# Impacts and risks of “realistic” global warming projections for the 21st
century

Nicola Scafetta

###### Abstract

The IPCC AR6 assessment of the impacts and risks associated with projected
climate changes for the 21st century is both alarming and ambiguous. According
to computer projections, global surface may warm from 1.3 to 8.0 °C by 2100,
depending on the global climate model (GCM) and the shared socioeconomic
pathway (SSP) scenario used for the simulations. Actual climate-change hazards
are estimated to be high and very high if the global surface temperature
rises, respectively, more than 2.0 °C and 3.0 °C above pre-industrial levels.
Recent studies, however, showed that a substantial number of CMIP6 GCMs run “
_too hot_ ” because they appear to be too sensitive to radiative forcing, and
that the high/extreme emission scenarios SSP3-7.0 and SSP5-8.5 are to be
rejected because judged to be _unlikely_ and _highly unlikely_ , respectively.
Yet, the IPCC AR6 mostly focused on such alarmistic scenarios for risk
assessments. This paper examines the impacts and risks of “realistic” climate
change projections for the 21st century generated by assessing the theoretical
models and integrating them with the existing empirical knowledge on global
warming and the various natural cycles of climate change that have been
recorded by a variety of scientists and historians. This is achieved by
combining the SSP2-4.5 scenario (which is the most likely SSP according to the
current policies reported by the International Energy Agency) and empirically
optimized climate modeling. According to recent research, the GCM macro-
ensemble that best hindcast the global surface warming observed from 1980–1990
to 2012–2022 should be made up of models that are characterized by a low
equilibrium climate sensitivity (ECS) ($1.5<ECS\lessapprox 3.0$ °C), in
contrast to the IPCC AR6 _likely_ and _very likely_ ECS ranges of 2.5-4.0 °C
and 2.0-5.0 °C, respectively. I show that the low-ECS macro-GCM with the
SSP2-4.5 scenario projects a global surface temperature warming of 1.68-3.09
°C by 2080–2100 instead of1.98-3.82 °C obtained with the GCMs with ECS in the
2.5-4.0 °C range. However, if the global surface temperature records are
affected by significant non-climatic warm biases — as suggested by satellite-
based lower troposphere temperature records and current studies on urban heat
island effects — the same climate simulations should be scaled down by about
30%, resulting in a warming of about 1.18-2.16 °C by 2080–2100. Furthermore,
similar moderate warming estimates (1.15-2.52 °C) are also projected by
alternative empirically derived models that aim to recreate the decadal-to-
millennial natural climatic oscillations, which the GCMs do not reproduce. The
proposed methodologies aim to simulate hypothetical models supposed to
optimally hindcast the actual available data. The obtained climate projections
show that the expected global surface warming for the 21st century will likely
be mild, that is, no more than 2.5-3.0 °C and, on average, likely below the
2.0 °C threshold. This should allow for the mitigation and management of the
most dangerous climate-change related hazards through appropriate low-cost
adaptation policies. In conclusion, enforcing expensive decarbonization and
net-zero emission scenarios, such as SSP1-2.6, is not required because the
Paris Agreement temperature target of keeping global warming below 2 °C
throughout the 21st century should be compatible also with moderate and
pragmatic shared socioeconomic pathways such as the SSP2-4.5.

1Department of Earth Sciences, Environment and Georesources, University of
Naples Federico II, Complesso Universitario di Monte S. Angelo, Via Vicinale
Cupa Cintia, 21, 80126 Naples (NA), Italy.

  * •

Keywords: Climate change; Climate models; Shared socioeconomic pathways; 21st-
century climate projections; Impacts and risks assessment.

  * •

Cite as: Scafetta, N.: 2024. Impacts and risks of “realistic” global warming
projections for the 21st century. _Geoscience Frontiers_ 15(2), 101774.
(https://doi.org/10.1016/j.gsf.2023.101774)

Highlights:

  * •

The IPCC AR6 assessment of likely impacts and risks of 21st-century climate
change is highly uncertain.

  * •

Most climate models, however, run too hot, and the SSP3-7.0 and SSP5-8.5
scenarios are unlikely.

  * •

New climate change projections for the 21st century were generated using best-
performing climate models, and

  * •

empirical climate modeling of natural cycles, and calibration on lower
troposphere temperature data.

  * •

Only the realistic and moderate SSP2-4.5 scenario is considered for climate-
change impact and risk assessments.

  * •

Net-zero emission policies are not necessary because SSP2-4.5 is sufficient to
limit climate change hazards to manageable levels.

##  1 Introduction

The IPCC Sixth Assessment Report (AR6) Working Group II (Pörtner et al., 2022)
assessed that the environmental impacts of climate changes in the 21st century
— such as rising sea levels and temperatures, increased extreme weather
events, droughts and floods, and the spread of wildfires — could significantly
affect environments, societies and many aspects of human economy and life.
Projected climate changes could also directly and indirectly impact human
health by inducing changes in many environmental factors associated with
infectious and respiratory diseases, changes in heat and cold-related
morbidity and mortality, changes in food production, changes in sociopolitical
tensions and conflicts, and many others (Rocque et al., 2021).

In fact, despite CO2 is, strictly speaking, not a pollutant, and its
atmospheric increase is actually greening the Earth due to its fertilization
effects (Zhu et al., 2016; Piao et al., 2019) and a warmer climate does also
present a number of benefits, today there is growing concern that, during the
21st century, global warming induced by anthropogenic greenhouse gas emissions
due to the persistent use of fossil fuels (which mostly emits CO2) could
exceed temperature levels that are considered sustainable with the current
adaptive capacity of most communities (Masson-Delmotte et al., 2018, 2021). It
is claimed that dangerous levels of global warming could be exceeded by as
soon as 2050 unless a number of expensive adaptation and mitigation strategies
are implemented such as energy policies attempting to reach net-zero emissions
by about 2050 (European Commission, 2023; Shukla et al., 2022).

The term "net-zero emissions" refers to achieving a net balance between man-
made greenhouse gas (GHG) emissions produced and GHG removed from the
atmosphere. Given the present limited ability to remove GHGs from the
atmosphere, net-zero requires swiftly limiting the use of fossil fuels - coal,
oil, and gas - and transitioning toward renewable green energy in all sectors
of the economy.

Depending on the magnitude and speed of the evolving physical processes,
climate changes may have both beneficial and harmful consequences. These
effects are under the control of complex and still poorly understood nonlinear
dynamical systems. As the temperature continues to warm, some nations may see
immediate advantages, but the long-term consequences could be harmful, while
others may see the opposite. Depending on elements including geographic
location, socioeconomic conditions, and capacity for adaptation, the
distribution of climate-change impacts and risks also considerably changes
from place to place. By aggregating several indicators, Tol (2015) estimated
that climate change could globally have a net positive economic welfare impact
only if the global surface warming remains roughly below 2.0 °C above its
1850–1900 pre-industrial levels throughout the 21st century. However, Tol
(2015) also highlighted the large uncertainty associated with such claims
because, for example, on eleven analyzed economical risk estimates for a
warming of 2.5 °C, it was found that the researchers disagree on the sign of
the net impact: four were positive and seven negative.

For example, most of the early net gains should occur because increasing
carbon dioxide in the atmosphere reduces water stress in plants, causing them
to grow quicker, and because wealthy countries are mostly concentrated in
temperate climatic zones, which should benefit more from a moderate warming.
However, if the temperature rises too much, the water stress in the plants
could grow. In general, although the situation varies from place to place, the
scientific literature suggests that on a global scale, even if there may be
initial economic net gains from global warming, such gains may be offset by
losses as the environment continues to warm (particularly above 2.5-3.0 °C) in
a too short time period (Pörtner et al., 2022). After then, societies will be
able to recover economically only after effectively adapting to the new
climatic conditions.

The Working Group I of the IPCC AR6 (Masson-Delmotte et al., 2021) stated that
the global surface temperature in the first two decades of the 21st century
(2001–2020) was about 1 [0.84 to 1.10] °C higher than the (1850–1900) pre-
industrial period. Current computer-based global climate models (GCMs) suggest
that the 1.5 °C warming level could, on average, be easily exceeded by as
early as 2030, and the 2 °C warming level could be exceeded by about 2050–2060
(Masson-Delmotte et al., 2018, 2021). Consequently, because the global surface
temperature is expected to rise very fast, climate changes are being
considered by the United Nations a kind of imminent threat, especially for the
impoverished societies that could lack the resources required for quickly cope
and/or adapt to it. Thus, aggressive, and expensive climate policies aimed at
significantly reducing anthropogenic greenhouse gas emissions from fossil
fuels are being advocated for mitigating future climate changes in annual COPs
(Conferences of the Parties) promoted by United Nations since 1995.

For example, in 2015 at COP-21 in Paris the UNFCCC (2023) reached a landmark
agreement to combat climate change by promoting international policies to keep
global warming “ _below 2 °C_ ” (Gao et al., 2017) while even pursuing
ambitious efforts “ _to limit the temperature increase to 1.5 °C above pre-
industrial levels_ ” by 2030 by enforcing Net-Zero by 2050 policies. Masson-
Delmotte et al. (2018) and Meinshausen et al. (2022) argued that only by
executing the Paris Agreement pledges of net-zero emissions by 2050 could
restrict global surface warming to less than the “safe” threshold of about 2
°C. For similar reasons, editors of geoscience and health journals have being
calling for urgent collective climate-mitigation actions (McNutt, 2015)
because “ _our planet is in crisis_ ” (Filippelli et al., 2021) and because
the projected global warming for the 21st century would be the “ _greatest
threat to global public health_ ” (Atwoli et al., 2021) if not immediately
mitigated as agreed in Paris. However, many severe uncertainties and concerns
on such claims remain.

In fact, fossil fuels continue to supply most of the inexpensive energy that
is today used worldwide, which is allowing developing countries to quickly
raise their living and welfare standards. Indeed, while wealthy Western
Nations seek to phase out coal power plants and, in general, are attempting to
substantially limit the use of fossil fuels through impractical, expensive and
unpopular rapid-decarbonization policies, the majority of the rest of the
world is significantly increasing their emissions (Crippa et al., 2023). In
particular, China, India, Indonesia, Japan and Vietnam, among other Asian
countries, are constructing about 400 coal-fired power plants and planning to
build another 400 units in the near future (GEM, 2023). Furthermore, the rapid
depletion of critical metals required for a rapid shift from fossil fuel to
renewable green-energies calls the possibility of such a technological
transition into serious doubt (Groves et al., 2023) and, in general, economic
meta-analyses indicate that the costs of implementing the net-zero policies
required to meet the Paris climate targets may outweigh the benefits even in
the worst-case-scenario global warming (Tol, 2023). As a result, there is no
political agreement among world nations on how to manage future climate-
change-related hazards.

World nations disagree on the actual threats, and, in fact, they prioritize
different issues, likely because of the scientific uncertainty about future
climate changes, as well as of their potential impacts and associated risks.
The still existing large scientific uncertainty on this matter may lead to
detrimental decisions either if a climatic alarm is overstated or understated.
Policy disparities among countries would make finding effective solutions to
potential climate-change-related hazards hard, while also risking harmful
economic imbalances in the interactions between and within states. It should
be evident to all that if a few wealthy countries significantly reduce their
emissions while all the others significantly raise them, total emissions may
not fall but, instead, increase and climate change will not be mitigated. For
example, net-zero emission policies are being strongly advocated in the 27
nations of the European Union that, however, in 2022 contributed only 6.7% of
the GHG global total emissions (Crippa et al., 2023); moreover, for the year
2022 about half of the EU countries – including Italy (6.70 t CO2eq/cap) and
France (6.50 t CO2eq/cap) – produced GHG per capita emissions less than the
world total (6.76 t CO2eq/cap).

The potential impacts and risks related to projected climate changes for the
21st century must be estimated more precisely by constraining the various
existing physical and economic uncertainties. This operation is necessary to
conduct proper cost-benefit analyses of the required mitigation and/or
adaptation policies in the hope that by being less controversial they could be
more likely accepted by all nations. In fact, either underestimating or
exaggerating the alarm of a possible climate-change crisis could have harmful
economic implications that can negatively impact human economy and life at
both global and local scales.

The main scientific uncertainties surrounding the physics of climate change
are reviewed in Section 2, along with how they could be significantly
constrained by using recent advances in scientific knowledge. Realistic
projections of climate changes for the 21st century and their associated
impacts and risks are evaluated in Section 4. Finally, Section 5 proposes an
alternative interpretation based on empirical climate change modeling for the
21st century that takes into account the implications of additional physical
uncertainties concerning the temperature data and the actual warming, and the
likely role of the Sun (Connolly et al., 2021; Scafetta, 2023c; Scafetta and
Bianchini, 2023; Soon et al., 2023) and natural oscillations (Scafetta, 2013,
2021b) that are not properly modeled by the GCMs. This article only evaluates
“realistic” climate change projections while assuming valid the economic and
welfare models used by the IPCC (Shukla et al., 2022) to assess the impacts
and risks that might be associated with projected levels of global warming.

##  2 Understanding the uncertainties

Future climate-change impacts and risks are determined by the extent to which
the global surface will warm during the 21st century. Projected climatic
changes are determined by the amount of greenhouse gases emitted and by how
the climate system responds to them (Hausfather and Moore, 2022). However, the
anthropogenic signal should be viewed as superimposed on top of natural
climate variability, which consists of cycles and fluctuations that occur at
all timescales; the natural variability occurring from the decadal to the
millennial scales is especially important for correctly interpreting the
climatic changes observed since 1850 (e.g.: Scafetta, 2013, 2021b).

GCM simulations for the 21st century, such as those shown in Figure 1 (Masson-
Delmotte et al., 2021; Pörtner et al., 2022), were used to assess global and
regional impacts and risks associated with global warming. Figure 1
illustrates that the GCM simulations differ significantly both amongst models
and because of the chosen SSP scenario adopted for the simulation. The entire
ensemble of climate projections estimates that, by 2100, global surface
temperatures might rise by 1.3 to 8.0 °C, meaning that climate change could
have a wide range of effects on civilizations and the environment throughout
the 21st century, ranging from beneficial to catastrophic. The main sources of
uncertainty are briefly described below.

Figure 1: CMIP6 GCM simulations adopted in the present study (Appendix A). The
curve’s color scales with the ECS of the models.

