Towards reliable projections of global mean surface temperature

TL;DR

This paper introduces physically motivated time-series methods using energy balance models to improve the reliability and reduce uncertainty in projections of global mean surface temperature, especially regarding exceeding critical warming thresholds.

Contribution

It proposes a new approach that constrains future GMST projections by learning forcing discrepancies, resulting in more reliable and narrower uncertainty bands compared to traditional model-based estimates.

Findings

Proposed methods produce reliable GMST projections in perfect model tests.

Application to observations yields lower mean warming and narrower uncertainty bands.

Results indicate a reduced probability of exceeding 2.0 K warming target.

Abstract

Quantifying the risk of global warming exceeding critical targets such as 2.0 K requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. These approaches give reliable projections of GMST when tested in a perfect model setting, and when applied to observations lead to well constrained projections with lower mean warming and narrower projection bands than previous estimates. Despite the reduced uncertainty, the lower warming leads to a greatly reduced probability of exceeding the 2.0 K warming target.