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Earth System Modeling—An Integrated Assessment Tool for
Environmental Studies
R. A. PIELKE, Sr.
Department of Atmospheric Science, Colorado State University,
Fort Collins, CO 80523, U.S.A.
Abstract—This paper overviews a wide spectrum of influences on the Earth’s
climate system. These include external effects, such as variations in the solar
energy output, and internal influences. Internal influences include both natural
and human-caused effects.
The feedbacks associated with the Earth’s climate system are nonlinear
and occur over a range of time and space scales. As a result, our ability to
predict the future of climate is hindered, and perhaps impossible. We need to
assess integrated Earth system climate models to assess the limits of
predictability, to evaluate when sudden transitions might occur, and to evaluate
the vulnerability of the climate to human-caused and natural forcings. These
subjects are discussed in this paper.
INTRODUCTION
The papers presented at the 14th Toyota Conference further demonstrate that
climate processes involve complex, nonlinear interactions within and between
the atmosphere, oceans, land, and ice-covered surfaces. The prediction of future
climate, as modified by human activities, therefore, is a much more complicated
process than implied, for example, by the Intergovernmental Panel on the Climate
Convention (IPCC, 1992, 1996) and the United States National Assessment. The
potential accuracy of climate prediction is limited because of the presence of
feedbacks within and between the components of the climate system which
operate on a variety of time scales. Moreover, these feedbacks can produce rapid
transitions within the climate system (e.g. Claussen et al., 1999).
As discussed by Pielke (1998, 2000), these feedbacks result in climate
prediction being a much greater challenge than weather prediction. With weather
prediction (as challenging as it is), many feedbacks can be ignored since their
time scale is longer than the time period for which a prediction is being made. In
addition, direct human intervention in the climate system occur on the same time
scale as the climate forecast, yet this human activity has in the past been
unpredictable.
Table 1 summarizes an undoubtedly incomplete variety of direct human
disturbances, and feedbacks within the climate system, which place limitations
on the predictability of climate. It is clear from this table that climate prediction
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Synoptic-scale snowfall and snowmelt
(Liston and Sturm, 1998)
Biophysical effect of CO2
(Sellers et al., 1992)
Biophysical effect of H2 O
Ice Surfaces
Land Surface
Atmosphere
Turbulent heat and moisture fluxes
(Pielke, 2000)
Indirect effect of aerosols as CCN and IN
(Rotstayn, 1999)
Oceans/Lakes
Short-term hours and less
Seasonal high latitude sea-ice melting/freezing
(Pielke et al., 2000; Parkinson, 1995)
Biogeochemical effect of CO2
(Eastman et al., 2001)
Seasonal high latitude snow cover variations
(Pielke et al., 2000)
Biogeochemical effect of H2 O
(Lu et al., 2001)
Biogeochemical effect of Nitrogen deposition
(Vitousek et al., 1997)
Sea surface temperature changes
(e.g. El Niño) (Castro et al., 2001)
Direct effect of aerosols on the radiation budget
(Kaneyasu et al., 2000)
Medium-range weeks to years
Landscape change
(Chase et al., 2000a)
North Atlantic Conveyer Belt
(Gray et al., 1997)
Solar output changes
(Bard et al., 2000)
Radiative effect of CO2
(Shine and Forster, 1999)
Introduction of other anthropogenic trace
gases
(IGACtivities, 2000)
Continental ice cap changes in size
Long-term decades to several
Table 1. Examples of effects on the global-scale climate system. A recent cite for each is listed as
an example of papers that demonstrate the importance of these effects.
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involves much more than accurate knowledge of the level of carbon dioxide (or
any other subset of climate forcing) in the future and its influence on the
atmospheric radiative budget. This perspective has received confirmation even
from proponents of the IPCC perspective. Trenberth (1999) stated that “Improving
predictions of what the climate will be, not just with idealized scenarios but
taking all factors (including solar radiation, land use changes, aerosol effects, and
biogeochemical feedbacks) into account is a high priority.” Shine and Forster
(1999), in their paper, which discusses the effect of human activity on radiative
forms of climate change, state that “...climate change can occur due to nonradiative processes, such as land use changes...”
