Bay of Bengal upper-ocean stratification and the sub-seasonal variability in convection: Role of rivers in a coupled ocean-atmosphere model

The Indian summer monsoon intraseasonal oscillations (MISOs) induce pronounced intraseasonal sea surface temperature (SST) variability in the Bay of Bengal (BoB), which has important feedbacks to atmospheric convection. An ocean general circulation model (OGCM) is employed to investigate the upper‐ocean processes affecting intraseasonal SST variability and its feedback to the MISO convection. In the BoB, the MISO induces intraseasonal SST variability predominantly through surface heat flux forcing with comparable contributions from shortwave radiation and turbulent heat flux, and to a much smaller extent through wind‐driven ocean mixed layer entrainment. The ocean salinity stratification, represented by mixed layer depth (MLD) and barrier layer thickness (BLT), has a strong control on SST but weak impact on convection of the MISO. The MLD is critical for the amplitude of SST response to various forcing processes, while the BLT mainly affects entrainment by determining the temperature difference between the mixed layer and the water below. From May to mid‐June, the shallow MLD and thin barrier layer greatly enhance intraseasonal SST anomalies, which can amplify convection fluctuations of the MISO through air‐sea interaction and leads to intense but short‐duration postconvection break spells. When either the MLD or the BLT is large, intraseasonal SSTs tend to be weak. Further investigation reveals that freshwater flux of the monsoon gives rise to the shallow MLD and thick barrier layer, and its overall effect on intraseasonal SSTs is a 20% enhancement. These results provide implications for improving the simulation and forecast of the MISO in climate models.

The northward propagation of the Monsoon Intraseasonal Oscillations (MISOs) in the Bay of Bengal (BoB) is an intrinsic characteristic of the Indian summer monsoon (ISM). Previous studies have demonstrated the critical role of air‐sea interactions in modulating MISO propagation. This study elucidates the intraseasonal variability of ocean heat content (OHC) in the upper 200 m of the BoB and its dynamic relationship with MISO. Similar to sea surface temperature (SST), the positive OHC anomalies lead MISO's northward propagation, showing two prominent maxima located east of Sri Lanka and in the northwestern BoB. The OHC anomalies are stronger east of Sri Lanka, penetrating the thick barrier layer during MISO events, whereas temperature anomalies in the northwestern BoB remain confined to the mixed layer. Diagnostic analyses reveal that the intraseasonal OHC variability, unlike that of SST, stems from intensified downward vertical advection driven by intraseasonal vertical velocity. In contrast to the wind‐dominated intraseasonal vertical velocity in NBOX, the pronounced intraseasonal OHC variability east of Sri Lanka stems from sea level anomaly generated by both westward‐propagating Rossby waves and MISO‐related winds. Subsequently, with the arrival of MISO, upward vertical advection and thick barrier layer prolong warm SST anomalies east of Sri Lanka, providing additional heat and moisture to enhance MISO convection and rainfall intensity. These results highlight the essential role of the memory effect of upper ocean heat exchange and redistribution processes in sustaining MISO propagation.

Active and break phases of the Indian summer monsoon are associated with sea surface temperature (SST) fluctuations at 30–90 days timescale in the Arabian Sea and Bay of Bengal. Mechanisms responsible for basin-scale intraseasonal SST variations have previously been discussed, but the maxima of SST variability are actually located in three specific offshore regions: the South-Eastern Arabian Sea (SEAS), the Southern Tip of India (STI) and the North-Western Bay of Bengal (NWBoB). In the present study, we use an eddy-permitting 0.25° regional ocean model to investigate mechanisms of this offshore intraseasonal SST variability. Modelled climatological mixed layer and upper thermocline depth are in very good agreement with estimates from three repeated expendable bathythermograph transects perpendicular to the Indian Coast. The model intraseasonal forcing and SST variability agree well with observed estimates, although modelled intraseasonal offshore SST amplitude is undere-stimated by 20–30 %. Our analysis reveals that surface heat flux variations drive a large part of the intraseasonal SST variations along the Indian coastline while oceanic processes have contrasted contributions depending of the region considered. In the SEAS, this contribution is very small because intraseasonal wind variations are essentially cross-shore, and thus not associated with significant upwelling intraseasonal fluctuations. In the STI, vertical advection associated with Ekman pumping contributes to ∼30 % of the SST fluctuations. In the NWBoB, vertical mixing diminishes the SST variations driven by the atmospheric heat flux perturbations by 40 %. Simple slab ocean model integrations show that the amplitude of these intraseasonal SST signals is not very sensitive to the heat flux dataset used, but more sensitive to mixed layer depth.

