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4
Mechanisms and Predictability
We have seen that climatic attributes directly affect human
activities and viability, and that these attributes are directly
connected to the major processes acting in the Earth's climate
system. To exploit beneficial climate variations and mitigate the
effects of the harmful variations, we must first understand the
operation of the system and then project what could happen in the
future.
By now it is well known that some features of our climate can be
forecast beyond the two-week limit of atmospheric deterministic
predictability. This chapter details the nature of predictability
on dec-cen (and shorter) time scales, and endeavors to distinguish
between the predictable aspects of natural variability and the
predictable consequences of possible anthropogenic changes. It
examines how predictability is related to the mechanisms of climate
variability and assesses the possibility of predicting climate on
dec-cen time scales, especially the attributes discussed in Chapter
2. Last, it speculates on the applications of such predictions
should they be attainable.
The Nature of Climate Prediction
Weather Prediction
The state of the atmosphere and ocean is governed by what is
generally agreed to be a set of deterministic equations: If the
initial state is exactly known, the future evolution of the system
is determined for all time. On the other hand, the atmosphere and
ocean are rife with instabilities and nonlinearities that imply
that the climate system is chaotic: Two sets of initial conditions
that appear to be very close to one another (but not identical)
will evolve along trajectories that inevitably diverge. Their
divergence cannot continue forever, though, given the bounds
imposed by the system' s finite energy. As a result, trajectories
continuously approach, as well as diverge from, each other,
generating the system' s chaotic, albeit deterministic,
behavior.
A numerical weather-forecast model also has deterministic
solutions, but any small error in the initial state guarantees that
the resulting forecast trajectory will diverge from a trajectory
that began with the "correct" initial state. The inevitable growth
of the initially specified error, and the subsequent mixing of
ever-changing trajectories, limits predictability. The pioneering
work of Lorenz (1963, 1969), and the research it spurred over the
years, established that the doubling rate of errors for large-scale
atmospheric flows is on the order of 2-3 days (Lorenz, 1982), so
that the global atmosphere is predictable only on scales of two
weeks or so, given the currently achievable accuracy of
initial-state determination. Similar studies for the detailed
predictability of the oceans are in their infancy, but the slower
growth and saturation times of oceanic instabilities suggest
potentially longer predictability times, on the order of months
rather than weeks. Still, all these times are much shorter than the
dec-cen time scale of interest here.
Climate Prediction
The obvious question is, if the ultimate limit of detailed
prediction for atmosphere and ocean weather is on the order of
weeks to months at best, how could we possibly expect to predict
climate on time scales of years or decades? The answer follows
directly from the definition of climate: Climate is the statistics
of the atmosphere (and other components of the climate system). We
have known for a long time that atmospheric statistics are
determined entirely by the boundary conditions of the atmosphere;
every atmospheric modeler uses this paradigm. The boundary
conditions for an atmospheric model (in particular SST and land and
sea ice) are specified, and the atmospheric circulation and
hydrologic cycle are allowed to come into equilibrium with these
specifications. Other boundary quantities are then determined by
internally coupling the atmospheric model to a land model, in
particular land-surface moisture and vegetation, and land snow and
ice cover. Whether or not climate is uniquely determined by the
boundary conditions is still undetermined, but for the purposes of
argument, we will assume here that it is.
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If, therefore, we can predict the boundary conditions of the
atmosphere at a specific time, particularly SST and sea ice, we
will have some information about the statistics of the atmosphere
at that time. We will not be able to predict the precise state of
the atmosphere, because it can vary in equilibrium with the
predicted boundary conditions, but we will know something about its
average conditions. We may be able to predict the average monthly
or seasonal precipitation over a region even if we cannot say on
what specific day the precipitation will fall. Knowing only a mean
value at a given time can still be helpful if the associated
variability is small. Predictions of tropical boundary conditions
at a certain time are likely to be useful because tropical climate
variability is low, while predictions of mid-latitude boundary
conditions would be less useful because mid-latitude variability,
especially during winter, is high.
Two more important points need to be made. First is the
distinction between initialized and uninitialized prediction. To
make a prediction about a specific time in the future, say the
summer of 2009, there must be some connection to the actual
conditions now. We call this estimation of the actual beginning
state of the system "initialization" (while recognizing that this
term is sometimes used elsewhere to mean the act of bringing a
model system to a state of equilibrium without estimating its
current conditions). If we do not make this initial estimation, we
will not be able to forecast the time at which the climate will
assume a given state, though we may still draw conclusions about
its statistics (that is, changes of its mean and the nature of its
variability). The difference between initialized and uninitialized
prediction becomes important in discussing greenhouse-warming
predictions versus ENSO predictions. The second important point is
the potential for making empirically or statistically based
(analog) predictions. Sufficient information is available from past
climate records to allow predictions to be made (with specified
uncertainty) whenever specific climate states exist that have in
the past been accompanied or followed by particular regional or
local climate conditions. "Climate state" is defined here, as in
NRC (1975), as the average of the complete set of atmospheric,
hydrospheric, and cryospheric variables over a specified period of
time in a specified domain of the earth-atmosphere system.
