arXiv subjects

B. N. Goswami

Publications and source records attributed to B. N. Goswami.

12 recordsLinked to original sources

arXiv2026

A Deep Learning Earth System Model Simulation of Indian Monsoon Intraseasonal and Interannual Variability

With the data-driven artificial intelligence/machine learning (AI/ML) models having demonstrated their ability to extend the prediction horizon of large-scale weather at a fraction of computational cost of numerical weather prediction models, a pertinent question is, could these models do the same for sub-seasonal to seasonal (S2S) prediction? A key challenge in developing a S2S prediction system is the requirement for a coupled ocean-atmosphere Earth system emulator that can stably simulate the observed intraseasonal and interannual variability with fidelity. In the rapidly evolving field of AI/ML weather models, such a deep learning 3D ocean-atmosphere coupled model has become available, called SamudrACE. With our interest in developing an AI/ML S2S model for Indian monsoon, here we examine the extent to which SamudrACE faithfully simulates Indian monsoon intraseasonal and interannual variability. Compared to observation, we found biases in SamudrACE's simulation of monsoon intraseasonal and interannual variability. Our systematic documentation and analyses of these biases provide a useful benchmark for improving not only SamudrACE but also coupled emulators in general and could fast track the development of a deep learning 3D global S2S prediction system.

physics.ao-ph↗

arXiv2026

Resolving the Paradox of Changing El Niño-Monsoon Relation through Synchronization of Chaotic Oscillators

For over a century, the relationship between Indian summer monsoon rainfall and El Nino-Southern Oscillation has been the foundation of 'long-range' prediction of Indian monsoon. This relation is estimated from correlations between Indian summer monsoon rainfall and a Pacific sea surface temperature-based index of El Nino-Southern Oscillation. However, a prominent multi-decadal variability in the correlation raises doubts on the realism of El Nino-Monsoon relation and stability of Indian monsoon predictability. Previous studies discussed that Pacific-based El Nino-Southern Oscillation indices do not represent El Nino's global influence completely, making their correlation with Indian monsoon unreliable. To address this limitation, a Global El Nino-Southern Oscillation framework based on the depth of the 20 degree Celsius isotherm is developed, integrating subsurface signal from all three tropical ocean basins and maximizing Indian monsoon teleconnections. Contrary to previous findings, the 20 degree Celsius isotherm-based Global El Nino-Southern Oscillation exhibits a strong and stable correlation (greater than 0.8) with Indian monsoon at an 18-month lead. Through a re-examination of the El Nino-Monsoon relationship with the superior Global El Nino-Southern Oscillation predictor, we show that the true relationship is independent of global warming and stationary in time. We discover that the stationarity in the El Nino-Monsoon relationship emerges as a natural consequence of chaotic synchronization between Indian summer monsoon rainfall and 20 degree Celsius isotherm at an 18-month lead. Such synchronization between chaotic climate variables provides a new physical basis for climate predictability beyond the conventional deterministic limit set by chaos. Our findings provide the foundation and renewed confidence in long range climate prediction.

physics.ao-ph↗

arXiv2026

Why is Seasonal Climate Predictable Beyond the Limit of Deterministic Predictability set by Chaos?

The Earth's climate is an ensemble of interacting, spatially extended oscillatory media ('climate systems') whose slow-changing averages coexist with chaotic, high-frequency weather fluctuations in quasi-equilibrium. The limit of deterministic predictability (LDP) for any climate system is determined by its fastest-growing errors. However, recent findings show that the Indian Summer Monsoon Rainfall (ISMR) can be predicted up to 18 months in advance-far beyond its LDP. Using a model of two interacting oscillatory media, we show that this extended predictability arises from lag synchronization between ISMR and its predictor, the Global El Nino-Southern Oscillation (G-ENSO), to which it is strongly coupled. We introduce complex order parameters representing the internal dynamics of the two climate systems. Their spatiotemporal evolution is governed by coupled Complex Ginzburg-Landau Equations, producing aperiodic yet strongly correlated time series at long lead times. Our findings have far-reaching consequences in advancing seasonal prediction across climate systems.