The time-lag relationship between precipitation and sea surface temperature (SST) variations depends upon the time scale. The present study compares the relationship between precipitation and SST variations on three time scales (synoptic, intraseasonal, and interannual) during boreal summer in the North Indian Ocean and the western North Pacific region. On interannual time scale, higher SST is followed by more rain rate in the Arabian Sea, but less rain rate in the Philippine Sea. On intraseasonal and synoptic time scales, higher SST precedes more rain rate. It is shown that the precipitation perturbation following the SST perturbation is more robust in the Philippine Sea on interannual and intraseasonal variations and less robust in the Arabian Sea on synoptic variations. The increase of rain rate perturbation leads to a decrease of SST on all the three time scales. The SST change in response to precipitation perturbation is more robust in the Philippine Sea and the South China Sea than in the Arabian Sea and the Bay of Bengal on interannual and intraseasonal variations. The correlation between intraseasonal precipitation and SST variations tends to be stronger when SST is above than below 29 °C in the South China Sea and the Philippine Sea. The correlation between intraseasonal precipitation perturbation and precipitation-induced SST change displays a decrease with total rain rate in the Arabian Sea and the Bay of Bengal.

Intraseasonal variations (ISV) of sea surface temperature (SST) in the Bay of Bengal (BoB) is highest in its northwestern part. An Indian Ocean model forced by QuikSCAT winds and climatological river discharge (QR run) reproduces ISV of SST, albeit with weaker magnitude. Air‐sea fluxes, in the presence of a shallow mixed layer, efficiently effect intraseasonal SST fluctuations. Warming during intraseasonal events is smaller (<1°C) for June ‐ July period and larger (1.5° to 2°C) during September, the latter due to a thinner mixed layer. To examine the effect of salinity on ISV, the model was run by artificially increasing the salinity (NORR run) and by decreasing it (MAHA10 run). In NORR, both rainfall and river discharge were switched off and in MAHA10 the discharge by river Mahanadi was increased tenfold. The spatial pattern of ISV as well as its periodicity was similar in QR, NORR and MAHA10. The ISV was stronger in NORR and weaker in MAHA10, compared to QR. In NORR, both intraseasonal warming and cooling were higher than in QR, the former due to reduced air‐sea heat loss as the mean SST was lower, and the latter due to enhanced subsurface processes resulting from weaker stratification. In MAHA10, both warming and cooling were lower than in QR, the former due to higher air‐sea heat loss owing to higher mean SST, and the latter due to weak subsurface processes resulting from stronger stratification. These model experiments suggest that salinity effects are crucial in determining amplitudes of intraseasonal SST variations in the BoB.

Intraseasonal variability in the eastern Pacific warm pool in summer is studied, using a regional ocean–atmosphere model, a linear baroclinic model (LBM), and satellite observations. The atmospheric component of the model is forced by lateral boundary conditions from reanalysis data. The aim is to quantify the importance to atmospheric deep convection of local air–sea coupling. In particular, the effect of sea surface temperature (SST) anomalies on surface heat fluxes is examined. Intraseasonal (20–90 day) east Pacific warm-pool zonal wind and outgoing longwave radiation (OLR) variability in the regional coupled model are correlated at 0.8 and 0.6 with observations, respectively, significant at the 99% confidence level. The strength of the intraseasonal variability in the coupled model, as measured by the variance of outgoing longwave radiation, is close in magnitude to that observed, but with a maximum located about 10° further west. East Pacific warm pool intraseasonal convection and winds agree in phase with those from observations, suggesting that remote forcing at the boundaries associated with the Madden–Julian oscillation determines the phase of intraseasonal convection in the east Pacific warm pool. When the ocean model component is replaced by weekly reanalysis SST in an atmosphere-only experiment, there is a slight improvement in the location of the highest OLR variance. Further sensitivity experiments with the regional atmosphere-only model in which intraseasonal SST variability is removed indicate that convective variability has only a weak dependence on the SST variability, but a stronger dependence on the climatological mean SST distribution. A scaling analysis confirms that wind speed anomalies give a much larger contribution to the intraseasonal evaporation signal than SST anomalies, in both model and observations. A LBM is used to show that local feedbacks would serve to amplify intraseasonal convection and the large-scale circulation. Further, Hovmöller diagrams reveal that whereas a significant dynamic intraseasonal signal enters the model domain from the west, the strong deep convection mostly arises within the domain. Taken together, the regional and linear model results suggest that in this region remote forcing and local convection–circulation feedbacks are both important to the intraseasonal variability, but ocean–atmosphere coupling has only a small effect. Possible mechanisms of remote forcing are discussed.