For climate prediction on all time scales, whether initialized
or not, the tool for predicting the boundary conditions of SST and
sea ice is the coupled climate model—a model that consistently
links the atmosphere, ocean, and ice together in responding to a
specified external forcing.
Short-, Medium-, and Long-Range
Climate Prediction
There is no accepted terminology describing the various time
scales for prediction. This report will use "short-range climate
prediction" to denote prediction on time scales up to interannual,
"medium-range climate prediction" to mean prediction at decadal
time scales, and "long-range climate prediction'' (sometimes called
"greenhouse prediction'') for prediction on centennial time
scales—the scale of a human lifetime.
Short-range climate prediction is an established enterprise:
Skill has been demonstrated for predicting the SST changes in the
tropical Pacific that are characteristic of the ENSO phenomenon on
lead times of 6 to 12 months. Atmospheric properties elsewhere may
then be inferred from these forecasts. These predictive skills,
which vary as a function of several factors (including season,
model type, and decade), have been well documented (Battisti and
Sarachik, 1995; Glantz, 1996; Latif et al., 1998). ENSO prediction
is initialized prediction (in the sense defined above), so a
real-time observing system in the tropical Pacific was put in place
by the TOGA research program. It has been kept in place even though
TOGA has ended, which should permit us to develop our skill
further.
Long-range climate prediction has so far been limited to
predicting forced climate change in response to the anthropogenic
addition of radiatively active gases and aerosols to the
atmosphere. Because this type of prediction is essentially
uninitialized, it cannot predict the actual state of the boundary
conditions at some specific future time. It can, however, be used
to derive the statistics of the boundary conditions (and therefore
the statistics of the atmosphere in equilibrium with the statistics
of the boundary conditions) at some future time. Thus, initialized
short-range climate prediction can predict the SST in the tropical
Pacific for January of 1999, say, while greenhouse predictions can
only say that annually averaged SST will be warmer in the year 2050
by some specified amount, or within a certain range. Such
greenhouse predictions are still valuable if the forcing changes
the mean boundary conditions enough for a difference beyond natural
variability to be apparent; again, small shifts of the mean may be
noticeable in the tropics where the variability is low, while
larger shifts may be masked in mid-latitudes where variability is
high. Long-range forecasts permit the assessment of shifts in
average precipitation, or length of the growing season, or changes
in patterns of runoff; as indicated by Karl et al. (1996), even
subtle shifts in the mean state can have considerable implications
for the frequency and magnitude of extreme climate events.
Medium-range climate prediction, prediction on time scales of a
decade or so, is the most problematic type of prediction. Its value
as uninitialized prediction is limited: The year-to-year
variability of climate, together with the relatively slow approach
of the climate system to equilibrium with anthropogenically added
radiatively active atmospheric constituents, limit the value of
prediction of the statistics of boundary conditions a decade in
advance. Even this type of prediction may be useful under certain
circumstances, however. When regional changes are fast and crossing
the threshold of a new climate state can be predicted,
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preparations can be made for change even if the exact time of
occurrence is not known.
The possibility of initialized medium-range climate prediction
is a real and intriguing one. As the fully coupled system is
allowed to evolve freely over the course of a decade from its
initial state, water parcels from deeper parts of the ocean reach
the surface and imprint themselves on the SST field. The question,
of course, is whether or not the evolving imprint of the initial
ocean state on the SST can survive both the mixing in the ocean (as
parcels wend their way to the surface) and the inevitable noise
from high-frequency atmospheric forcing.
Prediction and Mechanisms
The existence and nature of climatic predictability depends on
the nature of the mechanisms responsible for the variability. We
can distinguish two basic types of mechanisms for decadal
variability: variability forced by processes external to the
climate system, and variability generated by processes internal to
the climate system.
Externally Forced Atmospheric
Variability
In the external-forcing category are forcings by varying solar
output; by addition of aerosols due to major volcanic eruptions,
biomass burning, and industrial sources; and by the addition of
radiatively active gases to the atmosphere. Their effects are
discussed in detail in Chapter 5, "Atmospheric Composition and
Radiative Forcing."
It is generally true that variability generated by external
forcing is unpredictable when the forcing itself is unpredictable;
the decadal variations of solar radiative output and the
geodynamics of future eruptions of volcanoes are both poorly
understood. When volcanic aerosols are added to the stratosphere,
there is a period of a year or two in which the aerosols stay in
the stratosphere, so their radiative and chemical effects can be
predicted over the following year (see, e.g., Fiocco et al., 1996).
