Northeast monsoon rainfall variability over south peninsular India and its teleconnections

Rainfall over south peninsular India during the northeast (NE) monsoon season (Oct–Dec) shows significant interannual variation. In the present study, we relate the northeast monsoon rainfall (NEMR) over south peninsular India with the major oscillations like El Ni?o Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Equatorial Indian Ocean Oscillation (EQUINOO) in the Indian and Pacific Oceans. For establishing the teleconnections, sea surface temperature, outgoing long wave radiation, and circulation data have been used. The present study reveals that the positive phase of ENSO, IOD, and EQUINOO favor the NEMR to be normal or above normal over southern peninsular India. The study reveals that the variability of NEMR over south peninsula can be well explained by its relationship with positive phase of ENSO, IOD, and EQUINOO.     

The Variability of the Interannual Oscillations of the Indian Summer Monsoon Rainfall

A new method of analysis namely, Singular Spectrum Analysis (SSA) is applied to the Indian Summer Monsoon (June-September) Rainfall (ISMR) series. The method is efficient in extracting the statistically significant oscillations with periods 2.8 and 2.3 year from the white noise of the ISMR series. The study shows that 2.8 / 2.3 year cycle captures the variability of the ISMR related to Southern Oscillation / Quasi Biennial Oscillation. The temporal structure of these oscillations show that these are in phase in extreme (excess and drought) monsoon conditions as well as in El Nino Southern Oscillation (ENSO) years. Both these oscillations show minimum variability during the period 1920-1940 and there is an increasing trend in the variability of these oscillations in the recent decades. The study enables to obtain pure signal consisting of reconstructed time series using these two Oscillations, from the original white noise series.     

Surface pressure and summer monsoon rainfall over India

The relationship between the all-India summer monsoon rainfall and surface pressure over the Indian region has been examined to obtain a useful predictor for the monsoon rainfall. The data series of all-India monsoon rainfall and the mean pressures of three seasons before and after the monsoon season as well as the winter-to-spring pressure tendency (MAM-DJF) at 100 stations for the period 1951-1980 have been used in the analysis. The all-India monsoon rainfall is negatively correlated with the pressure of the spring (MAM) season preceding the monsoon and winter-to-spring seasonal difference as pressure tendency (MAM-DJF), at almost all the stations in India, and significantly with the pressures over central and northwestern regions. The average mean sea level pressure of six stations (Jodhpur, Ahmedabed, Bombay, Indore, Sagar and Akola) in the Western Central Indian (WCI) region showed highly significant (at 1% level) and consistent CCs of -0.63 for MAM and -0.56 for MAM-DJF for the period 1951–1980. Thus, the pre-monsoon seasonal pressure anomalies over WCI could provide a useful parameter for the long-range forecasting scheme of the Indian monsoon rainfall.     

Relationships between interannual variability in the Arabian Sea and Indian summer monsoon rainfall

Summary The interannual variability of the monthly mean upper layer thickness for the central Arabian Sea (5°N-15° N and 60° E-70° E) from a numerical model of the Indian Ocean during the period 1954–1976 is investigated in relation to Indian monsoon rainfall variability. The variability in the surface structure of the Somali Current in the western Arabian Sea is also briefly discussed. It is found that these fields show a great deal of interannual variability that is correlated with variability in Indian monsoon rainfall. Model upper layer thickness (H) is taken as a surrogate variable for thermocline depth, which is assumed to be correlated with sea surface temperature. In general, during the period 1967 to 1974, which is a period of lower than normal monsoon rainfall, the upper ocean warm water sphere is thicker (deeper thermocline which implies warmer surface water); in contrast, during the period 1954–1966, which is a period of higher than normal monsoon rainfall, the upper warm water sphere is thinner (shallower thermocline which implies cooler surface water). The filtered time series of uppper layer thickness indieates the presence of a quasi-biennial oscillation (QBO) during the wet monsoon period, but this QBO signal is conspicuously absent during the dry monsoon period.Since model H primarily responds to wind stress curl, the interannual variability of the stress curl is investigated by means of an empirical orthogonal function (EOF) analysis. The first three EOF modes represent more than 72% of the curl variance. The spatial patterns for these modes exhibit many elements of central Arabian Sea climatology. Features observed include the annual variation in the intensity of the summer monsoon ridge in the Arabian Sea and the annual zonal oscillation of the ridge during pre- and post-monsoon seasons. The time coefficients for the first EOF amplitude indicate the presence of a QBO during the wet monsoon period only, as seen in the ocean upper layer thickness.The variability in the model upper layer thickness is a passive response to variability in the wind field, or more specifically to variability in the Findlater Jet. When the winds are stronger, they drive stronger currents in the ocean and have stronger curl fields associated with them, driving stronger Ekman pumping. They transport more moisture from the southern hemisphere toward the Indian subcontinent, and they also drive a greater evaporative heat flux beneath the Findlater Jet in the Arabian Sea. It has been suggested that variability in the heat content of the Arabian Sea drives variability in Indian monsoon rainfall. The results of this study suggest that the opposite is true, that the northern Arabian Sea responds passively to variability in the monsoon system.With 10 Figures     

