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13
Model Performance
Adequate prediction of global surface warming caused by
increasing greenhouse gases will require coupled models of the
atmosphere, oceans, ice sheets and snow fields, and biosphere.
These coupled models will have to provide time-dependent
simulations that adopt, as input conditions, scenarios for future
emissions of greenhouse gases. And, like the proverbial chain, the
validity of these coupled models is governed by the weakest of the
component models. The IPCC scientific assessment (Intergovernmental
Panel on Climate Change, 1990) reviewed current results in these
and related topics. To date, most emphasis has been placed on the
development and testing of atmospheric models. Even though
policymakers' would like them, current capabilities do not allow
credible projections of regional effects. The panel finds that
these and other aspects of climate models have even greater
uncertainty than those associated with global mean temperature
projections. However, for purposes of assessing their limits for
policy decisions, the primary focus of the examination here is on
global mean temperature.
Considerable effort has been focused on atmospheric GCM
experiments in which the CO2
concentration of the atmosphere is instantaneously doubled and the
models are then allowed to achieve a new equilibrium climate.
Although these simulations do not provide information on
time-dependent (or "transient") climatic changes that would
accompany more realistic greenhouse gas accumulation scenarios,
they do allow a means of testing, understanding, and comparing
atmospheric GCMs. The IPCC scientific assessment (Intergovernmental
Panel on Climate Change, 1990) provided a convenient summary of
these simulations. For present purposes the only simulations
considered are those that utilize computed clouds; i.e., that
incorporate cloud feedback.
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Representative terms from entire chapter:
cloud feedback
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FIGURE 13.1 Global warming from 18 GCM
simulations for a doubling of atmospheric CO2.
The institutional designations are United Kingdom Meteorological
Office (UKMO), NOAA
Geophysical Fluid Dynamics Laboratory (GFDL), NASA Goddard
Institute for Space Studies (GISS),
Oregon State University (OSU), National Center for Atmospheric
Research (NCAR), Canadian Climate
Centre (CCC), and Australian Bureau of Meteorology Research Centre
(BMR).
The equilibrium global warming (i.e.,
dTs) produced by 18
different CO2 doubling simulations
1 is summarized in Figure 13.1. The
simulation numbers are in order of increasing projection of global
warming. Multiple simulations have been performed by five of the
seven involved GCMs; these serve as sensitivity studies for a
specific model. As an example, simulation numbers 4 and 5 (UKMO),
and 8 through 10 (GFDL), respectively, proceed to a finer
horizontal resolution. Note that neither model indicates a
significant influence of horizontal resolution on the
model-predicted global warming. The UKMO GCM produced both the
greatest (5.2°C (9.4°F)) and the smallest (1.9°C
(3.4°F)) global warming, and this notable variation is the
consequence of differences in assumptions about cloud parameters
(Mitchell et al., 1989).
The horizontal solution technique used in the seven GCMs is
either finite difference (UKMO, GISS, OSU) or spectral (GFDL, NCAR,
CCC, BMR). The spectral models are in much better agreement (dTs=
3.5° to 4.0°C (6.3° to 7.2°F)) than the finite
difference models (dTs = 1.9° to 5.2°C (3.4° to
9.4°F)). This is probably coincidental. Of the 19 models in
Figure 12.2, eight are finite difference and eleven are spectral,
and here neither group is
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found to exhibit better agreement than the other. It should be
noted that no persuasive comparison of model results with global
warming observations has yet been constructed.
It is important to realize that the global warming results in
Figure 13.1 include snow-ice feedback, whereas the sensitivity
parameters in Figure 12.2 do not, and this partially explains
differences between Figures 12.2 and 13.1. For example, simulation
numbers 10 (GFDL) and 14 (GISS) in Figure 13.1 produce quite
comparable global warming (4.0° and 4.2°C (7.2° and
7.6°F), respectively). The same GFDL and GISS models are,
however, models number 12 and 19 in Figure 12.2. Relative to clear
skies, Figure 12.2 shows rather modest positive cloud feedback in
the GFDL model, whereas there is a very strong positive feedback in
the GISS model.
That these two models agree well in Figure 13.1 is at least
partially due to compensatory differences in snow-ice albedo
feedback. Both modeling groups have provided feedback diagnostics
so that individual feedbacks may be progressively incorporated, and
this is demonstrated in Table 13.1. The two GCMs produce similar
warming in the absence of both cloud feedback and snow-ice albedo
feedback. The incorporation of cloud feedback, however, shows that
this is a stronger feedback in the GISS model, as is consistent
with Figure 12.2. But the additional incorporation of snow-ice
albedo feedback largely compensates for their differences in cloud
feedback. Thus, while the two models produce comparable global
warming, they do so for quite different reasons.
