arXiv subjects

Antonietta Capotondi

Publications and source records attributed to Antonietta Capotondi.

5 recordsLinked to original sources

arXiv2026

Knowledge-guided machine learning for disentangling Pacific sea surface temperature variability across timescales

Global weather and climate patterns are strongly influenced by dominant modes of anomalous Pacific sea surface temperature (SST) variability, including the El Niño-Southern Oscillation (ENSO), Pacific Meridional Mode (PMM), and Pacific Decadal Oscillation (PDO). However, disentangling these modes of variability remains challenging due to their spatial overlap and nonlinear coupling, which violate the assumptions of traditional linear methods. We develop a Knowledge-Guided AutoEncoder (KGAE) that uses spatiotemporal constraints and a gradient-based sparsity incentive to identify physically interpretable modes of detrended SST variability, each defined by a single broad characteristic timescale, without the need for predefined temporal filters or thresholds. The KGAE separates ENSO-like modes on 2- and 3-7-year timescales, as well as a decadal mode with characteristics reminiscent of the PDO and PMM, each with distinct spatial patterns. We perturb each latent dimension and use finite differences to characterize the state-independent and state-dependent sensitivities. We demonstrate that the decadal mode modulates ENSO diversity (central versus eastern Pacific) through both interference and nonlinear state dependence, and that a quasibiennial mode interacts with the interannual mode to characterize ENSO onset and decay. We assess the robustness of the KGAEs to random initialization and training stochasticity, to sampling bias induced by a time-stratified cross-validation scheme, and to unseen data (generalization). When applied to climate model output, KGAEs reveal model-specific biases in ENSO diversity and seasonal timing. Our results highlight how machine learning can uncover physically meaningful modes of Earth system variability and characterize their complex interactions across models and timescales.

physics.ao-ph↗

arXiv2025

A Hybrid Deep-Learning Model for El Niño Southern Oscillation in the Low-Data Regime

While deep-learning models have demonstrated skillful El Niño Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simulations that provide thousands of years of training data at the expense of introducing climate model biases. Simpler Linear Inverse Models (LIMs) trained on the much shorter observational record also make skillful ENSO predictions but do not capture predictable nonlinear processes. This motivates a hybrid approach, combining the LIMs modest data needs with a deep-learning non-Markovian correction of the LIM. For O(100 yr) datasets, our resulting Hybrid model is more skillful than the LIM while also exceeding the skill of a full deep-learning model. Additionally, while the most predictable ENSO events are still identified in advance by the LIM, they are better predicted by the Hybrid model, especially in the western tropical Pacific for leads beyond about 9 months, by capturing the subsequent asymmetric (warm versus cold phases) evolution of ENSO.

cs.LG↗

arXiv2024

Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction

Accurate ocean dynamics modeling is crucial for enhancing understanding of ocean circulation, predicting climate variability, and tackling challenges posed by climate change. Despite improvements in traditional numerical models, predicting global ocean variability over multi-year scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), the first data-driven 3D ocean model for seasonal to decadal prediction of global ocean circulation. ORCA-DL accurately simulates three-dimensional ocean dynamics and outperforms state-of-the-art dynamical models in capturing extreme events, including El Niño-Southern Oscillation and upper ocean heatwaves. This demonstrates the high potential of data-driven models for efficient and accurate global ocean forecasting. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections.

physics.ao-ph↗

arXiv2024

Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts

We introduce an interpretable-by-design method, optimized model-analog, that integrates deep learning with model-analog forecasting which generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify initial analog states that lead to shadowing target trajectories. The advantage of our method lies in its inherent interpretability, offering insights into initial-error-sensitive regions through estimated weights and the ability to trace the physically-based evolution of the system through analog forecasting. We evaluate our approach using the Community Earth System Model Version 2 Large Ensemble to forecast the El Niño-Southern Oscillation (ENSO) on a seasonal-to-annual time scale. Results show a 10% improvement in forecasting equatorial Pacific sea surface temperature anomalies at 9-12 months leads compared to the unweighted model-analog technique. Furthermore, our model demonstrates improvements in boreal winter and spring initialization when evaluated against a reanalysis dataset. Our approach reveals state-dependent regional sensitivity linked to various seasonally varying physical processes, including the Pacific Meridional Modes, equatorial recharge oscillator, and stochastic wind forcing. Additionally, forecasts of El Niño and La Niña are sensitive to different initial states: El Niño forecasts are more sensitive to initial error in tropical Pacific sea surface temperature in boreal winter, while La Niña forecasts are more sensitive to initial error in tropical Pacific zonal wind stress in boreal summer. This approach has broad implications for forecasting diverse climate phenomena, including regional temperature and precipitation, which are challenging for the model-analog approach alone.

physics.ao-ph↗

arXiv2024

Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO Variance

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific (CP) events are typically weaker than Eastern Pacific (EP) events. SSTA pattern and intensity undergo low-frequency modulations, affecting ENSO prediction skill and remote impacts. Yet, how different ENSO types contribute to these decadal variations and long-term variance trends remain uncertain. Here, we decompose the low-frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non-binary event category memberships. Our approach identifies two La Niña and three El Niño categories and shows that the shift of ENSO variance in the mid-1970s is associated with an increasing likelihood of strong La Niña and extreme El Niño events.

physics.ao-ph↗