The addition of radiatively active gases to the atmosphere produces
mean warming of the Earth, regional changes of mean temperature and
precipitation, and possible changes to natural climate cycles, such
as ENSO and the Pacific North American pattern, but prediction of
these mean changes relies on our ability to know the past and
future emissions of these gases. Those radiatively active gases
that also contain chlorine or oxides of nitrogen affect the
stratospheric burden of ozone, so to the extent that the
concentration of these gases is known and the chemistry of ozone is
understood, the concentration of ozone may be predicted.
Internally Forced Atmospheric
Variability
We may classify the internal decadal-variability mechanisms into
three distinct categories: those arising from high-frequency
forcing of the slow components of the climate system by the more
rapidly varying atmosphere; those arising from slow internal
variations in the ocean, atmosphere, cryosphere, or biosphere; and
those arising from the coupling of components of the climate system
that individually would not have such an effect (Sarachik et al.,
1996). Specific mechanisms associated with each of the components
of the climate system will be presented in Chapter 5.
Chapter 3 described a variety of patterns of variability, and
some of their co-varying aspects. In this section we discuss the
extent to which these patterns, or other broad-scale features of
the atmosphere, appear to be coupled to other parts of the climate
system, predominantly the ocean. These links are important to
predictability; when a fast and a slow component are coupled, and
the latter has mechanistic control of the pattern, the longer time
scales of the slower component can be capitalized on to make more
distant forecasts or predictions of the faster component (in this
case the atmosphere) than would otherwise be possible.
The Hasselmann (1976) theory of climate variability is a
convenient starting point for understanding the relevance of
different climatic time scales to climate prediction. Hasselmann's
theory asserts that the atmosphere produces, through instabilities
of various types, high-frequency variability that presents itself
as weather. At these frequencies, the variability may be considered
random. When a slower-reacting reservoir, such as the ocean, is
forced by such high-frequency variability, the high-frequency
variability is damped in the slower component. This basic climate
mechanism of Hasselmann seems to account for a great deal of
observed variability (see, e.g., Frankignoul and Hasselmann, 1977)
and of modeled natural variability in long, coupled climate
simulations (Manabe and Stouffer, 1996).
If the Hasselmann mechanism were the only operative
mechanism—say, the atmosphere acting on the ocean—the
temporal extent of the predictability would be the autocorrelation
time of the sea surface temperature. In this case, the best
forecast of SST would be a forecast of persistence (i.e., no
change) that fades to the norm with a time constant consistent with
the autocorrelation function. Over many parts of the world ocean,
persistence is on the order of months. If, however, the SST
generated by the Hasselmann mechanism feeds back to the atmosphere
in a coherent way, coupled modes may result, increasing
predictability notably. Similar couplings may exist with other
parts of the climate system, such as the perennial ice or snow
fields, though this possibility has not been explored to any great
extent.
Extension of these ideas to the stochastically forced coupled
atmosphere-ocean system suggests that such coupling will act to
significantly enhance the very-low-frequency variance in the
atmosphere (see, e.g., Barsugli and Battisti, 1998). This is the
simplest theory accounting for the presence of very-low-frequency
variability in the climate system, and is usually assumed to be
correct in the absence of other information. The Hasselmann
mechanism would
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also determine the maximum level of predictability of climate
anomalies, if the coupling between the atmosphere and the ocean (or
biosphere or cryosphere) did not support patterns of its own.
However, there are several climate phenomena on the decadal time
scale that are most likely the result of processes other than those
inherent in the Hasselmann mechanism. The best documented of these
coupled phenomena are discussed below, and the possible mechanisms
are summarized.
The essential reason for studying the mechanisms of decadal
variability (aside from their intrinsic scientific interest) is
that determining which mechanisms are operative will determine the
extent to which climate can be predicted. Some mechanisms (e.g.,
external forcing by volcanic eruptions) we will assume to have no
predictability, and thus to offer no improvement in predicting
climate. Some mechanisms have moderate predictability (e.g., random
forcing of SST by the atmosphere), and some have significant
predictability (e.g., SST variations in tropical Pacific caused by
coupled atmosphere-ocean modes). These are well worth
exploring.
