Interactions of temperature fluctuations of the Pacific, Indian and Atlantic oceans with Global atmospheric oscillation

2021 ◽  
Author(s):  
Ilya Serykh ◽  
Dmitry Sonechkin
Keyword(s):  
Indian Ocean ◽  
Water Temperature ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Short Term ◽  
Applied Geophysics ◽  
Atmospheric Oscillation ◽  
The Pacific ◽  
Indian And Atlantic Oceans ◽  
Pole Tides

<p>The predictability of El Niño and La Niña is investigated. In this case, the recently discovered so-called Global Atmospheric Oscillation (GAO) is considered (Serykh et al., 2019). Assuming GAO to be the main mode of short-term climatic variability, this study defines an index that characterizes the dynamics and relationships of the extratropical components of the GAO and El Niño – Southern Oscillation (ENSO). Due to the general propagation of the GAO’s spatial structure from west to east, another index – predictor of ENSO is defined. The cross-wavelet analysis between both of these indices and the Oceanic Niño Index (ONI) is performed. This analysis reveals a range of timescales within which the closest relationship between the GAO and ONI takes place. Using this relationship, it is possible to predict El Niño and La Niña with a lead-time of approximately 12 months (Serykh and Sonechkin, 2020a).</p><p>Using data on the distribution of temperatures in the Pacific, Indian, and Atlantic Oceans, large-scale structures of spatial and temporal variations of these temperatures are investigated (Serykh and Sonechkin, 2020b). A structure is found which is almost identical to the spatial and temporal sea surface temperature (SST) structure that is characteristic of the GAO. Variations in water temperature in a near-equatorial zone of the Pacific Ocean at depths up to about 150 meters behave themselves in the same way as variations in sea surface height and SST. At even greater depths, variations in water temperature reveal a "striped" structure, which is, however, overall similar to that of SST variations. Variations of water temperature at depths in all three oceans spread from east to west along the equator with a period of 14 months. This makes it possible to think that the dynamics of these temperatures are controlled by the so-called Pole tides. The surface North Pacific Pole Tide was found previously responsible for excitation of El Niño (Serykh and Sonechkin, 2019). The deep Pole tides in the Southern Atlantic and Southern Indian Ocean appear to be triggers of the Atlantic El Niño and Indian Ocean Dipole (IOD). Thus, IOD manifests itself at the depth of the thermocline more clearly than on the surface of the Indian Ocean. The out-of-phase behavior of El Niño and IOD is explained by the 180-degree difference in the longitudes of these phenomena.</p><p> </p><p><strong>References</strong></p><p>Serykh I.V., Sonechkin D.M. Nonchaotic and globally synchronized short-term climatic variations and their origin // Theoretical and Applied Climatology. 2019. Vol. 137. No. 3-4. pp 2639–2656. https://doi.org/10.1007/s00704-018-02761-0</p><p>Serykh I.V., Sonechkin D.M., Byshev V.I., Neiman V.G., Romanov Yu.A. Global Atmospheric Oscillation: An Integrity of ENSO and Extratropical Teleconnections // Pure and Applied Geophysics. 2019. Vol. 176. pp 3737–3755. https://doi.org/10.1007/s00024-019-02182-8</p><p>Serykh I.V., Sonechkin D.M. El Niño forecasting based on the global atmospheric oscillation // International Journal of Climatology. 2020a. https://doi.org/10.1002/joc.6488</p><p>Serykh I.V., Sonechkin D.M. Interrelations between temperature variations in oceanic depths and the Global atmospheric oscillation // Pure and Applied Geophysics. 2020b. Vol. 177. pp 5951–5967. https://doi.org/10.1007/s00024-020-02615-9</p>

The relationship between Indian Ocean sea–surface temperature and East African rainfall

2005 ◽  
Vol 363 (1826) ◽  
pp. 43-47 ◽  
Author(s):  
Emily Black
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Sea Surface ◽  
East African ◽  
African Rainfall ◽  
The Pacific ◽  
The Indian Ocean ◽  
The Relationship

