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Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs
Abstract
The simulation of the boreal summer intraseasonal oscillation (BSISO) has been analyzed in the historical run of the 32 models, which participated in the Coupled Model Intercomparison Project phase5 (CMIP5), and it is shown that the current state‐of‐the‐art general circulation models (GCMs) still have difficulties to properly simulate the BSISO. Compared to CMIP3 models, more CMIP5 models simulated the northward propagation of BSISO. The majority of the models could not simulate the spatial pattern of BSISO variance over the Asian summer monsoon (ASM) region and many of them failed to capture all the three peak centers of BSISO variance over the Indian summer monsoon region. Many of the models underestimated the BSISO variance over the equatorial Indian Ocean, and it is associated with the seasonal mean dry biases over this region. A reasonable representation of the intraseasonal sea surface temperature and its coupling to the convection over the equatorial Indian Ocean and an equatorial eastward propagation of convective anomalies beyond 100°E assure realistic simulation of BSISO over the ASM domain. We found that the models MIROC5, IPSL‐CM5A‐LR, GFDL‐CM3, CMCC‐CM, and MPI‐ESM‐LR are able to represent reasonable BSISO characteristics and can be used to unravel the modulation of BSISO characteristics due to various projected climate changes.
... They argued that the severe drought of 2009 in India is caused by the extended break phase initiated by MISO due to the divergent mode of the westward propagating Rossby waves. On the other hand, it is crucial to study the presentation of MISO in climate models; only a handful of studies analyzed MISO in the phase 5 of Coupled Model Intercomparison Project (CMIP5), e.g., Sabeerali et al. (2013) investigated the simulation of MISO in CMIP5 models and observed that most of the models still have difficulty simulating the MISO. They also observed that using the power spectrum analysis in most of the CMIP5 models, the MISO is propagated northward at a wavenumber of 1 and a period of 30-50 days. ...
... To understand the propagation characteristics of MISO, a finite domain power spectrum analysis is performed on the daily precipitation anomalies from 1998-2014 for JJAS season to TRMM and CMIP6 models (Teng and Wang 2003;Fu et al. 2003;Sabeerali et al. 2013). This power spectrum analysis decomposes and converts the data from the spacetime domain to the wavenumber-frequency domain (Sperber and Annamalai 2008). ...
... Although there is a large variability in wavenumber and magnitude of the maximum peak of power spectra observed in the southward propagation of MISO, it is weaker in all models in line with the observations. Comparable with the earlier studies done for CMIP5 models (Sabeerali et al. 2013), the majority of the CMIP6 models also simulate weak Northward propagating component of MISO. Expect, for few models (EC-Earth3, MIROC6, SAM0-UNICON, NorESM2-MM) which simulate the reasonable magnitude in a spectral peak maxima compared with the observations at wave number 1 and 40-day periodicity. ...
The Indian summer monsoon (ISM) rainfall undergoes a cycle of enhancement and weakening on the intra-seasonal time scales, known as active and break spells. This cycle is associated with the large-scale Monsoon Intra-seasonal Oscillation (MISO) which is the dominant mode of sub-seasonal monsoon variability. The synoptic scale low-pressure systems (LPSs) are observed to cluster in the active phase of the MISO. However, the LPS–MISO interaction in the climate models is not yet investigated. In the present study, we examine the relationship between MISO and LPSs in the historical simulations of 20 coupled models from the sixth phase of coupled model intercomparison project (CMIP6). We find that, as in the observations, the LPSs tend to cluster during the active phase of ISM in the model simulations. The observations show that the frequency of LPS genesis during the active phase of ISM is 2.6 time more than that during the break phase. In the model simulations also, the LPS genesis distribution is skewed towards the positive phase of MISO, albeit considerable inter-model variability. The observations show a 31% of total LPS genesis is due to the remnants of tropical cyclones from the West North Pacific, known as “downstream amplification ” and the remaining formed due to the in situ processes, while the ensemble mean of CMIP6 shows 29% downstream LPSs genesis. Irrespective of the genesis types, the LPSs are clustered in the active phase in observations and model simulations. A strong negative meridional shear of zonal wind at 850 hPa (dU850dϕ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{dU_{850}}{d\phi }$$\end{document}) is observed over the head Bay of Bengal, the core LPS genesis region, in both observations and CMIP6 models during the active phase of ISM. The vertical shear simulated by the models differs from the observations. The vertical shear simulated by the CMIP6 models does not vary much between active and break phases. The analyses suggest that part of the uncertainty in simulating the LPSs might be linked to the skill of models in simulating the low-level relative vorticity pattern over the Western North Pacific which is a key factor in triggering the downstream genesis of LPS.
... Fluctuations in cloudiness and winds due to MISOs are known to cause large intraseasonal variations in the net surface heat flux (SHF) and sea surface temperature (SST) in the western Pacific and the Bay of Bengal (BoB) (Krishnamurti et al., 1988;Sengupta & Ravichandran, 2001). A phase lag of about 7-10 days is observed between warm (cold) SST and enhanced (subdued) rainfall anomalies in the BoB (Sabeerali et al., 2013). The atmospheric subseasonal fluctuations mainly force the intraseasonal fluctuations in SST. ...
... Coupled models represent the intraseasonal variability better (Fu et al., 2003(Fu et al., , 2007Kemball-Cook et al., 2002;Lin et al., 2011). However, they still simulate weak intraseasonal variance over the monsoon region (Lin et al., 2008;Sabeerali et al., 2013). Though the representation of MISOs has improved in the models participating in the Coupled Model Intercomparison Project phase 5 compared to CMIP3 models, significant biases in simulating the intraseasonal variance still exist (Sabeerali et al., 2013). ...
... However, they still simulate weak intraseasonal variance over the monsoon region (Lin et al., 2008;Sabeerali et al., 2013). Though the representation of MISOs has improved in the models participating in the Coupled Model Intercomparison Project phase 5 compared to CMIP3 models, significant biases in simulating the intraseasonal variance still exist (Sabeerali et al., 2013). Coupled models also suffer from restricted inland propagation of LPS over the Indian landmass. ...
Many global climate models, including the Climate Forecast System version 2 (CFSv2), have a biased representation of subseasonal modes of variability of the Indian summer monsoon. For instance, they simulate a weaker summer mean monsoon low‐pressure systems (LPS) climatology, faster than observed northward propagation of monsoon intraseasonal oscillations (MISOs), and a systematic dry bias over Indian landmass. The Bay of Bengal (BoB), with its shallow mixed layers and unique thermal stratification, significantly modulates the convective activity in this region at subseasonal‐to‐seasonal timescales through modulation of sea surface temperature. The highly stratified upper ocean in the BoB is due to the enormous freshwater it receives from rains and rivers. A river routing model is coupled to the CFSv2 to account for the riverine freshwater and the improvements in modelling the upper‐ocean structure are analysed. Model simulations indicate that inclusion of temporally varying riverine freshwater improves the upper‐ocean state in the BoB and the observed mixed‐layer temperature gradients in the Bay are simulated reasonably after incorporating the time varying river runoff. This resulted in increased LPS lifetime and track density, and enhanced rainfall over central India. Better representation of the upper‐ocean stratification in the model leads to larger post‐convection shoaling of mixed layers at intraseasonal timescales, thereby forming thick barrier layers. Enhanced air–sea interactions restricted to the shallow mixed layer are associated with stronger vorticity, specific humidity and low‐level convergence to the north of the intraseasonal convection band. This enhanced low‐level moisture convergence north of the convection centre results in realistic northward propagation of MISO and aids LPS activity. It is demonstrated that better simulation of the upper‐ocean structure in coupled climate models can improve the representation of subseasonal modes of monsoon variability. These results bear important implications for operational forecasting.
... The performance of the models in representing the seasonal mean state of precipitation over the Indian region is estimated by using the bias Eq. (1) (Sabeerali et al., 2013;Konda and Vissa, 2021) ...
... Linear regression analysis is chosen as an analysis tool because it can objectively identify the development and propagation of wave system (Tsou et al., 2005). The space-time metrics of northward propagating BSISO and the northwest-southeast tilt of convection is obtained by using linear regression analysis (Sabeerali et al., 2013;Neena et al., 2017;Pathak et al., 2017;Konda and Vissa, 2021;Valdivieso et al., 2021). The northward propagation of BSISO is analyzed by using linear regression (here after linear regression is termed as regression) analysis with lead/lag of − 30-30 days (Sabeerali et al., 2013;Konda and Vissa, 2021). ...
... The space-time metrics of northward propagating BSISO and the northwest-southeast tilt of convection is obtained by using linear regression analysis (Sabeerali et al., 2013;Neena et al., 2017;Pathak et al., 2017;Konda and Vissa, 2021;Valdivieso et al., 2021). The northward propagation of BSISO is analyzed by using linear regression (here after linear regression is termed as regression) analysis with lead/lag of − 30-30 days (Sabeerali et al., 2013;Konda and Vissa, 2021). For regression analysis, area-averaged (70 • E-90 • E, 12 • N-22 • N) 20-100 day bandpass filtered precipitation anomalies are used. ...
Historical runs of 30 Coupled Model Intercomparison Project Phase-6 (CMIP6) General circulation models (GCMs) are evaluated for the representation of Boreal Summer Intraseasonal Oscillations (BSISO). Several statistical metrics were developed to evaluate the characteristic features of BSISO, such as propagation, phase speed, and exchange of air-sea fluxes at the air-sea interface over the major regions of the Indian Summer Monsoon (ISM). The mean state of the monsoon precipitation in the CMIP6 models is evaluated by using seasonal mean bias, pattern correlation, and root mean square error. The majority of CMIP6 models underestimate the precipitation over central India and overestimate the precipitation over the eastern equatorial region. Multi-model mean (MME) of the models shows good agreement of precipitation pattern with the observations. In the observations, the precipitation anomalies propagate northward from the equatorial latitudes to the northern latitudes over the ISM region (60oE-100oE longitudes). However, the initiation of northward propagating convection shows a significant variation with time in the CMIP6 models. Most of the models well simulated the BSISO propagation over the Bay of Bengal (BoB) and the Indian subcontinent. The majority of the models underestimate the phase speed of BSISO over the Arabian Sea (AS), and easterlies from the western north Pacific, which led to the failure of models in representing the northwest-southeast tilt of convection. Surface turbulent fluxes and zonal winds lag the deep convection over the North Indian Ocean on intraseasonal timescales. However, misrepresentation of air-sea fluxes in the CMIP6 models leads to the significant biases of intraseasonal variances. This study examines the simulation characteristic features of BSISO by CMIP6 models and is mainly attributes them to the atmospheric internal dynamics and air-sea interactions. The present study further suggests that improving atmospheric-oceanic feedback mechanisms, specific humidity, and low-level winds in the CMIP6 models is necessary to accurately predict the ISM intraseasonal variability.
... It has been shown in the previous studies that intra-seasonal variability of South Asian Monsoon is well separated from the small scale high frequency synoptic fluctuations (2-10 days) and observed preferably at a time scale of 10-90 days (Goswami 2005;Singh et al. 2019;Sabeerali et al. 2013;Sharmila et al. 2014). In earlier studies, presence of a quasi-biweekly oscillations in monsoon system parameters such as monsoon trough, cloud cover and rainfall was reported which was found to be associated with the westward propagation (Krishnamurti and Bhalme 1976;Yasunari 1979;Krishnamurti and Ardanuy 1980;Singh 2013;Singh and Dasgupta 2017;Singh et al. 2019). ...
... For e.g. Sabeerali et al. (2013) mentioned that CMIP5 models perform better in delineating the intraseasonal variability of monsoon in south Asian region as compared to their previous versions. Further to this authors also noted that, several of the CMIP5 models failed to capture the spatial pattern and variance of the ISOs. ...
... Considering aforementioned various improvements embedded in the CMIP6 models such as the improved model physics and enhanced model resolution, present study is devised to address the characteristics of the South Asian monsoon system at intra-seasonal scale for particularly high frequency oscillations which were largely excluded in coupled climate model based previous studies (Sharmila et al. 2013;Sabeerali et al. 2013Sabeerali et al. , 2014. Present study focuses on the 10-20 days and 30-60 days ISOs of SAM system to address the potential of CMIP6 models in resolving spatial-temporal and propagation characteristics of ISOs, and would be useful in understanding the characteristics of ISOs in various model components setup. ...
Understanding of intra-seasonal oscillations (ISOs) acts as a crucial bridge in deciding the fate of the seasonal total rainfall during monsoon season in densely populated South Asian monsoon (SAM) domain. Based on daily precipitation data sets from state-of-the-art coupled climate model cohort that participated in Coupled Climate Model Inter-comparison Project Phase 6 (CMIP6), this study brings out the efficacy of these models in resolving the intra-seasonal signatures of monsoon season rainfall. Out of 27 CMIP6 models considered in the present study, only 21 are able to simulate the annual cycle well within the acceptance bound derived from the observations based rainfall. CMIP6 models Can-ESM5, HadGEM3-GC31, MRI-ESM2, UKESM1, ACCESS-CM2 and ACCESS-ESM1-5 are failed to resolve the onset phase of the monsoon in the month of June. Subsequent analysis revealed reasonable skills of most of the 27 CMIP6 models in demarcating 2 dominant intra-seasonal oscillations (ISOs) of monsoon viz 10–20 and 30–60 days. It is noted from the present analysis that CMIP6 models resolve the 10–20 days ISOs signal significantly but unexpectedly show conspicuous shift in 30–60 days ISOs with respect to the observational data sets over the central Indian region which results in extended dry spell in the beginning of the monsoon season. Westward propagating 10–20 days and Northward propagating 30–60 days ISOs characteristics are well simulated by 50% of CMIP6 models considered in the present study. Multi model mean of 27 CMIP6 models seems to preserve the shape of the distribution of 10–20 days ISOs in good agreement with the observational datasets as compared to low frequency 30–60 days oscillations, nevertheless, considering huge inter-model variations in ISOs particularly for 30–60 days ISOs and inability of CMIP6 models in detecting climatological wet and dry spells of ISOs, caution must be taken while delineating the intra-seasonal variability of the South Asian monsoon using CMIP6 models. Inter-model spread amongst the CMIP6 models considered in the present study may be attributed to the pathways of the interaction of model components, variants, model physics and representation of feedback mechanism.
... This is consistent with the conclusion given by Kemball-Cook et al. (2002) based on the ECHAM4 general circulation model (GCM) coupled to a 2.5-layer intermediate ocean model. In comparison with the previous generation, the CMIP5 models show an improvement in simulating the northward propagation of MISO, but most models still have difficulties in accurately simulating a realistic MISO (Sabeerali et al. 2013;Sperber et al. 2013). More importantly, the underestimation of MISO variances has been attributed to the weak representation of mean vertical easterly shear. ...
... In comparison with the simulations from CMIP5, the models have an improvement in simulating the mean rainfall intensity. However, as in CMIP5 (Sabeerali et al. 2013), EC-Earth3-Veg (Fig. 2i), and MRI-ESM2-0 ( Fig. 2p) largely underestimate the MISO amplitude over the BoB region by about 1.5 mm day −1 , which is almost half of the observed intensity. Particularly, the STD of MISO is largely underestimated in the equatorial Indian Ocean in these models. ...
... Particularly, the STD of MISO is largely underestimated in the equatorial Indian Ocean in these models. Thereby, these models have poor skills in depicting the MISO activity in the tropical Indian Ocean, and these model biases are also seen in CMIP5 (Sabeerali et al. 2013). In general, few models can reproduce the realistic pattern of the MISO amplitude, as many models have a PCC lower than 0.7. ...
The simulations of the monsoon intraseasonal oscillation (MISO) during the Indian summer monsoon (ISM) are evaluated with 19 atmosphere–ocean coupled general circulation models (CGCMs) from phase 6 of the Coupled Model Inter-comparison Project (CMIP6). The focus is on the northward propagation of MISO. The CMIP6 models have great improvement in simulating the mean rainfall, as 17 out of 19 models can reasonably simulate the mean rainfall. However, many models fail to reproduce the realistic patterns of the mean rainfall and the MISO amplitude, particularly over land in the monsoon region. The underestimation of the MISO amplitude is still a notable model bias in CMIP6. Moreover, 9 out of 19 models cannot generate realistic northward propagation features, and some even reproduce a stationary MISO pattern. Process diagnostics based on the seasonal mean vertical zonal wind shear, low-level mean moisture, and vortex tilting are also examined. It is found that the accuracy of model simulations of vortex tilting is strongly associated with the northward propagation of MISO. In contrast, the model fidelity in MISO is not dependent on the simulation skill for the seasonal mean state. In addition, decomposition analysis of vortex tilting illustrates that the meridional shear of the intraseasonal vertical velocity is crucial to the tilting simulation. The poor model fidelity in vortex tiling is caused by the weak convection, particularly the absence of downdraft to the north of the convection center. The coupling between the moisture in the boundary layer and the tilting in the free troposphere may be responsible for capturing the vertical motions. In summary, vortex tilting can be a useful metric for evaluating the northward propagation of MISO.
... Simulations of the MISO are still generally poor in state-of-the-art coupled models (e.g. Goswami et al., 2013;Jayakumar et al., 2017;Sabeerali et al., 2013;Sharmila et al., 2013) and re-analysis products (e.g. Sanchez-Franks et Figure 8. MJO composite evolution for the boreal winter (November-April) averaged over latitudes 3-7 • S for the period of 1 November 1997 to 31 October 2010: (a) Log 10 Chl from Sea-WIFS satellite observations (shaded) and satellite-derived outgoing longwave radiation (contour); (b) wind speed (shaded) and zonal wind stress (contour), both from the cross-calibrated multiplatform (CCMP) data set; and (c) NOAA-OI satellite SST anomalies (shaded) and AVISO mean sea level anomaly (contour). ...
... Observations and models indicate that MISOs may be slowing down because of the warming in the Indian Ocean (Sabeerali et al., 2013), which needs to be understood better for providing reliable monsoon predictions and projections in this climate-vulnerable region. This is underscored by the observational evidence that climate variability and change are increasing the frequency of dry spells and the intensity of wet spells in the Indian summer monsoon, which are directly related to MISO (Singh et al., 2014). ...
... Observations and models indicate that MISOs may be slowing down because of the warming in the Indian Ocean (e.g. Sabeerali et al., 2013), which needs to be understood better for providing reliable monsoon predictions and projections in this climate vulnerable region. ...
Over the past decade, our understanding of the Indian
Ocean has advanced through concerted efforts toward measuring the ocean
circulation and air–sea exchanges, detecting changes in water masses, and
linking physical processes to ecologically important variables. New
circulation pathways and mechanisms have been discovered that control
atmospheric and oceanic mean state and variability. This review brings
together new understanding of the ocean–atmosphere system in the Indian
Ocean since the last comprehensive review, describing the Indian Ocean
circulation patterns, air–sea interactions, and climate variability.
