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 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.

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 The Impact of Indian Ocean Mean-State Biases in Climate Models on the Representation of the East African Short Rains

2018 ◽  
Vol 31 (16) ◽  
pp. 6611-6631 ◽  
Author(s):  
Linda Hirons ◽  
Andrew Turner
Keyword(s):  
Indian Ocean ◽  
Wind Stress ◽  
Equatorial Indian Ocean ◽  
East African ◽  
Easterly Wind ◽  
Low Level ◽  
The Indian Ocean ◽  
Short Rains ◽  
The Impact ◽  
Mean State

The role of the Indian Ocean dipole (IOD) in controlling interannual variability in the East African short rains, from October to December, is examined in state-of-the-art models and in detail in one particular climate model. In observations, a wet short-rainy season is associated with the positive phase of the IOD and anomalous easterly low-level flow across the equatorial Indian Ocean. A model’s ability to capture the teleconnection to the positive IOD is closely related to its representation of the mean state. During the short-rains season, the observed low-level wind in the equatorial Indian Ocean is westerly. However, half of the models analyzed exhibit mean-state easterlies across the entire basin. Specifically, those models that exhibit mean-state low-level equatorial easterlies in the Indian Ocean, rather than the observed westerlies, are unable to capture the latitudinal structure of moisture advection into East Africa during a positive IOD. Furthermore, the associated anomalous easterly surface wind stress causes upwelling in the eastern Indian Ocean. This upwelling draws up cool subsurface waters, enhancing the zonal sea surface temperature gradient between west and east and strengthening the positive IOD pattern, further amplifying the easterly wind stress. This positive Bjerknes coupled feedback is stronger in easterly mean-state models, resulting in a wetter East African short-rain precipitation bias in those models.

Warming of the Indian Ocean weakens the Atlantic Niño - El Niño Southern Oscillation connection 

2021 ◽  
Author(s):  
Shreya Dhame ◽  
Andréa Taschetto ◽  
Agus Santoso ◽  
Giovanni Liguori ◽  
Katrin Meissner
Keyword(s):  
Indian Ocean ◽  
Atlantic Ocean ◽  
Southern Oscillation ◽  
Warming Trend ◽  
Equatorial Pacific ◽  
Ocean Warming ◽  
The West ◽  
Indian Ocean Warming ◽  
The Indian Ocean ◽  
Mean State

<p>The tropical Indian Ocean has warmed by 1 degree Celsius since the mid-twentieth century. This warming is likely to continue as the atmospheric carbon dioxide levels keep rising. Here, we discuss how the warming trend could influence the El Niño Southern Oscillation (ENSO) via interaction with the Pacific and the Atlantic Ocean mean state and variability. The warming trend leads to the strengthening of easterlies in the western equatorial Pacific, subsequent downwelling and increase of the mixed later depth in the west, and an increase in the subsurface temperature gradient across the equatorial Pacific. In the eastern equatorial Pacific, the response of upwelling ocean currents to surface wind stress decreases, resulting in a weakening of ENSO amplitude. The Indian Ocean warming influences ENSO via the Atlantic Ocean as well. There, it is associated with the strengthening of equatorial easterly winds, and anomalous warming in the west and upwelling induced cooling in the east, especially in austral winter, during the peak of the Atlantic Niño. Consequently, this results in a decrease of the amplitude of Atlantic Niño events and weakening of the Atlantic Niño-ENSO teleconnection, thereby hindering the transition of El Niño events to La Niña events. Thus, the Indian Ocean warming trend is found to modulate tropical Pacific and Atlantic mean state and variability, with implications for ENSO predictability under a warming climate.</p>

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.

scholarly journals Indian Ocean Dynamic Sea Level, Variability And Projections In CMIP6 Models

2022 ◽  
Author(s):  
Abhisek Chatterjee ◽  
Sajidh C K
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Sea Level Rise ◽  
Sea Level ◽  
Sea Level Variability ◽  
Regional Sea Level ◽  
The Indian Ocean ◽  
High Resolution Models ◽  
Mean State ◽  
Dynamic Sea Level

Abstract The regional sea level variability and its projection amidst the global sea level rise is one of the major concerns for coastal communities. The dynamic sea level plays a major role in the observed spatial deviations in regional sea level rise from the global mean. The present study evaluates 27 climate model simulations from the sixth phase of the coupled model intercomparison project (CMIP6) for their representation of the historical mean states, variability and future projections for the Indian Ocean. Most models reproduce the observed mean state of the dynamic sea level realistically, however consistent positive bias is evident across the latitudinal range of the Indian Ocean. The strongest sea level bias is seen along the Antarctic Circumpolar Current (ACC) regime owing to the stronger than observed south Indian Ocean westerlies and its equatorward bias. Further, this equatorward shift of the wind field resulted in stronger positive windstress curl across the southeasterly trade winds in the southern tropical basin and easterly wind bias along the equatorial waveguide. These anomalous easterly equatorial winds cause upwelling in the eastern part of the basin and keeps the thermocline shallower in the model than observed, resulted in enhanced variability for the dipole zonal mode or Indian Ocean dipole in the tropics. In the north Indian Ocean, the summer monsoon winds are weak in the model causing weaker upwelling and positive sea level bias along the western Arabian Sea. The high-resolution models compare better in simulating the sea level variability, particularly in the eddy dominated regions like the ACC regime in interannual timescale. However, these improved variabilities do not necessarily produce a better mean state likely due to the enhanced mixing driven by parametrizations set in these high-resolution models. Finally, the overall pattern of the projected dynamic sea level rise is found to be similar for the mid (SSP2-4.5) and high-end (SSP5-8.5) scenarios, except that the magnitude is higher under the high emission situation. Notably, the projected dynamic sea level change is found to be milder when only the best performing models are used compared to the full ensemble.

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