Contribution of Monthly and Regional Rainfall to the Strength of Indian Summer Monsoon

Indian summer monsoon rainfall (ISMR; June–September) has both temporal and spatial variability causing floods and droughts in different seasons and locations, leading to a strong or weak monsoon. Here, the authors present the contribution of all-India monthly, seasonal, and regional rainfall to the ISMR, with an emphasis on the strong and weak monsoons. Here, regional rainfall is restricted to the seasonal rainfall over four regions defined by the India Meteorological Department (IMD) primarily for the purpose of forecasting regional rainfall: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). In this study, two rainfall datasets provided by IMD are used: 1) all-India monthly and seasonal (June–September) rainfall series for the entire Indian subcontinent as well as seasonal rainfall series for the four homogeneous regions for the period 1901–2013 and 2) the latest daily gridded rainfall data for the period 1951–2014, which is used for assessment at the extent to which the four regions are appropriate for the intended purpose. Rainfall during July–August contributes the most to the total seasonal rainfall, regardless of whether it is a strong or weak monsoon. Although NEI has the maximum area-weighted rainfall, its contribution is the least toward determining a strong or weak monsoon. It is the rainfall in the remaining three regions (NWI, CI, and SPIN) that controls whether an ISMR is strong or weak. Compared to monthly rainfall, regional rainfall dominates the strong or weak rainfall periods.

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Increase in summer monsoon rains in northeast India during ENSO periods: a multiproxy analysis

10.5194/egusphere-egu2020-376 ◽

2020 ◽

Author(s):

Arvind Singh ◽

Kiran Kumar Pullabotla ◽

Ramesh Rengaswamy

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Central India ◽

Northeast India ◽

Data Set ◽

Rainfall Variation ◽

Enso Events ◽

Proxy Records ◽

Rainfall Patterns

El-Ni&#241;o Southern Oscillation (ENSO) affects Indian summer monsoon. Most of the worst droughts - the most recent being in 2009 - in India have been triggered by ENSO. But given the heterogeneity in rainfall patterns over India, we revisited ENSO influence on Indian summer monsoon. Our analysis based on multiple isotopic (proxy-based) and satellite data set shows significant variation in the spatiotemporal patterns of rainfall over the Indian subcontinent and adjoining oceans. We observed a weaker summer monsoon over central India and relatively stronger summer monsoon over northeast India during strong El-Ni&#241;o events. Rainfall derived from isotope-enabled general circulation models reproduces weak and strong rainfall patterns during the El-Ni&#241;o events over central India and northeast India, respectively. These model derived &#948;18Orain (oxygen isotopic composition of rainfall) variation over central India during ENSO events mimic the weaker rainfall conditions. However, significant changes in the model derived rainfall and associated &#948;18Orain is not observed over northeast India during ENSO events. Based on multiple data analysis, we infer that the long term variations (trends) in the Indian summer monsoon strength were controlled by the long term variation in ENSO during the last 50 years (1965 &#8211; 2013).Since these observations were unprecedented and counterintuitive, we further verified our observations from the proxy records. Two speleothems (cave deposits) records from the central India and northeast India were analyzed for the rainfall variation and ENSO influence signatures. These paleo-proxy records showed a similar inverse relation of rainfall patterns over central India and northeast India during ENSO periods, confirming observed ENSO&#8217;s role on rainfall. Also, these proxy records showed a long-term pause in ENSO events or stronger La-Ni&#241;a like conditions, which were persisted during 1625 &#8211; 1715 and favored stronger (weaker) rainfall over central India (northeast India).

