Sea Surface Temperature Variability over the Tropical Indian Ocean during the ENSO and IOD Events in 2016 and 2017

Abstract

2016 and 2017 were marked by strong El Niño and weak La Niña events, respectively, in the tropical East Pacific Ocean. The strong El Niño and weak La Niña events in the Pacific significantly impacted the sea surface temperature (SST) in the tropical Indian Ocean (TIO) and were followed by extreme negative and weak positive Indian Ocean Dipole (IOD) phases in 2016 and 2017, which triggered floods in the Indian subcontinent and drought conditions in East Africa. The IOD is an irregular and periodic oscillation in the Indian Ocean, which has attracted much attention in the last two decades due to its impact on the climate in surrounding landmasses. Much work has been done in the past to investigate global climate change and its impact on the evolution of IOD. The dynamic behind it, however, is still not well understood. The present study, using various satellite datasets, examined and analyzed the dynamics behind these events and their impacts on SST variability in the TIO. For this study, the monthly mean SST data was provided by NOAA Optimum Interpolation Sea Surface Temperature (OISST). SST anomalies were measured on the basis of 30-year mean daily climatology (1981–2010). It was determined that the eastern and western poles of the TIO play quite different roles during the sequence of negative and positive IOD phases. The analysis of air-sea interactions and the relationship between wind and SST suggested that SST is primarily controlled by wind force in the West pole. On the other hand, the high SST that occurred during the negative IOD phase induced local convection and westerly wind anomalies via the Bjerknes feedback mechanism. The strong convection, which was confined to the (warm) eastern equatorial Indian Ocean was accompanied by east–west SST anomalies that drove a series of downwelling Kelvin waves that deepened the thermocline in the east. Another notable feature of this study was its observation of weak upwelling along the Omani–Arabian coast, which warmed the SST by 1 °C in the summer of 2017 (as compared to 2016). This warming led to increased precipitation in the Bay of Bengal (BoB) region during the summer of 2017. The results of the present work will be important for the study of monsoons and may be useful in predicting both droughts and floods in landmasses in the vicinity of the Indian Ocean, especially in the Indian subcontinent and East African regions.

Gardening Pocillopora spp. fragments and their potential for rebuilding reef systems in the southern Mexican Pacific

“…in an upwelling area of northern Costa Rica; according to their results, coral fragments grew faster during the non‐upwelling season. However, during January 2016, SST was 1.4°C above historic temperature in the study area and remained hotter thorough the year associated with El Niño 2016–2017 (Khan et al 2021). It may be argued that an anomalously hot period could reduce the upwelling cold water stress for corals and allow them to grow faster; in contrast, the hottest SST records could hamper coral growth in summer.…”

Alarming signs detected in the small pelagic fishery on the west coast of Sri Lanka

“…Dalpadado et al, (2021) pointed out that sea surface temperature (SST) has increased in last two decades and net primary production (NPP) showed large inter-annual variability in northern and central regions of the Indian Ocean, with slightly decreasing trends in a few northern regions. Furthermore, strong El Niño and weak La Niña events in the Paci c signi cantly impacted the sea surface temperature (SST) in the tropical Indian Ocean (TIO) and were followed by extreme negative and weak positive Indian Ocean Dipole (IOD) phases reported in 2016 and 2017 respectively (Khan et al, 2021). The inter-annual peaks in the catch rates including the extraordinary increase in the catch rates in 2017 could probably be resulted with extremely favorable oceanographic conditions such as increase of Chlroophyll-a (Marsac and Demarcq, 2017).…”

Spatiotemporal Variability of Drought and Its Multi-Scale Linkages with Climate Indices in the Huaihe River Basin, Central China and East China

“…The Empirical Orthogonal Function (EOF), also known as Eigenvector Analysis or Principal Component Analysis, is a method for extracting the eigenvector of variables. It was first applied to climatology and meteorology by Lorenz in the 1950s and is now widely used to highlight potential physical mechanisms related to climate variability [6,16,17]. The spatial patterns (EOFs), the temporal series (PCs), and the eigenvalues are the outputs of the EOF analysis.…”

“…Atmosphere 2021, 12,1446 2 of 20 In recent years, a lot of research has been conducted to detect the possible relation between large-scale climate phenomena and droughts or floods in specific geographical locations [2,5,[7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Wahiduzzaman concluded that major flood events over Bangladesh occurred during the monsoon period, and most of them occurred under the La Niña condition [24].…”