Oscillation Sea Surface Temperature Research Articles

Impact of Extratropical Northeast Pacific SST on U.S. West Coast Precipitation

AbstractThe rainfall over the U.S. West Coast is known to be highly influenced by large‐scale atmospheric circulation and tropical climate teleconnections. However, the role of North Pacific oceanic variability is less understood. Using high‐resolution regional atmospheric model simulations forced by sustained positive and negative phases of the extratropical Pacific Decadal Oscillation sea surface temperature anomalies (SSTa), we diagnose the precipitation changes over the U.S. West Coast during 2010–2020. We find that precipitation anomalies are up to 60% stronger (weaker) for the warm (cold) cases, especially over Northern and Central California during wintertime, and Baja California in the summertime. In both seasons, precipitation is predominantly modulated through changes in the water vapor flux, which are directed toward the coast in wintertime and away from the coast during summertime. These flux anomalies are primarily driven by large‐scale changes in the wind associated with the atmospheric adjustment to the strong ocean SSTa.

A change in the relation between the Subtropical Indian Ocean Dipole and the South Atlantic Ocean Dipole indices in the past four decades

Abstract. We utilized the global atmospheric reanalysis ERA5 and reconstructed sea surface temperature (SST) data from 1979 through 2020 to examine the stability of the relationship between the SST oscillations in the southern Indian Ocean and the Atlantic Ocean, as described by the Subtropical Indian Ocean Dipole (SIOD) and South Atlantic Ocean Dipole (SAOD) indices, respectively. We note a significant positive correlation between the two indices prior to the year 2000 but practically no correlation afterwards. We show that in the two decades prior to 2000, a positive phase of the SAOD is associated with more convective activities over the subtropical southern Atlantic Ocean and eastern Brazil, which trigger a stronger upper-atmosphere wavetrain. This produces stronger southern subtropical highs and surface anti-cyclonic circulations and therefore a stronger correlation between the two indices. The situation is reversed after 2000. Our results are potentially applicable to predictions of precipitation in southern Africa and South America.

Stream gauge clustering and analysis for non-stationary time series through complex networks

Stream gauge clustering enables diverse studies, such as the analysis of spatial patterns and the physical reasons for these patterns. The clustering of monitoring gauges through complex networks combined with community detection algorithms is a strong alternative to classical methods. However, this approach involves several particularities that impact the clustering results, and the non-stationarity commonly present in streamflow series is typically not accounted for in studies that use this methodology. This article presents the application of a new framework developed to cluster stream gauge stations and to analyse changes in the clustering results across time. Weighted networks were created through Mutual Information combined with an automated threshold. Complex networks were obtained for the entire series and for sliding windows to investigate if significant differences occur across time. A more in-depth analysis was carried out with selected time windows through the perspective of Network Science and Complex Network Analysis, and the results were compared to those from a classic clustering approach. The framework provided robust and physically coherent clustering results and a more detailed clustering result than the classic approach. The weighted networks construction procedure successfully diminished time delimited effects induced by non-stationarity. However, the sliding windows networks results demonstrated that significant changes occurred across time, and three different community configurations were obtained. These results indicate that the use a single network can result in a misrepresentation of local characteristic and lead to wrong conclusions. The communities’ evolution across time showed spatial-time coherence with both the physical phenomenology of the study area and previous studies. The changes observed were associated with phase shifts of low-frequency sea surface temperature oscillations of both the Atlantic and the Pacific Oceans through the phase shifts’ direct and indirect influence on the South Atlantic Convergence Zone. The in-depth analysis resulted in the identification of a spatially coherent transition zone within the clusters obtained and attested the reliability of the framework results based on network typologies. Thus, the proposed framework can aid in clustering problems and provide better comprehension of local characteristics. Although the framework was developed for stream gauge clustering, its use can be extended to the clustering of any data with nonlinear and nonstationary characteristics.