###  2.1 The SSP scenarios

The GCM simulations for the 21st century were obtained under different
greenhouse gas emission scenarios known as representative concentration
pathways (RCPs) and, more recently, five shared socioeconomic pathways (SSPs)
describing alternative socioeconomic developments (Riahi et al., 2017). The
latter were classified as SSP1 (sustainability), SSP2 (middle of the road),
SSP3 (regional rivalry), SSP4 (inequality), and SSP5 (fossil-fueled
development). These labels were combined with the expected level of radiative
forcing in the year 2100 relative to 1750, which varies from 1.9 to 8.5 W/m2.

The SSP scenarios explicitly adopted by the IPCC AR6 (Masson-Delmotte et al.,
2021) and herein considered are:

  * •

SSP1-2.6 — low GHG emissions and strong adaptation and mitigation (CO2
emissions are cut to net-zero around 2050–2075); this is a kind of SSP
scenarios compatible with the Paris Agreement pledges needed to limit the
global surface warming just below the “safe” threshold of 2 °C (Meinshausen et
al., 2022) by using the CMIP6 GCMs (Masson-Delmotte et al., 2018; Hausfather
et al., 2022; Hausfather and Moore, 2022).

  * •

SSP2-4.5 — intermediate GHG emissions and moderate adaptation and mitigation
(CO2 emission rates remain around current levels until 2050 then they fall but
do not reach net-zero by 2100);

  * •

SSP3-7.0 — high GHG emissions and average no policy (CO2 emissions double by
2100);

  * •

SSP5-8.5 — very high GHG emissions and worst-case no policy (CO2 emissions
triple by 2075).

The likelihood of the aforementioned scenarios were not estimated by the IPCC
AR6 (Masson-Delmotte et al., 2021, pages 54, 231 and 238), despite the fact
that having done so would have been essential for properly assessing plausible
climate-change impacts and risks. In particular, the SSP5-8.5 scenario
produces the most alarming global warming projections ranging from 4 to 8 °C
by 2100, and it has been the most popular climate scenario used in the
scientific literature (Pielke, 2021). For example, the IPCC AR6 (Masson-
Delmotte et al., 2021, table 12.12) highlighted the assessments of the
emergence of climate impact drivers (CIDs) for the 21st century using the
climate simulations derived from the SSP5-8.5 scenario.

However, the choice of highlighting the climate simulations and their risk
assessments made with the SSP5-8.5 scenario is questionable because a few
authors have investigated the SSP likelihood issue and also the IPCC AR6
briefly acknowledged that “ _the likelihood of high-emissions scenarios such
as RCP8.5 or SSP5-8.5 is considered low in light of recent developments in the
energy sector_ ” and that “ _studies that consider possible future emissions
trends in the absence of additional climate policies, such as the recent IEA
2020 World Energy Outlook ‘stated policy’ scenario (IEA, 2020), project
approximately constant fossil fuel and industrial CO_ _2_ _emissions out to
2070, approximately in line with the intermediate RCP4.5, RCP6.0 and SSP2-4.5
scenarios_ ” (Masson-Delmotte et al., 2021, pages 238-239). This is an
important statement since the IPCC and many works have considered the SSP5-8.5
extreme scenario to represent the ‘ _business as usual_ ’ case (Stocker et
al., 2013) despite it was meant to only be a very high-end, although still
plausible, no-policy scenario (van Vuuren et al., 2011).

In fact, Ritchie and Dowlatabadi (2017) affirmed that the “ _SSP5-RCP8.5
should not be a priority for future scientific research or a benchmark for
policy studies_ ” because it is very unlikely because such scenario is based
on the implausible assumption of a massive increase of the use of coal that is
supposed to substitute all the other forms of energy production. Hausfather
and Peters (2020) stated that the __ “ _the ‘business as usual’ story is
misleading_ ” and invited researchers “ _to stop using the worst-case scenario
for climate warming as the most likely outcome_ ” because only “ _more-
realistic baselines make for better policy_ ”. Hausfather and Peters (2020)
described SSP5-8.5 as _highly unlikely_ , SSP3-7.0 as _unlikely_ (as it
requires the reversal of some current policies) and SSP2-4.5 as _likely_
(because it is the most consistent with current policies).

The SSP5-8.5 and SSP3-7.0 scenarios were also recognized as unrealistic by
Burgess et al. (2020) and Pielke and Ritchie (2021a, b), who argued that the
IPCC might have misused these scenarios for more than a decade. Pielke et al.
(2022) and Burgess et al. (2023) also found that another and even more
moderate SSP scenario — the SSP2-3.4, which was ignored by the IPCC AR6 —
optimally agrees with the reported emissions from 2005 to 2020 and with the
most recent projections of the IEA (2020, 2021) up to 2050. This would imply
that the world could be on pace for even more moderate global warming by 2100
than what SSP2-4.5 implies.

For example, SSP3-7.0 and SSP5-8.5 demand that CO2 atmospheric concentrations
climb from around 420 ppm to almost 867 ppm and 1135 ppm, respectively, from
2020 to 2100, which looks to be excessively high; yet, SSP2-4.5 requires a
more plausible 602 ppm level by 2100 (Meinshausen et al., 2020). See also
Meinshausen et al. (2023) who reviewed the next generation of Earth system
model likely scenarios.

As a result, only the SSP2-4.5 baseline or other even more moderate scenarios
(Pielke et al., 2022) could be plausible. Thus, policies aimed to address
realistic impacts and risks of climate changes should be based only on such
moderate scenarios.

###  2.2 The GCM climate sensitivity uncertainty

The latest computer climate simulations were generated using the Global
Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6
(CMIP6), which is overseen by the World Climate Research Program (WCRP) of the
Working Group on Coupled Modelling (WGCM) (Eyring et al., 2016). More than 50
CMIP6 models were adopted to project future climates. The models used
historically estimated forcing functions from 1850 to 2014, which were
extended from 2015 to 2100 using the estimated forcing functions deduced from
the chosen SSP scenarios. The initial conditions and other internal model
parameters could be randomly varied within their estimated error ranges to
produce ensembles of climate simulations. The impacts and risks of the
projected climate changes are typically evaluated for prospective reference
periods in the short (2021–2040), mid-term (2041–2060), and long-term
(2081–2100). The ensembles of such climate projections were then used for
policy planning on adaptation, mitigation, and resilience.

However, as Figure 1 shows, the CMIP6 GCM simulations produce diverging
climate chronologies for the 21st century despite using equivalent forcing
functions. This significantly raises the level of uncertainty in calculating
the impacts and risks of future climate change; such uncertainty needs to be
restricted given the significant societal costs associated with climate
policies.

A core issue is that a significant fraction of GCMs appear to be functioning
too “ _hot_ ” (Hausfather et al., 2022), implying that climate change policy
should discount climate-change projections from the too-hot models. This
problem was also acknowledged in the IPCC AR6 reports where the projections
from the evidently too-hot GCMs were not used for impact and risk assessments
(Pörtner et al., 2022). In fact, the CMIP6 GCMs suggest that doubling
atmospheric CO2 concentrations from pre-industrial levels (from 280 to 560
ppm) would cause, at equilibrium, a global surface warming ranging from 1.8 to
5.7 °C (Masson-Delmotte et al., 2021). ECS values larger than 5 °C, on the
other hand, already exceeded those of the previous versions of global climate
models (CMIP3 and CMIP5 GCMs) used in the IPCC reports in 2007 and 2013.

Doubling the CO2 content in the atmosphere results in an increase in radiative
forcing of around 3.7 W/m2, which should cause a global warming of roughly 1
°C according to the Stefan–Boltzmann law (Rahmstorf, 2008). However, the
actual warming depends also on the feedback response of the climate system to
the applied forcing (Roe, 2009), but the magnitude of such a response is still
highly uncertain (Knutti et al., 2017).

There are different definitions of climate sensitivity. The equilibrium
climate sensitivity (ECS) is the long-term temperature rise (equilibrium
global mean near-surface air temperature) that is expected to result from a
doubling of the atmospheric CO2 concentration (Masson-Delmotte et al., 2021).
Once the CO2 concentration has stopped rising and the majority of the
feedbacks have had time to fully take effect, the model predicts a new global
mean near-surface air temperature level. However, after CO2 has doubled, it
can take centuries or even millennia to reach a new equilibrium temperature.
Another commonly used definition is the transient climate response (TCR),
which is defined as the change in the global mean surface temperature,
averaged over a 20-year period, centered at the time of atmospheric CO2
doubling, in a climate model simulation in which the atmospheric CO2
concentration increases at 1% per year until it doubles from 280 to 560 ppm
(Masson-Delmotte et al., 2021). This means to force the computer climate model
with a linearly increasing forcing from 0 to 3.7 $W/m^{2}$ for 70 years. As a
result, TCR can be assessed using simulations that last a shorter time than
those needed to assess ECS. In addition, TCR is smaller than ECS also because
slower feedbacks, which might further enhance a temperature increase, require
more time to fully respond to an increase in radiative forcing. For instance,
after a radiative perturbation, the deep ocean could take several centuries to
achieve a new steady state while continuing to cool the top ocean as a
heatsink. The CMIP6 GCMs calculate TCR values ranging between 1.2 and 2.8 °C.
In any case, ECS and TCR values of the CMIP6 GCMs are closely correlated
(Scafetta, 2023b).

Estimating the most realistic ECS and TCR ranges, on the other hand, has been
a highly controversial topic for more than 140 years because their values
strongly dependent on the physical assumptions that one makes, particularly on
how the water vapor and cloud feedbacks are treated. For example, Arrenius
(1896) estimated that doubling CO2 could result in a 5-6 °C increase in global
surface temperature; however, 10 years later, the same author concluded that
his previous estimate was incorrect and proposed lower ECS values ranging from
1.6 to 3.9 °C (Arrenius, 1906). Möller (1963) found that the ECS estimate
could vary greatly, up to one order of magnitude, according to how water vapor
and/or cloudiness responded to the CO2 perturbation, and concluded that such
an uncertainty implied that “ _the theory that climatic variations are
affected by variations in the CO_ _2_ _content becomes very questionable_ ”.
Manabe and Wetherald (1967) developed a one-dimensional climate model and
estimated that the ECS could be about 2.0 °C (which was related also to the
2.0 °C target for cost-benefit analyses; Gao et al., 2017). Later, however,
with a different model, Manabe and Wetherald (1975) estimated that the likely
ECS could be 2.93 °C.

In 1979, the US National Research Council concluded that ECS could _likely_
(or 66%) lie from 1.5 and 4.5 °C (Charney et al., 1979). In its two earlier
Assessment Reports — FAR (1990) and SAR (1996) — the IPCC concurred and
qualitatively estimated that the real ECS value had likely to lie between 1.5
and 4.5 °C. In its third Assessment Report, the IPCC changed a little bit the
likely range to lie between 1.75 and 4.25 °C. In 2007, the IPCC Fourth
Assessment Report claimed to have formally quantified the uncertainties and
that the likely ECS range was to lie between 2 and 4.5 °C. In 2013, however,
the IPCC Fifth Assessment Report claimed again that the ECS likely range had
to be between 1.5 and 4.5 °C. By accepting the analysis by Sherwood et al.
(2020) on the assessment of the Earth’s climate sensitivity to radiative
forcing using multiple lines of evidence, the latest Assessment Report of the
IPCC (AR6, Masson-Delmotte et al., 2021) concluded that the likely ECS (66%
confidence) should lie between 2.6 and 3.9 °C, and very likely (90%
confidence) between 2.3 and 4.7 °C. The likely TCR could range between 1.4 and
2.2 °C. Thus, the IPCC AR6 concluded that the ECS _likely_ range had to lie
between 2.5 and 4.0 °C, and _very-likely_ between 2.0 and 5.0 °C. This
suggested that to assess climate-related impacts and risks for the 21st
century and, as a result, to develop public policies aimed at addressing
and/or mitigating them, the CMIP6 GCMs that predicted ECS or TCR values
outside of such ranges do not have to be used. Rugenstein et al. (2023)
provided an overview of the “best” ECS estimations proposed since 1979 to the
present, all of which offer extremely broad uncertainty ranges.

However, as additional studies were concluded, in time the task of determining
the actual ECS has grown more and more compelling. In fact, Knutti et al.
(2017) stressed that not only the scientific literature proposes ECS values
ranging from 0.5 to 6.0 °C or even within a wider range, but that “ _evidence
from climate modelling favours values of ECS in the upper part of the ’likely’
range, whereas many recent studies based on instrumentally recorded warming —
and some from palaeoclimate — favour values in the lower part of the range_ ”.
Rugenstein et al. (2023) confirmed the conundrum by stating: “ _Early in the
2010s, a substantial discrepancy was noted between estimates of climate
sensitivity derived from climate models and estimates based on the observed
warming record and radiative balance … Estimates based on observed warming
pointed to much lower values than those derived from models_ ”. Such
conclusions indicate that there is a substantial dichotomy between empirical
and GCM studies regarding the actual climate sensitivity to radiative forcing,
which is likely owing to the GCMs’ inability to adequately hindcast natural
climate variability and its geographic patterns. The dilemma is serious since,
according to the scientific method, conclusions based on empirical data should
be given preference, but the IPCC and climate policies rely on impact and risk
assessments derived from questionable GCM projections. For example, Mauritsen
et al. (2019) and Mauritsen and Roeckner (2020) demonstrated that by slightly
altering the free parameters that regulate the cloud feedback, the model ECS
could be reduced from 7 °C to roughly 3 °C, effectively halving the expected
global warming by 2100.

