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General Circulation Models’ problems
There are problems in GCMs with determining carbon storage, with the effects of
aerosols, with clouds, and with CO2 forcing.(232,233,273) We discuss each of these
problem areas below.
However, these are not the only problems found with the current crop of GCMs. In a
2003 paper that identified a human effect on sea-level atmospheric pressure, the
researchers note that they “find increases in sea-level pressure over the subtropical North
Atlantic Ocean, southern Europe and North Africa, and decreases in the polar regions and
the North Pacific Ocean, in response to human influence.”(222) After this statement of
their important finding, Gillett et al. go on to compare the results from the different
GCMs they used in their research. “Our analysis also indicates that the climate models
substantially underestimate the magnitude of the sea-level pressure response.”(222)
The oceans seem to give rise to other model problems. As noted in the chapter, the
number of longterm ocean monitoring stations is small, and coverage is sparse, the lack of
sufficient data making it hard to distinguish among ocean climate models.(274) Despite this
problem, an analysis of carbon transport models (16 models and variants were tested),
found “an uptake of CO2 in the southern extratropical ocean less than that estimated from
ocean measurements, a result that is not sensitive to transport models or methodological
approaches.”(275) This is in distinction to their results for the Northern Hemisphere land
sink, where the “results show some sensitivity to transport differences among models,
especially in how they respond to seasonal terrestrial exchange of CO2.”(275)
Important climate connections are still being discovered (they must be recognized to be
included in climate models). One example of this is the connection between climate and
geology. Earthquakes have occurred because of climate change.(276)
The role of clouds and water
Water vapor is the most plentiful greenhouse gas. It is important to treat the latent heat
and moisture as correctly as possible within the limitations of a GCM. In the late 1980s,
divergent predictions of various models was worrisome, and the cause was not identified.
Cess et al. compared 14 climate models in 1989 and again in 1993, and found that the
differences—some as great as an order of magnitude—mainly arose from the respective
models’ treatment of clouds.(232,233) As discussed in the chapter, the models make three-
dimensional grids of the atmosphere and treat each volume (“cell”) separately. The cells
are joined along boundaries, and the boundaries must match. Anyone who has lived
through a partly cloudy day can appreciate the difficulty of including clouds within such
a scheme, so the models’ difficulties should come as no surprise.
Clouds have important local effects as well. A study of light absorbed in a rainforest
during the rainy season in which artificial light was introduced showed that rainy season
cloudiness strongly affects carbon storage (and that artificial light is not very effective in
countering the loss of natural light).(234)
This recognition of the basis for the difficulties led to a focus on the cloud problem within
the models and to development of improved treatment of clouds, which led to new
generations of models that are better able to describe what happened in the past, what is
happening now, and what may happen in the future. (This is not, of course, to say
current models are anywhere near perfect.)
The Department of Energy’s Atmospheric Radiation Measurement (ARM) program was
initiated in the early 1990s, around the time the models were shown to be in need of
improvement and partially in response to the problems, to gather better atmospheric data
for use both in weather prediction and in climate modeling.(273) New instruments were
deployed, such as lidar to be used to identify the molecular species present in clouds and
air through use of Raman scattering, and these provided data that added new perspectives
on the many issues involved and have helped improve the models.
Prior to ARM, what atmospheric measurements there were were mostly short term in
nature.(273) The ARM program was a bold step in that it would monitor the atmosphere
for an extended period—years or decades rather than weeks or months. As of 2003, there
were approximately a thousand instruments recording atmospheric properties.(273)
In addition, climate modelers were involved in creation of new models for clouds and in
generation of ideas that can be tested through new instrumentation. Theorists wrestle
with the statistical properties of clouds and how to incorporate them into models as well
as with how to introduce realistic cloudiness within grid cells.
We have seen that carbon dioxide acts as a trap for infrared radiation. Hence, increasing
the carbon dioxide content of the atmosphere warms Earth; this means more water vapor
in the air (the warmer the air, the more water it can hold), which warms the atmosphere
still more. The water becomes warmer, giving off CO2, further increasing the water vapor
and carbon dioxide concentration in the atmosphere. These atmosphere-ocean feedback
loops are complex and not yet entirely understood. Do clouds contribute to warming
overall by increasing the infrared absorption? Do they cause cooling by increasing Earth’s
albedo? Ramanathan and colleagues raised the question of the effect of clouds rather than
water vapor per se.(187)
Clear skies have no cloud-cover effects. The net cloud forcing term of the flux is broken
into its long wave (infrared) and short wave (visible) components, CLW and CSW. These
two components of forcing have been measured by satellite. The long wave forcing is
expected to be positive, that is, to cause warming. This is the familiar greenhouse effect.
The short wave forcing is expected to be negative, because clouds should (mostly) reflect
visible light. The preliminary satellite measurements (187) give CLW = 30.1 W/m2 and CSW
= -46.7 W/m2. The near cancellation observed in the experiment was unexpected. For
comparison, the doubling of the carbon dioxide concentration by 2050 is expected to
contribute 4 W/m2 and the melting of pack ice near the north pole could change the
regional flux by 50 to 100 W/m2. From this result, clouds would appear to cause slight
cooling overall.