Pre-monsoon surface pressure and summer monsoon rainfall over India

Summary The relationship between the surface air pressure field during the pre-monsoon months and the Indian summer monsoon rainfall is analysed using climate data from 105 stations situated in Eurasia between 0°–60° N and 20°–100° E. Moreover, grid-point data for the whole northern hemisphere are used. Pressure during April over an area around 50° N and 35° E is found to show a significant negative correlation with the subsequent monsoon rainfall. During May the pressure over a large part of the study area south of 40° N shows a significant correlation with its highest value in the heat low region over Pakistan. It is assumed that monitoring of pressure variations over this region may be useful in predicting monsoon rainfall, particularly the rainfall during the first half of the season. Certain limitations of the climate data in this region are also discussed.With 5 Figures     

Some Unique Characteristics of Atmospheric Interannual Variability in Rainfall Time Series over India and the United Kingdom

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Signatures of a universal spectrum for atmospheric interannual variability in some disparate climatic regimes

Summary Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations on all space-tie scales ranging from turbulence (centimetersseconds) to climate (kilometers-years). Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures ofself-organized criticality. Standard models in meteorological theory cannot explain satisfactorily the observed self-organized criticality in atmospheric flows. Mathematical models for simulation and prediction of atmospheric flows are nonlinear and do not possess analytical solutions. Finite precision computer realizations of nonlinear models give unrealistic solutions because ofdeterministic chaos, a direct consequence of round-off error growth in iterative numerical computations. Recent studies show that roundoff error doubles on an average for each iteration of iterative computations. Round-off error propagates to the main stream computation and gives unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. An alternative non-deterministic cell dynamical system model for atmospheric flows described in this paper predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics. The model provides universal quantification for self-organized criticality in terms of the statistical normal distribution. Model predictions are in agreement with a majority of observed spectra of time series of several standard climatological data sets representative of disparate climatic regimes. Universal spectrum for natural climate variability rules out linear trends. Man-made greenhouse gas related atmospheric warming will result in intensification of natural climate variability, seen immediately in high frequency fluctuations such as QBO and ENSO and even shorter timescales. Model concepts and results of analyses are discussed with reference to possible prediction of climate change.With 11 Figures     

New statistical models for long-range forecasting of southwest monsoon rainfall over India

The India Meteorological Department (IMD) has been issuing long-range forecasts (LRF) based on statistical methods for the southwest monsoon rainfall over India (ISMR) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the recent deficient monsoon years of 2002 and 2004. In this paper, we report the improved results of new experimental statistical models developed for LRF of southwest monsoon seasonal (June–September) rainfall. These models were developed to facilitate the IMD’s present two-stage operational forecast strategy. Models based on the ensemble multiple linear regression (EMR) and projection pursuit regression (PPR) techniques were developed to forecast the ISMR. These models used new methods of predictor selection and model development. After carrying out a detailed analysis of various global climate data sets; two predictor sets, each consisting of six predictors were selected. Our model performance was evaluated for the period from 1981 to 2004 by sliding the model training period with a window length of 23 years. The new models showed better performance in their hindcast, compared to the model based on climatology. The Heidke scores for the three category forecasts during the verification period by the first stage models based on EMR and PPR methods were 0.5 and 0.44, respectively, and those of June models were 0.63 and 0.38, respectively. Root mean square error of these models during the verification period (1981–2004) varied between 4.56 and 6.75% from long period average (LPA) as against 10.0% from the LPA of the model based on climatology alone. These models were able to provide correct forecasts of the recent two deficient monsoon rainfall events (2002 and 2004). The experimental forecasts for the 2005 southwest monsoon season based on these models were also found to be accurate.     

The Interannual variability of rainfall over homogeneous regions of Indian summer monsoon

Theoretical and Applied Climatology - The Indian subcontinent, due to its enormous variety of geographical features, is associated with inhomogeneity. Hence, in the present study, we have...     