Coordinated international focus on the Indian Ocean has motivated the
application of new technologies to deliver higher-resolution observations
and models of Indian Ocean processes. As a result we are discovering the
importance of small-scale processes in setting the large-scale gradients and
circulation, interactions between physical and biogeochemical processes,
interactions between boundary currents and the interior, and interactions between the
surface and the deep ocean. A newly discovered regional climate mode in the
southeast Indian Ocean, the Ningaloo Niño, has instigated more regional
air–sea coupling and marine heatwave research in the global oceans. In the
last decade, we have seen rapid warming of the Indian Ocean overlaid with
extremes in the form of marine heatwaves. These events have motivated
studies that have delivered new insight into the variability in ocean heat
content and exchanges in the Indian Ocean and have highlighted the critical
role of the Indian Ocean as a clearing house for anthropogenic heat. This
synthesis paper reviews the advances in these areas in the last decade.
... Earlier studies have highlighted that the majority of coupled global climate models participating in the Coupled Model Intercomparison Project (CMIP) overestimate light rain and underestimate moderate and heavy rain Sabeerali et al. (2013); Goswami et al. (2014); . We have analyzed the probability distribution (PDF) of rainfall from a few selected CMIP6 models (CESM2, HadGEM3, and IITM-ESM) along with CFSv2 as shown in Fig. 1. ...
... Rights reserved. Sabeerali et al. (2013); Hazra et al. (2015 as compared to the observations Pokhrel and Sikka (2013). Therefore, we have targeted improving the rainfall coming from the cloud microphysics scheme. ...
An unresolved problem of the current Global Climate Models (GCM) is the unrealistic distribution of rainfall over the Indian Summer Monsoon (ISM) region, which is also related to the persistent dry bias over the Indian landmass. Therefore, quantitative prediction of the intensity of rainfall events has remained a challenge for state-of-the-art GCMs. Based on observations, it is hypothesized that the insufficient growth of cloud droplets and the processes responsible for the cloud-to-rainwater conversion are the key components in distinguishing between shallow and convective clouds. The Eulerian–Lagrangian particle-by-particle-based small-scale model provides a path for reassessing the ‘autoconversion’ parameterization schemes and suggests the relative dispersion-based ‘autoconversion’ parameterization scheme for the climate model. The realistic information on cloud drop size distribution is incorporated into the microphysical parameterization scheme of the climate model. Two sensitivity simulations are conducted using the climate forecast system (CFSv2) model. The coupled climate model incorporates a relative dispersion-based Liu–Daum-type autoconversion parameterization scheme in place of the traditional Sundqvist-type autoconversion, which, based on small-scale model analysis, makes the model more accurate in simulating the probability distribution (PDF) of rainfall with accompanying specific humidity, liquid water content, and outgoing long-wave radiation (OLR). The improved simulation of rainfall PDF appears to have been aided by a significantly improved simulation of OLR, which led to a more accurate simulation of the ISM rainfall.
... Therefore, it is worth diagnosing the model's capability in simulating northward-convective bands and associated mechanisms. We estimated the northward and eastward propagation of the convective band following a similar approach of Sabeerali et al. 2013;Di Sante et al. 2019;. In this regard, the 20-100 day bandpass filtered precipitation anomalies are regressed for each grid against a reference time series. ...
... The convection over the equatorial Indian Ocean that propagates eastward controls ISM's intraseasonal variability (ISV), particularly prolonged breaks condition (Yasunari 1980;Sperber and Annamalai 2008;Sabeerali et al. 2013;Sharmila et al. 2013). Apart from this, the northward propagating convection from the Indian Ocean over the Indian region also contributes to the smaller-scale ISM variability (Webster and Yang 1992;Webster et al. 1999). ...
This study demonstrated the influence of downscaling using the regional climate model (RCM) driven by Era-Interim reanalysis (EIN) in simulating different aspects of the Indian summer monsoon (ISM). It is also examined, whether increasing the horizontal resolution of RCM will inevitably be capable of adding more information to ISM characteristics and its spatio-temporal variability. In this regard, two RCM (at 50 km: Reg50 and 25 km: Reg25) simulations were conducted for six years from 2000 to 2005 for the South Asia Coordinated Regional Downscaling Experiment (CORDEX) domain. The added value (AV) is found to be strongly dependent on region and considered metrics. A slight improvement towards increasing spatial resolution is observed in the simulation of the mean ISM characteristics, while considerable improvements are noticed for the frequency distribution of extremes. The notable improvement in the daily climatology of precipitation is observed over the region of northeast India (~ 35%) and the Hilly region (~ 32%) and the lowest improvement over north-central India (~ 8%). The reduction of anomalously strong northeasterly flow over the southeastern Arabian Sea and strengthening of the moisture leaden southeasterly wind flow from the Bay of Bengal in Reg25 compared to Reg50 is consistent with the reduction of dry bias over India in Reg25. The robust improvements are noticed for the heavy precipitation events (probability density function: PDF tails) and mean precipitation due to extreme precipitation events, particularly over the areas characterized by complex topographical features (e.g., the Western Ghats, Indo-Gangetic plains, and northeast India and Hilly regions) as well as over the areas having substantial bias (e.g., central India), indicating its strong sensitivity towards model resolution. The increasing latent heat flux in Reg25 contributes to increasing the moisture and hence rainfall over India. Both simulations apparently simulate many of the ISM characteristics better than the EIN, thereby emphasizing the usefulness of finer resolutions in the better simulation of the Indian monsoon, especially for heavy rainfall. However, the RegCM bias is comparable to or even greater in some places than the EIN bias. This suggests that high-resolution models are important for improving performance; however, it does not necessarily mean that they can have AV for every aspect and all places. Apart from this, the substantial difference in the AV over different regions or aspects highlights the importance of carefully selecting AV matrices for the different areas and characteristics being investigated. RegCM exhibits some systematic biases in precipitation despite substantial improvement due to misrepresentation of dynamical and thermodynamical processes, including northward and eastward propagating convective bands.
... In this study, the northward and eastward propagating convecting bands have been computed following the methodology of Sabeerali et al. (2013). Firstly 20-100 days bandpass filtered has been applied for precipitation to extract the intraseasonal mode. ...
... The eastward and northward propagating convective bands from the Indian Ocean (IO) significantly regulate the intraseasonal variability and amount of precipitation the ISV of ISMR (Sperber and Annamalai 2008;Sabeerali et al. 2013;Di Sante et al. 2019). However, limited efforts have been made to understand the role of air-sea coupling on these propagating bands (Di Sante et al. 2019). ...
A new high-resolution Regional Earth System Model, namely ROM, has been implemented over CORDEX-SA towards examining the impact of air–sea coupling on the Indian summer monsoon characteristics. ROM's simulated mean ISM rainfall and associated dynamical and thermodynamical processes, including the representation of northward and eastward propagating convention bands, are closer to observation than its standalone atmospheric model component (REMO), highlighting the advantage of air–sea coupling. However, the value addition of air–sea coupling varies spatially with more significant improvements over regions with large biases. Bay of Bengal and the eastern equatorial Indian Ocean are the most prominent region where the highest added value is observed with a significant reduction up to 50–500% precipitation bias. Most of the changes in precipitation over the ocean are associated with convective precipitation (CP) due to the suppression of convective activity caused by the negative feedback due to the inclusion of air–sea coupling. However, CP and large-scale precipitation (LP) improvements show east–west asymmetry over the Indian land region. The substantial LP bias reduction is noticed over the wet bias region of western central India due to its suppression, while enhanced CP over eastern central India contributed to the reduction of dry bias. An insignificant change is noticed over Tibetan Plateau, northern India, and Indo Gangetic plains. The weakening of moisture-laden low-level Somalia Jets causes the diminishing of moisture supply from the Arabian Sea (AS) towards Indian land regions resulting in suppressed precipitation, reducing wet bias, especially over western central India. The anomalous high kinetic energy over AS, wind shear, and tropospheric temperature gradient in REMO compared to observation is substantially reduced in the ROM, facilitating the favourable condition for suppressing moisture feeding and hence the wet bias over west-central India in ROM. The warmer midlatitude in ROM than REMO over eastern central India strengthens the convection, enhancing precipitation results in reducing the dry bias. Despite substantially improved ROM’performance, it still exhibits some systematic biases (wet/dry) partially associated with the persistent warm/cold SST bias and land–atmosphere interaction.
... MISO can explain approximately 60% of total precipitation variance over the Bay of Bengal (BoB) (Goswami 2005;Waliser 2006;Shukla 2014). To date, simulations and predictions of monsoonal precipitation and MISOs remain a great challenge for stateof-the-art models (e.g., Sabeerali et al. 2013;Wang 2015;Goswami and Chakravorty 2017;Hazra et al. 2017). The predictability of ISM arises from its close relationship with the El Niño-Southern Oscillation (ENSO, e.g., Gill et al. 2015), the Atlantic Niño (Pottapinjara et al. 2014) and the Indian Ocean Dipole/Zonal Mode (IODZM, e.g., Murtugudde et al. 2000;Ashok et al. 2001). ...
... A consistent conclusion was that a better depiction of the CIO mode in a model tends to yield a better simulation of northward-propagating MISO and heavier intraseasonal rainfall during the ISM. However, the CIO mode is not well captured in CESM, and the biases of seasonal mean and vertical shear also remain (Sabeerali et al. 2013;Zhao et al. 2014). As a result, the simulated monsoonal precipitation and the northward-propagating MISO are weaker than observed. ...
The simulation and prediction of the Indian summer monsoon (ISM) and its intraseasonal component in climate models remain a grand scientific challenge for numerical simulations. Recently, an intraseasonal mode was proposed over the tropical Indian Ocean, named central Indian Ocean (CIO) mode. The CIO mode index and the monsoon intraseasonal oscillations (MISO) have a high correlation. In this study, the simulations of the CIO mode in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models are examined. Although the coupled ocean-atmosphere feedbacks associated with the CIO mode are not fully reproduced, the results show that a better depiction of the CIO mode in CMIP6 models is favorable for a better simulation of northward-propagating MISO and heavy rainfall during the ISM. Dynamic diagnostics unveil that the rendition of the CIO mode is dominated by kinetic energy conversion from the background to the intraseasonal variability. Furthermore, kinetic energy conversion is controlled by the meridional shear of background zonal winds (u y), which is underestimated in most CMIP6 models, leading to a weak barotropic instability. As a result, a better simulation of u y is required for improving the CIO mode simulation in climate models, which helps to improve the simulation and prediction skill of northward-propagating MISO and monsoonal precipitation.
... Assessment of the low frequency tropical intraseasonal oscillations (e.g., MJO and BSISO) and associated Asian summer monsoon variability in climate models that have participated in the Coupled Model Intercomparison Project (CMIP) appears in several previous studies (e.g., Lin et al., 2006Sperber and Kim, 2012;Hung et al., 2013;Jena et al., 2016;Ahn et al., 2017;Preethi et al., 2019;Konda and Vissa, 2021). Evidence still abounds that many GCMs fall short at representing the basic features of these oscillations, including the northward propagating convection, because of resolution-sensitivity to parameterizations, large-scale dynamics, and representation of physical processes that vary from model to model (Sabeerali et al., 2013;Konda and Vissa, 2021). However, there has been a notable improvement in the robust simulation of intraseasonal variability from CMIP3 to CMIP5, due in part to increases in resolution and convective parameterization. ...
... However, there has been a notable improvement in the robust simulation of intraseasonal variability from CMIP3 to CMIP5, due in part to increases in resolution and convective parameterization. Comparing the simulated BSISO eastward propagating convective anomalies in CMIP5 with those of the CMIP3, Sabeerali et al. (2013) noted a modest improvement in BSISO simulation in CMIP5 models. Ogata et al. (2014) applied Taylor's skill metrics to assess the performance of 20 CMIP3 and 24 CMIP5 models at capturing the seasonal mean structures of the summer Asian monsoon and reported an improvement in the skills of the CMIP5 multi-model ensemble mean. ...
The boreal summer intraseasonal oscillation (BSISO) plays an important role in the intraseasonal variability of a wide range of weather and climate phenomena across the region modulated by the Asian summer monsoon system. This study evaluates the strengths and weaknesses of 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to reproduce the basic characteristics of BSISO. The models' rainfall and largescale climates are evaluated against GPCP and ERA5 reanalysis datasets. All models exhibit intraseasonal variance of 30–60-day bandpass-filtered rainfall and convection anomalies but with diverse magnitude when compared with observations. The CMIP6 models capture the structure of the eastward/northward propagating BSISO at wavenumbers 1 and 2 but struggle with the intensity and location of the convection signal. Nevertheless, the models show a good ability to simulate the power spectrum and coherence squared of the principal components of the combined empirical orthogonal function (CEOF) and can capture the distinction between the CEOF modes and red noise. Also, the result shows that some CMIP6 models can capture the coherent intraseasonal propagating features of the BSISO as indicated by the Hovmöller diagram. The contribution of latent static energy to the relationship between the moist static energy and intraseasonal rainfall over Southeast Asia is also simulated by the selected models, albeit the signals are weak. Taking together, some of the CMIP6 models can represent the summertime climate and intraseasonal variability over the study region, and can also simulate the propagating features of BSISO, but biases still exist.
... Despite the improvement in CMIP5 models, the poor representation of MISO is still a glaring shortcoming. Sabeerali et al. (2013) attributed the simulated slow northward propagation of MISO to the weak easterly wind shear simulated in CMIP5 models. ...
... ( Figure S2d) and EC-Earth3-Veg ( Figure S2g). It indicates that the poor MISO simulation is still a persistent bias in most contemporary CGCMs (Jiang et al., 2018;Neena et al., 2016;Sabeerali et al., 2013;Sperber & Annamalai, 2008;Sperber et al., 2013). ...
Plain Language Summary
The northward‐propagating monsoon intraseasonal oscillation (MISO) transports heat and momentum from the tropics to subtropics during the Indian summer monsoon (ISM), but the poor representation of MISO is still a glaring shortcoming for climate models. Here, the results show that vorticity anomalies lead MISO by about 5° latitude and the tilting of vorticity is an important component in the vorticity budget and plays a key role in the northward propagation of MISO, although the vorticity anomalies are in a balance between their components. During the ISM, the vertical shear of background zonal wind sets up a background horizontal vorticity, which is then tilted by the vertical velocity during MISO. Due to the meridional gradient of vertical velocity, to the north (south) of the convection, a positive (negative) vorticity anomaly is generated, and updraft (downdraft) is induced accordingly. In the 19 Phase 6 of Coupled Model Intercomparison Project (CMIP6) models, most models do not reproduce the tilting during ISM. Since the northward propagation of MISO depends on vortex tilting, most models yield a poor rendition of MISO. As a result, the intraseasonal rainfall and the mean rainfall during ISM are still much weaker in CMIP6 than in observations.
... In a recent study, Sabeerali et al. (2013) found that the ensemble means based on a small subset of the CMIP5 models simulate some critical aspects of the monsoon intraseasonal oscillations linked to the ISM quite well. The estimates of ISM have a wide range when different models are utilized (Kulkarni et al., 2022), and the ensemble mean summer monsoon rainfall indicated an upward tendency from the middle to the end of the century (Kripalani et al., 2007;Kumar et al., 2011;Turner & Annamalai, 2012). ...
The advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.
... Many previous studies have investigated the skill of seasonal forecasts, using GC or CFSv2, in predicting the ISM, and these studies have generally demonstrated similar biases to those in weather and climate simulations (Abhilash et al., 2014;George et al., 2016;Ramu et al., 2016;Johnson et al., 2017;Srivastava et al., 2017;Jain et al., 2019;Chevuturi et al., 2019;Martin et al., 2021;Joseph et al., 2023;Kolusu et al., 2023). Despite these biases, the seasonal forecasts do show skill, particularly at shorter lead times of up to 2 weeks Rao et al., 2019;Joseph et al., 2023;Kolusu et al., 2023), and can simulate the northward propagation of the monsoon intraseasonal oscillation (Abhilash et al., 2014;Sabeerali et al., 2013;Srivastava et al., 2023), low-pressure systems (Srivastava et al., , 2023 and the monsoon onset (Menon et al., 2018;Chevuturi et al., 2019;Pradhan et al., 2017) reasonably well. attributed this skill in CFSv2 to correctly capturing connections with the El Niño-Southern Oscillation with Indian Ocean coupled dynamics not adequately represented in CFSv2, and similar behaviour was demonstrated for GC by Johnson et al. (2017). ...
The Met Office Global Coupled Model (GC) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of timescales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems in order to provide insight into how they develop across timescales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer timescales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just 8 d, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2 and differs between the early part of the monsoon season and the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the boreal summer intraseasonal oscillation (BSISO) index as a measure of whether the large-scale dynamics favour increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.
... The under-prediction of the model inflow is a direct outcome of the dry bias simulated by the CFSv2 over the Indian continent (Saha et al., 2014;George et al., 2015;Ramu et al., 2016;Srivastava et al., 2017). Most of the current generation coupled models suffer from such a dry bias over India (Sabeerali et al., 2013), and concerted research efforts are being taken up in MM project to address this dry bias Krishna et al., 2019). This dry bias may be corrected using statistical bias correction techniques. ...
Despite the availability of reliable seasonal forecasts of Indian Summer Monsoon Rainfall (ISMR), the use of dynamical models driven by these forecasts for reservoir level management is limited. Reservoir water management can specially be useful if it can be done several months in advance, in view of an impending drought/flood scenario. The applicability of seasonal forecasts from the Monsoon Mission (MM) seasonal forecast model for seasonal and monthly inflow forecasts for tropical Indian reservoirs (Mula and Kangsabati) is studied using the Soil and Water Assessment Tool (SWAT) hydrological model, at a lead time of 3 months. Long-term observed inflow datasets are used for calibration and validation of SWAT-Calibration and Uncertainty Procedure (CUP) with Sequential Uncertainty Fitting (SUFI)-2 algorithm using insitumeteorological data. Observed inflows and inflow simulations are compared with simulated inflow using SWAT with same calibrated parameters, but with forcing derived from reforecasts from the MM model. The SWAT-CUP calibrated well with reasonable Nash Sutcliffe Efficiency (NSE) (Mula = 0.75, Kangsabati = 0.79) and Percentage Bias (PBIAS) (Mula =-28%, Kangsabati = 17%) for both reservoirs. The skill scores for streamflow predictions vary from 0.6-0.70 during the monsoon season, indicating reasonable accuracy for these predictions. The SWAT-MM model has a reasonable skill with 0.52-0.53 NSE and 26%-40% PBIAS. Therefore, SWAT-MM-based model has a good potential to forecast monthly and seasonal reservoir inflow for various agro-climatic zones of India. These forecasts when used in real-time, can serve as a guideline for managing the reservoir storage and release, and hence proving to be of great socioeconomic importance.
