scholarly journals Oceanic Influences on the Seasonal Cycle in Evaporation over the Indian Ocean

2001 ◽  
Vol 14 (6) ◽  
pp. 1199-1226 ◽  
Author(s):  
Roxana C. Wajsowicz ◽  
Paul S. Schopf
Keyword(s):  
Indian Ocean ◽  
Seasonal Cycle ◽  
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 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 Verification of reanalysis data for the tropical zone of the Indian ocean. Part 2. Characteristics of the averaged seasonal cycle and interannual variability

2020 ◽  
pp. 119-126
Author(s):  
A.B. Polonsky ◽  
◽  
A.V. Torbinskii ◽  
A.V. Gubarev ◽  
◽  
...  
Keyword(s):  
Indian Ocean ◽  
Interannual Variability ◽  
Seasonal Cycle ◽  
Potential Temperature ◽  
Tropical Indian Ocean ◽  
Reanalysis Data ◽  
Tropical Zone ◽  
Current Vector ◽  
Zonal Component ◽  
The Indian Ocean

The aim of the study is to evaluate the performance of ORAS5/SODA3/GLORYS re-analyses using the RAMA in the tropical Indian Ocean. To assess the reproducibility of the seasonal cycle and characteristics of interannual variability, we used the data on the potential temperature, salinity, and zonal component of the current vector obtained south of the equator for the period 2010–2014. It is shown that at 55°E south of the equator, the GLORYS re-analysis better reproduces the five-year averaged seasonal cycle and interannual variability than the SODA3 and ORAS5 re-analyses.

The Indian Ocean Dipole

2018 ◽  
Author(s):  
Saji N. Hameed
Keyword(s):  
Indian Ocean ◽  
Forest Fires ◽  
Indian Ocean Dipole ◽  
Seasonal Cycle ◽  
Southern Oscillation ◽  
Equatorial Indian Ocean ◽  
Eastern Indian Ocean ◽  
Boreal Summer ◽  
Ocean Atmosphere ◽  
The Indian Ocean

Discovered at the very end of the 20th century, the Indian Ocean Dipole (IOD) is a mode of natural climate variability that arises out of coupled ocean–atmosphere interaction in the Indian Ocean. It is associated with some of the largest changes of ocean–atmosphere state over the equatorial Indian Ocean on interannual time scales. IOD variability is prominent during the boreal summer and fall seasons, with its maximum intensity developing at the end of the boreal-fall season. Between the peaks of its negative and positive phases, IOD manifests a markedly zonal see-saw in anomalous sea surface temperature (SST) and rainfall—leading, in its positive phase, to a pronounced cooling of the eastern equatorial Indian Ocean, and a moderate warming of the western and central equatorial Indian Ocean; this is accompanied by deficit rainfall over the eastern Indian Ocean and surplus rainfall over the western Indian Ocean. Changes in midtropospheric heating accompanying the rainfall anomalies drive wind anomalies that anomalously lift the thermocline in the equatorial eastern Indian Ocean and anomalously deepen them in the central Indian Ocean. The thermocline anomalies further modulate coastal and open-ocean upwelling, thereby influencing biological productivity and fish catches across the Indian Ocean. The hydrometeorological anomalies that accompany IOD exacerbate forest fires in Indonesia and Australia and bring floods and infectious diseases to equatorial East Africa. The coupled ocean–atmosphere instability that is responsible for generating and sustaining IOD develops on a mean state that is strongly modulated by the seasonal cycle of the Austral-Asian monsoon; this setting gives the IOD its unique character and dynamics, including a strong phase-lock to the seasonal cycle. While IOD operates independently of the El Niño and Southern Oscillation (ENSO), the proximity between the Indian and Pacific Oceans, and the existence of oceanic and atmospheric pathways, facilitate mutual interactions between these tropical climate modes.

scholarly journals The Indian Summer Monsoon in MetUM-GOML2.0: Effects of air-sea coupling and resolution

2018 ◽  
Author(s):  
Simon C. Peatman ◽  
Nicholas P. Klingaman
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Seasonal Cycle ◽  
Global Ocean ◽  
Boreal Summer ◽  
Correction Technique ◽  
The Indian Ocean ◽  
The Mean ◽  
Mean State