Lorenz (1979) proposed the concept of forced and free variations of weather
and climate. He refers to forced variations as those caused by external conditions,
such as changes in solar irradiance. Volcanic aerosols also cause forced variations.
He refers to free variations as those that “are generally assumed to take place
independently of any changes in external conditions.” Day-to-day weather
variations are presented as an example of free variations. He also suggests that
“free climate variations in which the underlying surface plays an essential role
may therefore be physically possible.”
There have been no model experiments to assess climate prediction in which
all important atmosphere-ocean-land surface processes were included. Existing
papers on this subject have been limited to coupled atmosphere-ocean global
models (e.g., Cubasch et al., 1994; Larow and Krishnamurti, 1998) or atmospheric
models alone (e.g., Bengtsson et al., 1996). In Bengtsson et al. (1996), the ocean
sea surface temperature is prescribed and vegetation effects, in their words, are
“grossly simplified.”
However, if the ocean and lake surfaces, ice surfaces, and/or land surface
change over the same time period as the atmospheric changes, then the nonlinear
feedbacks (i.e., two-way fluxes) between the air, land, and water eliminate an
interpretation of the ocean-atmosphere and land-atmosphere interfaces as
boundaries. Rather than “boundaries” these interfaces become interactive
mediums. The fluxes that occur between the atmosphere, ocean and lakes, sea ice,
and the land surface, must therefore necessarily be considered as part of the
predictive system. On the time-scale of what we typically call short-term weather
prediction (days), important feedbacks include biophysical (e.g., vegetation
controls on the Bowen ratio), snow cover, clouds (e.g., in their effect on the
surface energy budget), and precipitation (e.g., that which changes the soil
moisture) processes. This time-scale is already considered an initial value
problem (Sivillo et al., 1997). Seasonal and interannual weather prediction
include the following feedbacks: biogeochemical (e.g., vegetation growth and
senesence), anthropogenic aerosols (e.g., through their effect on the long- and
short-wave radiative fluxes), sea ice, and ocean sea surface temperature (e.g.,
changes in upwelling such as associated with an El Niño) effects. For even longer
time periods (of years to decades and longer), the additional feedbacks include
biogeographical processes (e.g., changes in vegetation species composition and
distribution), human-caused landuse changes, and deep ocean circulation effects
Fig. 1. Schematic illustration of direct effects and feedback influences with respect to climate
prediction. The length of the arrows has no meaning here, although the possible sign each effect
could have is shown (from Pielke, 1998).
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on the ocean surface temperature and salinity. In the context of Lorenz’s (1979)
terminology, each of these feedbacks are free variations.
Figure 1, adapted from Pielke (1998), schematically illustrates direct and
feedback effects on global mean temperature, as an example of the complexity of
climate prediction. Using Lorenz’s definitions, the direct effects could be
considered forced variations, while the feedbacks are part of the free variations.
Each of these time-scales must be considered initial value problems because
the predictions are dependent on the initial value for at least some aspects of the
ocean-atmosphere-land surface coupled system. A range of recent work (Claussen,
1994, 1998; Foley, 1994; Texier et al., 1997) has shown that the initial specification
of the land surface exerts a strong control on the subsequent atmospheric
circulation in global climate prediction models. Takata and Kimoto (2000)
demonstrate that whether soils freeze or not in the winter has an effect on the
subsequent seasonal weather cycles over continental spatial scales. Cubasch et al.
(1994) suggests in a greenhouse gas warming experiment with a coupled oceanatmosphere model that the time evolution of the modeled global mean warming
is “strongly dependent on the initial state of the climate system.”
EXAMPLES OF THE EFFECTS GIVEN IN TABLE 1
Landuse change effects
Human-caused landscape change includes deforestation, overgrazing,
agricultural activities, urbanization, and reforestation. Leemans (1999), for
example, shows that most landuse change occurred in the 1900s and that landuse
Fig. 2. Estimate changes in land cover and population from 1700 to 1995. The land use from top to
bottom in this figure are “cropland”, “pasture”, “forest”, and “other”, respectively (adapted from
Klein, 2001).
