Ocean Sea Surface Temperature Variability Research Articles

A key hub for climate systems: deciphering from Southern Ocean sea surface temperature variability

A key hub for climate systems: deciphering from Southern Ocean sea surface temperature variability

Winter precipitation predictability in Central Southwest Asia and its representation in seasonal forecast systems

In Central Southwest Asia (CSWA; 22°N to 40°N and 30°E to 70°E), winter (November to February) precipitation contributes up to 70% of the annual mean, but substantial interannual variations exist. Dynamical models exhibit subpar predictability in this region, but the limits of their skills are not well established. Here, we identify the tropical and extratropical forcings that explain ~75% of area-averaged seasonal variability in CSWA winter precipitation. Tropical forcing comes from the indirect El Niño-Southern Oscillation (ENSO) pathway, the leading mode of tropical Indian Ocean precipitation variability. This mode is coupled with ENSO-related Pacific Ocean sea surface temperature variability. A direct ENSO influence on CSWA does not extend beyond its Indian Ocean connection. Extratropical forcing comes from a large-scale mode of internal atmospheric variability. The spatial structure, variability of tropical forcing, and teleconnection with CSWA winter precipitation are skillfully depicted in two seasonal forecasting systems: the fifth-generation seasonal forecasting system (SEAS5) and Seamless System for Prediction and Earth System Research (SPEAR). Extratropical forcing’s spatial structure is also produced skillfully in the two modelling systems; however, the representation of its interannual variability and teleconnection requires improvement. While SEAS5 displays skill in representing extratropical forcing influence on CSWA winter precipitation and marginal skill in reproducing interannual variability, SPEAR has negligible ability in both areas. Consequently, these models have limited predictive skills over CSWA in winter. While improvements in representing extratropical forcing may be inherently limited as it arises from internal atmospheric variability, further research is needed to establish its predictability limits fully.

Regional tree-ring oxygen isotope deduced summer monsoon drought variability for Kumaun-Gharwal Himalaya

The precipitation in the Kumaun-Gharwal (Uttarakhand) Himalaya (KGH) is predominantly regulated by the Indian summer monsoon (ISM) and has been declining over the last century. However, because of limited historical data, it is difficult to place this recent decreasing trend in the context of pre-anthropogenic influences and understand a fuller range of hydroclimate scenarios for the region. Thus, we developed a 508-year regional tree-ring stable oxygen isotope (δ18OTR) record for multiple coniferous taxa (Abies spectabilis, Cedrus deodara and Picea smithiana) for the KGH, which spanned 1508–2015 CE. The δ18OTR record explained 35.8% of instrumental June–August Palmer Drought Severity Index (JJAPDSI) variance modelled from a nearby meteorological station. The JJAPDSI reconstruction shows regional coherency with both instrumental and proxy-based independent hydroclimatic records providing confidence that it is a reliable proxy to assess the long term hydroclimatic variability of the region. We identified decreasing strength of JJAPDSI reconstruction in KGH during recent decades indicating intensified drought. This is in the context of pre-anthropogenic influences and understand a fuller range of hydroclimate scenarios for the region. The δ18OTR also has coherency with Pacific and Indian Ocean sea surface temperature variability illustrating the broader teleconnections that influence the regional hydroclimate.

Enhanced Impacts of Indian Ocean Sea Surface Temperature on the Dry/Wet Variations Over Northwest China

AbstractMost western Northwest China (WNWC) has experienced notable wetting in recent decades. Despite the widespread attention of scientific community, the causes of such climate shift are still not well‐understood. In this study, we find that the dominant mode of Indian Ocean sea surface temperature variability — Indian Ocean Basin mode (IOBM) plays a crucial role in the precipitation over WNWC. Nonetheless, such a close lagged relationship is unstable, and varies with Pacific Decadal Oscillation (PDO). When the PDO is in its warm phase, there is no robust lagged relationship between April‐June WNWC precipitation variations and January‐March IOBM. When the PDO is in its cold phase, the warm phase of the IOBM inspires a wavetrain across the Eurasian continent in the next April‐June, with an uncommon low pressure around Lake Baikal, thereby enhancing the westerlies and thus more moisture into the WNWC region. This enables the IOBM‐based statistical rainfall prediction scheme to estimate the precipitation in the WNWC region with some confidence. This remarkable contrast in the influence of IOBM between the two phases of the PDO highlights exciting new avenues for improving climate predictions over the WNWC.

