Beyond forecasting: Machine Learning exploring climate impacts
Machine learning has emerged as a transformative tool in climate research, evolving from theory to practice over the past few decades. In this context, CMCC is expanding its focus beyond traditional climate modeling to include the study of environmental, social and economic impacts of climate change through data-driven approaches. This transformation is expected to involve all-encompassing capacity building, with substantial infrastructure investments alongside efforts to enhance the knowledge base of scientists.
Over the past few decades, machine learning has evolved from a theoretical concept into a practical tool, thanks to advancements in computational architectures around the early 2000s. Initially applied predominantly in language and image processing contexts, it was only around 2015 that researchers began exploring its potential in climate science.
However, it was not until approximately two years ago that a significant breakthrough occurred, marking a pivotal moment in the field. “This happened when the emergence of fully data-driven models, which outperformed traditional numerical models, particularly in atmospheric weather forecasting, demonstrated the viability of data-driven approaches in climate modeling,” says Italo Epicoco, principal scientist at CMCC and Assistant Professor at the University of Salento, working on machine learning approaches for climate modeling.
This watershed moment has opened new avenues in climate research, ushering in an era where data-driven methodologies are becoming increasingly prominent in understanding and predicting complex climate systems.
In this framework, CMCC has joined this shift in the field with exploratory endeavors focused on downscaling techniques, which already yielded promising results in regional applications. “We must be careful not to flatten machine learning only on earth science modeling,” says Epicoco. “ CMCC is traditionally not only concerned with climate models, but also with models that describe how climate change impacts various contexts, such as the impacts on soil and agriculture, or on coastal erosion. These are examples where CMCC has already started working for some years now.”
CMCC has been actively exploring for some years contextualized machine learning approaches to address the socio-economic impacts of climate change, or to support coastal management, with successful applications in monitoring and fishing-related activities. Additionally, CMCC is working to harness machine learning techniques to analyze satellite data for land use classification, facilitating informed decision-making in agriculture and environmental management. Ongoing initiatives, such as fire monitoring, underscore CMCC’s commitment to integrating machine learning methodologies across various domains to enhance climate research and resilience efforts.
CMCC is seeking to identify, and if possible, anticipate the challenges of the future by developing comprehensive models that incorporate human behavior and economic dynamics. Epicoco acknowledges the complexity of this endeavor: “To address climate impacts and its interactions with socio-economic systems, we need to be able to manage such an amount of data that is much bigger than what you face in the case of forecasts,” he explains.
Machine learning as an innovative approach to climate research
For instance, the CLINT (CLimate INTelligence) project, a collaborative effort involving CMCC, Politecnico di Milano, and other European partners, utilizes Artificial Intelligence (AI) and Machine Learning techniques to enhance the detection, causation, and attribution of extreme events. Within this project, researchers at CMCC are investigating ways to improve the detection and forecasting of extreme weather events, particularly tropical cyclones and heatwaves, by integrating AI into standard detection methods.
Recent developments, such as the enhancement of global indices for tropical cyclone occurrence, demonstrate the potential of this collaboration to yield significant outcomes. Ongoing efforts within the CLINT project aim to refine detection mechanisms, improve the representation of extreme precipitation, and advance seasonal predictions. With progress made since its inception in 2021, CLINT is poised to make further strides in 2024, contributing to more accurate and timely forecasts of extreme weather phenomena.
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