{"id":277565,"date":"2023-09-06T20:34:57","date_gmt":"2023-09-06T18:34:57","guid":{"rendered":"https:\/\/climatescience.press\/?p=277565"},"modified":"2023-09-06T20:35:00","modified_gmt":"2023-09-06T18:35:00","slug":"do-cmip5-models-skillfully-match-actual-warming","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=277565","title":{"rendered":"Do CMIP5 models skillfully match actual warming?"},"content":{"rendered":"\n
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From Climate Etc.<\/a><\/p>\n\n\n\n

by Nic Lewis<\/p>\n\n\n\n

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Why matching of CMIP5 model-simulated to observed warming does not indicate model skill<\/p>\n\n\n\n

A well-known Dutch journalist, Maarten Keulemans of\u00a0De Volkskrant<\/em>, recently\u00a0tweeted<\/a>\u00a0an open letter to the Nobel-prizewinning physicist Professor Clauser in response to his signing of the\u00a0Clintel World Climate Declaration<\/a>\u00a0that \u201cThere is no climate emergency\u201d, asking for his response to various questions. One of these was:<\/p>\n\n\n\n

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The CLINTEL Declaration states that the world has warmed \u201csignificantly less than predicted by (the) IPCC\u201d. Yet, a simple check of the models versus observed warming demonstrates that \u201cclimate models published since 1973 have generally been quite skillful predicting future warming\u201d, as Zeke Hausfather\u2019s team at Berkeley Earth recently analysed.<\/em><\/p>\n<\/blockquote>\n\n\n\n

The most recent such analysis appears to be that shown for CMIP5 models in a\u00a0tweet<\/a>\u00a0by Zeke Hausfather, reproduced in Figure 1. While the agreement between modeled and observed global mean surface temperature (GMST) warming over 1970\u20132020 shown in the Figure 1 looks impressive, it is perhaps unsurprising given that modelers knew when developing and tuning their models what the observed warming had been over most of this period.<\/p>\n\n\n\n

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Figure 1<\/strong>. Zeke Hausfather\u2019s comparison of global surface temperature warming in CMIP5 climate models with observational records. Simulations based on the intermediate mitigation RCP4.5 scenario of global human influence on ERF through emissions of greenhouse gases, etc. were used to extend the CMIP5 Historical simulations beyond 2005.<\/figcaption><\/figure>\n\n\n\n

It is well-known that climate models have a higher climate sensitivity than observations indicate. Figure 2 compares equilibrium climate sensitivity (ECS) diagnosed in CMIP5 models and in the latest generation, CMIP6, models with the corresponding observational estimate on the same basis in\u00a0Lewis (2022)<\/a>\u00a0of 2.16\u00b0C and (likely range 1.75\u20132.7\u00b0C). Only one model has an ECS below the estimate in Lewis (2022), and most models have ECS values exceeding the upper bound of its likely range. CMIP6 models are generally even more sensitive than CMIP5 models, with half of them having ECS values above the top of the 2.5\u20134\u00b0C likely range given in the IPCC\u2019s 2021 Sixth Assessment Report: The Physical Science Basis (AR6 WG1).<\/p>\n\n\n\n

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Figure 2<\/strong>.\u00a0 Red bars: equilibrium climate sensitivity in CMIP5 and CMIP6 models per\u00a0Zelinka et al. (2020)<\/a>\u00a0Tables S1 & S2 estimated by the standard method (ordinary least squares regression over years 1\u2013150 of abrupt4xCO2 simulations). Blue line and blue shaded band: best estimate and likely (17%-83% probability) range for ECS in Lewis (2022), derived from observational evidence over the ~150 year historical period but adjusted to correspond to that estimated using the aforementioned standard method\u00a0 for models.<\/figcaption><\/figure>\n\n\n\n

So, how is it possible that Hausfather gets an apparently good match between models and observations in the period 1970-2020? Does it imply that the models correctly represent the effects of changes in \u201cclimate forcers\u201d, such as the atmospheric concentration of greenhouse gases and aerosols, on GMST, and accordingly that their climate sensitivities are correct?<\/p>\n\n\n\n

