I am quite familiar with most of the authors of this report. Most are considered to be climate skeptics, some more credible than others. Christy and Spencer have long been pro-Trump, anti-left, and religious conservatives, whose work has been strongly criticized, although they have pioneered and led work in satellite temperature measurement from NASA's Goddard Space Center. Curry has also been criticized, but mostly as a result of criticizing the zeal of catastrophist climate scientists. Koonin is actually an Obama-era skeptic who is perhaps a bit more mainstream. He wrote a book, arguing that climate science has more uncertainties than often reported. I am not familiar with McKitrick. I read and reviewed a book by Spencer and pointed out his embarrassing attempt to prove mathematically that Trump won the 2020 election. He has also been associated with some religious fanatics who see environmentalists as a kind of evil. I would say Spencer is most likely to be biased, although I largely agreed with an op-ed he wrote recently. Spencer and Christy were on science advisory boards in the first Trump administration. They developed tropospheric temperature measurement methodologies via satellite data and are associated with the “warming pause” in that data, which has been a matter of debate for many years. Christy did missionary work in Africa and, like Wright, saw the effects of energy poverty firsthand. I have had the opportunity to hear Curry speak, both live and in podcasts, and have read about her story of being attacked by other climate scientists merely considering the ideas of some skeptics and developing a more skeptical approach to climate science, seeing the prevailing paradigm as too ready to embrace catastrophism. She comes across to me as a sincere and knowledgeable scientist.
Energy Secretary Chris Wright
commissioned the report and selected the scientists to deliver it. He says they
are a diverse group, which may be partially true, but I would say they could
have been more diverse. Wright notes in the forward that he has invited public
comment to the report. He emphasizes in the forward that action on energy
poverty should trump action on climate change mitigation:
“Climate change is real, and it deserves attention. But
it is not the greatest threat facing humanity. That distinction belongs to
global energy poverty. As someone who values data, I know that improving the
human condition depends on expanding access to reliable, affordable energy.
Climate change is a challenge—not a catastrophe. But misguided policies based
on fear rather than facts could truly endanger human well-being.”
The report emphasizes the
uncertainties of climate science and the benefits of increased atmospheric CO2
to counter the focus on the dangers of greenhouse gases. The executive summary
notes the large range of predicted climate sensitivity, the average surface
warming under a doubling of the CO2 concentration, which varies from 1.8°C to
5.7°C, a range that has stayed more or less the same since the 1970s, with most
early estimates at (1.5-4.5). That is indeed a real uncertainty. They say that
model-driven climate sensitivity estimates are much higher than data-driven
estimates. I don’t think that is always the case, nor is there a consensus
about that. Do climate models overestimate warming? Some certainly do, but
others may not. It is noted that:
“Claims of increased frequency or intensity of
hurricanes, tornadoes, floods, and droughts are not supported by U.S.
historical data”
They note that forest
management is a major factor in the frequency of wildfires and that there are
several other factors influencing global mean sea level besides global
warming. They refute that sea level rise has accelerated. They acknowledge
ocean acidification, but think coral reefs are more resilient than predicted,
noting improvements at the Great Barrier Reef.
“Attribution of climate change or extreme weather events
to human CO2 emissions is challenged by natural climate variability, data
limitations, and inherent model deficiencies. Moreover, solar activity's
contribution to the late 20th century warming might be underestimated.”
It is noted that the report
was commissioned to challenge the consensus on climate change. Thus, in that
light, the selection of scientists is probably a good selection.
Chapter 1 is a very short
section noting the 2009 designation of CO2 and other GHGs as pollutants under
the Clean Air Act and as threats to public health. That ‘endangerment finding’
is in the process of being revoked. Atmospheric CO2 is currently at about
430ppm, increasing at about 2ppm per year. At about 5000ppm, it can begin to
have negative effects on humans, so that is not a likely scenario now or in the
future.
Chapter 2 is about CO2’s effects on
the environment. Here, they emphasize its beneficial effects on plant
fertilization and resultant “global greening.” These are positive effects that
have helped to improve crop yields and reforestation. The greening helps to
mitigate global warming as well by increasing the terrestrial uptake of CO2.