Historical and Projected Variations of Precipitation and Temperature and Their Extremes in Relation to Climatic Indices over the Gandaki River Basin, Central Himalaya

Changes in precipitation and temperature, especially in the Himalayan region, will have repercussions for socio-economic conditions in the future. Thus, this study aimed to understand the climatic trend and changes in one of the Himalayan River basins, i.e., Gandaki River Basin (GRB), Nepal. In particular, we analysed the historical (1985–2014) and projected (2015–2100) precipitation and temperature trend and their extremes using observation and 13 bias-corrected Coupled Model Intercomparison Project phase 6 (CMIP6) datasets. Additionally, the relationship between extreme precipitation/temperature indices and ocean-atmospheric circulation patterns were also analysed. The results showed an increasing trend of precipitation amount and temperature at annual and seasonal scales with the highest upward trend for precipitation in monsoon season and temperature in winter season. Among nine precipitation indices analysed, the wet extremes are projected to increase in all Shared Socioeconomic Pathways (SSP) scenarios; with the highest increment of high-intensity related extremes (R10 mm and R20 mm). In contrast, dry spells will decline in the distant-future (2075–2100) as compared to near (2015–2044) and mid-future (2045–2074). Further, increment in temperature trend resulted in a decrease in cold related temperature extremes and an increase in warm related extremes. Furthermore, it was observed that the changes in precipitation and temperature extremes over GRB were influenced by large-scale ocean-atmospheric circulation patterns. The Atlantic Multidecadal Oscillation (AMO), Sea Surface Temperature (SST) and Southern Oscillation Index (SOI) were found to have a major role in driving precipitation extremes while AMO, SST and Pacific Decadal Oscillation (PDO) have strong influence on temperature extremes. The results of this study will be useful for better understanding the implications of historical and future changes in precipitation and temperature and their extremes over the GRB.

A sub-seasonal oscillation of sea surface temperature in the Southern Indian Ocean during DJF and its excitation mechanism

Southern Indian Ocean dipole (SIOD) is a dipole SST anomaly in the Southern Indian Ocean that plays an important role in tropical climate variability. However, previous studies focused on the interannual and interdecadal scales. In this paper, the characteristics on sub-seasonal scale of SIOD are revealed from December to February (DJF) based on the ERA-Interim reanalysis dataset, and the excitation mechanisms of SIOD are also discussed. The results show that the two dominant modes of SST in the Southern Indian Ocean are the spatial distribution of Southwest-Northeast direction dipole (SIOD) and triple (SIOT) respectively, which has obvious period of quasi 50–60 days. Moreover, the intensity and zonal oscillation of the Mascarene high are conducive to the formation of SIOD (SIOT). The air–sea interaction during the formation process is composed of three stages. In the first stage the atmospheric forces the ocean to result in the Mascarene high westward (eastward) and enhances the abnormal anticyclone in the Southern Indian Ocean. There are abnormal northerly (southerly) flows on the west (east) sides of the abnormal anticyclone respectively, which weakens (enhances) the southeast trade wind in the climatology. The surface latent heat flux release decreases (increases) and SST is warmed (cooled). During the following stage, the ocean feedback the atmosphere. The warm SST continues to increase, resulting in low-level convective enhancement, which weakens the abnormal anticyclone. The third stage is again the atmosphere forcing the ocean. The abnormal anticyclone gradually turns into an abnormal cyclone and the meridional wind direction is reversed. The release of the latent heat flux increases (decreases) significantly which leads to the cooling (warming) of SST on the west (east) sides of an abnormal cyclone. In addition, the formation and extinction of SIOT are easier affected by the southern annular mode (SAM) than SIOD. The abnormal zonal wave train with wavenumber 4 (3) propagates the Southern Indian Ocean by the westerly jet waveguide and results in an SST anomaly of SIOD (SIOT), accompanied by an obvious sub-seasonal meridional variation of the precipitation in Southern Africa.