... CMIP5 simulations have been investigated for long-term studies of climate change on humans and the environment in various studies (Mallakpour and Villarini, 2015;Ahmadalipour et al., 2017;Doulabian et al., 2021). Choosing models that can accurately represent the climate of a region is a fundamental step before conducting an assessment of the impact of climate change in each region (Sabeerali et al., 2013;Ahmadalipour et al., 2017). Examining the average parameter during the base period shows a better simulation of the model. ...
In this study, temperature changes and its concentration distribution in the period of 1984-2015 and 2015-2100 were investigated under CanESM5 climate model and SSP126, SSP245 and SSP585 scenarios. By confirming the correlation (more than 0.96) and the efficiency coefficient of the model (more than 0.82), the trend of temperature values using modified Mann-Kendall test and temperature concentration index (TCI) values in the sub-basins of Zayanderood Dam, Iran was estimated. The results indicated a non-significant upward trend in the base period (1984-2015) and a significant increasing trend at the level of 5% in the future period (2015-2100) produced by the mentioned scenarios. According to the slope of the trend line, an increase of 1.45, 4 and 9.8 degrees Celsius is predicted during the period of 2015-2100 according to the SSP126, SSP245 and SSP585 scenarios, respectively. The evaluation of changes in TCI values in the studied area showed that in the future period, the distribution of rainfall patterns will be regular and the uniformity of temperature distribution in the SSP585 scenario is more than in the other two scenarios. The results of the temperature pattern distribution in the study area showed that according to the upcoming climate changes and under the studied scenarios, it is expected that while the study area is warming in the future, the uniformity of the temperature distribution will also appear in the months of the year. This shows the reduction of temperature fluctuations and the uniformity of the average temperature in the months of the year. The reduction of TCI values shows the equalization of average temperature changes in the seasons. The results of the investigations showed that the combination of climate change scenarios with the TCI can well show the concentration and distribution of the temperature in different periods.
... In this study, we utilize two widely used filtering techniques and do not reference the ISO indices proposed by other researchers (e.g., Kikuchi et al., 2012;Lin, 2013;Sabeerali et al., 2013;Suhas et al., 2013;Lee and Wang, 2016). However, we propose that it would be beneficial to also apply the diagnostic approach to these indices. ...
Two commonly used filter approaches for boreal summer intraseasonal oscillation (BSISO) are investigated with the observations during 1979-2021. One is the real-time BSISO index and the other is based on 20-80-day Lanczos bandpass filter. Both two filter approaches are able to isolate spatial structure and life cycle of BSISO realistically in the Indo-NWP region. But the BSISO phase pattern match does not guarantee consistent propagation characteristics and phase occurrence frequency. The real-time monitoring methods such as real-time BSISO index does not use future information beyond the current date, so it is influenced by seasonal mean anomalies induced by El Niño on the interannual timescale over the Indo-NWP region. During post-El Niño summers, the real-time BSISO index shows a weaker northward propagation than bandpass filter approach. Phases 2-4 of BSISO show relatively higher occurrence frequency than other phases during post-El Niño summers in real-time BSISO index, resulting in the seasonal mean of active eight BSISO phases largely resembling the El Niño induced mean state anomaly pattern with depressed convection and low-level anomalous anticyclone in the tropical northwest Pacific region. Bandpass filter approach exhibits a more uniform phase occurrence frequency for both normal and post-El Niño summers, which is consistent with the random nature of BSISO as one of atmospheric internal variability. Bandpass filter approach is recommended for studies of BSISO northward propagation and its relationship with low-frequency variability such as ENSO.
... On the other hand, the multi-model ensemble mean of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models could well reproduce strong CISOs over the ISM region, the Bay of Bengal, the SCS, and the EA region, although the simulated intensity is weaker, and the peak wet phases are two to three pentads later than the observed 54 . Climate models' capability in simulating and predicting CISO and the abrupt monsoon transition remains ample room for improvement [55][56][57][58] . ...
Abrupt monsoon onsets/retreats are indispensable targets for climate prediction and future projection, but the origins of their abruptness remain elusive. This study establishes the existence of three climatological Madden-Julian Oscillation (CMJO) episodes contributing to the rapid Australian summer monsoon retreat in mid-March, the South China Sea (or East Asian) summer monsoon onset in mid-May, and the Indian summer monsoon onset in early June. The CMJO displays a dynamically coherent convection-circulation structure resembling its transitionary counterpart, demonstrating its robustness as a convectively coupled circulation system and the tendency of the transient MJOs’ phase-lock to the annual cycle. The CMJO is inactive during the boreal winter due to destructive year-to-year modulations of El Niño-Southern Oscillation. We hypothesize that the interaction between atmospheric internal variability (MJO) and the insolation-forced slow annual cycle generates the sudden monsoon withdrawal/onset during the boreal spring. Understanding the factors determining the timing and location of the MJO’s phase-locking and its variability is vital for monsoon forecasting and climate projection.
... As a result, the moisture convergence is enhanced, destabilizing the lower atmosphere ) north of the convection center, and the convection center moves further northward. The ineludible role of the ocean on MISO dynamics (Webster et al. 1998;Ajayamohan et al. 2008) and better simulation of MISO phase and amplitude using coupled models (rather than standalone models) are being progressively recognized (Kemball-Cook and Wang 2001;Wu et al. 2002;Fu et al. 2003;Seo et al. 2007;Sabeerali et al. 2013;Sharmila et al. 2013;Li et al. 2018). Although most of the coupled models could capture the northward propagation of MISOs satisfactorily, the models have limited capability for the coevolution of atmospheric convection and sea surface state (Goswami et al. 2014). ...
State-of-the-art coupled models have several limitations in representing the phase and amplitude characteristics of monsoon intra-seasonal oscillations (MISO). Specifically, the models’ deficiencies in predicting stronger active spells have been widely reported in earlier studies. In the present study, we endeavour to overcome this limitation by improving the representation of the diurnal cycle of the sea surface temperature and the associated feedback processes. In the present study, we demonstrate that resolving the diurnal cycle rectification along with implementing a modern bulk surface-flux algorithm in a global coupled model improves the simulation of MISO characteristics. The present analysis showcases how rectification in the presence of a revised turbulent flux algorithm and diurnal skin temperature parameterisation can modulate the oceanic, atmospheric, and interfacial properties so that the coupled model can better simulate stronger active monsoon spells. The essential requirements for the coherent northward propagation mechanisms of MISOs are pronounced in the presence of intra-seasonal rectification by diurnal SSTs and air–sea interactive flux feedbacks.
... Many previous studies have investigated the skill of seasonal forecasts, using GC or CFSv2, in predicting the ISM, and these 40 studies have generally demonstrated similar biases to those in weather and climate simulations (Abhilash et al., 2014;George et al., 2016;Ramu et al., 2016;Johnson et al., 2017;Srivastava et al., 2017;Jain et al., 2019;Chevuturi et al., 2019;Joseph et al., 2023;Kolusu et al., 2023). Despite these biases, the seasonal forecasts do show skill, particularly at shorter lead times Rao et al., 2019;Joseph et al., 2023;Kolusu et al., 2023) and can reasonably well simulate the northward propagation of the monsoon intraseasonal oscillation (Abhilash et al., 2014;Sabeerali et al., 2013;45 Srivastava et al., 2023), low-pressure systems and the monsoon onset (Menon et al., 2018;Chevuturi et al., 2019;Pradhan et al. 2017). George et al. (2016) attributed this skill in CFSv2 to correctly capturing connections with the El Nino Southern Oscillation, with Indian Ocean coupled dynamics not adequately represented in CFSv2, and similar behaviour was demonstrated for GC by Johnson et al. (2017). ...
The Met Office Global Coupled Model (GC) and the NCEP Climate Forecast System (CFSv2) are both widely used for predicting and simulating the Indian summer monsoon (ISM), and previous studies have demonstrated similarities in the biases in both systems at a range of time scales from weather forecasting to climate simulation. In this study, ISM biases are studied in seasonal forecasting setups of the two systems, in order to provide insight into how they develop across time scales. Similarities are found in the development of the biases between the two systems, with an initial reduction in precipitation followed by a recovery associated with an increasingly cyclonic wind field to the north-east of India. However, this occurs on longer time scales in CFSv2, with a much stronger recovery followed by a second reduction associated with sea surface temperature (SST) biases, so that the bias at longer lead times is of a similar magnitude to that in GC. In GC, the precipitation bias is almost fully developed within a lead time of just eight days, suggesting that carrying out simulations with short time integrations may be sufficient for obtaining substantial insight into the biases in much longer simulations. The relationship between the precipitation and SST biases in GC seems to be more complex than in CFSv2, and is different during the early part of the monsoon season from during the later part of the monsoon season. The relationship of the bias with large-scale drivers is also investigated, using the Boreal Summer IntraSeasonal Oscillation (BSISO) index as a measure of whether the large-scale dynamics favours increasing, active, decreasing or break monsoon conditions. Both models simulate decreasing conditions the best and increasing conditions the worst, in agreement with previous studies and extending these previous results to include CFSv2 and multiple BSISO cycles.
... In this connection, the evaluation of climate models is very significant nowadays to understand the degree of reliability of the model in the case of climate projections. The GCMs which participated in CMIP5 namely CMCC-CM, GFDL-CM3, IPSL-CM5A-LR, MIROC5, and MPI-ESM-LR may be utilised to study the rainfall pattern during monsoon season under the various warming scenarios due to their better ability to represent Indian Summer Monsoon (ISM) over this region [13]. In addition, Kundu et al. [14] studied that GCMs participated in CMIP5 have a better ability to simulate rainfall patterns during the principal monsoon season over the northwest Himalayan region compared to RCMs participated in CORDEX-SA domains with respect to India Meteorological Department (IMD) rainfall data. ...
The present study is designed to assess the rainfall pattern from Climate Model Inter-comparison Project Phase 5 (CMIP5) based on the satellite-derived rainfall products, Tropical Rainfall Measuring Mission (TRMM) over the Indian Region utilising daily as well as monthly rainfall data during monsoon season, ranging from 1st June to 30th September (JJAS). In this context, five best defined global climate models (GCMs) that participated in CMIP5 archive along with its multi-model mean (MMM) have been analysed to investigate the rainfall pattern during JJAS in terms of spatial map and time series under the forcing scenarios i.e., Representative Concentration Pathway 4.5 (RCP 4.5) and Representative Concentration Pathway 8.5 (RCP 8.5) from 2006 to 2018 over Indian region. On the other hand, spatial maps and time series have also been generated using TRMM rainfall data at daily (TRMM 3B42v7) and monthly (TRMM 3B43v7) scales during the reference time period. Thereafter, comparative study of the JJAS rainfall pattern between CMIP5 models and TRMM products has been carried out, whether the GCMs are able to simulate rainfall data reasonably well compared to satellite-derived estimates or not under various forcing scenarios over this region? Based on the assessment, it is noted that CMIP5 models have the ability to simulate daily mean monsoon season rainfall; however, it underestimates the rainfall intensity at daily scale over the north-east and south-west parts of India. Moreover, statistical analysis indicated more biases in the western coast and the north-eastern parts of India where it receives the highest amount of rainfall during JJAS. The outcomes presented here may be useful for assessing the reliability of CMIP5 models to project the rainfall pattern in near future under the various warming scenarios over the Indian Region.
... It has the broad implication that models need to simulate the latitude dependence of dominant controls on convection correctly, which is important for their ability to capture the variability of convection. Several generations of Coupled Model Intercomparison Project (CMIP) models, including the latest CMIP6, are unable to reproduce this variability [60][61][62]. A recent study has reported an unduly weak relationship between MSE and precipitation in these models [63]. ...
Understanding controls on convection on various timescales is crucial for improved monsoon rainfall forecasting. Although the literature points to vertically homogeneous vorticity signatures preceding rainfall during the Indian summer monsoon, we show using reanalysis data that, for rainfall associated with northward propagating intraseasonal oscillations (ISOs), different controls are present at different latitude zones. For the latitude zone close to the equator (5°N-14°N) and including the southern Indian region, a conventional dynamical control on rainfall exists with barotropic vorticity leading ISO rainfall by about five days. In contrast, for the latitude zone away from the equator (15°N-24°N; covering the central Indian region), thermodynamic fields control ISO rainfall, with barotropic vorticity following rainfall by two days on average. Over central India, the pre-moistening of the boundary layer yields maximum moist static energy (MSE) about four days prior to ISO rainfall. Analyzing the statistics of individual events verifies these observations. Similar thermodynamic control is also present for the large-scale extreme rainfall events (LEREs) occurring over central India. These high rainfall events are preceded by positive MSE anomalies arising from the moisture preconditioning of the boundary layer. The resulting convection then leads to a maximum in barotropic vorticity 12 hours after the rainfall maximum. Characterizing these influences on convection occurring over various timescales can help identify the dominant mechanisms that govern monsoon convection. This can help reduce climate model biases in simulating Indian monsoon rainfall.
... The interaction between air-sea variables plays an important role in the maintenance and northward propagating of BSISO (Shinoda et al. 1998;Kemball-Cook and Wang 2001;Sengupta and Ravichandran 2001;Fu et al. 2007;Roxy and Tonimoto 2007;Roxy et al. 2013;Hendon et al. 2012;Konda and Vissa 2021). Recent modelling studies suggest that climate models with an interactive ocean component are able to simulate the BSISO characteristics more realistically compared to the stand-alone atmosphere models (Fu et al. 2002;Fu and Wang 2004;Seo et al. 2013;Sabeerali et al. 2013;Li et al. 2018;Konda and Vissa 2021;Rajendran et al. 2022). Sengupta and Ravichandran (2001) identified the role of net surface heat flux in modulating the intraseasonal variations of SST. ...
The present study focuses on the variability of subsurface ocean temperature and associated planetary waves (oceanic Kelvin and Rossby waves) in the Indian Ocean during the boreal summer intraseasonal oscillation (BSISO) phases in the Indian Ocean Dipole (IOD) years. To accomplish this, multi-sensor datasets have been considered for the period 1980-2021. During negative IOD (nIOD) years northward propagation of BSISO convection is consistent with the normal years, whereas incoherent patterns are evident in the positive (pIOD) years. Enhanced meridional gradient of sea surface temperature (SST) anomalies is evident during the nIOD years, which favours the coherent northward propagation of BSISO. Significant intraseasonal variations of subsurface ocean temperature anomalies are observed in the Equatorial Indian Ocean (EIO). Pronounced subsurface (50-150 m) temperature anomalies are evident during the different phases of BSISO during the nIOD years. Symmetric formation of equatorial downwelling Kelvin wave followed by upwelling Kelvin wave packet were observed during the nIOD BSISO phases. This study further suggests that the symmetric patterns of subsurface waves are evident during the nIOD years and led to clear variations of the northward propagation of BSISO.
... (CMIP climate models, developed around the globe by different modeling groups, are widely used to simulate and project changes in Indian monsoon rainfall. Evaluation of a large number of CMIP5 models showed that they were able to capture the broad climatological features of the Indian monsoon, including its mean and intraseasonal variability (Sabeerali et al. 2013). Their performance was found to improve when compared with older CMIP3 models (Sperber et al. 2013). ...
Indian Summer Monsoon Rainfall (ISMR) is one of the most well-documented areas of hydrometeorology; however, the processes associated with ISMR are not well understood. This attributes to the complexities associated with ISMR at multiple spatio-temporal scales. This further results in inconsistencies across the literature to assess the impacts of global warming on the monsoon, though this has huge relevance as a huge population of South Asia is dependent on the same. Here, we review and assess the existing literature on the Indian monsoon, its variability, and its trajectory in a warming scenario. We further synthesize the literature on its impacts on the hydrology of major river basins in South Asia. We also identify a few research questions, addressing which will add value to the understanding of the Indian monsoon and the associated water cycle. We have highlighted that there is a significant lack of understanding of how different large-scale and regional factors affect ISMR at different timescales. These impacts, in turn, get translated into hydrology and water sector in India. There is a need to know where we stand to combat the impacts of climate change on ISMR, which can be translated to adaptation by policy-making processes and water management practices in India.
HIGHLIGHTS
The study reviews the literature regarding the Indian monsoon in changing climate over time.;
Studies regarding the changing characteristics and the factors affecting the Indian monsoon have been reviewed.;
Few research questions are discussed that can be addressed to improve the understanding of the Indian monsoon.;
Study can be a guideline for future research regarding the simulation and prediction/projection of the Indian monsoon.;
... Earlier studies have highlighted that the majority of coupled global climate model participating in the Coupled Model Intercomparison Project (CMIP) is overestimated light rain and underestimated moderate and heavy rain [11][12][13]. It is important to note that the present generation CMIP6 models also overestimates (underestimates) lighter (moderate and heavy) rainfall ( Figure 2). ...
An unresolved problem of present generation coupled climate models is the realistic distribution of rainfall over Indian monsoon region, which is also related to the persistent dry bias over Indian land mass. Therefore, quantitative prediction of the intensity of rainfall events has remained a challenge for the state-of-the-art global coupled models. Guided by the observation, it is hypothesized that insufficient growth of cloud droplets and processes responsible for the cloud to rain water conversion are key components to distinguish between shallow to convective clouds. The new diffusional growth rates and relative dispersion based autoconversion from the Eulerian-Lagrangian particleby-particle based small-scale model provide a pathway to revisit the parameterizations in climate models for monsoon clouds. The realistic information of cloud drop size distribution is incorporated in the microphysical parameterization scheme of climate model. Two sensitivity simulations are conducted using coupled forecast system (CFSv2) model. When our physically based small-scale derived modified parameterization is used, a coupled climate model simulates the probability distribution (PDF) of rainfall and accompanying specific humidity, liquid water content, and outgoing long-wave radiation (OLR) with increasing accuracy. The improved simulation of rainfall PDF appears to have been aided by much improved simulation of OLR and resulted better simulation of the ISM rainfall.
... Many biases focus on the poor oceanic simulations, especially (Meng et al., 2019(Meng et al., , 2022Zhang et al., 2016;Gasparin et al., 2015). For instance, Sabeerali et al. (2013) concluded that the cold SST bias was one possible reason for weak monsoonal intraseasonal oscillation in all GFDL models. Meng et al. (2022) highlighted the shortcomings of oceanic reanalysis datasets, like simple ocean data assimilation ocean/ sea ice reanalysis (SODA, Carton et al., 2018) and the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2, Menemenlis et al., 2008), in capturing the intraseasonal dynamics within the upper ocean during the central Indian Ocean mode (Zhou et al., 2017). ...
