Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling

Year : 2025 | Volume : 15 | Issue : 03 | Page : 26 32
    By

    Anvesha Katti,

  • Priyanka Vashisht,

  1. Assistant Professor, Department of Computer Science and Engineering, Amity University, Haryana, India
  2. Associate Professor, Department of Computer Science and Engineering, Amity University, Haryana, India

Abstract

Climate modeling helps to predict the future of climate variations and human interference with environment. The traditional General Circulation Models (GCMs) are based on physics-derived mathematical equations but are very expensive in terms of computation. There are alternative ways to perform climate modeling in recent years with the rise and improvement of Artificial Intelligence (AI) based approaches in term of predictability, efficiency, and classification of extreme events compared to conventional. In this study, AI Models like XGBoost, Long Short-Term Memory (LSTM) networks and hybrid AI-physics models are assessed over GCM. Results show that AI models significantly decrease prediction errors by about 30%, with hybrid AI-physics models having the lower MAE and higher F1 score for extreme event classification. In addition, the AI models have improved computational power by 40% less time for processing and less energy consumption. These results highlight the possibility that AI based approaches can be highly effective for enhancing climate prediction accuracy, risk assessment, and informing policies.

Keywords: Climate modeling, artificial intelligence, machine learning, general circulation models, extreme event classification, hybrid ai-physics models, computational efficiency, climate prediction

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Anvesha Katti, Priyanka Vashisht. Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling. Current Trends in Information Technology. 2025; 15(03):26-32.
How to cite this URL:
Anvesha Katti, Priyanka Vashisht. Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling. Current Trends in Information Technology. 2025; 15(03):26-32. Available from: https://journals.stmjournals.com/ctit/article=2025/view=227956


References

  1. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ. Climate models and their evaluation. In Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR). Cambridge University Press; 2007; 589–662.
  2. Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, Prabhat F. Deep learning and process understanding for data-driven Earth system science. Nature. 2019 Feb 14; 566(7743): 195–204.
  3. Schneider T, Leung LR, Wills RC. Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence. Atmos Chem Phys. 2024 Jun 19; 24(12): 7041–62.
  4. Pawar S, San O, Aksoylu B, Rasheed A, Kvamsdal T. Physics guided machine learning using simplified theories. Phys Fluids. 2021 Jan 1; 33(1): 011701.
  5. Schultz MG, Betancourt C, Gong B, Kleinert F, Langguth M, Leufen LH, Mozaffari A, Stadtler S. Can deep learning beat numerical weather prediction? Philos Trans R Soc A. 2021 Apr 5; 379(2194): 20200097.
  6. Cowls J, Tsamados A, Taddeo M, Floridi L. The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. AI Soc. 2023 Feb; 38(1): 283–307.
  7. Rutledge GK, Alpert J, Ebisuzaki W. NOMADS: A climate and weather model archive at the National Oceanic and Atmospheric Administration. Bull Am Meteorol Soc. 2006 Mar;87(3):327-42.
  8. Chadwick RB. 6.7 Wind Profiler Demonstration System. Middle Atmosphere Program: Handbook for MAP. Vol. 20. ICSU, Scientific Committee on Solar-Terrestrial Physics; 1981; 336.
  9. Brenowitz ND, Henn B, McGibbon J, Clark SK, Kwa A, Perkins WA, Watt-Meyer O, Bretherton CS. Machine learning climate model dynamics: Offline versus online performance. arXiv preprint arXiv:2011.03081. 2020 Nov 5.
  10. Slater LJ, Arnal L, Boucher MA, Chang AY, Moulds S, Murphy C, Nearing G, Shalev G, Shen C, Speight L, Villarini G. Hybrid forecasting: blending climate predictions with AI models. Hydrol Earth Syst Sci. 2023 May 15; 27(9): 1865–89.
Regular Issue Subscription Review Article
Volume 15
Issue 03
Received 12/05/2025
Accepted 28/08/2025
Published 17/09/2025
Publication Time 128 Days
61

Login


My IP

PlumX Metrics