Results 111 to 120 of about 1,621,068 (173)
Coral Sr/Ca records provide realistic representation of eastern Indian Ocean cooling during extreme positive Indian Ocean Dipole events. [PDF]
Pfeiffer M +5 more
europepmc +1 more source
Reconstruction of the state space figure of indian ocean dipole
State space reconstruction is an important index for describing nonlinear time series. However, reconstruction of state space figure is difficult if the data is noisy. Hence, noise reduction is an important step for reconstructing state space figure.
Majumder, S +5 more
core +1 more source
Crustal fault reactivation facilitating lithospheric folding/buckling in the central Indian Ocean
High-quality, normal-incidence seismic reflection data confirm that tectonic deformation in the central Indian Ocean occurs at two spatial scales: whole lithosphere folding with wavelengths varying between 100 and 300 km, and compressional reactivation ...
Beekman, F. +3 more
core +1 more source
We investigate rainfall variability in Indonesia using the Empirical Orthogonal Function (EOF) method. The analysis starts by taking three main modes of EOF results, namely EOF1, EOF2, and EOF3.
Melly Ariska +4 more
doaj +1 more source
We propose a unified statistical method based on deep learning and heatmap analysis to quantify the relative contributions of the global oceans to El Niño–Southern Oscillation (ENSO) predictability. By incorporating subsurface signals in the Indian Ocean
Tang Li +3 more
doaj +1 more source
The southern tropical Indian Ocean (TIO) displays large mixed layer salinity (MLS) variation. Circulation in this region is governed by the Indian Ocean tropical gyre (IOTG), where the source water proportion and associated mixing remain unclear ...
Zhangzhe Zhao, Janet Sprintall, Yan Du
doaj +1 more source
Rainfall variability characteristics over the East African coast. [PDF]
Includes bibliographical references.This study explores inter-annual rainfall variability over the East African coast region (Kenya and Tanzania) for the period 1980-2010 and focuses on dry and wet spell characteristics during the two rainy seasons.
Gamoyo, Majambo Jarumani
core
Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. [PDF]
McKenna S +4 more
europepmc +1 more source
A machine learning based prediction system for the Indian Ocean Dipole. [PDF]
Ratnam JV, Dijkstra HA, Behera SK.
europepmc +1 more source
Two distinct types of the Indian Ocean Dipole. [PDF]
Zheng J, Wang C.
europepmc +1 more source