Assessment of CMIP6 models' skill for tropical Indian Ocean sea surface temperature variability

AbstractThe present study examines the ability of Coupled Model Inter‐comparison Project phase 6 (CMIP6) models in representing the dominant modes of tropical Indian Ocean (TIO) sea surface temperature (SST) variability on the interannual and decadal time scale. Historical simulations from 27 CMIP6 models are assessed against Extended Reconstructed SST over the period of 1854 to 2014. Spectrum analysis reveals that many models reproduce interannual and decadal variability of TIO SST but underestimate the amplitude of variability with some disparity in the periodicity. All models can reproduce the dominant basin‐wide mode of interannual and decadal variability of TIO SST reasonably well. Skill score analysis of TIO SST variability reveals that KACE‐1‐0‐G has highest skill, followed by FGOALS‐f3‐L, EC‐Earth3‐Veg‐LR, ACCESS‐ESM1‐5, CanESM5‐CanOE on the interannual timescale and FGOALS‐f3‐L, CanESM5‐CanOE, KACE‐1‐0‐G and CanESM5, respectively, showed highest skills for decadal variability. It is found that variations in radiation and latent heat flux are primarily responsible for interannual variability in TIO SST, the basin‐wide warming, in the observations. Taylor diagram analysis reveals that all the models exhibit better skill for the radiative flux; however, skill for the latent heat and momentum flux varies from model to model. It is important to note that the models in which the latent heat flux and zonal wind are better represented have produced better TIO SST variability compared to other models. A higher discrepancy in latent heat and zonal momentum flux leads to improper wind‐evaporation‐SST and wind‐circulation‐SST feedback, which in turn restricts the model skill. Besides, model that has realistic central and eastern Pacific SST variability show better skill for TIO SST variability in both interannual and decadal time scales. The present study advocates that better representation of latent heat flux and zonal wind in coupled models is important for the accurate simulation of interannual and decadal variability in TIO SST.

Recent changes in the major modes of Asian summer monsoon rainfall: influence of ENSO-IOD relationship

Indo-Pacific Ocean sea surface temperature (SST) anomalies in the form of Indian Ocean Dipole (IOD) and El Nino-Southern Oscillation (ENSO) have a major role in determining the climate of the tropics and extratropics. The tropical Pacific variability was dominated by canonical ENSO (EOF1) and El Nino Modoki (EOF2) during the 1980–1998 period (period 1), while during the 1999–2017 period (period 2), both the flavors of ENSO contributed to EOF1. This Pacific Ocean variability weakened the ENSO-IOD relationship also. The present study estimates the changes that occurred to the major modes of Asian summer monsoon (ASM) rainfall during these two periods and addresses the role of the recent changes in SST boundary forcing in ASM interannual variability. During period 1, the first mode (EOF1) of ASM rainfall was associated with ENSO and IOD with 32% variability. But after 1998, EOF1 depends only on the ENSO flavors (canonical and Modoki ENSO) and has slightly reduced variability (26%). The second mode was controlled by the decay phase of ENSO during the 1980–1998 period (12%), while the eastern Indian Ocean and central-northeast Pacific SST variability contributed to EOF2 (15%) during period 2. EOF3 is controlled by IOD in both periods. Further analysis indicates that the extended warming pattern from central to eastern Pacific of ENSO during period 2 weakened ENSO-IOD co-occurrence through the modification of tropical circulation. In the absence of IOD, ENSO-monsoon teleconnection becomes stronger during period 2 than in the previous period. Indian summer monsoon rainfall (ISMR) relationship with the developing phase of ENSO became stronger after 1998, and that of the decay phase of ENSO is decreased. The ISMR-ENSO Modoki relationship has a phase shift from summer maxima to winter maxima. Meanwhile, the ISMR-IOD relationship in the absence of ENSO became slightly stronger in the 1999–2017 period. Thus, the Pacific variability in these two periods influenced ASM rainfall variability through the modulation of its relationship with tropical Indo-Pacific SST anomalies.