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Fig. 3. Model output cloud and water vapor mixing ratio fields on the third nested grid (grid 4) at
21 GMT on 15 May 1991. The clouds are depicted by white surfaces with qc = 0.01 g/kg, with
the sun illuminating the clouds from the west. The vapor mixing ratio in the planetary boundary
layer is depicted by the green surface with qv = 8 g/kg. The tan surface is the ground. Areas
formed by the intersection of clouds or the vapor field with lateral boundaries are flat surfaces,
and visible ground implies qv < 8 g/kg. The vertical axis is height, and the backplanes are the
north and east sides of the grid domain (from Pielke et al., 1997).
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change is accelerating (Fig. 2). O’Brien (2000) provides additional information
on the accelerating rate of tropical deforestation. Pitman and Zhao (2000), and
Chase et al. (1996, 2000a) have presented results that indicate a substantial effect
on the Earth’s atmospheric circulation thousands of kilometers from where
historical landscape changes occurred. These teleconnections in the model, for
example, produce major shifts in the polar jet stream, with substantial higher
latitude regions of warming and cooling. Other examples of the influence of
landscape change on the climate system include Pielke (2000), Bonan (1997),
Lauenroth et al. (1999), Pitman et al. (1999), Wang and Eltahir (2000a, b), Baron
et al. (1998), Kabat (2001; see section A), Skinner and Majorowicz (1999), Sisk
(1999), and Lewis (1998). Burke et al. (2000) show how the heterogeneity of the
landscape can influence GCM modeled atmosphere circulations.
Figure 3 provides an example of the important role of land surface on deep
cumulus convection. Figure 3 (top) is the model result for a specific date of
cumulonimbus development over the central Great Plains of the United States
using the current landscape. Figure 3 (bottom) uses the natural landscape. Pielke
et al. (1997) provides a discussion of this experiment, while Shaw et al. (1997),
Grasso (1996), and Ziegler et al. (1997) provide validation of the results for the
current landscape situation. Clearly, the alteration of the landscape (with a
reduction in transpiration in the natural landscape) has resulted in a reduction in
cumulonimbus cloud activity in the model.
The published assessments of land-surface temperatures also ignored humancaused landscape changes, except for urbanization (Easterling et al., 1999).
Crowley (2000), and Delworth and Knutson (2000) for example, ignore landuse
change effects on temperature trends in their assessments of climate change.
Crowley ignores this effect in his conclusions regarding temperature changes
over the past 1000 years! These changes can affect both the local and regional
temperature record as the landscape cover is altered.
Pielke et al. (1999), for example, can explain the observed warming and
drying in July and August in south Florida as due to the conversion of the natural
landscape to its current human-dominated form. Pielke et al. (2000a) documented
substantial spatial variation in temperature trends in eastern Colorado, some of
which are undoubtedly associated with landscape change in the 20th century. For
instance, Segal et al. (1988, 1989) and Stohlgren et al. (1998), document the
major role of irrigation on the climate in this region including a cooling of
summer maximum temperatures along the eastern slope of the Colorado Rocky
Mountains. Without considering how landscape change has influenced surface
temperature change, why should it be concluded that surface temperature data
sets should be able to document trends due to increased anthropogenic greenhouse
gases and aerosols?
Global and regional temperature trends assessed using the NCEP Reanalysis
1000–850 mb thickness values for the period 1979–1996 are consistent with
trends evaluated for the lower troposphere using MSU satellite data (Pielke et al.,
1998a, b; Chase et al., 2000b). Both data sets found no consistent global
temperature change in the lower troposphere during this period. In contrast, the
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Fig. 4. RAMS/GEMTM coupled model results—the seasonal domain-averaged contributions to
maximum daily temperature, minimum daily temperature, precipitation, and leaf area index
(LAI) due to: f 1 = natural vegetation, f2 = 2 × CO2 radiation, f 3 = 2 × CO2 biology, f12 = interaction
of natural vegetation and 2 × CO 2 biology, f 13 = interaction of natural vegetation and 2 × CO 2
biology, f 23 = interaction of 2 × CO 2 for radiation and biology, and f 123 = the interaction of all
three factors (from Eastman et al., 2001).
surface data shows warming. If each data set are accurate, as implied by the recent
NRC Panel on the reconciliation of MSU and surface data (NRC, 2000; Hurrell
and Treberth, 1996), the radiative effect of increased carbon dioxide and other
greenhouse gases and aerosols must be processed differently by the real climate
system, than represented in the GCMs. Landuse change is one possible reason for
the differences, since the surface temperature data would respond more to local
landscape changes.