It is emphasized that Table 13.1 should not be used to estimate
the amplification factor due to cloud feedback, because feedback
mechanisms are interactive. From Table 13.1 the cloud feedback
amplifications for the GFDL and GISS models might be inferred to be
1.2 and 1.6, respectively, but only in the absence of snow-ice
albedo feedback. If snow-ice albedo feedback is incorporated before
cloud feedback, then the respective amplification factors are 1.3
and 1.8. These larger values are due to an amplification of cloud
feedback by snow-ice albedo feedback.
The change in precipitation that would be concurrent with global
warming is of considerable importance. Global mean precipitation
change, for
TABLE 13.1 Comparison of Global Warming (°C) for the
GFDL and GISS GCMs with the Progressive Additions of Cloud Feedback
and Snow-Ice Albedo Feedback
Feedbacks
GFDL
GISS
No cloud or snow-ice
1.7
2.0
Plus cloud
2.0
3.2
Plus snow-ice
4.0
4.2
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the same models as in Figure 13.1, is summarized in Figure 13.2
(Inter-governmental Panel on Climate Change, 1990; no value is
reported for simulation number 7). Although there is considerable
variability among the simulations, with reference to Figure 13.1
there is an obvious correlation between precipitation change and
global warming. This is to be expected, since global warming
enhances surface evaporation and hence precipitation.
To be more specific on this point, Figure 13.3 provides a
scatter plot of precipitation change versus global warming.
Although 17 simulations are represented here, there are four
coincident points (one finite difference and three spectral).
Clearly, differences among global warming simulation models are the
cause of much of the variance in the predictions of global
precipitation change. Note that the finite difference models
exhibit a considerably stronger correlation, and there is no
obvious explanation for this. The primary point of Figure 13.3 is
that global precipitation change and global warming are, as would
be expected, strongly coupled.
Of far more practical importance than the global averages are
the regional patterns of changes in both precipitation and soil
moisture. While global precipitation, for reasons discussed above,
will increase with global warming, one would anticipate that there
would be geographical regions where just the opposite occurs.
Frequently, arid subtropics are a consequence of the descending
branch of the tropical Hadley cell, and a shift in
FIGURE 13.2 Global precipitation change for 17
of the global warming simulations of Figure 13.1.
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FIGURE 13.3 Global precipitation change versus
global warming
for the 17 simulations. There are four coincident points.
its location should bring with it a reduction of precipitation.
Regional changes in soil moisture are even more difficult to
predict, because soil moisture is the difference between
precipitation and evaporation plus run-off, and errors in the
change of any of these quantities will produce magnified errors in
the fractional change of soil moisture.
As noted at the beginning of this chapter, persuasive
projections of future climatic changes will require the
availability of reliable coupled atmosphere, ocean, cryosphere, and
biosphere models. As of now, in most models, the role assigned to
the oceans does not include any horizontal transport. In many
studies, in fact, the ocean temperature has been postulated as a
boundary condition, often to provide a surrogate parameter from
which potentially useful insights can be drawn. Recently, more
elaborate models of the ocean have been coupled to GCMs in climate
change simulations (Stouffer et al., 1989; Manabe et al., 1990),
but efforts at this level are in their infancy. Clearly, there is a
need here for model improvements, particularly with respect to
cloud-climate interactions. And, of course, the other component
models, as they evolve, will have to undergo similar scrutiny.
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Note
1. Not the same set as cited in Figure 12.2.
References
Intergovernmental Panel on Climate Change. 1990. Climate Change:
The IPCC Scientific Assessment, J. T. Houghton, G. J. Jenkins, and
J. J. Ephraums, eds. New York: Cambridge University Press.
Manabe, S., K. Bryan, and M. J. Spelman. 1990. Transient
response of a global ocean-atmosphere model to a doubling of
atmospheric carbon dioxide. Journal of Physical Oceanography
20:722–749.
Mitchell, J. F. B., C. A. Senior, and W. J. Ingram. 1989.
CO2 and climate: A missing feedback?
Nature 341:132–134.
Stouffer, R. J., S. Manabe, and K. Bryan. 1989. Interhemispheric
asymmetry in climate responses to a gradual increase of atmospheric
CO2. Nature 342:660–662.
