scholarly journals Effects of the seasonal cycle on the development and termination of the Indian Ocean Zonal Dipole Mode

2006 ◽  
Vol 111 (C12) ◽  
Author(s):  
D. J. Halkides ◽  
Weiqing Han ◽  
Peter J. Webster
Keyword(s):  
Indian Ocean ◽  
Seasonal Cycle ◽  
Dipole Mode ◽  
The Indian Ocean

scholarly journals Annual, Seasonal, and Interannual Variability of Air–Sea Heat Fluxes in the Indian Ocean

2007 ◽  
Vol 20 (13) ◽  
pp. 3190-3209 ◽  
Author(s):  
Lisan Yu ◽  
Xiangze Jin ◽  
Robert A. Weller
Keyword(s):  
Heat Flux ◽  
Indian Ocean ◽  
Interannual Variability ◽  
Seasonal Cycle ◽  
Arabian Sea ◽  
Heat Fluxes ◽  
The Other ◽  
Net Heat Flux ◽  
The Indian Ocean ◽  
The Mean

Abstract This study investigated the accuracy and physical representation of air–sea surface heat flux estimates for the Indian Ocean on annual, seasonal, and interannual time scales. Six heat flux products were analyzed, including the newly developed latent and sensible heat fluxes from the Objectively Analyzed Air–Sea Heat Fluxes (OAFlux) project and net shortwave and longwave radiation results from the International Satellite Cloud Climatology Project (ISCCP), the heat flux analysis from the Southampton Oceanography Centre (SOC), the National Centers for Environmental Prediction reanalysis 1 (NCEP1) and reanalysis-2 (NCEP2) datasets, and the European Centre for Medium-Range Weather Forecasts operational (ECMWF-OP) and 40-yr Re-Analysis (ERA-40) products. This paper presents the analysis of the six products in depicting the mean, the seasonal cycle, and the interannual variability of the net heat flux into the ocean. Two time series of in situ flux measurements, one taken from a 1-yr Arabian Sea Experiment field program and the other from a 1-month Joint Air–Sea Monsoon Interaction Experiment (JASMINE) field program in the Bay of Bengal were used to evaluate the statistical properties of the flux products over the measurement periods. The consistency between the six products on seasonal and interannual time scales was investigated using a standard deviation analysis and a physically based correlation analysis. The study has three findings. First of all, large differences exist in the mean value of the six heat flux products. Part of the differences may be attributable to the bias in the numerical weather prediction (NWP) models that underestimates the net heat flux into the Indian Ocean. Along the JASMINE ship tracks, the four NWP modeled mean fluxes all have a sign opposite to the observations, with NCEP1 being underestimated by 53 W m−2 (the least biased) and ECMWF-OP by 108 W m−2 (the most biased). At the Arabian Sea buoy site, the NWP mean fluxes also have an underestimation bias, with the smallest bias of 26 W m−2 (ERA-40) and the largest bias of 69 W m−2 (NCEP1). On the other hand, the OAFlux+ISCCP has the best comparison at both measurement sites. Second, the bias effect changes with the time scale. Despite the fact that the mean is biased significantly, there is no major bias in the seasonal cycle of all the products except for ECMWF-OP. The latter does not have a fixed mean due to the frequent updates of the model platform. Finally, among the four products (OAFlux+ISCCP, ERA-40, NCEP1, and NCEP2) that can be used for studying interannual variability, OAFlux+ISCCP and ERA-40 Qnet have good consistency as judged from both statistical and physical measures. NCEP1 shows broad agreement with the two products, with varying details. By comparison, NCEP2 is the least representative of the Qnet variabilities over the basin scale.

scholarly journals Relationship between Indian Ocean dipole mode and summer monsoon

MAUSAM ◽  
2021 ◽  
Vol 59 (2) ◽  
pp. 167-172
Author(s):  
INDU BALA ◽  
O. P. SINGH
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Indian Ocean Dipole ◽  
Summer Monsoon Rainfall ◽  
Monsoon Onset ◽  
Dipole Mode ◽  
Peninsular India ◽  
Indian Ocean Dipole Mode ◽  
The Indian Ocean ◽  
Withdrawal Phase

Utilizing the Indian Ocean Dipole Mode (IODM) and Indian Summer Monsoon Rainfall (ISMR) data for the period 1960-2002 the relationships between the IODM and monsoon onset over Kerala and rainfall distribution over the country have been studied. It has been found that stronger/weaker western pole during April-May is associated with delayed/early monsoon onset over Kerala. Stronger eastern pole during March-April seems to be associated with enhanced seasonal (June-September) rainfall over peninsular India. The IODM index of July-August can provide good indications of summer monsoon activity over peninsular India during the withdrawal phase of the  monsoon, i.e., during September.

scholarly journals An assessment of the Indian Ocean mean state and seasonal cycle in a suite of interannual CORE-II simulations

2020 ◽  
Vol 145 ◽  
pp. 101503 ◽  
Author(s):  
H. Rahaman ◽  
U. Srinivasu ◽  
S. Panickal ◽  
J.V. Durgadoo ◽  
S.M. Griffies ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Seasonal Cycle ◽  
The Indian Ocean ◽  
Mean State

scholarly journals Improved Prediction of the Indian Ocean Dipole Mode by Use of Subsurface Ocean Observations

2017 ◽  
Vol 30 (19) ◽  
pp. 7953-7970 ◽  
Author(s):  
Takeshi Doi ◽  
Andrea Storto ◽  
Swadhin K. Behera ◽  
Antonio Navarra ◽  
Toshio Yamagata
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Dipole ◽  
Three Dimensional ◽  
Coupled Model ◽  
Tropical Indian Ocean ◽  
Seasonal Prediction ◽  
Dipole Mode ◽  
Indian Ocean Dipole Mode ◽  
Ocean Data Assimilation ◽  
The Indian Ocean

Abstract The numerical seasonal prediction system using the Scale Interaction Experiment–Frontier version 1 (SINTEX-F) ocean–atmosphere coupled model has so far demonstrated a good performance for prediction of the Indian Ocean dipole mode (IOD) despite the fact that the system adopts a relatively simple initialization scheme based on nudging only the sea surface temperature (SST). However, it is to be expected that the system is not sufficient to capture in detail the subsurface oceanic precondition. Therefore, the authors have introduced a new three-dimensional variational ocean data assimilation (3DVAR) method that takes three-dimensional observed ocean temperature and salinity into account. Since the new system has successfully improved IOD predictions, the present study is showing that the ocean observational efforts in the tropical Indian Ocean are decisive for improvement of the IOD predictions and may have a large impact on important socioeconomic activities, particularly in the Indian Ocean rim countries.

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