As the temperature is raised as a result of greenhouse warming, liquid water will be able to
hold less carbon dioxide gas in solution, further increasing the carbon dioxide
concentration in the atmosphere. Feedback loops in the ocean incorporate the fact that
current surface water is more acidic than the deep water. As this water circulates, it will
cause calcium carbonate to dissolve, releasing more carbon dioxide into the seawater; much
of this CO2 will be released to the atmosphere over a time period of several hundred
years.(277)
Facchini et al. suggest that their measurements of cloud water imply that the albedo of
clouds could be increased by the presence of organic molecules (and these are being
emitted in pollutants worldwide).(278) These brighter clouds would reflect more sunlight
away from Earth. Clouds could also become brighter because other sources of pollutants
limit drop size, such as nitric acid, HNO3.(279) Nitric acid appears to enhance formation
of high cirrus clouds.(280) The same volume of water is spread out more thinly, which
makes the cloud more reflective. Again, this could increase the cooling effect of clouds.
An effect that could run in the other direction is the formation of clouds by jet contrails.
Everyone has seen these contrails as they become clouds (they end up looking like cirrus
clouds). Jet contrails also enhance formation of high cirrus clouds.(281) Cloud cover over
the United States has increased by about 5% since the jet age, and this extra cloudiness
may be contribute extra warming, confusing the cloud issue in GCMs.(282)
The ban on air travel in the aftermath of the terrorist attack on the World Trade Center
was responsible for finding some answers. Analysis of data from around 4000 U.S.
weather stations as compared to data from those same stations over the 30-year period
from 1971 to 2000 showed that the difference between a day’s highest and lowest
recorded temperature (known as diurnal temperature range) increased by over 1 °C
compared to the longterm mean diurnal temperature range.(283) More important, the range
was 1.8 °C higher during this three-day period compared to the three-day periods
immediately before the flight ban and immediately after the lifting of the flight ban.(283)
Travis et al. compared deviations of this magnitude to the thirty-year record and found
none comparable. Nothing in the cloudiness experienced during the travel ban could have
given rise to such an anomalous result.(283)
There are suggestions that water vapor in the upper troposphere could be a source of
negative feedback. It is not even clear that changes in the water vapor column have been
occurring, and few experts believe that high-altitude water will decrease, but this has not
yet been resolved.(284) In fact, experimental evidence seems to show that the water vapor
has increased in this region by about 1%/yr.(285)
The IPCC says “The amount, location, height, lifespan, and optical properties of clouds
exert important controls on Earth’s climate, and changes in these properties might play an
important role in climatic change.” Note the use of the word “might.” We do not know.
Also, as discussed in the Chapter, clouds appear at scales much smaller than the scales of
even “complete” GCMs and must be dealt with parametrically. In the hoped-for future,
the scale size can be reduced below the threshold for seeing clouds, and cloudiness can
work as part of the climate model.
A 2002 attempt to test the coupled atmosphere-ocean model of the Centre National de
Recherches Météorologique did reasonably well at reproducing known streamflows over
the past half century, but found mixed results otherwise.(286) The main results are not
surprising. More water is available to cloud systems; water remains longer in the
atmosphere because warmer air can hold more water vapor than colder air; the moisture
convergence moves generally northward; and precipitation efficiency is decrreased,
especially during the Northern Hemisphere’s summer.(286)
It is known that modest increases in cloudiness can reduce radiation forcing
significantly.(287) The fifteen main models used in climate simulations do not agree among
themselves when clouds are treated. The reason that this is possible is that the models use
parameters, numbers, to characterize ignorance of certain processes (as discussed
above).(288) It is very important to try to learn more, because the range of possible
sensitivities (that is, distinguishing between the 1.5 °C and 4.5 °C future) is strongly
dependent on the effect of clouds.(289)
The role of aerosols
Aerosols are tiny particles suspended in the atmosphere. There are both natural sources
(natural fires, volcanic emissions, sea salt, VOCs emitted by vegetation) and
anthropogenic sources (fossil fuel burning, human-set fires) for aerosols. The best
estimate is that roughly 10% of aerosols are due to human activity.(290) The natural
particles are predominantly larger than 10 µm, while those from human-generated
processes contain significant amounts of smaller size (see Chapter 13). The major
problem for current climate models is that it is still not known whether, overall, the
aerosols cool or warm Earth in the models.(291)
Recent evidence from using a GCM incorporating aerosols fairly realistically to see
effects indicates that “extra climate forcing factors have a significant impact on both 20th
century climate change and the contemporary land and ocean carbon sinks. The additional
forcings act to delay by more than a decade the conversion of the land carbon sink to a
source, but ultimately result in a more abrupt rate of CO2 increase ... Future climate
change is therefore projected to be more rapid when these additional factors are included,
fueled partly by more abrupt carbon cycle feedbacks as the additional carbon accumulated
in the soils during the historical period is released to the atmosphere. By 2100 strong
positive feedbacks between climate and the carbon cycle have accelerated the rate of
global warming and CO2 increase.”(292)
There is experimental evidence to compare to the model predictions. Nature itself knows
whether aerosols warm or cool. The volcanic eruption from Mount Pinatubo in the
Philippines caused substantial cooling of the climate (the years immediately following the
eruption are the exception to the very warm decade of the 1990s). Figure E17.3.1 shows
the mechanism for such cooling.
