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Addressing Climate Change with Artificial Intelligence Methods
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Addressing Climate Change with Artificial Intelligence Methods

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 15424

Special Issue Editor

Dr. Antonello Pasini

E-Mail Website
Guest Editor
Institute of Atmospheric Pollution Research, National Research Council, 00010 Rome, Italy
Interests: climate modelling; complex systems; artificial intelligence; neural network modelling

Special Issue Information

Dear Colleagues,

Climate change is a hot topic in contemporary scientific research. Together with the study of historical climatology, dynamical modelling (via global climate/Earth system models) is the standard way to address the complexity of the climate and obtain knowledge about its past behaviour and possible future evolution.

In recent years, however, this complexity has also been addressed via the use of data-driven methods—artificial intelligence (AI) techniques in particular—as alternatives or complementary to dynamical models. The former applications include attribution or prediction studies (about global warming, but also individual phenomena), as well as research into large datsets using deep learning; the latter uses involve downscaling or finds application to specific impact studies, such as hydrological or extreme-event investigations. AI applications have shown also their usefulness in terms of extracting knowledge from large datasets (e.g., sets of satellite data) or addressing the social and economic impacts of climate change, as in cases of human migration.

In this framework, this Special Issue has the ambitious objective of publishing high-quality papers and presenting the latest research and studies dedicated to the application of AI methods to climate change topics. In particular, this Special Issue aims to publish innovative work within a large spectrum of applications.

Both research articles (for general applications and/or case studies) and reviews can be submitted.

Relevant topics of the call include (non-exhaustive list):

  • Detection and attribution of climate change by AI methods;
  • AI downscaling of dynamical models for obtaining better reconstruction of the past and/or prediction of high-resolution future scenarios;
  • Prediction through pure AI methods;
  • AI in the study of extreme events;
  • AI in impact studies (general or case studies in all possible applications).

Dr. Antonello Pasini
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence methods
  • climate change
  • AI climate modelling
  • AI climate detection/attribution
  • downscaling via AI methods
  • AI climate prediction
  • AI in extreme-event studies
  • climate impacts addressed by AI methods

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Published Papers (8 papers)