The surroundings of the Bay of Bengal (BoB) suffer a lot from the extreme rainfall events during Indian summer monsoon (ISM). Previous studies have proved that the sea-air interaction is an important factor for the monsoonal precipitation. Using the 6th Coupled Modol Inter-comparison Project (CMIP6) models, this study examined the biases of surface heat flux, which is the main connection between atmosphere and ocean. Results show that although CMIP6 have a better simulation of intraseasonal sea surface temperature (SST) anomalies over BoB than the previous ones, the “atmospheric blockage” still delays the response of latent heat flux to the oceanic forcing. Specifically, during the increment of positive latent heat flux in CMIP6, the negative contribution from wind effects covers most of the positive contribution from humidity effects, due to the underestimate of humidity effects. Further diagnostic analysis denote that the surface air humidity has a quarter of a phase ahead of warm SST in observation, but gets wet along with the warm SST accordingly in most CMIP6 models. As a result, the simulated transfer of intraseasonal moisture flux is hindered between ocean and atmosphere. Therefore, as a bridge between both sides, the atmospheric boundary layer is essential for a better sea-air coupled simulation, especially when the atmospheric and the oceanic variabilities involved in a climate model becomes increasingly sophisticated. The surface air humidity and boundary layer processes require more attention as well as better simulations.
... BNUESM, EC-EARTH, IPSL-CM5A-LR and MRI-CGCM3 (Table S4, Fig. S3). These GCMs have been widely used in climate impact studies in China (Sabeerali et al., 2013;Chen and Frauenfeld, 2014;Miao et al., 2014) and provide outputs that can meet the data requirements to run the CAST model. The Taylor diagrams for five GCMs of temperature, precipitation, and evaporation are shown in Fig. S4. ...
Quantifying soil structural dynamics and aggregate turnover is important in understanding soil organic carbon (SOC) stocks, particularly over decadal and larger time scales. Until now it has remained unclear clear how soil aggregate size and its associated carbon respond to both long-term soil fertility and climate change. Here, we explore changes in soil structure and aggregate organic C (OC) stocks under different fertilization practices by combining field chronosequence SOC measurements with dynamic and process modeling in a long-term wheat-maize field experiment on the North China Plain. The fertilization practices comprise no fertilization (CK), chemical fertilization (NPK), and combined manure and NPK treatments (MNPK). The experimental measurements included the mass of OC stocks in different soil aggregate size classes. We used this information to calibrate parameters of the Carbon, Aggregation, and Structure Turnover (CAST) model and to predict future changes in aggregate structure and the resulting OC stocks using the RCP2.6 scenarios that were defined by the outputs of five future climate models from IPCC projection. With trends towards a wetter climate and increasing soil moisture under the RCP2.6 scenarios for the region, soil OC stocks will increase in all three treatments, with the strongest increase under MNPK due to exogenous C inputs. The CAST model output further suggests that changes in microaggregate (250–53 μm) OC stocks in the NPK and MNPK treatments accounted for 78.6 % and 75.3 % of the calculated change in total SOC stocks between the early and late 21st century. In conclusion, our combined data and modeling approach describes changes in soil aggregate C, identifies the primary soil aggregate size class of microaggregates involved in C sequestration in an agricultural soil, and predicts the role of Fluvaquent soils on the North China Plain as a future C sink.
... anomalies (Sabeerali et al. 2013;Konda and Vissa 2021). On intraseasonal timescales, convection originated in the equatorial Indian Ocean and propagates northward to the SCI with a phase speed of 1.2°lat/day (Fig. 5a) (Wang et al. 2018;Karmakar and Mishra 2020). ...
This study uses the 30 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project phase-6 (CMIP6) to examine the simulations of the surplus/deficit Indian summer monsoon rainfall (ISMR) and its associated air-sea interactions on intraseasonal to interannual timescales. The majority of the CMIP6 models simulate the seasonal mean state of ISMR over the Indian mainland with systematic biases. Best performing models (BPM; AWI-ESM-1-1-LR, BCC-CSM2-MR, BCC-ESM1, CNRM-CM6-1, CNRM-ESM2-1, GFDL-CM4, INM-CM5-0, MIROC-ES2L, MIROC6, TaiESM1) well simulated the seasonal mean precipitation with Taylor skill score > 0.75 and normalized root mean square error (NRMSE) is < 0.7. However, the models are failed to simulate precipitation over the orographic regions (Western Ghats). Improving the simulations of low-level winds and sea surface temperature (SST) with high spatial resolutions would provide better precipitation simulations. B-MME (multimodel ensemble mean of BPM) can capture the negative IOD-like (Indian Ocean Dipole) pattern during deficit monsoon years and fail to capture the positive IOD-like pattern during surplus monsoon years. Models overestimate the moisture transport from the West Indian Ocean to the sub-continent of India during deficit monsoons, which plays a crucial role in modulating the precipitation and its associated intraseasonal variability. The present analysis identified that during deficit monsoon years, the faster moving 20–100 days oscillations are evident; however, these oscillations are sluggish during surplus monsoon years, which affects the duration of convection activity and causes dry conditions over the regions. During surplus monsoon years, the Bay of Bengal (Arabian Sea) responds strongly (slowly) to the atmosphere than the deficit monsoon years. However, models are fail to represent the ocean’s response to the atmosphere over the Bay of Bengal. The freshwater forcing improvement in the models simulates the ocean to atmosphere response over the Indian region. The present study further suggests that the improved simulation of the Indian summer monsoon (ISM) variability by the GCMs is possible by improving the ocean and atmosphere feedback mechanisms, sensitivities of the models among internal variables, and orographic features necessary for the accurate simulation of intraseasonal variability.
... Rights reserved. temperature (SST) and vertical shear of zonal wind over the key regions related to BSISO (supporting Fig. S2), i.e., the Indian Ocean-West Pacific region (Inness et al. 2003;Sabeerali et al. 2013). Compared to the ECMWF model, the CMA model shows relatively larger biases for the climatology fields (precipitation, zonal wind, and SST) at all lead times (supporting Fig. S2), corresponding to the fact that the ECMWF model has better prediction skill of BSISO1 than the CMA model. ...
The occurrence of summer extreme rainfall over southern China (SCER) is closely related to the boreal summer intraseasonal oscillation (BSISO), and whether operational models can reasonably predict the BSISO evolution and its modulation on SCER probability is crucial for disaster prevention and mitigation. Here, we find that the skill of subseasonal-to-seasonal (S2S) operational models in predicting the first component of BSISO (BSISO1) might determine the forecast skill of SCER. A systematic assessment is conducted on the reforecast data from two operational models that participated in the S2S project, i.e., the model of European Centre for Medium-Range Weather Forecasts (ECMWF) and the model of China Meteorological Administration (CMA). The results show that the ECMWF model can yield skillful prediction of the BSISO1 index up to 24 days in advance, while the skill of the CMA model is about 10 days. Accordingly, the SCER occurrence is correctly predicted by ECMWF (CMA) model at a forecast lead time of ~ 14 (7) days. The diagnostic results of modeled moisture processes further suggest that the anomalous moisture convergence (advection) induced by the BSISO1 activity serves as the primary (secondary) source of subseasonal predictability of SCER. With better prediction of the moisture convergence anomaly in the specific phases of BSISO1, higher skills can be obtained in the probability prediction of SCER. The present study implies that a further improvement in predicting the BSISO and its related moisture processes is crucial to promoting the subseasonal prediction skill of SCER probability.
... The monsoonal precipitation during the ISM is dominated by monsoon intraseasonal oscillations (MISO) (Goswami, 2005;Shukla, 2014), which accounts for approximately 60% of total precipitation variance over the Bay of Bengal (BoB) (Goswami, 2005;Waliser, 2006). Although monsoonal precipitation during the ISM has been widely studied, the monsoonal precipitation prediction skill remains low (e.g., Wang et al., 2004;Annamalai et al., 2007;Sabeerali et al., 2013;Wang et al., 2015). Thus, insight into subseasonal variabilities over the Indian Ocean can help facilitate better simulations and predictions of the ISM. ...
Prediction of precipitation during the Indian summer monsoon (ISM) is a persistent scientific challenge. The central Indian Ocean (CIO) mode was proposed as a subseasonal climate mode over the tropical Indian Ocean, and it has a close relation with monsoon intraseasonal oscillations (MISO) during the ISM both in observations and simulations. In this study, the prediction skill of the CIO mode in the subseasonal-to-seasonal (S2S) air–sea coupled models is examined. The ECMWF and UKMO models display significantly higher skills for up to about 2 and 3 weeks, respectively, which are longer than other S2S models. The decline of the CIO mode prediction skill is due to the reduced signal of subseasonal zonal winds at 850 hPa over the tropical central Indian Ocean (especially along the equator; 5°S–5°N, 70°E–85°E). Therefore, a better simulation of tropical subseasonal zonal winds is required to improve the CIO mode prediction in models, and the improvement will benefit a better MISO simulation and a higher prediction skill during the ISM.
... ISO is governed by the interaction between large-scale dynamics and organized convection. However, our understanding of the mechanism behind ISO northward migration is not clear, and the forecast skills in the dynamical models are more or less limited (Sabeerali et al. 2013). A recent study by Li et al. (2021) showed that many state-of-the-art models in the Coupled Model Intercomparison Project-6 (CMIP6) show weaker rainfall signals in intraseasonal timescale than in observation. ...
The governing dynamics behind the northward propagation of convection in the intraseasonal timescale over the Arabian Sea (AS) and Bay of Bengal (BoB) during summer monsoon is examined here using the vorticity budget equation. Previous theories of northward propagation suggested that generation of vorticity to the north of an existing convection center in the presence of mean easterly shear is essential. In this study, using observational analysis, we found that the tilting term in the vorticity equation leads the precipitation maxima by about 6–8 days over BoB and 2–3 days over AS. Tilting term exhibits stronger behavior over BoB as compared to AS. Further investigation shows that the component of the tilting term associated with the meridional gradients in vertical velocity in intraseasonal timescale acts to the vertical gradient of the zonal mean flow to generate positive anomalies in tilting. Convective updrafts are stronger and more vertically stretched over BoB, which could be responsible for the enhanced tilting. A component of tilting term associated with vertical shear of mean meridional winds shows a strong signal over AS, which could be related to the higher phase speed over AS compared to BoB. Moreover, although the beta effect contributes negatively to the vorticity equation, it induces an asymmetry in the meridional winds around the convection maxima over BoB, which drives dry air into the convection center and helps develop a new center to the north. This mechanism is relatively weak in the AS. This study highlights the importance of convection in northward propagation and provides a pathway to improve model performance for simulating intraseasonal variability and summer monsoon.
... The BoB, Arabian Sea, IO and adjoining parts of south-east Asia show overestimation of rainfall during the monsoon in the model, yet these regions have utmost importance for monsoon progression towards the Indian land surface. This model also often underestimates the MISO variance over the equatorial IO, which is an important source region for instigating MISO (Sabeerali et al., 2013). ...
The northward propagation of intra-seasonal oscillations (ISO) is closely associated with the seasonal and sub-seasonal cycle of the Indian summer monsoon (ISM). This northward propagating monsoon ISO (MISO) is prominent at 25–90 days during the ISM and are very complex in nature. In this study, we use a simulation with REGional Climate Model v4.7 (RegCM4.7) output to attempt to better understand one of the major modes of ISO during the period 1981 to 2016. The model forced with EIN75 initial and boundary condition and EIN75 SST forcing is found to successfully capture the major signals of MISO. The propagating features of the MISO circulation pattern are broadly characterized using extended empirical orthogonal function (EEOF) and two major MISO indices (MISO-1 and MISO-2). The regions between the Indian subcontinent and the Indian ocean (IO) are found to be crucial for the MISO propagation during the 25–90 day oscillation. Half of the lifecycle of the MISO is found to be 15 days, i.e., the period necessary for the alteration from break phase to active monsoon phase. Simulated MISO indexes in phase space diagrams also reveal northward propagating signals that further help to explain potential mechanisms behind the MISO rainfall propagation from the IO to the Himalayan foothills.
... Moreover, a strong inter-model spread in the simulated fields of these models, especially in the tropics (Li et al. 2018;Wu et al. 2019), limits the confidence in the model to project climate signals. The coarser horizontal and absence of regional (fine-scale) air-sea interaction restricting the use of GCMs or ESMs for many aspects of monsoon (Fu et al. 2007), particularly extremes and propagation of low-pressure systems (LPSs) (Stowasser et al. 2009;Levine et al. 2020), northward propagation of the boreal summer intraseasonal oscillation (BSISO) (Sabeerali et al. 2013;Li et al. 2018), and activebreak spells (Sharmila et al. 2015;Misra et al. 2018). ...
An effort is made to implement a regional earth system model (RESM); ROM, over CORDEX-South Asia (SA). The added value of RESM is assessed for mean precipitation, its variability (intraseasonal to interannual), extremes, and associated processes. In this regard, ROM’s fields are compared with the respective fields of its standalone version (REMO), the models belonging coupled model intercomparison project (CMIP5 and CMIP6), and regional climate models of CORDEX-CORE simulations. RESM shows substantial improvement for most of the Indian monsoon’s aspects; however, the magnitude of the value addition varies spatiotemporally and also with different aspects.. The improved representation of intraseasonal variability (active-break spell’s duration and intensity) and Interannual variability attributed to improved mean seasonal precipitation. Additionally, correct representation of sea surface temperature, Indian Ocean Dipole, and its underlying dynamics also contribute to improving the mean precipitation. The notable improvement is seen especially over the south-eastern regions of the Bay of Bengal (BoB) and South-Central India, where increasing (decreasing) low-pressure systems over Central India (BoB) are noticed as a consequence of air-sea coupling, leading to enhanced (reduced) precipitation over Central India (BoB), reducing dry (wet) bias found in REMO and the other models. Despite substantial improvements, RESM has a systematic wet bias in the mean precipitation associated with a warm bias over the western coast of the Arabian Sea. An overestimation of very high extreme precipitation due to the enhanced contribution of low-pressure systems indicates the model’s limitations, suggesting the need for further tuning of the RESM.
... The prediction skill of the ISM precipitation has been extensively studied, although the prediction remains challenging in contemporary climate models (e.g., Wang et al., 2004;Sabeerali et al., 2013). Overall, the forecast of monsoonal precipitation is constrained to about 3 weeks. ...
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.
... It has been aimed to facilitate a better understanding of the climatic system as well as to investigate the future climate under the climate change scenarios (Taylor et al., 2007). Out of 32 GCMs that have been participated in the CMIP5 archive, CESM1-CAM5, CMCC-CM, MIROC5, MPI-ESM-LR and MRI-CGCM3 along with their counterpart RCMs, participated in CORDEX-SA domain have been considered for the present comparative study due to their better rainfall simulation capacity during ISM (Sabeerali et al., 2013). Note that the CMIP5 counterparts IPSL CM5A-LR and GFDL-ESM2M are not included in this study because of coarser resolutions of respective models. ...
Present study is an attempt to evaluate the rainfall pattern obtained from the selected Regional Climate Models (RCMs) which participated in Coordinated Regional Climate Downscaling Experiment for the South Asia region (CORDEX‐SA) and the Global Climate Models (GCMs) which participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the high‐resolution ground‐based Indian Meteorological Department (IMD) rainfall data during the principal monsoon season (i.e., June 1 to September 30) from 1976 to 2000 over the states of Himachal Pradesh (HP) and Uttarakhand (UK) in North‐West Himalayan Region (NWH). Considering the inter‐model differences, the multimodel means (MMM) of both of the CORDEX‐SA and CMIP5 models have been compared with the IMD gridded rainfall data set so as to find out the suitable set of models (whether CORDEX‐SA or CMIP5) for assessing the rainfall variability over this region. The CORDEX‐MMM indicates a poor relationship with IMD data compared with the CMIP5‐MMM over both the NWH states of HP and UK. CMIP5‐MMM demonstrates better ability to represent extreme rainfall events as compared with the CORDEX‐MMM. Significant bias has been noted for both of the CORDEX‐MMM and CMIP5‐MMM with respect to the IMD data sets, nevertheless, differences were found to be minimal in case of CMIP5‐MMM in comparison to CORDEX‐MMM. In case of the simulated rainfall from CMIP5‐MMM over both HP and UK regions, RMSE, MAE and bias, all are much smaller than that of the CORDEX‐MMM simulations with respect to the IMD rainfall data. Further to this, CMIP5‐MMM notably performs better in case of delineating extreme cases of inter‐annual variability except CORDEX‐MMM for the state of UK. Results presented here encompass the entire spectrum of summer monsoon rainfall variability, for example, daily, monthly, seasonal, inter‐annual and extreme rainfall over the NWH region. Based on the comprehensive analysis of global and regional models, it is noted that the CMIP5‐MMM has better ability to simulate rainfall pattern during monsoon season compared with the finer resolution CORDEX‐MMM for both the states of UK and HP. Therefore, it is suggested that CMIP5 models may be utilized to investigate the rainfall variability at daily, seasonal and interannual scales over the NWH region for various forcing scenarios rather than the outputs from CORDEX‐SA domain.
... Hence, stratiform rain fraction plays a crucial role in the organization of clouds and precipitation in MISOs (Kumar et al., 2017). Based on this understandings, earlier studies (Ganai et al., 2016(Ganai et al., , 2019Hazra, Chaudhari, Saha, Pokhrel, & Goswami, 2017;Sabeerali et al., 2013;Saha, Pokhrel, et al., 2014) highlighted that the major bias in mean and MISO simulation in climate models originate from the production of more (less) convective (stratiform) precipitation. ...
Plain Language Summary
Simulation of the monsoon intraseasonal oscillations (MISOs), and Madden Julian Oscillations (MJO), in association with the seasonal Indian summer monsoon rainfall (ISMR), has been a real challenge for the state‐of‐the‐art global coupled climate models. Total rainfall in India during the monsoon season is useful for policymakers, farmers, and water managers who are keenly interested in the sub‐seasonal variations of rainfall. Thus, simulation of the MISOs, and MJO which have effects on the seasonal mean ISM rainfall has been evaluated here using coupled climate model simulations. Different autoconversion rates (convective and microphysics) which is a key process for the formation of rainfall in climate models are experimented to simulate MISO and MJO correctly. The proper combination of convective and microphysical autoconversion in climate models is needed to account for the sub‐seasonal variability by not only providing a better partition of cloud water and ice but also the better feedback between the large‐scale condensation and convective parameterization. Therefore, the results presented here demonstrate a road map for the improvement of MISO as well as MJO simulation by realistically simulating the physical processes associated with an accurate combination of autoconversion rate in a coupled climate model.
... The boreal summer intra-seasonal oscillation (BSISO) controls the majority of sub-seasonal variations in SAsiaM rainfall, as well as affecting the East Asian monsoon region. While CMIP5 models represented the BSISO better than CMIP3 (especially its characteristic northward propagation), the spatial pattern is still poorly simulated in most models (Sabeerali et al., 2013). ...
