AI-Driven Big Data Systems for Climate Change Impact Assessment
Keywords:
Climate Change, Impact Assessment, Big Data, AI Systems, Predictive Models, Scenario Modeling, Causal Analysis, Detection Methods, Attribution Analysis, Climate Projections, Risk Assessment, Vulnerability Mapping, Extreme Events, Uncertainty Analysis, Ecosystem Services, Carbon Balance, Regional Analysis, Environmental Data, Climate Analytics, Earth Systems.Abstract
AI-Powered Big Data Systems for Intelligent Climate Change Impact Assessment presents an objective, evidence-based account with formal structure and clear, discipline-appropriate critique. Growing climate knowledge enables analysis of weather events and future scenarios using AI and Big Data systems. However, such analyses must increasingly probe extreme impacts. While state-of-the-art statistical approaches are effective for specific detection and attribution, they cannot fully capture biophysical mechanisms or future scenarios. Probabilistic projections need a causal perspective.
An emergent intelligent climate change impact assessment and projection framework integrates detection, attribution, projection, and scenario modeling with these additional aspects. Multisource AI-assisted datasets support an international ensemble of studies on subcontinent-level impacts. A regional vulnerability mapping exercise interprets sensitivity to climate extremes, assesses uncertainty, and identifies regions at risk. Future scenarios measuring climate-disrupted carbon and ecosystem service balances are generated from a continental land model. Nevertheless, local risk assessments rely on simple calibrated approaches, thus remaining vulnerable to missing mechanisms in the statistical relationships.
References
1. Callaghan, M. W., Minx, J. C., & Forster, P. M. (2020). A topography of climate change research. Nature Climate Change, 10, 118–123.
2. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999–2049.
3. Sriram, H. K., Challa, K., Gadi, A. L., & Singreddy, S. (2025). AI and Cloud-Driven Transformation in Finance, Insurance, and the Automotive Ecosystem: A Multi-Sectoral Framework for Credit Risk, Mobility Services, and Consumer Protection. Anil Lokesh and singreddy, Sneha, AI and Cloud-Driven Transformation in Finance, Insurance, and the Automotive Ecosystem: A Multi-Sectoral Framework for Credit Risk, Mobility Services, and Consumer Protection (March 15, 2025).
4. Yuval, J., & O’Gorman, P. A. (2020). Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions. Nature Communications, 11, 3295.
5. Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020). WeatherBench: A benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems, 12, e2020MS002203.
6. Taddeo, M., Tsamados, A., Cowls, J., & Floridi, L. (2021). Artificial intelligence and the climate emergency: Opportunities, challenges, and recommendations. One Earth, 4(6), 776–779.
7. Seenu, A., Sheelam, G. K., Motamary, S., Meda, R., Koppolu, H. K. R., & Inala, R. (2025). AI-Driven Innovations in Infrastructure Management with 6G Technology. In 2025 2nd International Conference on Computing and Data Science (ICCDS) (pp. 1–6). IEEE. 2025 2nd International Conference on Computing and Data Science (ICCDS). https://doi.org/10.1109/iccds64403.2025.11209649
8. Wang, F., Tian, D., Lowe, L., Kalin, L., & Lehrter, J. (2021). Deep learning for daily precipitation and temperature downscaling. Water Resources Research, 57, e2020WR029308.
9. Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., & Shuckburgh, E. (2021). Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, 5124.
10. Callaghan, M. W., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., Hegarty, M., Jones, C., Lee, K., Lucas, A., van Maanen, N., Menke, I., Pfleiderer, P., Yesil, B., … Minx, J. C. (2021). Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature Climate Change, 11, 966–972.
11. Narasareddy Annapareddy, V., Pamisetty, A., Malempati, M., Kaulwar, P. K., & Bhardwaj Komaragiri, V. (2025, April). Enhancing Solar Power System Efficiency Through AI-Driven Predictive Maintenance and Cloud-Based Infrastructure Stability Solutions. In International Conference on Smart Computing and Informatics (pp. 328-337). Cham: Springer Nature Switzerland.
12. Davenport, F. V., & Diffenbaugh, N. S. (2021). Using machine learning to analyze physical causes of climate change: A case study of U.S. Midwest extreme precipitation. Geophysical Research Letters, 48, e2021GL093787.
13. Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021). Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nature Machine Intelligence, 3, 667–674.