Coupled Modes
Chapter 3 described a large-scale decadal mode of variability in
the Pacific that has ENSO-like SST characteristics in the tropical
regions and strong out-of-phase covariation of SST in the North
Pacific. The mechanisms responsible for this variability are not
yet known. Various investigators have postulated various reasons
for it, including:
• inherent nonlinearities in the physics of ENSO (e.g.,
Munnich et al., 1991);
• interaction between ENSO and the seasonal cycle (e.g.,
Jin et al., 1994, 1996);
• interaction between ENSO and other unstable coupled
atmosphere-ocean modes (e.g., Mantua and Battisti, 1994);
• stochastic forcing of a linearly stable coupled system
(e.g., Penland and Sardeshmukh, 1995); and
• low-frequency changes in the shallow equatorial
thermohaline circulation that may lead to changes in the amplitude
and frequency of the interannual ENSO variability (e.g., Pedlosky,
1987; McCreary and Lu, 1994; Gu and Philander, 1997).
Whatever the cause, a significant portion of the low-frequency
variability in the global climate system is ENSO-like in structure,
as can be seen in Figure 3-7. Decadal-scale changes in the state of
the tropical Pacific atmosphere-ocean system might also affect the
predictability of the higher-frequency ENSO variability. For both
of these reasons, the Pacific is an important focus for
understanding dec-cen variability in the climate system.
In addition to the ENSO-like coupled phenomenon, there is ample
evidence of variability in the mid-latitude North Pacific
atmosphere-ocean system on interannual time scales. Specifically,
wintertime variability is largely forced locally by the atmosphere;
about half of the variance in interannual SST anomalies is forced
by ENSO, and communicated to the North Pacific Ocean via the
atmospheric teleconnections. The variability in the North Pacific
climate on decadal and longer time scales is not yet fully
documented. It is known that, over the last half-century, a
substantial portion of the variability in the atmosphere-ocean
system on these time scales is associated with the global ENSO-like
structure displayed in Figure 3-7.
Recently, Latif and Barnett (1996) have suggested a mechanism by
which coupling between the atmosphere and upper ocean that takes
place in the mid-latitude North Pacific basin may give rise to
climate variability in the North Pacific and North America on the
multidecadal time scale. The spatial structure of the model
anomalies in the Latif and Barnett study is remarkably similar to
that of the mid-latitude anomalies in the global ENSO-like
structure of the observations displayed in Figure 3-7, which is
also primarily a low-frequency pattern of variability. However, the
observed anomalies clearly involve the tropical atmosphere-ocean
system. Hence, if the mechanism suggested by Latif and Barnett does
indeed operate in nature, it will be necessary to sort out how much
of the variance in the mid-latitudes comes from forcing in the
tropical Pacific that is teleconnected to the mid-latitudes, and
how much from atmosphere-ocean interactions that are specific to
the North Pacific.
Delworth et al. (1993) found multidecadal variability in the
North Atlantic of the coupled atmosphere-ocean GCM of Manabe and
Stouffer (1988). The pattern of the SST anomalies associated with
this variability is somewhat similar to that found in the
observations by Kushnir (1994) and in simpler coupled models (Chen
and Ghil, 1996). However, the mechanism associated with the
variability in both types of atmosphere-ocean models seems to be
internal to the ocean thermohaline circulation, and not inherently
a coupled atmosphere-ocean phenomenon.
Sarachik et al. (1996) review the current theories of mechanisms
for producing dec-cen climate variability from an ocean and
ocean-atmosphere modeling perspective. These theories include:
stochastic forcing of the ocean by white-noise, synoptic-scale
variability of the atmosphere (e.g., Hasselmann, 1976; Mikolajewicz
and Maier-Reimer, 1990), internal ocean variability (e.g., Delworth
et al., 1993; Chen and Ghil, 1995), coupled ocean-atmosphere modes
(Hirst, 1986; Latif and Barnett, 1994), and ENSO variability
(Trenberth and Hurrell, 1994; Wallace et al., 1995). Yin and
Sarachik (1994) proposed an oceanic advective and convective
mechanism. A completely different type of interannual (in the North
Atlantic) to decadal (in the North Pacific) variability has been
modeled by Jiang et al. (1995) via the nonlinear dynamics of the
double-gyre circulation's strength, heave, and wobble (see also
Cessi and Ierley, 1995, and Speich et al., 1995) and by Spall
(1996) via the nonlinear
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dynamics of the Gulf Stream and the deep western boundary
current.
Prospects for Climate Prediction
As stated, the nature of the mechanisms causing dec-cen
variability will determine whether or not it is possible to make
deterministic climate predictions on these time scales. It is clear
that the mechanisms for some of the most notable decadal phenomena
are unknown at present. If the natural decadal phenomena are forced
externally, they cannot be predicted a decade in advance because
those forcings are assumed to be unpredictable. If the Hasselmann
mechanism is the operative one, then the scale of predictability is
limited to the order of the autocorrelation time of the slower
component of the system, usually the ocean. If the ocean is the
driver of long-term climate-system variability, however, the fact
that its circulation is sluggish offers some hope for initialized
decadal-scale prediction: Initial experiments with low-resolution
coupled models (Griffies and Bryan, 1997) have indicated that
errors imposed on the initial (internal) state of the North
Atlantic grow slowly enough that SST and sea level may be
predictable as much as a decade in advance. Skill in forecasting
equatorial Pacific SST, which has been demonstrated out to two
years (Chen et al., 1995), may be extended to longer forecast
periods, perhaps approaching a decade.