Role of Ocean and Atmosphere Variability in Scale‐Dependent Thermodynamic Air‐Sea Interactions

AbstractThis study investigates the influence of oceanic and atmospheric processes in extratropical thermodynamic air‐sea interactions resolved by satellite observations (OBS) and by two climate model simulations run with eddy‐resolving high‐resolution (HR) and eddy‐parameterized low‐resolution (LR) ocean components. Here, spectral methods are used to characterize the sea surface temperature (SST) and turbulent heat flux (THF) variability and co‐variability over scales between 50 and 10,000 km and 60 days to 80 years in the Pacific Ocean. The relative roles of the ocean and atmosphere are interpreted using a stochastic upper‐ocean temperature evolution model forced by noise terms representing intrinsic variability in each medium, defined using climate model data to produce realistic rather than white spectral power density distributions. The analysis of all datasets shows that the atmosphere dominates the SST and THF variability over zonal wavelengths larger than ∼2,000–2,500 km. In HR and OBS, ocean processes dominate the variability of both quantities at scales smaller than the atmospheric first internal Rossby radius of deformation (R1, ∼600–2,000 km) due to a substantial ocean forcing coinciding with a weaker atmospheric modulation of THF (and consequently of SST) than at larger scales. The ocean forcing also induces oscillations in SST and THF with periods ranging from intraseasonal to multidecadal, reflecting a red spectrum response to ocean forcing similar to that driven by atmospheric forcing. Such features are virtually absent in LR due to a weaker ocean forcing relative to HR.

Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations

Grasshoppers are preeminent herbivores and perhaps the most significant rangeland pests in the United States (US). Despite the important ecosystem functions they provide, grasshopper populations often obtain densities that cause significant economic harm to grazing operations and agricultural production. Although numerous studies conducted at the level of individual field sites have examined potential mechanisms contributing to grasshopper population “boom and bust” cycles, there has yet to be a large, regional scaled analysis that quantified grasshopper variation across the Western US as a whole. While taking steps to account for data collection biases, mediating effects, and variable confounding, we assessed the influence of Pacific Ocean sea surface temperature oscillations on a 40-year record of grasshopper density in the Western US. Central to our analysis was employing spatially varying coefficients to model time and location-specific variation in grasshopper response to climate. Our results quantitatively demonstrated interannual changes in grasshopper density to be indirectly effected by seasonal El Niño/Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) variability and to exhibit spatial asynchrony and non-stationarity such that the relative influence of climate on grasshopper density varied through time and across geographic space. Our model is the first to incorporate climate indices as spatially varying coefficients for assessment of a terrestrial species and represents a critical step towards understanding causal drivers of regional grasshopper density.

Climatic Drivers of Deglacial SST Variability in the Eastern Pacific

AbstractWe explore the response of northeastern Pacific sea surface temperature (SST) to deglacial (16–7 ka) climate variability as recorded in ‐based SST reconstructions spanning 65°N to 10°S. Included in the analysis is a new 23 kyr SST record from core NH8P from the northwest Mexican Margin. We isolate spatiotemporal patterns in regional SSTs with trend empirical orthogonal function (TEOF) analysis. The dominant TEOF mode reflects deglacial warming associated with rising . Tropical and subtropical SSTs correlated most strongly with this mode, suggesting that the thermodynamic response of the tropical eastern Pacific to greenhouse gas forcing was the dominant driver of regional SST change during deglaciation. The second TEOF mode reflects millennial‐scale variability and is most strongly expressed in subpolar SSTs. The synchronous timing between North Pacific and North Atlantic SST oscillations is evidence for the rapid transmission of millennial‐scale climate perturbations between the basins, likely through an atmospheric teleconnection. SSTs at NH8P have no correlation with either leading TEOF mode as there is minimal change in SST at this site after 20 ka. A model simulation of the LGM indicates that glacial cooling was muted in much of the Eastern Pacific Warm Pool (EPWP), in which NH8P lies, due to reductions in latent heat flux. This suggests that the wind‐evaporation‐SST feedback was responsible for the attenuation of EPWP cooling. Overall, this study highlights the distinct latitudinal trends in the Pacific's response to deglaciation.

