Prince K. Xavier

Increasing trend of extreme rain events over India in a warming environment.

Against a backdrop of rising global surface temperature, the stability of the Indian monsoon rainfall over the past century has been a puzzle. By using a daily rainfall data set, we show (i) significant rising trends in the frequency and the magnitude of extreme rain events and (ii) a significant decreasing trend in the frequency of moderate events over central India during the monsoon seasons from 1951 to 2000. The seasonal mean rainfall does not show a significant trend, because the contribution from increasing heavy events is offset by decreasing moderate events. A substantial increase in hazards related to heavy rain is expected over central India in the future.

Clustering of synoptic activity by Indian summer monsoon intraseasonal oscillations

[1]xa0Active and break phases of the Indian summer monsoon are characterized by enhancement and decrease of precipitation over the monsoon trough region. Using genesis data of monsoon low pressure systems (LPS) and circulation data for the period 1954 to 1993, it is shown that the frequency of occurrence of LPS is nearly 3.5 times higher in the active phase of monsoon as compared to the break phase. In addition, the tracks of these synoptic systems are also strongly spatially clustered along the monsoon trough during the active phase of the monsoon. The enhanced (decreased) frequency of occurrence of LPS during active (break) phases is due to modulation of meridional shear of zonal winds and cyclonic vorticity along the monsoon trough by the intraseasonal oscillations (ISO).

ENSO control on the south Asian monsoon through the length of the rainy season

[1]xa0Being an integral effect of sub-seasonal rain spells over the season, the seasonal mean south Asian monsoon (SAM) rainfall could be affected by change in the length of the rainy season (LRS). An objective definition of the duration of the SAM season has, however, been lacking. Here we show that the meridional gradient of tropospheric temperature (ΔTT) over the SAM region controls the LRS and defines the SAM season. It is further shown that ENSO induces decreased SAM rainfall by regulating the LRS. The atmospheric response to tropical sea surface temperature (SST) over the tropical Pacific during an evolving El Nino reduces ΔTT over the SAM region and shortens LRS by delaying the onset and advancing the withdrawal. The strong negative correlation between LRS and ENSO related SST has remained steady and provides basis for improved prediction of seasonal mean SAM rainfall variability.

Dynamics of “internal” interannual variability of the Indian summer monsoon in a GCM

1] The poor predictability of the Indian summer monsoon ( ISM) appears to be due to the fact that a large fraction of interannual variability (IAV) is governed by unpredictable internal low frequency variations. Mechanisms responsible for the internal IAV of the monsoon have not been clearly identified. Here, an attempt has been made to gain insight regarding the origin of internal IAV of the seasonal ( June - September, JJAS) mean rainfall from internal IAV of the ISM simulated by an atmospheric general circulation model (AGCM) driven by fixed annual cycle of sea surface temperature (SST). The underlying hypothesis that monsoon ISOs are responsible for internal IAV of the ISM is tested. The spatial and temporal characteristics of simulated summer intraseasonal oscillations ( ISOs) are found to be in good agreement with those observed. A long integration with the AGCM forced with observed SST, shows that ISO activity over the Asian monsoon region is not modulated by the observed SST variations. The internal IAV of ISM, therefore, appears to be decoupled from external IAV. Hence, insight gained from this study may be useful in understanding the observed internal IAV of ISM. The spatial structure of the ISOs has a significant projection on the spatial structure of the seasonal mean and a common spatial mode governs both intraseasonal and interannual variability. Statistical average of ISO anomalies over the season ( seasonal ISO bias) strengthens or weakens the seasonal mean. It is shown that interannual anomalies of seasonal mean are closely related to the seasonal mean of intraseasonal anomalies and explain about 50% of the IAV of the seasonal mean. The seasonal mean ISO bias arises partly due to the broad-band nature of the ISO spectrum allowing the time series to be aperiodic over the season and partly due to a non-linear process where the amplitude of ISO activity is proportional to the seasonal bias of ISO anomalies. The later relation is a manifestation of the binomial character of rainfall time series. The remaining 50% of the IAV may arise due to land-surface processes, interaction between high frequency variability and ISOs, etc.

Potential predictability and extended range prediction of Indian summer monsoon breaks

Extended range prediction (two to three weeks in advance) of Indian summer monsoon active (rainy) and break (dry) phases are of great importance for agricultural planning and water management. Using daily rainfall and circulation data for 23 years, a fundamental property of the monsoon intraseasonal oscillations (ISOs) is discovered and shown that the potential predictability limit (~20 days) of monsoon breaks is significantly higher than that for active conditions (~10 days). An empirical model for prediction of monsoon ISOs is then constructed and feasibility of useful prediction of monsoon breaks up to 18 days in advance is demonstrated.

An Analog Method for Real-Time Forecasting of Summer Monsoon Subseasonal Variability

A physically based empirical real-time forecasting strategy to predict the subseasonal variations of the Indian summer monsoon up to four–five pentads (20–25 days) in advance has been developed. The method is based on the event-to-event similarity in the properties of monsoon intraseasonal oscillations (ISOs). This two-tier analog method is applied to NOAA outgoing longwave radiation (OLR) pentad averaged data that have sufficiently long records of observation and are available in nearly real time. High-frequency modes in the data are eliminated by reconstructing the data using the first 10 empirical orthogonal functions (EOFs), which together explain about 75% of the total variance. In the first level of the method, the spatial analogs of initial condition pattern are identified from the modeling data. The principal components (PCs) of these spatial analogs, whose evolution history of the latest five pentads matches that of the initial condition pattern, are considered the temporal PC analogs. Predictions are generated for each PC as the average evolution of PC analogs for the given lead time. Predicted OLR values are constructed using the EOFs and predicted PCs. OLR data for 1979–99 are used as the modeling data and independent hindcasts are generated for the period 2000–05. The skill of anomaly predictions is rather high over the central and northern Indian region for lead times of four–five pentads. The phases and amplitude of intraseasonal convective spells are predicted well, especially the long midseason break of 2002 that resulted in large-scale drought conditions. Skillful predictions can be made up to five pentads when started from an active initial state, whereas the limit of useful predictions is about two–three pentads when started from break initial conditions. An important feature of this method is that unlike some other empirical methods to forecast monsoon ISOs, it uses minimal time filtering to avoid any possible endpoint effects and hence may be readily used for real-time applications. Moreover, as the modeling data grow with time as a result of the increased number of observations, the number of analogs would also increase and eventually the quality of forecasts would improve.

Impacts of Boreal Winter Monsoon Cold Surges and the Interaction with MJO on Southeast Asia Rainfall

AbstractTRMM rainfall data from 1998–2012 are used to study the impacts and interactions of cold surges (CSs) and the Madden–Julian oscillation (MJO) on rainfall over Southeast Asia during the boreal winter season from November to February. CSs are identified using a new large-scale index. The frequencies of occurrences of these two large-scale events are comparable (about 20% of the days each), but the spatial pattern of impacts show differences resulting from the interactions of the general flow with the complex orography of the region. The largest impact of CSs occurs in and around the southern South China Sea as a result of increased low-level convergence on the windward side of the terrain and increased shear vorticity off Borneo that enhances the Borneo vortex. The largest impact of the MJO is in the eastern equatorial Indian Ocean, sheltered from CSs by Sumatra. In general CSs are significantly more likely to trigger extreme rainfall. When both systems are present, the rainfall pattern is mainly co...