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Research

Jump to: Review

16 pages, 2925 KiB  
Article
A Comprehensive AI Approach for Monitoring and Forecasting Medicanes Development
by Javier Martinez-Amaya, Veronica Nieves and Jordi Muñoz-Mari
Climate 2024, 12(12), 220; https://doi.org/10.3390/cli12120220 - 13 Dec 2024
Viewed by 327
Abstract
Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In [...] Read more.
Medicanes are rare cyclones in the Mediterranean Sea, with intensifying trends partly attributed to climate change. Despite progress, challenges persist in understanding and predicting these storms due to limited historical tracking data and their infrequent occurrence, which make monitoring and forecasting difficult. In response to this issue, we present an AI-based system for tracking and forecasting Medicanes, employing machine learning techniques to identify cyclone positions and key evolving spatio-temporal structural features of the cloud system that are associated with their intensification and potential extreme development. While the forecasting model currently operates with limited training data, it can predict extreme Medicane events up to two days in advance, with precision rates ranging from 65% to 80%. These innovative data-driven methods for tracking and forecasting provide a foundation for refining AI models and enhancing our ability to respond effectively to such events. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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13 pages, 4985 KiB  
Article
Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study
by Sergei Soldatenko and Yaromir Angudovich
Climate 2024, 12(11), 189; https://doi.org/10.3390/cli12110189 - 15 Nov 2024
Viewed by 573
Abstract
This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two [...] Read more.
This paper explores the capabilities of two types of recurrent neural networks, unidirectional and bidirectional long short-term memory networks, to build a surrogate model for a coupled fast–slow dynamic system and predicting its nonlinear chaotic behaviour. The dynamical system in question, comprising two versions of the classical Lorenz model with a small time-scale separation factor, is treated as an atmosphere–ocean research simulator. In numerical experiments, the number of hidden layers and the number of nodes in each hidden layer varied from 1 to 5 and from 16 to 256, respectively. The basic configuration of the surrogate model, determined experimentally, has three hidden layers, each comprising between 16 and 128 nodes. The findings revealed the advantages of bidirectional neural networks over unidirectional ones in terms of forecasting accuracy. As the forecast horizon increases, the accuracy of forecasts deteriorates, which was quite expected, primarily due to the chaotic behaviour of the fast subsystem. All other things being equal, increasing the number of neurons in hidden layers facilitates the improvement of forecast accuracy. The obtained results indicate that the quality of short-term forecasts with a lead time of up to 0.75 model time units (MTU) improves most significantly. The predictability limit of the fast subsystem (“atmosphere”) is somewhat greater than the Lyapunov time. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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15 pages, 3276 KiB  
Article
Rainfall Projections for the Brazilian Legal Amazon: An Artificial Neural Networks First Approach
by Luiz Augusto Ferreira Monteiro, Francisco Ivam Castro do Nascimento, José Francisco de Oliveira-Júnior, Dorisvalder Dias Nunes, David Mendes, Givanildo de Gois, Fabio de Oliveira Sanches, Cassio Arthur Wollmann, Michel Watanabe and João Paulo Assis Gobo
Climate 2024, 12(11), 187; https://doi.org/10.3390/cli12110187 - 15 Nov 2024
Viewed by 656
Abstract
Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an [...] Read more.
Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an initial approach, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with the observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7 mm to 11.2 mm, and the coefficient of determination (R2) was low in some series. Extensive analyses showed a low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, this study lays a foundation for future advancements in climate modeling and water resource management in the BLA. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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18 pages, 4326 KiB  
Article
Neural Network Downscaling to Obtain Local Precipitation Scenarios in the Italian Alps: A Case Study
by Cristina Iacomino and Antonello Pasini
Climate 2024, 12(9), 147; https://doi.org/10.3390/cli12090147 - 20 Sep 2024
Viewed by 1244
Abstract
Predicting local precipitation patterns over the European Alps remains an open challenge due to many limitations. The complex orography of mountainous areas modulates climate signals, and in order to analyse extremes accurately, it is essential to account for convection, requiring high-resolution climate models’ [...] Read more.
Predicting local precipitation patterns over the European Alps remains an open challenge due to many limitations. The complex orography of mountainous areas modulates climate signals, and in order to analyse extremes accurately, it is essential to account for convection, requiring high-resolution climate models’ outputs. In this work, we analyse local seasonal precipitation in Trento (Laste) and Passo Tonale using high-resolution climate data and neural network downscaling. Then, we adopt an ensemble and generalized leave-one-out cross-validation procedure, which is particularly useful for the analysis of small datasets. The application of the procedure allows us to correct the model’s bias, particularly evident in Passo Tonale. This way, we will be more confident in achieving more reliable results for future projections. The analysis proceeds, considering the mean and the extreme seasonal anomalies between the projections and the reconstructions. Therefore, while a decrease in the mean summer precipitation is found in both stations, a neutral to positive variation is expected for the extremes. Such results differ from model’s, which found a clear decrease in both stations in the summer’s mean precipitation and extremes. Moreover, we find two statistically significant results for the extremes: a decrease in winter in Trento and an increase in spring in Passo Tonale. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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14 pages, 4013 KiB  
