Intraseasonal Sea Surface Temperature Research Articles

Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations

Abstract. This study uses the Community Atmosphere Model 5.3 coupled to a 1-D ocean model to investigate the effects of intraseasonal sea surface temperature (SST) feedback frequency on Madden–Julian oscillation (MJO) simulations with intervals at 30 min and 1, 3, 6, 12, 18, 24, and 30 d. The large-scale nature of the MJO in simulations remains intact with decreasing feedback frequency, although it becomes increasingly unrealistic in both structure and amplitude, until 1 per 30 d when the intraseasonal fluctuations are overwhelmingly dominated by unorganized small-scale perturbations in both atmosphere and ocean, as well as at the atmosphere–ocean interface where heat and energy are rigorously exchanged. The main conclusion is that the less frequent the SST feedback, the more unrealistic the simulations. Our results suggest that more spontaneous atmosphere–ocean interaction (e.g., ocean response once every time step to every 3 d in this study) with high vertical resolution in the ocean model is a key to the realistic simulation of the MJO and should be properly implemented in climate models.

Contributions of Air‐Sea Coupling to Northward‐Propagating Intraseasonal Convection Over the South China Sea

AbstractNorthward‐propagating convection associated with intraseasonal oscillation (ISO) over the South China Sea (SCS) is closely linked to the evolution of the East China rainy season. In this study, we quantitatively examined the contribution of air‐sea coupling to northward propagation of intraseasonal convection over the SCS based on observational diagnosis and model experiments. While oceanic feedback plays a limited role in northward propagation of Indian Ocean and western Pacific ISO, its positive effect on SCS ISO propagation is much larger and nearly comparable with that of atmospheric internal dynamics over the central SCS. Warm intraseasonal sea‐surface temperature (SST) anomalies ahead of the convection induce boundary‐layer convergence and surface turbulent fluxes, largely favoring growth of moist static energy to the north of the ISO convection. Conversely, weak negative SST anomalies induce divergence and reduced fluxes over the convective center. The underlying SST anomalies strongly promote northward propagation of SCS ISO, but destroy the maintenance of deep convection. We then verified these oceanic feedbacks using atmosphere‐only (SPA) and atmosphere–ocean‐mixed‐layer coupled (SPK) configurations of the Super‐Parameterized Community Atmospheric Model. The SPA simulations produced stronger intraseasonal convection but weaker propagation signals than the SPK simulations, confirming the positive contribution of intraseasonal SST to ISO propagation observed over the SCS.

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

The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.

Impacts of Sea Surface Temperature Gradient on Monsoon Intraseasonal Oscillation

Abstract The northward-propagating monsoon intraseasonal oscillation (MISO) is the most pronounced variability over the tropical Indian Ocean during the Indian summer monsoon (June–September). MISO is accompanied by significant air–sea interactions; however, the mechanism of the oceanic feedback to MISO is still a great scientific challenge. In this study, the role of the intraseasonal sea surface temperature (SST) gradient in MISO is diagnosed using reanalysis products and model sensitivity experiments. It is found that the positive meridional gradient of intraseasonal SST induces positive wind convergence in the planetary boundary layer (PBL) and leads convection by about 1–2 days. This accounts for approximately half of the total wind convergence in the PBL to the north of convection during MISO. The warm SST anomalies before convection accelerate the intraseasonal northerly wind in the PBL due to the enhanced downward transport of momentum from aloft. By contrast, the cold SST anomalies behind deep convection weaken the downward vertical momentum transport, thereby inducing a deceleration in the intraseasonal northerly. Consequently, changes in the speed of intraseasonal northerly along the meridional direction strengthen the PBL wind convergence ahead of deep convection. It finally results in the intensification of intraseasonal rainfall associated with MISO over the summer monsoon region. In addition, a strong (weak) SST meridional gradient at an intraseasonal time scale amplifies (lessens) the MISO intensity in the model simulation. Thus, this study highlights the role of SST meridional gradient in the feedback to MISO, which differs from the weak contribution of the warm SST itself to the wind convergence mentioned in previous studies. Collectively, these findings indicate that consideration of oceanic feedback is necessary to improve the understanding and simulation of MISO.