The best hope for initialized prediction is offered by the
existence of true atmosphere-ocean modes. Depending on the degree
of coupling and the internal ocean phenomena that set the time
scale for the oscillation, the initialization of those ocean
phenomena captures the wherewithal for the coupled evolution in
part of its cycle, and guarantees that parts of its evolution will
continue into the future. ENSO on shorter time scales is an example
of this; the initial state is the thermocline's configuration,
which, when specified at the initial time, determines the future
evolution of the upper tropical ocean and hence its temperature
structure. This kind of prediction also seems to work for the modal
structures involved with the Atlantic subtropical dipole (Chang et
al., 1998).
The possibility of long-range (uninitialized) greenhouse
prediction has been demonstrated by many coupled predictions over
the years (see IPCC, 1996a, for a complete review). As yet,
however, only the grossest measures of warming have been used.
Also, it is clear that other climate system components besides
temperature must change in response to a significant change in
greenhouse gases—for example, the warming may be kept to a
minimum by changes in the atmospheric moisture distribution. These
changes, such as those in the hydrologic cycle or cloudiness, may
actually be more important to society than the temperature effect.
Also, the regional usefulness of current predictions is limited
both by the uncertainty of the prediction of the mean itself and by
the regional variability that masks modest changes in the mean. The
general usefulness of uninitialized greenhouse prediction is
defined by the magnitude of the forced response relative to the
existing variability. These factors, combined with the
impossibility of demonstrating the correctness of such a prediction
until the extremely long prediction time has passed, make the use
of such predictions particularly challenging.
The Uses of Climate Prediction
This section considers the uses of decadal and longer
prediction, both initialized and uninitialized, should the skill of
such prediction turn out to be significant. In order to determine
whether the climate attributes that affect humankind (discussed in
Chapter 2) are likely to be predictable, we must first demonstrate
that some related physical quantity (e.g., SST) is predictable.
While we do not know enough yet to do this properly, we can
indicate what is known and what still needs to be known in order to
make predictions of each of the climate attributes.
Uses of Medium-Range Climate
Prediction (Initialized)
The discussion of decadal-scale prediction here deals with
initialized predictions only. There are two ways they can apply:
directly (for the region from which the prediction is derived) or
remotely (when teleconnections exist between that region and
another).
Precipitation and Freshwater
Availability
At this time, there are a few predictable phenomena that clearly
affect rainfall over vulnerable populations: The Atlantic
subtropical dipole that influences rainfall in Brazil and Africa
(Hastenrath and Heller, 1977; Lamb, 1978; Hastenrath, 1990) is one,
and the extreme ENSO states influencing western U.S. rainfall
events (Cayan and Peterson, 1989) are another. Other phenomena
affect rainfall and are probably predictable (e.g., the large-scale
variability over the northern Pacific), but so far they have not
been shown to be predictable on decadal time scales.
The variation of rainfall in the Brazilian Nordeste clearly has
both interannual and decadal components. It has been related to the
location of the ITCZ over the tropical Atlantic, which in turn has
been related to SST variations in both the tropical and subtropical
Atlantic and Pacific (most recently in Uvo et al., 1997). The
possibility therefore exists that prediction of SST will lead to
better estimates of the location of the Atlantic ITCZ and of
consequences of future rainfall in Brazil and in the Sahel. SST has
come to be predicted better on short (seasonal-to-interannual) time
scales in the tropical oceans; the population dislocations in the
Nordeste caused by years with low precipitation during
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the growing season have gradually been reduced by actions taken
on the basis of predictions made a few months to a year in advance
(Moura, 1994). Short-term forecasts for Morocco have proven quite
successful as well. These are clear examples of how useful
prediction of future climate can be to society.
The indication by Chang et al. (1998) that the decadal variation
of tropical to subtropical Atlantic SST anomalies may be predicted
years in advance offers the possibility of longer-range planning
for the allocation of water resources to agriculture, drinking
water, and hydroelectric power for northeastern Brazil and
northwestern Africa. A prediction of dry conditions for the next
five years might shift resource planning from power generation to
irrigation, while a prediction of wet conditions might do the
opposite. To the extent that precipitation in the Sahel and Morocco
could be predicted, resources could be directed to agricultural
infrastructure on a forecast of good rainfall, or to food-relief
infrastructure on a prediction of poor rainfall.