Several recent attempts have been made to better constrain the ECS likely
range. For example, Nijsse et al. (2020) derived that the likely ECS range
should be 1.9-3.4 °C and Lewis (2023) directly challenged the assessments
proposed by Sherwood et al. (2020), which were adopted by the IPCC (Masson-
Delmotte et al., 2021). Lewis’ corrections and revisions of the methodologies
used by Sherwood et al. (2020) lead to a likely (17–83%) ECS range between
1.75 and 2.7 °C and a 5–95% range between 1.55 and 3.2 °C. Scafetta (2021c,
2022, 2023a, 2023b) tested how well the CMIP6 GCMs hindcast the global surface
warming observed from 1980–1990 to 2011–2021 in function of their ECS and TCR
values. Such a period was chosen because it is claimed to be characterized by
climate records with the smallest uncertainty and, moreover, both surface and
satellite records are available for comparison and alternative
interpretations. It was discovered that, as climate sensitivity to radiative
forcing diminishes, the performance of the CMIP6 GCMs improves. By aggregating
the CMIP6 GCMs into three sub-ensembles (herein labeled “macro-GCMs”)
according to their ECS value — low-ECS ($1.5<\mathrm{ECS}\leq 3.0$ °C),
medium-ECS ($3.0<\mathrm{ECS}\leq 4.5$ °C), and high-ECS
($4.5<\mathrm{ECS}\leq 6.0$ °C) — only the low-ECS macro-GCM was found to
produce a warming of $0.60\pm 0.12$ °C, which hindcasts sufficiently well the
0.5-0.6 °C warming observed in various global surface temperature records
(Hersbach et al., 2020; Ishihara, 2006; Lenssen et al., 2019; Morice et al.,
2012, 2021; Rohde and Hausfather, 2020; Zhang et al., 2019). On the contrary,
the medium and high-ECS macro-GCMs produced a warming of $0.79\pm 0.10$ °C and
$0.94\pm 0.23$ °C, respectively, which consistently showed a warm bias,
suggesting that these two macro-GCMs may not be reliable climate predictors
for climate-change policies for the 21st century. Also Spencer and Christy
(2023) found that the actual value of the ECS should be on the lower end of
the range produced by current GCMs and, therefore, confirmed Scafetta (2022).
It is worth noting that Schmidt et al. (2023)’s critique of Scafetta (2022)
was refuted because it was shown to be based on statistical and physical flaws
(Scafetta, 2023d).

Thus, current research suggests that only the low-ECS macro-GCM could be used
for assessing the impacts and risks of climate change for the 21st century
because the actual ECS should be lower than about 3 °C while the IPCC AR6
assumed an ECS likely range between 2.5 and 4.0 °C.

###  2.3 The surface and satellite 1980–2022 global warming divergence

Scafetta (2023a, b) also argued that even the low-ECS macro-GCM should be
considered _too hot_ if the 0.4 °C of warming reported by the UAH-MSU
satellite lower troposphere (lt) global temperature record (Spencer et al.,
2017) from 1980–1990 to 2011–2021 accurately represents actual surface warming
better than what global surface temperature records claim. In fact, McKitrick
and Christy (2020) and Mitchell et al. (2020) discovered that all CMIP6 GCMs
appear to be affected by a strong warm bias throughout the troposphere,
particularly above the tropics and after 2000, where the models hindcast a
hotspot at roughly 10 km height that is not observed in the data. The
difference in model and satellite tropospheric warming rates was acknowledged
by Santer et al. (2017) and is reported by the IPCC AR6 (Masson-Delmotte et
al., 2021, figure 3.10). The logical conclusion would be that the actual ECS
value could be at or below the bottom of the CMIP6 GCM range, that is below
about 2 °C, which would imply an even lower projected warming for the 21st
century.

In general, according to the atmospheric physics used in the models, the
troposphere should warm quicker than the surface since greenhouse gases are
expected to warm the troposphere first by activating water vapor and negative
lapse rate feedbacks. As a result, the warming of the lower troposphere should
be larger, not smaller, than the warming at the surface. Thus, if the surface
warming of the Earth were caused by greater greenhouse gas concentrations, as
the GCMs suggest, the warming rate in the lower troposphere may be viewed as
the upper limit of the possible warming rate at the surface.

The discrepancy between satellite-based and surface-based surface temperature
data could be due to non-climatic warm biases generated by contamination from
urban heat island (UHI) or other local non-climatic warming sources existing
on the surface. In fact, Scafetta and Ouyang (2019) and Scafetta (2021a)
demonstrated that non-climatic warming biases could affect extended land
regions because, in comparison to GCM simulations, nocturnal minimum
temperatures warmed too quickly relative to diurnal maximum temperatures, even
in already homogenized temperature records. Furthermore, comparisons of
surface temperature records between GCM simulations and lower troposphere UAH-
MSU satellite measurements revealed that the land region warmed far more
quickly than the ocean region. By contrast, the warming of the ocean area is
roughly replicated by the satellite microwave derived temperatures (Scafetta,
2021a, 2023a, 2023b). Scafetta (2021c) also investigated and compared
alternative surface temperature data (ERA5-T2m, HadCRUT4, GISTEMP 250km, NOAA
v5) utilizing regional distributions of warming patterns from 1980–1990 to
2011–2021; it was discovered that the land regions were frequently much warmer
than what the UAH-MSU v. 6.0 lt and the ERA5 t850 temperature records
revealed. There exists also a so-called “ _divergence problem_ ”, in which
proxy temperature records based on three-ring width chronologies show much
less warming than the temperature data from land stations of similar latitudes
since the 1970s (Büntgen et al., 2021; Spencer et al., 2017), which further
suggests a non-climatic warm bias affecting global surface temperature records
(Büntgen et al., 2021; Spencer et al., 2017). Indeed, Connolly et al. (2021)
and Soon et al. (2023) showed that a Northern Hemisphere land surface
temperature record derived entirely from confirmed rural-only stations
demonstrates up to 40% less warming relative to the preindustrial period
(1850–1900) than records derived from both urban and rural stations.
Additional studied on a significant UHI influence on the surface temperature
records are being conducted (Spencer, 2023).

Scafetta (2023b, figure 5F) also compared three consecutive versions of the
HadCRUT global surface temperature records — HadCRUT3 (discontinued after May
2014), HadCRUT4 (discontinued after December 2021), and non-infilled data and
infilled HadCRUT5 (the most recent version) — and discovered that the global
surface temperature warming trend since 2000 has been progressively increased
by consecutive adjustments of the data. The 2000–2014 temperature “ _pause_ ”
or “ _hiatus_ ”, which was clearly visible in HadCRUT3 and acknowledged by the
IPCC AR5 (Stocker et al., 2013), has now completely disappeared in the
infilled HadCRUT5 record, which adopts model predictions for filling the
regional and temporal gaps in the observations. This situation cannot be
explained simply as the result of optimized temperature data because the lower
troposphere satellite microwave derived temperature records (UAH-MSU v6) still
indicate a significant “pause” from 2000 to 2014. The same is true for
Connolly et al. (2021) and Soon et al. (2023)’s rural-only derived surface
temperature record: see Scafetta (2023b, figure 5B). It is possible that the
new adopted homogenization algorithms added some spurious warming to the land
records as a result of a mathematical artifact known as “ _urban blending_ ”
(Katata et al., 2023; O’Neill et al., 2022), and/or the models used to fill in
the temporal and areal data gaps introduced some spurious warming because of
the warming bias that the climate models present.

It should, however, be noticed that the satellite-based UAH-MSU lt temperature
record by Spencer et al. (2017) was deemed controversial because, after some
data adjustments made in 2014 in the remote sensing system (RSS), the RSS
alternative lower troposphere global temperature record by Mears and Wentz
(2016) showed a warming trend that appeared more compatible with those
presented by the surface-based temperature records; the NOAA-STAR v. 4.0
troposphere temperature dataset presented trends similar to RSS (Santer et
al., 2017) and, therefore, yielded a similar conclusion contradicting UAH-MSU.
However, Zou et al. (2023) recently revised the NOAA-STAR troposphere
temperature records by addressing certain errors in their previous version.
The new NOAA-STAR v. 5.0 troposphere records now closely reflect and,
therefore, confirms the lower warming trend of the UAH-MSU temperature
records. The discovery adds to the growing body of evidence that global
surface temperature data are warm-biased, as the CMIP6 GCMs predict that the
troposphere should warm faster than the surface.

###  2.4 Issues related to the natural climate variability not reproduced by
the GCMs

Climate records are characterized by large secular and millennial oscillations
throughout the Holocene (e.g.: Alley, 2000; Bond et al., 2001; Kutschera et
al., 2017; Neff et al., 2001), with the millennial ones responsible for well
documented warm periods such as the Roman (approximately 2000 years ago) and
Medieval (around 1000 years ago) ones (Ljungqvist, 2010; Luterbacher et al.,
2016; Christiansen and Ljungqvist, 2012; Lasher and Axford, 2019; Büntgen et
al., 2021). Moreover, spectral examinations of global (land and ocean) surface
temperature data show that the climate system is characterized by a
significant number of distinct natural oscillations (for example, with periods
of roughly 5.2, 5.9, 6.6, 7.4, 9.1, 10.5, 14, 20 e 60 years) (Scafetta, 2010,
2013, 2021b). The quasi 60-year oscillation appears to be rather important and
is found in a vast range of climatic datasets such as global sea level,
Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO)
records dating back for centuries and millennia (Knudsen et al., 2011;
Scafetta, 2014a; Wyatt and Curry, 2013). The Holocene also experienced a
Climate Optimum, particularly in the Northern Hemisphere, between roughly
9,500 and 5,500 years ago, with a thermal maximum around 8000 years ago, which
is not reproduced by models, indicating the need to improve our understanding
of natural climate forcings and feedbacks, as well as their representation in
GCMs (Kaufman and Broadman, 2023).

The quasi millennial and 60-year oscillations appear to have been responsible
for at least 50% of the 1900–2000 and 1970–2000 warming, respectively. The
latter was also responsible for the substantial warm period that occurred in
the 1940s and subsequent cooling observed globally from the 1940s to the 1970s
(Scafetta, 2010, 2013). It has been observed that a synchronous quasi-60-year
modulation appears in specific solar activity reconstructions over the last
150 years that closely match the quasi-60-year modulation observed in climate
data (Connolly et al., 2023; Scafetta, 2023c; Soon et al., 2023). In general,
all climatic oscillations from the inter-annual to the multi-millennial
timescales appear to be spectrally coherent with solar and/or astronomical
oscillations (Czymzik et al., 2016; Eddy, 1976; Kerr, 2001; Kirkby, 2007; Neff
et al., 2001; Scafetta, 2010, 2014b, 2020; Scafetta and Bianchini, 2023;
Schmutz, 2021; Steinhilber et al., 2012, and many others).

Scafetta (2012a, 2013, 2021b) demonstrated that the GCMs are unable to
reproduce these natural oscillations and, as a result, they incorrectly
attribute nearly 100% of the global surface warming observed from 1850–1900 to
the present and, in particular, that observed from the 1970s to the 2000s, to
anthropogenic greenhouse emissions. Indeed, it appears that the millennial
natural oscillation has been warming the climate system since 1700, that is
since the coldest era of the Little Ice Age, and may have been responsible for
a large percentage of the global surface warming of the twentieth century.

For example, Figure 3.2 of the IPCC AR6 (Masson-Delmotte et al., 2021, pp.
432) highlights the inability of the CMIP6 climate models to hindcast the
Medieval Warm Period, which normally lasted from roughly 800 to 1350. Thus,
these models are not able to reproduce the millennial oscillation. In fact,
the IPCC AR6 (pp. 433) explicitly acknowledges that “ _before the year 1300_ ”
there are “ _larger disagreements between models and temperature
reconstructions_ ” even by using the paleoclimate reconstruction proposed by
the PAGES2k Consortium (2017), which appears to attenuate the Medieval Warming
in comparison to other paleotemperature reconstructions (e.g.: Moberg et al.,
2005; Loehle and McCulloch, 2008; Mann et al., 2008; Ljungqvist, 2010;
Christiansen and Ljungqvist, 2012; Ge et al., 2017; Kutschera et al., 2017;
Luterbacher et al., 2016; Lüning et al., 2019) as those reported by the IPCC
AR5 (Stocker et al., 2013, figure 5.7a). The IPCC AR6 is ambiguous on the
physical cause of such disagreements because it only states (pp. 433) that
they could just _“be expected because forcing and temperature reconstructions
are increasingly uncertain further back in time (specific causes have not yet
been identified conclusively)_ ”. More specifically, AR6 did not acknowledge
that several empirical studies have already suggested that the cause could
have been a Medieval high solar activity (Bond et al., 2001; Eddy, 1976; Kerr,
2001; Kirkby, 2007; Steinhilber et al., 2012; Scafetta, 2013; Scafetta and
Bianchini, 2023), whose real effect on the climate could not be reproduced by
the CMIP6 models.

Several authors proposed empirical models for global climate change based on
the above results. Here I will review the main model that I proposed because
it passed several tests (Scafetta, 2010, 2013, 2021c, 2023c). The empirical
modeling of natural climate oscillations not reproduced by the GCMs suggests
that the climate system is significantly less sensitive to variations in
greenhouse gas concentrations while being significantly oversensitive to
variations in solar activity changes. I estimated that the actual ECS could be
between 1 and 2 °C. Equally low ECS estimates were also found by others
(Bates, 2016; Lindzen and Choi, 2011; McKitrick and Christy, 2020; Monckton et
al., 2015; Stefani, 2021).

The climate system could appear to be over-sensitivity to solar activity
changes because the Sun should impact climate change not only through total
solar irradiance (TSI) variations, as the GCMs assume, but also through
variations in its ultraviolet light and magnetic field. UV forcing directly
modifies stratospheric ozone, whereas solar magnetic activity modifies fluxes
of interplanetary charged particles – cosmic rays, solar wind, and
interplanetary dust – that could directly influence the electric properties of
the atmosphere and its cloud system (Scafetta et al., 2016, 2020; Scafetta,
2023c; Shaviv, 2002; Svensmark et al., 2016; Svensmark, 2022). In particular,
Scafetta (2023c) showed that direct TSI forcing could even explain only 20% of
the total solar effect on climate change. That TSI variations by alone cannot
explain how to Sun could modulate the atmosphere of a planet was recently
demonstrated by Chavez et al. (2023), who discovered that, despite Neptune
being 30 times farther from the Sun than Earth, its cloud activity exhibits
remarkable relationships with the 11-year solar activity cycles; the (still
unknown) physical mechanism could not be TSI variations since on Neptune the
latter are 900 times smaller than on Earth.

