# CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints

arXiv: https://arxiv.org/abs/2502.13185 · HTML: https://mlsanity.com/paper/2502.13185/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023)
- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses](https://mlsanity.com/paper/2411.11268/) (2024)
- [Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling](https://mlsanity.com/paper/1906.06622/) (2019)
- [Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1](https://mlsanity.com/paper/2410.21920/) (2024)

## Built on by (most cited first, 6 of 6)

- [Explainable AI: Learning from the Learners](https://mlsanity.com/paper/2601.05525/) (2026)
- [climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer](https://mlsanity.com/paper/2604.21085/) (2026)
- [Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts](https://mlsanity.com/paper/2503.00332/) (2025)
- [Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems](https://mlsanity.com/paper/2608.22277/) (2026)
- [Neptune: An AI model for Global Ocean Subseasonal Prediction](https://mlsanity.com/paper/2609.08606/) (2026)
- [S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales](https://mlsanity.com/paper/2610.03106/) (2026)

## Abstract (arXiv)

Accurate and efficient climate simulations are crucial for understanding Earth's evolving climate. However, current general circulation models (GCMs) face challenges in capturing unresolved physical processes, such as cloud and convection. A common solution is to adopt cloud resolving models, that provide more accurate results than the standard subgrid parametrisation schemes typically used in GCMs. However, cloud resolving models, also referred to as super paramtetrizations, remain computationally prohibitive. Hybrid modeling, which integrates deep learning with equation-based GCMs, offers a promising alternative but often struggles with long-term stability and accuracy issues. In this work, we find that water vapor oversaturation during condensation is a key factor compromising the stability of hybrid models. To address this, we introduce CondensNet, a novel neural network architecture that embeds a self-adaptive physical constraint to correct unphysical condensation processes. CondensNet effectively mitigates water vapor oversaturation, enhancing simulation stability while maintaining accuracy and improving computational efficiency compared to super parameterization schemes. We integrate CondensNet into a GCM to form PCNN-GCM (Physics-Constrained Neural Network GCM), a hybrid deep learning framework designed for long-term stable climate simulations in real-world conditions, including ocean and land. PCNN-GCM represents a significant milestone in hybrid climate modeling, as it shows a novel way to incorporate physical constraints adaptively, paving the way for accurate, lightweight, and stable long-term climate simulations.