arXiv subjects

Elena Provenzano

Publications and source records attributed to Elena Provenzano.

2 recordsLinked to original sources

arXiv2026

CNN-based forecasting of early winter NAO using sea surface temperature

The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability over the North Atlantic sector, influencing temperature and precipitation across Europe. While the NAO's impact on North Atlantic sea surface temperatures (SSTs) is well understood, the NAO can also be driven by SST anomalies. However, this NAO response to SST anomalies is believed to be weak and nonlinear. Former studies highlight that during early winter (November-December), El Nino Southern Oscillation (ENSO) events modulate the NAO, with El Nino (La Nina) events being linked to positive (negative) NAO phases, and an opposite effect observed in late winter (January-February). Indian Ocean SSTs and the North Atlantic Horseshoe SST anomaly have also been suggested as contributors to early winter NAO variability. However, climate models often struggle to capture these SST-NAO teleconnections, particularly in early winter. To address this, a statistical framework based on convolutional neural networks (CNNs) is developed to predict the early winter NAO using observed SST fields one-, two-, and three-month before. A linear model serves as a benchmark, and both models are trained on ERA5 reanalysis data from 1940 to 2023. A sensitivity analysis is used to interpret the CNN's decision-making process, revealing that it focuses on regions such as the tropical Pacific and North Atlantic, confirming results from previous works. The CNN outperforms the linear model, highlighting the value of capturing nonlinear SST-NAO relationships. Prediction skill appears to be linked to ENSO, with strong ENSO events associated with greater skill in forecasting the NAO than neutral events. These findings underscore the potential of deep learning to build medium-range NAO prediction.

physics.ao-ph↗

arXiv2026

Disentangling Internal and Forced Climate Variability with Convolutional Neural Networks using Multivariate Fields

Long-term climate data exhibit variations composed of internal and forced components. Internal variability arises from natural processes that could occur within a stable climate. Forced variability, on the other hand, reflects climate changes induced, for example, by anthropogenic greenhouse gas and aerosol emissions. Accurately distinguishing between these types of variability is crucial for attributing climate fluctuations and understanding internal variability processes and impacts. In this study, we apply a U-Net convolutional network, a model commonly used in computer vision, to separate forced and internal climate variability from 1950 to 2022 using a multi-model dataset. The dataset includes multiple fields from five single-model initial-condition large ensembles. Cross-validation is conducted by training the U-Net using the data from the ensembles of four models, leaving out the data from one model to assess performance. Validation results yield errors ranging from 0.1°C to 0.4°C for the forced variability of local-scale monthly surface air temperature, which is no more than half the magnitude of external forcing. The U-Net achieves better performance than a simple approach based on a fourth-order polynomial trend for estimating forced variability. The error is mainly due to insufficient sampling and poor agreement among the models, as the U-Net underestimates the warming for the model with the highest transient climate sensitivity during validation. Performance is lower for variables like sea-level pressure and precipitation because of their low ratio of forced to internal variability. This framework might be enhanced by incorporating a larger multi-model dataset in the training and validation.

physics.ao-ph↗