# An Antidote for Hawkmoths: On the prevalence of structural chaos in non-linear modeling

## TL;DR

This paper investigates whether structural chaos due to model uncertainty significantly impacts forecast reliability in non-linear systems like climate models, questioning the prevalence and mathematical understanding of such chaos.

## Contribution

It critically evaluates existing claims of structural chaos in climate modeling, arguing that these claims are unconvincing and highlighting the need for more rigorous mathematical analysis.

## Key findings

- Structural chaos may occur but is not as prevalent as some literature suggests
- Current mathematical results are insufficient to conclusively establish structural chaos
- Model error can sometimes dominate traditional chaos effects in forecasts

## Abstract

This paper deals with the question of whether uncertainty regarding model structure, especially in climate modeling, exhibits a kind of "chaos." Do small changes in model structure, in other words, lead to large variations in ensemble predictions? More specifically, does model error destroy forecast skill faster than the ordinary or "classical" chaos inherent in the real-world attractor? In some cases, the answer to this question seems to be "yes." But how common is this state of affairs? And are there precise mathematical results that can help us answer this question? We examine some efforts in the literature to answer this last question in the affirmative and find them to be unconvincing.