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Narang U, Juneja K, Upadhyaya P, Salunke P, Chakraborty T, Behera SK, Mishra SK, Suresh AD. Artificial intelligence predicts normal summer monsoon rainfall for India in 2023. Sci Rep 2024; 14:1495. [PMID: 38233406 PMCID: PMC10794699 DOI: 10.1038/s41598-023-44284-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 10/05/2023] [Indexed: 01/19/2024] Open
Abstract
Inaccuracy in the All Indian Summer Monsoon Rainfall (AISMR) forecast has major repercussions for India's economy and people's daily lives. Improving the accuracy of AISMR forecasts remains a challenge. An attempt is made here to address this problem by taking advantage of recent advances in machine learning techniques. The data-driven models trained with historical AISMR data, the Niño3.4 index, and categorical Indian Ocean Dipole values outperform the traditional physical models, and the best-performing model predicts that the 2023 AISMR will be roughly 790 mm, which is typical of a normal monsoon year.
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Affiliation(s)
- Udit Narang
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology Delhi, Delhi, India
| | - Kushal Juneja
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology Delhi, Delhi, India
| | - Pankaj Upadhyaya
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India
| | - Popat Salunke
- Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Tanmoy Chakraborty
- Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India.
| | - Swadhin Kumar Behera
- Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
| | - Saroj Kanta Mishra
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India.
| | - Akhil Dev Suresh
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Delhi, India
- Department of Physics, Indian Institute of Science Education and Research Tirupati, Tirupati, India
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Athira KS, Roxy MK, Dasgupta P, Saranya JS, Singh VK, Attada R. Regional and temporal variability of Indian summer monsoon rainfall in relation to El Niño southern oscillation. Sci Rep 2023; 13:12643. [PMID: 37542113 PMCID: PMC10403600 DOI: 10.1038/s41598-023-38730-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/13/2023] [Indexed: 08/06/2023] Open
Abstract
The Indian summer monsoon rainfall (ISMR) exhibits significant variability, affecting the food and water security of the densely populated Indian subcontinent. The two dominant spatial modes of ISMR variability are associated with the El Niño Southern Oscillation (ENSO) and the strength of the semi-permanent monsoon trough along with related variability in monsoon depressions, respectively. Although the robust teleconnection between ENSO and ISMR has been well established for several decades, the major drivers leading to the time-varying relationship between ENSO and ISMR patterns across different regions of the country are not well understood. Our analysis shows a consistent increase from a moderate to substantially strong teleconnection strength between ENSO and ISMR from 1901 to 1940. This strengthened relationship remained stable and strong between 1941 and 1980. However, in the recent period from 1981 to 2018 the teleconnection decreased consistently again to a moderate strength. We find that the ENSO-ISMR relationship exhibits distinct regional variability with time-varying relationship over the north, central, and south India. Specifically, the teleconnection displays an increasing relationship for north India, a decreasing relationship for central India and a consistent relationship for south India. Warm SST anomalies over the eastern Pacific Ocean correspond to an overall decrease in the ISMR, while warm SST anomalies over the Indian Ocean corresponds to a decrease in rainfall over the north and increase over the south of India. The central Indian region experienced the most substantial variation in the ENSO-ISMR relationship. This variation corresponds to the variability of the monsoon trough and depressions, strongly influenced by the Pacific Decadal Oscillation and North Atlantic Oscillation, which regulate the relative dominance of the two spatial modes of ISMR. By applying the PCA-Biplot technique, our study highlights the significant impacts of various climate drivers on the two dominant spatial modes of ISMR which account for the evolving nature of the ENSO-ISMR relationship.
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Affiliation(s)
- K S Athira
- Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India.
- Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Mohali, Punjab, India.
- College of Climate Change and Environmental Sciences, Kerala Agricultural University, Thrissur, India.
