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Elsa Barrio-Torres

Publications and source records attributed to Elsa Barrio-Torres.

2 recordsLinked to original sources

Evaluating the performance of GCM trajectories using Weather Type frequencies for persistence and transitions: the Iberian Peninsula and Lamb classification

This study evaluates the performance of 36 historical CMIP6 GCM trajectories (1979-2005) in reproducing atmospheric circulation over the Iberian Peninsula in the summer months (June-September) using the Lamb Weather Type (WT) classification scheme. Using ERA5 reanalysis as the observational reference, we introduce a methodological framework-applicable to any region worldwide-to evaluate GCM performance. This approach extends traditional daily frequency analysis by evaluating both the daily frequency distribution of WTs and their 24-hour dynamic evolution (i.e., transition probabilities and persistence). Model performance is quantified using the Overlap coefficient. A filtering process is applied where only trajectories that successfully reproduce both daily and conditional distributions with a minimum Overlap threshold $t_{sim}$ across a set number of grid points are retained. The findings show that while several models can adequately reproduce daily WT frequencies (16 out of 36), some struggle to capture day-to-day atmospheric transitions. This leads to a final selection of 12 trajectories over the Iberian Peninsula. Model performance across the region is then evaluated using integrated metrics assessing daily reproduction, conditional reproduction, and transition dynamics. Overall, models from the ec earth3 family-specifically the ec earth3 aerchem trajectory-exhibit the best and most consistent performance across the region. Additionally, the results highlight a geographical performance gap: while models generally represent circulation well in the northwest, they face significant challenges in the central and southern Mediterranean regions of the Peninsula. Ultimately, this study establishes that assessing WT persistence and transitions provides a far more discriminative, objective tool for GCM selection than evaluating daily distributions alone.

stat.ME

Prediction of Maximum Temperature record in Spain 1960-2023 by the means of ERA5 atmospheric geopotentials

The increasing frequency of extreme temperature events, such as daily maximum temperature ($T_x$) records, underscores the need for robust tools to understand their drivers and predict their occurrence. Previous studies have identified increasing and non-stationary trends in $T_x$ records across the Iberian Peninsula, particularly during summer, the literature directly exploring their connection with upper-level atmospheric covariates remains limited. This work develops and applies an innovative methodological framework to model the occurrence of $T_x$ records and their relationship with geopotential height fields. We used daily $T_x$ data from 36 Spanish stations (1960-2023) provided by ECA&D and geopotential height data at 300, 500, and 700 hPa from ERA5. Exploratory analysis revealed a non-stationary trend in records, a higher frequency in the interior of the peninsula, and decreasing spatial co-occurrence with distance. We designed a hierarchical spatio-temporal logistic regression algorithm prioritizing interpretability and high-dimensionality reduction. The approach involves: (1) fitting local models per station; (2) applying a spatial consensus filter based on statistical significance to reduce the initial 1620 covariates to 17 in a base model (M1); and (3) a controlled incorporation of interaction terms. Among the tested models, a global model (M2) that enhances M1 with geodetic interactions was selected for its optimal balance between predictive performance (AUC) and complexity. Model M2 demonstrates high predictive accuracy at interior stations and good performance at coastal stations. It also adequately reproduces key observed properties, including the persistence of record streaks and patterns of spatial co-occurrence. This study provides a novel tool for predicting upcoming record events with high accuracy while maintaining a concise and interpretable structure.

stat.AP