Intraseasonal and Interannual Variability of Rainfall over India

The present study has examined the combined effect of MJO, ENSO and IOD on the intraseasonal and interannual variability of northeast monsoon rainfall over south peninsular India. The study has revealed that the intraseasonal variation of daily rainfall over south peninsular India during NEM season is associated with various phases of eastward propagating MJO life cycle. Positive rainfall anomaly over south peninsular India and surrounding Indian Ocean (IO) is observed during the strong MJO phases 2, 3 and 4; and negative rainfall anomaly during the strong MJO phases 5,6,7,8 and 1. Above normal (below normal) convection over south peninsular India and suppressed convection over east Indian and West Pacific Ocean, high pressure (low pressure) anomaly over West Pacific Ocean, Positive (negative) SST anomalies over equatorial East and Central Pacific Ocean and easterly wind anomaly (westerly anomaly) over equatorial Indian Ocean are the observed features during the first three MJO (5, 6, 7) phases and all these features are observed in the excess (drought) NEMR composite. This suggests that a similar mode of physical mechanism is responsible for the intraseasonal and interannual variability of northeast monsoon rainfall. The number of days during the first three phases (last four phases) of MJO, where the enhanced convection and positive rainfall anomaly is over Indian Ocean (East Indian ocean and West Pacific Ocean), is more (less) during El Nino and IOD years and less during La Nina and NIOD years and vice versa. The observed excess (deficit) rainfall anomaly over west IO and south peninsular India and deficit (excess) rainfall anomaly over east IO including Bay of Bengal and West Pacific Ocean suggest that the more (less) number of first three phases during El Nino and IOD (La Nina and Negative IOD) is due to the interaction between eastward moving MJO and strong easterlies over equatorial IO present during El Nino and IOD years. This interaction would inhibit the development of long duration MJO and would result in short duration high frequency MJO type which confined over Indian Ocean and south peninsular India and hence make all the El Nino and IOD years to be excess rainfall years for NEM season.

Intraseasonal variability of rainfall over the Indian subcontinent (IS) and the Tibetan Plateau (TP) has been discussed widely but often separately. In this study, we investigate the covariability of rainfall across the IS and the TP on intraseasonal time scales and its impact on interannual variability of regional rainfall. The most dominant mode of rainfall intraseasonal variability across the region features a dipole pattern with significant out-of-phase rainfall anomalies between the southeastern TP and the central and northern IS. This dipole rainfall pattern is associated with intraseasonal oscillations (ISOs) of 10–20 days and 30–60 days, especially the latter. An active spell of rainfall in the central and northern IS (southeastern TP) is associated with the strengthening (northward shift) of water vapor transport of the Indian summer monsoon, resulting in more water vapor entering into the central and northern IS (southeastern TP) and thus more rainfall. The 10–20-day ISO of the dipole rainfall pattern is caused by the 10–20-day atmospheric ISO in both the tropics and the extratropics, whereas the 30–60-day ISO of the dipole rainfall pattern is only associated with atmospheric ISO in the tropics. The dipole rainfall pattern resembles the most dominant mode of interannual variability of July–August mean rainfall. The 30–60-day ISO of the dipole rainfall pattern has an important contribution to the dipole pattern of July–August mean rainfall anomalies on an interannual time scale due to the different frequencies of occurrence of the active and break phases.

The paper deals with the variability of summer-monsoon rainfall during normal, flood and drought years over India. During flood years the monsoon rainfall increases mostly all over parts of the country and large area less than 100 cm isohytel covers Orissa and adjoining Madhya Pradesh. During drought years the rainfall amount decreases over the entire country and isohytel of 100 cm shrinks to almost a point. The variability of monsoon rainfall from flood to normal to drought years depends upon the number of depression/low-pressure area which form over the North Bay and move inland. To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies has been performed by using the Empirical Orthogonal Function analysis. Further Empirical Orthogonal Function (EOF) analysis is carried out on these data to find out the nature of rainfall distribution in different monsoon categories namely normal, flood and drought years. This technique thus serves to identify spatial and temporal patterns characteristics of possible physical significance.

