CMIP6 GCM ensemble members versus global surface temperatures

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Noname manuscript No.

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CMIP6 GCM ensemble members versus global surface temperatures

Nicola Scafetta

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Abstract The Coupled Model Intercomparison Project (phase 6) (CMIP6) global circulation models (GCMs) predict equi-

librium climate sensitivity (ECS) values ranging between 1.8◦C and 5.7◦C. To narrow this range, we group 38 GCMs into

low, medium and high ECS subgroups and test their accuracy and precision in hindcasting the mean global surface

warming observed from 1980-1990 to 2011-2021 in the ERA5-T2m, HadCRUT5, GISTEMP v4, and NOAAGlobTemp

v5 global surface temperature records. We also compare the GCM hindcasts to the satellite-based UAH-MSU v6 lower

troposphere global temperature record. We use 143 GCM ensemble averaged simulations under four slightly different

forcing conditions, 688 GCM member simulations, and Monte Carlo modeling of the internal variability of the GCMs

under three different model accuracy requirements. We found that the medium and high-ECS GCMs run too hot up

to over 95% and 97% of cases, respectively. The low ECS GCM group agrees best with the warming values obtained

from the surface temperature records, ranging between 0.52◦C and 0.58◦C. However, when comparing the observed and

GCM hindcasted warming on land and ocean regions, the surface-based temperature records appear to exhibit a sig-

niﬁcant warming bias. Furthermore, if the satellite-based UAH-MSU-lt record is accurate, actual surface warming from

1980 to 2021 may have been around 0.40◦C (or less), that is up to about 30% less than what is reported by the surface-

based temperature records. The latter situation implies that even the low-ECS models would have produced excessive

warming from 1980 to 2021. These results suggest that the actual ECS may be relatively low, i.e. lower than 3◦C or even

less than 2◦C if the 1980-2021 global surface temperature records contain spurious warming, as some alternative studies

have already suggested. Therefore, the projected global climate warming over the next few decades could be moderate

and probably not particularly alarming.

Keywords: CMIP6 climate models; temperature records; equilibrium climate sensitivity; global warming; model validation and

testing

Cite this paper as: Scafetta, N., 2022. CMIP6 GCM ensemble members versus global surface temperatures. Climate Dynamics.

https://doi.org/10.1007/s00382-022-06493-w

1 Introduction

The Coupled Model Intercomparison Project (phase 6) (CMIP6) collects several simulations of global climate models

(GCM) currently used to interpret past and future climate changes (Eyring et al., 2016; IPCC, 2021). However, these

GCMs calculate equilibrium climate sensitivity (ECS) values ranging from 1.8◦C to 5.7◦C (IPCC, 2021). The ECS is the

most important climatic parameter as it measures the long-term increase in air temperature near the surface that should

result from an increase in radiative forcing of approximately 3.8 W/m2, which corresponds to a doubling of the atmo-

spheric CO2concentration from 280 ppm (which is deﬁned as the preindustrial level) to 560 ppm. The uncertainty of the

ECS is highly problematic as it indicates that the climate system is still poorly understood and modeled. Consequently,

also the extent of future climate change is rather uncertain as the impact of anthropogenic CO2emissions on the climate

cannot yet be adequately quantiﬁed (cf. Knutti et al., 2017).

The uncertainty of the ECS stems from the fact that various climate feedback mechanisms – in particular water vapor

and cloud cover – are still too little known and modeled, as already found sixty years ago by Möller (1963). In the absence

of climate feedback mechanisms, the Stefan-Boltzmann law for blackbodies predicts that a doubling of the atmospheric

Department of Earth Sciences, Environment and Georesources, University of Naples Federico II, Complesso Universitario di Monte S. Angelo,

via Cinthia, 21, 80126 Naples, Italy. Email: nicola.scafetta@unina.it

arXiv:2210.11414v1 [physics.ao-ph] 14 Oct 2022

2 Nicola Scafetta

CO2concentration could cause an increase in global surface temperature of about 1◦C. Therefore, only strong positive

climate feedbacks could signiﬁcantly increase the ECS above such a value, but their existence is still debated.

