Indian Ocean Sea Surface Temperature Variability Research Articles

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.

Effect of Indian Ocean SST on Tibetan Plateau Precipitation in the Early Rainy Season

The onset of the South Asian summer monsoon (SASM) indicates the beginning of the rainy season in the South Asia region. It is not only critical for the local agriculture and animal husbandry but also important for water and life security. Precipitation in the early rainy season (May) increases rapidly and has a large interannual variability, especially in the Tibetan Plateau (TP) region. One of the starting mechanisms of the monsoon system is the land–sea thermal contrast (LSTC) between the Indian Ocean (IO) and South Asia region. Therefore, the IO can be considered as a crucial factor for the intensity of the monsoon system, as well as the TP precipitation. In this study, the relationships between IO sea surface temperature (SST) and TP precipitation on the interannual time scale are investigated. Correlation maps show that IO SST variability contains a portion that is independent from the tropical Pacific Ocean SST and is negatively correlated with the TP precipitation. Here the authors define an LSTC index to determine the thermal condition over the IO and South Asia region. The SASM reveals an out-of-phase relationship with LSTC between land and ocean, which means it would be suppressed by the enhanced LSTC. The daily data are used to further analyze the relationship between the SASM and TP precipitation in detail. Results show that the anomalous TP precipitation in May is mainly caused by the Bay of Bengal monsoon and that the Indian monsoon is responsible for the TP precipitation in June. More specifically, warmer SST enlarges the LSTC between the IO and South Asia region. The SASM is weaker than the mean state, resulting in less precipitation over the TP. In negative years the opposite occurs.

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.

Temporal and spatial variability of the cropping limit in South Australia

Since its establishment in 1865, Goyder’s Line in South Australia has represented the division between land suitable for cropping and land seen as viable for grazing. Despite this, current cropping areas extend beyond Goyder’s Line. In this study, the 220 mm growing season (April to October) rainfall isohyet is used as a proxy for Goyder’s Line to assess its temporal and spatial variability. Using indices of the El Nino Southern Oscillation, Indian Ocean sea surface temperature variability, Southern Annular Mode and subtropical ridge, it is shown that climate state significantly influences the location of the 220 mm growing season rainfall isohyet. This implies that the boundary between viable and non-viable cropping areas (i.e. Goyder’s Line or ‘nature’s limit’) is non- stationary. These results also indicate the key influences on South Australia’s climate and have important implications globally for agricultural practices operating in or bordering semi-arid environments.

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.

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.

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.

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.

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.

Southern Indian Ocean SST variability and its relationship with Indian summer monsoon

Sea surface temperature (SST) variability in the Southern Indian Ocean (SIO) region and its relationship to Indian summer monsoon rainfall is investigated. A correlation analysis is used to examine the relation between SIO SST variability and Indian monsoons with lag‐lead times of up to two years (eight seasons). A significant positive correlation is found between tropical SIO SST and the All India Rainfall Index (AIRI) in the March‐May and December‐February seasons before the onset of monsoon. The SST in the region south of 35°S is positively correlated with AIRI six, seven, and eight seasons before the onset of monsoon. A maximum correlation of 0.47 is found for the region south of 35°S, with a confidence level of 99%. Based on this correlation, we have defined SST indices for the central SIO (CSIO), the northwest of Australia (NWA), the SIO, and the Antarctic Circumpolar Current (ACC). These indices seem to be early predictors of Indian monsoons when their relationship with AIRI is examined. The predictive skill of these indices is also tested by multivariate linear regression. The consistency of this relationship is verified by the removal of the El Niño Southern Oscillation (ENSO) signal from SST data and is found to be unaffected by the ENSO signal, except in the region west of Australia.

Southwest Indian Ocean SST Variability: Its Local Effect and Remote Influence on Asian Monsoons*

Abstract An atmospheric general circulation model (AGCM) is used to examine the role of Indian Ocean sea surface temperature (SST) anomalies in regional climate variability. In particular, the authors focus on the effect of the basinwide warming that occurs during December through May after the mature phase of El Niño. To elucidate the relative importance of local and remote forcing, model solutions were sought for experiments where SST anomalies are inserted in the (i) tropical Indo-Pacific Oceans, (ii) tropical Pacific Ocean, and (iii) tropical Indian Ocean. A 10-member ensemble simulation is carried out for each of the three forcing scenarios. The model solutions demonstrate that precipitation variations over the southwest Indian Ocean are tied to local SST anomalies and are highly reproducible. Changes in the Indian Ocean–Walker circulation suppress precipitation over the tropical west Pacific–Maritime Continent, contributing to the development of a low-level anticyclone over the Philippine and South China Seas. Our model results indicate that more than 50% of the total precipitation anomalies over the tropical west Pacific–Maritime Continent is forced by remote Indian Ocean SST anomalies, offering an additional mechanism for the Philippine Sea anticyclone apart from Pacific SST. This anticyclone increases precipitation along the East Asian winter monsoon front from December to May. The anomalous subsidence over the Maritime Continent in conjunction with persistent anomalies of SST and precipitation over the Indian Ocean in spring prevent the northwestward migration of the ITCZ and the associated deep moist layer, causing a significant delay in the Indian summer monsoon onset in June by 6–7 days. At time scales of 5 days, however, the reproducibility of the northward progression of the ITCZ during the onset is low. Results indicate that Indian Ocean SST anomalies during December through May that develop in response to both atmospheric and oceanic processes to El Niño need to be considered for a complete understanding of regional climate variability, particularly around the Indian Ocean rim.