The summer monsoon rainfall over Orissa, a state of eastern India, shows characteristic intraseasonal and interannual variability, due to interaction of basic westerly flow with orography and the synoptic scale monsoon disturbances including low-pressure systems and cyclonic circulations extending upto mid-tropospheric level (LPSC). These systems normally develop over the north Bay of Bengal and move west-northwestwards along the monsoon trough. The essence of this study is to find out the main features of the intraseasonal variability of daily monsoon rainfall over Orissa in relation to synoptic systems like LPSC and its implication on the interannual variation of rainfall. For this purpose, the actual and mean daily rainfall data of 31 uniformly distributed stations, six homogeneous regions and Orissa as a whole during monsoon season (June–September) over a period of 20 years (1980–1999) are subjected to auto-correlation and power spectrum analyses. The actual and average daily scores of significant EOFs and actual daily occurrence along with daily probability of occurrence of the LPSC influencing rainfall over Orissa during the same period are also subjected to auto-correlation and power spectrum analyses.

The Indo-Gangetic Plains (IGPs) are densely populated and agriculturally productive areas with strong interannual and intraseasonal rainfall variability. The intraseasonal rainfall variability over IGPs is due to variation in sea surface temperature in the equatorial Indian Ocean. The intense rainfall activity over IGPs is mainly convection-driven, which may be linked with Madden–Julian Oscillation (MJO). The threshold for exceptionally heavy rainfall during the period 1979–2012 is based on the analysis of heavy rainfall episodes (percentage departure in daily rainfall (PDR ≥ 700%). The thresholds for extremely strong MJO events show highest departure in MJO amplitude (PDA ≤ 200%). The present study aims to find the simultaneous relationship between 60 and 30-day cycles of rainfall variability over IGPs and linked with MJO amplitude variability for the period 1979–2012. Further, the 30-day cycle of rainfall variability is elaborately studied for different phases of MJO. The monthly and daily variability of IGPs rainfall as well as MJO amplitude is analysed to find important intense rainfall and MJO events. The results suggest that the monthly rainfall variability is caused due to synoptic scale weather systems like monsoon trough oscillation and corresponding pressure fluctuations over IGPs. The exceptionally intense rainfall activity during onset and retreat phases is observed to be associated with MJO phases 6–8. The intense rainfall activity during active-break phase is observed to be associated with MJO phases 3–5. The intense rainfall events during break phase are observed along foothills of Himalaya. The day-to-day rainfall variability is due to interaction between monsoon circulation and MJO.

Flash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

The space–time structure of the active and break periods of the Indian monsoon has been studied using 70-yr-long high-resolution gridded daily rainfall data over India. The analysis of lagged composites of rainfall anomalies based on an objective categorization of active and break phases shows that the active (break) cycle, with an average life of 16 days, starts with positive (negative) rainfall anomalies over the Western Ghats and eastern part of central India and intensifies and expands to a region covering central India and parts of north India during the peak phase, while negative (positive) anomalies cover the sub-Himalayan region and southeast India. During the final stage of the active (break) period, the positive (negative) rainfall anomalies move toward the foothills of the Himalayas while peninsular India is covered with opposite sign anomalies. The number of days on which lows and depressions are present in the region during active and break periods is consistent with the rainfall analysis. The number of depressions during the active phase is about 7 times that during the break phase.Using multichannel singular spectrum analysis of the daily rainfall anomalies, the seasonal monsoon rainfall is found to consist of two dominant intraseasonal oscillations with periods of 45 and 20 days and three seasonally persisting components. The 45- and 20-day oscillations are manifestations of the active and break periods but contribute very little to the seasonal mean rainfall. The seasonally persisting components with anomalies of the same sign, and covering all of India, have a very high interannual correlation with the total seasonal mean rainfall. These results support a conceptual model of the interannual variability of the monsoon rainfall consisting of seasonal mean components and a statistical average of the intraseasonal variations. The success in the prediction of seasonal mean rainfall depends on the relative strengths of the seasonally persisting components and intraseasonal oscillations.