Biogeochemical effects
As carbon dioxide concentrations continue to increase, this enrichment will
provide a more favorable environment for plant growth with, as of yet, unknown
consequences on the Earth’s climate system. Anthropogenic nitrogen deposition
(and other trace gas deposition) also significantly alters the biochemistry of the
vegetation. Hayden (1998) summarizes ecosystem feedbacks on climate at the
landscape scale. Doherty et al. (2000) and de Noblet-Duchoudré et al. (2000)
demonstrate the significant role of vegetation feedbacks within the climate
system.
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In the central grasslands of the United States, using a coupled atmosphericbiogeochemical model Eastman et al. (2001) found that the effect of doubled
carbon dioxide would be improved water use efficiency and greater vegetation
growth. The net effect, on time periods as short as a growing season according to
the coupled models, would be a cooler daytime environment and a warmer
nighttime.
Figure 4 reproduced from Eastman et al. (2001) shows that landuse change
and the enrichment effect of carbon dioxide have more immediate effects on the
regional weather during the growing season, than the radiative effect of carbon
dioxide. While this result certainly does not mean we should not be concerned
about the radiative effect of increased carbon dioxide and other anthropogenic
greenhouse gases over longer time periods, it does mean that the biological effect
of enhanced carbon dioxide is likely to be important in influencing climate on the
regional scale (and probably global scale) in the long term.
Indirect effect of aerosols
The collidial stability of clouds that results when pollution cloud condensation
nuclei (CCN) are inserted into the atmosphere has been proposed as a major
component altering the Earth’s climate system (Pielke, 1984; Cotton and Pielke,
1995, and references therein). In recent work, Rotstayn (1999) alters the parameters
in the microphysical representation in a GCM to show, for example, a resultant
major shift in the geographical pattern of rainfall in the Equatorial Pacific Ocean.
He demonstrates that the generation of rainfall from clouds is a very nonlinear
process. Combined with the direct albedo effect of increased CCN on albedo, the
net result of this spatially and temporally varying anthropogenic input of aerosols
into the atmosphere is a large and nonlinear response within the Earth’s climate
system. Other papers, which discuss the significant indirect effect of aerosols on
climate, include Lohmann and Feichter (2001), Lohmann et al. (2000), and
Rotstayn et al. (2000).
Sea-ice feedbacks
The melting of sea ice within GCMs is perhaps the largest warming feedback
in these models. However, the exposure of the Arctic Ocean could promote
enhanced snowfall on the adjacent land masses (Ewing and Donn, 1956).
Figures 5(a)–(d) illustrate for the summer the complicated feedbacks between
the sea ice and adjacent land masses that can occur. To create these figures, the
RAMS model (Liston and Pielke, 2000) was integrated for the warm season of the
year with assimilated observed sea ice and with the ice removed. As evident in the
figures the near surface air temperatures are actually warmer over the Arctic
Ocean (by over 1°C in large areas) when the sea ice absorbs solar radiation and
transfers some of this energy as sensible heat back into the atmosphere. Without
the sea ice, while the Earth system gains heat through the reduction of reflected
solar insolation, the atmosphere is cooler on this time scale. This difference in
surface temperature is communicated into the troposphere and could result in a
weaker arctic frontal region when sea ice is present, than if it were absent.
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Fig. 5. Simulated mean surface (2 m) temperature (K) for the month of June 1995, for runs (a)
without sea-ice data and (b) with sea-ice data. Both model runs used assimilated sea-surface
temperature fields and simulated ice-phase physics in the soil column. (c) Mean sea-ice
concentration for the month of June 1995 based on NOAA 1° × 1° data, and (d) mean difference
(with ice-without ice) in surface temperature for June 1995. (Model runs completed by Liston
and McFadden (2000), Colorado State University.)
Biogeographic effects
The movement of entire biomes based on GCM simulations of atmospheric
changes ignores the feedbacks that can occur due to changes in vegetation species
composition. Foley (1994), and others have shown that feedbacks from the
vegetation are essential for realistic simulations of paleoclimate conditions.
Neilson and Marks (1994) found that different water use efficiencies within a
biogeographic model produced dramatically different vegetation distributions in
response to the same GCM climate change experiment.
