Posts by Collection

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

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.", Geosci. Model Dev., 14, 3159–3184. https://gmd.copernicus.org/articles/14/3159/2021/

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

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 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

Determination of Exposures of Mediterranean Touristic Resources by Using Regional Climate Modeling

Published: April 10, 2019

Summer tourism in the Mediterranean Basin is one of the most important contributors to the countries’ GDPs, and is highly dependent on the climatic conditions. In this study, it is aimed to determine the exposures of the most visited touristic resources in the Mediterranean Basin via Tourism Climate Index [1] which is an ideal indicator of tourism exposure to the hazard of changes to the mean climate [2]. For this purpose, the outputs of the MPI-ESM-MR global climate model of the Max Planck Institute for Meteorology are downscaled to 50km by the use of Regional Climate Model (RegCM4. 4) of the Abdus Salam International Centre for Theoretical Physics (ICTP). To make future projections for the period of 2021-2050 and 2070-2099 with respect to the reference period of 1971-2000, RCP 4.5 and RCP 8.5 scenarios are used. Tourism Climate Index (TCI) for projected periods are computed by using the 30-year monthly mean temperature, relative humidity, precipitation, wind and sunshine outputs of the RegCM4. 4. Thereafter, the TCI values are plotted to see the changes throughout the months.

Detecting Extreme Temperature Events Using Gaussian Mixture Models

Published: April 24, 2023

Extreme events are rare atmospheric phenomena that cause significant damage to humans and natural systems, but detecting extreme events in the future in a changing climate can be challenging. Traditionally, temperature distributions were assumed to follow a normal distribution and certain thresholds were used to define extreme events. However, the mean and the variance of temperatures are expected to change in a future climate, which might limit the application of traditional methods for detecting extreme events.

Understanding European Heatwaves with Variational Autoencoders

Published: March 11, 2026

Understanding the dynamics of heatwaves is critical for accurate climate risk assessment. Traditional definitions, based solely on surface temperature thresholds, often overlook the complex, multivariate nature of heatwaves. In this study, we use a spatiotemporal Variational Autoencoder (VAE), an unsupervised machine learning method, to identify compact representations of year-round multivariate heatwave patterns. We extract eleven-day multivariate (e.g., circulation, humidity, temperature, geopotential height, cloud cover, stream function, and radiation) heatwave samples from ERA5 reanalysis data over the North Atlantic, based on near-surface temperature extremes in Western Europe. The VAE model is trained on heatwave samples from 1941–1990 and evaluated using 2001–2022 samples. The VAE model effectively clusters heatwave events by season, and composite maps reveal the interplay and temporal evolution between different atmospheric variables in their contributions to heatwaves over Western Europe, such as summer blocking highs and winter omega blocks. Notably, recent summer heatwaves form a distinct cluster within the latent space, pointing to a shift in atmospheric dynamics consistent with climate change. This approach offers a powerful framework to explore the spatiotemporal evolution of multivariate extremes. As a next step, we plan to apply the VAE model to CMIP simulations to evaluate how well climate models reproduce observed heatwave patterns and their associated atmospheric dynamics. This can provide a promising diagnostic to assess model skill and improve projections of heatwaves under climate change.

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.