Tropical Intraseasonal Variability Response to Zonally Asymmetric Forcing in an Idealized Moist GCM

Abstract The tropical intraseasonal variability in an idealized moist general circulation model (GCM) coupled to a slab ocean is investigated. The model has a simple moist convection scheme and realistic radiative transfer, but no parameterization of cloud processes. In a zonally symmetric aquaplanet state, variability is dominated by westward-propagating Rossby waves. Enforcing zonal asymmetry through the application of a prescribed ocean heat flux in the bottom boundary leads to the development of a slow, eastward propagating mode that bears some of the characteristics of the observed Madden–Julian oscillation (MJO). When the ocean heat flux is made stronger, high-frequency Kelvin waves exist alongside the MJO mode. The strength of the disturbances and the spatial distribution of their precipitation anomalies are sensitive to the strength of intraseasonal sea surface temperature (SST) anomalies. The greatest resemblance to the MJO is observed when shallow slab ocean depths (1 m) are used, but the mode still exists at deeper slabs. Sensitivity experiments to the parameters of the convection scheme suggest that the simulated MJO mode couples to convection in a way that is distinct from both Kelvin and Rossby waves generated by the model. Analysis of the column moist static energy (CMSE) budget of the MJO mode suggests that column radiative heating plays only a weak role in destabilizing the mode relative to the stabilizing contribution of vertical advection. The CMSE budget analysis highlights the importance of the life cycle of horizontal advection for the destabilization and propagation of the MJO. Synergies between the generated MJO mode and linear theories of the MJO are discussed as well.

Predictability Limit of Monsoon Intraseasonal Precipitation: An Implication of Central Indian Ocean Mode

The prediction of monsoonal precipitation during Indian summer monsoon (ISM) remains difficult. Due to the high correlation between the Central Indian Ocean (CIO) mode index and the ISM precipitation variability, the predictability limit of the CIO mode index is investigated by the non-linear local Lyapunov exponent (NLLE) method in observations. Results show that the predictability limit of the CIO mode index can reach 38 days during boreal summer (from June to September), which is close to the upper predictability limit of intraseasonal precipitation (up to 40 days), and higher than the predictability limits of dynamical monsoon indices (under 3 weeks) and boreal summer intraseasonal oscillation (BSISO) indices (around 30 days). Such high predictability limit of the CIO mode index is mainly attributable to the long predictability limits from the intraseasonal sea surface temperature (SST) and intraseasonal zonal wind, which are the components of the CIO mode. As a result, the CIO mode is expected to extend the predictability of monsoonal precipitation, and benefits to improve the prediction skills of the ISM.

Climate Process Team: Improvement of Ocean Component of NOAA Climate Forecast System Relevant to Madden‐Julian Oscillation Simulations

AbstractGiven the increasing attention in forecasting weather and climate on the subseasonal time scale in recent years, National Oceanic and Atmospheric Administration (NOAA) announced to support Climate Process Teams (CPTs) which aim to improve the Madden‐Julian Oscillation (MJO) prediction by NOAA’s global forecasting models. Our team supported by this CPT program focuses primarily on the improvement of upper ocean mixing parameterization and air‐sea fluxes in the NOAA Climate Forecast System (CFS). Major improvement includes the increase of the vertical resolution in the upper ocean and the implementation of General Ocean Turbulence Model (GOTM) in CFS. In addition to existing mixing schemes in GOTM, a newly developed scheme based on observations in the tropical ocean, with further modifications, has been included. A better performance of ocean component is demonstrated through one‐dimensional ocean model and ocean general circulation model simulations validated by the comparison with in‐situ observations. These include a large sea surface temperature (SST) diurnal cycle during the MJO suppressed phase, intraseasonal SST variations associated with the MJO, ocean response to atmospheric cold pools, and deep cycle turbulence. Impact of the high‐vertical resolution of ocean component on CFS simulation of MJO‐associated ocean temperature variations is evident. Also, the magnitude of SST changes caused by high‐resolution ocean component is sufficient to influence the skill of MJO prediction by CFS.

Modulation of observed sea surface temperature variation by the quasi‐biweekly oscillation in the tropical western Pacific during boreal summer

AbstractUsing 17‐year in situ buoy observations, this study investigates how sea surface temperature (SST) over the tropical western Pacific Ocean is modulated by the quasi‐biweekly oscillation (QBWO) during boreal summer. The 10‐buoy mean phase‐by‐phase composites of SST indicate that SST evolution lags behind the convection cycle by approximately one quarter, with the maximum SST occurring in Phase −90° (29.68°C) and the minimum SST occurring in Phase 90° (29.37°C). The diurnal cycle of SST (dSST) is in phase with the convection cycle, with the minimum dSST occurring during the convectively active phase (0.34°C) and the maximum dSST occurring during the convectively suppressed phase (0.61°C). The fluctuation in the dSST reaches 0.27°C, which is similar to that in the SST (0.31°C). This kind of oscillation can be well explained by the evolution of the sea surface forcing parameters, especially the shortwave radiation and wind speeds, observed from the buoys. In addition, the composite range of the dSST suggests an interesting asymmetry between Phase −90° and 90°. The interaction of the background summer mean state and the 10–30‐day intraseasonal anomalies causes the asymmetry in wind speed between Phase −90° and 90°, which in turn results in the asymmetry in the dSST between these two phases. Since QBWOs are more likely to occur during El Niño developing summers than during El Niño decaying summers, the 10–30‐day intraseasonal SST and dSST variances during El Niño developing summers are stronger than those during El Niño decaying summers.