Fig. E17.3.1 Volcanoes emit large amounts of ash and sulfur dioxide. These aerosols affect climate becausethey are carried high into the atmosphere by the eruption. S(NASA, Ref. 290)
Evidence for the aerosol effect is the observed faster warming of the Southern
Hemisphere, when the opposite would be predicted without the aerosol effects.(148,293)
Ice core evidence shows that glaciation occurred in Greenland simultaneously with
volcanic eruptions as logged in dust in Antarctic ice cores.(294) Mt. Pinatubo’s 1991
eruption caused a temporary cooling with a peak forcing of - 4 W/m2 in early 1992.(295)
For reasons that are still mysterious, just after the Mt. Pinatubo eruption the rise of
carbon dioxide, methane, and nitrous oxide slowed and the atmospheric concentration of
carbon monoxide dropped suddenly.(296)
Most of North America does not seem to be warming substantially; it may even be
cooling. Why is North America deviating from global warming? Some have argued that
this deviation is proof that the greenhouse effect is overstated. Aerosols, primarily
sulfates, as discussed in Chapter 14, scatter light; they also reflect solar radiation and
increase the number of clouds.(143,284) The effect of reflection is to lower the amount of
sunlight reaching Earth by 0.2% to 0.3%, taking into account that half of Earth is cloudy
on average.(143,288) Globally, this could cause a cooling forcing of -1 to -2
W/m2.(148,293,297)
Measurements also show that the sun has “dimmed” due to pollution in the atmosphere
by as much as 37%.(298) It is predicted that this will have small effect on agriculture in
distinction to what would happen if the sun really dimmed.(298) There is support for the
“global dimming” due to pollutants in experiments in pan evaporation (basically, set a pan
in the sun and see how quickly evaporation occurs).(299) Cloudiness also “dims” the sun
in a similar way.(300) In addition, an examination of Earthshine on the moon indicates that
Earth dims and brightens for reasons not yet known. Are these changes in abbedo related
to atmospheric pollutants?(62)
The atmospheric residence time is long (decades) for CO2 and short (weeks) for sulfates
and other aerosols. Because of the vast differences in residence times, cooling is immediate
while greenhouse warming is spread over a century. (The aerosol cooling through
enhancment of cloud reflectivity is known as the Twomey effect.) Indirect effects of
aerosols from burning weaken the Twomey effect.(301) Liu and Daum find that
“anthropogenic aerosols exert an additional effect on cloud properties that is derived from
changes in the spectral shape of the size distribution of cloud droplets in polluted air and
acts to diminish this cooling.”(301) This is not the case with most natural aerosols, those
produced by the oceans and attendant ecosystems. Human-caused aerosols are more
complex chemically than marine aerosols and have many more very small particles.
The combined direct and indirect aerosol effects have been estimated to contribute –1.4 ±
0.5 W/m2 (not including systematic error). Thus, effects from aerosols reduce global
warming (short term) by around 60%.(302)
The overall warming is about four times greater than cooling. The present “standoff”
results because of growing burning (the total warming is near zero as long as carbon
emission grows exponentially).(303) Cleaning up sulfate pollution may bring the
greenhouse with a vengeance. On the other hand, this is an opportunity to benefit from
reductions.
As discussed in the Chapter and in Chapter 14, aerosols (including soot) are still not well-
understood. This uncertainty carries over into the models’ treatments of aerosols. The
three major aerosols considered in the models are sulfate aerosols (SO4) resulting from
fossil fuel combustion, soot (carbon black), and the VOCs also emitted from combustion
and vegetation. While dust may have an effect, it is not usually included, but is discussed
by the IPCC.(304) While dust has been thought to add cloud condensation nuclei,
increasing rainfall, experimentally it seems to work the other way.(305)
The radiative forcing measured in the Indian Ocean experiment (IDOEX) differed by a
factor of three between the surface and the upper stratosphere in clear sky
conditions.(306) This suggests that the soot from burning near Indonesia affects the water
cycle in the atmosphere.(306,307) The value from this INDOEX experiment is equivalent
to a forcing of - 26 W/m2.(306) Since soot has increased in the last century and a half of
industrial output by roughly a factor of three,(308) the behavior of soot is extremely
important to a full understanding of climate. The INDOEX value is much larger than the -
0.3 W/m2 found to result from Brazilian biomass burning.(309)
Carbon black (soot) was estimated to be produced at 50 to 200 kilotonnes per year.(310)
A new approach has led to a realization that soot emissions from the burning biomass and
fossil fuels can interfere with aerosol reflectivity, making them absorb more radiation.(311)
This realization led to the formation of AERONET, a network of over 250 sites
worldwide to monitor aerosol optical depth and absorption optical depth.(312)
AERONET data implies that the amount of black carbon has been underestimated before
this measurement by a factor of 2 to 4 (implying emission of 200 to 800 kt/yr).(312) As
the authors of Ref. 312 point out, both developed and developing countries contribute
soot. Developed countries’ soot comes mainly from diesel emissions, but emissions from
developing countries comprises not only vehicles but heavily polluting industries and
cookstoves (see Chapter 14, and especially Extension 23.3, Cookstoves). As is clear,
“large emissions of soot aerosols in developing countries have negative impacts on human
health, agricultural productivity, regional climate, and global warming.”(312)
As we pointed out in Chapter 16, African dust can travel to America, bringing
possibilities of coral disease and fungal infestations. But the dust has other effects, both
on health and climate. As Prospero and Lamb point out, “[d]ust could also affect climate
through cloud microphysical processes, possibly suppressing rainfall and conceivably
leading to the perpetuation and propagation of drought. Over south Florida, clouds are
observed to glaciate at relatively warm temperatures in the presence of African dust, an
effect that could alter cloud radiative processes, precipitation, and cloud lifetimes.”(313)
They go on to state that iron in the dust fertilizes the ocean (see also Extension 17.6,
Methods of removing or reducing CO2 and trace gases), and that “during intense drought
phases, the concentration of respirable dust over the Caribbean probably exceeds the U.S.