The key question is this. Matching by CMIP5 climate models, in aggregate, with observed GMST changes would only be evidence that models correctly represent the effects of changes in \u201cclimate forcers\u201d, such as the atmospheric concentration of greenhouse gases and aerosols, on GMST if resulting changes in their combined strength in models matched best estimates of the actual changes in those forcers. The standard measure of strength of changes in climate forcers, in terms of their effect on GMST, is their \u201ceffective radiative forcing\u201d (ERF), which measures the effect on global radiative flux at the top of the Earth\u2019s atmosphere once it and the land surface have adjusted to the changes in climate forcers (see IPCC AR6 WG1 Chapter 7<\/a>, section 7.3)<\/p>\n\n\n\n

It is therefore important to compare changes in total ERF as diagnosed in CMIP5 models during their Historical and RCP4.5 scenario simulations over 1970\u20132020 with the current best estimates of their actual changes, which I will take to be those per IPCC AR6 WG1 Annex III<\/a>, extended from 2019 to 2020 using the almost identical Climate Indicator Project ERF time series<\/a>.<\/p>\n\n\n\n

Historical and RCP4.5 ERF (referred to as \u201cadjusted forcing\u201d) in CMIP5 models was diagnosed in Forster at al. (2013)<\/a>, for the 20 models with the necessary data<\/a>. I take the mean ERF for that ensemble of models[1]<\/a> as representing the ERF in the CMIP5 models used in Figure 1.<\/p>\n\n\n\n

Figure 3 compares the foregoing estimates of mean ERF in CMIP5 models with the best estimates given in IPCC AR6. Between the early 1980s and the late 2000s CMIP5 and AR6 ERF estimates agreed quite closely, but they diverged both before and (particularly) after that period. The main reason for their divergence since 2007 appears to be that aerosol ERF, which is negative, is now estimated to have become much smaller over that period than was projected under the RCP4.5 scenario. Updated estimates of aerosol ERF also appears likely to account for about half of their lesser divergence prior to 1983, with the remainder mainly attributable to differences in ERF changes for land use and various other forcing agents.<\/p>\n\n\n\n

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Figure 3<\/strong>. Effective radiative forcing (ERF) over 1970\u20132020 as estimated in CMIP5 models (mean across 19 models) and the best estimate given in the IPCC Sixth Assessment Scientific Report (AR6 WG1). The ERF values are relative to their 1860\u201379 means.<\/figcaption><\/figure>\n\n\n\n

The IPCC AR6 best estimate of the actual  ERF change between 1970 and 2020 is 2.53 Wm\u22122<\/sup>. The linear trend change over 1970\u20132020 given by ordinary least squares regression is 2.66 Wm\u22122<\/sup>, while the change between the means of the first and last decades in the period, scaled to the full 50 year period, is 2.59 Wm\u22122<\/sup>.<\/p>\n\n\n\n

By comparison, the mean ERF change for CMIP5 models between 1970 and 2020 is 1.67 Wm\u22122<\/sup>. The linear trend change over 1970\u20132020 is 1.92 Wm\u22122<\/sup>, and the scaled change between the first to last decades\u2019 means is 1.76 Wm\u22122<\/sup>.<\/p>\n\n\n\n

It is evident that the AR6 estimate of the actual 1970\u20132020 ERF change is far greater than that in CMIP5 models. Based on the single years 1970 and 2020, the AR6-to-CMIP5 model ERF change ratio is 1.51. Based on linear trends that ratio is 1.39, while based on first and last decades\u2019 means it is 1.46. The last of these measures is arguably the most reliable, since single year ERF estimates may be somewhat unrepresentative, and due to intermittent volcanism the ERF has large deviations from a linear relationship to time. As there is some uncertainty I will take the ratio as being in the range 1.4 to 1.5.<\/p>\n\n\n\n

So, CMIP5 models matched the observed 1970\u20132020 warming trend, but the estimated actual change in ERF was 1.4 to 1.5 times greater than that in CMIP5 models. On the assumption that both the CMIP5 model ERF estimates and the IPCC AR6 best estimates of ERFs are accurate, it follows that:<\/p>\n\n\n\n