Indian Monsoon Rainfall Research Articles

Forecasting Monthly Rainfall using Bio-Inspired Artificial Algae Deep Learning Network

Rainfall forecasting is the most critical and challenging task because of its dependence on different climatic and weather parameters. Hence, robust and accurate rainfall forecasting models need to be created by applying various machine learning and deep learning approaches. Several automatic systems were created to predict the weather, but it depends on the type of weather pattern, season and location, which leads in maximizing the processing time. Therefore, in this work, significant artificial algae long short-term memory (LSTM) deep learning network is introduced to forecast the monthly rainfall. During this process, Homogeneous Indian Monthly Rainfall Data Set (1871–2016) is utilized to collect the rainfall information. The gathered information is computed with the help of an LSTM approach, which is able to process the time series data and predict the dependency between the data effectively. The most challenging phase of LSTM training process is finding optimal network parameters such as weight and bias. For obtaining the optimal parameters, one of the Meta heuristic bio-inspired algorithms called Artificial Algae Algorithm (AAA) is used. The forecasted rainfall for the testing dataset is compared with the existing models. The forecasted results exhibit superiority of our model over the state-of-the-art models for forecasting Indian Monsoon rainfall. The LSTM model combined with AAA predicts the monsoon from June–September accurately.

Is the Indian monsoon rainfall linked to the Southern Ocean sea ice conditions?

In the background of global warming, the necessity for finding a new predictor of Indian summer monsoon rainfall (ISMR) is realised recently as the relationship between the ISMR and the predictive physical parameters keeps changing both in space and time. We developed a linkage between the satellite-derived sea ice over the Southern Ocean and ISMR through the interaction of physical processes occurring between the cryosphere, hydrosphere, and atmospheric environment. The statistical and possible physical linkage were explored using consistent and reliable 38-year time series observations. Lead-lag cross correlation indicated a significant relationship between Southern Ocean sea ice (SOSI) extent and ISMR over the Bellingshausen–Amundsen Sea (BAS). The most significant relationship was obtained in austral autumn (r = −0.5, p < 0.05) with no time lag. We analysed atmospheric circulation pattern over the BAS and Ross Sea corresponding to extreme rainfall events. Excess (deficit) rainfall years showed anomalous cyclonic (anticyclonic) wind patterns that led to anomalous positive (negative) air temperature that facilitated anomalous negative (positive) sea ice conditions in BAS. Hence, the excess rainfall (deficit) events were observed corresponding to a large negative (positive) anomalous sea ice condition in the BAS. This linkage likely occurred through El Niño–Southern Oscillation (ENSO) associated sea surface temperature (SST) variability. To find out the dominant mode of variability and common frequency distribution in the SOSI extent, Niño3.4 SST and ISMR, we have performed the wavelet analysis for exploring the coherence between the respective variables. The spectral analysis showed that both the SOSI extent and ISMR have dominant oscillations in 2–8 years frequency bands similar to the ENSO variability. This observed relationship can be used as the input for future works for physically linking the BAS sea ice condition with the ISMR through the dynamic and thermodynamic ocean-atmospheric components.

Spatio‐temporal variability in the stratiform/convective rainfall contribution to the summer monsoon rainfall in India

AbstractSpatio‐temporal variability of contributions of stratiform and convective rainfall to Indian monsoon (June–September) rainfall have been investigated using hourly rainfall data of well distributed 126 stations in India for the period 1969–2015. The criteria used for identifying stratiform rainfall are hourly rainfall ≤5 mm and spatial homogeneity. The study showed that Indian monsoon rainfall exhibits two distinctive features, viz. stratiform component dominating over peninsular (southern) India and convective component dominating over northern India. The diurnal variation shows domination of convective activity in the afternoon hours over the northern parts of India. Intra‐seasonal variability in the stratiform rainfall is the lowest over the West Coast stations through‐out the season. While it shows low values during the onset (June) and withdrawal (September) phases of monsoon and higher values during July–August in Peninsular India. Inter‐annual variations in the convective rainfall are larger than that of stratiform rainfall all over the country. Trend analysis indicates that both stratiform and convective rainfall are decreasing over the central parts of the country and increasing significantly along West Coast and western parts of the country. During excess monsoon year, convective rainfall activity is more than in deficit monsoon year, in Central India. The results brought out in the study will be useful as a proxy for understanding the spatial and temporal variability of the latent heating fields over India in the monsoon season and validation of model simulations of clouds and rainfall types.

