Robust detection of forced warming in the presence of potentially large climate variability.

Sebastian Sippel,Reto Knutti,Flavio Lehner,Erich Fischer,Angeline G. Pendergrass,Enikő Székely,Nicolai Meinshausen

doi:10.1126/sciadv.abh4429

Abstract

Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records.

Highlights

The key goal of climate change detection and attribution (D&A) is to assess the causes of observed changes in the climate system [1]
Illustration of D&A based on decadal-scale internal variability (DIV) anchor We start by illustrating the trade-off between prediction performance and robustness to DIV in the “Coupled Model Intercomparison Project (CMIP) train-test split” (Fig. 2A)
We introduced an approach to climate change D&A that explicitly increases robustness to the large structural uncertainty in magnitude and patterns of DIV across state-of-the-art climate models [40]

Summary

Introduction

The key goal of climate change detection and attribution (D&A) is to assess the causes of observed changes in the climate system [1]. Traditional D&A typically uses model simulated patterns (so-called fingerprints) that encapsulate the physics-based expectation of the forced climate response to individual or combined external forcings to reliably quantify the magnitude of a climate signal in observations [2, 3]. The probability of such a signal occurring in an unforced climate is assessed via a systematic comparison of the strength of the fingerprint in observations and in the unforced variability of climate model preindustrial control simulations [2,3,4,5,6]. The observed 40-year global mean temperature (GMT) trend at Earth’s surface far exceeds variability in unforced control simulations (Fig. 1A)

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