Constraining the ECS value is an urgent task of climatology. In fact, at least two-thirds of the CMIP6 GCMs could be

severely defective. For example, by grouping models into low (1.5 <ECS ≤3.0 ◦C), medium (3.0 <ECS ≤4.5 ◦C) and

high (4.5 <ECS ≤6.0 ◦C) sensitivity values, if, say, the actual ECS is less than 3°C, the GCMs with ECS >3◦C should

be ignored. Therefore, it is very important that detailed evaluations of the models are carried out in order to determine

if, where and how the models should improve both on a global scale – as proposed, for example, in this work – and on

regional scales, as done in numerous other studies (e.g.: Heo et al., 2014; Seo et al., 2018, and many others).

Constraining ECS also has important policy implications because the expected warming for the 21st century depends

on the value of the model’s ECS (Grose et al., 2017; Scafetta, 2022): the higher the ECS, the greater the expected warming

due to GHG emissions. For example, Huntingford et al. (2020) found that the wide ECS range of CMIP6 GCMs implies

that at thermal equilibrium the global surface temperature could warm up between 1.0◦C and 3.3◦C above the pre-

industrial period (1850-1900) even if anthropocentric emissions cease today.

Scientists already wondered whether a strong response to greenhouse gases could be realistic (Voosen, 2019). Indeed,

high ECS CMIP6 models have already been found to perform poorly (e.g.: Ribes et al., 2021; Scafetta, 2022; Tokarska et

al., 2020; Zhu et al., 2020) while the medium and even the low ECS models are being carefully evaluated.

For example, Nijsse et al. (2020) derived that the most likely ECS interval should be 1.9-3.4◦C while alternative

studies, often empirical based, have suggested that the actual ECS could be even lower, probably between 1◦C and

2.5◦C (e.g.: Lewis and Curry, 2018; Lindzen and Choi, 2011; Scafetta, 2013; Stefani, 2021; van Wijngaarden and Happer,

2020). Most GCMs seem to overestimate the observed surface warming since 1980 (Scafetta, 2021b, 2022) and also that

observed in the global (McKitrick and Christy, 2020) and tropical troposphere (Mitchell et al., 2020), in particular at

its top (200-300 hPa) where the CMIP6 GCMs predict an unobserved hot spot (McKitrick and Christy, 2018). A similar

situation also occurred with the previous CMIP3 and CMIP5 GCMs (Fu et al., 2011; Scafetta, 2012a, 2013). Actually, as

Knutti et al. (2017) acknowledged, there is a dichotomy between the observed and modeled ECS as GCMs tend to favor

sensitivity values at the top of the probable range, while several studies based on instrumentally recorded warming and

some from paleoclimate favor values in the lower part of the range. Therefore, not only the models with high ECS, but

also those with medium ECS should be and are being seriously questioned.

Scafetta (2021a, 2022) showed that the performance of the GCMs improves as their ECS decreases and, in any case,

the low ECS GCMs appear to be the best performing models. However, even low-ECS GCMs need further evaluation

because biases in some regions (e.g. on land) could be offset by opposite biases in other regions (e.g. on ocean). Further-

more, serious uncertainties remain in the solar forcing and in the temperature records themselves (Connolly et al., 2021;

D’Aleo, 2016). These uncertainties question the warming trend reported by the available climate records and, directly or

indirectly, the models themselves. Finally, climate systems seem to be regulated by various natural oscillations from the

decadal to the millennial scales, which the GCMs are unable to reproduce, the presence of which would also imply low

ECS values, probably between 1 and 2◦C (Scafetta, 2012a, 2013, 2021c).

Focusing on the performance of the CMIP6 GCMs, Scafetta (2022) proposed that the probable ECS range could be

constrained by statistical investigation to ﬁnd which GCM group – low, medium or high ECS – best reproduces the ob-

served global surface warming between the 1980-1990 and 2011-2021 as reported by ERA5-T2m (Hersbach et al., 2020;

Simmons et al., 2021). The period 1980-2021 was chosen because it is optimally covered by all available climatic tempera-

ture records. Scafetta (2022) analyzed the “average” simulations provided by the Koninklijk Nederlands Meteorologisch

Instituut (KNMI) Climate Explorer (Oldenborgh, 2020) of 38 CMIP6 GCMs with three shared socioeconomic pathways

(SSP) emission scenarios, which also counted for a partial evaluation of the internal variability of the models. The low

ECS GCM group was found to be perfectly compatible, at least on a global scale, with the 2011-2021 warming relating to

the 1980-1990 period. Conversely, both GCM groups with medium and high ECS showed too high warming trends.