Results from the United Kingdom Meteorological Office (UKMO) coupled climate model have been analysed over the Indian subcontinent in order to validate the model's performance and to assess the changes in climate and its variability in a simulation with a 1 per cent increase per year in CO2 (compound).The model produces a reasonable simulation of present‐day climate over the Indian subcontinent. At the time of CO2 doubling, the model simulates temperature increases of the order of 1 K to 4 K over the Indian subcontinent during winter and monsoon seasons. The model‐simulated monsoon circulation shifts by 10° latitude towards the north and intensifies by approximately 10 per cent in the warmer atmosphere. The interannual variability of monsoon onset dates and intraseasonal variability of monsoon rainfall are not significantly different when the CO2 concentration doubles. However, the model simulates increased interannual variability of the monsoon rainfall and a greater number of heavy rainfall days during the monsoon.

A recently developed non‐deterministic cell dynamic system model for atmospheric flows is summarized in this paper. The model predicts quantum‐like mechanics for atmospheric flows with inherent long‐range spatiotemporal correlations, now identified as signatures of self‐organized criticality or deterministic chaos. The model enables quantification of the power spectrum of temporal fluctuations of atmospheric flows in terms of the universal and unique characteristics of the statistical normal distribution. The model predictions are in agreement with continuous periodogram analysis of three sets each of 30 years (1871–1900, 1901–1930, 1956–1985) and one set of 25 years (1931–1955) summer monsoon rainfall time series for 29 meteorological subdivisions in the Indian region. The important results of the present study are as follows. (i) The power spectrum of the temporal fluctuations of rainfall follows the universal inverse power law form of the statistical normal distribution, with the square of the eddy amplitude representing the eddy probability density corresponding to the normalized standard deviation t equal to (logλlogλ50)‐1; where λ is the period length in years and λ50 the period up to which the cumulative percentage contribution to total variance is equal to 50 and t=0. (ii) Periodicities in rainfall up to 3–4 years contribute to as much as 50 per cent of the total variance.A universal spectrum for interannual variability in summer monsoon rainfall indicates predictability of the overall pattern of rainfall fluctuations. A relatively recent and short period rainfall time series, such as the 30‐year period 1956–1985 in this study, enables identification of the universal structure of atmospheric variability. Further, short period fluctuations that are major contributors to interannual variability can be identified accurately in the 30‐year data sets and provide means for estimating the near future (up to 4 years) rainfall variation.

Large intraseasonal rainfall variations are identified over the southern South China Sea (SSCS), tropical southeastern Indian Ocean (SEIO), and east coast of the Philippines (EPHI) in boreal winter. The present study contrasts origins and propagations and investigates interrelations of intraseasonal rainfall variations on the 10–20- and 30–60-day time scales in these regions. Different origins are identified for intraseasonal rainfall anomalies over the SSCS, SEIO, and EPHI on both time scales. On the 10–20-day time scale, strong northerly or northeasterly wind anomalies related to the East Asian winter monsoon (EAWM) play a major role in intraseasonal rainfall variations over the SSCS and EPHI. On the 30–60-day time scale, both the intraseasonal signal from the tropical Indian Ocean and the EAWM-related wind anomalies contribute to intraseasonal rainfall variations over the SSCS, whereas the EAWM-related wind anomalies have a major contribution to the intraseasonal rainfall variations over the EPHI. No relation is detected between the intraseasonal rainfall variations over the SEIO and the EAWM on both the 10–20-day and 30–60-day time scales. The anomalies associated with intraseasonal rainfall variations over the SSCS and EPHI propagate northwestward and northeastward, respectively, on the 10–20- and 30–60-day time scales. The intraseasonal rainfall anomalies display northwestward and northward propagation over the Bay of Bengal, respectively, on the 10–20- and 30–60-day time scales.