Changing Indian monsoon rainfall patterns under the recent warming period 2001–2018

Changing Indian monsoon rainfall patterns under the recent warming period 2001–2018

Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d−1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

Forecast skill of the Indian monsoon and its onset in the ECMWF seasonal forecasting system 5 (SEAS5)

Accurate forecasting of variations in Indian monsoon precipitation and progression on seasonal time scales remains a challenge for prediction centres. We examine prediction skill for the seasonal-mean Indian summer monsoon and its onset in the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system 5 (SEAS5). We analyse summer hindcasts initialised on 1st of May, with 51 ensemble members, for the 36-year period of 1981–2016. We evaluate the hindcasts against the Global Precipitation Climatology Project (GPCP) precipitation observations and the ECMWF reanalysis 5 (ERA5). The model has significant skill at forecasting dynamical features of the large-scale monsoon and local-scale monsoon onset tercile category one month in advance. SEAS5 shows higher skill for monsoon features calculated using large-scale indices compared to those at smaller scales. Our results also highlight possible model deficiencies in forecasting the all India monsoon rainfall.

Performance of the RegCM-MITgcm Coupled Regional Model in Simulating the Indian Summer Monsoon Rainfall

An effort is made to evaluate the performance of an atmosphere–ocean coupled regional model in simulating the Indian summer monsoon rainfall during the years 1995–2015. The RegCM and MITgcm models are taken as the atmospheric and oceanic components of the coupled model, respectively. It is found that the coupling helps to improve the SST of the Arabian Sea and Bay of Bengal regions of the model domain. The air–sea coupling also improves the wind speed simulation. The RegCM-MITgcm coupled model correctly represents the intraseasonal variability of the Indian summer monsoon rainfall as compared to the RegCM stand-alone model. It is shown that the very high and extreme rainfall intensity events are correctly simulated by the RegCM-MITgcm coupled model, whereas the RegCM stand-alone model underestimates the precipitation intensities. It is argued that the atmosphere–ocean coupling helps to increase the predictability skill of the Indian monsoon rainfall. For example, the limit of predictability of the Indian monsoon rainfall obtained from the coupled model (22 days) is closer to that obtained from the IMD observation (24 days). The corresponding limit of predictability from the stand-alone model is 20 days.

Proportional Trends of Continuous Rainfall in Indian Summer Monsoon

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns.

Observations of contrasted glacial-interglacial dissolution of foraminifera above the lysocline in the Bay of Bengal, northeastern Indian Ocean

Site U1446 (19°50′N, 85°44′E, at water depth 1 430 m) was drilled during Expedition 353 (Indian monsoon rainfall) of the International Ocean Discovery Program (IODP). It is located in the Mahanadi offshore basin, on the northern Bay of Bengal. Sedimentation rates and contents of biocarbonates are high at this relatively shallow site. Using a micropaleontological approach, we examined planktonic and benthic foraminifera in the upper around 40 m of this site, spanning the last around 190 ka. A striking feature of the foraminiferal record is the occurrence of strong but varying dissolution although the site is located well above the modern lysocline. Such strong dissolution has never been reported in this area. We estimated the flux of foraminifera and quantified the ratio of benthic foraminifera over total foraminifera (benthic/total foraminifera) along with the foraminifer fragmentation index in order to characterize past changes in this above-lysocline dissolution. This study reveals a clear glacial-interglacial contrast, with a stronger dissolution during marine isotope stages (MISs) 1 and 5 than during MISs 2–4 and 6. Such a difference in preservation is likely to have a strong impact on geochemical proxies measured on foraminifera. Our new observations call for an in-depth study of the causes of such above-lysocline dissolution in the region, and an evaluation of its impact on the foraminifera-based proxies used for paleoenvironmental reconstruction.