Long-term variability in the date of monsoon onset over western India

The date of onset of the southwest monsoon in western India is critical for farmers as it influences the timing of crop plantation and the duration of the summer rainy season. Identifying long-term variability in the date of monsoon onset is difficult, however, as onset dates derived from the reanalysis of instrumental rainfall data are only available for the region from 1879. This study uses documentary evidence and newly uncovered instrumental data to reconstruct annual monsoon onset dates for western India for the period 1781–1878, extending the existing record by 97 years. The mean date of monsoon onset over the Mumbai (Bombay) area during the reconstruction period was 10 June with a standard deviation of 6.9 days. This is similar to the mean and standard deviation of the date of monsoon onset derived from instrumental data for the twentieth century. The earliest identified onset date was 23 May (in 1802 and 1839) and the latest 22 June (in 1825). The longer-term perspective provided by this study suggests that the climatic regime that governs monsoon advance over western India did not change substantially from 1781 to 1955. Monsoon onset over Mumbai has occurred at a generally later date since this time. Our results indicate that this change is unprecedented during the last 230 years. Following a discussion of the results, the nature of the relationship between the date of monsoon onset and the El Niño-Southern Oscillation is discussed. This relationship is shown to have been stable since 1781.     

Surface and upper air temperatures over India in relation to monsoon rainfall

Summary The relationship between the all-India summer monsoon rainfall and surface/upper air (850, 700, 500 and 200 mb levels) temperatures over the Indian region and its spatial and temporal characteristics have been examined to obtain a useful predictor for the monsoon rainfall. The data series of all-India and subdivisional summer monsoon rainfall and various seasonal air temperatures at 73 surface observatories and 9 radiosonde stations (1951–1980) have been used in the analysis. The Correlation Coefficients (CCs) between all-India monsoon rainfall and seasonal surface air temperatures with different lags relative to the monsoon season indicate a systematic relationship.The CCs between the monsoon rainfall and surface-air temperature of the preceding MAM (pre-monsoon spring) season are positive over many parts of India and highly significant over central and northwestern regions. The average surface air temperature of six stations i.e., Jodhpur, Ahmedabad, Bombay, Indore, Sagar and Akola in this region (Western Central India, WCI) showed a highly significant CC of 0.60 during the period 1951–1980. This relationship is also found to be consistently significant for the period from 1950 to present, though decreasing in magnitude after 1975. WCI MAM surface air temperature has shown significant CCs with the monsoon rainfall over eleven sub-divisions mainly in northwestern India, i.e., north of 15 °N and west of 80 °E.Upper air temperatures of the MAM season at almost all the stations and all levels considered show positive CCs with the subsequent monsoon rainfall. These correlations are significant at some central and north Indian stations for the lower and middle tropospheric temperatures.The simple regression equation developed for the period 1951–1980 isy = – 183.20 + 8.83x, wherey is the all-India monsoon rainfall in cm andx is the WCI average surface air temperature of MAM season in °C. This equation is significant at 0.1% level. The suitability of this parameter for inclusion in a predictive regression model along with five other global and regional parameters has been discussed. Multiple regression analysis for the long-range prediction of monsoon rainfall, using several combinations of these parameters indicates that the improvement of predictive skill considerably depends upon the selection of the predictors.With 9 Figures     

Impact of MJO on the intraseasonal variation of summer monsoon rainfall over India