Rwanda is a landlocked country in central-eastern Africa. As a country highly dependent on rain-fed agriculture, Rwanda is vulnerable to rainfall variability. Observational data show that there are two rainy seasons in Rwanda, i.e., the long rainy season and the short rainy season. This study mainly focuses on the dominant intraseasonal rainfall mode during the long rainy season (February–May), and evaluates the forecast skill for the intraseasonal variability (ISV) over Rwanda and its surrounding regions in a state-of-the-art dynamic model. During the long rainy season, observational results reveal that the dominant intraseasonal rainfall mode in Rwanda exhibits a significant variability on the 10–25-day time scale. One-point-correlation analysis further unveils that the 10–25-day intraseasonal rainfall variability in Rwanda co-varies with that in its adjacent areas, indicating that the overall 10–25-day rainfall variability in Rwanda and its adjacent regions (8°S–3°N, 29°–37°E) should be considered collectively when studying the dominant intraseasonal rainfall variability in Rwanda. Composite results show that the development of the 10–25-day rainfall variability is associated with the anomalous westerly wind in Rwanda and its surrounding regions, which may trace back to a pair of westward-propagating equatorial Rossby waves. Based on the observational findings, an ISO_rainfall_index and an ISO_wind_index are proposed for quantitatively evaluating the forecast skill. The ECMWF model has a comparable skill in predicting the wind index and the rainfall index, with both indices showing a skill of 18 days.摘要非洲中东部地区的经济主要依靠自给农业支撑, 该地区农业经济对降水的变化尤为敏感. 本文以卢旺达为例, 观测分析指出卢旺达的次季节降雨主要集中在10–25天; 根据次季节尺度降水变率的单点相关方法, 发现卢旺达的次季节降水变率和周围区域变化一致; 进一步合成结果显示该地区次季节降水变率与异常西风有关, 这可追溯到赤道地区西传的赤道Rossby波. 最后, 本文评估了当前动力模式ECMWF对卢旺达地区(即非洲中东部)次季节降水变率的预报能力, 发现EC模式在对该区域降水和相关风场指数的预报技巧都在18天左右, 且预报技巧表现出一定的年际差异, 这可能与热带太平洋的背景海温信号有关. 该工作增进了当前对非洲中东部地区的次季节降水变率和预测水平的认知, 并且对该地区国家粮食安全和防灾减灾具有启示性意义.

Chapter 5 - Climate variability, observed climate trends, and future climate projections for Sri Lanka

