Development of non-stationary temperature duration frequency curves for Indian mainland

Mohan, Meera G.; S., Adarsh

doi:10.1007/s00704-023-04606-x

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Moreover, the escalating global temperature is a significant concern, and its pattern is becoming more intricate, introducing non-stationary features. This emphasizes the need to construct temperatureduration-frequency (TDF) curves using robust methods to address heatwave and drought disasters [62]. Any significant change in persistence could affect the accuracy of predictions, similar to relying on a stationarity assumption in a non-stationary setting resulting from climatic changes.…”

Section: Multifractal Analysis In 10-year Rolling Windowmentioning

confidence: 99%

Unravelling the Fractal Complexity of Temperature Datasets across Indian Mainland

Sankaran,

Plocoste,

Geetha Raveendran Nair

et al. 2024

Fractal Fract

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Studying atmospheric temperature characteristics is crucial under climate change, as it helps us to understand the changing patterns in temperature that have significant implications for the environment, ecosystems, and human well-being. This study presents the comprehensive analysis of the spatiotemporal variability of scaling behavior of daily temperature series across the whole Indian mainland, using a Multifractal Detrended Fluctuation Analysis (MFDFA). The analysis considered 1° × 1° datasets of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), and diurnal temperature range (DTR) (TDTR = Tmax − Tmin) from 1951 to 2016 to compare their scaling behavior for the first time. Our results indicate that the Tmin series exhibits the highest persistence (with the Hurst exponent ranging from 0.849 to unity, and a mean of 0.971), and all four-temperature series display long-term persistence and multifractal characteristics. The variability of the multifractal characteristics is less significant in North–Central India, while it is highest along the western coast of India. Moreover, the assessment of multifractal characteristics of different temperature series during the pre- and post-1976–1977 period of the Pacific climate shift reveals a notable decrease in multifractal strength and persistence in the post-1976–1977 series across all regions. Moreover, for the detection of climate change and its dominant driver, we propose a new rolling window multifractal (RWM) framework by evaluating the temporal evolution of the spectral exponents and the Hurst exponent. This study successfully captured the regime shifts during the periods of 1976–1977 and 1997–1998. Interestingly, the earlier climatic shift primarily mitigated the persistence of the Tmax series, whereas the latter shift significantly influenced the persistence of the Tmean series in the majority of temperature-homogeneous regions in India.

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Section: Multifractal Analysis In 10-year Rolling Windowmentioning

confidence: 99%

Unravelling the Fractal Complexity of Temperature Datasets across Indian Mainland

Sankaran,

Plocoste,

Geetha Raveendran Nair

et al. 2024

Fractal Fract

Self Cite

View full text Add to dashboard Cite

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Predicting maximum temperatures over India 10-days ahead using machine learning models

Ratnam,

Behera,

Nonaka

et al. 2023

Sci Rep

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In the months of March-June, India experiences high daytime temperatures (Tmax), which sometimes lead to heatwave-like conditions over India. In this study, 10 different machine learning models are evaluated for their ability to predict the daily Tmax anomalies 10 days ahead in the months of March-June. Several model experiments were carried out to identify an optimal model to predict daily Tmax anomalies over India. The results indicate that the AdaBoost regressor with Multi-layer Perceptron as the base estimator is an optimal model to predict the Tmax anomalies over India in the months of March-June. The optimal model predictions are benchmarked against 10-day persistence predictions and the predictions from the Climate Forecast System (CFS) reforecast. The results indicate that the machine learning model skill is higher than persistence and comparable to CFS reforecast 10-day predictions in April and May. In March and June, the machine learning models have low skill scores and perform no better than persistence. These results indicate that the machine learning models are promising tools to predict the surface air maximum temperature anomalies over India in April and May and can complement predictions from more sophisticated numerical models.

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Development of non-stationary temperature duration frequency curves for Indian mainland

Cited by 2 publications

References 48 publications

Unravelling the Fractal Complexity of Temperature Datasets across Indian Mainland

Unravelling the Fractal Complexity of Temperature Datasets across Indian Mainland

Predicting maximum temperatures over India 10-days ahead using machine learning models

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