Indian Journal of Recent Development Systems for Digitization

ISSN: 2584-1009 (Online)

The use of AI and Machine Learning in Climate Modeling

Author(s)

Sivakumar, R.D.

Assistant Professor, Department of Computer Science, Bell Institute, Sivakasi

Abstract

The combination of AI and ML algorithms in order to improve climate models shows a constitutive progress in the corresponding branch of science. This abstract is a presentation of the many aspects of the advancements and disruptions that AI and ML bring into the climate modelling, with the idea that both these technologies can revolutionize our knowledge on this intricate climate system. Through the use of huge sets of data and extremely complex algorithms, these technologies offer the level of accuracy and predictability, which in turn, fills the long-term gap and support in the process of designing and forecasting. The study focuses on different techniques within the AI and ML realm used for climate model implementation, including examples of deep learning algorithms as well as neural networks. Among the major issues that arise for the validity of the AI assisted models is whether AI-enhanced models are able to reflect subtle climate patterns and feedback cycles which may go unrecognized by traditional models. The complicated and multidimensional factor that cause climate dynamics change can be deeply understood through the adaptability and learning capability feature of machine learning algorithms. The adaptive nature of the models is such that they are able to fine-turn their forecasts even when using live data. This feature also helps in the creation of better and improved climate models. This leads to gaining more deep understanding of climate-related aspects when it is ranging from weather patterns in the local area to global climate trends. However, one cannot realize that with these most encouraging improvements but difficulties and obstacles will come to the ethical and practical aspects of AI implementation. Ethical considerations involve multiple functions, but the key ones are privacy of data, bias in algorithm, and undesirable effects as a result of system failures. The use of historical data for train the AI models design may raise questions about those deriving from its own prejudice types. Furthermore, AI was more likely to reinforces the complexity of AI algorithms, thus making it tricky to identify as well as to trace the outcomes and the accountability. Achieving an equilibrium between technological innovation and moral commitment at this juncture is imperative. The work specifically targets the emerging AI and ML trends in the climate modeling scene as the technologies advance to the next level. Given that the climate research is an interdisciplinary endeavor, a collaborative effort between climate scientists, computer scientists, and ethicists will undoubtedly be needed to address the intricacies that surround the incorporation of AI into existing computer models. And by the same token, the adoption of new techniques and the perfection of available algorithms promote a field that is dynamic and in constant change. A key point in the planning of AI and ML applications for climate modeling is the possibility for introduce the openness to information to all people in the world. The scaling aspect of such technologies enable wider participation in climate research project, thus all scientists across the globe can become contributors doing their little part in gathering a more comprehensive knowledge on how climate systems work. Such knowledge democratization is the key in bringing about collective actions to meet the plea of global effect of climate change.

Keywords : Artificial Intelligence (AI), Machine Learning (ML), Climate Modeling, Climate Science, Environmental Modeling, Predictive Modeling, Data Analysis, Deep Learning, Neural Networks, Data Integration, Real-time Data, Algorithmic Bias, Ethical Considerations and Global Climate Trends.

Volume : 1

Issue : 4

Pages : 37-50

Date of Publication : March 2024

DOI : 10.5281/zenodo.10828278 

Published By

IJRDSD

Published In

Indian Journal of Recent Development Systems for Digitization