A monsoon refers to a seasonal transition of regimes in atmospheric circulation and precipitation in response to the annual cycle of solar insolation and the distribution of moist static energy (Wang and Ding, 2008; Wang et al., 2014; Biasutti et al., 2018). A global monsoon can be objectively identified based on precipitation contrasts in the solstice seasons to encompass all monsoon regions (Wang and Ding, 2008). In AR5, regional monsoon domains were identified starting from the definition of the global monsoon tailored over the continents and adjacent oceans, as in Kitoh et al. (2013). This Annex contains the definition of the global monsoon as used in AR6 (Section AV.2), it explains the rationale for the different definition of AR6 regional monsoons compared to AR5 (Section AV.3) and provides the definition and basic characteristics of each regional monsoon assessed (Section AV.4).
... Monsoon systems are land-atmosphere-ocean coupled systems; it is vital to know the morphology of precipitation over the AS and BOB along with the Indian landmass for their better representation in the climate models (Mukhopadhyay et al. 2010;Sabeerali et al. 2013;Ajayamohan et al. 2016). Using the tropical rainfall measuring mission (TRMM) precipitation radar (PR) data and model outputs, Chattopadhyay et al. (2009) showed that the convective rain exhibits weak northward propagation, while organized stratiform strongly modulates the northward propagation. ...
Recent studies emphasize the significance of precipitation characteristics at intraseasonal time scales for better predicting the monsoonal rainfall. In this connection, to understand the differences in characteristics of the vertical structure of precipitation during wet and dry spells over the Arabian Sea (AS) and Bay of Bengal (BOB) from the southwest monsoon (SWM) to northeast monsoon (NEM) using 16 years of tropical rainfall measuring mission (TRMM) version#7 datasets. On average, the wet and dry spells durations are more during NEM (> 5 days) than SWM (4 days) over BOB, while the durations are identical (5 days) in all spells over AS. Irrespective of the season, shallow systems' occurrence and rain fraction are more in dry spells than the respective wet spells over AS and BOB. During the dry spells of BOB, both rain fraction and occurrence of stratiform and convective rain decreases while shallow rain increases from SWM to NEM. The increase in shallow systems occurrence results bimodal distribution (3 and 5.5 km) in storm height and reflectivity distributions. During wet spells, for different rain types, the occurrence and rain fraction changes are minimal in both seasons and seas. The prevalence of deeper systems than shallow systems is due to changes in atmospheric background conditions from dry to wet spells. The latent heating distributions are broader during SWM than NEM in both spells of two seas. The observed bimodal distribution of latent heating profiles in the dry spells during SWM and NEM over AS, and only during NEM over BOB results from a higher occurrence of shallow rain in these spells.
... Several inter-comparison studies of GCMs have shown that the GCMs simulate the mean seasonal features quite well (Gadgil and Sajani 1998;Rajeevan and Nanjundiah 2009;Lee, 2010;Sperber et al. 2012;Sabeerali et al. 2013;Nageswararao et al. 2016;Pillai et al. 2018).The ability of the present day GCMs in simulating the seasonal climate has improved quite a lot over the past decade Stephenson and Doblas-Reyes 2000;Krishnamurti et al. 2000;Kharin and Zwiers 2003;Yun et al. 2003;Palmer 2004;Kar et al. 2006Kar et al. , 2011. However, the predictability of summer monsoon rainfall and ENSO is poor due to the presence of spring predictability barrier and large biases affecting both Indian summer monsoon simulation as well as Indo-Pacific mean climate state in dynamical models (Kriplani et al., 2007;Pokhrel et al. 2012;Chaudhari et al. 2013;Saha et al. 2013;Annamalai et al. 2017;Ashok et al. 2019). ...
Seasonal prediction of Indian summer monsoon rainfall has been considered as one of the important factors to decide the social and economic aspect of India because of its multi-sectorial dependencies. This study evaluates the performance of seven state-of-the-art GCMs in simulating the summer monsoon rainfall on a seasonal scale over the period of 1982–2008 using the GCM reforecasts. The rainfall simulated by the models is compared with the IMD observed rainfall dataset at 0.25° × 0.25°. Preliminary analysis of spatial pattern and statistics shows that the models IITM-CFSv2, NCEP-CFSv2 and ECMWF are some of the prominent models that capture the seasonal rainfall pattern and possess good skill. ECMWF performs very well in simulating the rainfall pattern as well as the rainfall intensities. Comprehensive statistical analysis such as standard deviation ratio and skill scores concludes that the IITM-CFSv2 produces the rainfall pattern as well as the variability of the summer monsoon better than its counterparts. The multi-model simple mean also tends to improve with the addition of IITM-CFSv2. Though the rainfall trend and variance simulated by IITM-CFSv2 is quite in agreement with the observed, there lies a significant dry bias over the north-west India. The mean simulated rainfall is quite less with the CFSv2 models. Though the IITM-CFSv2 simulates lesser rainfall at all the four-lead times, it is quite capable in capturing the rainfall variability. The models ECMWF and GFDLA04 are well performers in terms of mean rainfall estimates whereas the models CFSv2 is better in terms of reproducing the rainfall variability.
... Despite great progress in the climate model development, the capability in BSISO simulation and prediction is still limited (Waliser et al. 2003;Sobel et al. 2008;Fang et al. 2016). Most current models show deficiency in simulating the spatial structure, amplitude, evolution and northward propagation of BSISO (Sabeerali et al. 2013;Hu et al. 2017;Neena et al. 2017). In the latest Subseasonal to Seasonal (S2S) Prediction Project, most state-of-the-art operational models exhibit useful BSISO forecast skill of about 2 weeks in advance (Jie et al. 2017). ...
... The meteorological inputs as seasonal means from coupled general circulation models (CGCMs) are not enough for the proper planning of the entire crop season (Capa-Morocho et al., 2016). Seasonal forecasts of all India averaged rainfall limit its applicability to agriculture, hydrology, energy, and other sectors (Manzanas et al., 2018;Ramu et al., 2017;Sabeerali et al., 2013;S. K. Saha et al., 2016). ...
The usefulness of dynamical downscaling of seasonal reforecasts of Indian Monsoon is explored to address the seasonal mean biases in the reforecasts. Almost all the current generation global coupled models, including the Climate Forecast System version 2 (CFSv2, T126 ~110 km), exhibit systematic mean dry bias over the central Indian region during the summer monsoon season. Cold sea surface temperature (SST) biases in the Indian Ocean and a weak monsoon circulation due to a colder tropospheric temperature contribute to this dry bias. Such systematic biases restrict the use of skillful forecasts from these models in climate applications (such as agriculture or hydrology). Dynamical downscaling of seasonal forecasts (~110 km resolution) using the Weather Research and Forecasting (WRF) model coupled to a simple ocean mixed layer model (OML; WRFOML) at 38 km resolution significantly reduces the majority of the systematic biases reported earlier. The seasonal mean dry bias reduces to 16% in WRFOML as compared to 44% (33%) in the CFSv2-T126 (WRFCTL) over the Indian land region. Warmer SSTs in the Indian Ocean and a more robust monsoon circulation emanating from a realistic simulation of the tropospheric temperature reduced the systematic biases in WRFOML compared to CFSv2-T126 and WRFCTL. Additionally, category-wise rainfall distributions are also improved drastically in the downscaled simulations (WRFOML). Downscaled reforecasts with reduced systematic biases have better suitability for climate applications.
... The multi-model ensemble median (MEM) of CMIP6 shows excess/deficit over the western side of WCI and CNI for 1981-2000(Kim et al. 2020. It is suggested that such large variability in simulated rainfall may be due to different resolutions of the atmospheric and oceanic components as well as in the incorporated physical and chemical process schemes (Kang et al. 2002;Lin et al. 2008) in the CCMs (Fu et al. 2003;Waliser et al. 2003;Fu and Wang 2004;Taylor et al. 2012;Sabeerali et al. 2013;Sperber et al. 2013). Hence, this may be summarised that the simulated pattern of monthly rainfall in BCC-CSM2-MR and BCC-ESM1 over WCI, PI and CNI is well represented. ...
The reliability of the projection of Indian Summer Monsoon Rainfall (ISMR) and associated wind circulation in the simulation of the Coupled Climate Models (CCMs) is based on their ability to reproduce themselves in the control experiment. The performances of CCMs, namely BCC-CSM2-MR and BCC-ESM1 of the Beijing Climate Center, China, and MPI-ESM1-2-HR and MPI-ESM1-2-LR of Max Planck Institute (MPI) Germany, are evaluated under control experiment of CMIP6, since they had better performed in Coupled Model Intercomparison Project phase 5 (CMIP5). Under the historical experiment (control experiment) in the period from 1979 to 2014, the simulated wind circulation, relative humidity, and rainfall are evaluated on the seasonal and sub-seasonal scales during the Indian summer monsoon season (i.e. June-July-August-September). The simulated wind at pressure levels of 1000, 850, 700 and 200 hPa and relative humidity at similar pressure levels are considered for the evaluation. The India Meteorological Department (IMD) observed rainfall (0.25° x 0.25°) is taken to validate the model’s simulated rainfall. Further, to validate the zonal wind (u component), meridional wind (v component) and relative humidity, the reanalysed data (0.25° × 0.25°) at the pressure levels of 1000, 850, 700 and 200 hPa are taken from ERA5 of the European Centre for Medium-Range Weather Forecasting (ECMWF). The seasonal and monthly mean wind and relative humidity are vertically averaged from the levels of 1000 to 700 hPa, while monthly mean wind at the level of 200 hPa is considered for upper-level analysis. The models BCC-CSM2-MR, BCC-ESM1, MPI-ESM1-2-HR and MPI-ESM1-2-LR simulated wind, humidity and rainfall on the monthly and seasonal scales are validated against the respective observed/reanalysed data. The evaluations show that the CMIP6 model BCC-CSM2-MR performs well in reproducing relative humidity over the Arabian Sea and the Bay of Bengal. The model BCC-CSM2-MR and BCC-ESM1 perform well in simulating JJAS rainfall in comparison to observed rainfall of IMD.
Drought is an occurrence that brings about significant changes to the structure of areas. Its influence is especially noticeable in important regions with dry and semi-dry weather patterns, leading to a range of difficulties including interruptions in food distribution systems, lack of water, health problems, economic declines, increased migration, and inadequate energy supply. The Ardabil plain, located in Asia and the northern-western region of Iran, plays a pivotal role in crop productions within an arid environment and holds significant political importance for the country. The main objective of this study is to enhance environmental sustainability in this critical and vulnerable region, particularly in anticipation of imminent droughts. The study focuses on examining the financial impacts on agriculture and selection a crop using the SWAT model, HWA method and climate scenarios under the RCP8.5 pathway for the future period (2040–2050). Results for the near future indicate a notable decline in rainfall of around 38 %, a reduction in wheat production by approximately 25 %, and an increase in temperature of around 30 %. At present, the Ardabil Plain produces a total of 284,182 tons of wheat, with 204,980 tons from irrigated crops and 79,202 tons from rain-fed crops. However, the projected future scenario indicates a decrease in total wheat production to 202,926 tons, with 153,855 tons from irrigated crops and 49,071 tons from rain-fed crops. This decline in production is expected to lead to a total net income loss of approximately -$139,372,437, with -$87,690,344 attributed to irrigated crops and -$51,682,092 to rain-fed crops. The comprehensive hierarchy of crop choices yielded by the HWA method is outlined as follows: barley holds a superior position, followed by wheat, soybeans, and potatoes. The study findings suggest that the availability of water sources in certain regions may prompt a shift in farming land from the north to the south of the plain to promote environmental sustainability.
We present the Monsoon Mission Coupled Forecast System version 2.0 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. The latest 25 years (1998–2022) of retrospective seasonal coupled hindcast simulations of the Indian summer monsoon with April initial conditions from Coupled Forecast System Reanalysis are discussed. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except over India and North America. The dry bias over these regions remained like in MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centers and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of Indian summer monsoon rainfall (ISMR) by 17 % and enhances the amplitude skill (normalized root mean square error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2.
Cumulus parameterization (CP) in state‐of‐the‐art global climate models is based on the quasi‐equilibrium assumption (QEA), which views convection as the action of an ensemble of cumulus clouds, in a state of equilibrium with respect to a slowly varying atmospheric state. This view is not compatible with the organization and dynamical interactions across multiple scales of cloud systems in the tropics and progress in this research area was slow over decades despite the widely recognized major shortcomings. Novel ideas on how to represent key physical processes of moist convection‐large‐scale interaction to overcome the QEA have surged recently. The stochastic multicloud model (SMCM) CP in particular mimics the dynamical interactions of multiple cloud types that characterize organized tropical convection. Here, the SMCM is used to modify the Zhang‐McFarlane (ZM) CP by changing the way in which the bulk mass flux and bulk entrainment and detrainment rates are calculated. This is done by introducing a stochastic ensemble of plumes characterized by randomly varying detrainment level distributions based on the cloud area fraction of the SMCM. The SMCM is here extended to include shallow cumulus clouds resulting in a unified shallow‐deep CP. The new stochastic multicloud plume CP is validated against the control ZM scheme in the context of the single column Community Climate Model of the National Center for Atmospheric Research using data from both tropical ocean and midlatitude land convection. Some key features of the SMCM CP such as it capability to represent the tri‐modal nature of organized convection are emphasized.
We describe the Monsoon Mission Coupled Forecast System version 2 (MMCFSv2) model, which substantially upgrades the present operational MMCFSv1 (version 1) at the India Meteorology Department. We evaluate MMCFSv2 based on the latest 25 years (1998–2022) of retrospective coupled hindcast simulations of the Indian Summer Monsoon with April initial conditions from Coupled Forecast System Reanalysis. MMCFSv2 simulates the tropical wind, rainfall, and temperature structure reasonably well. MMCFSv2 captures surface winds well and reduces precipitation biases over land, except in India and North America. The dry bias over these regions remained similar to MMCFSv1. MMCFSv2 captures significant features of the Indian monsoon, including the intensity and location of the maximum precipitation centres and the large-scale monsoon circulation. MMCFSv2 improves the phase skill (anomaly correlation coefficient) of the interannual variation of ISMR by 17 % and enhances the amplitude skill (Normalized Root Mean Square Error) by 20 %. MMCFSv2 shows improved teleconnections of ISMR with the equatorial Indian and Pacific oceans. This 25-year hindcast dataset will serve as the baseline for future sensitivity studies of MMCFSv2.
Northward propagating summer monsoon intraseasonal oscillations (MISOs) in the Indian Ocean region remain poorly understood and difficult to predict. Here we examine a free-running high-resolution regional atmospheric model (RegCM4.7 with 25km resolution), forced distantly at the boundaries by atmospheric observations (ERA-Interim, 0.75 degrees) and forced locally by observed SST over the period 1979-2016, to assess its ability to reproduce key aspects of these MISOs. We find that the model MISO exhibits spatial structures and northward propagation characteristics broadly similar to observed MISO when confining the analysis to the 25-90 day period band. The MISO precipitation anomalies are then shown to be consistent with previously known observed relationships to broad-scale sea-level pressure patterns, ITCZ positioning, and changes in the regional Hadley Cell component. The total simulated seasonal (JJAS) rainfall anomalies over India are not significantly correlated with observations, indicating that intrinsic variations in the regional model atmosphere dominate most of the precipitation variability. However, the bandpass-filtered MISO anomalies surprisingly exhibit a significant correlation (0.61) with observations. This suggests that instabilities in the regional broad-scale atmospheric circulation, e.g., linked to the ITCZ position or strength, may be partly controlled by the large-scale atmospheric flows specified at the domain boundaries and/or that specified local SST anomalies may help to guide some fraction of the developing model MISO to follow observations. This result motivates further research on MISO initiation and development using this type of regional atmospheric model.
State-of-the-art coupled models have several limitations in representing the phase and amplitude characteristics of monsoon intra-seasonal oscillations (MISO). Specifically, the models' deficiencies in predicting stronger active spells have been widely reported in earlier studies. In the present study, we endeavour to overcome this limitation by better representing the diurnal cycle of the sea surface temperature and the associated feedback processes. In the present study, we demonstrate that resolving the diurnal cycle rectification in a state-of-the-art global coupled model improves the simulation of MISO. The present analysis showcases how rectification can modulate the oceanic, atmospheric and interfacial properties so that the coupled model can better simulate stronger active monsoon spells. The essential requirements for the coherent northward propagation mechanism of MISOs are pronounced in the presence of intra-seasonal rectification by diurnal SSTs.
The monsoon intraseasonal oscillation (MISO) is the dominant variability over the Indian Ocean during the Indian summer monsoon (ISM) season and is characterized by pronounced northward propagation. Previous studies have shown that general circulation models (GCMs) still have difficulty in simulating the northward-propagating MISO, and that the role of air-sea interaction in MISO is unclear. In this study, 14 atmosphere-ocean coupled GCMs (CGCMs) and the corresponding atmosphere-only GCMs (AGCMs) are selected from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) to assess their performance in reproducing MISO and the associated vortex tilting mechanism. The results show that both CGCMs and AGCMs are able to well simulate the significant relationship between MISO and vortex tilting. However, 80% of CGCMs show better simulation skills for MISO than AGCMs in CMIP6. In AGCMs, the poor model fidelity in MISO is due to the failure simulation of vortex tilting. Moreover, it is found that failure to simulate the downward motion to the north of convection is responsible for the poor simulation of vortex tilting in AGCMs. In addition, it is observed that there is a significant relationship between the simulated sea surface temperature gradient and simulated vertical velocity shear in the meridional direction. These findings indicate that air-sea interaction may play a vital role in simulating vertical motions in tilting and MISO processes. This work offers us a specific target to improve the MISO simulation and further studies are needed to elucidate the physical processes of this air-sea interaction coupling with vortex tilting.
Within the summer monsoon, the circulation and rainfall over the Indian region exhibit large variations over the synoptic scale of 3-7 days and the supersynoptic scales of 10 days and longer. In this paper we discuss some facets of intraseasonal variation on the supersynoptic scale on the basis of existing observational studies and some new analysis. The major variation of the summer monsoon rainfall on this scale is the active-break cycle. The deep convection over the Indian region on a typical day in the active phase is organized over thousands of kilometers in the zonal direction and is associated with a tropical convergence zone (TCZ). The intraseasonal variations on the supersynoptic scale are also coherent on these scales and are related to the space-time variation of the large-scale TCZ. The latitudinal distribution of the occurrence of the TCZ is bimodal with the primary mode over the heated continent and a secondary mode over the ocean. The variation of the continental TCZ is generally out of phase with that of the oceanic TCZ. During the active spells, the TCZ persists over the continent in the monsoon zone. The revival from breaks occurs either by northward propagation of the TCZ over the equatorial Indian Ocean or by genesis of a disturbance in the monsoon zone (often as a result of westward propagations from W. Pacific). The mechanisms governing the fluctuation between active spells and breaks, the interphase transition and the complex interactions of the TCZ over the Indian subcontinent with the TCZ over the equatorial Indian Ocean and the W. Pacific, have yet to be completely understood.
