Publications

Detecting and understanding extreme temperature events and heatwaves using machine-learning

Published in Universität Bremen, 2025

Heatwaves are becoming more frequent, more intense, and more dangerous. Traditional methods for studying them often fall short. This dissertation uses machine learning to reveal that extreme heat events are occurring far more often than previously estimated, and that recent European heatwaves represent a genuinely new atmospheric pattern not seen in the historical record.

Recommended citation: Paçal, A. (2025). Detecting and understanding extreme temperature events and heatwaves using machine-learning [Dissertation, Universität Bremen]. https://doi.org/10.26092/elib/4746 https://doi.org/10.26092/elib/4746

Artificial intelligence for modeling and understanding extreme weather and climate events

Published in Nature Communications, 2025

This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models.

Recommended citation: Camps-Valls, G., Fernández-Torres, MÁ., Cohrs, KH. et al. Artificial intelligence for modeling and understanding extreme weather and climate events. Nat Commun 16, 1919 (2025). https://doi.org/10.1038/s41467-025-56573-8 https://www.nature.com/articles/s41467-025-56573-8

Detecting Extreme Temperature Events Using Gaussian Mixture Models

Published in Journal of Geophysical Research: Atmospheres, 2023

Extreme temperature events are detected with Gaussian Mixture Models to follow a multimodal rather than a unimodal distribution.

Recommended citation: Paçal, A., Hassler, B., Weigel, K., Kurnaz, M. L., Wehner, M. F., & Eyring, V. (2023). Detecting Extreme Temperature Events Using Gaussian Mixture Models. Journal of Geophysical Research: Atmospheres, 128, e2023JD038906. https://doi.org/10.1029/2023JD038906

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP

Published in Geoscientific Model Development, 2021

This paper complements a series of now four publications that document the release of the Earth System Model Evaluation Tool (ESMValTool) v2.0.

Recommended citation: Weigel, K., Bock, L., Gier, B. K., Lauer, A., Righi, M., Schlund, M., Adeniyi, K., Andela, B., Arnone, E., Berg, P., Caron, L.-P., Cionni, I., Corti, S., Drost, N., Hunter, A., Lledó, L., Mohr, C. W., Paçal, A., Pérez-Zanón, N., Predoi, V., Sandstad, M., Sillmann, J., Sterl, A., Vegas-Regidor, J., von Hardenberg, J., and Eyring, V. (2020). "Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP.&quot, Geosci. Model Dev., 14, 3159–3184. https://gmd.copernicus.org/articles/14/3159/2021/

Future Holiday Climate Index (HCI) Performance of Urban and Beach Destinations in the Mediterranean

Published in Atmosphere, 2020

HCI scores for the reference (1971–2000) and future (2021–2050, 2070–2099) periods were computed with the use of two latest greenhouse gas concentration trajectories, RCP 4.5 and 8.5, based on the Middle East North Africa (MENA) Coordinated Regional Downscaling Experiment (CORDEX) domain and data.

Recommended citation: Demiroglu, O. C., Saygili-Araci, F. S., Pacal, A., Hall, C. M., & Kurnaz, M. L. (2020). "Future Holiday Climate Index (HCI) Performance of Urban and Beach Destinations in the Mediterranean." Atmosphere. 11(9), 911. https://www.mdpi.com/2073-4433/11/9/911