Furthermore, it should be noticed that the CMIP6 GCMs minimize the solar
effect on the climate also because they use a solar forcing derived from a
specific TSI reconstruction which presents a very little and nearly constant
secular variability (Matthes et al., 2017), although the scientific literature
also recommends other TSI reconstructions that show a much larger secular
variability (e.g. Hoyt and Schatten, 1993; Egorova et al., 2018; Penza et al.,
2022) and that also appear to be better correlated with uncontroversial TSI
satellite measurements (Scafetta et al., 2019; Connolly et al., 2021;
Scafetta, 2023c). Also the CMIP6 models used for the millennial simulation
were forced with a very low variability TSI reconstruction such as the PMIP4
SATIRE-M (Wu et al., 2018), which could be erroneous because several other TSI
reconstructions exist and show a larger secular and millennial variability
(cf. Scafetta et al., 2019; Scafetta, 2023c; Connolly et al., 2023). In
particular, Scafetta (2021b, figure 7) explicitly demonstrated that the GCMs
cannot reproduce the Medieval Warm Period (and likely any other warm periods
of the Holocene) because they assume only a nearly constant radiative solar
forcing and, therefore, there is no net radiative forcing capable of
reproducing a significant warming during the Medieval period because volcanic
forcing is sporadic and anthropogenic forcing is also absent. This (apparently
arbitrary) choice taken by the CMIP6 GCM modelers regarding solar forcing also
contributes to both the GCMs’ high ECS values (which are required to
reconstruct the 20th-century warming using anthropogenic forcing) and to their
failure in reconstructing the other warm periods of the Holocene.

See Connolly et al. (2021), Scafetta (2023c) and Soon et al. (2023) for
additional commentary, modeling and supporting bibliography regarding how to
empirically assess of the role of the Sun in climate change using various
multi-proxy solar records.

##  3 Addressing the “too hot” model problem

As discussed in Section 2, a number of evidence point to the conclusion that
at least the majority of CMIP6 GCMs are running “too hot”, even more than what
the IPCC (Masson-Delmotte et al., 2021) and Hausfather et al. (2022) have
already admitted. These findings suggest that the CMIP6 GCM climate
sensitivity to radiative forcings is significantly overestimated. The CMIP3
and CMIP5 GCMs were already running too hot (Scafetta, 2012a, 2013), but the
situation has gotten so critical with the CMIP6 GCMs that also the UN climate
panel has raised concerns (Voosen, 2019, 2021).

The most recent research on direct comparison of the CMIP6 GCM hindcasts with
global surface temperature records since 1980 indicates that, at most, only
the CMIP6 GCMs with low climate sensitivity (e.g. for ECS $\leq 3$ °C) could
adequately hindcast the observed warming since 1980 and could be used to
evaluate the risk associated with projected climate changes for the 21st
century (Scafetta, 2022, 2023a, 2023d). Other authors have reached the same
result (Lewis, 2023; Spencer and Christy, 2023). Furthermore, various
evidences suggest that actual global warming since 1980 may have been less
than what is generally reported by the global surface temperature records
(Scafetta, 2023b). If this is the case, even the low-ECS CMIP6 macro-GCM would
be functioning too hot, rendering all CMIP6 GCMs untrustworthy for guiding
public policy targeted at addressing the impacts and risks of projected
climate changes for the 21st century. Finally, alternative climate modeling
based on actual evidence of underestimated natural cycles and solar influences
also suggests low ECS values to radiative forcing. In fact, according to these
evidences, the true ECS may likely be in the range of 1 to 2 °C (e.g.:
Scafetta, 2013, 2021b, 2023c; Scafetta and Bianchini, 2023).

In the following, I will assess the impacts and risks of climate change
suggested by the IPCC (Pörtner et al., 2022) using the SSP2.4.5 scenario and
the 21st-century warming projected by the low-ECS CMIP6 macro-GCM. I also
provide alternative calculations that account for the possibility that even
the low sensitivity models are running too hot because, based on satellite low
troposphere temperature records, the actual 1980–2022 warming may be less than
what claimed in Masson-Delmotte et al. (2021). Finally, alternative risk
assessments are provided by employing the empirical global surface temperature
models for the 21st century proposed by Scafetta (2013, 2021b).

##  4 Re-assessment: selection of reliable GCMs

Climate simulations from 42 CMIP6 GCMs are analyzed here. The computer
simulations were created using historical forcings (1850–2014) further
extended up to 2100 with hypothetical forcing functions deduced from 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). These four scenarios are nearly indistinguishable
until 2022. Thus, from 1850 to 2022, the four simulation ensembles can be
considered independent assessments of the same models under nearly identical
forcing conditions. This variability also helps to assess in first
approximation the spreading related with the chaotic internal variability of
the models: see also Scafetta (2023a) where the GCM internal variability issue
is extensively discussed.

A total of 156 simulations were analyzed. 142 simulations were “average”
records provided by the Koninklijk Nederlands Meteorologisch Instituut (KNMI)
Climate Explorer (van Oldenborgh, 2020). Other 14 simulations were missing in
Climate Explorer and were taken from the supplementary of Hausfather et al.
(2022). The latter include those produced by four GCMs: CMCC-ESM2, GFDL-CM4,
IITM-ESM and TaiESM1. The ECS and TCR values of the GCMs were taken from Table
7.SM.5 of the IPCC AR6 (Masson-Delmotte et al., 2021). Some missing values
were taken from the supplementary of Hausfather et al. (2022). The ECS of FIO-
ESM-2-0 was hypothesized to be 4.27 °C because its TCR falls between those of
CNRM-CM6-1 and EC-Earth3.

Macro-GCM | Model Name | ECS (°C) | Macro-GCM | Model Name | TCR (°C)
---|---|---|---|---|---
High-ECS | CIESM | 5.63 | High-TCR | UKESM1-0-LL | 2.79
CanESM5 p1 | 5.62 | CanESM5 p1 | 2.74
CanESM5 p2 | 5.62 | CanESM5 p2 | 2.74
CanESM5-CanOE p2 | 5.62 | CanESM5-CanOE p2 | 2.74
HadGEM3-GC31-LL-f3 | 5.55 | NESM3 | 2.72
HadGEM3-GC31-MM f3 | 5.42 | EC-Earth3-Veg | 2.62
UKESM1-0-LL | 5.34 | HadGEM3-GC31-MM f3 | 2.58
CESM2 | 5.16 | HadGEM3-GC31-LL f3 | 2.55
CNRM-CM6-1 f2 | 4.83 | CNRM-CM6-1-HR f2 | 2.48
CNRM-ESM2-1 f2 | 4.76 | CIESM | 2.39
CESM2-WACCM | 4.75 | TaiESM1 | 2.34
KACE-1-0-G | 4.75 | IPSL-CM6A-LR | 2.32
ACCESS-CM2 | 4.72 | Medium-TCR | EC-Earth3 | 2.30
NESM3 | 4.72 | FIO-ESM-2-0 | 2.22
IPSL-CM6A-LR | 4.56 | CNRM-CM6-1 f2 | 2.14
Medium-ECS | EC-Earth3-Veg | 4.31 | ACCESS-CM2 | 2.10
TaiESM1 | 4.31 | CMCC-CM2-SR5 | 2.09
CNRM-CM6-1-HR f2 | 4.28 | AWI-CM-1-1-MR | 2.06
FIO-ESM-2-0 | 4.27 | CESM2 | 2.06
EC-Earth3 | 4.26 | KACE-1-0-G | 2.04
GFDL-CM4 | 3.89 | GFDL-CM4 | 2.00
ACCESS-ESM1-5 | 3.87 | CESM2-WACCM | 1.98
MCM-UA-1-0 | 3.65 | ACCESS-ESM1-5 | 1.95
CMCC-ESM2 | 3.58 | FGOALS-f3-L | 1.94
CMCC-CM2-SR5 | 3.52 | MCM-UA-1-0 | 1.94
AWI-CM-1-1-MR | 3.16 | CMCC-ESM2 | 1.92
MRI-ESM2-0 | 3.15 | CNRM-ESM2-1-f2 | 1.86
BCC-CSM2-MR | 3.04 | MPI-ESM1-2-LR | 1.84
Low-ECS | FGOALS-f3-L | 3.00 | Low-TCR | GISS-E2-1-G p3 | 1.80
MPI-ESM1-2-LR | 3.00 | CAMS-CSM1-0 | 1.73
MPI-ESM1-2-HR | 2.98 | BCC-CSM2-MR | 1.72
FGOALS-g3 | 2.88 | IITM-ESM | 1.71
GISS-E2-1-G p3 | 2.72 | MPI-ESM1-2-HR | 1.66
MIROC-ES2L | 2.68 | MRI-ESM2-0 | 1.64
GFDL-ESM4 | 2.65 | GFDL-ESM4 | 1.63
MIROC6 | 2.61 | MIROC-ES2L | 1.55
NorESM2-LM | 2.54 | MIROC6 | 1.55
NorESM2-MM | 2.50 | FGOALS-g3 | 1.54
IITM-ESM | 2.37 | NorESM2-LM | 1.48
CAMS-CSM1-0 | 2.29 | INM-CM5-0 | 1.41
INM-CM5-0 | 1.92 | INM-CM4-8 | 1.33
INM-CM4-8 | 1.83 | NorESM2-MM | 1.33
Table 1: CMIP6 GCMs analyzed in the present study. They are ranked according
to their ECS (left) and TCR (right) values. (The ECS of FIO-ESM-2-0 is
hypothesized).

Table 1 lists the adopted GCMs with their estimated ECS and TCR values, which
are approximately correlated to each other (Scafetta, 2023b). The GCMs are
also grouped in three macro-GCMs according to their ECS value: low-ECS
($1.5<\mathrm{ECS}\leq 3.0\;{{}^{\circ}}\mathrm{C}$); medium-ECS
($3.0<\mathrm{ECS}\leq 4.5\;{{}^{\circ}}\mathrm{C}$), and high-ECS
($4.5<\mathrm{ECS}\leq 6.0\;{{}^{\circ}}\mathrm{C}$), as already proposed by
Scafetta (2023b, a, 2022).

Figure 1 shows the adopted 156 CMIP6 GCM simulations which are baselined in
1850–1900. The curve’s color scales with the ECS value of the models from blue
(low sensitivity) to red (high sensitivity). The figure shows that, as the ECS
increases, the warming projected by the models during the 21st century tends
to increase as well. Similar results can be obtained by coloring the curves in
function of the TCR value of their GCM.

Figure 2: CMIP6 GCM simulations adopted in the present study divided into
three macro-GCMs according to their ECS value. The curve’s color scales with
the ECS of the models. The synthetic records are compared against the HadCRUT
v. 5.0 (infilled data) global surface temperature and the satellite-based UAH-
MSU lt v. 6.0 (Spencer et al., 2017).

Figure 2 shows the adopted 156 CMIP6 GCM simulations, which are now baselined
in 1980–1990 to better assess their performance since 1980, when satellite
temperature records are also available. The data from the three macro-GCMs are
displayed on the three panels. The synthetic records are contrasted to the
HadCRUT v. 5.0 global surface temperature (infilled data, which present the
most warming of any other HadCRUT records) and the satellite-based UAH-MSU lt
v. 6.0 (Spencer et al., 2017), which grows warmer significantly less.

The warming from 1980–1990 to 2011–2022 shown by the (infilled) HadCRUT5
record is nearly identical to that from other global surface temperature
records: ERA5-T2m (Hersbach et al., 2020), GISTEMP (Lenssen et al., 2019), and
Berkeley Earth Land/Ocean temperature (Rohde and Hausfather, 2020). Instead,
the warming presented by the UAH-MSU lower troposphere temperature record is
nearly identical to that shown by the recent NOAA-STAR v. 5.0 one (data from
Zou et al., 2023).

Figure 2 reveals that, since 1980, simulations from the medium and high-ECS
macro-GCMs appear to be too hot. However, also the low-ECS macro-GCM appears
to produce too hot simulations in comparison with the satellite UAH-MSU lt
record. These results are confirmed also by considering the 688 GCM individual
ensemble simulations available on Climate Explorer (see Scafetta, 2023a,
figures 1 and 2).

Figure 3: Temperature changes from 1980–1990 to 2012–2022 produced the adopted
42 GCMs against the warming of HadCRUT v. 5.0 ($0.605\pm 0.02$ °C) and UAH-MSU
lt v. 6.0 ($0.432\pm 0.03$ °C): see Table 2. Top: ECS ranking; Bottom: TCR
ranking.

Figure 3 confirms the above expectations by using the same approach proposed
by Scafetta (2022, 2023a, 2023b). It shows the temperature changes from
1980–1990 to 2012–2022 produced by the adopted 42 GCMs against the
correspondent warming shown by HadCRUT v. 5.0 ($0.605\pm 0.02$ °C, 95%
confidence) and UAH-MSU lt v. 6.0 ($0.432\pm 0.03$ °C, 95% confidence). The
top panel shows the results against the ECS ranking; the bottom panel shows
the same against the TCR ranking. The figure demonstrates that, as ECS or TCR
increase, the warming hindcasted by the GCMs increases as well. The GCM
results are listed in Table 2, which also reports the [2.5%, 17%, 50%, 83%,
97.5%] percentiles for each macro-GCM.