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Response of a Global Spectral Model for Simulation of Indian Summer Monsoon Rainfall

Journal of Climate Change ◽

10.3233/jcc200010 ◽

2020 ◽

Vol 6 (2) ◽

pp. 33-46

Author(s):

Srabanti Ballav ◽

Sandipan Mukherjee ◽

Ashok P. Dimri

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Monsoon Rainfall ◽

Indian Summer Monsoon Rainfall ◽

Model Performance ◽

Summer Monsoon Rainfall ◽

Seasonal Rainfall ◽

Spectral Model ◽

Global Spectral Model ◽

Rainfall Forecast

The present work highlights response of a global spectral model T80L18 with respect to Indian summer monsoon rainfall (ISMR) during 8 years period of 1996-2003. The model performance is evaluated for day-1, day-3 and day-4 retrospective 24-hour accumulated rainfall forecasts from 0300 UTC to the next day 0300 UTC using in-situ rainfall observations of 4491 stations. The model performance is evaluated by assessing: (i) percentage departure and root mean square error (RMSE) of seasonal rainfall forecast, (ii) coefficient of variation (CoV) of seasonal rainfall forecast and observation, along with percentage departure of monthly rainfall forecast and (iii) model performance during a drought and a normal year of 2002 and 2003, respectively. Generally, it is noted that the T80L18 model underestimated high rainfall and overestimated low rainfall, however, with increasing forecast duration prediction over low rainfall areas improved. The model RMSE over central and western India is found to increase with increasing forecast duration; however, the same was found to decrease over Jammu and Kashmir. The CoV of day-1 rainfall forecast is found to be low over all India in comparison to the observed data. In the case of model performance evaluation during a drought and a normal year of 2002 and 2003, it is noted that the model produced higher rainfall over the rainfall deficit regions of observed distribution; whereas the heaviest observed rainfall region (>250 cm) is not well resolved by the model. In general, the T80L18 model performance is noted to be better over central India for mean seasonal rainfall prediction.

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The synergistic impact of ENSO and IOD on the Indian Summer Monsoon Rainfall in observations and climate simulations - an information theory perspective

10.5194/esd-2020-50 ◽

2020 ◽

Author(s):

Praveen Kumar Pothapakula ◽

Cristina Primo ◽

Silje Sørland ◽

Bodo Ahrens

Keyword(s):

Interannual Variability ◽

Summer Monsoon ◽

Indian Summer Monsoon ◽

Information Exchange ◽

Climate Model ◽

Monsoon Rainfall ◽

Indian Summer Monsoon Rainfall ◽

Indian Subcontinent ◽

Summer Monsoon Rainfall ◽

Core Region

Abstract. El-Niño southern oscillation (ENSO) and Indian Ocean Dipole (IOD) are two well-know temporal oscillations in the sea surface temperature (SST), which both are thought to influence the interannual variability of the Indian Summer Monsoon Rainfall (ISMR). Until now, there has been no measure to assess the simultaneous information exchange (IE) from both ENSO and IOD to ISMR. This study explores the information exchange from two source variables (ENSO and IOD) to one target (ISMR). First, in order to illustrate the concepts and quantification of two-source IE to a target, we use idealized test cases consisting of linear as well as non-linear dynamical systems. Our results show that these systems exhibit net synergy (i.e., the combined influence of two sources on a target is greater than the sum of their individual contributions), even with uncorrelated sources in both the linear and non-linear systems. We test IE quantification with various estimators (the Linear, Kernel, and Kraskov estimators) for robustness. Next, the two-source IE from ENSO and IOD to the ISMR is investigated in observations, reanalysis, three global climate model (GCM) simulations, and three nested, higher-resolution simulations using a regional climate model (RCM). This (1) quantifies IE from ENSO and IOD to ISMR in the natural system, and (2) applies IE in the evaluation of the GCM and RCM simulations. The results show that both ENSO and IOD contribute to the ISMR interannual variability. Interestingly, significant net synergy is noted in the central parts of the Indian subcontinent, which is India's monsoon core region. This indicates that both ENSO and IOD are synergistic predictors in the monsoon core region. But, they share significant net redundant information in the southern part of Indian subcontinent. The IE patterns in the GCM simulations differ substantially from the patterns derived from observations and reanalyses. Only one nested RCM simulation IE pattern adds value to the corresponding GCM simulation pattern. Only in this case, the GCM simulation shows realistic SST patterns and moisture transport during the various ENSO and IOD phases. This confirms, once again, the importance of the choice of the GCM in driving a higher-resolution RCM. This study shows that two-source IE is a useful metric that helps in better understanding the climate system and in process-oriented climate model evaluation.