| | - M K Roxy
- Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
| | - Panini Dasgupta
- Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
- Department of Meteorology and Oceanography, College of Science and Technology, Andhra University, Visakhapatnam, India
- Future Innovation Institute, Seoul National University, Siheung, 15011, Seoul, Republic of Korea
| | - J S Saranya
- Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
- College of Climate Change and Environmental Sciences, Kerala Agricultural University, Thrissur, India
- School of Earth and Environmental Sciences/Research Institute of Oceanography, Seoul National University, Seoul, 08826, Republic of Korea
| | - Vineet Kumar Singh
- Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
- Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India
- Typhoon Research Center, Jeju National University, Jeju, South Korea
| | - Raju Attada
- Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Mohali, Punjab, India
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Nakazato M, Kido S, Tozuka T. Mechanisms of asymmetry in sea surface temperature anomalies associated with the Indian Ocean Dipole revealed by closed heat budget. Sci Rep 2021; 11:22546. [PMID: 34824293 PMCID: PMC8617302 DOI: 10.1038/s41598-021-01619-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 11/01/2021] [Indexed: 11/09/2022] Open
Abstract
The Indian Ocean Dipole (IOD) is an interannual climate mode of the tropical Indian Ocean. Although it is known that negative sea surface temperature (SST) anomalies in the eastern pole during the positive IOD are stronger than positive SST anomalies during the negative IOD, no consensus has been reached on the relative importance of various mechanisms that contribute to this asymmetry. Based on a closed mixed layer heat budget analysis using a regional ocean model, here we show for the first time that the vertical mixing plays an important role in causing such asymmetry in SST anomalies in addition to the contributions from the nonlinear advection and the thermocline feedback proposed by previous studies. A decomposition of the vertical mixing term indicates that nonlinearity in the anomalous vertical temperature gradient associated with subsurface temperature anomalies and anomalous vertical mixing coefficients is the main driver of such asymmetry. Such variations in subsurface temperature are induced by the anomalous southeasterly trade winds along the Indonesian coast that modulate the thermocline depth through coastal upwelling/downwelling. Thus, the thermocline feedback contributes to the SST asymmetry not through the vertical advection as previously suggested, but via the vertical mixing.
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Affiliation(s)
- Mai Nakazato
- Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Shoichiro Kido
- Application Laboratory (APL), Research Institute for Value-Added-Information Generation (VAiG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
| | - Tomoki Tozuka
- Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. .,Application Laboratory (APL), Research Institute for Value-Added-Information Generation (VAiG), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan.
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Regional and Local Impacts of the ENSO and IOD Events of 2015 and 2016 on the Indian Summer Monsoon—A Bhutan Case Study. ATMOSPHERE 2021. [DOI: 10.3390/atmos12080954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The Indian Summer Monsoon (ISM) plays a vital role in the livelihoods and economy of those living on the Indian subcontinent, including the small, mountainous country of Bhutan. The ISM fluctuates over varying temporal scales and its variability is related to many internal and external factors including the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). In 2015, a Super El Niño occurred in the tropical Pacific alongside a positive IOD in the Indian Ocean and was followed in 2016 by a simultaneous La Niña and negative IOD. These events had worldwide repercussions. However, it is unclear how the ISM was affected during this time, both at a regional scale over the whole ISM area and at a local scale over Bhutan. First, an evaluation of data products comparing ERA5 reanalysis, TRMM and GPM satellite, and GPCC precipitation products against weather station measurements from Bhutan, indicated that ERA5 reanalysis was suitable to investigate ISM change in these two years. The reanalysis datasets showed that there was disruption to the ISM during this period, with a late onset of the monsoon in 2015, a shifted monsoon flow in July 2015 and in August 2016, and a late withdrawal in 2016. However, this resulted in neither a monsoon surplus nor a deficit across both years but instead large spatial-temporal variability. It is possible to attribute some of the regional scale changes to the ENSO and IOD events, but the expected impact of a simultaneous ENSO and IOD events are not recognizable. It is likely that 2015/16 monsoon disruption was driven by a combination of factors alongside ENSO and the IOD, including varying boundary conditions, the Pacific Decadal Oscillation, the Atlantic Multi-decadal Oscillation, and more. At a local scale, the intricate topography and orographic processes ongoing within Bhutan further amplified or dampened the already altered ISM.
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A machine learning based prediction system for the Indian Ocean Dipole. Sci Rep 2020; 10:284. [PMID: 31937896 PMCID: PMC6959259 DOI: 10.1038/s41598-019-57162-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 12/24/2019] [Indexed: 11/08/2022] Open
Abstract
The Indian Ocean Dipole (IOD) is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole off Sumatra and the other pole near East Africa. An IOD event starts sometime in May-June, peaks in September-October and ends in November. Through atmospheric teleconnections, it affects the climate of many parts of the world, especially that of East Africa, Australia, India, Japan, and Europe. Owing to its large impacts, previous studies have addressed the predictability of the IOD using state of the art coupled climate models. Here, for the first-time, we predict the IOD using machine learning techniques, in particular artificial neural networks (ANNs). The IOD forecasts are generated for May to November from February-April conditions. The attributes for the ANNs are derived from sea surface temperature, 850 hPa and 200 hPa geopotential height anomalies, using a correlation analysis for the period 1949–2018. An ensemble of ANN forecasts is generated using 500 samples with replacement using jackknife approach. The ensemble mean of the IOD forecasts indicates the machine learning based ANN models to be capable of forecasting the IOD index well in advance with excellent skills. The forecast skills are much superior to the skills obtained from the persistence forecasts that one would guess from the observed data. The ANN models also perform far better than the models of the North American Multi-Model Ensemble (NMME) with higher correlation coefficients and lower root mean square errors (RMSE) for all the target months of May-November.
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