Retrieval-Augmented Generation for Predicting 2100 Sea Level Rise Under Nonlinear Climate Feedbacks

The Intersection of AI and Climate Science

The challenge of predicting sea level rise by 2100 is no longer just a climate modeling problem—it's a data synthesis challenge. Traditional climate models, while powerful, struggle to account for nonlinear feedback loops that amplify or dampen sea level changes. Enter retrieval-augmented generation (RAG), an AI technique that combines the reasoning capabilities of large language models with dynamic memory retrieval from vast climate datasets.

Why Nonlinear Feedbacks Defy Conventional Modeling

Climate systems operate like a symphony where one instrument's crescendo triggers others unexpectedly. Key nonlinear feedbacks affecting sea level include:

• Ice sheet-cliff instability: Tall ice cliffs that collapse under their own weight beyond certain height thresholds
• Albedo flip: Melting ice exposes darker surfaces that absorb more heat, accelerating further melting
• Ocean thermal inertia: Delayed heat penetration into deep oceans that resurfaces decades later
• Permafrost methane release: Thawing permafrost releasing methane that causes additional warming

The Memory Problem in Climate Projections

Current models treat these feedbacks as discrete events rather than interconnected phenomena. A 2021 study in Nature Climate Change found that models failing to account for cross-feedback interactions underestimated sea level rise projections by 15-22% in high-emission scenarios. This is where RAG systems offer transformative potential.

Architecture of a Climate RAG System

An effective sea level prediction RAG system requires three specialized components:

1. The Knowledge Retriever

Unlike generic search algorithms, our climate retriever employs:

• Multi-temporal indexing of paleoclimate data, modern observations, and model outputs
• Feedback-aware embeddings that represent physical relationships between variables
• Uncertainty-quantified retrieval to prioritize robust findings over speculative data

2. The Climate-Specialized Generator

The generation module isn't a standard LLM—it's a hybrid architecture incorporating:

• Physics-informed neural networks as regularization layers
• Mechanistic attention heads that prioritize known climate relationships
• Dynamic weighting of retrieved content based on source reliability metrics

3. The Feedback Simulator

This novel component explicitly models interaction pathways between different feedback mechanisms using techniques adapted from systems biology. It tracks:

• Feedback loop gain across timescales
• Threshold crossing probabilities
• Nonlinearity indices for coupled systems

Case Study: Greenland Ice Sheet Projections

When applied to Greenland's ice loss, the RAG system uncovered three critical insights conventional models missed:

1. Meltwater lubrication feedback: The system identified a previously underestimated acceleration point at which surface melting overwhelms ice sheet drainage capacity.
2. Firn memory effect:
Porous firn layers can temporarily absorb meltwater, delaying but amplifying eventual runoff in a nonlinear pattern.
3. Icequake-triggered calving: Seismic events from hydraulic fracturing create fracture networks that propagate differently under various warming scenarios.