Influence of the Eastern Pacific and Central Pacific Types of ENSO on the South Asian Summer Monsoon

Based on observational and reanalysis data, the relationships between the eastern Pacific (EP) and central Pacific (CP) types of El Nino-Southern Oscillation (ENSO) during the developing summer and the South Asian summer monsoon (SASM) are examined. The roles of these two types of ENSO on the SASM experienced notable multidecadal modulation in the late 1970s. While the inverse relationship between the EP type of ENSO and the SASM has weakened dramatically, the CP type of ENSO plays a far more prominent role in producing anomalous Indian monsoon rainfall after the late 1970s. The drought-producing El Nino warming of both the EP and CP types can excite anomalous rising motion of the Walker circulation concentrated in the equatorial central Pacific around 160°W to the date line. Accordingly, compensatory subsidence anomalies are evident from the Maritime Continent to the Indian subcontinent, leading to suppressed convection and decreased precipitation over these regions. Moreover, anomalously less moisture flux into South Asia associated with developing EP El Nino and significant northwesterly anomalies dominating over southern India accompanied by developing CP El Nino, may also have been responsible for the Indian monsoon droughts during the pre-1979 and post-1979 sub-periods, respectively. El Nino events with the same "flavor" may not necessarily produce consistent Indian monsoon rainfall anomalies, while similar Indian monsoon droughts may be induced by different types of El Nino, implying high sensitivity of monsoonal precipitation to the detailed configuration of ENSO forcing imposed on the tropical Pacific.

Performance Evaluation of CMIP5 and CMIP6 Models on Heatwaves in Korea and Associated Teleconnection Patterns

AbstractThis study assesses the performance of the Coupled Model Intercomparison Project (CMIP) models for simulating summer heatwaves in Korea during a historical simulation period (1979–2014) using four diagnostic indices that represent the teleconnection mechanism of summer heatwaves in Korea. Four skill metrics are used for the model evaluation, namely, relative error (RE), interannual variability skill‐score (IVS), correlation coefficient (CC), and total ranking (TR) based on daily maximum temperature (TMAX) in Korea and the four diagnostic indices. The results show that the REs of CMIP5 models do not differ significantly from those of the CMIP6 models while the IVSs in the CMIP6 models are significantly improved compared with the CMIP5 models. Observations show that the heatwave circulation index (HWCI) contributes more to the interannual variability in TMAX in Korea than that of the Indian Monsoon Rainfall Index (IMRI), indicating that the teleconnection from the northwestern Pacific is more important than that from northwestern India. Interestingly, the CMIP6 models simulate this property better than the CMIP5 ensemble. The higher TR of CMIP6 models than CMIP5 supports that CMIP6 models are better overall in simulating heatwaves in Korea and the associated diagnostic indices.

Performance of CMIP5 atmospheric general circulation model simulations over the Asian summer monsoon region

Performance of twenty-eight state of art atmosphere general circulation models (AGCMs) in simulating regional characteristic features of summer monsoon rainfall and circulation over the south Asian and the western-north Pacific regions are examined. AGCMs depict good representation of climatological spatial distribution of monsoon rainfall over both the regions. Interestingly, the year-to-year variability of the south Asian (Indian) monsoon rainfall is well simulated by the models compared to the circulation features. Whereas, for the western-north Pacific region, the AGCMs depict poor representation of monsoon rainfall variability, but circulation features are simulated with better skill. Thus suggesting that, models with good performance of monsoon rainfall variability need not necessarily have better representation of monsoon circulation features. Further analysis indicate that most models with good skill in simulating monsoon variability have better representation of monsoon-SST teleconnections with the Indo-Pacific SSTs. For the south Asian region, better simulating models have good representation of monsoon-ENSO teleconnection whereas most models have poor representation of monsoon-IOD relationship, suggesting requirement for realistic representation of these teleconnections in the models. For the western-north Pacific region, most models have unrealistic relationship of monsoon rainfall with SSTs during preceding winter and concurrent monsoon season, indicating that the poor (better) skill in simulating rainfall (circulation) probably arise from the unrealistic (realistic) representation of monsoon-SST teleconnections. Thus, the importance of realistic representation of monsoon-SST teleconnections in the climate models have been brought out for better simulation of monsoon variability over the south Asian and the western-north Pacific regions. • Performance of twenty-eight state of art atmosphere general circulation models in representing the summer monsoon rainfall and circulation over the south Asia and the western-north Pacific are examined. • Study brings out the importance of realistic representation of monsoon-SST teleconnections in the climate models for better simulation of the monsoon over the south Asia and the western-north Pacific. • The models which depict higher skill in simulating the interannual variability of monsoon have a better representation of monsoon-SST teleconnection over most of the Indo-Pacific region.