Environmental Protection Agency’s 24-hour standard.”(313)
Research has identified the influence of another pollutant in climate, the
chlorofluorocarbons that deplete ozone. Observations of Southern Hemisphere changes
seen in spring and summer are due to ozone depletion in the stratosphere.
Chlorofluorocarbons do not appear to be implicated, however, in the winter Southern
Hemisphere climate changes.(314)
Carbon storage
Carbon storage is an observational as well as a model problem. Since the CO2
concentration prior to the industrial age was constant over several millennia, it is clear that
the sources and sinks of carbon dioxide were in balance at that time. That does not,
however, tell us where the carbon came from nor where it went. With the industrialization
of Europe and North America, the release of greenhouse gases has changed that balance
substantially. Europe now absorbs only 7 to 12 percent of its carbon emissions.(315) And
in the present world, the sources and destinations of carbon are not particularly well-
understood, although as Quay points out, much “of the interannual variability in
atmospheric CO2 is driven by changes in uptake by terrestrial biota.”(274) As Ref. 241
points out, “[u]nexpected changes in the flow of carbon between the atmosphere and
terrestrial biosphere and/or the oceans could occur.” This makes it difficult to capture
carbon in the GCMs. Abrupt changes due to humans can occur as well. Page et al.
identified Indonesian peat fires, for example, as a major cause of carbon dioxide rise in the
late 1990s.(28)
In recent models, a CO2 “fertilization factor,” implying increased plant production, is
included as a fitted parameter as, for example, was the case for the 1992 IPCC model.(316)
Depending on the way this parametrization is done, the models can give different
predictions. Work is in progress to determine how this works in nature, but while some
results are in, the situation is still not well-determined. For instance, Arctic tundra seems
to exhibit both carbon uptake and efflux.(80,105,317-319)
The assessment of Ref. 320 is particularly helpful in setting out the uncertainties inherent
in attempting to quantify the carbon stocks. Refer especially to Fig. 4.5 and 4.7 of Ref.
306 for a sense of this uncertainty (every ecosystem considered in these figures could be
either a net source or a net sink for carbon).(320) This question is discussed in more detail
in Extension 17.7, Planting trees.
Climate sensitivity, forcing, and uncertainty
The climate sensitivity is the response to a given forcing (that is, a certain number of
watts per square meter will cause a temperature increase of a definite amount).
Technically, after the IPCC Second Assessment Report, it is defined as “[t]he radiative
forcing of the surface-troposphere system due to the perturbation in or the introduction
of an agent (say, a change in greenhouse gas concentrations) is the change in net (down
minus up) irradiance (solar plus long-wave; in W/m2) at the tropopause AFTER allowing
for stratospheric temperatures to readjust to radiative equilibrium, but with surface and
tropospheric temperatures and state held fixed at the unperturbed values.”(321)
The IPCC defines the climate sensitivity parameter λ as
∆Ts/∆F = λ,
where ∆Ts is the response of global mean temperature to a change (in °C), and ∆F is the
extra radiative forcing (in W/m2). Often the sensitivity is stated instead as the temperature
change due to a doubling of preindustrial carbon dioxide (or equivalent). This means that
to find the desired outcome (∆Ts), we need to know both the sensitivity (λ) and the
forcing (∆F).
As the reader is aware by now, this sensitivity is roughly 1.5 °C to 4.5 °C for doubling
the carbon dioxide and trace gases in the climate system. That is a large uncertainty
(actually larger after the IPCC Third Assessment Report, in which it is given as 1.4 °C to
5.8 °C, a much higher final possible temperature).(211) A more natural way to express
sensitivity is as temperature change per watt per square meter of radiative forcing: it is
0.75 ± 0.25 °C/(W/m2).(a) Expressed in this way, it does not imply that a doubling of
carbon dioxide is inevitable (which may be inferred from the popular method of
expressing sensitivity), but merely that increasing forcing will lead to a corresponding
temperature increase.
The Third Assessment Report treated uncertainties more carefully than the two preceding
reports, putting the ends at the 95% confidence interval,(322) and these numbers translate
to a 90% probability that the warming will be 1.7 °C to 4.9 °C.(270) The confidence in the
climate models has grown, but the uncertainty has increased because of the greater
thought and care that went into IPCC descriptions.(322) Obviously this must be decreased
if we are really to pretend we can predict the climate system.
The other underlying uncertainty is the individual forcing terms from each of the elements
in the system that do provide forcing. Hansen et al. believe that this uncertainty is even
less well known than the sensitivity.(152,220) It, too, must be addressed.