Knowledge of the processes that control East African rainfall is essential for the development of seasonal forecasting systems, which may mitigate the effects of flood and drought. This study uses observational data to unravel the relationship between the Indian Ocean Dipole (IOD), the El Niño Southern Oscillation (ENSO) and rainy autumns in East Africa. Analysis of sea–surface temperature data shows that strong East African rainfall is associated with warming in the Pacific and Western Indian Oceans and cooling in the Eastern Indian Ocean. The resemblance of this pattern to that which develops during IOD events implies a link between the IOD and strong East African rainfall. Further investigation suggests that the observed teleconnection between East African rainfall and ENSO is a manifestation of a link between ENSO and the IOD.

scholarly journals The Pacific–Indian Ocean associated mode in CMIP5 models

Ocean Science ◽  
2020 ◽  
Vol 16 (2) ◽  
pp. 469-482 ◽  
Author(s):  
Minghao Yang ◽  
Xin Li ◽  
Weilai Shi ◽  
Chao Zhang ◽  
Jianqi Zhang
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Models ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
The Pacific ◽  
Basin Scale

Abstract. The Pacific–Indian Ocean associated mode (PIOAM), defined as the first dominant mode (empirical orthogonal function, EOF1) of sea surface temperature anomalies (SSTAs) in the Pacific–Indian Ocean between 20∘ S and 20∘ N, is the product of the tropical air–sea interaction at the cross-basin scale and the main mode of ocean variation in the tropics. Evaluating the capability of current climate models to simulate the PIOAM and finding the possible factors that affect the simulation results are beneficial in the pursuit of more accurate future climate change prediction. Based on the 55-year Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) dataset and the output data from 21 Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) models, the PIOAM in these CMIP5 models is assessed. Instead of using the time coefficient (PC1) of the PIOAM as its index, we chose to utilize the alternative PIOAM index (PIOAMI), defined with SSTA differences in the boxes, to describe the PIOAM. It is found that the explained variance of the PIOAM in almost all 21 CMIP5 models is underestimated. Although all models reproduce the spatial pattern of the positive sea surface temperature anomaly in the eastern equatorial Pacific well, only one-third of these models successfully simulate the El Niño–Southern Oscillation (ENSO) mode with the east–west inverse phase in the Pacific Ocean. In general, CCSM4, GFDL-ESM2M and CMCC-CMS have a stronger capability to capture the PIOAM than the other models. The strengths of the PIOAM in the positive phase in less than one-fifth of the models are slightly greater, and very close to the HadISST dataset, especially CCSM4. The interannual variation of the PIOAM can be measured by CCSM4, GISS-E2-R and FGOALS-s2.

scholarly journals FFNN-LSCE: A two-step neural network model for the reconstruction of surface ocean pCO<sub>2</sub> over the Global Ocean

2018 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac ◽  
Carlos Mejia
Keyword(s):  
Neural Network ◽  
Indian Ocean ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Regional Scale ◽  
Sea Surface ◽  
Global Ocean ◽  
Co2 Fluxes ◽  
Surface Ocean ◽  
Data Coverage

Abstract. A new Feed-Forward Neural Network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) reconstruction of pCO2 climatology and (2) reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity and temperature (SSS and SST), sea surface height (SSH), chlorophyll a (Chl), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step 2 augmented by their anomalies. During each step, the FFNN model reconstructs the non-linear relations between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1º x 1º grid for the period 2001–2016. Global ocean pCO2 was reconstructed with a satisfying accuracy compared to independent observational data from SOCAT. However, errors are larger in regions with poor data coverage (e.g. Indian Ocean, Southern Ocean, subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skills over the Equatorial Pacific associated with ENSO (El Niño Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variabilty between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern hemisphere and in particular, the Southern Pacific and the Indian Ocean, as these regions suffer from poor data-coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea-air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

scholarly journals The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean

2007 ◽  
Vol 20 (13) ◽  
pp. 2872-2880 ◽  
Author(s):  
Gary Meyers ◽  
Peter McIntosh ◽  
Lidia Pigot ◽  
Mike Pook
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
El Nino Southern Oscillation ◽  
La Nina ◽  
The Indian Ocean

