUSSION: In addition to revealing previously unrecognized dynamics of the IO, our results explain the lack of consensus in model predictions of future changes in SST variability in this ocean (9–12). Not all models show increasing SST variability under future greenhouse warming because the equatorial mode does not become active because of muted changes in mean state. Activation might require a change in direction of surface winds along the equator—at least seasonally—so that large-scale upwelling can be established along the equatorial IO. Larger changes rather than just a reversal in winds might be required so that the balance of positive and negative feedbacks in the EEIO favors unstable growth of SSTAs. Addressing these questions could help clarify the interpretation of model simulations, which show consistent predictions of a strengthening thermocline feedback, yet equivocal results regarding changes in SST variability (9–12). Additional questions must be answered to accurately predict this disruptive outcome, such as whether the changes in the mean state after 2050 will be sufficiently large to favor activation of the equatorial mode. The magnitude of these changes will depend on whether they are amplified by coupled feedbacks, an issue that remains hotly contested (33, 34). All available observational evidence, however, supports predictions of large changes in mean state potentially amplified by coupled feedbacks (fig. S2). Historical observations show pronounced changes in the east-west SST gradient, particularly during the season when coupled feedbacks are stronger (3–5). Here, we showed that only models with equatorial mode activation can simulate changes in the SST gradient as observed (fig. S2). Furthermore, multiple paleoclimate datasets from the LGM show large changes in mean state potentially amplified by coupled feedbacks (24) along with much stronger climate variability (27), attesting to this ocean’s ability to experience large changes in mean state and variability via coupled feedbacks. In summary, we have demonstrated that the IO can sustain an equatorial mode of climate variability under altered mean states predicted for the second half of the 21st century. This mode manifests as cold and warm interannual events with large-scale SSTAs spanning the central and EEIO. These events, particularly warm ones, represent a marked departure from current variability, characterized by weaker and more spatially confined warm IOD events. Because of their basin-wide and stronger SSTAs, future warm events could drive unprecedented hydrological extremes across the basin. They could bring more frequent droughts to East Africa and southern India, in addition to increased rainfall over Indonesia, exacerbating the effect of a warmer climate on these hydrological extremes (11). Cold and warm events are governed by physical processes similar to those driving El Niño and La Niña and could therefore be predictable at least a season in advance. However, further research on its predictability and global impacts will be needed to improve adaptation efforts to climate change. The emergence of the equatorial mode is supported by a consistent link between changes in variability and mean state across climate models, although a sufficiently large change is required for its activation. These predictions are supported by paleoclimate data from the LGM, which show mean state changes of a magnitude comparable to those predicted under high emissions (24) along with an active equatorial mode (27). Furthermore, the activation of the equatorial mode appears to be less sensitive to common biases in the simulation of seasonal climate by CMIP models (fig. S6 and Supplementary Text 3), supporting our conclusion that this disruptive outcome will be largely determined by the magnitude of the changes in mean state. Further work is needed to accurately assess threshold behavior in this key component of the climate system, particularly under lower-emission scenarios or past climatic states other than the LGM.
  4. Present-day conditions do not favor the coupled interactions required for the mode’s emergence, and indeed, historical observations do not show evidence of the occurrence of these extreme events. A potential activation under greenhouse warming, however, could lead to record-breaking SST and rainfall fluctuations, rendering the emergence of the mode a main factor determining future climate risks, including more frequent and devastating wildfires, flooding, and droughts.

 

 

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  • chaamjamal: Thank you Paul. This is a 50-year study at a decadal time scale. The effective sample size is about 5. There can't be a lot of statistical power in th
  • chaamjamal: Autocorrelation refers to correlations among different time spans of the same time series.
  • chaamjamal: The correlations reported are those between different time series over the same time span.
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