The Southern Ocean has a critical influence on the global climate, and any long-term variability in the Southern Ocean can have both regional and global impacts significantly. However, sparse observations limit the study of the long-term variation. To test the quality of models simulating the natural sea surface temperature (SST) variability, the SST variability in the global oceans is evaluated in simulations of the Climate Model Intercomparison Project Phase 3 (CMIP3) and CMIP5 models. The result shows that some models demonstrate good skill in simulating the observed spatial structure of the SST variability in the tropical domains and less so in the extra-tropical domains. The CMIP5 ensemble exhibits some improvement over the CMIP3 ensemble, mostly in the tropical domains on SST variability simulation. Further, the spatial structure of the SST modes of the CMIP3 and CMIP5 super ensemble is more realistic than any single model, which is mostly used for the following study. Several SST leading modes in the Southern Ocean are discussed on decadal and even larger time scales using CMIP5 data set based on EOF analysis. We compare the modes against several simple null hypotheses, such as isotropic diffusion (red noise) and a Slab Ocean model, to investigate the sources of decadal variability and the factors affecting the propagation and decay of long-term anomalies. The result reveals that the annular mode with largest amplitudes in the Pacific, the basin-wide monopole mode and South Pacific dipole are the principle patterns with low-frequency variability, which contain the dual effects of internal intrinsic processes as well as external forcing and teleconnections. The annular mode is mostly affected by El Nino Southern Oscillation (ENSO) via teleconnection especially in the South Pacific domain and by local Southern Annular Mode (SAM) over the whole Southern Ocean. The monopole mode and South Pacific dipole mode, while they both demonstrate pronounced multi-decadal and longer time scales variability, are firstly inducted by the Wave-3 patterns in the atmosphere and further developed via ocean dynamics. The causes and characteristics of interannual-decadal SST variability in the Southern Ocean are further investigated with an ocean general circulation model and a simplified band ocean model. Possible factors are examined affecting the generation, propagation and decay of long-term anomalies with a series of sensitivity experiments. We found that the atmospheric forcing not only affects the SST modes on shorter time-scales directly, but also shows its influence on longer time scales via air-sea interaction, amplification and oceanic feedback. The deep mixed layer in the Southern Ocean is an essential element to maintain the long-term SST variability. The ocean dynamics connect the entire ocean and create the homogeneous-like spatial patterns. The ocean advection is the key factor to create SST spectral structure, which concentrates the spectrum on interannnual scale synchronizing with the transport of Antarctic Circumpolar Current (ACC).

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO.

