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ANN-Based Assessment of the Influence of Natural and Anthropogenic Forcing on Surface Air Temperature Variability Over the Indian Subcontinent

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Abstract

We examine here the relative influence of natural and anthropogenic forcing on surface air temperature (SAT) variability over the Indian subcontinent (IND) through the analysis of the normalized data of maximum (Tmax), minimum (Tmin), and mean (Tmean/INDSAT) temperatures, total solar irradiance (TSI), El-Nino Southern Oscillations (ENSO), Pacific Decadal Oscillations (PDO), North Atlantic Oscillations (NAO), sea surface temperature (SST), all-India rainfall (AIR), and CO2 using artificial neural networks (ANN). Sensitivity analysis reveals the dependency of each of the natural (e.g. TSI, ocean-atmospheric circulations [OAC, e.g. ENSO, PDO, NAO], SST, AIR) and anthropogenic (CO2) parameters on SAT as follows: (i) SAT is highly sensitive to continuous emission of anthropogenic CO2, (ii) intensive influence of TSI at 11- and 22-year cycles, and (iii) intermittent influence of OAC and SST at low intensive solar radiance. These dependent parameters are used to develop a model to predict future temperatures using multi-perceptron nonlinear feed-forward and back-propagation schemes with a NARX network encoded Bayesian regularization (BR) training algorithm. We used TSI, CO2, OAC, SST, and AIR data as input and Tmean/INDSAT as target (supervised learning). In order to ensure the stable and best possible result, we trained the network successively with different numbers (1–10) of neurons and delay parameters. Optimum mean square error (MSE) and R values (minimum MSE and maximum R value) with eight neurons were found appropriate for the present analysis. We therefore, chose eight neurons to develop a model for training and testing the data and obtained MSE = 0.00039976 and R = 0.98933. This model was validated from 2001 to 2007 and predicted the temperatures from 2001 to 2019 with error ≤ 20%.

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Acknowledgements

The authors thank the Director, CSIR-NGRI, for his consent to publish this work. Padmavathi is grateful to DST for awarding the DST-WOSA fellowship and to AcSIR for allowing registration for Ph.D. The authors thank the IMD and IITM for making available the data of Indian temperature and rainfall. The authors also acknowledge the help of Dr. Raju Mandal for providing the average maximum, minimum, and mean surface air temperature time series from 2008 to 2019 and rainfall data from 2017 to 2019, and Wolter and Timlin for providing ENSO Index data, Coddington et al. for the TSI data, Mantua et al. for PDO data, Jones et al. for the NAO data, Kennedy et al. for SST data, and Etheridge et al. for the CO2 data from KNMI Climate Explorer.

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  1. CSIR-National Geophysical Research Institute, Uppal Road, Hyderabad, India

    B. Padmavathi, R. K. Tiwari & V. M. Tiwari

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    B. Padmavathi

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Padmavathi, B., Tiwari, R.K. & Tiwari, V.M. ANN-Based Assessment of the Influence of Natural and Anthropogenic Forcing on Surface Air Temperature Variability Over the Indian Subcontinent. Pure Appl. Geophys. 178, 1911–1926 (2021). https://doi.org/10.1007/s00024-021-02724-z

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