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

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

doi:10.5194/egusphere-egu21-9611

Abstract

&lt;p&gt;Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&amp;A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (&amp;#8220;control&amp;#8221;) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&amp;A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).&lt;/p&gt;&lt;p&gt;For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76&amp;#176;C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (&gt; 5&amp;#963;), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&amp;A confidence estimates.&lt;/p&gt;&lt;p&gt;Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model&amp;#8217;s natural variability estimates are used.&lt;/p&gt;&lt;p&gt;Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&amp;A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.&lt;/p&gt;&lt;p&gt;Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science.&amp;#160;&lt;em&gt;Nat Clim Change&lt;/em&gt;&amp;#160;&lt;strong&gt;9&lt;/strong&gt;(3), pp. 180-182.&lt;/p&gt;

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 [2–6]. The observed 40-year global mean temperature (GMT) trend at Earth’s surface far exceeds variability in unforced control simulations (Fig. 1A)

Objectives

Methods

Results

Conclusion