Temperature
Temperature is the variable most relevant to the intensity and
timing of cryospheric melting. To the extent that rivers are fed by
glaciers and/or snowpack, their streamflow is partly determined by
temperature. A prediction of increased average temperature would
imply earlier melt, and therefore an early peak in streamflow. For
those regions requiring summer water, the earlier peak may leave
the summer unreasonably dry. Believable decadal (half-decadal)
forecasts of temperature and precipitation—and, more
important, streamflow, particularly in arid and semi-add regions
such as the U.S. West—will be critical to water managers in
developing new operating rules that are better adapted to the
changing conditions.
High temperatures over land also affect vegetation and soil
moisture, especially during summer, both directly, by increasing
evaporation, and indirectly, by affecting precipitation. A decrease
in soil moisture has a feedback effect that increases surface
temperature, as does reduced evapo-transpiration by vegetation.
Believable early warning of a prolonged period of increased
temperature and decreased precipitation, especially in the prime
agricultural lands of the Northern Hemisphere, would provide
opportunities to avert global food insecurity and attendant
disruptions: Irrigation infrastructure could be built, markets
could be stabilized by futures hedging, drought-resistant seed
could be stocked, and so on.
Changes in atmospheric temperature over the oceans eventually
work their way into the interior of the ocean, where they affect
ocean volume and may lead to noticeable changes in sea level.
Although multiple decades generally pass before the impact of
temperature change is felt, approximately 50 percent of the
sea-level rise in this century may be attributed to the global
warming over this time period.
Storms
Advanced numerical models can simulate and predict the
short-term evolution of storms with a remarkable degree of success.
However, the science of describing and predicting the statistical
properties of storms on time scales of a month or longer is still
in its infancy. Of major interest to climatologists is the
relationship between extreme weather events (which are associated
both with tropical and extra-tropical storms and with climatic
factors such as regional SST) and the strength and position of the
semi-permanent features of the atmospheric circulation. It has been
demonstrated in several observational studies that a close
relationship exists between the intensity and distribution of
storms and climate variability. Changes in tropical cyclone
activity have been observed to be linked to variations in regional
SST and SLP (Shapiro, 1982; Emanuel, 1987), as well as to the
entire state of the tropical atmosphere associated with ENSO (Gray
et al., 1992; Nicholls, 1985). Extratropical cyclonic storms are
linked to the state of the "teleconnections" in the monthly mean
circulation (Lau, 1988; Rogers, 1990). Similarly, the
high-frequency storm events exert an influence on low-frequency
climatic fluctuations (Gray, 1979; Lau and Nath, 1991).
Existing observations are not adequate to elucidate the
climatology of severe storms and their link to climate variability.
The limited extent of the instrumental record and inconsistencies
in reports of storm activity are major obstacles to a satisfactory
resolution of these issues. Attempts have been made to extract
useful information on severe weather from GCMs, but the results are
at best rudimentary (Broccoli and Manabe, 1990; Haarsma et al.,
1993). Once the evolution of large-scale climatic factors can be
predicted, it is plausible to envision the emergence of an ability
to predict the degree of storminess. In fact, schemes to predict up
to a year in advance the characteristics of an upcoming
tropical-cyclone season, given atmospheric and ocean variables,
have already been demonstrated with some success (Nicholls, 1985;
Gray et al., 1992).
Ecosystems
Temperature changes will alter the stability of the oceans.
Changes to the stability-sensitive upwelling of nutrient-rich
waters, as well as direct thermal effects (among other things), may
modify the patterns of biological productivity in the world's
oceans (IPCC, 1996b). Only recently has it been shown that the
salmon fisheries of the Pacific Northwest undergo decadal
variability in phase with an index of the North Pacific SST (Mantua
et al., 1997; see Figure 2-18). Currently Alaska is producing a
prodigious amount of salmon, while the Columbia River basin is
producing very little. Just knowing this variability exists is
useful; much time, effort, and money are spent in reviving salmon
fisheries, when in fact their decline might be due to natural
factors rather than fishing practices. A prediction of this
pattern
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several years in advance would allow resources to be applied
more efficiently.
Uses of Long-Range Climate Prediction
(Uninitialized)
This section deals with uninitialized prediction on interdecadal
time scales, and in particular the response of climate to the
anthropogenic addition of radiatively active constituents (gases
and aerosols) to the atmosphere. This topic has been treated in
considerable detail by the IPCC which was established to assess
available scientific information on climate change, assess the
environmental and socioeconomic impacts of climate change, and
formulate response strategies. Indeed, the three volumes of the
second IPCC (1996a,b,c) assessment devote some two thousand pages
to doing just that, so only a few salient points connected with the
climate attributes of Chapter 2 are noted here.