CO2 both enhances photosynthesis and reduces leaf-level transpiration,
resulting in increased crop water productivity, the yield per unit of water
used. The authors think that the IPCC has underemphasized the benefits of CO2
to plant growth.
In discussing ocean
acidification, they note that it is a misnomer since the ocean is not likely to
become acidic (pH below 7.0) but merely less alkaline. Thus, ‘ocean
neutralization’ would be a more accurate term. Below is a graph of changes to
ocean pH from 1985-2022, where it dropped from about 8.11 to about 8.05. For
comparison, they note that:
“…boron isotope proxy measurements show that ocean pH was around 7.4 or 7.5 during the last glaciation (up to about 20,000 years ago) increasing to present-day values as the world warmed during deglaciation.”
They conclude that the
effects of increased oceanic CO2 on pH are exaggerated.
Chapter 3 discusses human
influences on climate. They note that the IPCC and others have chosen to focus
on the more extreme climate models, which they say are implausible. They note
that there is much natural climate variability on different time scales and
that it is difficult to estimate anthropogenic influences, but also acknowledge
them.
“Human activities influence climate through changing
land use and land cover. Humans are also changing the composition of the
atmosphere by emissions of CO2 and other greenhouse gases and by altering the
concentration of aerosol particles in the atmosphere.”
Below are graphics of
estimates of radiative forcing components.
“These graphs show that the total radiative forcing is
comprised of both natural and anthropogenic components. Carbon dioxide is the
largest human influence on the climate and the one most relevant to the
influence of fossil fuel use. It exerts a warming influence by decreasing the
cooling power of the atmosphere.”
Below are the changes in
atmospheric CO2 concentrations since 1955, with the thresholds for C3 and C4
plants and the minimum CO2 concentration during maximum glaciation. The second
graph compares model predictions with actual observations. As can be seen, most
past model predictions show CO2 increasing faster than has been observed,
although the more current models are more in line with observations. They note
that about half of anthropogenic emissions accumulate in the atmosphere.
“The carbon cycle accommodates about 50 percent of
humanity’s small annual injection of carbon into the air by naturally
sequestering it through plant growth and oceanic uptake, while the remainder
accumulates in the atmosphere.”
They note that land uptake of
CO2 has increased over time, but while ocean uptake has also increased, it is
more difficult to measure. It is certain that land uptake has increased faster
than oceanic uptake.
They explore the effects of
urbanization, particularly the urban heat island effect, on surface temperature
measurements. They note that it is challenging to measure the heat island
effects. The argument, made in papers by McKitrick and others, notes that heat
island effects are likely to result in overestimates of surface temperatures.
“In summary, while there is clearly warming in the land
record, there is also evidence that it is biased upward by patterns of
urbanization and that these biases have not been completely removed by the data
processing algorithms used to produce climate data sets.”
Chapter 4 explores climate
sensitivity to CO2 forcing. They argue that there is growing recognition that
climate models can’t be used to estimate climate sensitivity and that
data-driven models can be constrained by some parameters having limited or
low-quality data, particularly data and proxies that rely on the past, such as
paleoclimatic reconstructions and historical data. They note that data-driven
estimates of Equilibrium Climate Sensitivity (ECS) tend to be lower than
model-based estimates.
As noted, the ECS has
remained an estimate with a wide range, although several groups and scientists
have tried to confine it to a smaller range. There is still much debate and
disagreement. The variation in ECS estimated from climate models is shown below
to be from 1.83°C to 5.67°C.
Energy Balance Models are
used to derive data-driven ECS estimates. These utilize surface and ocean
temperature records. They also make assumptions about climate forcings and
ocean heat storage. Thus, they can have similar uncertainty factors as climate
models, since both rely on assumptions. They go into some detail about how ECS
is estimated, the many uncertainties of both climate model estimates and
data-driven estimates, and the ongoing debates about ECS ranges.
Below, they explain another
metric that could be used in addition to ECS, a metric that is more constrained
and less exposed to uncertainties than ECS, the Transient Climate Response
(TCR):
Chapter 6: Discrepancies
Between Models and Instrumental Observations – I think that perhaps the central
argument of this report is that climate models are too variable and too beset
with uncertainty and bias to accurately predict future warming scenarios. Thus, they
emphasize discrepancies between models and direct observations. They argue as
well that many of these models fail to accurately predict what happened in the
past, where we have historical data and proxies to compare with the models.