During summer, the northern Indian Ocean exhibits significant atmospheric intraseasonal variability associated with active and break phases of the monsoon in the 30–90 days band. In this paper, we investigate mechanisms of the Sea Surface Temperature (SST) signature of this atmospheric variability, using a combination of observational datasets and Ocean General Circulation Model sensitivity experiments. In addition to the previously-reported intraseasonal SST signature in the Bay of Bengal, observations show clear SST signals in the Arabian Sea related to the active/break cycle of the monsoon. As the atmospheric intraseasonal oscillation moves northward, SST variations appear first at the southern tip of India (day 0), then in the Somali upwelling region (day 10), northern Bay of Bengal (day 19) and finally in the Oman upwelling region (day 23). The Bay of Bengal and Oman signals are most clearly associated with the monsoon active/break index, whereas the relationship with signals near Somali upwelling and the southern tip of India is weaker. In agreement with previous studies, we find that heat flux variations drive most of the intraseasonal SST variability in the Bay of Bengal, both in our model (regression coefficient, 0.9, against ~0.25 for wind stress) and in observations (0.8 regression coefficient); ~60% of the heat flux variation is due do shortwave radiation and ~40% due to latent heat flux. On the other hand, both observations and model results indicate a prominent role of dynamical oceanic processes in the Arabian Sea. Wind-stress variations force about 70–100% of SST intraseasonal variations in the Arabian Sea, through modulation of oceanic processes (entrainment, mixing, Ekman pumping, lateral advection). Our ~100 km resolution model suggests that internal oceanic variability (i.e. eddies) contributes substantially to intraseasonal variability at small-scale in the Somali upwelling region, but does not contribute to large-scale intraseasonal SST variability due to its small spatial scale and random phase relation to the active-break monsoon cycle. The effect of oceanic eddies; however, remains to be explored at a higher spatial resolution.

This study addresses the air–sea interaction processes and mixed layer variability, which cause the intraseasonal oscillations in the sea surface temperature (SST) during January 2013–December 2014 using the Regional Ocean Modeling System (ROMS). We have analyzed the SST variability at three locations—northern Bay of Bengal (BoB)/15°N, 90°E (R15), central BoB/12°N, 90°E (R12), and equatorial Indian Ocean (EIO)/0°N, 80.5°E (R0). During northeast monsoon (NEM) and southwest monsoon (SWM), intraseasonal SST variability is respectively controlled by the intense outgoing fluxes (sum of longwave radiation, latent heat flux, and sensible heat flux) and zonal wind stress. The intraseasonal SST variability in spring and fall is modulated by intense incoming shortwave radiation. There is a profound impact of mixed layer depth (MLD) variations on the intraseasonal SST oscillations in the BoB and EIO. At R15, and R12, deepened simulated MLD is associated with the lowered SST variability in the NEM and SWM. In spring and fall, the shallow MLD variability corresponds to higher intraseasonal SST variability at the buoy locations. In the northern BoB, ROMS cannot capture barrier layer (BL) and temperature inversion (TI) accurately in the winter and premonsoon season due to salinity bias, resulting in the difference between simulated and actual MLD. But the simulated MLD bias does not affect the intraseasonal SST structures in the NEM and pre-SWM. In the northern BoB, the proper representation of salinity structure may represent BL, TI, and MLD, accurately in the simulation during the winter and premonsoon season.

This study investigates sea surface temperature (SST) and precipitation variations in the eastern Arabian Sea (EAS) induced by the northward-propagating Indian summer monsoon (ISM) intraseasonal oscillations (MISOs) through analyzing satellite observations and the Climate Forecast System Reanalysis (CFSR) and performing ocean general circulation model (OGCM) experiments. MISOs in the EAS achieve the largest intensity in the developing stage (May–June) of the ISM. The MISOs induce intraseasonal SST variability primarily through surface heat flux forcing, contributed by both shortwave radiation and turbulent heat flux, and secondarily through mixed layer entrainment. The shallow mixed layer depth (MLD < 40 m) in the developing stage and decaying stage (September–October) of the ISM significantly amplifies the heat flux forcing effect on SST and causes large intraseasonal SST variability. Meanwhile, the high SST (>29°C) in the developing stage leads to enhanced response of MISO convection to SST anomaly. It means that the ocean state of the EAS region during the developing stage favors active two-way air–sea interaction and the formation of the strong first-pulse MISO event. These results provide compelling evidence for the vital role played by the ocean in the MISO mechanisms and have implications for understanding and forecasting the ISM onset. Compared to satellite observation, MISOs in CFSR data have weaker SST variability by ~50% and biased SST–precipitation relation. Reducing these biases in CFSR, which provides initial conditions of the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2), may help improve the ISM rainfall forecast.