Reconstructions of past climates
The public has heard about global warming, but often misconstrued what was meant by
the scientists who were responsible for the research. According to Patt and Schrag, IPCC
used “specific language to describe probability ranges. ... There is a problem with this
strategy, however, in that it uses words differently from the way lay readers of the
assessment typically do.”(323) The mischaracterization of scientific uncertainty by the
press could be responsible for public misapprehension of climate change, one exploited
by industry and political blocs. Because the public thinks it is misinformed, Zehr argues,
it is excluded from the debate.(324) Bond et al. also argue that accurate knowledge is the
most important determinant for individual action on climate change.(325)
Such considerations were involved in public reaction to the 1995 IPCC report. Patt and
Schrag write “[t]he IPCC strategy could result in miscommunication, leading readers to
underestimate the probability of high-magnitude possible outcomes.”(323) Uncertainties
were treated in a confusing way in the report, causing environmental groups to seize on
the unequivocal nature of the IPCC statement that humans have a discernible effect on
climate, and climate doubters pointed to the uncertainty.(326) As a result of this rancor,
climate scientists Stephen Schneider and Richard Moss are working to persuade fellow
scientists to characterize their model uncertainties better.(326)
In the chapter, we present several detailed reconstructions of past temperatures. As
presented, we see generally warmer temperatures in the first two centuries, followed by
much cooler temperatures fora period of 500 to 600 years, followed by warming,
followed by spectacular warming over the past half century. We should ask how reliable
this picture is. The scale of the current warming swamps every part of the past record in
size.
It is common in the scientific literature to show data with “error bars,” which indicate the
one standard deviation excursions from the measured values. If two data points with
errors have a part of the range in common, we interpret this in almost all cases as
measurements of the same number. Figure E17.3.2 shows the situation for deciding
whether or not two measurements are of the same quantity.
Fig. E17.3.2 a. Two ranges are shown as [ ] and ( ). There is no evidence that the the two ranges are thesame (so we say they represent different measurements). b. There is no evidence that the two ranges aredifferent (so we say they represent a measurement of the same thing). The region where the two rangesoverlap is exactly that of the smaller measurement, which must represrent the best value of themeasurement. c. It is possible that the two measurements are (or are not) the same because the two rangesoverlap in part (so we say they might represent the same measurement).
This direct comparison is not usually done in making these reconstructions, but it is
clearly understood that the farther back in time before the instrumented era (starting at
roughly 1880), the less certain the reconstructions will be. That is due to the use of
proxies to measure the temperature (see Extension 16.3, Proxy measurements). Even in
the instrumented era, not every point on the globe is represented by a measured
temperature, so there is still some uncertainty; it is lower than in the proxy measurement
era. Obviously, the ability to reconstruct past climates efficiently and accurately is
important to us in that we will have to place trust on these models to “see” the future.
In many cases, only some reconstructions using differing models agree, and others
disagree, within specified values of the parameters. Is the agreement a signal that the
models are trustworthy? Are the disagreements a signal that the models are not
trustworthy? The answer to this question is clearly arguable. Therefore, it is important to
build some sort of system to determine whether or not the models are trustworthy, or,
more specifically, how trustworthy the results of the model reconstructions are.
One of the earliest, and longest term, reconstructions was of 6,000 years Mann, Bradley,
and Hughes.(92) This paper reported that the current warming was unprecedented in the
record. Is this long a reconstruction reasonable? In many parts of the time series, only one
proxy was used. Mann has since argued that multiple proxies should be used to reduce
uncertainties.(327)
Mann and Jones used these multiple proxies in a gutsy 2,000 year reconstruction. The
intent was to use the backwards-extended record to see whether the late twentieth
century warming remains anomalous.(328) Mann and Jones found that “late 20th century
warmth is unprecedented for at least roughly the past two millennia for the Northern
Hemisphere.” (328)
An important study of the success in reproducing past climates was undertaken by means
of a GCM that is run over the past 1,000 years with random degraded idealized proxy
temperature indications (that is, the data are “noisy”) and plausible (on the basis of the
historical record) external forcings.(329) The results of the model under the differing
conditions were intercompared. The specific model used in the research was the coupled
atmosphere-ocean GCM known as European Centre Hamburg 4–Hamburg Ocean
Primitive Equation–G (ECHO-G). The crux of the matter for this research is, as the
researchers say, “that the model simulates a reasonable, internally consistent climate, and
the external forcing lies within the envelope of possible values.”(329) They found a loss of
variance; that is, the predicted reconstructions were less extreme than the “actual” data
underlying the climate reconstruction. This variance was particularly noted at the
hundred-year scale, and to a smaller extent in the decadal scale.
It should come as no surprise that reducing the noise resulted in less deviation from the
“true” record. The researchers also tested whether the sparseness of proxy points
affected the reconstruction and whether inclusion of more data would make a difference.