A numerical study on the mixed layer depth variability and its influence on the sea surface temperature during 2013–2014 in the Bay of Bengal and Equatorial Indian Ocean

This study addresses the air–sea interaction processes and mixed layer variability, which cause the intraseasonal oscillations in the sea surface temperature (SST) during January 2013–December 2014 using the Regional Ocean Modeling System (ROMS). We have analyzed the SST variability at three locations—northern Bay of Bengal (BoB)/15°N, 90°E (R15), central BoB/12°N, 90°E (R12), and equatorial Indian Ocean (EIO)/0°N, 80.5°E (R0). During northeast monsoon (NEM) and southwest monsoon (SWM), intraseasonal SST variability is respectively controlled by the intense outgoing fluxes (sum of longwave radiation, latent heat flux, and sensible heat flux) and zonal wind stress. The intraseasonal SST variability in spring and fall is modulated by intense incoming shortwave radiation. There is a profound impact of mixed layer depth (MLD) variations on the intraseasonal SST oscillations in the BoB and EIO. At R15, and R12, deepened simulated MLD is associated with the lowered SST variability in the NEM and SWM. In spring and fall, the shallow MLD variability corresponds to higher intraseasonal SST variability at the buoy locations. In the northern BoB, ROMS cannot capture barrier layer (BL) and temperature inversion (TI) accurately in the winter and premonsoon season due to salinity bias, resulting in the difference between simulated and actual MLD. But the simulated MLD bias does not affect the intraseasonal SST structures in the NEM and pre-SWM. In the northern BoB, the proper representation of salinity structure may represent BL, TI, and MLD, accurately in the simulation during the winter and premonsoon season.

Important role of North Atlantic air–sea coupling in the interannual predictability of summer precipitation over the eastern Tibetan Plateau

Tibetan Plateau (TP) summer precipitation, whose first leading mode presents a dipole oscillation between the northeastern and southeastern TP, plays important roles in the water cycle and atmospheric circulation. Using a weakly coupled data assimilation (WCDA) system to constrain the atmospheric component in a coupled model with a global atmospheric reanalysis product, this study demonstrates significant improvements in predicting the interannual variation of summer precipitation over the eastern TP, capturing the strong reversal mode between the northeastern and southeastern TP. These improvements are mainly attributed to the strong air–sea coupling over the North Atlantic (NA) and its remote impacts on the TP precipitation. This coupling allows observation-constrained atmosphere to significantly influence initial conditions (ICs) of sea surface temperature (SST) in the NA area and then obviously affect predictions of both NA SST and summer North Atlantic Oscillation (SNAO), which affects the downstream TP atmospheric circulation through the wave train of Eliassen-Palm (EP) stationary wave flux. This study highlights the importance of NA air–sea coupling in the interannual predictability of TP precipitation and suggests a new source of interannual predictability of TP precipitation.

Interdecadal Pacific Oscillation Drives Enhanced Greenland Surface Temperature Variability During the Last Glacial Maximum

AbstractStable oxygen isotope records from central Greenland suggest disproportionally large long‐term surface air temperature (SAT) variability during the Last Glacial Maximum (LGM) relative to preindustrial times. Large perturbations in mean atmospheric circulation and its variability forced by extensive Northern Hemisphere ice sheet coverage have been suggested as cause for the enhanced Greenland SAT variability. Here, we assess the factors driving Greenland SAT variability during the LGM by means of dedicated climate model simulations and find remote forcing from the Pacific of critical importance. Atmospheric teleconnections from the Interdecadal Pacific Oscillation (IPO), a multidecadal oscillation of sea surface temperature in the Pacific Ocean, strongly intensify under LGM conditions, driving enhanced surface wind variability over Greenland, which in turn amplifies SAT variability by anomalous atmospheric heat transport. A major role of the IPO in forcing Greenland SAT variability also is supported by a number of models from the Paleoclimate Modeling Intercomparison Project Phase III.