Rainfall over Africa varies across timescales of a few days to several weeks due to several tropical and extratropical modes of variability. Excessive rains or prolonged drought regularly result in natural disasters and have thus a severe impact on the local economy, agriculture, spread of diseases, and entire ecosystems. The dynamical nature of the atmosphere allows the existence of planetary balanced modes, which are called Rossby waves, and smaller-scale unbalanced inertio-gravity (IG) waves. The former, which are more rotational, arise from the horizontal pressure gradient force, while for the latter gravity acts as the restoring force, making their flow pattern more divergent. The main source of variability in the extratropics stems from Rossby waves. At the equator, further types of convectively coupled equatorial waves (CCEWs) exist, namely Kelvin and mixed Rossby-gravity (MRG) waves. As the slowest intraseasonal tropical mode, the Madden–Julian Oscillation (MJO), which is related to Kelvin and Rossby waves, acts on a timescale of 30 to 90 days. Although it is primarily a planetary mode, the MJO has a specific “flavor” over the African continent. On the short intraseasonal timescale of 10 to 25 days, equatorial Rossby (ER) waves and the internal modes of the West African monsoon, the quasi-biweekly zonal dipole (QBZD) and the Sahel mode, modulate rainfall. On the synoptic timescale of a few days to a week, African easterly waves (AEWs) are a dominant mode over West Africa, whereas Kelvin waves predominantly modulate rainfall over equatorial Africa. Extratropical influences on northern and southern Africa manifest themselves in Rossby wave trains, which modulate synoptic to intraseasonal rainfall through tropical rainfall plumes, cold air surges, and upper-tropospheric dry air intrusions. Furthermore, the Saharan heat low (SHL) acts as a link between the northern hemispheric extratropics and tropics. Finally, the Indian monsoon, the Atlantic, Indian, and the Pacific Oceans can remotely affect the intraseasonal variability of African rainfall. Forecasting synoptic to intraseasonal rainfall variability is an integral part of seamless prediction between the weather and climate regimes. In the early 21st century, numerical weather prediction (NWP) systems can forecast larger intraseasonal signals such as the MJO several weeks into the future, but they still struggle to forecast shorter scale features reliably. Besides NWP, statistical models can successfully forecast intraseasonal variability of rainfall. Due to the relevance of synoptic to intraseasonal rainfall variability for African societies, early warning systems (EWSs) have been developed to mitigate impacts.

Observed summer (May–October) rainfall in Myanmar for the period 1981–2010 was used to investigate the interannual variability of summer monsoon rainfall over Myanmar. Empirical orthogonal function, the sequential Mann-Kendall test, power spectrum analysis, and singular value decomposition (SVD) were deployed in the study. Results from spectral analysis showed that the variability of rainfall over Myanmar exhibits a 2- to 6-year cycle. An abrupt change in rainfall over the country was noted in 1992. There was a notable increasing rainfall trend from 1989. After the sudden change, the mean rainfall increased by 36.1 mm, compared with the mean rainfall before the sudden change, and was associated with a rise in temperature of about 0.2 °C. An increase in heavy rainfall days was observed from the early 1990s to 2010. IOD and ENSO play an important role in the interannual variability of the summer rainfall over Myanmar. The covariability between rainfall over Myanmar and Indian Ocean SST generally suggests that a positive IOD mode is associated with suppressed rainfall in the central and northern parts of Myanmar. During a negative IOD mode, nearly the whole Myanmar experiences enhanced rainfall, which is associated with devastating socioeconomic impacts. The covariability between the rainfall over Myanmar and the sea surface temperature in the Pacific Ocean in the first and second SVD modes was dominated by warming in the east and central Pacific—an El Nino-like pattern—resulting in dry conditions in central Myanmar.

A realistic simulation of rainfall over the Tibetan Plateau (TP) is a big challenge for both regional and global climate models. In this study, we investigate the simulations of summer rainfall over the TP from 1979 to 2010 by the Weather Research and Forecasting (WRF) model with various horizontal resolutions and cumulus schemes, with a focus on the difference in the model's skill in simulating interannual variability of rainfall between early and high summer. The WRF captures spatial pattern of climatological summer mean rainfall over the TP. However, it produces apparent wet bias, especially in southern and eastern edges of the TP. Despite this climatological bias, WRF skilfully reproduces the interannual variability of summer rainfall over the southeastern TP, where maximum rainfall is located. An increase in horizontal resolution or an appropriate cumulus scheme mostly improves the simulation of climatological mean rainfall. However, the phase of interannual variability of simulated rainfall is not sensitive to horizontal resolution and cumulus scheme. Instead, it is sensitive to mechanisms responsible for interannual variability of rainfall. WRF has a high skill in simulating the interannual variability of rainfall over the southeastern TP in July and August, but a low skill in June. The WRF's high skill is attributed to that the interannual variability of July–August rainfall is largely driven by large‐scale circulation, while its low skill for June rainfall may be ascribed to the overestimation of snow and its relationship with rainfall.