In both latter cases, there was a small effect, but not a major effect in making the
change.(329)
The final result of the analysis is that “[t]he centennial variability of the NH [Northern
Hemisphere] temperature is underestimated by the regression-based methods applied
here, suggesting that past variations may have been at least a factor of 2 larger than
indicated by empirical reconstructions.”(329)
Von Storch et al. are not alone in their concern about the uncertainties. Alley has
recognized that the models do not give the full range of uncertainty. According to him,
“An assessment of the effect of CO2, particularly during deglacial warming but also in
looking at the warm climate of the mid-Cretaceous, indicates that the average behaviour of
the models underpinning the IPCC somewhat underestimates climate sensitivity, although
the more sensitive of the models are rather accurate. Similarly, models often exhibit some
skill in simulating abrupt climate changes of the past, but with a tendency to
underestimate the size, extent, or rate of the changes.”(330) Tol questions the ability of
GCMs to lead to meaningful cost-benefit analysis because of the models’ uncertainties:
“The bottom line of all this is that it seems as if the uncertainty about climate change is
too large to apply cost-benefit analysis.”(331)
There are additional problems. A fluctuation analysis indicates that the scale
independence of the actual data is not reproduced by popular GCMs. That would
indicate that predicted global temperatures could be overestimated in the models.(332)
In addition, Peters et al. point out that catastrophic events (such as changes in the oceanic
thermohaline circulation) involve feedbacks among diverse elements nature, and of the
models that describe nature.(333) These feedbacks can lead to what they call “cross scale
interactions,” in which a local event, for example, a wildfire, could grow extremely rapidly
because of larger geographic scale weather patterns that encourage the fire’s spread. Peters
et al. see threshold effects as possible outcomes of cross-scale interactions at fine scales
that create effects at larger scales (certainly these thresholds need to be dealt with
properly whatever the origin) and emphasize the importance of the nonlinearity of
highly-interconnected systems.(333) As with the electric grid, the complexity has both
good and bad features—good in that small fluctuations can be minimized through
interactions, bad in that the complexity means that a larger failure can cascade out of
control. If the models we use do not reflect the cross-scale interactions properly, their
predictions probably cannot be trusted.
As pointed out in Chapter 17, the most abrupt change could be that of the thermohaline
circulation of the ocean conveyer. This is an area of open research. There is clear ice core
evidence that the conveyer changed around the start of the Younger Dryas. It is clear that
oceanic changes in the past controlled large-scale climate changes.(334) Still unknown is
how the thermohaline circulation caused changes in the tropics that led to atmospheric
changes.(335)
Another way to gauge reconstructions is to compare them to others. If there is agreement
with other reconstructions, it is common (though not certain, as we have noted above) to
believe that the reconstruction must be reasonable. One case in which this argument was
made is a reconstruction for the Netherlands between 764 and 1705, and extended through
actual observations to 1998.(336) The overlap with other reconstructions extends back
only to about 1000, for example, a multiproxy reconstruction of European temperatures
to 1500,(337) while the earliest reconstruction is unique. The Low Countries Temperature
index compares well to the other reconstructions. Most interesting, given the result of von
Storch, is that there were strong variations found both in the tenth and the fifteenth
centuries. In this reconstruction, as in all others, the twentieth century was “by far (three
standard errors) the warmest century of the last millennium in terms of winter
temperatures,” while somewhat unexpectedly, “the 13th century was warmest in terms of
summer temperatures (by the narrow margin of one standard error).”(336) This variability,
by virtue of its being so large, may be more realistic than for other reconstructions.
Murphy et al. adopted a different approach. They build a model ensemble by varying the
parameters and construct a “probability density function” to represent the models
reliability.(338) They present their predicted future (doubled carbon dioxide) temperature
in terms of 5% and 95% probability limits: 2.4 to 5.4 °C. They discover “a range of
regional changes much wider than indicated by traditional methods based on scaling the
response patterns of an individual simulation.”(338)
Future climates and their effects
If postdicting the past is chancy, predicting the future is much riskier. Nevertheless, the
IPCC has proceeded to do so, and research groups make these sorts of predictionas all the
time. Using a Monte Carlo simulation with an ensemble of climate models, Knutti et al.
find a 40% probability that the global temperature rise will exceed the IPCC range, while
finding only a 5% probability it will fall below the IPCC range.(339) Stott and
Kettleborough take a completely different tack, examining several emissions scenarios in a
GCM. Their result is that “in the absence of policies to mitigate climate change, global-
mean temperature rise is insensitive to the differences in the emissions scenarios over the
next four decades.”(340) Stott and Kettleborough take the uncertainty very seriously and
find that uncertainties in climate response dominate the near term, while after about 2040,
emissions scenarios do matter. These studies are among the first to be emerging in such a
way that policymakers can use directly.(341)
The uncertainty problem is exacerbated in making projections for policymakers because
of lack of certainty as to the value of the climate sensitivity. If the sensitivity is near the
high end of the scale (4.5 °C per doubling), the effort required to offset greenhouse gas
emissions is much lower than if the sensitivity is high, in which case the situation
approaches emergency status. As Caldiera, Jain, and Hoffert write,
in summary, the amount of global mean temperature change produced by a
change in atmospheric CO2 content is known perhaps only to factor of three.
This uncertainty propagates from climate stabilization pathways, to allowable
carbon dioxide emissions, and ultimately to carbon emissions–free power
requirements. Climate sensitivity uncertainty introduces much greater un-
certainty in allowable CO2 emissions than does carbon cycle uncertainty. For
CO2 stabilization by year 2150 leading to a CO2-induced global mean warming
of 2 °C, estimated allowable carbon emissions later this century could be less
than 0 GtC or greater than 13 GtC (1 GtC \ 1012 kg C) per year, depending on
whether climate sensitivity is 4.5° or 1.5 °C per CO2 doubling, respectively.
A metastudy showed that, with the exception of forestry, every sector that was studied
will be worse off in the future.(342) Hitz and Smith conclude that “beyond several degrees
of GMT [global mean temperature], damages tend to be adverse and increasing.”(342)
Two important points made by Hansen et al.(a) in a paper titled “Earth’s energy
imbalance” are that thermal inertia in Earth’s system (especially the oceans) causes a
delay in warming, and that that in turn implies that there is warming “in the pipeline,”
that is inevitable. That is, the authors maintain that a certain amount of future warming is
already committed to regardless of whether humans stopped their use of fossil fuels
immediately.