Quasi‐Biweekly Mode of the Asian Summer Monsoon Revealed in Bay of Bengal Surface Observations

AbstractAsian summer monsoon has a planetary‐scale, westward propagating “quasi‐biweekly” mode of variability with a 10–25 day period. Six years of moored observations at 18°N, 89.5°E in the north Bay of Bengal (BoB) reveal distinct quasi‐biweekly variability in sea surface salinity (SSS) during summer and autumn, with peak‐to‐peak amplitude of 3–8 psu. This large‐amplitude SSS variability is not due to variations of surface freshwater flux or river runoff. We show from the moored data, satellite SSS, and reanalyses that surface winds associated with the quasi‐biweekly monsoon mode and embedded weather‐scale systems, drive SSS and coastal sea level variability in 2015 summer monsoon. When winds are calm, geostrophic currents associated with mesoscale ocean eddies transport Ganga‐Brahmaputra‐Meghna river water southward to the mooring, salinity falls, and the ocean mixed layer shallows to 1–10 m. During active (cloudy, windy) spells of quasi‐biweekly monsoon mode, directly wind‐forced surface currents carry river water away to the east and north, leading to increased salinity at the moorings, and rise of sea level by 0.1–0.5 m along the eastern and northern boundary of the bay. During July–August 2015, a shallow pool of low‐salinity river water lies in the northeastern bay. The amplitude of a 20‐day oscillation of sea surface temperature (SST) is two times larger within the fresh pool than in the saltier ocean to the west, although surface heat flux is nearly identical in the two regions. This is direct evidence that spatial‐temporal variations of BoB salinity influences sub‐seasonal SST variations, and possibly SST‐mediated monsoon air‐sea interaction.

The synergistic impact of ENSO and IOD on Indian summer monsoon rainfall in observations and climate simulations – an information theory perspective

Abstract. The El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are two well-known temporal oscillations in sea surface temperature (SST), which are both thought to influence the interannual variability of Indian summer monsoon rainfall (ISMR). Until now, there has been no measure to assess the simultaneous information exchange (IE) from both ENSO and IOD to ISMR. This study explores the information exchange from two source variables (ENSO and IOD) to one target (ISMR). First, in order to illustrate the concepts and quantification of two-source IE to a target, we use idealized test cases consisting of linear and nonlinear dynamical systems. Our results show that these systems exhibit net synergy (i.e., the combined influence of two sources on a target is greater than the sum of their individual contributions), even with uncorrelated sources in both the linear and nonlinear systems. We test IE quantification with various estimators (linear, kernel, and Kraskov estimators) for robustness. Next, the two-source IE from ENSO and IOD to ISMR is investigated in observations, reanalysis, three global climate model (GCM) simulations, and three nested higher-resolution simulations using a regional climate model (RCM). This (1) quantifies IE from ENSO and IOD to ISMR in the natural system and (2) applies IE in the evaluation of the GCM and RCM simulations. The results show that both ENSO and IOD contribute to ISMR interannual variability. Interestingly, significant net synergy is noted in the central parts of the Indian subcontinent, which is India's monsoon core region. This indicates that both ENSO and IOD are synergistic predictors in the monsoon core region. But, they share significant net redundant information in the southern part of the Indian subcontinent. The IE patterns in the GCM simulations differ substantially from the patterns derived from observations and reanalyses. Only one nested RCM simulation IE pattern adds value to the corresponding GCM simulation pattern. Only in this case does the GCM simulation show realistic SST patterns and moisture transport during the various ENSO and IOD phases. This confirms, once again, the importance of the choice of GCM in driving a higher-resolution RCM. This study shows that two-source IE is a useful metric that helps in better understanding the climate system and in process-oriented climate model evaluation.

Three‐to‐Six‐Day Air–Sea Oscillation in Models and Observations

AbstractAnalysis of coupled ocean‐atmosphere numerical simulations revealed a widespread oscillation of sea surface temperature (SST) and surface wind anomalies with a cyclic nature characterized by a period of 3 to 6 days. Similar oscillations were also found in buoy data at different locations around the globe and in an atmospheric reanalysis, albeit the oscillations were noisier and stronger in the observations, and they were weaker and longer period in the atmospheric reanalysis. These oscillations did not, however, develop in an atmosphere‐only simulation, suggesting the importance of ocean–atmosphere coupling for simulating this phenomenon. We propose a mechanism for this interaction involving a feedback between SST and surface wind.