|  | Hist + |  |
---|---|---|---|---
Model | ECS (°C) | SSP1-2.6 | SSP2-4.5 | SSP3-7.0 | SSP5-8.5 | percentile | °C
CIESM | 5.63 | 0.71 | 0.81 | NaN | 0.76 | 97.5% | 1.31
CanESM5-p1 | 5.62 | 1.25 | 1.25 | 1.26 | 1.28 | 83.0% | 1.24
CanESM5-p2 | 5.62 | 1.26 | 1.28 | 1.28 | 1.29 | 50.0% | 0.97
CanESM5-CanOE-p2 | 5.62 | 1.19 | 1.21 | 1.22 | 1.21 | 17.0% | 0.76
HadGEM3-GC31-LL-f3 | 5.55 | 1.32 | 1.32 | NaN | 1.14 | 2.5% | 0.66
HadGEM3-GC31-MM-f3 | 5.42 | 0.86 | NaN | NaN | 0.91 |  |
UKESM1-0-LL-f2 | 5.34 | 1.19 | 1.17 | 1.14 | 1.17 |  |
CESM2 | 5.16 | 0.82 | 0.77 | 0.78 | 0.84 |  |
CNRM-CM6-1-f2 | 4.83 | 0.71 | 0.69 | 0.68 | 0.72 |  |
CNRM-ESM2-1-f2 | 4.76 | 0.68 | 0.65 | 0.65 | 0.69 |  |
CESM2-WACCM | 4.75 | 1.02 | 0.95 | 0.89 | 0.97 |  |
KACE-1-0-G | 4.75 | 1.02 | 1.03 | 1.00 | 1.03 |  |
ACCESS-CM2 | 4.72 | 0.88 | 0.87 | 0.92 | 0.92 |  |
NESM3 | 4.72 | 1.03 | 1.00 | NaN | 1.08 |  |
IPSL-CM6A-LR | 4.56 | 0.80 | 0.80 | 0.80 | 0.81 |  |
EC-Earth3-Veg | 4.31 | 0.89 | 0.90 | 0.87 | 0.91 | 97.5% | 1.07
TaiESM1 | 4.31 | NaN | 1.03 | 1.08 | 1.11 | 83.0% | 0.89
CNRM-CM6-1-HR-f2 | 4.28 | 0.81 | 0.75 | 0.76 | 0.77 | 50.0% | 0.80
FIO-ESM-2-0 | 4.27 | 0.79 | 0.80 | NaN | 0.81 | 17.0% | 0.68
EC-Earth3 | 4.26 | 0.88 | 0.79 | 0.92 | 0.91 | 2.5% | 0.46
GFDL-CM4 | 3.89 | NaN | 0.80 | NaN | 0.85 |  |
ACCESS-ESM1-5 | 3.87 | 0.86 | 0.87 | 0.88 | 0.85 |  |
MCM-UA-1-0 | 3.65 | 0.90 | 0.71 | 0.68 | 0.78 |  |
CMCC-ESM2 | 3.58 | NaN | 0.44 | 0.47 | 0.46 |  |
CMCC-CM2-SR5 | 3.52 | 0.74 | 0.63 | 0.71 | 0.71 |  |
AWI-CM-1-1-MR | 3.16 | 0.84 | 0.85 | 0.80 | 0.83 |  |
MRI-ESM2-0 | 3.15 | 0.79 | 0.78 | 0.67 | 0.86 |  |
BCC-CSM2-MR | 3.04 | 0.68 | 0.68 | 0.68 | 0.71 |  |
FGOALS-f3-L | 3.00 | 0.78 | 0.72 | 0.72 | 0.72 | 97.5% | 0.79
MPI-ESM1-2-LR | 3.00 | 0.60 | 0.59 | 0.59 | 0.57 | 83.0% | 0.73
MPI-ESM1-2-HR | 2.98 | 0.56 | 0.59 | 0.63 | 0.60 | 50.0% | 0.62
FGOALS-g3 | 2.88 | 0.79 | 0.64 | 0.62 | 0.63 | 17.0% | 0.53
GISS-E2-1-G-p3 | 2.72 | 0.49 | 0.61 | 0.75 | 0.46 | 2.5% | 0.44
MIROC-ES2L-f2 | 2.68 | 0.66 | 0.63 | 0.60 | 0.58 |  |
GFDL-ESM4 | 2.65 | 0.72 | 0.76 | 0.72 | 0.71 |  |
MIROC6 | 2.61 | 0.53 | 0.50 | 0.50 | 0.55 |  |
NorESM2-LM | 2.54 | 0.73 | 0.66 | 0.78 | 0.73 |  |
NorESM2-MM | 2.50 | 0.79 | 0.82 | 0.70 | 0.76 |  |
IITM-ESM | 2.37 | NaN | 0.51 | NaN | 0.53 |  |
CAMS-CSM1-0 | 2.29 | 0.44 | 0.45 | 0.41 | 0.44 |  |
INM-CM5-0 | 1.92 | 0.66 | 0.63 | 0.69 | 0.65 |  |
INM-CM4-8 | 1.83 | 0.58 | 0.56 | 0.56 | 0.62 |  |
Table 2: Temperature changes from 1980–1990 to 2012–2022 produced the adopted
42 GCMs, with percentiles for each macro-GCM. NaN indicates missing
simulations. (The ECS of FIO-ESM-2-0 is hypothesized).

Only the low-ECS macro-GCM ($1.5<ECS\lessapprox 3.0$ °C and
$1.3<TCR\lessapprox 1.8$ °C) appears to generate a distribution of hindcasts
that best incorporates the observed warming. These ECS and TCR ranges
significantly restrict those of the CMIP6 GCMs (1.5-6.0 °C and 1.3-2.8 °C,
respectively) and are compatible with those estimated by Lewis (2023).
However, if the actual global warming from 1980 to 2022 is compatible with
that shown by the lower troposphere estimates of UAH-MSU v. 6.0, Figure 3
indicates that even the low-ECS macro-GCM would be running too hot (cf.
McKitrick and Christy, 2020). Each model is represented by four separate
simulations (where available), which provide a first order estimate of the
dispersion due to the models’ internal variability.

Figure 3 also shows that a few GCMs with medium to high ECS and TCR values may
be approaching the observed warming. However, because climate projections are
created by multi-model ensembles rather than individual models or simulations,
a very tiny number of favorable instances appearing in the distribution’s
tails should be statistically classed as outliers. For example, CNRM-ESM2-1
appears to approach the HadCRUT5 warming within the allowed range of $\pm 0.1$
°C (cf. Scafetta, 2023a) only because the model’s extraordinarily high ECS
(4.76 °C) is compensated by its comparatively low TCR (1.86 °C), which slows
down the global surface temperature rise. However, relying on just one GCM to
judge whether such a scenario is conceivable would be insufficient; additional
GCMs must be created and statistically proven to be consistent with both
extremely high ECS values and relatively low TCR values.

Figure 4: Boxplots indicating the temperature changes from 1850–1900 to
2040–2060 (left) and 2080–2100 (right) produced the adopted 42 GCMs divided
for the Hist + SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 emission scenarios.
(a) All CMIP6 GCMs; (b) CMIP6 GCMs with ECS ranging between 2.5 and 4.0 °C
(the likely IPCC AR6 range); (c) High-ECS CMIP6 GCMs ($4.5<ECS\leq 6.0$ °C);
(d) Medium-ECS CMIP6 GCMs ($3.0<ECS\leq 4.5$ °C); (e) Low-ECS CMIP6 GCMs
($1.5<ECS\leq 3.0$ °C).

Figure 4 shows boxplots indicating the temperature changes from 1850–1900 to
2040–2060 (left) and 2080–2100 (right) produced the adopted 42 GCMs for the
Hist + SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 emission scenarios. The
boxplots indicate the distribution of the GCM forecasts based on a five-number
summary (“minimum”, first quartile [Q1 = 25%], median [Q2 = 50%], third
quartile [Q3 = 75%], “maximum”) plus outliers, which are displayed as red
crosses. Each line of the figure represents a different selection of GCMs: (a)
is based on all CMIP6 GCMs; (b) uses only the GCMs with ECS ranging between
2.5 and 4.0 °C, which corresponds to the IPCC AR6 likely range; (c) uses only
the high-ECS GCMs ($4.5<ECS\leq 6.0$ °C); (d) uses only the medium-ECS CMIP6
GCMs ($3.0<ECS\leq 4.5$ °C); (e) uses only the low-ECS CMIP6 GCMs
($1.5<ECS\leq 3.0$ °C). Table 3 summaries the quartile statistics of the
temperature changes from 1850–1900 to 2040–2060 and 2080–2100 produced by the
adopted 42 GCMs, similar to that produced by the boxplots depicted in Figure 4
but with a different percentage descriptions [2.5%, 17%, 50%, 83%, 97.5%],
which highlights the median, the (likely) 66% and the (very likely) 95%
confidence ranges.

|  |  Hist+SSP1-2.6 (°C) |  Hist+SSP2-4.5 (°C) |  Hist+SSP3-7.0 (°C) |  Hist+SSP5-8.5 (°C)
---|---|---|---|---|---
| percentile | 2040-2060 | 2080-2100 | 2040-2060 | 2080-2100 | 2040-2060 | 2080-2100 | 2040-2060 | 2080-2100
Fig. 4A | 97.5% | 2.81 | 2.90 | 3.19 | 4.25 | 3.57 | 6.03 | 3.79 | 7.06
83% | 2.40 | 2.76 | 2.62 | 3.72 | 2.71 | 4.79 | 3.03 | 5.98
50% | 1.95 | 2.04 | 2.20 | 3.11 | 2.28 | 4.09 | 2.61 | 4.99
17% | 1.50 | 1.46 | 1.70 | 2.26 | 1.89 | 3.22 | 2.10 | 3.75
2.5% | 1.22 | 1.29 | 1.39 | 1.97 | 1.52 | 2.76 | 1.71 | 3.37
Fig. 4B | 97.5% | 2.49 | 2.82 | 2.61 | 3.82 | 2.68 | 4.71 | 2.98 | 5.76
83% | 2.13 | 2.32 | 2.45 | 3.53 | 2.52 | 4.48 | 2.83 | 5.43
50% | 1.81 | 1.84 | 2.04 | 2.69 | 2.14 | 3.64 | 2.47 | 4.63
17% | 1.43 | 1.43 | 1.62 | 2.24 | 1.77 | 3.20 | 1.98 | 3.76
2.5% | 1.20 | 1.26 | 1.37 | 1.98 | 1.49 | 2.87 | 1.77 | 3.63
Fig. 4C | 97.5% | 2.81 | 2.91 | 3.20 | 4.27 | 3.58 | 6.04 | 3.79 | 7.07
83% | 2.76 | 2.89 | 3.16 | 4.22 | 3.54 | 6.00 | 3.72 | 6.99
50% | 2.25 | 2.47 | 2.58 | 3.62 | 2.74 | 4.85 | 3.01 | 5.97
17% | 1.95 | 2.20 | 2.13 | 3.21 | 2.25 | 4.20 | 2.64 | 5.45
2.5% | 1.72 | 1.87 | 1.98 | 2.88 | 2.05 | 4.14 | 2.32 | 4.98
Fig. 4D | 97.5% | 2.49 | 2.82 | 2.62 | 3.83 | 2.68 | 4.72 | 2.99 | 5.77
83% | 2.36 | 2.68 | 2.47 | 3.64 | 2.53 | 4.56 | 2.91 | 5.51
50% | 2.06 | 2.11 | 2.34 | 3.29 | 2.45 | 4.11 | 2.73 | 5.19
17% | 1.80 | 1.80 | 1.99 | 2.62 | 2.15 | 3.74 | 2.45 | 4.54
2.5% | 1.64 | 1.56 | 1.59 | 2.25 | 2.13 | 3.53 | 2.33 | 3.95
Fig. 4E | 97.5% | 1.95 | 1.98 | 2.25 | 2.83 | 2.44 | 3.89 | 2.58 | 4.63
83% | 1.68 | 1.69 | 1.84 | 2.46 | 2.11 | 3.38 | 2.19 | 4.00
50% | 1.50 | 1.46 | 1.77 | 2.28 | 1.92 | 3.23 | 2.10 | 3.74
17% | 1.28 | 1.33 | 1.55 | 2.11 | 1.61 | 2.94 | 1.85 | 3.59
2.5% | 1.20 | 1.26 | 1.36 | 1.96 | 1.48 | 2.70 | 1.64 | 3.12
Table 3: Percentile statistics similar to the boxplot statistics depicted in
Figure 4.

For the SSP2-4.5 case, the 2.5-4.0 ECS GCMs project a likely (or 66%)
2080–2100 warming ranging between 2.24 and 3.53 °C with median 2.69 °C, which
well corresponds to the 2.2-3.4 °C (with median 2.7 °C) range estimated by the
Climate Action Tracker (2022) as the likely warming that will occur by the end
of the 21st century on the basis of real-world current policies.

Figure 4 shows that the reported ranges vary greatly according to the adopted
SSP scenario, and the selection of models used for the simulations. In
general, the ensemble of all simulations (apart from those referring to the
SSP1-2.6 scenario) appears to easily exceed the 2.0 °C (safe) threshold
suggested by Gao et al. (2017) by as early as 2050. This would be true whether
one chooses to use all CMIP6 GCM simulations or the selection of them that
corresponds to the IPCC likely ECS range (between 2.5 and 4.0 °C). Only the
SSP1-2.6 scenario would ensure an average warming of roughly 1.8 °C [1.26-2.82
°C] by 2080–2100 still using the GCMs corresponding to the IPCC likely ECS-
range and could satisfy the Paris Agreement warming target. The SSP1-2.6
scenario is rather aggressive since it requires us to take the “green road” of
achieving a net-zero global CO2 emission condition as soon as 2050, which
makes such policy extremely expensive and potentially harmful for society.

However, Figure 4 and Table 3 suggest that in the eventuality that only the
simulations from the low-ECS macro-CGM are considered, also the moderate
SSP2-4.5 scenario could be rather feasible for preventing a climatic crisis
because by 2050 the average warming will still be below 2.0 °C (roughly 1.77
°C [1.36-2.25 °C]) and by 2080–2100 it will rise to just 2.28 °C [1.96-2.83
°C], instead of 2.69 °C [1.98-3.82 °C] under the IPCC’s assumptions. SSP2-4.5
is a “middle of the road” scenario where CO2 emissions stay about the same as
today until the middle of the century, when they begin to progressively fall
but, even by 2100, they do not reach net-zero. Thus, this SSP scenario
requires no discernible changes in the historical trends of socioeconomic
elements for the next 2-3 decades. Development and income growth are uneven,
and sustainability progress is gradual. Adaptation policies are assumed to be
adequate to mitigate major climate-related hazards.

The climate change projection ensembles used here primarily come from KNMI
Climate Explorer’s GCM average simulations for each SSP. A critique could be
formulated that the entire ensemble of individual simulations, rather than
just the ensemble average simulation or a single member simulation for each
model and SSP, should be employed, resulting in larger projection ensembles.
The topic is controversial and, in any case, impossible to assess because the
number of individual runs for each model varies significantly among models.
Scafetta (2023a) for example, emphasized that, because physical models must be
both accurate and precise, the main prediction of the models is their average
simulation, whereas the error-range from such average should be considered a
user requirement rather than a GCM property due to its actual internal
variability. The user’s acceptable error-range for each model can be bounded
to the temperature variability within the decadal-bidecadal scales because the
models are supposed to reproduce the longer scales, and this variability was
estimated to be of the order of about $\pm 0.1$ °C (Scafetta, 2023a,
Appendix). Such error can be statistically ignored because it is significantly
smaller than the difference observed among the various GCM average
simulations. In fact, also Hausfather et al. (2022) evaluated the projection
ensembles using one simulation for each model and SSP, which is what I did
here.