The summer monsoon rainfall over India exhibits strong intraseasonal variability. Earlier studies have identified Madden Julian Oscillation (MJO) as one of the most influencing factors of the intraseasonal variability of the monsoon rainfall. In this study, using India Meteorological Department (IMD) high resolution daily gridded rainfall data and Wheeler?CHendon MJO indices, the intra-seasonal variation of daily rainfall distribution over India associated with various Phases of eastward propagating MJO life cycle was examined to understand the mechanism linking the MJO to the intraseasonal variability. During MJO Phases of 1 and 2, formation of MJO associated positive convective anomaly over the equatorial Indian Ocean activated the oceanic tropical convergence zone (OTCZ) and the resultant changes in the monsoon circulation caused break monsoon type rainfall distribution. Associated with this, negative convective anomalies over monsoon trough zone region extended eastwards to date line indicating weaker than normal northern hemisphere inter tropical convergence zone (ITCZ). The positive convective anomalies over OTCZ and negative convective anomalies over ITCZ formed a dipole like pattern. Subsequently, as the MJO propagated eastwards to west equatorial Pacific through the maritime continent, a gradual northward shift of the OTCZ was observed and negative convective anomalies started appearing over equatorial Indian Ocean. During Phase 4, while the eastwards propagating MJO linked positive convective anomalies activated the eastern part of the ITCZ, the northward propagating OTCZ merged with monsoon trough (western part of the ITCZ) and induced positive convective anomalies over the region. During Phases 5 and 6, the dipole pattern in convective anomalies was reversed compared to that during Phases 1 and 2. This resulted active monsoon type rainfall distribution over India. During the subsequent Phases (7 and 8), the convective and lower tropospheric anomaly patterns were very similar to that during Phase 1 and 2 except for above normal convective anomalies over equatorial Indian Ocean. A general decrease in the rainfall was also observed over most parts of the country. The associated dry conditions extended up to northwest Pacific. Thus the impact of the MJO on the monsoon was not limited to the Indian region. The impact was rather felt over larger spatial scale extending up to Pacific. This study also revealed that the onset of break and active events over India and the duration of these events are strongly related to the Phase and strength of the MJO. The break events were relatively better associated with the strong MJO Phases than the active events. About 83% of the break events were found to be set in during the Phases 7, 8, 1 and 2 of MJO with maximum during Phase 1 (40%). On the other hand, about 70% of the active events were set in during the MJO Phases of 3 to 6 with maximum during Phase 4 (21%). The results of this study indicate an opportunity for using the real time information and skillful prediction of MJO Phases for the prediction of break and active conditions which are very crucial for agriculture decisions.     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

Stratospheric zonal wind and temperature in relation to summer monsoon rainfall over India

Summary  The interannual variability of the Indian summer monsoon (June–September) rainfall is examined in relation to the stratospheric zonal wind and temperature fluctuations at three stations, widely spaced apart. The data analyzed are for Balboa, Ascension and Singapore, equatorial stations using recent period (1964–1994) data, at each of the 10, 30 and 50 hPa levels. The 10 hPa zonal wind for Balboa and Ascension during January and the 30 hPa zonal wind for Balboa during April are found to be positively correlated with the subsequent Indian summer monsoon rainfall, whereas the temperature at 10 hPa for Ascension during May is negatively correlated with Indian summer monsoon rainfall. The relationship with stratospheric temperatures appears to be the best, and is found to be stable over the period of analysis. Stratospheric temperature is also significantly correlated with the summer monsoon rainfall over a large and coherent region, in the north-west of India. Thus, the 10 hPa temperature for Ascension in May appears to be useful for forecasting summer monsoon rainfall for not only the whole of India, but also for a smaller region lying to the north-west of India. Received July 30, 1999 Revised March 17, 2000     

东亚、东南亚、南亚地区降水的年变化和年际变化

本文应用了1951—1980年4—9月月平均和1961—1970年4—9月旬平均降水资料,研究了东亚、南亚和东南亚降水的年变化和年际变化,分析了中国东部、西部降水差异及长江流域夏季旱涝的特征.结果指出,东亚、南亚和东南亚4—9月200mm以上大雨区分布的总趋势呈西南-东北向,它和西南季风的走向近于一致.在这条大雨带之中,包括了三种不同类型的降水.此外,中国江淮地区旬降水量在7—9月存在周期约为20天左右的准周期振荡现象,它和中国西部的降水分布完全不同.中国华南和华中的降水与El Nino现象具有相反的关系.统计结果表明,长江中下游夏季发生持续性多雨和持续性少雨的机率只有23％,大部分年份属于正常降水年份.最后也讨论了影响长江中下游地区夏季旱涝的环流因素.     

Climatology and Interannual Variability of the Southeast Asian Summer Monsoon

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On the relations of the rainfall variability and distribution with the mean rainfall over India

Summary Along with averages, rainfall variability and distribution are important climatological information. In this study, using 114 years (1871–1984) data of 306 stations, it is demonstrated that the variability and spatial distribution of annual, summer monsoon and monthly rainfall are highly dependent upon the respective period mean rainfall variation over India. The magnitude of three selected absolute measures of variability, e.g. standard deviation, absolute mean deviation and mean absolute interannual variability is found to increase linearly with mean rainfall.In order to describe the relation between the rainfall frequency distribution and the mean rainfall, a linear regression between the rainfall amount expected with a specified exceedance/non-exceedance probability and the mean rainfall amount is presented. Highly significant linear curves for a large number of probabilities specified in an average probability diagram clearly demonstrate the dependence of the rainfall frequency distribution on mean rainfall over India.With 8 Figures