Spatiotemporal complexity and time-dependent networks in sea surface temperature from mid- to late Holocene

In climate science regime transitions include abrupt changes in modes of climate variability and shifts in the connectivity of the whole system. While important, their identification remains challenging. This paper proposes a new framework to investigate regime transitions and connectivity patterns in spatiotemporal climate fields. Firstly, local regime shifts are quantified by means of information entropy; secondly, their spatial heterogeneity is examined by identifying the underlying spatial domains of the entropy field; finally, a weighted, direct and time-dependent network is inferred to capture the linkages between these domains. The spatiotemporal variability in sea surface temperature (SST) in two simulations of the last 6000 years is investigated with the proposed approach. The largest regional regime shifts emerge as abrupt transitions from low to high-frequency SST oscillations, or vice versa, in both simulations. Furthermore, the variability in time of the two climate networks is studied in terms of their network density. Generally, rapid and sudden transitions in the degree of connectivity of the system are observed in both simulations but, in most cases, at different times, with few exceptions. This suggests that our ability to predict the climate system may be hampered by its inherent complexity resulting from internal variability.

El Niño–Southern Oscillation (ENSO) Impact on Tea Production and Rainfall in South India

AbstractEl Niño–Southern Oscillation (ENSO) is an aperiodic oscillation of sea surface temperature (SST)-induced interannual rainfall variability in south India (SI) that has a direct impact on rain-fed agricultural production and the economy of the region. The study analyzed the influence of ENSO-related rainfall variability on crop yield of south Indian tea-growing regions (SITR) for the period of 1971–2015. The relationship between SST anomalies from June to August over the Niño-3 sector of the tropical Pacific Ocean and tea production anomalies of SI shows a positive correlation. However, SST has a negative relationship with rainfall in the regions of the southwest monsoon but not with the northeast monsoon region of the Nilgiris. The correlation between rainfall and crop yield in SI (r = 0.045) is positively weak and statistically insignificant (p > 0.05). Tea production is influenced more by the cold phase than the warm phase of ENSO, whereas rainfall is greatly influenced by the warm phase. Tea production across the regions indicated that none of the ENSO phase categories based on Niño-3 has significantly greater production than any of the other ENSO phases. Therefore, the predictability of tea production on the basis of ENSO phases is limited. Our findings highlight that the crop production of SITR appeared to be less responsive to the ENSO phases. This may be due to improvements in production technology that mitigated the problems associated with rainfall variability.

Local climate impacts of dipole-like sea surface temperature oscillations in the Southern Hemisphere

Abstract Dipole phenomena in ocean-atmospheric variability such as the Indian Ocean Dipole have been recognized as important factors that greatly affect local climates. This study presents evidence of two dipole modes in sea surface temperature anomaly (SSTA) over high latitude Southern Hemisphere (one in South Pacific and one in South Indian Ocean), identified using empirical orthogonal functions and cross-correlation analysis. These dipole modes have interannual periodicity, which is also explored for their seasonal variability and modes. Herein, a dipole mode is defined as a quasi-periodic oscillation between positive and negative phases in the various climate proxies, though predominantly in SST, which is supported by the signal's synchronized relationship with atmospheric variability (as recorded by pressure and wind records). In addition, the dipole modes have a clear synchronization relationship to local precipitation records, which is described in this paper. For this purpose, an index to represent the time-dependent evolution of each dipole mode and to better define and understand the teleconnections of the dipole modes with other climate variables was defined. The findings described here provide a more precise and unique understanding of the globally distributed SSTA teleconnections and climate's synchronized dynamics than that has currently been studied.