Wigley makes similar points and calls the future “pipeline” warming due to the up-to-the-
present anthropogenic changes in atmospheric composition the warming commmitment.(b)
Wigley also remarks that sea level rise is also in the future (see Extension 17.5, What
does sea level rise mean?). He finds in his model the rise will be about 10 centimeters per
century with large uncertainty if the atmospheric composition stops changing now, and
about 25 centimeters per century (also with large uncertainty) if emissions do not change
from present values.(b) Wigley calls this expected rise, together with the warming
commitment, the climate change commitment.(b) Wigley’s conclusion is sobering:
The CC [constant composition] results are potentially more alarming,
because they are based on a future scenario that is clearly impossible to
achieve and so represent an extreme lower bound to climate change over the
next few centuries. For temperature, they show that the inertia of the climate
system alone will guarantee continued warming and that this warming may
eventually exceed 1 °C.
Hansen et al.(a) use the GISS model and run past and future climates, with attention both
to land surface and oceans. They use the results of Levitus et al.(137,138,c) and compare to
their models for their calculated effective forcing of ~ 1.8 W/m2. They find a result for
thermal storage in the top 750 m of ocean of 6 ± 1 W yr/m2 for 10 years, compared to the
measured result of 5.5 W yr/m2 for the top 700 m.(137,138,c) Barrett and coworkers found
evidence that the Levitus-discovered ocean warming was incontrovertible;(139,d) and,
moreover, in Ref. d, are quoted as stating that the statistical confidence that “human-
produced greenhouse gases are behind real-world warming” was “much greater than 95%.”
Hansen et al.(a) find contributions from the forcings totaling ~ 1.8 W/m2 as calculated by
the group. This total is then matched to the observations between 1880 and 2003, which
give a change in temperature of 0.6 to 0.7 °C, corresponding to a forcing of 1 W/m2. This
means that “[o]f the 1.8 W/m2 forcing, 0.85 W/m2 remains, i.e., additional global warming
of 0.85 x 0.67 ~ 0.6 °C is ‘in the pipeline’ and will occur in the future even if atmospheric
composition and other climate forcings remain fixed at today’s values.” This leads the
authors to write:(a)
The present 0.85 W/m2 planetary energy imbalance, its consistency with
estimated growth of climate forcings over the past century (Fig. 1A), and its
consistency with the temporal development of global warming based on a
realistic climate sensitivity for doubled CO2 (Fig. 1B) offer strong support for
the inference that the planet is out of energy balance because of positive
climate forcings.
In another paper, Hansen and Sato argue that there is still time for humans to adapt to the
seemingly irreversible rise in carbon dioxide by reducing other greenhouse gas emissions
substantially.(e) They write “that a decline of non-CO2 forcings allows climate forcing to
be stabilized with a significantly higher transient level of CO2 emissions.”
Hansen et al. build on this conclusion when they write:(a)
The effect of the inertia is to delay Earth’s response to climate forcings, i.e.,
changes of the planet’s energy balance that tend to alter global temperature.
This delay provides an opportunity to reduce the magnitude of anthropogenic
climate change before it is fully realized, if appropriate action is taken. On
the other hand, if we wait for more overwhelming empirical evidence of
climate change, the inertia implies that still greater climate change will be in
store, which may be difficult or impossible to avoid.
A similar problem came to light in future food production. The Earth system is so
intertwined that if food production is increased in one region, it may adversely affect food
production is a different region because of the effect on water vapor.(f) The Conterminous
USA Integrated Assessment study(g) summary states that their “results show that
negative impacts of climate change on crop yields could be mitigated by elevated CO2
concentrations. On the other hand, the stress of climate change on unmanaged ecosystems
could be increased by the effects of increasing human population and its associated
activities.” The studies observed “very substantial changes in ecosystem productivity.”(g)
This is not to say that the developed countries such as the United States are necessarily
immune to the injurious consequences of global warming. One study points out that
“regions that rely on agricultural exports for relatively large shares of their income,” for
instance, Australia and New Zealand, “may be vulnerable not only to direct climate-
induced agricultural damages, but also to positive impacts induced by greenhouse gas
emissions elsewhere.”(h)
In contrast, in the less developed world, losses will be common. This is probed in the case
of Mali, and losses there are estimated $70 to $142 million.(i) In China, the situation is
more complex than in desert-bound Mali. The geography resembles that of North
America: the continental climate will produce “winners” in the northern part of the
country and “losers” in the northwest and southwest.(j)
Any positive outcomes for temperate-climate agriculture in these foregoing studies all rest
on the carbon dioxide fertilization effect.(f,g,h,i,j) A warning must be sounded here: a
study in fungi showed greatly differing effects of carbon dioxide fertilization depending on
whether the carbon dioxide concentration went up abruptly (as in the FACE experiments
upon which the carbon fertilization assumptions rest) or gradually (as is most likely to
occur in nature). The experiment showed negligible differences between the fungi for
which concentrations were raised gradually and controls, while simultaneously showing
large differences between control and the fungal groups exposed to abrupt increases.(k)
Anthropogenic climate changes under way in the Mediterranean basin may lead to
increased desertification.(l) The region seems perilously close to the loss of essential
summer storms. Local air pollution also seems to be having a major effect on the radiative
budget of the basin.(l)
Yet another example of the uncertain future is the strength of future hurricanes. The
destructiveness of hurricanes appears subjectively to be rising, but that might have been
due to a greater amount of building in vulnerable geographic areas. A review of the recent
literature by Trenberth finds that “[t]rends in human-influenced environmental changes
are now evident in hurricane regions. These changes are expected to affect hurricane
intensity and rainfall, but the effect on hurricane numbers remains unclear. The key
scientific question is not whether there is a trend in hurricane numbers and tracks, but
rather how hurricanes are changing.”(m)
The message of these predictions is similar to what other climate scientists have written
(for example, in the IPCC conclusions): Take action while time remains to mitigate human
actions. This is a simple variation of old folk wisdom: “A stitch in time saves nine.”