As an illustration of this effect, Figs. 6 through 8, from Liston et al. (2001)
show alterations in snow melt and associated turbulent energy fluxes if a portion
of the Arctic tundra were converted to shrubs from tundra (Fig. 6(a) illustrates the
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Fig. 5. (continued).
observed vegetation, while Fig. 6(b) illustrates the vegetation when moist tundra
and wet tundra were replaced with shrub tundra). The higher holding capacity of
shrubs results in a delayed melt period, as the wind blown snow in the winter is
more evenly distributed across the landscape. Even though this experiment did
not permit a change in the larger-scale atmospheric wind flow or temperature, a
significant feedback still occurred due to the change in vegetation type.
Other effects
The prevalence of natural and man-caused fires is also a major component
of the Earth’s climate system. Many of their effects on the Earth’s environment
are summarized in Levine et al. (1999). The biogenic aerosol emitted clearly has
a radiative effect, as discussed in Shine and Forster (1999), but the alteration of
the land surface energy and water budget is also important, but as of yet, relatively
little research has been performed to look at this effect. Amiro et al. (1999) found,
for example, that burned regions in the boreal forest of Canada were up to 6°C
Fig. 6. Simulated 1987 end-of-winter snowdepth distributions for (a) observed vegetation, and (b)
shrub-enhanced vegetation. The wind arrow shows the prevailing storm-wind direction that
redistributes the snow. End-of-winter snowdepth histograms for (c) observed vegetation from
(a) and (d) shrub-enhanced vegetation (from (b)) (from Liston et al., 2001).
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Fig. 7. Time evolution of spring snow disappearance for observed vegetation and shrub-enhanced
snowcover in 1987 (top) and 1990 (bottom). In the shrub-enhanced simulation, ridges became
snow-free earlier (dark shading), and shrub-covered areas became snow-free later (light
shading), compared to the observed vegetation simulation (from Liston et al., 2001).
Fig. 8. Difference in snowfree date resulting from shrub enhancement for 1987. Positive (negative)
numbers indicate the number of days longer (shorter) the vegetation surface remained snowcovered when shrubs were enhanced (from Liston et al., 2001).
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Fig. 9. Carbon sequestration as an integrated Earth system issue.
warmer than surrounding areas, and remained warmer even 15 years after the fire.
Pinty et al. (2000), in a recent paper, suggests that human-caused fires affect the
Earth surface albedo at continental scales.
Shepherd (2000) summarizes the role of the stratosphere within the climate
system. He discusses the two-way coupling that occurs between the stratosphere
and the troposphere which includes both chemical and dynamical interactions.
Thus if an anthropogenic perturbation of the troposphere occurs, there are
consequences associated with this coupling which reach into the stratosphere,
and then feedback into the troposphere.
Carbon sequestration
To reduce carbon dioxide in the atmosphere, it has been proposed to
sequester carbon in the soil through deliberate agricultural management practices
(Houghton et al., 1993; Unruh, 1995). However, this procedure has not been
assessed in an integrated Earth system perspective (Showstack, 2000). If, for
example, water vapor flux into the atmosphere is increased and/or the albedo of
the Earth’s surface is reduced, the net radiative effect of sequestering carbon
dioxide could be an increase of anthropogenic heating of the atmosphere!
Figure 9 illustrates this effect.
This example of an Earth system issue illustrates why the evaluation of the
carbon cycle must be coupled with the water and energy cycles. They cannot be
evaluated independently. The proposed National Science Foundation WaterEnergy-Biota (WEB) initiative (http://cires.colorado.edu/hydrology; James, 2000)
recommends this type of integrated Earth system perspective. The recognition
that carbon is just one component of the Earth’s environmental system is echoed
by Running (2000) who states that “Atmospheric carbon dioxide in itself is not
dangerous, it actually helps plants grow faster. But scientists see it as a canary in
the coal mine, the leading indicator of other global scale human impacts on the
biosphere, the sum total of living organisms on the land and in the oceans.”
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FALLACY OF DOWNSCALING FOR LONG-TERM CLIMATE MODELING
The application of statistical and dynamic downscaling as applied to numerical
weather prediction is a very appropriate and valuable tool to improve the spatial
and temporal skill of weather projections. However, dynamic downscaling from
the radiative effects of CO2 and aerosol GCM sensitivity experiments cannot
provide improved skill for several reasons.