physics.ao-ph↗

arXiv2026

Prediction and Predictability of the Wet-Season Rainfall over Southeast India

The challenge in predicting sub-regional climate within the Indian monsoon region is exacerbated by its increasing variability in a warming world. While exploring the seasonal predictability of rainfall over the state of Tamil Nadu in southeast India, we identify an overall increase in the monthly rainfall and its variability in recent years due to an increase in surface temperature, water vapour and moisture convergence. We attribute the increasing excess rainfall to a long-term reduction in convective inhibition. We further find an increasing trend in the length of the rainy season due to an earlier onset and a delayed withdrawal of the large-scale monsoon over the southeastern and southwestern regions of southern peninsular India, respectively. Further, the simultaneous (0- month lead) predictability of the primary wet-season (October-December, OND) rainfall over Tamil Nadu is dominated by sea surface temperature (SST) anomalies in the North Indian Ocean. However, a global tropical SST climate network reveals a high potential predictability and potential to realize significant forecast skill at a lead time of up to 10 months. The long-lead predictability arises from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. Our findings provide a robust data-driven methodology for skillful seasonal rainfall prediction over Tamil Nadu, despite the increasing rainfall variability.

physics.ao-ph↗

arXiv2026

Sub-seasonal Modulation and Predictability of Indian monsoon hourly Rainfall Extremes

Hourly rainfall extremes cause some of the most destructive weather disasters, yet numerical weather prediction models still struggle to forecast them, and a physical basis for their predictability remains unclear. Here, we identify a trivariate clustering of hourly rainfall extremes with surface temperature, phases of the Monsoon Intraseasonal Oscillation (MISO), and precipitable water vapor, establishing a physical foundation for the medium range predictability of these events. This clustering arises from multiscale interactions in which extremes organize into storm systems embedded within mesoscale convective clusters and synoptic low-pressure systems during active MISO phases. We develop an algorithm to identify, track, and monitor these storm systems. Although rapid error growth limits the prediction of isolated hourly extremes, our results provide basis for a physics informed training of deep learning, data driven models to forecast organized clusters of hourly rainfall extremes more than a week in advance, offering substantial potential to reduce losses from extreme rainfall.

physics.ao-ph↗

arXiv2025

Large-scale patterns of small-scale vorticity interactions foster moist convection during cyclogenesis

The formation and intensification of a tropical cyclone is a complex phenomenon involving several feedback interactions between momentum and energetics of the storm, and across multiple spatio-temporal scales. Background vorticity interactions in the turbulent atmosphere play a crucial role in the formation of cyclones. How these vorticity interactions lead to convective organization and sustain a disastrous cyclonic vortex amidst a turbulent atmosphere remains elusive. Moreover, what processes distinguish depressions that develop into a cyclone from those that do not? Here, we investigate the role of small-scale vorticity interactions in the background flow in sustaining large-scale organization during the emergence of a cyclone. We construct time-varying complex networks where geographical locations are nodes and connections between nodes represent short-time vorticity correlations. Only those nodes are connected that are in spatial proximity corresponding to sub-meso length scales. Each network is constructed for 29 hours of data; consecutive networks are separated by three hours, thus revealing the evolution of local coherence in vorticity dynamics. We discover that small-scale vorticity interactions manifest as large-scale emergent patterns. Further, we establish that organized moist convection is significantly correlated to regions of locally coherent vorticity dynamics during the intensification of a depression that forms a cyclone; however, such correlations are not sustained during non-developing cases. Using modal analysis of time-evolving network connectivity, we show that these large-scale patterns are essentially large-scale modes of propagation of coherence in small-scale vorticity dynamics. We explain that such propagation is facilitated by moisture feedback at small-scales and self-organized patterns at large-scales.

physics.ao-ph↗

arXiv2025

Climatic Phase Transitions Unravel the Onset and Withdrawal of Indian Monsoon

The livelihood and food security of more than a billion people depend on the Indian monsoon (IM). Yet, a universal definition of the large-scale season and progress of IM is missing. Even though IM is a planetary-scale convectively coupled system arising largely from seasonal migration of the Intertropical Convergence Zone (ITCZ), the definitions of its onset and progression are based on local weather observations, making them practically inutile due to the detection of bogus onsets. Using climate networks, we show that small-scale clusters of locally defined rainfall onsets coalesce through two abrupt climatic phase transitions defining large-scale monsoon onsets over Northeast India and the Indian peninsula, respectively. These abrupt transitions are interspersed with continuous growth of clusters. Breaking the conventional wisdom that IM starts from southern peninsula and expands northward and westward, we unveil that IM starts from Northeast India and expands westward and northward, covering the entire country. We show that the large-scale monsoon onset over the Indian peninsula is critically dependent on the characteristics of monsoon onset over Northeast India. Unlike existing definitions, a rapid and consistent northward propagation of rainfall establishing the ITCZ manifests after our network-based onset dates. Thus, our definition captures the IM onset better than the existing definitions.