Four-dimensional structure and sub-seasonal regulation of the Indian summer monsoon multi-decadal mode

A better understanding of the drivers and teleconnection mechanisms responsible for the multi decadal mode (MDM) of variability of the Indian summer monsoon rainfall (ISMR) with major socio-economic impacts in the region through clustering of large-scale floods or droughts is key to improving the poor simulation of ISMR MDM by most climate models. Here, using the longest instrumental record of ISMR available (1813–2006) and longest atmospheric and oceanic re-analyses, the global four dimensional (space–time) structures of atmospheric and oceanic fields of the multi-decadal mode of ISMR and sub-seasonal evolution of the teleconnection mechanism are brought out, essential for understanding underlying drivers but lacking so far. The relationships between the spatial structure of winds, Sea Surface Temperature (SST) and thermocline depth with the ISMR MDM indicate that the tropical ocean over the Indo-Pacific domain is passive responding primarily to the surface winds associated with the mode. A close association between the Atlantic Meridional Overturning Circulation (AMOC), north Atlantic (NA) SST, NA sea surface salinity (SSS) and the ISMR MDM indicate a slow oceanic pathway linking NA SST and the ISMR. In addition to strong correlation (~ 0.9) between global spatial patterns of JJAS SST associated with the MDMs of ISMR, NA SST and AMOC, strong temporal coherence (correlations ~ 0.9) between them is suggestive of regulation of the ISMR MDM (T ~ 65-years) by the NA SST associated with the Atlantic Multidecadal Oscillation (AMO) through a ‘fast’ atmospheric bridge. On a seasonal time scale, the atmospheric bridge manifests in the form of a stationary Rossby wave train generated by an anticyclonic (cyclonic) barotropic vorticity located above positive (negative) SST anomaly over NA in two phases of the AMO. That the AMO SST is the driver of the ISMR MDM is further supported when we unravel the sub-seasonal face of the teleconnection between the two. We show that phase locking of active (break) spells with annual cycle during positive (negative) phases of the ISMR MDM are forced by a similar phase locking of barotropic anticyclonic (cyclonic) vorticity over the NA SST with the annual cycle through the generation of a quasi-stationary Rossby wave train with an anticyclonic (cyclonic) vorticity at upper level over the Indian region with the NA columnar vorticity leading Indian monsoon rainfall by about a week. Our findings provide a basis for enhanced predictability of tropical climate through slow modulation by extra-tropical SST.

Potential of Reef Building Corals to Study the Past Indian Monsoon Rainfall Variability

Potential of Reef Building Corals to Study the Past Indian Monsoon Rainfall Variability

Myths and Realities of Indian Monsoon (Onset and Rainfall) Forecasts

Myths and Realities of Indian Monsoon (Onset and Rainfall) Forecasts

Prediction of Indian Monsoon Rainfall by Interval based Simplified High Order Fuzzy Time Series Model

Rain is of uttermost importance for agriculture based economies. Most of the Asian countries, India in particular largely depend on a good rainfall. The prediction of rainfall will not only help government to make better future policies but also farmers and agro based companies can make better future management. Rainfall forecasting involves high degree of uncertainty and for such conditions fuzzy time series and other soft computing techniques are best to deal with. The utility of a forecasting method lies with the accuracy with the predicted values. In this paper rainfall prediction by fuzzy time series model is proposed in which two difference values of the interval corresponding to the fuzzified forecasted value is proposed. This model is tested on real time data of average monsoon rainfall in India. The predicted values are compared with Chen model. The results show that the proposed model have less error compared to Chen’s model.

Decomposition of the Atlantic Multidecadal Variability in a Historical Climate Simulation

AbstractThe Atlantic multidecadal variability (AMV) modulates various climate features worldwide with enormous societal and economic implications, including variations in hurricane activity in the Atlantic, sea level, West African and Indian monsoon rainfall, European climate, and hemispheric-scale surface temperature. Leading hypotheses regarding the nature and origin of AMV focus primarily on its links with oceanic and coupled ocean–atmosphere internal variability, and on its response to external forcing. The role of another possible process, that of atmospheric noise forcing of the ocean, has received less attention. This is addressed here by means of historical coupled simulations and diagnostic experiments, which isolate the influences of external and atmospheric noise forcings. Our findings show that external forcing is an important driver of the simulated AMV. They also demonstrate that weather noise is key in driving the simulated internal AMV in the southern part (0°–60°N) of the AMV region, and that weather noise forcing is responsible for up to 10%–20% of the multidecadal internal SST variability in some isolated areas of the subpolar gyre region. Ocean dynamics independent from the weather noise forcing is found to be the dominant cause of multidecadal SST in the northern part of the AMV region.