Below, they argue that climate models are very complex and explain the effects
of subgrid assumptions.
The graphic below (from
Scarletta, 2023) shows that climate modeling of surface temperatures has fairly
consistently overestimated temperatures from what has been observed. It can
also be observed that in the low ESC models, there was a better fit of observed
data to the models than in medium and high ECS models.
McKitrick and Christy have
worked together as co-authors arguing that tropospheric temperatures have also
been consistently overestimated in climate models from 1979-2014. Updating in
2025, they note that the discrepancy between modeled and observed tropospheric
temperatures has gotten larger. They note that the IPCC has long acknowledged
the discrepancy between models and observed data, but lament that they have
only medium confidence that there is a warming bias in modeling. However, one
might also interpret that to mean they do acknowledge the warming bias. They
show some more examples of discrepancies between models and observed data in
vertical warming patterns in the tropics and in the tropical troposphere. They
note that models that predicted stratospheric cooling did not prove correct
since 2000, when stratospheric warming has been observed. Stratospheric
temperature changes are also influenced by ozone depletion and recovery, to
which they have been attributed by some scientists. Northern Hemisphere snow
cover has not dropped as models have predicted. Below is a chart of a very
large discrepancy of climate model temperature predictions with observed data
in the U.S Corn Belt from 1973-2022.
Chapter 6 covers extreme
weather. The chapter summary is below, noting that long-term trends do not
support the media rhetoric we often hear of these trends getting consistently
worse:
Chapter Summary
“Most types of extreme weather exhibit no statistically
significant long-term trends over the available historical record. While there
has been an increase in hot days in the U.S. since the 1950s, a point
emphasized by AR6, numbers are still low relative to the 1920s and 1930s.
Extreme convective storms, hurricanes, tornadoes, floods and droughts exhibit
considerable natural variability, but long-term increases are not detected.
Some increases in extreme precipitation events can be detected in some regions
over short intervals but the trends do not persist over long periods and at the
regional scale. Wildfires are not more common in the U.S. than they were in the
1980s. Burned area increased from the 1960s to the early 2000’s, however it is
low compared to the estimated natural baseline level. U.S. wildfire activity is
strongly affected by forest management practices.”
In this chapter, they analyze
data on hurricanes, cyclones, temperature extremes, heat waves, extreme
precipitation, tornadoes, flooding, droughts, and wildfires. They acknowledge
an increase in heavy precipitation since the 1950s.
Chapter 7 explores sea level
changes. They note in the summary:
“Since 1900, global average sea level has risen by about
8 inches. Sea level change along U.S. coasts is highly variable, associated
with local variations in processes that contribute to sinking and also with
ocean circulation patterns.”
They explain later:
“At the global level, warming raises sea level through
thermal expansion of sea water and through melting of glaciers and ice sheets.
Variations in land water storage are another important factor. At
the regional scale, sea level change is influenced by large-scale ocean
circulation patterns, and geologic processes and deformation from the
redistribution of ice and water. Locally, vertical land motion from geologic
processes, ground water withdrawal, and fossil fuel extraction are also important.”
They examine sea level data around the U.S. and then consider the projections of sea level rise. Some of the models produce estimates for the acceleration of sea level rise that seem out of synch with historical trends, as the graphic below compares NOAA predictions to historical averages dating back to 1920. NOAA and others are predicting a pretty big acceleration in sea level rise, which has yet to be confirmed. The authors do not believe the past data show any acceleration in global sea level rise.
In chapter 8, they criticize
climate change attribution science, explaining it in the summary and noting the
challenges and difficulties of it:
“Attribution” refers to identifying the cause of some
aspect of climate change, specifically with reference to anthropogenic
activity. There is an ongoing scientific debate around attribution methods,
particularly regarding extreme weather events. Attribution is made difficult by
high natural variability, the relatively small expected anthropogenic signal,
lack of high-quality data, and reliance on deficient climate models. The IPCC
has long cautioned that methods to establish causality in climate science are
inherently uncertain and ultimately depend on expert judgement.”