On the relationship between Indian Ocean sea surface temperature variability and tropical cyclogenesis in the southwest Pacific

ABSTRACTIn the southwest Pacific (SWP) tropical cyclones (TCs) account for 76% of the regions natural disasters and have substantial economic, physical and environmental impacts on people and places. Therefore, information is needed to better understand when and where TCs are likely to occur, as this can aid in preparedness and planning. While there is a well‐established relationship between Pacific Ocean sea surface temperature (SST) variability and tropical cyclogenesis (TC genesis) in the SWP, it does not fully explain the historical spatial and temporal variability observed. Therefore, this study aims to look beyond the Pacific and establish a new relationship between Indian Ocean SST variability and SWP TC genesis. This is achieved by statistically relating indices of Indian Ocean SST variability to SWP TC genesis positions. The physical mechanisms driving these observed relationships are then established by studying changes in the environmental conditions conducive to TC genesis. This analysis shows that Indian Ocean SST variability significantly modulates the clustering of SWP TC genesis, where warmer (cooler) SSTs in the eastern and western regions of the Indian Ocean result in a statistically significant north/east (south/west) migration of TC genesis by up to 950 km. Importantly, this relationship is shown to be consistent when the El Niño/Southern Oscillation (ENSO, the dominant Pacific mode) is in an inactive phase (ENSO neutral). Favourable TC genesis parameters including warm SSTs, increased relative humidity, anomalously negative 700 hPa vorticity, anomalously negative and low absolute 200–850 hPa vertical wind shear account for the observed shift in clustering. Furthermore, we show that the combined effect of ENSO/Indian Ocean SST variability results in varying risk profiles for island nations of the region, with the two climate modes either enhancing or suppressing individual impacts. Significantly, the findings from this study provide an opportunity for meteorological agencies to improve seasonal SWP TC outlooks.

Middle to Late Pleistocene vegetation and climate change in subtropical southern East Africa

In this study we investigate Pleistocene vegetation and climate change in southern East Africa by examining plant leaf waxes in a marine sediment core that receives terrestrial runoff from the Limpopo River. The plant leaf wax records are compared to a multi-proxy sea surface temperature (SST) record and pollen assemblage data from the same site. We find that Indian Ocean SST variability, driven by high-latitude obliquity, exerted a strong control on the vegetation of southern East Africa during the past 800,000 yr. Interglacial periods were characterized by relatively wetter and warmer conditions, increased contributions of C3 vegetation, and higher SST, whereas glacial periods were marked by cooler and arid conditions, increased contributions of C4 vegetation, and lower SST. We find that Marine Isotope Stages (MIS) 5e, 11c, 15e and 7a–7c are strongly expressed in the plant leaf wax records but MIS 7e is absent while MIS 9 is rather weak. Our plant leaf wax records also record the climate transition associated with the Mid-Brunhes Event (MBE) suggesting that the pre-MBE interval (430–800 ka) was characterized by higher inputs from grasses in comparison to relatively higher inputs from trees in the post-MBE interval (430 to 0 ka). Differences in vegetation and SST of southern East Africa between the pre- and post-MBE intervals appear to be related to shifts in the location of the Subtropical Front. Comparison with vegetation records from tropical East Africa indicates that the vegetation of southern East Africa, while exhibiting glacial–interglacial variability and notable differences between the pre- and post-MBE portions of the record, likely did not experience such dramatic extremes as occurred to the north at Lake Malawi.

Role of Indian Ocean SST variability on the recent global warming hiatus

Previous studies have shown a slowdown in the warming rate of the annual mean global surface temperature in the recent decade and it is referred to as the hiatus in global warming. Some recent studies have suggested that the hiatus in global warming is possibly due to strong cooling in the tropical Pacific. This study investigates the possible role of the Indian Ocean warming on the tropical Pacific cooling. Despite the continued rise in sea surface temperature (SST) over the tropical Indian Ocean, SST over the tropical Pacific has shown a cooling trend in the recent decade (2002−2012). It is well known fact that the Indian Ocean and the Pacific Ocean are strongly coupled to each other and the Indian Ocean basin wide warming is triggered by El Niño on interannual time scale. However, in the recent decade, this relationship is weakening. The recent Indian Ocean warming is triggering a Matsuno-Gill type response in the atmosphere by generating anomalous cyclonic circulations on either side of equator over the tropical Indian Ocean and anomalous easterlies along the tropical Pacific Ocean. These anomalous easterlies result in Ekman divergence in the equatorial Pacific and produce upwelling Kelvin waves, cools the tropical Pacific and therefore indirectly contributes to the hiatus in global warming.

