# A Hybrid Deep-Learning Model for El Ni~no Southern Oscillation in the Low-Data Regime

arXiv: https://arxiv.org/abs/2412.03743 · HTML: https://mlsanity.com/paper/2412.03743/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 66 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Neural networks for post-processing ensemble weather forecasts](https://mlsanity.com/paper/1805.09091/) (2018)
- [AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning](https://mlsanity.com/paper/2308.13280/) (2023)
- [Will Artificial Intelligence supersede Earth System and Climate Models?](https://mlsanity.com/paper/2101.09126/) (2021)
- [The World as a Graph: Improving El Ni~no Forecasts with Graph Neural Networks](https://mlsanity.com/paper/2104.05089/) (2021)
- [R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting](https://mlsanity.com/paper/1709.03159/) (2017)
- [Teleconnection patterns of different El Ni~no types revealed by climate network curvature](https://mlsanity.com/paper/2203.07035/) (2022)
- [Decadal Forecasts with ResDMD: a Residual DMD Neural Network](https://mlsanity.com/paper/2106.11111/) (2021)

## Built on by (most cited first, 2 of 2)

- [Distillation of CNN Ensemble Results for Enhanced Long-Term Prediction of the ENSO Phenomenon](https://mlsanity.com/paper/2509.06227/) (2025)
- [Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms](https://mlsanity.com/paper/2607.05101/) (2026)

## Abstract (arXiv)

While deep-learning models have demonstrated skillful El Ni~no 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.