physics.ao-ph↗

arXiv2022

Climate Change and Potential Demise of the Indian Deserts

In contrast to the wet gets wetter and dry gets drier paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi-arid northwest parts of India and Pakistan has increased by 10 to 50 percent during 1901 to 2015 and is expected to increase by 50 to 200 percent under moderate greenhouse gas (GHG) scenarios, e.g, SSP2 4.5. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon (ISM) rainfall and is a result of a westward expansion of the inter-tropical convergence zone (ITCZ), facilitated by a westward expansion of the Indian Ocean warm pool. While an adaptation strategy to increased hydrological disasters is a must, harvesting the increased rainfall would lead to a significant increase in food productivity, bringing transformative changes in the socio-economic condition of people in the region.

physics.ao-ph↗

arXiv2021

High ENSO-based 18-month lead Potential Predictability of Indian Summer Monsoon Rainfall

Scientific basis for long-lead seasonal prediction of Indian summer monsoon rainfall (ISMR) critical for water resource and crop strategy planning is lacking. Using a new predictor discovery method, here we show that the depth of 20 degree isotherm (D20) is least influenced by atmospheric noise and that the 18-month lead forecasts of ISMR have high potential skill (r = 0.86). The high potential predictability is due to smaller initial errors associated with the 18-month lead initial conditions and their slow growth associated with the El Nino and Southern Oscillation (ENSO). The potential skill arises not only from the correlation between ISMR and large-scale slowly varying D20 but also contributed significantly by that with the interannual small-scale D20 anomalies indicating a seminal role of the nonlinearity on the potential predictability. It is, therefore, imperative that a nonlinear predictor discovery as well as nonlinear prediction model is essential for realizing this potential predictability.

physics.ao-ph↗

arXiv2020

Electrical Route to Realising Intensity Simulation of Heavy Rain Events in Tropics

In the backdrop of a revolution in weather prediction by Numerical Weather Prediction (NWP) models, quantitative prediction of intensity of heavy rainfall events and associated disasters has remained a challenge. Encouraged by compelling evidence of electrical influences on cloud/rain microphysical processes, here we propose a hypothesis that modification of raindrop size distribution (RDSD) towards larger drop sizes through enhanced collision-coalescence facilitated by cloud electric fields could be one of the factors responsible for intensity errors in weather/climate models. The robustness of the hypothesis is confirmed through a series of simulations of strongly electrified (SE) rain events and weakly electrified (WE) events with a convection-permitting weather prediction model incorporating the electrically modified RDSD parameters in the model physics. Our results indicate a possible roadmap for improving hazard prediction associated with extreme rainfall events in weather prediction models and climatological dry bias of precipitation simulation in many climate models.

physics.ao-ph↗

arXiv2019

Role of the North Atlantic in Indian Monsoon Droughts

The forecast of Indian monsoon droughts has been predicated on the notion of a season-long rainfall deficit linked to warm anomalies in the equatorial Pacific. Here, we show that in fact nearly half of all droughts over the past century were sub-seasonal, and characterized by an abrupt decline in late-season rainfall. Furthermore, the potential driver of this class of droughts is a coherent cold anomaly in the North Atlantic Ocean. The vorticity forcing associated with this oceanic marker extends through the depth of the troposphere, and results in a wavetrain which curves towards the equator and extends to East-Asia. This upper-level response triggers an anomalous low-level anticyclonic circulation late in the season over India. This teleconnection from the midlatitudes offers an avenue for improved predictability of monsoon droughts.

physics.ao-ph↗

arXiv2018

Unraveling the Mystery of Indian Summer Monsoon Prediction: Improved Estimate of Predictability Limit

Large socio-economic impact of the Indian Summer Monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather estimated low potential predictability limit (PPL) of seasonal prediction of the ISM, contributed significantly by 'internal' interannual variability was considered insurmountable. Here we show that the 'internal' variability contributed by the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g. El Nino and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PPL. We establish a much higher actual limit of predictability (r~0.82) through an extensive re-forecast experiment (1920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near future.

physics.ao-ph↗