14. Challa, S. R., Burugulla, J. K. R., Pamisetty, A., Challa, K., & Paleti, S. (2025, April). AI and ML-Powered Cybersecurity Strategies for Cloud Computing: Ensuring Infrastructure Stability in Financial and Retail Sectors. In International Conference on Smart Computing and Informatics (pp. 315-327). Cham: Springer Nature Switzerland.
15. Fang, W., Xue, Q., Shen, L., & Sheng, V. S. (2021). Survey on the application of deep learning in extreme weather prediction. Atmosphere, 12(6), 661.
16. Watt-Meyer, O., Brenowitz, N. D., Clark, S. K., Henn, B., McGibbon, J., & Perkins, W. A. (2021). Correcting weather and climate models by machine learning nudged historical simulations. Geophysical Research Letters, 48, e2021GL092555.
17. Singireddy, S., Pandiri, L., Motamary, S., Kummari, D. N., Somu, B., & Lakkarasu, P. (2025, August). Blockchain-Powered Claims Validation for Enhancing Trust in Health and Auto Insurance Ecosystems. In International Conference on Artificial Intelligence: Theory and Applications (pp. 26-38). Cham: Springer Nature Switzerland.
18. Leal Filho, W., Wall, T., Mucova, S. A. R., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Azam, F. M. S., Alves, F., Guevara, Z., Matandirotya, N. R., Skouloudis, A., Tzachor, A., Malakar, K., & Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662.
19. Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12, 518–527.
20. Dutta, P., Adusupalli, B., Koppolu, H. K. R., Dodda, A., Yağanoğlu, M., Banerjee, J. S., & Chakraborty, A. (2025, March). Textual Social Data Disinformation Analysis Using a Hybrid Context-Enhanced Deep Learning Model. In Doctoral Symposium on Human Centered Computing (pp. 342-352). Singapore: Springer Nature Singapore.
21. Hernanz, A., García-Valdecasas Ojeda, M., & Manzanas, R. (2022). A critical view on the suitability of machine learning techniques to downscale climate change projections: Illustration for temperature with a toy experiment. Atmospheric Science Letters, 23, e1087.
22. Watson-Parris, D., Rao, Y., Olivié, D. J. L., Seland, Ø., & others. (2022). ClimateBench v1.0: A benchmark for data-driven climate projections. Journal of Advances in Modeling Earth Systems, 14, e2021MS002954.
23. Singireddy, J., Kaulwar, P. K., Somu, B., Meda, R., Dodda, A., & Yellanki, S. K. (2025, August). Reinforcement Learning-Based Asset Allocation in Algorithmic Trading for Banking Institutions. In International Conference on Artificial Intelligence: Theory and Applications (pp. 357-369). Cham: Springer Nature Switzerland.
24. Bochenek, B., & Ustrnul, Z. (2022). Machine learning in weather prediction and climate analyses—Applications and perspectives. Atmosphere, 13(2), 180.
25. Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., & Anandkumar, A. (2022). FourCastNet: A global data-driven high-resolution weather model using adaptive Fourier neural operators. Advances in Neural Information Processing Systems.
26. Chattopadhyay, A., Mustafa, M., Hassanzadeh, P., Bach, E., & Kashinath, K. (2022). Towards physics-inspired data-driven weather forecasting: Integrating data assimilation with a deep spatial transformer-based U-Net in a case study with ERA5. Geoscientific Model Development, 15, 2221–2237.
27. Vadisetty, R., Nuka, S. T., Kalisetty, S., Pandugula, C., Burugulla, J. K. R., & Annapareddy, V. N. (2025, February). Generative AI for Advanced Recycling Processes in Polyethylene and Polypropylene Manufacturing. In International Ethical Hacking Conference (pp. 269-284). Singapore: Springer Nature Singapore.
28. Bonev, B., Kurth, T., Hundt, C., Pathak, J., Baust, M., Kashinath, K., & Anandkumar, A. (2023). Spherical Fourier neural operators: Learning stable dynamics on the sphere. Proceedings of the 40th International Conference on Machine Learning, 2806–2823.
29. Hobeichi, S., Nishant, N., & others. (2023). Using machine learning to cut the cost of dynamical downscaling. Earth’s Future, 11, e2022EF003291.
30. Challa, S. R., Kaulwar, P. K., Koppolu, H. K. R., Adusupalli, B., & Suura, S. R. (2025, April). Big Data and AI in Wealth Management: Leveraging Cloud Computing for Secure and Scalable Financial Infrastructure. In International Conference on Smart Computing and Informatics (pp. 307-318). Cham: Springer Nature Switzerland.