##  5 Re-assessment: adoption of optimized empirical climate models

If the actual global warming from 1980 to 2022 is compatible with that
provided by UAH-MSU (or NOAA-STAR v. 5.0) lower troposphere temperature data
(Spencer et al., 2017; Zou et al., 2023), the actual impacts and risks
connected with predicted twenty-first-century climate changes are much
smaller, and the CMIP6 GCM simulations should not be used to make policy. In
such instance, all CMIP6 GCMs would be inadequate for accurately estimating
climate changes in the 21st century because the actual ECS would be roughly
one-third lower than even the low-ECS macro-GCM, that is between 1 and 2 °C
(cf. McKitrick and Christy, 2020; Scafetta, 2013, 2023c; Stefani, 2021), which
would rule out nearly all CMIP6 GCMs.

There are not enough models that span the ECS range from 1 to 2 °C. However,
the case could be approximated by empirically scaling the CMIP6 macro-GCM
simulations to best reflect the warming documented by satellite data from 1980
to 2022. The purpose is to produce hypothetical GCM simulations that best
hindcast the available data, so that their twenty-first-century projections
could be trusted for policy.

The proposed scaling algorithm works in first approximation because on a
global scale the performance of a GCM is mainly determined by its ECS and TCR
values. Moreover, it is easy to calculate that scaling down the medium and
high-ECS macro-GCMs to mimic the warming of the low-ECS macro-GCM from
1980–1990 to 2011–2022, the three macro-GCM ensembles would roughly overlap
throughout the 21st century. In fact, by using the data from Table 2, the
ratio between the 2011–2022 medians of the medium- and high-ECS ensembles and
the low-ECS ensemble are $0.80/0.62\approx 1.3$ and $0.97/0.62\approx 1.6$,
respectively; similar ratios are found using the data from Table 3. By using
the 2080–2100 medians: for SSP1-2.6 the ratios are $2.1/1.5\approx 1.4$ and
$2.5/1.5\approx 1.7$; for SSP2-4.5 the ratios are $3.3/2.3\approx 1.4$ and
$3.6/2.3\approx 1.6$; for SSP3-7.0 the ratios are $4.1/3.2\approx 1.3$ and
$4.8/3.2\approx 1.5$; and for SSP5-8.5 the ratios are $5.2/3.7\approx 1.4$ and
$6.0/3.7\approx 1.6$, respectively. These ratios remain approximately the same
for all other percentile levels.

As a result, because the performance of one macro-GCM can be approximated by
an appropriate linear scaling of another one, it is legitimate to modify the
climate projection ensembles of the available macro-GCMs to generate plausible
climate projection ensembles from hypothetical macro-GCMs by scaling them to
best reproduce the available data. Another advantage of the proposed
methodology is that it can be applied to a wide range of circumstances where
the existing GCMs are unsatisfactory. For example, the three proposed macro-
GCMs can be scaled to recreate both global surface and lower troposphere
temperatures, or they can be scaled and integrated with climate models that
empirically reconstruct natural cycles and extra solar components that the
CMIP GCMs do not reproduce, as Scafetta (2013, 2021b) already proposed.

Let us now address these two situations in detail.

###  5.1 GCM scaling on the surface and lower troposphere temperature records

As explained in Section 2, climate-change impacts and risks for the 21st
century are assessed by the IPCC (Pörtner et al., 2022) using the CMIP6 GCMs
simulations under different SSP scenarios. However, Hausfather and Peters
(2020) suggested that the SSP2-4.5 scenario is the most plausible. According
to Pielke et al. (2022), only the SSP2-3.4 and SSP2-4.5 scenarios should be
deemed realistic since their emission trajectories are the most compatible
with recent history and present reference estimates of the expected evolution
of the global energy system over the next three decades, but here only the
SSP2-4.5 GCM simulations are available. Thus, it is here suggested that the
Hist+SSP2-4.5 scenario simulations from the CMIP6 GCMs could serve as a basis
for realistic climate change projections for the 21st century.

Moreover, according to the analysis presented in Sections 2 and 4, the best-
performing subset of CMIP6 GCMs appears to be the one with ECS values between
1.5 and 3.0 °C (Lewis, 2023; Scafetta, 2023a). The low-ECS macro GCM is
composed of 15 GCMs. From this list it might be possible to exclude the
FGOALS-f3-L model and to add the BCC-CSM2-MR ($ECS=3.04$ °C) and MRI-ESM2-0
($ECS=3.15$ °C) models. FGOALS-f3-L may be excluded because, despite its ECS
is on the border limit ($3.0$ °C), its TCR appears to be too high (1.94 °C).
Instead, the other two models might be included since, despite having an ECS
slightly higher than 3.0 °C, their TCR is low (1.72 and 1.64 °C,
respectively). In fact, a low-TCR macro-GCM would have a TCR equal to or less
than 1.8 °C. All the other CMIP6 GCMs should be avoided for climate-change
policy since they are characterized by too high ECS or TCR values and, as an
ensemble, they overestimate the warming observed from 1980 to 2022.

In any instance, the optimal selection of GCMs that must be chosen might be
considered arbitrary. I propose an alternative methodology that considers all
available simulations. The ensemble projections of each of the three macro-
GCMs can be empirically scaled to best fit the observed warming from 1980–1990
to 2012–2022 to ensure an ideal outcome compatible with the observed warming
during the same period (and add a little bit more dispersion associated with
the internal variability of the models). In this study, the HadCRUT5 and UAH-
MSU lt v. 6.0 temperature records are deemed representative of the surface-
based and satellite-based global surface temperature estimations.

The necessary scaling factors are obtained from the median values reported in
Table 2 for each of the three ECS macro-GCMs, as well as the warming of 0.605
°C and 0.432 °C shown by the HadCRUT5 and UAH-MSU lt v. 6.0 records from 2012
to 2022 relative to 1980–1990, respectively. Thus, we have:

  * •

Case #1 uses HadCRUT5 — the Hist+SSP2-4.5 GCM simulations of the high-ECS
group are scaled by the factor $0.605/0.97=0.62$, those of the medium-ECS
group are scaled by the factor $0.605/0.80=0.77$, and those of the low-ECS
group are scaled by the factor $0.605/0.620=0.98$;

  * •

Case #2 uses UAH-MSU lt v. 6.0 — the Hist+SSP2-4.5 GCM simulations of the
high-ECS group are scaled by the factor $0.432/0.97=0.45$, those of the
medium-ECS group are scaled by the factor $0.432/0.80=0.54$, and those of the
low-ECS group are scaled by the factor $0.432/0.620=0.70$.

The above ratios are characterized by a statistical relative error of about
$\pm$ 5%.

In Case #1, the climate change projection ranges for the 21st century do not
differ much from those determined only from the low-ECS macro GCM simulations
mentioned in Section 4 and reported in Table 3 because the scaling factor in
such a case is close to one. Case #2, on the other hand, necessitates a 30%
reduction in the climate simulations of the low-ECS macro GCM. The [2.5%, 17%,
50%, 83%, 97.5%] percentiles of the simulated warming distributions from
1850–1900 covering each decade from 2000 to 2100 are shown in Table 4.

|  | Temperature change from 1850–1900 (°C)
---|---|---
| Percentile | 2005 | 2015 | 2025 | 2035 | 2045 | 2055 | 2065 | 2075 | 2085 | 2095
| 97.5% | 1.11 | 1.30 | 1.59 | 1.85 | 2.06 | 2.36 | 2.57 | 2.75 | 2.96 | 3.09
Case #1 | 83.0% | 0.95 | 1.14 | 1.41 | 1.66 | 1.89 | 2.13 | 2.35 | 2.55 | 2.70 | 2.82
Fig. 5A | 50.0% | 0.74 | 0.90 | 1.15 | 1.35 | 1.59 | 1.80 | 1.97 | 2.10 | 2.22 | 2.34
(HadCRUT5) | 17.0% | 0.48 | 0.65 | 0.86 | 1.04 | 1.23 | 1.43 | 1.58 | 1.72 | 1.86 | 1.94
| 2.5% | 0.30 | 0.52 | 0.71 | 0.90 | 1.05 | 1.24 | 1.38 | 1.51 | 1.62 | 1.68
| 97.5% | 0.80 | 0.93 | 1.13 | 1.32 | 1.47 | 1.68 | 1.84 | 1.96 | 2.11 | 2.21
Case #2 | 83.0% | 0.67 | 0.82 | 1.01 | 1.19 | 1.34 | 1.52 | 1.68 | 1.82 | 1.92 | 2.02
Fig. 5B | 50.0% | 0.53 | 0.64 | 0.82 | 0.96 | 1.13 | 1.29 | 1.41 | 1.50 | 1.59 | 1.67
(UAH-MSU lt v. 6.0) | 17.0% | 0.34 | 0.46 | 0.62 | 0.74 | 0.87 | 1.03 | 1.13 | 1.23 | 1.33 | 1.39
| 2.5% | 0.22 | 0.37 | 0.51 | 0.65 | 0.75 | 0.89 | 0.99 | 1.07 | 1.16 | 1.20
| 97.5% | 1.18 | 1.26 | 1.34 | 1.50 | 1.67 | 1.98 | 2.25 | 2.38 | 2.52 | 2.52
Case #3 | 83.0% | 1.00 | 1.10 | 1.20 | 1.30 | 1.51 | 1.75 | 2.02 | 2.17 | 2.26 | 2.26
Fig. 7A | 50.0% | 0.80 | 0.85 | 0.94 | 1.00 | 1.21 | 1.42 | 1.64 | 1.73 | 1.78 | 1.77
(HadCRUT4.6) | 17.0% | 0.54 | 0.60 | 0.65 | 0.69 | 0.85 | 1.06 | 1.25 | 1.34 | 1.41 | 1.37
| 2.5% | 0.37 | 0.47 | 0.51 | 0.55 | 0.68 | 0.87 | 1.05 | 1.13 | 1.18 | 1.11
Table 4: Percentiles [2.5%, 17%, 50%, 83%, 97.5%] of the distributions of the
best estimated projected warming from 1850–1900 to the 11-year periods
covering each decade from 2000 to 2100. The curves are depicted in Figures 5
and 7. Modeling

Due to the used scaling factors, the warming hindcasted by the models from
1850–1900 to 1980–1990 may not match the observed warming. This is unimportant
because past data are characterized by increasing uncertainty (Morice et al.,
2021). Furthermore, if surface temperature records from 1980 to 2022 had to be
scaled down to match the satellite-based records, the real warming from the
pre-industrial period to the present would be less than what was reported (cf.
Connolly et al., 2023; Scafetta, 2021a).

Figure 5: [A] Estimated global temperature changes using the Hist+SSP2-4.5
scenario and scaling the CMIP6 GCM simulations to optimally hindcast the
HadCRUT5 global surface temperature warming from 1980–1990 to 2012–2022. [B]
The same as in [A] using CMIP6 GCM simulations scaled to optimally hindcast
the UAH-MSU lt v. 6.0 temperature warming from 1980–1990 to 2012–2022. Both
temperature records are baselined with the modeled hindcast mean from 1980 to
1990. Table 4 tabulates the depicted percentile ranges. [Right] Burning ember
diagrams (in function of the global temperature warming) of the main five
global reason for concern (RFC) assuming low to no adaptation reported by the
IPCC AR6 (Pörtner et al., 2022).

Figure 5A’s left panel depicts Case #1. It shows the estimated global
temperature changes from 1980 to 2100, as well as their percentile — likely
(66%) and very likely (95%) — ranges, based on the Hist+SSP2-4.5 scenario and
scaling the CMIP6 GCM simulations to best hindcast the warming shown by the
HadCRUT5 global surface temperature record from 1980–1990 to 2012–2022. The
temperature record is baselined with the modeled hindcast mean from 1980 to
1990.

Figure 5B’s left panel depicts Case #2. It is the same as in Figure 5A, but
the CMIP6 GCM simulations are now scaled to best hindcast the warming seen in
the UAH-MSU lt v. 6.0 temperature record from 1980-1990 to 2012-2022. This
scenario should be used if it is demonstrated that non-climatic warm biases
influence global surface temperature records and that the satellite lower
troposphere temperatures better represent the actual global surface warming
from 1980 to 2022. The temperature record is baselined with the modeled
hindcast mean from 1980 to 1990.

The displayed percentile ranges of the estimated global temperature changes
shown in Figures 5A and 5B are reported in Table 4 for each decade from 2000
to 2100. The right panels of Figure 5 depict the burning ember diagrams (in
function of the global temperature warming) of the main five global reason for
concern (RFC) assuming low to no adaptation as reported in the IPCC AR6
(Pörtner et al., 2022). The five RFCs are described as:

  * •

RFC1 (unique and threatened systems) — ecological and human systems with
limited geographic ranges due to climate-related factors and high endemism or
other distinguishing characteristics such as coral reefs, the Arctic and its
indigenous peoples, mountain glaciers, and biodiversity hotspots;

  * •

RFC2 (extreme weather events) — impacts and risks to human health,
livelihoods, assets and ecosystems from extreme weather events such as
heatwaves, heavy rain, drought and associated wildfires, and coastal flooding;

  * •

RFC3 (distribution of impacts) — impacts and risks that disproportionately
affect specific groups as a result of uneven distribution of physical climate
change hazards, exposure, or vulnerability;

  * •

RFC4 (global aggregate impacts) — impacts on socio-ecological systems that may
be aggregated globally into a single metric, such as monetary damages, lives
lost, species extinction, or global ecosystem deterioration;

  * •

RFC5 (large-scale singular events) — relatively significant, rapid, and
potentially permanent changes in systems driven by global warming, such as ice
sheet collapse or thermohaline circulation slowdown.

Figure 5A suggests that the impacts and risks of climate changes for all five
RFCs could be relatively moderate (yellow-orange flag) until 2050, and they
will gradually increase until 2100. Only some unique and particularly
critically threatened environmental systems may be at higher risk. In fact,
Table 4 shows that the very likely (95% confidence) warming should range
1.15-2.21 °C (median 1.70 °C) by 2040–2060, and 1.65-3.03 °C (median 2.28 °C)
by 2080–2100.

Figure 5B shows the 21st-century climate projection depicted in Figure 5A is
reduced by roughly 30% to fit the lower troposphere temperature records: the
95% confidence warming ranges from 0.82 to 1.58 °C (median 1.21 °C) by
2040–2060, and from 1.18 to 2.16 °C (median 1.63 °C) by 2080–2100. These
temperature projections are much lower than the IPCC AR6 estimates. For
example, simply using the GCMs with ECS between 2.5 and 4.0 °C, which is the
IPCC likely ECS-range, the global warming is estimated to be between 1.37 and
2.61 °C by 2040–2060 and 1.98-3.82 °C by 2080–2100 (Table 3).