Observed sea surface temperature (SST) in the Bay of Bengal exhibits intraseasonal variability during summer monsoon. An ocean general circulation model (OGCM) driven by satellite derived daily winds and heat fluxes during summer of 2000 is able to reproduce aspects of intraseasonal variability (10–20 days) in SST. The intra‐seasonal SST simulated using satellite derived shortwave flux compares more favorably with buoy data than that produced using the NCEP reanalysis flux. Analysis of the terms in the heat balance equation of MLD suggests that in the central Bay of Bengal local net heat flux and vertical diffusive mixing changes drive the SST ISOs. Thus, the SST ISOs in this region are governed primarily by one‐dimensional processes.

Sea surface temperature (SST) variations associated with the atmospheric intraseasonal oscillation in the tropical Indian and western Pacific Oceans, are examined using a one-dimensional mixed layer model. Surface fluxes associated with 10 well-defined intraseasonal events from the period 1986–93 are used to force the model. Surface winds from the European Centre for Medium-Range Weather Forecasts daily analyses and SST from the mixed layer model are used to compute latent and sensible heat fluxes and wind stress with the TOGA COARE bulk flux algorithm. Surface freshwater flux is estimated from the Microwave Sounding Unit precipitation data. Net shortwave radiation is estimated, via regression analysis, from outgoing longwave radiation. An idealized diurnal cycle of shortwave radiation is also imposed. The intraseasonal SST variation from the model, when forced by the surface fluxes estimated from gridded analyses, agrees well with the SST observed at a mooring during the COARE. The model was then integrated for the 10 well-defined intraseasonal events at grid points from 75° to 175°E at 5°S, which spans the warm pool of the equatorial Indian and western Pacific Oceans. The one-dimensional model is able to simulate the amplitude of the observed intraseasonal SST variation throughout this domain. Variations of shortwave radiation and latent heat flux are equally important for driving the SST variations in the western Pacific, while latent heat flux variations are less important in the Indian Ocean. The phasing of the intraseasonal variation of precipitation relative to wind stress results in little impact of the freshwater flux variation on the intraseasonally varying mixed layer. The diurnal cycle of shortwave radiation is found to significantly increase the intraseasonal amplitude of SST over that produced by daily mean insolation.

The northern Bay of Bengal is characterized by freshwater supply from the Ganges and Brahmaputra Rivers. The resulting shallow haline stratification and thick barrier layer lead to temperature inversions in fall and winter, that is, cool surface water overlaying warm subsurface water. This study examines sea surface temperature (SST) variability off Bangladesh and shows that temperature inversions play an essential role in generating seasonal and interannual SST variability there. Two satellite SST datasets reveal that the magnitude of SST variability has a local peak near the coast of Bangladesh on seasonal and interannual time scales. Output from a high-resolution ocean general circulation model, which is validated by satellite SST and Argo float observations, is used to calculate the mixed layer heat budget. Results show that inverted temperature profiles lead to SST warming on the seasonal time scale via heat exchange at the bottom of the mixed layer, which balances climatological atmospheric cooling in fall and winter. On interannual time scales, surface heat flux tends to damp SST variability, whereas heat exchange at the base of the mixed layer contributes to the growth of SST anomalies. SST off Bangladesh tends to be anomalously high in the year after an El Niño event and in the year of negative Indian Ocean dipole and La Niña events. The atmospheric circulations related to these climate modes force anomalous Ekman pumping, which advects more subsurface warm water to the surface in fall and winter, resulting in anomalous mixed layer warming. The deepening of the mixed layer entrains more subsurface warm water, which also contributes to anomalous warming.

The purpose of this study is to document the zonal evolution of processes affecting sea surface temperature (SST) variability on intraseasonal timescales in the equatorial Pacific Ocean. Data primarily from the Tropical Atmosphere Ocean (TAO) array of moored buoys are used, focusing on four sites along the equator with decade-long time series. These sites are located in the western Pacific warm pool (165°E), the eastern Pacific equatorial cold tongue (110° and 140°W), and the transition zone between these two regions (170°W). Results indicate that SST variability on intraseasonal timescales is most significantly influenced by local surface heat fluxes in the western Pacific (165°E), zonal advection in the central Pacific (170°W), and vertical advection and entrainment in the eastern Pacific (110° and 140°W). East of the date line, oceanic equatorial Kelvin waves strongly mediate dynamical processes controlling intraseasonal SSTs variations, while surface fluxes tend to damp these dynamically gene...