A numerical study on the mixed layer depth variability and its influence on the sea surface temperature during 2013–2014 in the Bay of Bengal and Equatorial Indian Ocean

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.

Rectification of the Intraseasonal SST Variability by the Diurnal Cycle of SST Revealed by the Global Tropical Moored Buoy Array

AbstractIt has been suggested that the intraseasonal sea surface temperature (SST) variability in the tropical oceans can be amplified by the diurnal cycle of SST (dSST). Here, by analyzing the global tropical moored buoy array for the first time, we find that the intraseasonal SST variability is indeed amplified by the dSST in most of the tropical oceans, especially in the Indo‐Pacific warm pool, but weakened in the equatorial cold tongues of the Pacific and Atlantic Oceans. Such a divergent response is associated with the difference in atmosphere‐ocean interaction processes over these two regions. In the warm pool region, SST responds to the intraseasonal atmospheric variability, resulting in in‐phase intraseasonal fluctuations between SST and dSST that amplify the intraseasonal SST variability. However, in the cold tongue region, SST drives the atmospheric changes, which leads to out‐of‐phase intraseasonal fluctuations between SST and dSST and thus the inhibition of the intraseasonal SST variability.

Intraseasonal SST\u2013precipitation relationship in a coupled reanalysis experiment using the MRI coupled atmosphere\u2013ocean data assimilation system

To evaluate the atmosphere–ocean coupled data assimilation system developed at the Meteorological Research Institute, the lead-lag relation between the intraseasonal variations (with a time scale of 20–100 days) in precipitation and sea surface temperature (SST) is examined in the tropics. It is shown that the relationship over the tropical western Pacific in the coupled reanalysis experiment (CDA) follows the observed relationship more closely than that in the uncoupled reanalysis experiment (UCPL). However, the lead-lag correlations with the observed SST are almost identical between precipitations in CDA and UCPL, indicating that the atmospheric component is strongly constrained by atmospheric observations and hardly affected by the SSTs as boundary conditions. Better representation of the SST–precipitation relationship in CDA is, thus, mostly due to the SST variation modified by the model physics. Comparison with additional reanalysis experiments using coupled and uncoupled systems that assimilate only in-situ observations without satellite observations suggests that the coupled model's physics complements the relatively weak observation constraints and reduces the degradation of the SST–precipitation relationship. Additional analysis for CDA suggests that the warming-to-cooling (cooling-to-warming) transition of the surface net flux, which is in phase with precipitation, is delayed from the positive (negative) peak of SST due to downward heat propagation in the ocean. Comparison of the oceanic near-surface temperature field with observation data indicates that the downward propagation of heat signals is too fast in CDA, resulting in smaller lags of transitions of the net heat flux and precipitation behind SST peaks.

Predictable Mode of Tropical Intraseasonal Variability in Boreal Summer

AbstractThis work uses a 19-yr ensemble hindcast of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the average predictable time (APT) method to detect the most predictable tropical intraseasonal variability (ISV) mode. The first and most predictable mode (APT1) of tropical ISV is similar to a joint merger of the two Madden–Julian oscillation (MJO) modes with more weight on the second mode and is characterized by a tripole pattern with two positive centers in the equatorial western Indian Ocean and central Pacific Ocean and a negative center over the Maritime Continent. The APT1 doubles the skillful prediction period made by the MJO defined by a correlation skill of 0.5 (approximately 25 days in the ECMWF model), demonstrating its potential to become a skillful prediction target and to offer powerful subseasonal prediction sources. The underlying physical process and predictability source of the APT1 are further analyzed. The APT1 is very similar to the pattern triggered by the most predictable tropical intraseasonal sea surface temperature (SST) anomalies mode, suggesting its oceanic origin. Tropical ocean–atmosphere interaction plays a critical role in the APT1 by enhancing the evolution of tropical convection cells under WES (wind–evaporation–SST) and Bjerknes feedbacks. The internal atmospheric processes also have an important impact on the formation and maintenance of the APT1.

