A hierarchical statistical framework for emergent constraints: application to snow-albedo feedback

TL;DR

This paper introduces a hierarchical statistical framework for emergent constraints in climate modeling, linking current and future climate states with observations, and applies it to snow-albedo feedback to improve projection accuracy.

Contribution

The paper presents a novel hierarchical emergent constraint framework that analytically relates future climate predictions to current data, accounting for signal-to-noise ratio and correlations.

Findings

Predicted snow-albedo feedback interval: (-1.25, -0.58)%K^{-1}

Neglecting SNR and correlation causes bias and underestimation of uncertainty

Framework applicable to general Earth System modeling

Abstract

Emergent constraints use relationships between future and current climate states to constrain projections of climate response. Here, we introduce a statistical, hierarchical emergent constraint (HEC) framework in order to link future and current climate with observations. Under Gaussian assumptions, the mean and variance of the future state is shown analytically to be a function of the signal-to-noise (SNR) ratio between data-model error and current-climate uncertainty, and the correlation between future and current climate states. We apply the HEC to the climate-change, snow-albedo feedback, which is related to the seasonal cycle in the Northern Hemisphere. We obtain a snow-albedo-feedback prediction interval of $(-1.25, -0.58)$ \%$K^{-1}$. The critical dependence on SNR and correlation shows that neglecting these terms can lead to bias and under-estimated uncertainty in constrained projections. The flexibility of using HEC under general assumptions throughout the Earth System is discussed.