Contrasting Indian Ocean SST variability with and without ENSO influence: A coupled atmosphere-ocean GCM study

In this study, we perform experiments with a coupled atmosphere-ocean general circulation model (CGCM) to examine ENSO’s influence on the interannual sea-surface temperature (SST) variability of the tropical Indian Ocean. The control experiment includes both the Indian and Pacific Oceans in the ocean model component of the CGCM (the Indo-Pacific Run). The anomaly experiment excludes ENSO’s influence by including only the Indian Ocean while prescribing monthly-varying climatological SSTs for the Pacific Ocean (the Indian-Ocean Run). In the Indo-Pacific Run, an oscillatory mode of the Indian Ocean SST variability is identified by a multi-channel singular spectral analysis (MSSA). The oscillatory mode comprises two patterns that can be identified with the Indian Ocean Zonal Mode (IOZM) and a basin-wide warming/cooling mode respectively. In the model, the IOZM peaks about 3–5 months after ENSO reaches its maximum intensity. The basin mode peaks 8 months after the IOZM. The timing and associated SST patterns suggests that the IOZM is related to ENSO, and the basin-wide warming/cooling develops as a result of the decay of the IOZM spreading SST anomalies from western Indian Ocean to the eastern Indian Ocean. In contrast, in the Indian-Ocean Run, no oscillatory modes can be identified by the MSSA, even though the Indian Ocean SST variability is characterized by east–west SST contrast patterns similar to the IOZM. In both control and anomaly runs, IOZM-like SST variability appears to be associated with forcings from fluctuations of the Indian monsoon. Our modeling results suggest that the oscillatory feature of the IOZM is primarily forced by ENSO.

Potential seasonal predictability of the observed Euro‐Atlantic atmospheric variability using SST forced ECHAM4‐T42 simulations

AbstractTo estimate the potential seasonal predictability of the Euro‐Atlantic atmospheric variability, canonical correlation analysis is used, and a comparison is made between the NCEP reanalysis with ensemble simulations of ECHAM4‐T42 forced with observed sea surface temperature (SST) and sea‐ice boundaries for 1951–94 and changing CO2 concentration. The method identifies those atmospheric modes of variability that have similar temporal evolution in the observations and the ensemble mean of the simulations. Signals due to long‐term changes in the forcing were first reduced by removing a third‐order polynomial from all data.Significant covariability in the 500 hPa geopotential height over the Euro‐Atlantic region is found from autumn to spring. The best agreement between the patterns is seen in late winter, where a mixed Pacific–North America (PNA) and tropical/northern‐hemisphere teleconnection pattern is the dominant signal. Although it tends to modulate the North Atlantic Oscillation (NAO), the direct influence over Europe is limited. In all cases, the covariability seems to be due to remote forcing by tropical Pacific SST. However, the model response to the El Niño Southern Oscillation (ENSO) always shows a PNA pattern, while the related observed signal undergoes large seasonal changes.The sea level pressure (SLP) over the Euro‐Atlantic region seems to be much less sensitive to remote ENSO forcing, although traces of its influence can be detected in late winter. On the other hand, a highly significant covariability is found between modelled and observed SLP anomalies in autumn, reflecting the influence of the North Atlantic SST on the NAO. However, the model does not reproduce the observed SLP structure. It is also shown that the reproducibility of the NAO in the undetrended ECHAM4 ensemble simulations in winter that was found by Latif et al. in the same simulations is related to long‐term trends in tropical Pacific and Indian Ocean SST variability. However, its origin cannot be determined as the CO2 concentration was specified from the observations in the simulations. Copyright © 2003 Royal Meteorological Society

Potential seasonal predictability of the observed Euro-Atlantic atmospheric variability using SST forced ECHAM4-T42 simulations

To estimate the potential seasonal predictability of the Euro-Atlantic atmospheric variability, canonical correlation analysis is used, and a comparison is made between the NCEP reanalysis with ensemble simulations of ECHAM4-T42 forced with observed sea surface temperature (SST) and sea-ice boundaries for 1951–94 and changing CO2 concentration. The method identifies those atmospheric modes of variability that have similar temporal evolution in the observations and the ensemble mean of the simulations. Signals due to long-term changes in the forcing were first reduced by removing a third-order polynomial from all data. Significant covariability in the 500 hPa geopotential height over the Euro-Atlantic region is found from autumn to spring. The best agreement between the patterns is seen in late winter, where a mixed Pacific–North America (PNA) and tropical/northern-hemisphere teleconnection pattern is the dominant signal. Although it tends to modulate the North Atlantic Oscillation (NAO), the direct influence over Europe is limited. In all cases, the covariability seems to be due to remote forcing by tropical Pacific SST. However, the model response to the El Niño Southern Oscillation (ENSO) always shows a PNA pattern, while the related observed signal undergoes large seasonal changes. The sea level pressure (SLP) over the Euro-Atlantic region seems to be much less sensitive to remote ENSO forcing, although traces of its influence can be detected in late winter. On the other hand, a highly significant covariability is found between modelled and observed SLP anomalies in autumn, reflecting the influence of the North Atlantic SST on the NAO. However, the model does not reproduce the observed SLP structure. It is also shown that the reproducibility of the NAO in the undetrended ECHAM4 ensemble simulations in winter that was found by Latif et al. in the same simulations is related to long-term trends in tropical Pacific and Indian Ocean SST variability. However, its origin cannot be determined as the CO2 concentration was specified from the observations in the simulations. Copyright © 2003 Royal Meteorological Society