A possible objection to the analysis proposed in Scafetta (2022) is that temperature records should be compared

with actual members of the CMIP6 GCM ensemble instead of their ensemble averages because the unforced internal

variability of the models produces different results due to uncertainties in the initial conditions as well as in the internal

parameters of the models. This problem will be addressed in this paper considering that:

1. physical models, including the GCMs, should be accurate and precise (see Appendix B);

2. there are still open issues regarding the reliability of the available global surface temperature records.

In fact, theoretical models must reproduce observations within a reasonably small error. In our case, it should be evident

that the poor precision of a GCM cannot be used as a pretext to justify its poor accuracy. For example, a low-precision

model could produce a very wide range of different hindcasts due to its internal variability. In this situation, even if

some of its hindcasts ﬁt the observations, the result should still be considered unsatisfactory if the mean of the GCM set

diverges too much from the actual data. Similarly, if an ECS GCM group produces a set of hindcasts that too sparsely

encompass the observations, the ECS values that characterize that group should be considered unrealistic even though

some of the models in the same group might perform better than others. In general, the accuracy, precision and ECS

category of the GCMs must be evaluated simultaneously.

CMIP6 GCM ensemble members versus global surface temperatures 3

Furthermore, surface-based temperature records appear to exhibit non-climatic warming biases due to poorly cor-

rected urban heats or other local surface phenomena (e.g.: Connolly et al., 2021; D’Aleo, 2016; Scafetta, 2021a). To account

for this problem, the satellite temperature measurements of the lower troposphere using microwave resonance units

(MSU) proposed by the U. of Alabama Huntsville (UAH-MSU-lt v6) (Spencer et al., 2017) will also be analyzed.

UAH-MSU-lt is the temperature record that features the lowest global warming trend (about +0.13 °C/decade) from

1980 to 2021 among all available global temperature records. According to GCM simulations, the troposphere is expected

to warm up faster than the surface (up to a factor of 3) because greenhouse gases are expected to warm the atmosphere

ﬁrst (Mitchell et al., 2020). Consequently, the global warming trend of the troposphere estimated from satellite measure-

ments should be further reduced to simulate the global warming trend at the surface. Here, these corrections are ignored

and UAH-MSU-lt is assumed to represent the possible lowest limit for the global warming trend of the surface. There-

fore, comparison with this satellite temperature record could help assess the presence of non-climatic warming bias in

the surface temperature records, particularly on land where large contaminated areas appear to exist (cf. Scafetta and

Ouyang, 2019; Scafetta, 2021a).

Indeed, preliminary analyzes have shown that the land seems to have warmed too much and too quickly compared

to the ocean (Scafetta, 2021a). Connolly et al. (2021) used data from rural stations only and showed that the warming of

the Northern Hemisphere’s land surface should be signiﬁcantly lower than what reported by the available surface-based

temperature records based on both rural and urban stations. Watts (2022) examined the quality of the U.S. temperature

stations from which ofﬁcial temperature records are obtained and concluded that approximately 96% of them could not

meet the National Oceanic and Atmospheric Administration (NOAA) requirements for “acceptable placement” because

they could be signiﬁcantly contaminated by different heat sources. In general, the surface temperature records and the

homogenization algorithms used to adjust them present several problems that may have exaggerated the warming.

Thus, the integrity of the available global surface temperature records and, therefore, the ability to correctly determine

the global warming trend of the 20th and 21st century should be questioned as well (Connolly et al., 2021; D’Aleo, 2016).