High- and low-latitude forcing on the south Yellow Sea surface water temperature variations during the Holocene

Abstract Sea surface temperature (SST) of the modern Yellow Sea (YS) is sensitive to forcing by both the East Asian monsoon and the North Pacific western boundary current, however, detailed mechanisms for explaining the Holocene SST evolution remains to be understood. Here we present two decadal sampling resolution alkenone SST records from the south YS spanning the last 8.9 kyr to understand forcing mechanisms on different timescales. The results show that the two sites from the south YS experienced SST variations on millennial timescale, displaying mean high values of 16.5 °C (A03-B) and 15.2 °C (YS01) from 7.7 to 5.9 ka, followed by low values of 16.4 °C (A03-B) and 14.8 °C (YS01) from 5.9 to 2.9 ka, then by high values of 16.9 °C (A03-B) and 15.5 °C (YS01) from 2.9 to 0.6 ka. After the entrance of the YS Warm Current at about 7.5 ka, evolution of the south YS SSTs paralleled the pattern of the main Kuroshio Current intensity, i.e., low (high) SST coeval with weak (strong) Kuroshio Current, suggesting that millennial-scale SST variations in the south YS were primarily controlled by the Kuroshio Current. Superimposing on the millennial variations, seven abrupt centennial-scale cold events were observed during the Holocene, with SST oscillations broadly coeval with those of North Atlantic ice-rafted detritus and East Asian winter monsoon rather than SSTs from the Okinawa Tough and Indo-Pacific warm pool. Hence, we propose that the East Asian winter monsoon was the top-down conveyor transmitting North Atlantic cooling signals to the south YS on centennial timescale. Thus, our study reveals both high- and low-latitude climate forcing on the evolution of the south YS SST during the Holocene, with low-latitude forcing on millennial-scale and high-latitude forcing on centennial-scale.

Estimation of Monsoon Seasonal Precipitation Teleconnection with El Niño-Southern Oscillation Sea Surface Temperature Indices over the Western Ghats of Karnataka

The Western Ghats (WG) of India are basically north-to-south oriented mountains with three distinct meteorological divisions. These mountains exhibit the characteristic features of precipitation and distribution during the summer monsoon season and possess latitudinal variations. It is a well-known fact that sea surface temperature (SST) combined with the El Nino-Southern Oscillation (ENSO) enacts a predominant role in the precipitation over the entire Western Ghats during the summer monsoon season. Whereas the Nino regions affect the variability of the Western Ghats’ precipitation in an asymmetric relationship. Nevertheless, the simulation of precipitation has been evidenced to be difficult. The current study attempts to predict the seasonal precipitation over the coastal region and the Western Ghats of Karnataka. The relationship between summer monsoon precipitation (SMP) and SST is examined up to eight seasons by conducting the correlation analysis with three seasons that lag before the onset of the monsoon season. The significant and positively correlated lagged Nino indices with the SMP index are identified as the predictors. The selected predictors are used for predicting the SMP by using statistical models, the multiple linear regression model and the artificial neural network (ANN) model. The statistical models are based on the combined lagged indices and the principle component as the predictor. The results of the statistical models on comparison suggest that neural network models have a better predictive skill than the linear regression models. Neural network models with combined lagged indices being used as predictors are slightly better, but a few more climatic parameters must be verified and the usage of this method on other meteorological divisions of the West Coast of India needs to be further investigated.

Natural Climate Oscillations may Counteract Red Sea Warming Over the Coming Decades

AbstractRecent reports of warming trends in the Red Sea raise concerns about the response of the basin's fragile ecosystem under an increasingly warming climate. Using a variety of available Sea Surface Temperature (SST) data sets, we investigate the evolution of Red Sea SST in relation to natural climate variability. Analysis of long‐term SST data sets reveals a sequence of alternating positive and negative trends, with similar amplitudes and a periodicity of nearly 70 years associated with the Atlantic Multidecadal Oscillation. High warming rates reported recently appear to be a combined effect of global warming and a positive phase of natural SST oscillations. Over the next decades, the SST trend in the Red Sea purely related to global warming is expected to be counteracted by the cooling Atlantic Multidecadal Oscillation phase. Regardless of the current positive trends, projections incorporating long‐term natural oscillations suggest a possible decreasing effect on SST in the near future.