Study on the changes in the East Asian precipitation in the mid‐1990s using a high‐resolution global downscaled atmospheric data set

AbstractA high‐resolution global atmospheric data set (DA126) is used to understand the East Asian summer precipitation variability. It is found that a fine resolution of the DA126 precipitation data is able to reveal the detailed structures of the rainfall variability over East Asia and southern China in comparison with global analysis precipitation data sets such as the Climate Prediction Center Merged Analysis of Precipitation (CMAP). The first two empirical orthogonal functions (EOFs) of the DA126 precipitation data over East Asia accurately reflect a decadal shift in rainfall over southern China in the mid‐1990s. Furthermore, the first EOF‐related precipitation of the DA126 is related to the tropical Pacific sea surface temperature (SST) variability (i.e., El Niño/Southern Oscillation), and the second EOF‐related precipitation is associated with the Indian Ocean SST variability. Consequently, the tropical Pacific and the Indian Ocean SSTs have different associations with the East Asian monsoon precipitation variability. However, it is difficult to find such a relationship in the first two EOFs of the CMAP data set over East Asia. Using the DA126 precipitation data set, our further analysis indicates that warming of both the tropical Pacific and the Indian Ocean causes an increase in the rainfall anomaly over southern China after the mid‐1990s, which results in a decadal shift in the rainfall anomaly after the mid‐1990s. In addition, the first EOF‐related precipitation is associated with both the Pacific‐Japan‐like (PJ‐like) pattern and the Eurasian‐like pattern. In contrast, the second EOF‐related precipitation is only associated with the PJ‐like wave trains from the western Pacific to East Asia.

Teleconnections and predictive characteristics of Australian seasonal rainfall

A new methodology is proposed that allows patterns of interannual covariability, or teleconnections, between the intraseasonal and slow components of seasonal mean Australian rainfall and the corresponding components in the Southern Hemisphere atmospheric circulation to be estimated. In all seasons, the dominant rainfall–circulation teleconnections in the intraseasonal component are shown to have the characteristic features associated with well-known intraseasonal dynamical and statistical atmospheric modes and their relationship with rainfall. Thus, for example, there are patterns of interannual covariability that reflect rainfall relationships with the intraseasonal Southern Annular Mode, the Madden-Julian Oscillation and wavenumber 3 and 4 intraseasonal modes of variability. The predictive characteristics of the atmospheric circulation–rainfall relationship are shown to reside with the slow components. In all seasons, we find rainfall–circulation teleconnections in the slow components related to the El Nino-Southern Oscillation. Each season also has a coupled mode, with a statistically significant trend in the time series of the atmospheric component that appears to be related to recent observed trends in rainfall. The slow Southern Annular Mode also features in association with southern Australian rainfall, especially during austral winter and spring. There is also evidence of an influence of Indian Ocean sea surface temperature variability on rainfall in southeast Australia during austral winter and spring.

Impact of overestimated ENSO variability in the relationship between ENSO and East Asian summer rainfall

AbstractEl Niño–Southern Oscillation (ENSO) events in the preceding winter are an important predictor used to forecast the subsequent East Asian summer rainfall (EASR). This study investigates the relationship between the preceding winter ENSO and the EASR in coupled general circulation models, by analyzing the simulated results of 18 Coupled Model Intercomparison Project Phase 3 models. It is found that more than half of these models can approximately reproduce the ENSO's delayed impact on the EASR, and five models can capture the significant ENSO–EASR relationship. All of these five models overestimate the intensity of the ENSO variability, and they are almost the models that most seriously overestimate the ENSO variability, strongly suggesting that overestimated ENSO variability can help coupled models reproduce the relationship between the ENSO and EASR. Further analyses indicate that all of the five “best” models also overestimate the intensity of tropical Indian Ocean sea surface temperature (SST) variability, and they simulate the strongest intensity of Indian Ocean SST variability among the 18 models.

Cholera outbreak in Senegal in 2005: was climate a factor?

Cholera is an acute diarrheal illness caused by Vibrio cholerae and occurs as widespread epidemics in Africa. In 2005, there were 31,719 cholera cases, with 458 deaths in the Republic of Senegal. We retrospectively investigated the climate origin of the devastating floods in mid-August 2005, in the Dakar Region of Senegal and the subsequent outbreak of cholera along with the pattern of cholera outbreaks in three other regions of that country. We compared rainfall patterns between 2002 and 2005 and the relationship between the sea surface temperature (SST) gradient in the tropical Atlantic Ocean and precipitation over Senegal for 2005. Results showed a specific pattern of rainfall throughout the Dakar region during August, 2005, and the associated rainfall anomaly coincided with an exacerbation of the cholera epidemic. Comparison of rainfall and epidemiological patterns revealed that the temporal dynamics of precipitation, which was abrupt and heavy, was presumably the determining factor. Analysis of the SST gradient showed that the Atlantic Ocean SST variability in 2005 differed from that of 2002 to 2004, a result of a prominent Atlantic meridional mode. The influence of this intense precipitation on cholera transmission over a densely populated and crowded region was detectable for both Dakar and Thiès, Senegal. Thus, high resolution rainfall forecasts at subseasonal time scales should provide a way forward for an early warning system in Africa for cholera and, thereby, trigger epidemic preparedness. Clearly, attention must be paid to both natural and human induced environmental factors to devise appropriate action to prevent cholera and other waterborne disease epidemics in the region.