Effects of high-frequency surface wind on the intraseasonal SST associated with the Madden-Julian oscillation

The effect of high-frequency (< 20 days) wind on the intraseasonal sea surface temperature (SST) anomaly associated with the Madden-Julian oscillation (MJO) is examined by diagnosing reanalysis and outputs from a set of oceanic general circulation model (OGCM) experiments. Warm SST anomaly (SSTA) ahead of MJO convective center induces anomalous boundary-layer convergence, favoring the eastward propagation of the MJO. To understand the key physical processes contributing to the warm SSTA, the mixed-layer heat budget equation is diagnosed. The time change of SSTA ($$\partial \left\langle T \right\rangle /\partial t$$) mostly comes from shortwave radiative heating, while latent heat flux (LHF) plays the secondary role. Due to the strong nonlinearity of LHF, the high-frequency (< 20 days) wind may affect the intraseasonal LHF variability via interacting with the background state, resulting in changes in intraseasonal SSTA. Our diagnosis shows that the upscale feedback associated with high-frequency wind variability accounts for around 23% of the intraseasonal LHF in the intraseasonal SST warming region, supporting the growth of $$\partial \left\langle T \right\rangle /\partial t$$. Sensitivity experiments are then designed using an OGCM that simulates the upper-ocean temperature well, to verify the high-frequency wind effect on the intraseasonal SST variability. Once the high-frequency component of surface winds is removed in the model integration, the amplitudes of intraseasonal LHF and $$\partial \left\langle T \right\rangle /\partial t$$ are decreased, leading to reduced SSTA. The modeling results confirm the positive role of high-frequency wind in supporting the tropical intraseasonal SST variation. The findings of this study suggest that an accurate representation of high-frequency disturbances and their interaction with other components are crucial for MJO simulation and prediction.

Atmospheric Mixed Layer Convergence from Observed MJO Sea Surface Temperature Anomalies

ABSTRACTThe Madden–Julian oscillation (MJO) dominates tropical weather on intraseasonal 30–90-day time scales, yet mechanisms for its generation, maintenance, and propagation remain unclear. Although surface moist static energy (MSE) flux is greatest under strong winds in the convective phase, sea surface temperature (SST) warms by ~0.3°C in the clear nonconvective phase of the MJO. Winds converging into the hydrostatic low pressure under warm air over the warm SST increase the vertically integrated MSE. We estimate column-integrated MSE convergence using a model of mixed layer (ML) winds balancing friction, planetary rotation, and hydrostatic pressure gradients. Small (0.3 K) SST anomalies associated with the MJO drive 7 W m−2net column MSE convergence averaged over the equatorial Indian Ocean ahead of MJO deep convection. The MSE convergence is in the right phase to contribute to MJO generation and propagation. It is on the order of the total MSE tendency previously assessed from reanalysis, and greater than surface heat flux anomalies driven by intraseasonal SST fluctuations.

Seasonality in Intraseasonal Sea Surface Temperature Variability Along the Sumatra‐Java Southern Coast

AbstractThis paper evaluates the intraseasonal variability of sea surface temperature (SST) along the Sumatra‐Java southern coast using available satellite‐derived oceanic and atmospheric data combined with output from a numerical model. The result reveals that the intraseasonal variability of SST is greater during boreal summer–fall (June–October) than during boreal winter–spring (November–May). Composite analysis shows a correlation between positive/negative intraseasonal SST variabilities and coastal downwelling/upwelling, as well as onshore/offshore Ekman transport during summer–fall. During this period, with the significantly increasing role of oceanic advection, oceanic processes are evidently enhanced and dominate the intraseasonal variability of SST. Meanwhile, the contribution of atmospheric processes drops by 67%. During winter–spring, the intraseasonal SST is primarily contributed by atmospheric processes but has a nonsignificant relationship with sea level anomalies. Intraseasonal SST anomalies vary out of phase with surface wind anomalies. The result also shows a relatively small contribution by vertical processes throughout the year, with the maximum in April and the minimum during August–September. Further analysis reveals that the alternating dominance of atmospheric and oceanic processes on intraseasonal variability of SST is responsible for the seasonality along the Sumatra‐Java southern coast. Moreover, the result indicates that the seasonality in intraseasonal SST is different in the eastern Indonesian Seas, which tends to be relatively strong in boreal winter. Distinct dominance of atmospheric and oceanic processes in intraseasonal SST is the main reason for these differences in seasonal variation characteristics.