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Long-Lead Statistical Forecasts of the Indian Summer Monsoon Rainfall Based on Causal Precursors

Weather and Forecasting ◽

10.1175/waf-d-19-0002.1 ◽

2019 ◽

Vol 34 (5) ◽

pp. 1377-1394 ◽

Cited By ~ 5

Author(s):

G. Di Capua ◽

M. Kretschmer ◽

J. Runge ◽

A. Alessandri ◽

R. V. Donner ◽

...

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Monsoon Rainfall ◽

Indian Summer Monsoon Rainfall ◽

A Priori ◽

Low Frequency ◽

India Meteorological Department ◽

Internal Variability ◽

Summer Monsoon Rainfall ◽

Lead Times

Abstract Skillful forecasts of the Indian summer monsoon rainfall (ISMR) at long lead times (4–5 months in advance) pose great challenges due to strong internal variability of the monsoon system and nonstationarity of climatic drivers. Here, we use an advanced causal discovery algorithm coupled with a response-guided detection step to detect low-frequency, remote processes that provide sources of predictability for the ISMR. The algorithm identifies causal precursors without any a priori assumptions, apart from the selected variables and lead times. Using these causal precursors, a statistical hindcast model is formulated to predict seasonal ISMR that yields valuable skill with correlation coefficient (CC) ~0.8 at a 4-month lead time. The causal precursors identified are generally in agreement with statistical predictors conventionally used by the India Meteorological Department (IMD); however, our methodology provides precursors that are automatically updated, providing emerging new patterns. Analyzing ENSO-positive and ENSO-negative years separately helps to identify the different mechanisms at play during different years and may help to understand the strong nonstationarity of ISMR precursors over time. We construct operational forecasts for both shorter (2-month) and longer (4-month) lead times and show significant skill over the 1981–2004 period (CC ~0.4) for both lead times, comparable with that of IMD predictions (CC ~0.3). Our method is objective and automatized and can be trained for specific regions and time scales that are of interest to stakeholders, providing the potential to improve seasonal ISMR forecasts.

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Interannual oxygen isotope variability in Indian summer monsoon precipitation reflects changes in moisture sources

Communications Earth & Environment ◽

10.1038/s43247-021-00165-z ◽

2021 ◽

Vol 2 (1) ◽

Author(s):

Gayatri Kathayat ◽

Ashish Sinha ◽

Masahiro Tanoue ◽

Kei Yoshimura ◽

Hanying Li ◽

...

Keyword(s):

Oxygen Isotope ◽

Summer Monsoon ◽

Indian Summer Monsoon ◽

Climate Model ◽

Monsoon Rainfall ◽

Isotope Composition ◽

Indian Subcontinent ◽

Rainfall Variability ◽

Summer Monsoon Rainfall ◽

Monsoon Circulation

AbstractThe primary influences on the spatio-temporal variability of oxygen isotope compositions in precipitation over the Indian summer monsoon domain are inadequately constrained by the limited observational record. Consequently, the climatic significance of isotopic signatures of precipitation preserved in proxy archives from the region remains unclear. Here we present simulations with an isotope-enabled climate model (IsoGSM2) with the moisture-tagging capability to investigate the role of relative contributions of moisture from oceanic and terrestrial sources to the interannual variability in oxygen isotope composition in summer monsoon rainfall. During weak monsoon years, the moisture contribution from the Arabian Sea dominates precipitation over the Indian subcontinent while the remote oceanic and terrestrial sources have a greater influence during strong monsoon years. We suggest that changes in monsoon circulation, moisture source, and precipitation intensity are interrelated and that speleothem oxygen isotope records from the region can potentially help reconstruct interannual to decadal monsoon rainfall variability.

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