Cases #1 and #2 imply that the impacts and risks of climate change could be
moderate (yellow-orange flag) in all five RFCs until 2100 also using the
SSP2-4.5 scenario.

###  5.2 GCM scaling assuming natural oscillations

Scafetta (2010, 2013, 2021c) proposed empirical models for global climate
change based on the evidence that climate records appear to be characterized
by several oscillations that could be related to solar or astronomical
harmonics. From a physical point of view it might be possible that cloud
formation, which the GCMs partially parameterize with some free parameters
that are carefully tuned (Mauritsen et al., 2019; Mignot et al., 2021), may
also strongly depend on some non-radiative forcing (cosmic rays,
interplanetary dust, etc.) modulated by solar magnetic activity (Scafetta et
al., 2020; Scafetta, 2023c; Svensmark et al., 2016; Svensmark, 2022), which
the state-of-the-art climate models ignore. In fact, the GCMs can also be
directly tuned just to obtain an improved match to the instrumental
temperature record by changing their free parameters (Mauritsen and Roeckner,
2020), which leaves the possibility of missing mechanisms (cf. Kaufman and
Broadman, 2023).

The proposed model assumes that the warming indicated by global surface
temperature records (e.g., HadCRUT3 and HadCRUT4) is sufficiently accurate. It
is made of a multi-harmonic natural component simulating the hypothesized and
empirically-derived astronomical/solar effect superimposed on an empirically
estimated anthropogenic-plus-volcanic effect scaled from the GCM simulations.
The empirical model also includes a quasi 9.1-year oscillation that appears to
be caused by solar-lunar tidal forcings. Scafetta (2010, 2012a, 2013)
estimated that if such natural solar/astronomical harmonic contribution is
considered, the climate’s sensitivity to anthropogenic and volcanic forcings
should be approximately halved. This means that the empirically determined
anthropogenic-plus-volcanic effect can be approximated by multiplying the
ensemble average of all GCM simulations by a factor of about 0.5. For more
information, see Scafetta (2013, 2023c). The underlying hypothesis is that the
harmonics that have characterized climate change in recent decades, centuries,
and millennia will continue to do so in the 21st century.

Figure 6: Comparison of the climate simulations and projections with their
relative global aggregated impacts and risks obtained with (A) the ensemble
average CMIP6 GCM simulations and (B and C) the harmonic empirical global
climate modeling proposed by Scafetta (2010, 2013, 2021b). The temperature
data used are the (infilled) HadCRUT v.5 (Morice et al., 2021) and the UAH-MSU
lt v. 6.0 lower troposphere global temperature records (Spencer et al., 2017).

Figure 6 compares the original CMIP6 GCM ensemble average simulations to the
proposed empirical model. The latter is based on Scafetta (2021b)’s
13-harmonics set (representing the solar-astronomical induced variability of
the climate system), which adds 7 interannual identified harmonics to Scafetta
(2013)’s original six harmonics model covering the decadal-to-millennial
scales. The model is completed by an empirically derived anthropogenic-plus-
volcanic signal, which is obtained by halving the ensemble average of the GCM
simulations, implying that the actual ECS could range between 0.9 and 2.8 °C,
as recently evaluated by independent studies (Lewis, 2023; Scafetta, 2023b;
Spencer and Christy, 2023).

The proposed equation for the empirical global surface temperature model is

$T(t)=T_{0}+\sum_{i=1}^{13}A_{i}\sin\left[2\pi\left(f_{i}\cdot(t-2000)+\alpha_{i}\right)\right]+0.5\cdot
GCM(t),$ (1)

where the coefficients $A_{i}$, $f_{i}$ and $\alpha_{i}$ per $i=1,...,13$ are
reported in Table 5, $GCM(t)$ is the ensemble average of the GCM simulations
for each SSP scenarios (which were downloaded from KNMI Climate Explorer), and
$T_{0}$ is the reference baseline of the temperature anomalies that can be
chosen, for example, to make $T_{1850-1900}=0$. The harmonic coefficients were
derived from astronomical considerations and optimized for best data fitting
using a Montecarlo approach (Scafetta, 2021b). Throughout the Holocene, the
period of the quasi millennial cycle was theoretically and empirically
estimated to be about 983 years (Scafetta, 2012b), but $f_{1}$ in Eq. 1
corresponds to a period of 760 years that was chosen to simulate the skewness
of the millennial cycle, which could be modeled with the addition of the quasi
2318-year Hallstatt solar cycle (Scafetta et al., 2016; Scafetta, 2020).
Indeed, paleoclimate temperature reconstructions from the last millennium
typically show a minimum around 1680 (corresponding to the coldest period of
the Little Ice Age during the Maunder solar minimum) and a millennial maximum
around 1077; the next solar millennial grad-maximum is expected around 2060.
Scafetta (2012b, figure 8) showed that the proposed semi-empirical model well
hindcast the quasi-millennial cycle observed in climate records throughout the
Holocene despite both its phase and period were deduced from solar-
astronomical considerations alone. Scafetta (2012a, figure 5B) validated an
early version of the same harmonic model by independently calibrating its
decadal and multi-decadal harmonics for the years 1850–1950 and 1950–2011,
demonstrating that the two independent climate hindcasts closely coincided
from 1850 to 2050 and optimally reproduced the global surface temperature
record from 1850 to 2011. Scafetta (2021b, figures 11 and 14) also used the
proposed model to reproduce temperatures since the Medieval period, and
Scafetta (2021b, figures 15) demonstrated that the model, which was calibrated
using data up to 2014, could also forecast the warm periods that occurred in
2015–2016 and around 2020, as well as the approaching 2023–2024 warm period.

$i$ |  $A_{i}$ (°C) |  $f_{i}$ (1/y) | $\alpha_{i}$
---|---|---|---
1 | 0.3228 | 0.001318 | 0.1711
2 | 0.05843 | 0.008696 | 0.4239
3 | 0.08583 | 0.01639 | 0.1516
4 | 0.03339 | 0.0500 | 0.1482
5 | 0.02407 | 0.09615 | 0.0198
6 | 0.02651 | 0.1075 | 0.4973
7 | 0.02163 | 0.1340 | 0.7107
8 | 0.02721 | 0.1666 | 0.6167
9 | 0.02598 | 0.1909 | 0.4086
10 | 0.03257 | 0.2086 | 0.9312
11 | 0.02758 | 0.2752 | 0.7666
12 | 0.02537 | 0.2812 | 0.7917
13 | 0.02472 | 0.3480 | 0.9746
Table 5: Coefficients for the harmonic component of the empirical climate
model given by Eq. 1.

The left panels of Figure 6 zoom in on the right panels between 1980 and 2030
to better show the models’ performance over the last 50 years. The temperature
data used here are the (infilled) HadCRUT v.5 (Morice et al., 2021), which is
chosen as an average representative of the most recent global surface
temperature records (Scafetta, 2023a), and the UAH-MSU lt v 6.0 global
temperature record by Spencer et al. (2017), which is nearly identical to the
NOAA STAR v. 5.0 lt record (Zou et al., 2023). The lower troposphere
temperature records are adopted as representative of the real surface
temperature change since 1980 if the surface records are influenced by non-
climatic biases (Scafetta, 2021a; Connolly et al., 2021, 2023; Soon et al.,
2023). Finally, the picture depicts global aggregated impact estimates based
on the burning ember diagram proposed by the IPCC AR6 (Pörtner et al., 2022).
The data represent temperature anomalies relative to the pre-industrial period
of 1850–1900.

Figure 6A depicts the original GCM simulations. Only the net-zero emission
SSP1-2.6 scenario ensures that global surface temperatures do not
significantly exceed the 2 °C (safe) threshold (Gao et al., 2017; Tol, 2015),
which satisfies to the Paris Agreement warming targets. However, as shown in
6-A1, these average models are running too hot in relation to both global
surface and lower troposphere temperature records.

Figure 6B compares the four SSP average simulations generated by the proposed
empirical model of Eq. 1 to the HadCRUT5 record. The figure shows that, in
addition to the (net-zero emission) SSP1-2.6 scenario, also the moderate
SSP2-4.5 scenario would ensure a global surface temperature of less than 2 °C
throughout the 21st century, with an average of about 1.8 °C by 2080–2100.
Furthermore, the extreme (and highly unlikely) SSP5-8.5 scenario is projected
to warm the global climate on average by only 3 °C relative to pre-industrial
levels, which is only moderately concerning. However, as illustrated in 6-B1,
the empirical model appears to underestimate the HadCRUT5-measured global
surface temperature warming. Yet, it agrees well with the HadCRUT3 and
HadCRUT4 records (Scafetta, 2013, 2021b); therefore, the current discrepancy
with the (infilled) HadCRUT5 appears to be attributable to the adjustments
made to the latter, which conceal the 2000–2014 temperature “hiatus” or
“pause” that is still revealed by the satellite temperature records.

In fact, Figure 6C is like Figure 6B, except the simulations are now compared
to the lower troposphere temperature record from UAH-MSU. The empirical model
(first proposed by Scafetta, 2010, 2013) appears to have accurately predicted
the observed warming. As a result, if satellite temperature data better
represent the actual global surface warming from 1980 to 2022, the model’s
empirical calibration can be assumed validated, potentially making its future
climate projections trustworthy as well.

Figure 7A compares the harmonic empirical global climate model with the
SSP2-4.5 scenario to the HadCRUT4.6 record (Morice et al., 2012), which is
used here as a compromise between the (infilled) HadCRUT5 and the lower
troposphere satellite temperature records. Scafetta (2021b) calibrated his
empirical model until 2014 using the HadCRUT4.6 record. The model projection
range is equivalent to Figure 5A. Figure 7A also shows that the proposed
harmonic empirical model reconstructs the multidecadal modulation (consisting
of quasi 60-year oscillations forming a sequence of quasi-30-year warming or
cooling periods) observed in the temperature record since 1850 much better
than the GCM simulations, which show only monotonically increasing patterns
interrupted by sporadic volcano eruptions (see Figures 1 and 6-A2); the same
quasi-60-year modulation can be reconstructed with appropriate total solar
activity records, as demonstrated by Scafetta (2023c).

By extending the same natural modulation up to 2080–2100, the empirical
climate projection predicts that the temperature will range from 1.15 to 2.52
°C (median 1.78 °C), which roughly matches the 1.26-2.82 °C range produced by
the SSP1-2.6 scenario using GCMs with ECS ranging from 2.5 to 4.5 °C as
reported by the IPCC. Table 4 reports the depicted percentile ranges from 2000
to 2100.

The warming rate is expected to remain relatively low from 2000 to 2035 and
from 2065 to the end of the century because the empirical model predicts
negative phases of the Atlantic Multidecadal Oscillation (AMO) during these
periods, whereas the warming rate is expected to increase from 2035 to 2065
when the AMO is forecasted to be positive. The AMO highlights a well-known
large quasi-60-year oscillation of the climate system that is not predicted by
GCMs (Scafetta, 2013) but has been observed for millennia (Knudsen et al.,
2011; Scafetta, 2014a; Wyatt and Curry, 2013); it is most likely of
solar/astronomical origin, along with other larger oscillations of the climate
system (Neff et al., 2001; Scafetta et al., 2013; Scafetta, 2014b, 2020,
2023c; Scafetta et al., 2020).

Figure 7: (A) The harmonic empirical global climate model with the SSP2-4.5
scenario, against the HadCRUT4.6 record (1850–2021) (Morice et al., 2012). (B)
Burning ember diagrams (in function of the global temperature warming) of the
main five global reason for concern (RFC) assuming low to no adaptation
reported by the IPCC AR6 (Pörtner et al., 2022). Table 4 tabulates the
depicted percentile ranges from 2000 to 2100.

##  6 Summary of the results

Figures 5 and 7 show three “realistic” global warming projections for the 21st
century. They imply that the impacts and risks of realistic climatic changes
are significantly lower than what the IPCC expected, even when only the
SPP2-4.5 scenario was offered.

Figure 8: [Left] Estimated warming in 2040–2060 and 2080–2100 according to the
SSP2-4.5 scenario and four cases: (O-original) using the GCMs with ECS between
2.5 and 4 °C (IPCC option); (1) using the GCMs scaled on the low-ECS macro GCM
and the HadCRUT5 record (Figure 5A); (2) using the GCMs scaled on the low-ECS
macro GCM and the UAH-MSU lt v. 6.0 record (Figure 5B); (3) using the
empirical harmonic model (Eq. 1) and the HadCRUT4.6 record (Figure 7A).
[Right] Burning ember diagrams (in function of the global temperature warming)
representing the estimated global and local impacts and risks of global
warming for several ecosystems and health outcomes as reported in the IPCC AR6
(Pörtner et al., 2022, Summary for Policymakers).

The left panels of Figure 8 summarize the estimated warming in 2040–2060 and
2080–2100 according to the SSP2-4.5 scenario and the warming projected ranges
at 66% and 95% confidence as deduced in four cases (see Tables 3 and 4):

  * •

Ranges #O are the original ones and derive from the GCMs with ECS values
between 2.5 and 4 °C, which are in line with the IPCC’s prediction of its
likely ECS range;

  * •

Ranges #1 derive from the GCMs scaled on the low-ECS macro GCM and assumes the
1980–2022 warming of the HadCRUT5 record, as shown in Figure 5A;

  * •

Ranges #2 derive from the GCMs scaled on the low-ECS macro GCM and assumes the
1980–2022 warming of the UAH-MSU LT v. 6.0 record, as shown in Figure 5B.

  * •

Ranges #3 derive from the empirical harmonic model (Eq. 1) depicted in Figure
7A.

The right panels of Figure 8 show a selection of the same burning ember
diagrams (in function of the global temperature warming) of regional and
global risk assessments relative to several ecosystems, and disease-health
situations adopted by the IPCC AR6. A detailed explanation of each RFC is
found in Pörtner et al. (2022). The first line represents examples of
ecosystem risk assessments; the second line represents examples of climate
sensitive health outcomes under limited, incomplete, and proactive adaptation
scenarios; the third and fourth lines represents examples of regional level
risk assessments. Unless otherwise specified, the burning ember diagrams
represent situations in which little or no adaptation is planned. As more
sophisticated adaptation policies are implemented, the color scale shifts
upward in a way like what is shown in the second row of Figure 8.