There is a different MSU record (Mears and Wentz, 2016), which shows warming trends that is more compatible

with those presented by the surface-based temperature records. However, this alternative satellite-based record is not

analyzed here because it would overlap the results of the surface-based temperature records. In any case, adopting it

in the present study may not be optimal because it only covers the latitude range from 70.0◦S to 82.5◦N and because it

appears to perform worse than UAH-MSU-lt that better agrees with the radiosonde temperature database (Christy et

al., 2018).

Here, we signiﬁcantly expand the analysis presented by Scafetta (2022) by testing 143 GCM average simulations and

all 688 GCM member simulations available on the KNMI website against four surface-based global temperature records

(ERA5-T2m, HadCRUT5, GISTEMP v4, NOAAGlobTemp v5) and the UAH-MSU-lt v6 satellite-based record. Since we

wish to narrow the ECS range, we again group the models into three classes corresponding to low, medium and high

ECS values, as proposed in Scafetta (2022). ECS GCM groups that produce systematically biased trends (e.g. too hot

or too cold relative to the observed temperatures) should be questioned and not used for policy even though some

simulations may appear to reproduce the observations. Finally, we compare the GCM hindcasts with observed land and

ocean warming values to determine whether the surface-based records could be regionally biased and whether the ECS

should be further constrained towards lower values.

2 Data and methods

We analyze the monthly reanalysis ﬁeld near-surface air temperature (ERA5-T2m) record from 1980 to 2021 (Hersbach

et al., 2020; Simmons et al., 2021). We repeat the same analysis using the HadCRUT5 (Morice et al., 2021), GISTEMP

v4 (Lenssen et al., 2019), and NOAAGlobalTemp v5 (Zhang et al., 2019) global surface temperature records. Some of

these records, however, may not cover the entire surface of the globe from 1980 to 2021. There are other global surface

temperature records such as those proposed by the Japanese Meteorological Agency (JMA, Ishihara, 2006) and by the

Berkeley Earth group (BE, Rohde and Hausfather, 2020), which will also be discussed brieﬂy. For completeness, as

explained in the Introduction, we add a comparison with the UAH-MSU-lt v6 temperature measurements (Spencer et

al., 2017).

We also analyze all 143 “average” surface air temperature (tas) records and all 688 ensemble member records from 38

different CMIP6 GCMs downloadable from KNMI Climate Explorer. These simulations were produced using historical

forcings (1850-2014) further extended up to 2100 with four different SSP scenarios: SSP1-2.6 (low GHG emissions), SSP2-

4.5 (intermediate GHG emissions), SSP3-7.0 (high GHG emissions ) and SSP5-8.5 (very high greenhouse gas emissions)

(IPCC, 2021). These four scenarios are nearly indistinguishable until 2021. Thus, from 1850 to 2021 the four simulation

sets can be considered independent assessments of the same models under nearly identical forcing conditions, which

also helps to assess in ﬁrst approximation the internal variability of the models.

The 1980-2021 period was chosen to better evaluate the performance of the CMIP6 GCMs. This period is optimally

covered by numerous climatic temperature records including those based on satellite measurements that are alternative

4 Nicola Scafetta

Fig. 1 (Left) GCM global surface temperature simulations (colored curves) and (right) ±1σGCM global surface temperature ensembles (yellow

area) versus the ERA5-T2m record (black, 12-month moving average).

to those based on land and oceanic measurements that could be affected by various non-climatic biases, which are

difﬁcult to eliminate (D’Aleo, 2016; Watts, 2022). In fact, going back in time from 1980 to 1850, the temperature records

are affected by ever-larger uncertainties and uncovered areas, which makes evaluating the CMIP6 models even more

difﬁcult. A possible advantage of the present study is that the previous studies evaluating the performance of the CMIP6

models attempted to constrain the ECS by comparing GCM simulations only with surface climate records from 1850 to

2020 (Ribes et al., 2021) or from 1981 to 2014 (Tokarska et al., 2020), or even using uncertain paleoclimate records (Zhu

et al., 2020) and concluded that only high-ECS models (ECS >4.5 ◦C) could be excluded. However, there are open

questions as to whether cooling adjustments applied to different Earth surface temperature records from 1850 to 1980

are justiﬁed (D’Aleo, 2016) and whether in more recent periods the global surface climate records are affected by non-

climatic warming biases (Connolly et al., 2021; Scafetta, 2021a). These biases could have exaggerated the 20th century

warming trend and incorrectly provided support for the medium-ECS GCMs.

