In Global Warming in Historical Context: - a Proposed Exogenous Cause of the Global Temperature Hiatus (II)

Environmental Research Letters TOPICAL REVIEW • OPEN ACCESS Recent citations The ‘pause’ in global warming in historical context: - A Proposed Exogenous Cause of the Global Temperature Hiatus (II). Comparing models to observations Norman Treloar - Influence and seepage: An evidence- To cite this article: Stephan Lewandowsky et al 2018 Environ. Res. Lett. 13 123007 resistant minority can affect public opinion and scientific belief formation Stephan Lewandowsky et al - A fluctuation in surface temperature in historical context: reassessment and View the article online for updates and enhancements. retrospective on the evidence James S Risbey et al This content was downloaded from IP address 193.174.18.1 on 01/03/2019 at 11:09 Environ. Res. Lett. 13 (2018) 123007 https://doi.org/10.1088/1748-9326/aaf372 TOPICAL REVIEW The ‘pause’ in global warming in historical context: (II). Comparing OPEN ACCESS models to observations RECEIVED 6 February 2017 Stephan Lewandowsky1,2,3 , Kevin Cowtan4, James S Risbey3 , Michael E Mann5, Byron A Steinman6, REVISED Naomi Oreskes7 and Stefan Rahmstorf8,9 11 November 2018 1 University of Bristol, Bristol, United Kingdom ACCEPTED FOR PUBLICATION 2 23 November 2018 University of Western Australia, Crawley, WA, Australia 3 CSIRO Oceans & Atmosphere, Hobart, Tasmania, Australia PUBLISHED 4 Department of Chemistry, University of York, York, United Kingdom 19 December 2018 5 Department of Meteorology and Atmospheric Sciences, Pennsylvania State University, State College, United States of America 6 Department of Earth and Environmental Sciences and Large Lakes Observatory, University of Minnesota Duluth, Duluth, United States Original content from this of America work may be used under 7 Department of the History of Science, Harvard University, Cambridge, United States of America the terms of the Creative 8 Commons Attribution 3.0 Potsdam Institute for Climate Impact Research, Potsdam, D-14473, Germany licence. 9 University of Potsdam, Institute of Physics and Astronomy, Potsdam, Germany Any further distribution of E-mail: [email protected] this work must maintain attribution to the Keywords: climate models, climate projections, ‘pause’ in global warming author(s) and the title of the work, journal citation and DOI. Abstract We review the evidence for a putative early 21st-century divergence between global mean surface temperature (GMST) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. We provide a systematic comparison between temperatures and projections using historical versions of GMST products and historical versions of model projections that existed at the times when claims about a divergence were made. The comparisons are conducted with a variety of statistical techniques that correct for problems in previous work, including using continuous trends and a Monte Carlo approach to simulate internal variability. The results show that there is no robust statistical evidence for a divergence between models and observations. The impression of a divergence early in the 21st century was caused by various biases in model interpretation and in the observations, and was unsupported by robust statistics. 1. Introduction models over-estimated warming (Fyfe et al 2013). The question of whether GMST deviates from model pro- A presumed slowdown in global warming during the jections has often been conflated with, but is con- first decade of the 21st century, and an alleged ceptually distinct from, questions relating to the divergence between projections from climate models observed warming rate. For example, one might ask and observations, have attracted considerable research whether warming has ceased or ‘paused’ or entered a attention. Even though the Earth’s climate has long ‘hiatus’. Answers to this question involve tests of the been known to fluctuate on a range of temporal scales statistical hypothesis that the warming trend is equal (Climate Research Committee, National Research to zero. A different question might be whether warm- Council 1995), the most recent fluctuation has been ing has slowed significantly, in which case the statis- singled out as a seemingly unique phenomenon, being tical question is whether there is a change in the long- identified as ‘the pause’ or ‘the hiatus.’ By the end of term rate of warming. A third question, at issue in this 2017, the ‘pause’ had been the subject of more than article, is whether the observations diverge from 200 peer-reviewed articles (Risbey et al 2018). model-derived expectations. Here, we focus on one aspect of the putative Existing research on the ‘pause’ has often conflated ‘pause’; namely, an alleged divergence between model the distinct questions that can be asked about short- projections and observed global mean surface temper- term warming trends. This conflation can be proble- ature (GMST); in particular the claim that climate matic because it is possible, in principle, for the © 2018 The Author(s). Published by IOP Publishing Ltd Environ. Res. Lett. 13 (2018) 123007 S Lewandowsky et al Figure 1. Historically conditioned trends for major GMST datasets; Berkeley (Cowtan and Way 2014); the U.K. Met Office’s HadCRUT (Brohan et al 2006, Morice et al 2012); NASA’s GISTEMP (Hansen et al 2010); Cowtan and Way’s improved version of HadCRUT (Cowtan and Way 2014); and NOAA’s dataset (Vose et al 2012). Each solid line plots the best-fitting least squares trend from the start year of 1998 to the end year (vantage year) as shown on the x-axis. The thick lines for each dataset correspond to the version available and current at a given vantage year, with the vertical gray lines indicating major changes in version. The thin lines provide retrospective trends that show what the trends would have been if later versions of the data (represented by solid points) had been available earlier. The solid points indicate the dates of versions of each dataset that were available for analysis. The trends are incremented from monthly data which results in some fine scale variation. observations to diverge from model-derived expecta- retrospective (‘hindsight’) trends calculated back to tions even though warming continues unabated. earlier vantage points as if the later versions of the Under those circumstances it would be misleading to dataset had been available then. discuss a ‘pause’ or ‘slowdown’, notwithstanding any The figure shows that different versions of the divergence between projected and observed trends. same dataset can yield substantially different trend A further difficulty in interpreting research on the estimates, as indicated by the difference between each ‘pause’ is that this period of intense research activity solid line and its thinner retrospective counterparts. coincided with notable improvements to observa- This is particularly pronounced for HadCRUT, which tional datasets. Specifically, GMST datasets are evol- shows a distinct jump in 2012 when HadCRUT3 was ving over time as they extend coverage (Morice et al replaced by HadCRUT4. In consequence, a data ana- 2012), introduce or modify interpolation methods lyst using HadCRUT3 in early 2012 would have con- that fill-in data for areas in which observations are cluded that the warming trend since 1998 had been sparse (Cowtan and Way 2014), or remove biases aris- slightly negative, whereas the same analyst using Had- ing from issues such as the transition of sea surface CRUT4 some months later would have concluded that temperature (SST) measurement from ships to buoys warming since 1998 had been positive. Likewise, (Karl et al 2015, Hausfather et al 2017). In con- although the datasets are known to yield similar long- sequence, research on short-term warming trends term estimates of global warming (e.g. from 1970 to may come to different conclusions, depending on the present), there were considerable differences what version of a dataset is being used. Figure 1, adap- between datasets for short-term trends early in the ted from the companion article by Risbey et al (2018), 21st century. See Risbey et al (2018) for details. shows that the consequences of revisions to GMST In this article, we apply the same historical con- datasets are far from trivial. The figure shows GMST ditioning to our analysis of the putative divergence trends starting in 1998 and ending at the times marked between models and observations during the period on the x-axis (vantage points). Each solid line shows known as the ‘pause.’ That is, we use the variants of the what we call ‘historically-conditioned’ trends, which datasets that were available at the time when assessing reflect only data that were available at each vantage evidence for the divergence between models and point. The thin lines, by contrast, show the observations, and we also condition the model 2 Environ. Res. Lett. 13 (2018) 123007 S Lewandowsky et al projections on the estimates of the forcings on the cli- realizations (extended by the RCP4.5 forcing scenario mate system that were available at any given time. The for the period 2006–2012). The IPCC stated that historical conditioning of both models and observa- ‘...111 out of 114 realizations show a GMST trend over tions provides the most like-with-like assessment of 1998–2012 that is higher than the entire HadCRUT4 the divergence between models and observations. trend ensemble ... This difference between simulated In order to assess claims made about this putative and observed trends could be caused by some combi- divergence we searched the literature for articles (pub- nation of (a) internal climate variability, (b) missing or lished through 2016) that referred to a ‘pause’ or ‘hia- incorrect radiative forcing and (c) model response tus’ in GMST in the title or abstract. The search was error’ (Flato et al 2013, p 769). The consensus view completed in December 2017 and yielded 225 peer- expressed by the IPCC therefore pointed to a diver- reviewed articles (see Risbey et al 2018 for a complete gence between modeled and observed temperature list). On the basis of the abstracts, 82 of those articles trends, putatively caused by a mix of three factors.

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