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A Unified procedure for the probabilistic assessment and forecasting temperature characteristics under global climate change

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Abstract

Accurate assessment and forecasting of temperature characteristics in relation to climate change are essential for making effective climate policies. The industrial revolution is considered one of the primary causes of climate change, resulting in global warming and spatio-temporal variation in temperature around the world. This study introduces a novel, unified approach called Generalized Probabilistic Standardized Temperature Index (GPSTI) to, monitoring, forecasting and evaluate the acceleration of temperature fluctuations with consideration of climate change impact. In application, this research considered meteorological data from 41 locations across various regions of Pakistan. Additionally, different machine learning techniques that include Autoregressive Integrated Moving Average (ARIMA), TBATS, Extreme learning machine (ELM), and Artificial Neural Network – Multilayer Perceptron (MLP) are used to predict the value of the GPSTI. The results indicate that the TBATS model has been demonstrated to be the best performer among all the evaluated models by continuously achieving lower RMSE values at most of the stations (Faisalabad, 0.9307; Karachi, 0.3836; Kohat, 0.4448; Gilgit, 0.4626; and Kotli, 0.3900) during the testing stage. Outcomes associated with this research shows that the GPSTI can be used for future forecasting under various machine learning and probabilistic approach. The key advantages of GPSTI include its ability to facilitate regional comparisons and its utility for future forecasting. Overall, the computational evidence strongly supports a significant shift toward higher temperatures over time, potentially influenced by the industrial revolution and its associated factors. These results support the widely accepted scientific consensus on global warming and provide additional empirical evidence for the ongoing discussion on climate change.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

AIC:

Akaike information criterion

ARIMA:

Autoregressive integrated moving average

BIC:

Bayesian information criterion

CCKP:

Climate change knowledge portal

CDF:

Cumulative density function

ELM:

Extreme learning machine

FFNN:

Feed-forward neural network

GEV:

Generalized extreme value

GPSTI:

Generalized probabilistic standardized temperature index

GWO:

Grey Wolf’s Optimization

ITI:

Incremental temperature index

MAE:

Mean absolute error

MLP:

Multilayer perceptron

NASA:

National aeronautics and space administration

RMSE:

Root mean square error

SLFN:

Single hidden layer feedforward neural network

SPI:

Standardized precipitation index

UN:

United nation

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Funding

The study was supported by National Natural Science Foundation of China (grant numbers 52079005, 52239003), and Natural Science Foundation of Guangdong (grant numbers 2022A1515010898).

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Authors and Affiliations

  1. College of Statistical Science, University of the Punjab, Lahore, 54000, Pakistan

    Wajiha Batool Awan, Aamina Batool & Zulfiqar Ali

  2. College of Water Sciences, Beijing Normal University, Beijing, 100875, China

    Zongxue Xu

  3. Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing, 100875, China

    Zongxue Xu

  4. Department of Statistics, Quaid-i-Azam University, Islamabad, 44000, Pakistan

    Rizwan Niaz

  5. Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate 32011, Baqubah, Iraq

    Saad Sh. Sammen

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  1. Wajiha Batool Awan
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Contributions

Conceptualization, methodology, software, Wajiha Batool Awan; validation, Zulfiqar Ali and Aamina Batool; formal analysis, Wajiha Batool Awan and Aamina Batool; data curation, Rizwan Niaz; writing—original draft preparation, Wajiha Batool Awan; writing—review and editing, Saad Sh. Sammen; supervision, Zulfiqar Ali; funding acquisition, Zongxue Xu. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zulfiqar Ali.

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Awan, W.B., Batool, A., Ali, Z. et al. A Unified procedure for the probabilistic assessment and forecasting temperature characteristics under global climate change. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05020-7

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