On the relationship between Indian summer monsoon withdrawal and Indo-Pacific SST anomalies before and after 1976/1977 climate shift

A clear shift in the withdrawal dates of the Indian Summer Monsoon is observed in the long term time series of rainfall data. Prior (posterior) to the 1976/1977 climate shift most of the withdrawal dates are associated with a late (an early) withdrawal. As a result, the length of the rainy season (LRS) over the Indian land mass has also undergone similar changes (i.e., longer (shorter) LRS prior (posterior) to the climate shift). In this study, probable reasons for this significant shift in withdrawal dates and the LRS are investigated using reanalysis/observed datasets and also with the help of an atmospheric general circulation model. Reanalysis/observational datasets indicate that prior to the climate shift the sea surface temperature (SST) anomalies in the eastern equatorial Pacific Ocean and the Arabian Sea exerted a strong influence on both the withdrawal and the LRS. After the climate shift, the influence of the eastern equatorial Pacific Ocean SST has decreased and surprisingly, the influence of the Arabian Sea SST is almost non-existent. On the other hand, the influence of the southeastern equatorial Indian Ocean has increased significantly. It is observed that the upper tropospheric temperature gradient over the dominant monsoon region has decreased and the relative influence of the Indian Ocean SST variability on the withdrawal of the Indian Summer Monsoon has increased in the post climate shift period. Sensitivity experiments with the contrasting SST patterns on withdrawal dates and the LRS in the pre- and post- climate shift scenarios, confirm the observational evidences presented above.

Do Climate Models Capture the Tropical Influences on North Pacific Sea Surface Temperature Variability?

Abstract This study evaluates the ability of global climate models to reproduce observed tropical influences on North Pacific Ocean sea surface temperature variability. In an ensemble of climate models, the study finds that the simulated North Pacific response to El Niño–Southern Oscillation (ENSO) forcing is systematically delayed relative to the observed response because of winter and spring mixed layers in the North Pacific that are too deep and air–sea feedbacks that are too weak. Model biases in mixed layer depth and air–sea feedbacks are also associated with a model mean ENSO-related signal in the North Pacific whose amplitude is overestimated by about 30%. The study also shows that simulated North Pacific variability has more power at lower frequencies than is observed because of model errors originating in the tropics and extratropics. Implications of these results for predictions on seasonal, decadal, and longer time scales are discussed.

Pacific Ocean sea-surface temperature variability and predictability of rainfall in the early and late parts of the Indian summer monsoon season

For central India and its west coast, rainfall in the early (15 May–20 June) and late (15 September–20 October) monsoon season correlates with Pacific Ocean sea-surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies can be used to predict such rainfall. The patterns of SST anomalies that correlate best include the equatorial region near the dateline, and for the early monsoon season (especially since ~1980), a band of opposite correlation stretching from near the equator at 120°E to ~25°N at the dateline. Such correlations for both early and late monsoon rainfall and for both regions approach, if not exceed, 0.5. Although correlations between All India Summer Monsoon Rainfall and typical indices for the El Nino-Southern Oscillation (ENSO) commonly are stronger for the period before than since 1980, these correlations with early and late monsoon seasons suggest that ENSO continues to affect the monsoon in these seasons. We exploit these patterns to assess predictability, and we find that SSTs averages in specified regions of the Pacific Ocean in April (August) offer predictors that can forecast rainfall amounts in the early (late) monsoon season period with a ~25% improvement in skill relative to climatology. The same predictors offer somewhat less skill (~20% better than climatology) for predicting the number of days in these periods with rainfall greater than 2.5 mm. These results demonstrate that although the correlation of ENSO indices with All India Rainfall has decreased during the past few decades, the connections with ENSO in the early and late parts have not declined; that for the early monsoon season, in fact, has grown stronger in recent decades.