References in addition to those listed for this chapter are shown in red in the text, and
listed below:
a. J. Hansen, L. Nazarenko, R. Ruedy, M. Sato, J. Willis, A. Del Genio, D. Koch, A.Lacis, K. Lo, S. Menon, T. Novakov, J. Perlwitz, G. Russell, G. A. Schmidt, and N.Tausnev, “Earth’s energy imbalance: confirmation and implications,” Science 308, 1431(2005).
b. T. M. L. Wigley, “The climate change commitment,” Science 307, 1766 (2005).
c. S. Levitus, J. I. Antonov, and T. P. Boyer, “Climatological annual cycle of ocean heatcontent,” Geophys. Res. Lett. 32, L02604 (2004).
d. R. A. Kerr, “Ocean warming model again points to a human touch,” Science 307, 1190(2005).
e. J. Hansen and M. Sato, “Greenhouse gas growth rates,” Proc. Natl. Acad. Sci. 101,16109 (2004). See also
f. L. J. Gordon, W. Steffen, B. F. Jönsson, C. Folke, M. Falkenmark, and Å. Johannessen,“Human modification of global water vapor flows from the land surface,” Proc. Natl.Acad. Sci. 102, 7612 (2005).
g. J. A. Edmonds and N. J. Rosenberg, “Climate change impacts for the ConterminousUSA: An Integrated Assessment: Summary,” Clim. Change 69, 151 (2005). Individualparts of the study Climate change impacts for the Conterminous USA: An IntegratedAssessment are:S. J. Smith, A. M. Thomson, N. J. Rosenberg,R. C. Izaurralde, R. A. Brown, and T. M.L. Wigley, “Part 1. Scenarios and context,” Clim. Change 69, 7 (2005); A. M. Thomson,N. J. Rosenberg, R. C. Izaurralde, and R. A. Brown, “Part 2: Models and validation,”Clim. Change 69, 27 (2005); A. M. Thomson, R. A. Brown, N. J. Rosenberg, R. C.Izaurralde, and V. Benson, “Part 3. Dryland production of grain and forage crops,” Clim.Change 69, 43 (2005); A. M. Thomson, R. A. Brown, N. J. Rosenberg, R. Srinivasan, and
R. C. Izaurralde, “Part 4: Water resources,” Clim. Change 69, 67 (2005); A. M.Thomson, N. J. Rosenberg, R. C. Izaurralde, and R. A. Brown, “Part 5. Irrigatedagriculture and national grain crop production,” Clim. Change 69, 89 (2005); R. C.Izaurralde, A. M. Thomson, N. J. Rosenberg, and R. A. Brown, “Part 6. Distribution andproductivity of unmanaged ecosystems,” Clim. Change 69, 107 (2005); and R. D. Sandsand J. A. Edmonds, “Part 7. Economic analysis of field crops and land use with climatechange,” Clim. Change 69, 127 (2005).
h. R. Darwin, “Effects of greenhouse gas emissions on world agriculture, foodconsumption, and economic welfare,” Clim. Change 66, 191 (2004).
i. T. A. Butt, B. A. McCarl, J. Angerer, P. T. Dyke, and J. W. Stuth, “The economic andfood security implications of climate change in Mali,” Clim. Change 68, 355 (2005).
j. H. Liu, X. Li, G. Fischer, and L. Sun, “Study on the impacts of climate change onChina’s agriculture,” Clim. Change 65, 125 (2004). P. Kirshen, M. McCluskey, R. Vogel,and K. Strzepek, “Global analysis of changes in water supply yields and costs underclimate change: A case study in China,” Clim. Change 68, 303 (2005)
k. J. N. Klironomos, M. F. Allen, M. C. Rillig, J. Piotrowski, S. Makvandi-Nejad, B. E.Wolfe, and J. R. Powell, “Abrupt rise in atmospheric CO2 overestimates communityresponse in a model plant–soil system,” Nature 433, 621 (2005).
l. M. M. Millán, M. J. Estrela, M. J. Sanz, E. Mantilla, M. Martín, F. Pastor, R.Salvador, A. R. Vallejo, L. Alonso, G. Gangoiti, J. L. Ilardia, M. Navazo, A. Albizuri, B.Artíñano, P. Ciccioli, G. Kallos, R. A. Carvalho, D. Andrés, A. Hoff, J. Werhahn, G.Seufert, and B. Versino, “Climatic feedbacks and desertification: The Mediterraneanmodel,” J. Climate 18, 684 (2005).
m. K. Trenberth, “Uncertainty in hurricanes and global warming,” Science 308, 1753(2005).
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