Most of the time, for cases #1-3, the impacts and risks of climate changes
would be moderate (yellow-orange flag) until 2050. The SSP2-4.5 scenario may
also ensure an average global surface temperature of less than 2 °C by 2100,
and hence also this moderate SSP should be considered compatible with the
Paris Agreement warming objective. As a result, while suitable adaptation
techniques may always be required, they may be rather affordable because the
impacts and risks of actual future climate change are expected to be moderate.

##  7 Discussion and conclusion

The IPCC used the CMIP6 GCMs to assess the probable risks and impacts of
climate change on global and regional scales over the next century (Masson-
Delmotte et al., 2021; Pörtner et al., 2022). Climate change estimates are
influenced by the climate’s sensitivity to radiative forcing as well as by the
projected greenhouse gas emissions, which are linked to varying rates and
kinds of socioeconomic developments. However, there is a great deal of
uncertainty in both conditions that must be restricted to properly assessing
realistic climatic hazards for the 21st century and developing appropriate
climate policies to optimally address them.

The ECS of the CMIP6 GCMs ranges between 1.8 and 5.7 °C, but the IPCC AR6
acknowledged the existence of a “ _hot_ ” model problem and claimed that the
actual ECS may likely range between 2.5 and 4.0 °C, with a best estimate of
around 3.0 °C (Sherwood et al., 2020; Hausfather et al., 2022). However,
recent research suggests that the expected ECS range should vary within lower
values between 1.5 and 3 °C (Nijsse et al., 2020; Scafetta, 2022, 2023a,
2023b; Lewis, 2023; Spencer and Christy, 2023). Furthermore, according to a
number of empirical studies, the actual ECS values could even be significantly
lower, ranging between 1 and 2 °C (Bates, 2016; Lindzen and Choi, 2011;
McKitrick and Christy, 2020; Scafetta, 2013, 2023c; Stefani, 2021).

The IPCC AR6 investigates a range of SSP scenarios for the 21st century
without assigning a probability to their plausibility. In any case, despite
the questionable visibility given to SSP5-8.5 (the worst-case scenario), which
yields the largest and most alarming projected global warming of up to 4-8 °C
(66%) by 2080–2100, Table 12.12 of the IPCC AR6 (Masson-Delmotte et al., 2021,
pp. 1856) already reports for the entire 21st century low confidence in the
direction of any change in the frequency, severity or extent of frost, river
floods, landslides, aridity, hydrological drought, agricultural and ecological
drought, fire weather, mean wind speed, severe wind storms, tropical cyclones,
sand and dust storms, heavy snowfall and ice storms, hail, snow avalanche,
coastal floods, coastal erosion, marine heatwaves, air pollution weather or
radiation at earth’s surface. Medium and high confidence of changes are mostly
expected in climatic impact-driver types more directly associated with
increasing atmospheric CO2 concentration at surface and global warming such as
increasing mean air temperature, extreme heat, sea level, mean ocean
temperature, ocean salinity and ocean acidity; with decreasing cold spell,
snow, glacier and ice sheet, permafrost, lake, river and sea ice, and
dissolved oxygen; mean precipitation will increase in some regions and
decrease in others.

However, recent research argued that the alarming SSP3-7.0 and SSP5-8.5
scenarios are _likely_ and _very likely_ unrealistic, respectively (Burgess et
al., 2020; Hausfather and Peters, 2020; Pielke and Ritchie, 2021a, b). These
studies indicate that the radiative forcing functions derived from the
SSP2-4.5 (or even SSP2-3.4) scenario are the most plausible. The SSP2-4.5 is a
moderate scenario; it projects about half of the 21st-century warming than
what the SSP5-8.5 scenario does (Figure 1) and is thus far less alarming.

With the aforementioned factors in mind, it was proposed here to use only the
SSP2-4.5 scenario and the GCMs with ECS $\leq 3$ °C to more precisely assess
“realistic” global and regional impacts and risks that could be associated
with climate changes that are expected to occur in the 21st century, and to
compare them with the Paris Agreement warming target of keeping global surface
temperature below 2 °C above the pre-industrial levels throughout the 21st
century. To optimize the result even more, the simulation ensembles containing
the low, medium, and high-ECS macro-GCMs were linearly scaled to best reflect
the real global surface warming recorded from 1980–1990 to 2012–2022.

According to the IPCC, if there is little-to-no adaptation, the impacts and
risks of projected climate change will be moderate-high (orange-red flag) by
2040–2060, and the situation might worsen considerably by 2100 even if the
SSP2-4.5 moderate scenario is implemented. In fact, according to the analysis
reported in Table 3, the GCMs within the IPCC’s preferred ECS range of 2.5-4.0
°C project a warming of 1.98-3.82 °C by 2080–2100. Thus, the IPCC (Masson-
Delmotte et al., 2018) concludes that only net-zero-emission scenarios like
the SSP1-2.6 (which could produce a warming of 1.26-2.82 °C by 2080–2100)
should be adopted to avoid too alarming climatic changes, which are expected
to begin if global surface temperatures rise more than 2-2.5 °C above the
1850–1900 level in a few decades (Tol, 2015). Climate-change alarmism and
world-wide proposals for prompt implementations of net-zero emission policies
are based on such claims.

However, using only the low-ECS models (ECS $\lessapprox 3.0$ °C) and the
SSP2-4.5 scenario, Table 3 suggests that global warming in the 21st century
will be moderate, ranging from 1.36 to 2.25 °C (median 1.77 °C) by 2050 and
from 1.96 to 2.83 °C (median 2.28 °C) by 2080–2100, which partially overlaps
with the upper warmer half of the climate projection obtained using the
SSP1-2.6 scenario and the GCMs with ECS of 2.5-4.0 °C. Thus, climate change
impacts and risks will worsen by the end of the 21st century, albeit at a
slower rate than predicted by the IPCC using the same SSP2-4.5 scenario. As a
result, the SSP2-4.5 scenario, which is moderate and affordable, may be close
enough to roughly meet the Paris Agreement warming target, whereas the
SSP2-3.4 scenario, which could be even more realistic (Pielke et al., 2022),
should more likely fully meet it.

I also proposed an alternative methodology for estimating “realistic” 21st-
century climate projections and assessing their respective impacts and risks.
In fact, the low-ECS macro-GCM appears to be slightly warmer than global
surface temperature records and there are serious concerns about the
reliability of the global surface temperature records, which cannot be
ignored. In fact, their warming appears to be excessive in comparison to
alternative temperature records, such as satellite-based ones relative to the
lower troposphere (Spencer et al., 2017; Zou et al., 2023), and there are
various evidences suggesting their contamination from urban heat islands and
other non-climatic surface factors (Scafetta, 2021a, 2023b; Connolly et al.,
2021; Soon et al., 2023). There are also concerns regarding the ability of the
GCMs to properly reconstruct decadal to millennial natural climate
oscillations (Scafetta, 2013, 2021b). As a result, all GCMs may be grossly
inadequate for estimating climate change in the 21st century, as also
McKitrick and Christy (2020) concluded. Thus, the models need to be corrected
and upgraded with new relevant physical mechanisms. It is possible to agree
with McCarthy and Caesar (2023) who recently showed the inability of the CMIP5
and CMIP6 GCMs in properly hindcasting the Atlantic Meridional Overturning
Circulation and concluded “ _if these models cannot reproduce past variations,
why should we be so confident about their ability to predict the future?_ ”.

To address the above issues, I have proposed an alternative methodology that
uses empirical modifications of the actual GCM projection ensembles via
appropriate linear scaling in such a way to simulate the outputs of
hypothetical climate models that could accurately represent the warming
observed from 1980 to 2022. The 1980–2022 period was selected because it is
covered by a variety of temperature records with low statistical errors. This
methodology would essentially simulate hypothetical GCMs that are supposed to
optimally reproduce the data. Simple testing validates the proposed
methodology because scaling the projection ensembles of the three macro-GCMs
to a similar level from 1080–1990 to 2011–2022 results in projection ensembles
that approximately overlap throughout the 21st century.

The proposed methodology may also be justified by considering that the GCMs
are extremely sensitive to small modifications of their internal free
parameters, in particular to those regarding cloud formation, and even GCM
modelers adopt complex tuning approaches to explicitly calibrating them to
better match historical data (Mauritsen and Roeckner, 2020; Mignot et al.,
2021). Section 4 proposes and investigates several of these modeling
approaches, the results of which are depicted in Figures 5 and 7.

If the warming of the HadCRUT5 record from 1980–1990 to 2011–2022 is assumed
correct, it is found that the SSP2-4.5 scenario produces climate projections
similar to those produced by the low-ECS macro-GCM. In fact, the projected
warming ranged from 1.65 to 3.03 °C by 2080–2100, with a median of 2.28 °C
(Table 4, case #1). This conclusion is unsurprising given that the low-ECS
macro-GCM has already successfully recreated the HadCRUT5 warming.

However, if the reference warming is that reported by lower troposphere
satellite temperature data (Spencer et al., 2017; Zou et al., 2023), the
warming of the low-ECS macro-GCM simulations must be lowered by about 30%. As
a result, global warming by 2080–2100 is projected to range from 1.18 to 2.16
°C (median 1.63 °C) above pre-industrial levels using the SSP2-4.5 scenario
(Table 4, case #2), which is well below the (safe) threshold of 2.0 °C and is
even cooler than the 1.26-2.82 °C estimate obtained with the GCMs with ECS
within the IPCC likely range of 2.5 and 4.0 °C using the SSP1-2.6 net-zero
emission scenario.

A similar result was obtained with an empirical climate model that assumes
that the global surface temperature record is sufficiently accurate, but also
takes into account temperature changes caused by empirically identified large
climate cycles and/or solar effects that the CMIP6 GCMs do not replicate
(Scafetta, 2010, 2013, 2021b); this case projects a warming ranging from 1.15
to 2.52 °C with median 1.78 °C by 2080–2100. Unfortunately, the IPCC ignores
such semi-empirical modeling of the climate system although it has been
developed and discussed in the scientific literature, and it should not be
dismissed lightly given that the GCMs fail to reproduce the natural
oscillations observed throughout the Holocene. They do not, for example,
reproduce any of the Holocene warm periods, such as the Roman and Medieval
warm periods, which indicate a quasi-millennial oscillation, a quasi-60-year
oscillation, and many other natural climate oscillations. Also this kind of
empirical modeling predicts very modest ECS values, ranging at least between 1
and 3 °C, but more likely between 1 and 2 °C (Bates, 2016; Lindzen and Choi,
2011; Scafetta, 2013, 2021b, 2023c; Stefani, 2021).

In conclusion, as Hausfather and Peters (2020) pointed out, it is past time to
stop treating worst-case climate change scenarios (e.g., SSP3-7.0 and
SSP5-8.5) as the most likely outcomes, because only realistic and pragmatic
scenarios, such as SSP2-4.5 or SSP2-3.4, can lead to sound policies that can
be accepted by all nations. Furthermore, net-zero scenarios such as SSP1-2.6
look to be equally unattainable, as the depletion of crucial metals required
for low-carbon solar and wind technologies, as well as electric vehicles and
their chargers, appears to make low-carbon technology production impossible on
the very large scale required to substitute fossil fuels (Groves et al.,
2023). In fact, despite the IPCC AR6 reports are rather alarming because
global surface temperatures were projected to rise by up to 4-8 °C above pre-
industrial levels according to alarming but unrealistic shared socioeconomic
pathways (see Figure 1 and Masson-Delmotte et al., 2021), with catastrophic
consequences in many situations (Pörtner et al., 2022), Figures 5, 7 and 8
show that “realistic” climate change impacts and risks for the 21st century
will likely be much more moderate than what the IPCC claims. This is because
there is a growing body of evidence that the actual ECS may be rather low
(1.5-3.0 °C, or even 1-2 °C) for a variety of reasons derived from direct
CMIP6 GCM assessments, likely warming biases affecting global surface
temperature records, and a (likely solar induced) natural variability that the
current climate models do not reproduce. According to the semi-empirical
climate modeling proposed above, the climate system will likely warm by less
than 2.0-2.5 °C by 2080–2100, and on average less than 2.0 °C, also if the
moderate SSP2-4.5 scenario is implemented. As a result, rapid decarbonization
and net-zero emission scenarios such as the SSP1-2.6 are shown to be
unnecessary to maintain global surface temperature below 2 °C throughout the
21st century.

Figure 9: Summary and comparison of the impacts and risks of global warming
projections for the 2080–2100 period herein obtained (Tables 3B and 4) versus
the climate “thermometer” proposed by Climate Action Tracker (2022).

Figure 9 employs the climate “thermometer” proposed by Climate Action Tracker
(2022) to summarize the above findings by contrasting the projections derived
from the IPCC climate assumptions, where only the SSP1-2.6 net-zero emission
scenario could satisfy the 2.0 °C target, with the new proposed assessments of
“realistic” global warming impacts and risks obtained using the three semi-
empirical models discussed above with the pragmatic SSP2-4.5 scenario that
approximately agrees with the real world action based on current policies
(Tables 3B and 4).

As a result, despite predictions that the climate system would continue to
warm throughout the 21st century, there is no compelling evidence of an
impending global disaster caused by manmade greenhouse gas emissions. The 2.0
°C Paris-agreement warming target for the 21st century can likely be met even
under the feasible and moderate SSP2-4.5 emission scenario because future
climate change is expected to be modest enough that any potential related
hazards can be addressed efficiently through effective and low-cost adaptation
strategies, without the need for implementing rapid, expensive, and
technologically likely impossible net-zero decarbonization policies.

## Declarations

### Funding

The author declares that no funds, grants, or other support were received
during the preparation of this manuscript.

### Competing Interests

The author has no relevant financial or non-financial interests to disclose.

### Author Contributions

NS has performed the analysis and written the study.

### Data availability

The data used in the paper can be downloaded from (accessed on 01/10/2023):

  * •

KNMI Climate Explorer: https://climexp.knmi.nl/selectfield_cmip6.cgi

  * •

Supplementary data from Hausfather et al. (2022)

  * •

CRUTEM5 Global Temperature: https://www.metoffice.gov.uk/hadobs/crutem5/

  * •

CRUTEM4 Global Temperature: https://www.metoffice.gov.uk/hadobs/crutem5/

  * •

UAH MSU v. 6.0 Global Temperature:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt

## Appendix A. Supplementary data

The supplementary data file contains the temperature simulations depicted in
Figures 1, 5, 6 and 7.
https://ars.els-cdn.com/content/image/1-s2.0-S1674987123002414-mmc1.xlsx

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