Spatial variation in long‐lead predictability of summer monsoon rainfall using a time‐varying model and global climatic indices

AbstractLong‐lead prediction of summer monsoon rainfall in India is a challenging task, especially at finer spatial scale. The spatial variability in the long‐lead (one or two season in advance) prediction plays a vital role in planning of hydrological and agricultural aspects of the society. One of the major issues in this field is the climate‐induced time‐varying characteristics (non‐stationarity) that lead to a deteriorating model performance as the time passes by after the model calibration. This study proposes the use of time‐varying approaches in order to check such deteriorating performance over time. Considering the time‐varying association between Indian summer monsoon rainfall and large‐scale climatic indices (e.g., El Niño‐Southern Oscillation, Equatorial Indian Ocean Oscillation, North Atlantic Oscillation, Pacific Decadal Oscillation, and El Niño Modoki Index), a time‐varying approach based on hybrid graphical modelling (GM) and vine copula (GM‐Copula) is demonstrated for rainfall prediction over five homogeneous monsoon regions (HMRs) in India. The time‐varying characteristic is imparted in the GM‐Copula approach by recursively updating the model inputs and the corresponding model parameters at a regular time interval (τ) through recalibration. In the time‐varying framework, the parameter τ is referred to as optimum recurrence interval of model recalibration and it is identified as 5 years for the regions with moderate rainfall and 3 years for regions with above and below moderate rainfall. The developed time‐varying approach is able to yield reasonably good prediction performance (mean absolute percentage error being within 4–10% across HMRs) with a prediction lead time of 5 months. HMR‐wise seasonal rainfall predictions with such quality and lead time are expected to be highly useful.

Integration of Wavelet Transform with ANN and WNN for Time Series Forecasting: an Application to Indian Monsoon Rainfall

Indian agricultural activity relies on monsoon rainfall; hence, its forecasting is indispensable for proper planning. Artificial neural network (ANN) is one of the popular approaches for rainfall forecasting. Recent research activity demonstrates that consolidating diverse model/techniques improves the exactness of forecasting when contrasted with the individual models. Therefore, the present study proposed a hybrid forecasting framework for rainfall forecasting combining wavelet transform, ANN and wavelet neural network (WNN). As a case study, Indian monsoon rainfall time series data have been considered to assess the forecasting performance of the proposed forecasting framework. The proposed approach has been compared with ANN and WNN. Observational outcomes uncover that the forecasting accuracy of the proposed strategy is superior to ANN and WNN.

Coherent response of the Indian Monsoon Rainfall to Atlantic Multi-decadal Variability over the last 2000 years

Indian Summer Monsoon (ISM) rainfall has a direct effect on the livelihoods of two billion people in the Indian-subcontinent. Yet, our understanding of the drivers of multi-decadal variability of the ISM is far from being complete. In this context, large-scale forcing of ISM rainfall variability with multi-decadal resolution over the last two millennia is investigated using new records of sea surface salinity (δ18Ow) and sea surface temperatures (SSTs) from the Bay of Bengal (BoB). Higher δ18Ow values during the Dark Age Cold Period (1550 to 1250 years BP) and the Little Ice Age (700 to 200 years BP) are suggestive of reduced ISM rainfall, whereas lower δ18Ow values during the Medieval Warm Period (1200 to 800 years BP) and the major portion of the Roman Warm Period (1950 to 1550 years BP) indicate a wetter ISM. This variability in ISM rainfall appears to be modulated by the Atlantic Multi-decadal Oscillation (AMO) via changes in large-scale thermal contrast between the Asian land mass and the Indian Ocean, a relationship that is also identifiable in the observational data of the last century. Therefore, we suggest that inter-hemispheric scale interactions between such extra tropical forcing mechanisms and global warming are likely to be influential in determining future trends in ISM rainfall.