The 1980-2021 warming for each record is calculated by evaluating the 2011-2021 average temperature anomaly

relative to the 1980-1990 period. 11-year intervals are used to bypass biases due to interannual ﬂuctuations such as those

related to ENSO and the 11-year solar cycle. Then, we apply standard statistical tests to decide if and how the observed

warming values for each of the temperature records are reproduced by the three ECS GCM groups.

The ERA5-T2m global surface temperature average warming from 1980-1990 to 2011-2021 is estimated to be:

∆Tmean =0.578◦C. (1)

The other temperature records give: HadCRUT5 (inﬁlled data), ∆Tmean =0.581◦C; GISTEMP v4, ∆Tmean =0.570◦C;

NOAAGlobalTemp v5, ∆Tmean =0.523◦C. HadCRUT5, GISTEMP, and ERA5-T2m give nearly identical warmings. We

also observe that HadCRUT5 (non-inﬁlled data) gives ∆Tmean =0.549◦C and HadCRUT4 gives ∆Tmean =0.521◦C. BE

gives ∆Tmean =0.591◦C and JMA gives ∆Tmean =0.557◦C, which do not differ much from the above estimates. Thus,

from 1980 to 2021, the available surface-based global temperature records measure that the global surface warming from

CMIP6 GCM ensemble members versus global surface temperatures 5

Fig. 2 GCM global surface temperature ensembles (yellow area, ±1σ) versus HadCRUT5, GISTEMP v4, NOAAGlobTemp v5, and UAH-MSU-

lt v6 temperature records (black, 12-month moving average).

1980-1990 to 2011-2021 has been between 0.52◦C and 0.59◦C, or approximately between 0.50◦C and 0.60◦C, with an av-

erage of 0.56◦C. In contrast, the satellite-based UAH-MSU-lt v6 temperature record gives ∆Tmean =0.402◦C, suggesting

that 2011-2021 actual warming may have been even less than 0.40◦C because, as explained in the introduction, according

to the GCMs the temperature trend of the troposphere should be scaled down to make it compatible with the surface

warming trend.

For the temperature records, since 1980 the error of the average over an 11-year period can be estimated to be very

small, ¯

σ95% ≈0.01◦C (see Appendix A), which represents about 2% of the warming from 1980-1990 to 2011-2021, and is

less than the differences between the various temperature records.

As explained in the Introduction, the proposed analysis groups the CMIP6 GCMs into three subsets characterized by

low (1.5 <ECS ≤3.0◦C), medium (3.0 <ECS ≤4.5 ◦C) and high (4.5 <ECS ≤6.0 ◦C) sensitivity values. This choice

is based on the following heuristic considerations. In fact, the IPCC (2013) estimated that the ECS had to have a “likely”

range of 1.5 – 4.5◦C. Also Wigley et al. (1997) suggested the same interval although the best-ﬁt sensitivity was found to

be 2.5°C. This range can be heuristically divided into at least two equal parts: 1.5 <ECS ≤3.0 ◦C and 3.0 <ECS ≤4.5

◦C. In 2013, the CMIP5 GCMs were used. However, the IPCC (2021) adopted the CMIP6 GCMs that extended the ECS

range up to 6◦C so that an equally large third range, 4.5 <ECS ≤6.0 ◦C, could be added to the previous two. Zelinka

et al. (2020) explained that the causes of the increased climate sensitivity in the CMIP6 models were due to stronger

positive cloud feedbacks due to decreased extratropical cloud cover and albedo that, however, might be questionable.

Therefore, the interval 1.5 <ECS ≤3.0 ◦C collects the GCMs with ECS values most consistent with different em-

pirical results, as discussed in the Introduction; the interval 3.0 <ECS ≤4.5 ◦C collects the other GCMs that also the

IPCC (2013) would have considered acceptable; ﬁnally, the interval 4.5 <ECS ≤6.0 ◦C collects the GCMs included in

the IPCC (2021) but which in 2013 the IPCC itself considered to predict an unlikely high ECS.

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