arXiv subjects

Dillon J. Amaya

Publications and source records attributed to Dillon J. Amaya.

2 recordsLinked to original sources

arXiv2026

Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble

Understanding the sources of ENSO prediction skill and error is essential for improving long-range climate prediction. Here we examine ENSO precursors in CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts initialized in February and August during 1970-2019, using ensemble sensitivity analysis and an extended nonlinear recharge oscillator (XRO) benchmark. SMYLE exhibits meaningful long-lead skill, with February-initialized predictions remaining skillful through the following winter, and August-initialized predictions retaining useful skill at the December target sixteen months ahead. However, the predictions are under-dispersive in a state-dependent manner. Rank histograms reveal overconfidence issues for strong ENSO events and a warm bias for weak events. February predictability arises from canonical Bjerknes feedback dynamics reinforced by subtropical-tropical interactions resembling the North Pacific Meridional Mode (NPMM). The best February-initialized predictions feature warm western Pacific subsurface anomalies and La Nina anomalies at initialization, with equatorially confined anomalies and relatively weak sensitivities to extratropical influences thereafter. Second-year ENSO predictability in August-initialized predictions instead emerges through a delayed pathway characterized by cross-basin preconditioning. Skillful cases begin with a cold tropical equatorial South Atlantic and a warmer tropical western Indian Ocean, while Pacific processes play a reduced role until the following year. SMYLE matches the skill of the cross-validated XRO at both December targets, and its sensitivities agree with the observationally fitted benchmark, though the Atlantic couplings appear misrepresented. These findings suggest that improving the initial Pacific state and cross-basin coupling fidelity in GCMs may help improve ENSO prediction.

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↗