What question did this study set out to answer?

This research aims to address the increasing uncertainties in conventional climate models by introducing a new modeling approach.

February 5, 2026

Climate modelling without irrelevant weather details: The Half-order Energy Balance Equation

Key Points

This research aims to address the increasing uncertainties in conventional climate models by introducing a new modeling approach.
Developed the Half-order Energy Balance Equation (HEBE) for climate modeling.
Focused on relevant climatic details instead of irrelevant weather elements.
Conducted hindcasts and projections to 2100 based on energy conservation and scaling principles.
HEBE demonstrates low uncertainty in climate hindcasts and projections.
Results support a shift from traditional models that emphasize weather details.

Abstract

Uncertainties in conventional numerical climate models –as measured by the spread between competing models - have for the first time in over four decades increased (IPCC, AR6, 2021). This approach is in crisis and the community is increasingly turning to Machine Learning i.e. to black boxes that “emulate” the standard (nearly) black box climate models. The root problem is that these models are based in the weather regime, i.e. they spend almost all their effort calculating irrelevant weather details. In this paper we summarize recent developments in a multidecadal effort to develop new models focused on the relevant details. We focus on the Half-order Energy Balance Equation (HEBE), that currently is the most promising candidate. It is based conservation laws (energy) and scale symmetries (scaling) and can already make low uncertainty hindcasts and projections to 2100.

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