Thus, attributing causality has very high margins of error due to the uncertainty inherent in the assumptions. Below is an IPCC explanation of the difference between detecting changes and attributing them to specific causes. The former is much easier to do with accuracy than the latter. They point out one reason for this, that direct experiments on climate are usually not possible or very limited due to the timescales it takes to measure climatic changes.
Attribution methods often
seek to determine how much of certain observed climate change effects are
attributed to natural causes vs. human causes. That is a very difficult problem
to solve, despite what some researchers believe. The IPCC has long acknowledged
uncertainty in attribution science. Both detection and attribution rely on
statistical analysis. The authors explore some of the IPCC’s statistical
attribution methods, which are summarized below.
The authors present three
criticisms of the IPCC's attribution methods: natural climate variability,
inappropriate statistical methods, and discrepancies between models and
observations. They go through the first two, having already covered the
discrepancies. They explore solar variability and variability in large-scale
ocean circulations. They argue that one of the statistical attribution methods,
optimal fingerprinting, the most used method, is inherently unreliable and note
that others have called it inherently biased. Time series methods are not
model-based but depend on assumptions. There is no general consensus on their
accuracy.
Below, they discuss planetary
albedo and the possibility of short-term climate drivers being associated with
the recent record warmth:
8.4 Declining planetary albedo and recent record warmth
A sharp recent increase in global average temperatures has
raised the question of short-term drivers of climate. One such candidate is the
fraction of absorbed solar radiation which has also increased abruptly in
recent years. The question is whether the change is an internal feedback to
warming caused by greenhouse gases, or whether something else increased the
fraction of absorbed radiation which then caused the recent warming.
The planetary albedo is the fraction of incoming solar
radiation that is reflected back into space rather than being absorbed by the
planet. Highly reflective surfaces like cloud tops and snow and ice
are most important in this regard. The Earth's albedo is
approximately 30 percent, meaning almost a third of the sunlight that reaches
Earth is directly reflected back to space. A lower albedo implies more solar
energy is absorbed by the planet to be then re-radiated as heat. Hence, other
things being equal, a decline in planetary albedo is associated with a warming
of the Earth.
Arguably the most striking change in the Earth’s climate
system during the 21st century is a significant reduction in planetary albedo
since 2015, which has coincided with at least two years of record global
warmth. Figure 8.2 shows the planetary albedo variations since 2000,
when there are good satellite observations. The 0.5 percent reduction in
planetary albedo since 2015 corresponds to an increase of 1.7 W/m2 in absorbed
solar radiation averaged over the planet (Hansen and Karecha, 2025). For
comparison, Forster et al. (2024) estimate the current forcing from the
increase in atmospheric CO2compared to preindustrial times to be 2.33 W/m2.
Looking at the graph and
comparing to recent record warming, there is a correlation, but no causation
has been established. It also corresponds to a global decline in cloud cover.
This could be a positive cloud feedback responding to warming, or it could be a
temporary fluctuation due to natural variability, they suggest. Below is a
table showing the emergence of anthropogenic signals in the historical period
for climate impact drivers.
There is a section on extreme
event attribution where they emphasize the inherent ambiguity of such
attributions. That uncertainty can be exploited by drawing clear and
overwhelming connections between climate change and extreme events, but such
attributions cannot be proven or even convincingly suggested, despite the
media's insistence on bringing up climate change every time an extreme event
occurs.
Chapter 9 explores climate
change and agriculture. Here they return to CO2’s fertilization effect. I am
not sure why they added this as a separate chapter from Chapter 2, which covers
global greening. The positive effect of CO2 on crop yields is well-known and
undisputable.
Chapter 10 – Managing the
Risks of Extreme Weather delves into things like monetary disaster losses,
disaster mortality, and the comparison of the risks of heat and cold. Many have
argued that the risks of cold are worse, and others that the risks of heat are
worse, so there is much debate. Data has shown conclusively that monetary
losses due to disasters are based much more on the overdevelopment of
vulnerable areas and much less on changes in disaster rates and intensities.