Correlation and coherence analysis between sea surface temperature (SST) and surface wind in the Equatorial Western Sumatra Waters

Equatorial Western Sumatra Waters have a tremendous dynamic in ocean characteristics. It directly faces the Indian Ocean exactly located below the equator. Consequently, Equatorial Western Sumatra waters are influenced by the tropical climatic factors such as monsoons and climate variabilities, such as Indian Ocean Dipole (IOD) and Madden Julian Oscillation (MJO) that control sea surface temperature (SST) fluctuation in the Indian Ocean regions. This study aimed to review the correlation and coherence of SST distributed by surface wind in the Equatorial Western Sumatra waters. Wavelet method (cross wavelet transforms and wavelet coherence) was used to analyze the correlation and coherency between SST and surface wind. The annual variation of SST for 365 days period is the strongest event throughout the year caused by either monsoon or the changes of wind speed in the surface. Otherwise, the strongest intra-seasonal SST variation of 35 - 60 days observed from December 2012 to March 2013. The highest surface wind speed occurs in the southern and western waters. The surface wind plays a role in evoking SST distribution of 35 - 60 days period (intra-seasonal variability). Besides, the surface wind with 6 months period (semi-annual variability) influences the SST distribution which is identified only in the southern waters and the Indian Ocean regions. These conditions are possibly predicted as the influence of monsoons.

Role of SST feedback in the prediction of the boreal summer monsoon intraseasonal oscillation

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.

Diagnosing Ocean Feedbacks to the BSISO: SST‐Modulated Surface Fluxes and the Moist Static Energy Budget

AbstractThe oceanic feedback to the atmospheric boreal summer intraseasonal oscillation (BSISO) is examined by diagnosing the sea surface temperature (SST) modification of surface fluxes and moist static energy on intraseasonal scales. SST variability affects intraseasonal surface latent heat (LH) and sensible heat (SH) fluxes, through its influence on air‐sea moisture and temperature gradients (∆q and ∆T, respectively). According to bulk formula decomposition, LH is mainly determined by wind‐driven flux perturbations, while SH is more sensitive to thermodynamic flux perturbations. SST fluctuations tend to increase the thermodynamic flux perturbations over active BSISO regions, but this is largely offset by the wind‐driven flux perturbations. Enhanced surface fluxes induced by intraseasonal SST anomalies are located ahead (north) of the convective center over both the Indian Ocean and the western Pacific, favoring BSISO northward propagation. Analysis of budgets of column‐integrated moist static energy (⟨m⟩) and its time rate of change (∂⟨m⟩/∂t) shows that SST‐modulated surface fluxes can influence the development and propagation of the BSISO, respectively. LH and SH variability induced by intraseasonal SSTs maintain 1–2% of ⟨m⟩ day−1 over the equatorial western Indian Ocean, Arabian Sea, and Bay of Bengal but damp about 1% of ⟨m⟩ day−1 over the western North Pacific. The contribution of intraseasonal SST variability to ∂⟨m⟩/∂t can reach 12–20% over active BSISO regions. These results suggest that SST variability is conducive, but perhaps not essential, for the propagation of convection during the BSISO life cycle.

Summer precipitation–SST relationship on different time scales in the northern tropical Indian Ocean and western Pacific

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.

Summer Intraseasonal Surface Heat Flux‐Sea Surface Temperature Relationship Over Northern Tropical Indo‐Western Pacific in Climate Models

AbstractThe present study documents the relationship between intraseasonal net surface heat flux (NHF, positive for downward flux) and sea surface temperature (SST) variations over the North Indian Ocean and tropical western North Pacific during May through September in climate models. A distinct NHF‐SST relationship is identified in the NCC.NorESM1‐M model. NHF has a weak correlation with the SST tendency but a large negative correlation with SST in the NCC.NorESM1‐M model, whereas NHF has a large positive correlation with the SST tendency and a weak correlation with SST in observations and some other climate models. The sea‐air humidity difference has a dominant contribution to latent heat flux variations in relation to a bias toward larger mean wind speed in the NCC.NorESM1‐M model. In contrast, surface wind speed overcomes the sea‐air humidity difference in contributing to latent heat flux variations due to a bias toward larger mean sea‐air humidity difference in the INM.inmcm4 model. Detailed analysis reveals different processes in intraseasonal SST variations in the South China Sea between the INM.inmcm4 and NCC.NorESM1‐M models. In the INM.inmcm4 model, atmosphere‐induced shortwave radiation and latent heat flux have a main contribution to the SST change. In the NCC.NorESM1‐M model, the SST change is mainly due to oceanic processes. The ocean‐induced positive SST anomalies increase evaporation and surface air humidity. This destabilizes the atmospheric boundary layer in favor of convection and reduced downward shortwave radiation. NHF has a damping effect on the SST change.