Key Words tropical convergence zone, active-weak cycles, break monsoon, monsoon ocean coupling s Abstract For over 300 years, the monsoon has been viewed as a gigantic land-sea breeze. It is shown in this paper that satellite and conventional observations support an alternative hypothesis, which considers the monsoon as a manifestation of seasonal migration of the intertropical convergence zone (ITCZ). With the focus on the Indian monsoon, the mean seasonal pattern is described, and why it is difficult to simulate it is discussed. Some facets of the intraseasonal variation, such as active-weak cycles; break monsoon; and a special feature of intraseasonal variation over the region, namely, poleward propagations of the ITCZ at intervals of 2–6 weeks, are considered. Vertical moist stability is shown to be a key parameter in the variation of monthly convection over ocean and land as well as poleward propagations. Special features of the Bay of Bengal and the monsoon brought out by observations during a national observational experiment in 1999 are briefly described.
The broad-scale fluctuations of cloudiness over the Eastern Hemisphere during the northern summer monsoon were investigated by using daily satellite mosaic pictures taken from June 1 to September 30, 1973.
Spectral analysis revealed two dominant periodicities, of around 40 days and around 15 days. Cross-spectral, time-sectional, time-lag correlation and phase-lag vector analysis were applied to reveal the characteristics of these two modes in the time-space field.
The fluctuation of 40-day period shows marked northward movement of cloudiness from the equatorial zone to the mid-latitudes (around 30°E) over the whole Asian monsoon area, and southward movement over Africa and the central Pacific. The northward movement is most apparent over the India-Indian Ocean sector. The fluctuation of this mode is associated with the major “active”-“break” cycle of the monsoon over the whole Asian monsoon area.
The fluctuation of 15-day period shows similar features to that of 40-day period, but includes two clockwise rotations, one over India and Southeast Asia and the other over the western Pacific. A southward movement from the equatorial zone to the Southern Hemisphere middle latitudes is also prominent to the east and west of Australia. The fluctuation of this mode seems to correspond with the movements of equatorial, monsoon (or tropical), and westerly disturbances.
It is also suggested that the fluctuation of 40-day period may be closely connected with the global-scale zonal oscillation in the equatorial zone and that of 15-day period may exist as a result of meridional wave interactions.
The Indian Ocean sea surface temperature (SST) during the boreal summer has shown a significant warming of 0.3 °C in the recent decade (2001-2010) compared to a former decade (1979-1988) and it is most pronounced in the central tropical Indian Ocean. By using reanalysis and satellite-derived datasets, we investigated how the monsoon intraseasonal oscillation (MISO) over the south Asian summer monsoon (ASM) region has been influenced by the recent warming in the Indian Ocean. It is found that the MISO variance has increased over the ASM region in the recent period compared with the earlier decade. It is also noted that the characteristic northward propagation of the MISO has slowed over 2001-2010, resembling more of a standing oscillation near the equator. Mechanisms implicated in the observed MISO changes are explored by conducting several model sensitivity experiments with an atmospheric general circulation model (AGCM). The model experiments suggest that the mean SST increase over the Indian Ocean and the associated changes in the air sea interaction, the increased mean moisture convergence, and changes in the large-scale circulation are responsible for the changes in the characteristics of the MISO. The influence of the recent Indian Ocean warming on the MISO characteristics must be understood fully since they determine the seasonal amount of rainfall over the Indian subcontinent. An examination of future projections of the MISO using the MPI-ESM-LR model from the CMIP5 archive also gives consistent result.
We have evaluated the simulation of Indian summer monsoon and its intraseasonal oscillations in the National Centers for Environmental Prediction climate forecast system model version 2 (CFSv2). The dry bias over the Indian landmass in the mean monsoon rainfall is one of the major concerns. In spite of this dry bias, CFSv2 shows a reasonable northward propagation of convection at intraseasonal (30–60 day) time scale. In order to document and understand this dry bias over the Indian landmass in CFSv2 simulations, a two pronged investigation is carried out on the two major facets of Indian summer monsoon: one, the air–sea interactions and two, the large scale vertical heating structure in the model. Our analysis shows a possible bias in the co-evolution of convection and sea surface temperature in CFSv2 over the equatorial Indian Ocean. It is also found that the simulated large scale vertical heat source (Q1) and moisture sink (Q2) over the Indian region are biased relative to observational estimates. Finally, this study provides a possible explanation for the dry precipitation bias over the Indian landmass in the simulated mean monsoon on the basis of the biases associated with the simulated ocean–atmospheric processes and the vertical heating structure. This study also throws some light on the puzzle of CFSv2 exhibiting a reasonable northward propagation at the intraseasonal time scale (30–60 day) despite a drier monsoon over the Indian land mass.
The boreal summer Asian monsoon has been evaluated in 25 Coupled Model Intercomparison Project-5 (CMIP5) and 22 CMIP3 GCM simulations of the late twentieth Century. Diagnostics and skill metrics have been calculated to assess the time-mean, climatological annual cycle, interannual variability, and intraseasonal variability. Progress has been made in modeling these aspects of the monsoon, though there is no single model that best represents all of these aspects of the monsoon. The CMIP5 multi-model mean (MMM) is more skillful than the CMIP3 MMM for all diagnostics in terms of the skill of simulating pattern correlations with respect to observations. Additionally, for rainfall/convection the MMM outperforms the individual models for the time mean, the interannual variability of the East Asian monsoon, and intraseasonal variability. The pattern correlation of the time (pentad) of monsoon peak and withdrawal is better simulated than that of monsoon onset. The onset of the monsoon over India is typically too late in the models. The extension of the monsoon over eastern China, Korea, and Japan is underestimated, while it is overestimated over the subtropical western/central Pacific Ocean. The anti-correlation between anomalies of all-India rainfall and Niño3.4 sea surface temperature is overly strong in CMIP3 and typically too weak in CMIP5. For both the ENSO-monsoon teleconnection and the East Asian zonal wind-rainfall teleconnection, the MMM interannual rainfall anomalies are weak compared to observations. Though simulation of intraseasonal variability remains problematic, several models show improved skill at representing the northward propagation of convection and the development of the tilted band of convection that extends from India to the equatorial west Pacific. The MMM also well represents the space–time evolution of intraseasonal outgoing longwave radiation anomalies. Caution is necessary when using GPCP and CMAP rainfall to validate (1) the time-mean rainfall, as there are systematic differences over ocean and land between these two data sets, and (2) the timing of monsoon withdrawal over India, where the smooth southward progression seen in India Meteorological Department data is better realized in CMAP data compared to GPCP data.
The wet/dry spells of the Indian summer monsoon (ISM) rainfall are governed by northward propagating boreal summer monsoon intraseasonal oscillations (MISO). Unlike for the Madden Julian Oscillation (e.g. RMM indices, Wheeler and Hendon in Mon Weather Rev 132:1917–1932, 2004), a low dimensional real-time monitoring and forecast verification metric for the MISO is not currently available. Here, for the first time, we present a real time monitoring index developed for identifying the amplitude and phase of the MISO over the ISM domain. The index is constructed by applying extended empirical orthogonal function (EEOF) analysis on daily unfiltered rainfall anomalies averaged over the longitudinal domain 60.5°E–95.5°E. The gravest two modes of the EEOFs together explain about 23 % of the total variance, similar to the variance explained by MISO in observation. The pair of first two principal components (PCs) of the EEOFs is named as MISO1 and MISO2 indices which together represent the evolution of the MISOs in a low dimensional phase space. Power spectral analysis reveals that the MISO indices neatly isolate the MISO signal from the higher frequency noise. It is found that the current amplitude and phase of the MISO can be estimated by preserving a memory of at least 15 days. Composite pictures of the spatio-temporal evolution of the MISOs over the ISM domain are brought out using the MISO indices. It is further demonstrated that the MISO indices can be used in the quantification of skill of extended range forecasts of MISOs. Since the MISO index does not rely on any sort of time filtering, it has great potential for real time monitoring of the MISO and may be useful in developing some prediction scheme.
The reanalysis at National Centers for Environmental Prediction (NCEP) focuses on atmospheric states reports generated by a constant model and a constant data assimilation system. The datasets have been exchanged among national and international partners and used in several more reanalyses. The new data assimilation techniques have been introduced including three-dimensional variational data assimilation (3DVAR), 4DVAR, and ensembles of analyses such as ensemble Kalman filter (EnKF), which produce not only an ensemble mean analysis but also a measure of the uncertainty. The new climate forecast system reanalysis (CFSR) was executed to create initial states for the atmosphere, ocean, land, and sea ice that are consistent as possible with the next version of the climate forecast system (CFS) version 2, which is to be implemented operationally at NCEP in 2010. Several graphical plots were generated automatically at the end of each reanalyzed month and were displayed on the CFSR Web site in real time.
A series of small-perturbation experiments has been conducted to demonstrate that an atmosphere ocean coupled model and an atmosphere-only model produce significantly different intensities of boreal summer intraseasonal oscillation (BSISO) and phase relationships between convection and underlying SST associated with BSISO. The coupled model not only simulates a stronger BSISO than the atmosphere-only model, but also generates a realistic phase relationship between intraseasonal convection and underlying SST. In the coupled model, positive (negative) SST fluctuations are highly correlated with more (less) precipitation with a time lead of 10 days as in the observations, suggesting that intraseasonal SST is a result of atmospheric convection, but at the same time, positively feeds back to increase the intensity of the convection. In the atmosphere-only model, however, SST is only a boundary forcing for the atmosphere. The intraseasonal convection in the atmosphere-only model is actually less correlated with underlying SST. The maximum correlation between convection and SST occurs when they are in phase with each other, which is in contrast to the observations. These results indicate that an atmosphere ocean coupled model produces a more realistic ISO compared to an atmosphere-only model.
The spatial and temporal structures of the northward-propagating boreal summer intraseasonal oscillation (BSISO) are revealed based on the analysis of both the ECHAM4 model simulation and the NCEP NCAR reanalysis. The BSISO structure and evolution characteristics simulated by the model bear many similarities to those derived from the NCEP NCAR reanalysis. The most notable features are the remarkable meridional asymmetries, relative to the BSISO convection, in the vorticity and specific humidity fields. A positive vorticity perturbation with an equivalent barotropic structure appears a few latitude degrees north of the convection center. The maximum specific humidity also shows a clear northward shift in the lower troposphere.Two internal atmospheric dynamics mechanisms are proposed to understand the cause of the northward propagation of the BSISO. The first is the vertical shear mechanism. The key process associated with this mechanism is the generation of barotropic vorticity due to the coupling between the free-atmosphere baroclinic and barotropic modes in the presence of the vertical shear of the mean flow. The induced barotropic vorticity in the free atmosphere further causes a moisture convergence in the planetary boundary layer (PBL), leading to the northward shift of the convective heating. The second mechanism is the moisture convection feedback mechanism. Two processes contribute to the northward shift of the low-level moisture. One is the moisture advection by the mean southerly in the PBL. Another is the moisture advection by the BSISO wind due to the mean meridional specific humidity gradient. The asymmetric specific humidity contributes to the northward shift of the convective heating.A theoretical framework is constructed to investigate the instability of the northward-propagating BSISO mode and the relative roles of various mechanisms including air sea interactions. An eigenvalue analysis indicates that the northward propagation of the BSISO is an unstable mode of the summer mean flow in the monsoon region. It has a typical wavelength of 2500 km. While the easterly shear contributes to the northward propagation primarily north of 5°N, the moisture feedback and the air sea interaction also contribute significantly, particularly in the region near and south of the equator. The internal atmospheric dynamics are essential in causing the northward propagation of the BSISO over the tropical Indian Ocean.
A Fourier method of filtering digital data called Lanczos filtering is described. Its principal feature is the use of ″sigma factors″ which significantly reduce the amplitude of the Gibbs oscillation. A pair of graphs is developed that can be used to determine filter response quality given the number of weights and the value of the cutoff frequency, the only two inputs required by the method. Examples of response functions in one and two dimensions are given and comparisons are made with response functions from other filters. The simplicity of calculating the weights and the adequate response make Lanczos filtering an attractive filtering method.
While many of the previous positive Indian Ocean dipole (IOD) years were associated with above (below)normal monsoon rainfall over central (southern) India during summer monsoon months [June-September (JJAS)], the IOD event in 2008 is associated with below (above)-normal rainfall in many parts of central (southern peninsular) India. Because understanding such regional organization is a key for success in regional prediction, using different datasets and atmospheric model simulations, the reasons for this abnormal behavior of the monsoon in 2008 are explored. Compared to normal positive IOD events, sea surface temperature (SST) and rainfall in the southern tropical Indian Ocean (STIO) in JJAS 2008 were abnormally high. Downwelling Rossby waves and oceanic heat advection played an important role in warming SST abnormally in the STIO. It was also found that the combined influence of a linear warming trend in the tropical Indian Ocean and warming associated with the IOD have resulted in abnormal warming of the STIO. This abnormal SST warming resulted in enhancement of convection in the southwest tropical Indian Ocean and forced anticyclonic circulation anomalies over the Bay of Bengal and central India, leading to suppressed rainfall over this region in JJAS 2008. The above mechanism is tested by conducting several model sensitivity experiments with an atmospheric general circulation model (AGCM). These experiments confirmed that the subsidence over central India and the Bay of Bengal was forced mainly by the anomalous warming in the STIO region driven by coupled ocean-atmosphere processes. This study provides the first evidence of combined Indian Ocean warming, associated with global warming, and IOD-related warming influence on Indian summer monsoon rainfall. The combined influence may force below-normal rainfall over central India by inducing strong convection in the STIO region. The conventional seesaw in convection between the Indian subcontinent and the eastern equatorial Indian Ocean may shift to the central equatorial Indian Ocean and the Bay of Bengal if the central Indian Ocean consistently warms in the global warming scenario.
Using a hybrid atmosphere-ocean coupled model, it is shown that during the boreal summer northward-propagating, intraseasonal oscillations (NPISOs) are strongly coupled to the underlying sea surface temperature (SST) in the Indian Ocean sector. On the one hand, the intraseasonal atmospheric convection changes the SST through solar radiation, latent heat flux, and mixed-layer entrainment; on the other, the induced SST fluctuations feed back to affect the intraseasonal convection. The preferential northward, rather than southward, propagation of boreal summer ISOs in the Indian Ocean is partially explained by an interaction among the summer-mean climate state, the atmospheric disturbances, and the ocean surface temperature.A solution to an atmosphere-only model forced with daily SST produces much stronger NPISOs than a similar solution forced with monthly mean SST (AMIP-type run). The atmosphere-only model, however, even when it is forced by daily SST from the coupled model (with a small amount of noise in the initial and/or boundary conditions), is unable to reproduce the NPISOs in the coupled case. In the coupled system, intraseasonal SST anomalies are forced by intraseasonal atmospheric convection, and hence are in quadrature with the convection. In the stand-alone atmospheric model, however, SST acts only as a boundary forcing, and the resultant atmospheric convection has almost the same phase with the underlying SST. One consequence is that the intensity of the SST-forced intraseasonal convection in the stand-alone atmospheric model is considerably weaker than in the coupled model.Finally, solutions indicate that the northward movement of the off-equatorial convection in the northern Indian Ocean is more closely related to local intraseasonal SST anomalies than to the equatorial eastward-moving Madden-Julian oscillation: Positive (negative) SST anomalies in the northern Indian Ocean lead the active (break) phases of the intraseasonal convection by about 2 pentads (10 days). Therefore, intraseasonal SST anomalies in the northern Indian Ocean are potentially a useful index to forecast active (break) spells of the south Asian summer monsoon.
Based on FGGE 1,cvel IIIb data, the structural features of 45 day perturbations over a tropical belt (15°N-15°S) during the 1979 summer are detailed. At the equator, 45 day perturbations which are primarily associated with the zonal wind components of wavenumber 1, propagate eastward (8° of longitude per day) and upward (0.7 km per day), probably indicating downward energy flux. In the Southern Hemisphere tropics (0°-15°S), the 45 day zonal mean wind perturbations propagate downward with an approximate phase speed of 0.8 km per day. In the Northern Hemisphere tropics, they are largely of standing character with the maximum amplitude (3 m s1) near 200 mb at 15°N.There exists a strong association between monsoon activity over South Asia and changes in the intensity of the equatorial Walker circulation. When active monsoons occur over South Asia, the Walker circulation becomes stronger than usual with prominent 850 mb easterlies (200 mb easterlies) over the eastern Pacific east of the date line and above normal 850 mb westerlies (200 mb easterlies) over the Indian Ocean and the western Pacific west of the date line. Equatorial convective activity appears to be above normal near the date line, as evident by abnormally strong ascending motions. During the break monsoon phase, the equatorial Walker circulation is depressed below normal.There are two bridges (one in the Eastern Hemisphere from approximately 50° to 150°E and another in the Western Hemisphere between 170° and 70°W,through which the 45 day perturbations of the Southern Hemisphere tropics at 200 mb interact with those in the Northern Hemisphere tropics. In comparison, the central Pacific between approximately 150° and 120° is the only favorable channel for interhemispheric interaction due to transient disturbances with time scales shorter than about 30 days. This interhemispheric interaction, due to short-period transient disturbances at 200 mb, is abnormally enhanced when the Asiatic monsoon is active.
The One-Degree Daily (1DD) technique is described for producing globally complete daily estimates of precipitation on a 1° × 1° lat/long grid from currently available observational data. Where possible (40°N-40°S), the Threshold-Matched Precipitation Index (TMPI) provides precipitation estimates in which the 3-hourly infrared brightness temperatures (IR Tb) are compared with a threshold and all "cold" pixels are given a single precipitation rate. This approach is an adaptation ot the Geostationary Operational Environmental Satellite Precipitation Index, but for the TMPI the IR Tb threshold and conditional rain rate are set locally by month from Special Sensor Microwave Imager-based precipitation frequency and the Global Precipitation Climatology Project (GPCP) satellite-gauge (SG) combined monthly precipitation estimate, respectively. At higher latitudes the 1DD features a rescaled daily Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) precipitation. The frequency of rain days in the TOVS is scaled down to match that in the TMPI at the data boundaries, and the resulting nonzero TOVS values are scaled locally to sum to the SG (which is a globally complete monthly product). The GPCP has approved the 1DD as an official product, and data have been produced for 1997 through 1999, with production continuing a few months behind real time (to allow access to monthly input data). The time series of the daily 1DD global images shows good continuity in time and across the data boundaries. Various examples are shown to illustrate uses. Validation for individual gridbox values shows a very high mean absolute error, but it improves quickly when users perform time/space averaging according to their own requirements.