Article
Harnessing Machine Learning to Decode the Mediterranean’s Climate Canvas and Forecast Sea Level Changes
by Cristina Radin, Veronica Nieves, Marina Vicens-Miquel and Jose Luis Alvarez-Morales
Climate 2024, 12(8), 127; https://doi.org/10.3390/cli12080127 - 22 Aug 2024
Viewed by 1521
Abstract
Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs, gaps in historical data, and bridging the gap between short-term [...] Read more.
Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs, gaps in historical data, and bridging the gap between short-term and long-term forecasting intervals. Our study addresses these challenges by combining advanced machine learning techniques to provide region-specific sea level predictions in the Mediterranean Sea. By integrating high-resolution sea surface temperature data spanning 40 years, we employed a tailored k-means clustering technique to identify regions of high variance. Using these clusters, we developed RNN-GRU models that integrate historical tide gauge data and sea surface height data, offering regional sea level predictions on timescales ranging from one month to three years. Our approach achieved the highest predictive accuracy, with correlation values ranging from 0.65 to 0.84 in regions with comprehensive datasets, demonstrating the model’s robustness. In areas with fewer tide gauge stations or shorter time series, our models still performed moderately well, with correlations between 0.51 and 0.70. However, prediction accuracy decreases in regions with complex geomorphology. Yet, all regional models effectively captured sea level variability and trends. This highlights the model’s versatility and capacity to adapt to different regional characteristics, making it invaluable for regional planning and adaptation strategies. Our methodology offers a powerful tool for identifying regions with similar variability and providing sub-regional scale predictions up to three years in advance, ensuring more reliable and actionable sea level forecasts for Mediterranean coastal communities. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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17 pages, 6144 KiB  
Article
The Machine Learning Attribution of Quasi-Decadal Precipitation and Temperature Extremes in Southeastern Australia during the 1971–2022 Period
by Milton Speer, Joshua Hartigan and Lance Leslie
Climate 2024, 12(5), 75; https://doi.org/10.3390/cli12050075 - 17 May 2024
Cited by 2 | Viewed by 1906
Abstract
Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe [...] Read more.
Much of eastern and southeastern Australia (SEAUS) suffered from historic flooding, heat waves, and drought during the quasi-decadal 2010–2022 period, similar to that experienced globally. During the double La Niña of the 2010–2012 period, SEAUS experienced record rainfall totals. Then, severe drought, heat waves, and associated bushfires from 2013 to 2019 affected most of SEAUS, briefly punctuated by record rainfall over parts of inland SEAUS in the late winter/spring of 2016, which was linked to a strong negative Indian Ocean Dipole. Finally, from 2020 to 2022 a rare triple La Niña generated widespread extreme rainfall and flooding in SEAUS, resulting in massive property and environmental damage. To identify the key drivers of the 2010–2022 period’s precipitation and temperature extremes due to accelerated global warming (GW), since the early 1990s, machine learning attribution has been applied to data at eight sites that are representative of SEAUS. Machine learning attribution detection was applied to the 52-year period of 1971–2022 and to the successive 26-year sub-periods of 1971–1996 and 1997–2022. The attributes for the 1997–2022 period, which includes the quasi-decadal period of 2010–2022, revealed key contributors to the extremes of the 2010–2022 period. Finally, some drivers of extreme precipitation and temperature events are linked to significant changes in both global and local tropospheric circulation. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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14 pages, 3263 KiB  
Article
Machine Learning Identification of Attributes and Predictors for a Flash Drought in Eastern Australia
by Milton Speer, Joshua Hartigan and Lance M. Leslie
Climate 2024, 12(4), 49; https://doi.org/10.3390/cli12040049 - 8 Apr 2024
Cited by 3 | Viewed by 5978
Abstract
Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of [...] Read more.
Flash droughts (FDs) are natural disasters that strike suddenly and intensify quickly. They occur almost anywhere, anytime of the year, and can have severe socio-economic, health and environmental impacts. This study focuses on a recent FD that began in the cool season of the Upper Hunter region of Eastern Australia, an important energy and agricultural local and global exporter that is both flood- and drought-prone. Here, the authors investigate the FD that started abruptly in May 2023 and extended to October 2023. The FD followed floods in November 2021 and much above-average May–October 2022 rainfall. Eight machine learning (ML) regression techniques were applied to the 60 May–October periods from 1963–2022, using a rolling windows attribution search from 45 possible climate drivers, both individually and in combination. The six most prominent climate drivers, and likely predictors, provide an understanding of the major contributors to the FD. Next, the 1963–2022 data were divided into two shorter timespans, 1963–1992 and 1993–2022, generally accepted as representing the early and accelerated global warming periods, respectively. The key attributes were markedly different for the two timespans. These differences are readily explained by the impacts of global warming on hemispheric and synoptic-scale atmospheric circulations. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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Review

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20 pages, 2914 KiB  
Review
Applying Machine Learning in Numerical Weather and Climate Modeling Systems
by Vladimir Krasnopolsky
Climate 2024, 12(6), 78; https://doi.org/10.3390/cli12060078 - 26 May 2024
Cited by 1 | Viewed by 1638
Abstract
In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The [...] Read more.
In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered as a simple introduction to the problem. Full article
(This article belongs to the Special Issue Addressing Climate Change with Artificial Intelligence Methods)
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