31. Jones, A., Kuehnert, J., Fraccaro, P., Meuriot, O., Ishikawa, T., Edwards, B., Stoyanov, N., Remy, S. L., Weldemariam, K., & Assefa, S. (2023). AI for climate impacts: Applications in flood risk. npj Climate and Atmospheric Science, 6, 63.
32. Zhang, Y., Long, M., Chen, K., Xing, L., Wang, J., Jin, R., & Jordan, M. I. (2023). Skilful nowcasting of extreme precipitation with NowcastNet. Nature, 619, 526–532.
33. Singireddy, J., Kaulwar, P. K., Somu, B., Meda, R., Dodda, A., & Yellanki, S. K. (2025, August). Reinforcement Learning-Based Asset Allocation in Algorithmic Trading for Banking Institutions. In International Conference on Artificial Intelligence: Theory and Applications (pp. 357-369). Cham: Springer Nature Switzerland.
34. Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., & Tian, Q. (2023). Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619, 533–538.
35. Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., & Battaglia, P. (2023). Learning skillful medium-range global weather forecasting. Science, 382, 1416–1421.
36. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
37. Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K., & Grover, A. (2023). ClimaX: A foundation model for weather and climate. Proceedings of the 40th International Conference on Machine Learning, 25904–25938.
38. Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez-Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Ben Bouallegue, Z., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., & Sha, F. (2024). WeatherBench 2: A benchmark for the next generation of data-driven global weather models. Journal of Advances in Modeling Earth Systems, 16, e2023MS004019.
39. KOTHAPALLI, S. L., SEENU, A., DILEEP, V., & YASMEEN, Z. (2025). THE FUTURE OF CUSTOMER ENGAGEMENT IN RETAIL BANKING: EXPLORING THE POTENTIAL OF AUGMENTED REALITY AND IMMERSIVE TECHNOLOGIES. INTERNATIONAL JOURNAL, 73(1), 72-79.
40. Rampal, N., Hobeichi, S., Gibson, P. B., Baño-Medina, J., & others. (2024). Enhancing regional climate downscaling through advances in machine learning. Artificial Intelligence for the Earth Systems, 3(2).
41. Salcedo-Sanz, S., Pérez-Aracil, J., Ascenso, G., Del Ser, J., Casillas-Pérez, D., Kadow, C., Fister, D., Barriopedro, D., García-Herrera, R., Giuliani, M., & Castelletti, A. (2024). Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: A review. Theoretical and Applied Climatology, 155, 1–44.
42. Ande, R., Mehta, D., Pandugula, C., Krishna AzithTejaGanti, V., & Kalisetty, S. (2025). Modeling the Som Kamla Amba sub-watershed using Artificial Neural Networks (ANN) and the SWAT tool. Available at SSRN 5341755.
43. Buster, G., Benton, B. N., Glaws, A., & King, R. N. (2024). High-resolution meteorology with climate change impacts from global climate model data using generative machine learning. Nature Energy, 9, 894–906.
44. Eyring, V., Collins, W. D., Gentine, P., Beucler, T., Barnes, E. A., Barreiro, M., Bretherton, C. S., Bocquet, M., & others. (2024). Pushing the frontiers in climate modelling and analysis with machine learning. Nature Climate Change, 14, 916–928.
45. Pandugula, C., Ganti, V. K. A. T., Kalisetty, S., Ande, R., & Mehta, D. (2025). Modeling the Som Kamla Amba Sub-Watershed Using Artificial Neural Networks (ANN) and the SWAT Tool. Available at SSRN 5341614.
46. Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024). Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives. WIREs Climate Change, 15(6), e914.
47. Conti, S. (2024). Artificial intelligence for weather forecasting. Nature Reviews Electrical Engineering, 1, 8.
48. Maguluri, K. K. (2025). Ethical challenges in artificial intelligence. How Artificial Intelligence is Transforming Healthcare IT: Applications in Diagnostics, Treatment Planning, and Patient Monitoring, 132.
49. Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., Hatfield, S., Battaglia, P., & others. (2024). Neural general circulation models for weather and climate. Nature, 632, 1060–1066.
50. Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., & Willson, M. (2025). Probabilistic weather forecasting with machine learning. Nature, 637, 84–90.
51. Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Allen, A., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J. A., Dong, H., Gupta, J. K., Thambiratnam, K., Archibald, A. T., Wu, C.-C., Heider, E., Welling, M., Turner, R. E., & Perdikaris, P. (2025). A foundation model for the Earth system. Nature, 641, 1180–1187.
52. Camps-Valls, G., Fernández-Torres, M.-Á., Cohrs, K.-H., Höhl, A., Castelletti, A., Pacal, A., Robin, C., & others. (2025). Artificial intelligence for modeling and understanding extreme weather and climate events. Nature Communications, 16, 1919.
53. ADUSUPALLI, B. (2025). Redefining Financial Risk Strategies: The Integration of Smart Automation, Secure Access Systems, and Predictive Intelligence in Insurance, Lending, and Asset Management. JAIBDD.
54. Ukoba, K., Onisuru, O. R., Jen, T.-C., Madyira, D. M., & Olatunji, K. O. (2025). Predictive modeling of climate change impacts using artificial intelligence: A review for equitable governance and sustainable outcome. Environmental Science and Pollution Research, 32, 10705–10724.
55. Ayadi, R., Forouheshfar, Y., & Moghadas, O. (2025). Enhancing system resilience to climate change through artificial intelligence: A systematic literature review. Frontiers in Climate, 7, 1585331.
56. Lange, S., & Büchner, M. (2021). ISIMIP3b bias-adjusted atmospheric climate input data. ISIMIP Repository.
57. Rong, X., Li, J., Chen, H., Su, J., Hua, L., Zhang, Z., & Xin, Y. (2021). The CMIP6 historical simulation datasets produced by the climate system model CAMS-CSM. Advances in Atmospheric Sciences, 38, 285–295.
58. Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., & others. (2024). The ERA5 global reanalysis from 1940 to 2022. Quarterly Journal of the Royal Meteorological Society, 150, 4014–4048.
59. Kurth, T., Subramanian, S., Harrington, P., Pathak, J., Mardani, M., Hall, D., Miele, A., Kashinath, K., & Anandkumar, A. (2023). FourCastNet: Accelerating global high-resolution weather forecasting using adaptive Fourier neural operators. Proceedings of the Platform for Advanced Scientific Computing Conference, 1–11.
60. Gao, Z., Shi, X., Han, B., Wang, H., Jin, X., Maddix, D., & others. (2022). Earthformer: Exploring space-time transformers for earth system forecasting. Advances in Neural Information Processing Systems.
61. Wang, J., Long, M., & others. (2023). Precipitation nowcasting with deep learning and multimodal atmospheric data. Geophysical Research Letters.
62. Sun, D., Zhang, X., Li, Y., Chen, H., & Wang, J. (2022). Machine learning applications in climate prediction and extreme weather event detection. Atmospheric Research, 276.
63. Miao, C., Sun, Q., Duan, Q., & Wang, Y. (2021). Evaluation and application of machine learning methods for climate and hydrological prediction. Journal of Hydrology, 603.
64. Fang, K., Pan, M., & others. (2021). Machine learning approaches for drought prediction and hydrological impact assessment. Water Resources Research, 57.
65. Shen, C. (2021). A transdisciplinary review of deep learning research and applications in water resources. Water Resources Research, 57.
66. Nearing, G. S., Kratzert, F., Klotz, D., Frame, J. M., Pelissier, C. S., Pelissier, C., & others. (2021). What role does hydrological science play in the age of machine learning? Water Resources Research, 57.
67. Kratzert, F., Klotz, D., Hochreiter, S., & Nearing, G. (2022). A note on machine learning benchmark datasets for hydrological modeling. Hydrology and Earth System Sciences, 26.
68. Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., & others. (2020). Deep learning and process understanding for Earth system science. Earth System Science Data, 12.
69. Boukabara, S.-A., Krasnopolsky, V., Penny, S. G., Chevallier, F., & others. (2021). Outlook for artificial intelligence and machine learning in atmospheric and Earth system sciences. Bulletin of the American Meteorological Society, 102.
70. Amiri, Z., Heidari, A., & Navimipour, N. J. (2024). Comprehensive survey of artificial intelligence techniques and strategies for climate change mitigation. Energy, 308, 132827.
71. Lewis, J. I., Toney, A., & Shi, X. (2024). Climate change and artificial intelligence: Assessing the global research landscape. Discover Artificial Intelligence, 4, 64.