An investigation is presented of the daily variation of the maximum cloud zone (MCZ) and the 700 mb trough in the Northern Hemisphere over the Indian longitudes 70-90oE during April-October for 1973-77. It is found that during June-September there are 2 favorable locations for a MCZ over these longitudes - on a majority of days the MCZ is present in the monsson zone N of 15oN, and often a secondary MCZ occurs in the equatorial region (0-10oN).-from Authors
This study evaluates the subseasonal variability associated with the Asian summer monsoon in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of each model's twentieth-century climate simula- tion are analyzed. The authors focus on the three major components of Asian summer monsoon: the Indian summer monsoon (ISM), the western North Pacific summer monsoon (WNPSM), and the East Asian summer monsoon (EASM), together with the two dominant subseasonal modes: the eastward- and northward- propagating boreal summer intraseasonal oscillation (BSIO) and the westward-propagating 12-24-day mode. The results show that current state-of-the-art GCMs still have difficulties and display a wide range of skill in simulating the subseasonal variability associated with Asian summer monsoon. During boreal summer (May-October), most of the models produce reasonable seasonal-mean precipitation over the ISM region, but excessive precipitation over the WNPSM region and insufficient precipitation over the EASM region. In other words, models concentrate their rain too close to the equator in the western Pacific. Most of the models simulate overly weak total subseasonal (2-128 day) variance, as well as too little variance for BSIO and the 12-24-day mode. Only 4-5 models produce spectral peaks in the BSIO and 12-24-day frequency bands; instead, most of the models display too red a spectrum, that is, an overly strong persistence of precipitation. For the seven models with three-dimensional data available, five reproduce the precondi- tioning of moisture in BSIO but often with a too late starting time, and only three simulate the phase lead of low-level convergence. Interestingly, although models often have difficulty in simulating the eastward propagation of BSIO, they tend to simulate well the northward propagation of BSIO, together with the westward propagation of the 12-24-day mode. The northward propagation in these models is thus not simply a NW-SE-tilted tail protruding off of an eastward-moving deep-tropical intraseasonal oscillation.
The Tropical Ocean-Global Atmosphere (TOGA) program sought to determine the predictability of the coupled ocean-atmosphere system. The World Climate Research Programme's (WCRP) Global Ocean-Atmosphere-Land System (GOALS) program seeks to explore predictability of the global climate system through investigation of the major planetary heat sources and sinks, and interactions between them. The Asian-Australian monsoon system, which undergoes aperiodic and high amplitude variations on intraseasonal, annual, biennial and interannual timescales is a major focus of GOALS. Empirical seasonal forecasts of the monsoon have been made with moderate success for over 100 years. More recent modeling efforts have not been successful. Even simulation of the mean structure of the Asian monsoon has proven elusive and the observed ENSO-monsoon relationships has been difficult to replicate. Divergence in simulation skill occurs between integrations by different models or between members of ensembles of the same model. This degree of spread is surprising given the relative success of empirical forecast techniques. Two possible explanations are presented: difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydrodynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. To this end, a thorough description of observed monsoon variability and the physical processes that are thought to be important is presented. Prospects of improving prediction and some strategies that may help achieve improvement are discussed.
The space-time evolution of the ocean and atmosphere associated with 1998–2000 monsoon intraseasonal oscillations (ISO) in the Indian Ocean and west Pacific is studied using validated sea surface temperature (SST) and surface wind speed from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager, and satellite outgoing longwave radiation. Monsoon ISO consist of alternating episodes of active and suppressed atmospheric convection moving northward in the eastern Indian Ocean and the South China Sea. Negative/positive SST anomalies generated by fluctuations of net heat flux at the ocean surface move northward following regions of active/suppressed convection. Such coherent evolution of SST, surface heat flux and convection suggests that air-sea interaction might be important in monsoon ISO.
The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses.
The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR– AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.
A clear shift in the withdrawal dates of the Indian Summer Monsoon is observed in the long term time series of rainfall data. Prior (posterior) to the 1976/1977 climate shift most of the withdrawal dates are associated with a late (an early) withdrawal. As a result, the length of the rainy season (LRS) over the Indian land mass has also undergone similar changes (i.e., longer (shorter) LRS prior (posterior) to the climate shift). In this study, probable reasons for this significant shift in withdrawal dates and the LRS are investigated using reanalysis/observed datasets and also with the help of an atmospheric general circulation model. Reanalysis/observational datasets indicate that prior to the climate shift the sea surface temperature (SST) anomalies in the eastern equatorial Pacific Ocean and the Arabian Sea exerted a strong influence on both the withdrawal and the LRS. After the climate shift, the influence of the eastern equatorial Pacific Ocean SST has decreased and surprisingly, the influence of the Arabian Sea SST is almost non-existent. On the other hand, the influence of the southeastern equatorial Indian Ocean has increased significantly. It is observed that the upper tropospheric temperature gradient over the dominant monsoon region has decreased and the relative influence of the Indian Ocean SST variability on the withdrawal of the Indian Summer Monsoon has increased in the post climate shift period. Sensitivity experiments with the contrasting SST patterns on withdrawal dates and the LRS in the pre- and post- climate shift scenarios, confirm the observational evidences presented above.
The influence of the Indian Ocean dipole (IOD) on the poleward propagation of boreal summer intraseasonal oscillations (BSISOs) is examined using observed datasets. This study finds that coherent (incoherent) poleward propagation of precipitation anomalies from 5°S to 25°N are observed during negative (positive) IOD years. Disorganized poleward propagation of BSISO in the south equatorial Indian Ocean is observed during positive IOD years. The rationale behind such an anomaly in the poleward propagation of BSISO in contrasting IOD years is identified based on the theory of northward-propagating BSISO, which suggests the influential role of air–sea interaction on the genesis and propagation of BSISO. It is found that the mean structure of moisture convergence and meridional specific humidity distribution undergoes radical changes in contrasting IOD years, which in turn influences the meridional propagation of BSISO. This study assumes significance, considering the critical role of BSISO in modulating the seasonal mean summer monsoon rainfall.
The link between realism in simulation of the seasonal mean precipitation and summer tropical intraseasonal oscillations and their dependence on cumulus parameterization schemes is investigated using the Florida State University Global Spectral Model (FSUGSM). Forty-member model ensemble simulations of the northern summer season are generated for three different cumulus parameterization schemes [namely, Arakawa–Schubert (Naval Research Laboratory; NRL), Zhang and McFarlane (National Center for Atmospheric Research; NCAR), and Emanuel (Massachusetts Institute of Technology; MIT)]. The MIT scheme simulates the regional pattern of seasonal mean precipitation over the Indian monsoon region well but has large systematic bias in simulating the precipitation over the western Pacific and the Maritime Continent. Although the simulation of details of regional distribution of precipitation over the Indian monsoon region by the NRL and NCAR schemes is not accurate, they simulate the spatial pattern of precipitation over the tropical Indo–Pacific domain closer to observation. The NRL scheme seems to captures the observed northward and eastward propagation of intraseasonal precipitation anomalies realistically. However, the simulations of the NCAR and MIT schemes are dominated by a westward propagating component. The westward propagating mode seen in the model as well as observations is indicated to be an equatorial Rossby wave modified by the northern summer mean flow. An examination of the relationship between simulation of the model climatology and eastward propagating character of monsoon intraseasonal oscillations (ISOs) in a limited sample shows that the scheme that simulates better seasonal mean pattern of rainfall over the tropical Indo–Pacific domain also simulates better intraseasonal variance and more realistic eastward propagation of monsoon ISOs. Among the parameters known to be important for meridional propagation of the summer monsoon ISOs, the meridional gradient of mean humidity in the lower atmosphere seems to be crucial in determining the northward propagation in the equatorial Indian Ocean (between 10°S and 10°N). For better prediction of the seasonal mean Indian monsoon, therefore, the model climatology should have minimum bias not only over the Indian monsoon region but also over the
entire Indo–Pacific basin.
How and to what extent the intraseasonal oscillations (ISOs) influence the seasonal mean and its interannual variability of the Indian summer monsoon is investigated using 42-yr (1956–97) daily circulation data from National Centers for Environmental Prediction–National Center for Atmospheric Research 40-Year Reanalysis and satellite-derived outgoing longwave radiation data for the period of 1974–97. Based on zonal winds at 850 hPa over the Bay of Bengal, a criterion is devised to define “active” and “break” monsoon conditions. The underlying spatial structure of a typical ISO cycle in circulation and convection that is invariant over the years is constructed using a composite technique. A typical ISO has large-scale horizontal structure similar to the seasonal mean and intensifies (weakens) the mean flow during its active (break) phase. A typical active (break) phase is also associated with enhanced (decreased) cyclonic low-level vorticity and convection and anomalous upward (downward) motion in...
How and to what extent the intraseasonal oscillations (ISOs) influence the seasonal mean and its interannual variability of the Indian summer monsoon is investigated using 42-yr (1956-97) daily circulation data from National Centers for Environmental Prediction-National Center for Atmospheric Research 40-Year Reanalysis and satellite-derived outgoing longwave radiation data for the period of 1974-97. Based on zonal winds at 850 hPa over the Bay of Bengal, a criterion is devised to define ''active'' and ''break'' monsoon conditions. The underlying spatial structure of a typical ISO cycle in circulation and convection that is invariant over the years is constructed using a composite technique. A typical ISO has large-scale horizontal structure similar to the seasonal mean and intensifies (weakens) the mean flow during its active (break) phase. A typical active (break) phase is also associated with enhanced (decreased) cyclonic low-level vorticity and convection and anomalous upward (downward) motion in the northern position of the tropical convergence zone (TCZ) and decreased (increased) convection and anomalous downward (upward) motion in the southern position of the TCZ. The cycle evolves with a northward propagation of the TCZ and convection from the southern to the northern position of the TCZ. It is shown that the intraseasonal and interannual variations are governed by a common mode of spatial variability. The spatial pattern of standard deviation of intraseasonal and interannual variability of low-level vorticity is shown to be similar. The spatial pattern of the dominant mode of ISO variability of the low-level winds is also shown to be similar to that of the interannual variability of the seasonal mean winds. The similarity between the spatial patterns of the two variabilities indicates that higher frequency of occurrence of active (break) conditions would result in ''stronger'' (''weaker'') than normal seasonal mean. This possibility is tested by calculating the two-dimensional probability density function (PDF) of the ISO activity in the low-level vorticity. The PDF estimates for ''strong'' and ''weak'' monsoon years are shown to be asymmetric in both the cases. It is seen that the strong (weak) monsoon years are associated with higher probability of occurrence of active (break) conditions. This result is further supported by the calculation of PDF of ISO activity from combined vorticity and outgoing longwave radiation. This clear signal indicates that the frequency of intraseasonal pattern determines the seasonal mean. Because the ISOs are essentially chaotic, it raises an important question on predictability of the Indian summer monsoon.
The summer monsoons in East and Southeast Asia are characterized, respectively, by the Mei-yu (in eastern China)–Baiu (in Japan) front (MBF) and by the monsoon trough stretching from northern Indochina to the Philippine Sea. These two major monsoon elements are separated by the North Pacific anticyclone. As indicated by the 850-mb zonal wind and cumulus convection over some key areas, a distinct opposite-phase intraseasonal variation exists between the two monsoon elements. Two approaches are adopted to explore the cause of this opposite-phase variation (which reflects the coupling between the two monsoon components): 1) the correlation coefficient patterns between the 850-mb zonal-wind monsoon index and the 850-mb streamfunction field and 2) the composite 850-mb streamline charts and the 120E zonal-wind cross sections. It is shown that the opposite-phase variation between the two monsoon elements is caused by the anomalous circulation associated with the northward-migrating 30–60-day monsoon trough/ridge from the equator to 20N and with the westward-prop-agating 12–24-day monsoon low–high along the latitude of 15–20N. Results obtained in this study are used to address two often discussed phenomena of the East Asian monsoon: 1) the rapid northward shift of the MBF across the Yangtze River basin during the Mei-yu onset is related to the north–south meridional oscillation of the MBF, and 2) the three longitudinally oriented location zones of extremely heavy rain events in eastern China are formed by the alternation of deep cumulus convection zones associated with the intraseasonal monsoon vortices (centered in the northern part of the South China Sea) between extreme monsoon conditions.
1] The role of Indian Ocean Dipole (IOD) on poleward propagation of boreal summer intraseasonal oscillations (BSISO) is examined using long simulation of a coupled ocean-atmosphere general circulation model. The model (SINTEX-F1) simulates the salient features of BSISO realistically. It is found that coherent (incoherent) poleward propagation of precipitation anomalies from 5°S to 25°N are observed during negative (positive) IOD years. The probable mechanisms behind such an anomaly in poleward propagation of BSISO in contrasting IOD years are identified. We find that the mean structure of meridional specific humidity distribution undergoes cardinal changes in contrasting IOD years, which in turn influences the meridional propagation of BSISO. Enhanced (decreased) air-sea interaction in negative (positive) IOD years also supports coherent (incoherent) poleward propagation of BSISO anomalies. This study has important implications, considering the critical role of BSISO in modulating the seasonal mean summer monsoon rainfall.
Key Words tropical convergence zone, active-weak cycles, break monsoon, monsoon ocean coupling s Abstract For over 300 years, the monsoon has been viewed as a gigantic land-sea breeze. It is shown in this paper that satellite and conventional observations support an alternative hypothesis, which considers the monsoon as a manifestation of seasonal migration of the intertropical convergence zone (ITCZ). With the focus on the Indian monsoon, the mean seasonal pattern is described, and why it is difficult to simulate it is discussed. Some facets of the intraseasonal variation, such as active-weak cycles; break monsoon; and a special feature of intraseasonal variation over the region, namely, poleward propagations of the ITCZ at intervals of 2–6 weeks, are considered. Vertical moist stability is shown to be a key parameter in the variation of monthly convection over ocean and land as well as poleward propagations. Special features of the Bay of Bengal and the monsoon brought out by observations during a national observational experiment in 1999 are briefly described.
Ensembles of hindcasts from seven models are analyzed to evaluate dynamical seasonal predictability of 850-hPa wind and rainfall for the Asian summer monsoon (ASM) during 1987, 1988, and 1993. These integrations were performed using observed sea surface temperatures and from observed initial conditions. The experiments were designed by the Climate Variability and Predictability, Working Group on Seasonal to Interannual Prediction as part of the Seasonal Prediction Model Intercomparison Project. Integrations from the European Union Pre-diction of Climate Variations on Seasonal to Interannual Timescales experiment are also evaluated. The National Centers for Environmental Prediction–National Center for Atmospheric Research and European Centre for Medium-Range Weather Forecasts reanalyses and observed pentad rainfall form the baseline against which the hindcasts are judged. Pattern correlations and root-mean-square differences indicate errors in the simulation of the time mean low-level flow and the rainfall exceed observational uncertainty. Most models simulate the subseasonal EOFs that are associated with the dominant variations of the 850-hPa flow during the ASM, but not with the fidelity exhibited by the reanalyses as determined using pattern correlations. Pattern correlations indicate that the first EOF, associated with the tropical convergence zone being located over the continental landmass, is best simulated. The higher-order EOFs are less well simulated, and errors in the mag-nitude and location of their associated precipitation anomalies compromise dynamical seasonal predictability and are related to errors of the mean state. In most instances the models fail to properly project the subseasonal EOFs/principal components onto the interannual variability with the result that hindcasts of the 850-hPa flow and rainfall are poor. In cases where the observed EOFs are known to be related to the boundary forcing, the failure of the models to properly project the EOFs onto the interannual variability indicates that the models are not setting up observed teleconnection patterns.
By using the cloudiness data from 1966 to 1972, a quasi-stationary appearance of 30 to 40 day period was confirmed during the summer monsoon season over India, which is recently found in the data for 1973 as a predominant mode of the monsoon fluctuations (Yasunari, 1979). In the same manner as the result of Yasunari (1979), the fluctuation of this mode shows a northward movement over India-Indian Ocean area in each year. This periodicity seems to appear annually except for the severe drought years such as 1972.
Future projections of the Indian summer monsoon rainfall (ISMR) and its large-scale thermodynamic driver are studied by using CMIP5 model outputs. While all models project an increasing precipitation in the future warming scenario, most of them project a weakening large-scale thermodynamic driver arising from a weakening of the upper tropospheric temperature (UTT) gradient over south Asian summer monsoon region. The weakening of the UTT gradient under global warming scenarios is related to the increase in sea surface temperature (SST) over the equatorial Indian Ocean (EIO) leading to a stronger increase of UTT over the EIO region relative to the northern Indian region, a hypothesis supported by a series of Atmospheric General Circulation Model (AGCM) experiments forced by projected SSTs. To diagnose the inconsistency between the model projections of precipitation and the large-scale thermodynamic driver, we have examined the rate of total precipitation explained by convective and stratiform precipitations in observations and in CMIP5 models. It is found that most models produce too much (little) convective (stratiform) precipitation compared to observations. In addition, we also find stronger precipitable water—precipitation relationship in most CMIP5 models as compared to observations. Hence, the atmospheric moisture content produced by the model immediately gets converted to precipitation even though the large-scale thermodynamics in models weaken. Therefore, under global warming scenarios, due to increased temperature and resultant increased atmospheric moisture supply, these models tend to produce unrealistic local convective precipitation often not in tune with other large-scale variables. Our results questions the reliability of the ISMR projections in CMIP5 models and highlight the need to improve the convective parameterization schemes in coupled models for the reliable projections of the ISMR.
The climatological characteristics and interannual variations of the development of the South Asian summer monsoon (SASM) in early summer are studied using output from a 200-yr simulation of a coupled atmosphere–ocean general circulation model (CM2.1). Some of the model results are compared with corresponding observations. Climatological charts of the model and observational data at pentadal intervals indicate that both the precipitation and SST signals exhibit a tendency to migrate northward. Enhanced monsoonal precipitation at a given site is accompanied by a reduction in incoming shortwave radiation and intensification of upward latent heat flux, and by oceanic cooling.
An extended empirical orthogonal function analysis is used to identify the dates for initiation of the northward march of SASM in individual summers. It is noted that early monsoon development prevails after the mature phase of La Niña events, whereas delayed development occurs after El Niño.
Sensitivity experiments based on the atmospheric component of CM2.1 indicate that the effects of SST forcings in the tropical Pacific (TPAC) and Indian Ocean (IO) on monsoon development are opposite to each other. During El Niño events, the atmospheric response to remote TPAC forcing tends to suppress or postpone monsoon development over South Asia. Conversely, the warm SST anomalies in IO, which are generated by the “atmospheric bridge” mechanism in El Niño episodes, lead to accelerated monsoon development. The net result of these two competing effects is an evolution scenario with a timing that is intermediate between the response to TPAC forcing only and the response to IO forcing only.