Abstract. The fidelity of the simulated Indian Summer Monsoon is analysed in the UK Met Office Unified Model Global Ocean Mixed Layer configuration (MetUM-GOML2.0), in terms of its boreal summer mean state and propagation of the Boreal Summer IntraSeasonal Oscillation (BSISO). The model produces substantial biases in mean June--September precipitation, especially over India, in common with other MetUM configurations. Using a correction technique to constrain the mean seasonal cycle of ocean temperature and salinity, the effects of regional air-sea coupling and atmospheric horizontal resolution are investigated. Introducing coupling in the Indian Ocean degrades the atmospheric basic state, compared with prescribing the observed seasonal cycle of sea surface temperature (SST). This degradation of the mean state is attributable to small errors (±0.5 C) in mean SST. However, coupling slightly improves the simulation of northward BSISO propagation over the Indian Ocean, Bay of Bengal and India. Increasing resolution from 200 km to 90 km grid spacing (approximate value at the equator) improves the atmospheric mean state, but increasing resolution again to 40~km offers no substantial improvement. The improvement to intraseasonal propagation at finer resolution is similar to that due to coupling.

scholarly journals The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

2018 ◽  
Vol 11 (11) ◽  
pp. 4693-4709 ◽  
Author(s):  
Simon C. Peatman ◽  
Nicholas P. Klingaman
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Seasonal Cycle ◽  
Global Ocean ◽  
Boreal Summer ◽  
Correction Technique ◽  
The Indian Ocean ◽  
The Mean ◽  
Mean State

Abstract. The fidelity of the simulated Indian summer monsoon is analysed in the UK Met Office Unified Model Global Ocean Mixed Layer configuration (MetUM-GOML2.0) in terms of its boreal summer mean state and propagation of the boreal summer intraseasonal oscillation (BSISO). The model produces substantial biases in mean June–September precipitation, especially over India, in common with other MetUM configurations. Using a correction technique to constrain the mean seasonal cycle of ocean temperature and salinity, the effects of regional air–sea coupling and atmospheric horizontal resolution are investigated. Introducing coupling in the Indian Ocean degrades the atmospheric basic state compared with prescribing the observed seasonal cycle of sea surface temperature (SST). This degradation of the mean state is attributable to small errors (±0.5 ∘C) in mean SST. Coupling slightly improves some aspects of the simulation of northward BSISO propagation over the Indian Ocean, Bay of Bengal, and India, but degrades others. Increasing resolution from 200 to 90 km grid spacing (approximate value at the Equator) improves the atmospheric mean state, but increasing resolution again to 40 km offers no substantial improvement. The improvement to intraseasonal propagation at finer resolution is similar to that due to coupling.

Interannual Variability of Indian Ocean Heat Transport

2006 ◽  
Vol 19 (6) ◽  
pp. 1013-1031 ◽  
Author(s):  
Galina Chirokova ◽  
Peter J. Webster
Keyword(s):  
Indian Ocean ◽  
Interannual Variability ◽  
Heat Transport ◽  
Time Scales ◽  
Seasonal Cycle ◽  
Intraseasonal Variability ◽  
Ocean Model ◽  
Ocean Heat Transport ◽  
The Indian Ocean ◽  
One Year

Abstract The work in this paper builds upon the relatively well-studied seasonal cycle of the Indian Ocean heat transport by investigating its interannual variability over a 41-yr period (1958–98). An intermediate, two-and-a-half-layer thermodynamically active ocean model with mixed layer physics is used in the investigation. The results of the study reveal that the Indian Ocean heat transport possesses strong variability at all time scales from intraseasonal (10–90 days) to interannual (more than one year). The seasonal cycle dominates the variability at all latitudes, the amplitude of the intraseasonal variability is similar to the seasonal cycle, and the amplitude of the interannual variability is about one-tenth of the seasonal cycle. Spectral analysis shows that a significant broadband biennial component in the interannual variability exists with considerable coherence in sign across the equator. While the mean annual heat transport shows a strong maximum between 10° and 20°S, interannual variability is relatively uniform over a broad latitudinal domain between 15°N and 10°S. The heat transport variability at all time scales is well explained by the Ekman heat transport, with especially good correlations at the intraseasonal time scales. The addition of the Indonesian Throughflow does not significantly affect the heat transport variability in the northern part of the ocean.

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