Abstract The Indian Ocean zonal dipole is a mode of variability in sea surface temperature that seriously affects the climate of many nations around the Indian Ocean rim, as well as the global climate system. It has been the subject of increasing research, and sometimes of scientific debate concerning its existence/nonexistence and dependence/independence on/from the El Niño–Southern Oscillation, since it was first clearly identified in Nature in 1999. Much of the debate occurred because people did not agree on what years are the El Niño or La Niña years, not to mention the newly defined years of the positive or negative dipole. A method that identifies when the positive or negative extrema of the El Niño–Southern Oscillation and Indian Ocean dipole occur is proposed, and this method is used to classify each year from 1876 to 1999. The method is statistical in nature, but has a strong basis on the oceanic physical mechanisms that control the variability of the near-equatorial Indo-Pacific basin. Early in the study it was found that some years could not be clearly classified due to strong decadal variation; these years also must be recognized, along with the reason for their ambiguity. The sensitivity of the classification of years is tested by calculating composite maps of the Indo-Pacific sea surface temperature anomaly and the probability of below median Australian rainfall for different categories of the El Niño–Indian Ocean relationship.

scholarly journals Short-Term Variation of the Surface Flow Pattern South of Lombok Strait Observed from the Himawari-8 Sea Surface Temperature

2019 ◽  
Vol 11 (12) ◽  
pp. 1491 ◽  
Author(s):  
Naokazu Taniguchi ◽  
Shinichiro Kida ◽  
Yuji Sakuno ◽  
Hidemi Mutsuda ◽  
Fadli Syamsudin
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Flow ◽  
Sea Surface ◽  
Surface Velocity ◽  
Thermal Plume ◽  
Short Term ◽  
Flow Variation ◽  
The Indian Ocean

Spatial and temporal information on oceanic flow is fundamental to oceanography and crucial for marine-related social activities. This study attempts to describe the short-term surface flow variation in the area south of the Lombok Strait in the northern summer using the hourly Himawari-8 sea surface temperature (SST). Although the uncertainty of this temperature is relatively high (about 0.6 ∘ C), it could be used to discuss the flow variation with high spatial resolution because sufficient SST differences are found between the areas north and south of the strait. The maximum cross-correlation (MCC) method is used to estimate the surface velocity. The Himawari-8 SST clearly shows Flores Sea water intruding into the Indian Ocean with the high-SST water forming a warm thermal plume on a tidal cycle. This thermal plume flows southward at a speed of about 2 m / s . The Himawari-8 SST indicates a southward flow from the Lombok Strait to the Indian Ocean, which blocks the South Java Current flowing eastward along the southern coast of Nusa Tenggara. Although the satellite data is limited to the surface, we found it useful for understanding the spatial and temporal variations in the surface flow field.

scholarly journals 300 years of hydrological records and societal responses to droughts and floods on the Pacific coast of Central America

2018 ◽  
Vol 14 (2) ◽  
pp. 175-191 ◽  
Author(s):  
Alvaro Guevara-Murua ◽  
Caroline A. Williams ◽  
Erica J. Hendy ◽  
Pablo Imbach
Keyword(s):  
Central America ◽  
Pacific Coast ◽  
Southern Oscillation ◽  
Ice Age ◽  
Guatemala City ◽  
Short Term ◽  
Inter Tropical Convergence Zone ◽  
Hydrological Variability ◽  
The Pacific ◽  
Dry Conditions

Abstract. The management of hydrological extremes and impacts on society is inadequately understood because of the combination of short-term hydrological records, an equally short-term assessment of societal responses and the complex multi-directional relationships between the two over longer timescales. Rainfall seasonality and inter-annual variability on the Pacific coast of Central America is high due to the passage of the Inter Tropical Convergence Zone (ITCZ) and the El Niño–Southern Oscillation (ENSO). Here we reconstruct hydrological variability and demonstrate the potential for assessing societal impacts by drawing on documentary sources from the cities of Santiago de Guatemala (now Antigua Guatemala) and Guatemala de la Asunción (now Guatemala City) over the period from 1640 to 1945. City and municipal council meetings provide a rich source of information dating back to the beginning of Spanish colonisation in the 16th century. We use almost continuous sources from 1640 AD onwards, including > 190 volumes of Actas de Cabildo and Actas Municipales (minutes of meetings of the city and municipal councils) held by the Archivo Histórico de la Municipalidad de Antigua Guatemala (AHMAG) and the Archivo General de Centro América (AGCA) in Guatemala City. For this 305-year period (with the exception of a total of 11 years during which the books were either missing or damaged), information relating to Catholic rogation ceremonies and reports of flooding events and crop shortages were used to classify the annual rainy season (May to October) on a five-point scale from very wet to very dry. In total, 12 years of very wet conditions, 25 years of wetter than usual conditions, 34 years of drier conditions and 21 years of very dry conditions were recorded. An extended drier period from the 1640s to the 1740s was identified and two shorter periods (the 1820s and the 1840s) were dominated by dry conditions. Wetter conditions dominated the 1760s–1810s and possibly record more persistent La Niña conditions that are typically associated with higher precipitation over the Pacific coast of Central America. The 1640s–1740s dry period coincides with the Little Ice Age and the associated southward displacement of the ITCZ.

