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).