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.

Predictability of summer northwest Pacific climate in 11 coupled model hindcasts: Local and remote forcing

The skills of 11 coupled ocean‐atmosphere general circulation models (CGCMs) are investigated in the prediction of seasonal rainfall and circulation anomalies over the northwest (NW) Pacific for the period 1980–2001, with a focus on the summer following the mature phase of El Niño (hereafter JJA(1)). It is shown that the first empirical orthogonal function (EOF) mode of sea level pressure is closely tied to the second EOF mode of rainfall variability over the NW Pacific during JJA(1), indicative of strong feedback between circulation and convection. Most coupled models and the associated multimodel ensemble well predict these EOF modes and their relationship with high fidelity. Coupled models are capable of predicting suppressed rainfall over the NW Pacific in JJA(1). A few models fail to predict the concurrent weak negative sea surface temperature (SST) anomalies on the southeastern flank of the anomalous anticyclone. This suggests that remote forcing via teleconnections is important for NW Pacific rainfall prediction in those models. In some models, local air‐sea interactions seem also to play a role. Specifically, remote forcing by tropical Indian Ocean (TIO) SST variability is identified as influential on NW Pacific climate during JJA(1). TIO SST affects the atmosphere over the NW Pacific by two mechanisms, via the equatorial Kelvin wave and the intensification of the subtropical westerly jet. Overall, models are successful in predicting the antisymmetric patterns of precipitation and winds over TIO during spring, which are critical in sustaining the TIO warming through the subsequent summer.

An objective analysis of the observed spatial structure of the tropical Indian Ocean SST variability

The observed interannual Indian Ocean sea surface temperature (SST) variability from 1950 to 2008 is analyzed in respect to the spatial structure of the variability. The analysis is based on an objective comparison of the leading empirical orthogonal function modes against the stochastic null hypothesis of spatial red noise (isotropic diffusion). Starting from this red noise assumption, the analysis searches for those structures that are most distinct from the red noise hypothesis. This objective approach will put previously well and less known modes of variability into the context of the multivariate SST variability. The Indian Ocean SST variability is marked by relatively weak SST variability, which is strongly dominated by a basin wide monopole pattern that is caused by different processes. The leading modes of variability are the El Nino Southern Oscillation (ENSO) variability and the warming trend, which both project onto the basin wide monopole structure. Other more characteristic spatial patterns of internal variability are much less dominant in the tropical Indian Ocean, which is quite different from all other ocean basin, where characteristic teleconnection patterns exist. The remaining, ENSO independent, detrended variability is dominated by multi-pole patterns from the southern Indian Ocean reaching into the tropical Indian Ocean, which are probably primarily caused by extra-tropical atmospheric forcings. The large scale tropical Indian Ocean internal variability itself has no dominant structure. The currently often used dipole mode index (DMI) does not appear to present a dominant teleconnection pattern of the Indian Ocean internal SST variability. In the context of the objective analysis presented here, the DMI partly reflects the ENSO variability and is also a representation of the multi-dimensional, chaotic spatial red noise (isotropic diffusion) process. As such the DMI cannot be interpreted as a coherent teleconnection between the two poles.

Is the Indian Ocean SST variability a homogeneous diffusion process?

Whereas the predominance of El Nino Southern Oscillation (ENSO) mode in the tropical Pacific sea surface temperature (SST) variability is well established, no such consensus seems to have been reached by climate scientists regarding the Indian Ocean. While a number of researchers think that the Indian Ocean SST variability is dominated by an active dipolar-type mode of variability, similar to ENSO, others suggest that the variability is mostly passive and behaves like an autocorrelated noise. For example, it is suggested recently that the Indian Ocean SST variability is consistent with the null hypothesis of a homogeneous diffusion process. However, the existence of the basin-wide warming trend represents a deviation from a homogeneous diffusion process, which needs to be considered. An efficient way of detrending, based on differencing, is introduced and applied to the Hadley Centre ice and SST. The filtered SST anomalies over the basin (23.5N–29.5S, 30.5E–119.5E) are then analysed and found to be inconsistent with the null hypothesis on intraseasonal and interannual timescales. The same differencing method is then applied to the smaller tropical Indian Ocean domain. This smaller domain is also inconsistent with the null hypothesis on intraseasonal and interannual timescales. In particular, it is found that the leading mode of variability yields the Indian Ocean dipole, and departs significantly from the null hypothesis only in the autumn season.