First, with respect to dynamic downscaling, there is not a feedback upscale
to the GCM from the regional model, even if all of the significant large-scale
(GCM scale) human-caused disturbances were included. Second, the GCMs also
have a spatial resolution that is inadequate to properly define the lateral boundary
conditions of the regional model. As shown by Anthes and Warner (1978), the
lateral boundary conditions are the dominant forcing of regional atmospheric
models associated with propagating features in the polar westerlies. With numerical
weather prediction, the atmospheric observations used in the analysis to initialize
a model retain a component of realism even when degraded to the coarser model
resolution of a global model.
This realism persists for a period of time (up to a week or so), when used as
lateral boundary conditions for a regional numerical weather prediction model.
This is not true with the GCM climate change experiments where observed data
does not exist to influence the predictions. A regional model cannot reinsert
model skill, that is dependent on lateral boundary conditions, no matter how good
the regional model.
If this conclusion is disagreed with, the first step to demonstrate that the
regional climate model has predictive skill is to integrate an atmospheric GCM
with observed SSTs for several seasons into the future. The GCM output would
then be downscaled using the regional climate model. There is expected to be
some regional skill associated with the seasonal scale inertia of SSTs (Castro et
al., 2001), and this needs to be quantified. This level of skill, however, will
necessarily represent the maximum skill theoretically even possible with GCMs
applied to forecasts years and decades into the future since, for these periods,
SSTs must also be predicted, and not specified! Such experiments have not been
systematically completed. Indeed, does the concept of predictive skill even make
sense when we cannot verify the models until decades into the future (Sarewitz
et al., 2000)?
The statistical downscaling from large-scale climate change experiments,
besides requiring that the GCMs are accurate predictions of the future, also
require that the statistical equations that are used for downscaling remain
invariant under changed regional atmospheric and land-surface conditions. There
is no way to test this hypothesis. In fact, it is unlikely to be valid since the regional
climate is not passive to larger-scale climate conditions, but is expected to change
over time, both due to direct human-caused landscape changes and in response
and feedback to the larger-scale atmospheric conditions.
Examples of the current paradigm of downscaling are illustrated in Climate
Impacts LINK (1997, their figure 5) and Atmosphere (1999). In the Atmosphere
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Fig. 10. 1979–1997 trends in degrees C/19 years for December–January–February NCEP 1000–
500 mb layer-average temperature (from Chase et al., 2000b).
article, model results are presented for the year 2050 as if they were predictions.
Climate Impacts LINK (1999, their figure 3) similarly present model results (in
this case their GCM output) for the decades of the 2050s. However, if the Earth
system feedbacks occur across the spectrum of space and time scales, and first
order human perturbations are left out of the GCMs, they cannot possibly provide
plausible forecasts of the Earth system 50 years from now.
A more appropriate procedure to use regional climate models is to apply
them as sensitivity experiments where the relative effect of regional human
disturbance is investigated (e.g., the radiative and biological effect within the
regional climate system of increased CO2; landuse change—both historical and
estimates of future possibilities; the radiative and biogeochemical effects of air
pollution, such as ozone, nitrogen deposition, etc.). The knowledge of these
sensitivities would be much more useful to policymakers than the existing
downscaled regional climate results, which are inappropriately communicated as
projections. Since many decisions are regional and local in scale, the understanding
of the involvement of the regional climate system within the entire spectrum of
environmental change and variability (and not just the radiative effect of increased
CO2 and aerosols) is a more effective methodology to protect society from
environmental threats.
SUDDEN SHIFTS IN THE EARTH’S CLIMATE
The natural Earth’s climate system has abrupt changes over short time
periods. Stahle et al. (2000), for example, documents a vast multi-decadal
megadrought in the 16th century over North America which would have
catastrophic economic impact if it occurred in the next 50 years. This drought far
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Fig. 11. Simulated January reference height temperature difference (current-natural, C; from Chase
et al., 2000a).
exceeded any drought of the 20th Century, including the 1930s Dust Bowl years.
Song (2000) documents protracted wet and dry periods in China since 1470 A.D.