scholarly journals Interannual to Decadal Variability of Tropical Indian Ocean Sea Surface Temperature: Pacific Influence versus Local Internal Variability

2020 ◽  
pp. 1-50
Author(s):  
Lei Zhang ◽  
Gang Wang ◽  
Matthew Newman ◽  
Weiqing Han
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Dipole ◽  
Tropical Indian Ocean ◽  
Tropical Pacific ◽  
Sea Surface ◽  
External Influence ◽  
Sst Variability ◽  
The Pacific ◽  
The Indian Ocean ◽  
The Tropical Pacific

AbstractThe Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal processes has not been well understood particularly on decadal and longer timescales, and the external influence from the Tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the Tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a Linear Inverse Model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean Basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal timescales. On the other hand, the Indian Ocean Dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal timescale. The third mode is partly associated with the Subtropical Indian Ocean Dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian-Pacific Ocean interaction is a two-way process.

scholarly journals Using Indicators of ENSO, IOD, and SAM to Improve Lead Time and Accuracy of Tropical Cyclone Outlooks for Australia

2020 ◽  
Vol 59 (11) ◽  
pp. 1901-1917
Author(s):  
Andrew D. Magee ◽  
Anthony S. Kiem
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Lead Time ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Southern Annular Mode ◽  
Sea Surface ◽  
Selection Algorithm ◽  
Statistical Framework ◽  
The Indian Ocean

AbstractCatastrophic impacts associated with tropical cyclone (TC) activity mean that the accurate and timely provision of TC outlooks are important to people, places, and numerous sectors in Australia and beyond. In this study, we apply a Poisson regression statistical framework to predict TC counts in the Australian region (AR; 5°–40°S, 90°–160°E) and its four subregions. We test 10 unique covariate models, each using different representations of the influence of El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and southern annular mode (SAM) and use an automated covariate selection algorithm to select the optimum combination of predictors. The performance of preseason TC count outlooks generated between April and October for the AR TC season (November–April) and in-season TC count outlooks generated between November and January for the remaining AR TC season are tested. Results demonstrate that skillful TC count outlooks can be generated in April (i.e., 7 months prior to the start of the AR TC season), with Pearson correlation coefficient values between r = 0.59 and 0.78 and covariates explaining between 35% and 60% of the variance in TC counts. The dependence of models on indices representing Indian Ocean sea surface temperature highlights the importance of the Indian Ocean for TC occurrence in this region. Importantly, generating rolling monthly preseason and in-season outlooks for the AR TC season enables the continuous refinement of expected TC counts in a given season.

Seasonal climate summary for the southern hemisphere (spring 2016): strong negative Indian Ocean Dipole ends, bringing second wettest September to Australia

2019 ◽  
Vol 69 (1) ◽  
pp. 273
Author(s):  
Blair Trewin ◽  
Catherine Ganter
Keyword(s):  
Indian Ocean ◽  
Southern Hemisphere ◽  
Indian Ocean Dipole ◽  
Southern Oscillation ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
The Pacific ◽  
The Indian Ocean ◽  
Low Levels ◽  
Equatorial Climate

This summary looks at the southern hemisphere and equatorial climate patterns for spring 2016, with particular attention given to the Australasian and equatorial regions of the Pacific and Indian Ocean basins. Spring 2016 was marked by the later part of a strong negative phase of the Indian Ocean Dipole, alongside cool neutral El Niño–Southern Oscillation conditions. September was exceptionally wet over much of Australia, contributing to a wet spring with near-average temperatures. The spring was one of the warmest on record over the southern hemisphere as a whole, with Antarctic Sea ice extent dropping to record low levels for the season.

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