The summertime intraseasonal oscillation (ISO) is an important component of the south Asian monsoon. Lagged regressions of intraseasonally filtered (25-80 days) outgoing longwave radiation (OLR) reveal that centers of convection move both northward and eastward from the central equatorial Indian Ocean subsequent to the initiation of an ISO. Eastward movement of convection is also seen at Indian subcontinent latitudes (10 8-208N). Based on the regression results, the summertime ISO convection signal appears as a band tilting northwestward with latitude and stretching from the equator to about 208N. Viewed along any meridian, convection appears to propagate northward while equatorial convection propagates to the east. To examine the robustness of the connection between eastward and northward movement, individual ISOs are categorized and composited relative to the strength of the large-scale eastward component of convection in the central equatorial Indian Ocean. It is found that the majority of ISOs that exhibit northward movement onto the Indian subcontinent (42 out of 54 ISOs, or 78%) also exhibit eastward movement into the western Pacific Ocean. It is also found that when convection in the central Indian Ocean is not followed within 10-20 days by convection in the western Pacific Ocean (12 out of 54 ISOs, or 22%), the independent northward movement of convection in the Indian Ocean region is somewhat stunted. The link between the eastward and northward movement of convection is consistent with an interpretation of the summertime ISO in terms of propagating equatorial modes. The northward moving portion of convection is forced by surface frictional convergence into the low pressure center of the Rossby cell that is excited by equatorial ISO convection. A similar convergence pattern is seen for the northern winter ISO, but it does not generate poleward movement due to relatively cool SSTs underlying the surface convergence.
The tropical intraseasonal oscillation (ISO) exhibits pronounced seasonality. The boreal summer ISO is more complex than its winter counterpart due to the coexistence of equatorial eastward, off-equatorial westward, and northward propagating, low-frequency modes and their interactions. Based on observational evidence and results obtained from numerical experiments, a mechanism is proposed for the boreal summer ISO in which the Northern Hemisphere summer monsoon (NHSM) circulation and moist static energy distribution play essential roles.With a climatological July mean basic state, the life cycle of model low-frequency waves consists of four processes: an equatorial eastward propagation of a coupled Kelvin-Rossby wave packet, an emanation of moist Rossby waves in the western Pacific, a westward propagation and amplification of the Rossby waves in South Asian monsoon regions, and a reinitiation of the equatorial disturbances over the central Indian Ocean. The life cycle spans about one month and provides a mechanism for self-sustained boreal summer ISO.Analyses of the model experiments reveal that the monsoon mean flows and spatial variation of moist static energy trap equatorial disturbances in the NHSM domain. The reduction of moist static energy over the eastern central Pacific suppresses equatorial convection, leading to disintegration of the equatorial Kelvin-Rossby wave packet and the emanation of Rossby waves in the western North Pacific. Strong easterly vertical shears and seasonally enhanced boundary layer humidity in the NHSM further amplify the Rossby waves (of the gravest meridional mode), making their structures highly asymmetric about the equator. The intensified Rossby waves start to stall and decay when approaching the Arabian Sea due to the `blocking' of the sinking dry air mass over North Africa, meanwhile triggering equatorial convection. The mean Hadley circulation plays a critical role in reinitiation of the equatorial Kelvin-Rossby wave packet over the equatorial Indian Ocean.
A finite-domain wavenumber frequency analysis was proposed to objectively measure the interannual variability of the boreal summer intraseasonal oscillation (ISO) in the Asian Pacific region. The strongest interannual variations of the ISO are found in the off-equatorial western North Pacific (WNP). In summers when El Niño is developing, both the westward- and northward-propagating waves with periods of 15 40 and 8 10 days are enhanced in July October. The northward-propagating ISO in the Indian summer monsoon region, however, has little linkage with El Niño Southern Oscillation (ENSO).ENSO affects the northwestward-propagating ISO mode in the WNP through changing the mean circulation. During July October in the El Niño developing year, the easterly vertical shears over the tropical western Pacific are considerably increased, which in turn promote development and northwestward emanation of Rossby waves away from the equatorial western-central Pacific, reinforcing the WNP ISO. In the Indian summer monsoon region, the ENSO-induced circulation changes are too weak to significantly modify the strong easterly sheared monsoon mean circulation. Therefore, the northward-propagating ISO is insensitive to ENSO.Unlike the wintertime Madden Julian oscillation (MJO), which is uncorrelated with ENSO, the May July MJO is strengthened during El Niño developing years. The questions of why there is a seasonal dependence of the MJO ENSO relationship and how ENSO directly affects the May July MJO require further investigations.
Three regionally coupled experiments are conducted to examine the role of Indian and Pacific sea surface temperature (SST) in Indian summer monsoon intraseasonal variability using the National Centers for Environmental Prediction’s Climate Forecast System, a coupled general circulation model. Regional coupling is employed by prescribing daily mean or climatological SST in either the Indian or the Pacific basin while allowing full coupling elsewhere. The results are compared with a fully coupled control simulation. The intraseasonal modes are isolated by applying multichannel singular spectrum analysis on the daily precipitation anomalies.
It is found that the amplitude of the northeastward-propagating mode is weaker when the air–sea interaction is suppressed in the Indian Ocean. The intraseasonal mode is not resolved clearly when the Indian Ocean SST is reduced to daily climatology. Intraseasonal composites of low-level zonal wind, latent heat flux, downward shortwave radiation, and SST provide a picture consistent with the proposed mechanisms of air–sea interaction for the northward propagation. The Pacific SST variability does not seem to be critical for the existence of this mode. The northwestward-propagating mode is obtained in the cases where the Indian Ocean was prescribed by daily mean or daily climatological SST. Intraseasonal SST composites corresponding to this mode are weak.
The synoptic structure of the 10–20-day monsoon mode and this intraseasonal monsoon mode's relationship with the Indian monsoon rainfall are examined with the 1979 summer First GARP Global Experiment IIIb data of the European Centre for Medium-Range Weather Forecasts and the daily 1° × 1° rainfall estimates retrieved from the satellite data by the Goddard Laboratory for Atmospheres. The major findings of this study are as follows. 1) The 10–20-day monsoon mode exhibits a double-cell (either double-high or double-low) structure; one cell is centered at about 15°–20°N and the other at the equator. 2) Both cells of the 10–20-day monsoon mode propagate coherently westward along the Indian monsoon trough and along the equator, respectively. 3) Based upon the zonal wind and local Hadley circulation, the vertical structure of the 10–20-day monsoon mode does not exhibit a phase change. 4) A significant rainfall occurs around low centers of the 10–20-day monsoon mode through the modulation of this monsoon...
Applying harmonic analyses to outgoing longwave radiation (OLR) data at a 2.5° longitude-latitude resolution over a tropical belt between 45°N and 45°S, the harmonics (m = 1–15) were computed for each year in 1975–1977 and 1970–1983. The sum of the first three harmonics (m = 1–3), which is referred to as Y(t), corresponds to the seasonal cycles. The sum of m = 4–15 represents low-frequency oscillations, denoted as L(t), with an approximate period range of 24 to 91 days.
The onset and withdrawal of the Australian summer monsoon appears to be determined by the phase changes of Y(t) and L(t). In comparison, L(t) is more important than Y(t) in determining the onset and withdrawal of the summer monsoon over southern Asia. Over the Northern Hemisphere monsoon region, the low-frequency modes exhibit seasonality, namely, they are more pronounced during the summer than the winter. The same is also true over the Southern Hemisphere monsoon region.
Gridded fields (analyses) of global monthly precipitation have been constructed on a 2.5° latitude-longitude grid for the 17-yr period from 1979 to 1995 by merging several kinds of information sources with different characteristics, including gauge observations, estimates inferred from a variety of satellite observations, and the NCEP-NCAR reanalysis. This new dataset, which the authors have named the CPC Merged Analysis of Precipitation (CMAP), contains precipitation distributions with full global coverage and improved quality compared to the individual data sources. Examinations showed no discontinuity during the 17-yr period, despite the different data sources used for the different subperiods. Comparisons of the CMAP with the merged analysis of Huffman et al. revealed remarkable agreements over the global land areas and over tropical and subtropical oceanic areas, with differences observed over extratropical oceanic areas. The 17-yr CMAP dataset is used to investigate the annual and interannual variability in large-scale precipitation. The mean distribution and the annual cycle in the 17-yr dataset exhibit reasonable agreement with existing long-term means except over the eastern tropical Pacific. The interannual variability associated with the El Niño-Southern Oscillation phenomenon resembles that found in previous studies, but with substantial additional details, particularly over the oceans. With complete global coverage, extended period and improved quality, the 17-yr dataset of the CMAP provides very useful information for climate analysis, numerical model validation, hydrological research, and many other applications. Further work is under way to improve the quality, extend the temporal coverage, and to refine the resolution of the merged analysis.
Impacts of the ocean surface on the representation of the northward-propagating boreal summer intraseasonal oscillation (NPBSISO) over the Indian monsoon region are analyzed using the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and its atmospheric component, the NCEP Global Forecast System (GFS). Analyses are based on forecasts of five strong NPBSISO events during June–September 2005–07.
The inclusion of an interactive ocean in the model is found to be necessary to maintain the observed NPBSISO. The atmosphere-only GFS is capable of maintaining the convection that propagates from the equator to 12°N with reasonable amplitude within the first 15 days, after which the anomalies become very weak, suggesting that the atmospheric internal dynamics alone are not sufficient to sustain the anomalies to propagate to higher latitudes. Forecasts of the NPBSISO in the CFS are more realistic, with the amplitude of precipitation and 850-mb zonal wind anomalies comparable to that in observations for the entire 30-day target period, but with slower northward propagation compared to that observed. Further, the phase relationship between precipitation, sea surface temperature (SST), and surface latent heat fluxes associated with the NPBSISO in the CFS is similar to that in the observations, with positive precipitation anomalies following warm SST anomalies, which are further led by positive anomalies of the surface latent heat and solar radiation fluxes into the ocean.
Additional experiments with the atmosphere-only GFS are performed to examine the impacts of uncertainties in SSTs. It is found that intraseasonal SST anomalies 2–3 times as large as that of the observational bulk SST analysis of Reynolds et al. are needed for the GFS to produce realistic northward propagation of the NPBSISO with reasonable amplitude and to capture the observed phase lag between SST and precipitation. The analysis of the forecasts and the experiments suggests that a realistic representation of the observed propagation of the oscillation by the NCEP model requires not only an interactive ocean but also an intraseasonal SST variability stronger than that of the bulk SST analysis.
The boreal summer intraseasonal variability (BSISV) associated with the 30–50-day mode is represented by the coexistence of three components: poleward propagation of convection over the Indian and tropical west Pacific longitudes and eastward propagation along the equator. The hypothesis that the three components influence each other has been investigated using observed outgoing longwave radiation (OLR), NCEP–NCAR reanalysis, and solutions from an idealized linear model. The null hypothesis is that the three components are mutually independent. Cyclostationary EOF (CsEOF) analysis is applied on filtered OLR to extract the life cycle of the BSISV. The dominant CsEOF mode is significantly tied to the observed spatial rainfall pattern associated with the active/break phases over the Indian subcontinent. The components of the heating patterns from CsEOF analysis serve as prescribed forcings for the dry version of the linear model. This allows one to investigate the possible roles that the regional heat sources and sinks play in driving the large-scale monsoon circulation at various stages of the BSISV life cycle. To understand the interactive nature between convection and circulation, the moist version of the model is forced with intraseasonal SST anomalies.
The linear models reproduce the major features of the BSISV seen in the reanalysis. The linear model suggests three new findings: (i) The circulation anomalies that develop as a Rossby wave response to suppressed convection over the equatorial Indian Ocean associated with the previous break phase of the BSISV results in low-level convergence and tropospheric moisture enhancement over the equatorial western Indian Ocean and helps trigger the next active phase of the BSISV. (ii) The development of convection over the tropical west Pacific forces descent anomalies to the west. This, in conjunction with the weakened cross-equatorial flow due to suppressed convective anomalies over the equatorial Indian Ocean, reduces the tropospheric moisture over the Arabian Sea and promotes westerly wind anomalies that do not recurve over India. As a result the low-level cyclonic vorticity shifts from India to Southeast Asia and break conditions are initiated over India. (iii) The circulation anomalies forced by equatorial Indian Ocean convective anomalies significantly influence the active/break phases over the tropical west Pacific.
The model solutions support the hypothesis that the three components of the BSISV influence each other but do not imply that such an influence is responsible for the space–time evolution of the BSISV. Further, the applicability of the model results to the observed system is constrained by the assumption that linear interactions are sufficient to address the BSISV and that air–sea interaction and transient forcing are excluded.
A composite study of the life cycle of the boreal summer intraseasonal oscillation (BSISO) was performed using data from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis and National Oceanic and Atmospheric Administration polar-orbiting satellites. Because of pronounced differences in their climatologies, the boreal summer periods May-June (MJ) and August-October (AO) were composited separately. Characteristics of the BSISO life cycle common to MJ and AO were initiation and eastward propagation of the convective anomaly over the Indian Ocean, followed by poleward propagation, with the northward-moving branch having greater amplitude than the southward-moving branch. The transition of convection from the Indian Ocean to the western Pacific occurred next, followed by dissipation of the current cycle and initiation of the subsequent cycle. The MJ and AO life cycles were found to have several significant differences. The MJ shows strong eastward movement of convection along the equator in both the Indian and western Pacific Oceans. Convection in AO has a weaker eastward-propagating signal along the equator and displays a discontinuous jump from the Indian Ocean to the western Pacific. In marked contrast to MJ, AO shows strong northwestward propagation of convection in the western Pacific during the latter half of the BSISO life cycle. The change in the BSISO life cycle from MJ to AO reflects the seasonal shift in the distributions of vertical wind shear and low-level specific humidity from early to late summer. Rossby waves emitted by equatorial convection play a critical role in the BSISO in both the Indian and western Pacific Oceans. These waves are instrumental in the northward propagation of convection in MJ and AO. Both MJ and AO composites suggest that air-sea interactions are present in the BSISO, fostering both eastward and northward propagation of convective anomalies in the Indian Ocean and in the western Pacific. The complexity and pronounced seasonal dependence of the BSISO reflected in the composites suggest that its simulation is a rigorous test for general circulation models.
In this paper the elements of a monsoon system are defined, and its
oscillations are determined from spectral analysis of long observational
records. The elements of the monsoon system include pressure of the
monsoon trough, pressure of the Mascarene high, cross-equatorial
low-level jet, Tibetan high, tropical easterly jet, monsoon cloud cover,
monsoon rainfall, dry static stability of the lower troposphere, and
moist static stability of the lower troposphere. The summer monsoon
months over India during normal monsoon rainfall years are considered as
guidelines in the selection of data for the period of this study. The
salient result of this study is that there seems to exist a
quasi-biweekly oscillation in almost all of the elements of the monsoon
system. For some of these elements, such as the surface pressure field,
monsoon rainfall, low-level cross-equatorial jet and monsoon cloudiness,
the amplitude of this oscillation in quasi-biweekly range is very
pronounced. For the spectral representation of the time series, the
product of the spectral density times frequency is used as the ordinate
and the log of the frequency as the abscissa. Dominant modes are also
found in the shorter time scales (<6 days). A sequential ordering of
elements of the monsoon systems for the quasi-biweekly oscillation is
carried out in terms of their respective phase angle. The principal
result here is that soon after the maximum dry and moist static
instabilities are realized in the stabilizing phase, there occur in
sequence an intensification of the monsoon trough, satellite brightness,
Mascarene high, Tibetan high and the tropical easterly jet. Soon after
that the rainfall maximum over central India, arising primarily from
monsoon depressions, is found to be a maximum.In the second part of this
paper we offer some plausible mechanisms for these quasi-biweekly
oscillations.
The relationship between subseasonal and interannual variability of the Asian summer monsoon has been investigated through analysis of the dominant modes of variability in the 40‐year NCEP/NCAR Re‐analysis, with complementary satellite and surface‐based precipitation data. The hypothesis that the characteristics of monsoon subseasonal variability (i.e. weather regimes) are modulated on interannual time‐scales in a systematic and therefore predictable manner has been tested. The null hypothesis is that predictability of the seasonal mean monsoon behaviour requires that the effects of the slowly varying components of the climate system be correctly simulated.
An interannual mode of monsoon variability has been identified which is closely related to the observed seasonal mean all‐India Rainfall (AIR). A counterpart of this mode has also been identified at subseasonal time‐scales which projects strongly on to the daily AIR, confirming that a common mode of monsoon variability exists on sub‐seasonal and interannual time‐scales. It has been shown that the temporal behaviour of this subseasonal mode, as described by the probability distribution function (PDF) of the principal component time series, does not show any evidence of bimodality, the shape of the PDF being Gaussian. Further, it has been shown that the mean of the PDF is systematically and significantly perturbed towards negative (positive) values in weak (strong) monsoon years as categorized in terms of the seasonal mean AIR. This translation in the mean of the PDF, rather than a change in shape of the PDF, suggests that anomalous monsoons are not associated with changes in weather regimes. Further analysis has confirmed that low‐frequency modulation of the basic state is primarily responsible for these shifts in the subseasonal PDFs, supporting the null hypothesis that predictability of the seasonal mean monsoon requires that the effects of the slowly varying components of the climate system be correctly simulated. Thus, model improvements to reduce systematic errors in the mean simulation and the response to low‐frequency boundary forcing may improve the prospects for dynamical seasonal prediction.
However, the results indicate that only a subset of the subseasonal modes are systematically perturbed either by the El Nino Southern Oscillation or in weak vs. strong monsoon years, suggesting that predictability is likely to be limited by the chaotic, internal variability of the monsoon system.
The quasi-biweekly mode (QBM) and the 30–60 day mode are two major intraseasonal oscillations (ISOs) in the tropics. The QBM is known to have a major influence in determining the active and break conditions of the Indian monsoon during the northern summer. A westward-propagating equatorial Rossby wave with quasi-biweekly period influences the Australian monsoon during the northern winter. Universality between the summer and winter QBM is established through analysis of daily circulation and convection data for 10 years. It is shown that the mean spatial structure of the QBM in circulation and convection resembles that of a gravest meridional mode equatorial Rossby wave with wavelength of about 6000 km and westward phase speed of approximately 4.5 m s⁻¹. However, the maximum zonal wind occurs at around 5°N (5°S) during the northern summer (winter). The wave structure appears to be translated northward (southward) by about 5° during the northern summer (winter). The relationship between outgoing long-wave radiation and circulation data indicates that the mode is driven unstable by coupling with moist convection. Similarity in temporal and spatial characteristics of the mode during the two seasons leads us to propose that the same mechanism governs the genesis and scale selection of the mode in both the seasons.