Precipitation and Freshwater
Availability
Since the earliest days of coupled modeling and its use to
determine the climatic effects of increases in radiatively active
gases, an unambiguous and robust result has been that the
hydrologic cycle will run faster in a warmer world (Manabe and
Wetherald, 1975). What has been in some dispute is where and how
the additional rainfall would occur, what the impact of the
increased evaporation potential on land and vegetation would be,
and how the frequency and intensity of extreme precipitation events
would be altered. In most models, the increase in surface
irradiance leads to increases in summer evaporation and reduced
soil moisture (with subsequent vegetation stress). The greater
evaporation also implies higher surface temperatures in continental
interiors, which will reduce precipitation in those regions. The
excess precipitation occurs over oceans and in high latitudes (see,
e.g., IPCC, 1996a) where it is not beneficial to human activity.
There are also suggestions that extreme precipitation events would
increase (see, e.g., Trenberth, in press).
The patterns identified earlier (NAO, PNA, the Atlantic dipole,
and ENSO-like decadal variability) all influence rainfall, by
controlling the location of the storm tracks, by controlling the
location of the ITCZ, or, in the case of ENSO, by controlling
rainfall directly through SST expression or remotely through
teleconnections. Thus it becomes important to know how these
patterns of variability change as radiatively active constituents
are added to the atmosphere. Unfortunately, current climate models
cannot answer this question unambiguously.
Since much of the distribution of precipitation and its
subsequent return to the ocean is topographically determined, it
becomes necessary to more fully resolve model orography and
streamflow. This cannot be done with the current generation of
models that predict the response to radiative gases; for practical
reasons, they cannot have high resolution if they are to be run for
a hundred years. There is increasing evidence (see, e.g., Giorgi
and Marinucci, 1996) that embedding high-resolution models within
global-prediction models gives better distributions of
precipitation, and offers the hope that future predictions of
climate response to radiatively active gases will be specific
enough for more accurate planning. In addition, statistically-based
climate-downscaling methodologies are showing promise (Hewitson and
Crane, 1996; Zorita et al., 1995).
The implications of having accurate medium-range forecasts of
precipitation are immense. They are particularly important for
water-resource management and planning, though they are relevant to
a great many other aspects of society as well. More regionally
accurate precipitation forecasts will be important in planning for
the world's food security in the face of rising population.
Temperature
Until now, the basic question has been how the surface
temperature of the globe, and of various regions, would be altered
by the addition of radiatively active constituents to the
atmosphere. As we have seen, this question must be broadened to
include possible changes to the naturally occurring climate
patterns, since such changes affect both the mean and variations of
climate. Until we know whether natural cycles will change under the
addition of radiatively active constituents, we will not be able to
predict the regional and global temperature responses. It should
also be noted that statements about detection of global warming are
usually statements about detecting a shift of the mean in the
presence of a naturally varying background. When the mean is partly
the result of the phase-locking of various natural cycles, and
these cycles themselves may be affected by the mean they help to
produce, the question of surface temperature alteration must be
deepened and reformulated.
Storms
Projections of changes in storminess due to anticipated changes
in greenhouse-gas concentration remain debatable (IPCC, 1990; Hall
et al., 1994; Lighthill et al., 1994). In the words of the IPCC
(1996a): "Overall, there is no evidence that extreme weather
events, or climate variability, has increased, in a global sense,
through the 20th century, although data and analyses are poor and
not comprehensive. On regional scales there is clear evidence of
changes in some extremes and climate variability indicators. Some
of these changes have been toward greater variability; some have
been toward lower variability." Further discussion of changes in
storms, as they relate to changes in flood frequency, is included
in the "Hydrologic Cycle" section of Chapter 5.
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Representative terms from entire chapter:
boundary conditions
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Sea Level
Long-term prediction is particularly relevant for changes in sea
level. The changes in relative sea and land levels measured at the
coast by tide gauges can be usefully divided four ways, into the
local and the global changes of the land and the sea level. These
distinctions are important for understanding the time scales and
causes of ongoing changes, and predicting their future evolution.
Land levels are significantly influenced by global-scale tectonic
effects—the adjustment of the Earth's mantle to the removal of
the glacial-era icesheets, for example. Local changes in land level
can result from altered sedimentation rates, or from subsidence due
to extraction of groundwater or oil. Sea level is also subject to
local changes forced by local winds, river runoff, and the passage
of oceanic waves of various frequencies. The global sea level is
determined primarily by the mass of water in the ocean and its
temperature structure.