Chapter 11 explores the
economics of climate change and the social cost of carbon. Regarding the social
cost of carbon, they write:
“Social Cost of Carbon (SCC) estimates are highly
uncertain due to unknowns in future economic growth, socioeconomic pathways,
discount rates, climate damages, and system responses. The SCC is not
intrinsically informative as to the economic or societal impacts of climate
change. It provides an index connecting large networks of assumptions about the
climate and the economy to a dollar value. Some assumptions yield a high SCC
and others yield a low or negative SCC (i.e. a social benefit of emissions). The
evidence for or against the underlying assumptions needs to be established
independently; the resulting SCC adds no additional information about the
validity of those assumptions. Consideration of potential tipping points does
not justify major revisions to SCC estimates.”
They show some work on
disaster economic impacts by Roger Pielke Jr., another skeptic author with good
scientific credentials. They go through economist William Nordhaus’s climate
change economic predictions, which suggest they will not be as bad as some have
predicted. His Dynamic Integrated Model of the Climate and Economy model, or
DICE) was used in developing the IPCC’s Integrated Assessment Models (IAMs).
These economic predictions far into the future have many inherent
uncertainties, including changing capabilities to adapt due to things like
technology. They note that even the Biden Administration acknowledged that the
effects of climate change on the U.S. economy are likely to be small. They
examine in some detail the way SCC is estimated in the IAMs. They propose that
the effective SCC is low due in part to the beneficial effects of atmospheric
carbon enhancing the economy and offsetting some of the detrimental effects. I
think that is a fair argument. They note that when discrete catastrophic outcomes
such as tipping points are incorporated into models, the SCC becomes inflated.
We still don’t know enough about equilibrium points in different Earth systems
and global cycles to accurately incorporate predicted tipping points into
economic modeling. They note that a Global Tipping Points Report published at
COP28 in 2023 listed the following as potential tipping points:
“Greenland ice sheet disintegration, West Antarctic ice
sheet disintegration, summertime disappearance of Arctic sea ice, Amazon
rainforest dieback, coral reef dieoff, thawing of permafrost and methane
hydrates, Atlantic Meridional Overturning Circulation collapse, boreal forest
shift, West African monsoon shift, and Indian Monsoon shift.”
Chapter 12 is Global Climate
Impacts of U.S. Emissions Policies. The key point of this chapter is that U.S.
actions to reduce emissions are likely to have negligible impacts. They compare
air pollutant impacts to greenhouse gas impacts, noting a big difference in
time scales of effects.
“The emissions rates and atmospheric concentrations of
criteria air contaminants are closely connected because their lifetimes are
short and their concentrations are small; when local emissions are reduced the
local pollution concentration drops rapidly, usually within a few days. But the
global average CO2 concentration behaves very differently, since emissions mix
globally and the global carbon cycle is vast and slow. Any change in local CO2
emissions today will have only a very small global effect, and only with a long
delay.”
As a case study, they examine
potential effects of aggressive regulation of greenhouse gases from U.S.
transportation. Of course, reducing GHGs also often results in the co-benefit
of reducing air pollutants, especially in places vulnerable to poor air quality
like California. They note that even aggressive efforts are likely to have
negligible effects.
“Consequently, in contrast to the case of local air
contaminants like particulates and ozone, even the most aggressive regulatory
actions on GHG emissions from U.S. vehicles cannot be expected to remediate
alleged climate dangers to the U.S. public on any measurable scale.”
Below are the concluding
thoughts. I think this was a very good report overall, with the skepticism
evident but not overdone. I believe it should be widely read and considered.
The paper is presented as a critical review, and that is what it is. Thus, it
is critical of some of the mainstream views of climate change science and
impact analysis.
References:
A
Critical Review of Impacts of Greenhouse Gas Emissions on the U.S. Climate by
Climate Working Group. (John Christy, Ph.D., Judith Curry, Ph.D., Steven
Koonin, Ph.D., Ross McKitrick, Ph.D. and Roy Spencer, Ph.D.) U.S. Department of
Energy. July 23, 2025. DOE_Critical_Review_of_Impacts_of_GHG_Emissions_on_the_US_Climate.pdf
No comments:
Post a Comment