Particular effort has gone into understanding the global
sea-level component of the tide-gauge measurement, because it is
expected to change with climate. Tide-gauge records longer than 50
years are needed to eliminate spurious trends due to low-frequency
variability. Once records have been corrected for post-glacial
rebound, a trend over the past 100 years of about 1.8 mm per year
emerges (Douglas, 1991). There is no firm evidence of an increase
in the rise, nor would it be expected from the change in climate
that has occurred over that period. Archeological and geological
studies indicate that the variation of sea level over the previous
two millennia was no more than a few tens of centimeters. The time
of onset of the current rise is not known.
While uncertainty about the measured rise remains, because of
the lack of global coverage and the possible influence of coastal
subsidence, uncertainties about the components of the rise are far
larger. Two factors contribute significantly to the change of
global sea level with climate: thermal expansion of the ocean, and
redistribution of water between land and sea. Surface thermal
anomalies penetrate down into the ocean's interior via the wind and
thermohaline-driven overturning. These circulations have a range of
time scales from decadal to millennial. Existing direct
observations of ocean temperature are insufficient to reveal the
past global warming of the ocean, although significant local
changes have been observed. Models of ocean circulation have
therefore been used to calculate the thermal-expansion part of the
observed sea-level rise. These models yield estimates ranging from
0.2 to 0.7 mm per year (IPCC, 1996a). This calculation is
inherently uncertain, however, since the boundary
conditions—wind stress, surface temperature, and
salinity—are not well known. The calculation becomes even more
tenuous when made for future climate scenarios with additional
greenhouse gases.
Ninety-nine percent of the world's land ice is contained in the
Greenland and Antarctic ice sheets. The response of these ice
sheets to climate change is difficult to predict (see, e.g.,
Oppenheimer, 1998). Since the mass balance of these ice sheets
reflects long time scales, they are likely still adjusting to past
climate changes. In general, the increased supply of moisture in a
warmer climate is expected to dominate the increased melting for
the Antarctic ice sheet, while the reverse is expected for the
Greenland ice sheet. Current observations are insufficient to
detect a mass imbalance in either. Here, a climate prediction might
attempt at least to determine the relative change in the mass
balance of the ice sheets, when models can determine temperature
and precipitation in the high latitudes.
To interpret observations of sea-level and ice-volume changes,
they must be placed in the context of the past and compared with
projections of the future. It is clearly of interest to know when
the current rise began and whether there were past rises of
comparable magnitude and duration. Paleo-studies and data
"archeology" (recovery of unpublished records) can help address
these issues. Most projections of sea-level response to
anthropogenic forcing have been based on simple models (e.g.,
one-dimensional up-welling-diffusion ocean models). Sea level is
fully embedded in the climate system, however, and a coupled
ocean-atmosphere-ice model must be used to maintain consistency in
all the elements. Furthermore, the dynamic response of the ocean to
climate change gives rise to regional changes in sea level that may
be of a magnitude comparable to that of the global mean change.
Continued improvement of these sophisticated models will be
necessary if useful projections are to be made. Such projections
will prove invaluable, though, because sea-level rise can have such
a large and devastating impact on the vastly developed and densely
occupied coastal regions of the world.
Ecosystems
Parameterizations of climate-induced ecosystem changes are
rapidly improving. To predict ecosystem changes under scenarios of
elevated greenhouse gases, earlier models simply mapped the
recently observed biomes to the GCM-predicted locations with
similar climatic conditions. Some of the latest models include
vegetation interactions with nutrients, CO2 fertilization, and fire (VEMAP, 1995;
IPCC, 1998). Recent integrated-assessment models of climate change
even include climate-vegetation and carbon cycle-vegetation
feedbacks, as well as the effects of changing land use (see, e.g.,
CIESIN, 1995). While the climate scenarios that have been explored
with these models are often derived from transient coupled
ocean-atmosphere GCMs, the ecosystem models themselves tend to be
designed to simulate an equilibrium land-surface biosphere, rather
than the transient ecosystem compositions that will precede the
equilibrium state.
The veracity of potential (i.e., omitting land-use changes)
vegetation-distribution predictions made from uninitialized climate
forecasts is as yet unknown. Because
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between one-third and one-half of terrestrial biological
production is used or dominated by human action (Vitousek et al.,
1997), and human-induced land-use changes are difficult to
anticipate, the prediction of future vegetation distributions is a
difficult undertaking. However, even in the absence of local-scale
ecosystem forecasting skill, there is value in assessing the
large-scale response of modeled ecosystems to uninitialized
climate-model forecasts. Such models can be used to assess the
future, large-scale response of terrestrial carbon sinks to altered
climate or to anthropogenic inputs, for instance, or the effects of
large-scale, vegetation-related albedo or surface roughness changes
on climate. As longer time series of the relevant vegetation data
become available for testing ecosystem-climate models, and these
models are more rigorously validated and improved, the value of
their forecasts will increase, particularly to the societies and
institutions that depend most directly on ecosystems.