On the more recent time scales, where human intervention within the Earth’s
climate system has occurred, there have been diverse regional and temporal
trends. Wang and Allard (1995), for example, report on nearly continuous surface
air cooling of a region in northern Quebec for the period 1947–1992 despite
warming of the western Arctic region of North America. This large spatial
variation in temperature trends was confirmed by Chase et al. (2000b) who
documented strong lower tropospheric cooling in northeastern Canada from
1979–1997 and strong lower tropospheric warming in northwestern North America
(Fig. 10). Chase et al. (2000a) shows that landuse change, particularly in the
tropics, could have caused much of the observed lower tropospheric change since
1979 (Fig. 11).
Other examples of abrupt changes in climate during the past century include
Yonetani and McCabe (1994), Changnon et al. (1993), and Schuster et al. (2000).
Schwing and Moore (2000) illustrate how such abrupt changes in climate can
have an immediate effect on the biosphere. They documented a change in sea
surface temperatures off of the California coast that went in two years from the
warmest on record to the lowest in decades. Species composition in zooplankton
communities off the Oregon and British Columbia coast shifted from warm water
and oceanic species to exclusively subarctic species.
Multiple climate equilibrium associated with biosphere-atmosphere
interactions are discussed by Claussen (1998), and Wang and Eltahir (2000c),
which is one explanation for abrupt climate changes. The concept of chaos,
including multiple equilibria, is reviewed in Zeng et al. (1993). Schuster et al.
(2000), for example, documented from ice cores in south central North America
that the termination of the Little Ice Age occurred abruptly in the decade around
1845 A.D. and continues to the present day in this warmer regime.
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Fig. 12. Schematic of different classes of prediction (from Pielke, 2000).
CONSEQUENCES
As presented earlier in this paper, neither the GCMs or the regional climate
models include all of the significant human effects on the climate system. These
additional human perturbations of the climate system, and the variety of feedbacks
across a wide spectrum of time and space scales reinforce the conclusion of a
“discernible human influence”, but also limit our ability to predict the future
climate. The combined effects of human landuse change, the biogeochemical
effect on the atmosphere due to increased CO2, and the microphysical effect of
pollution aerosols, for example, have not yet been included in these models. As
stated by Shine and Forster (1999), who reviewed the effect of human activity on
radiative forcing, the conclusions of the IPCC may not prove to be robust when
a broader set of human-caused radiative effects are included. Thus, the IPCC
model runs should only be interpreted as sensitivity experiments, not forecasts,
projections, or even scenarios. They are certainly not Earth system models.
Figure 12 presents a schematic where climate model prognostic calculations
can be presented as one of four types:
•
sensitivity studies;
•
scenarios;
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Fig. 13. Use of ecological vulnerability/susceptibility in environmental assessment (from Pielke and
Guenni, 1999, adapted from Tenhunen and Kabat, 1999).
•
projections;
•
perfect foresight.
In sensitivity analysis, one of the important forcings on climate is perturbed (e.g.,
a doubling of CO 2 concentrations and its radiative effect). This is the approach
used to produce the IPCC reports. This method could, in principle, generate
skillful predictions but only if other climate forcings, such as landuse change and
the biological effect of doubled CO2 were unimportant. A scenario approach
would apply if all of the important direct and feedback effects on climate were
included, yet the results are sensitive to initial conditions. A scenario is then a
realization out of an ensemble of possible simulations. If the realizations could
cover the ensemble space, then the model results could be considered realistic
projections. The term “prediction” is variously misused to describe sensitivity
studies and scenarios. In contrast to weather prediction, where we feel that we
understand all of the important physical effects and feedbacks, the climate system
is a much more complex dynamical system.
If skillful climate prediction is not possible beyond some time scale, a
vulnerability assessment approach is the preferred scientific approach to provide
policymakers useful information (Pielke Jr., 1998). Figure 13 as reproduced from
Pielke and Guenni (1999), illustrates an example of this approach with respect to
water resources. With this technique, needs of the policymaker are better met in
that the focus is on decisions and not predictions (Sarewitz et al., 2000). This
vulnerability perspective is further discussed in Pielke and Guenni (1999), and
Kabat (2001; section E).
The Earth system modeling approach should be constructed around the
vulnerability/susceptibility paradigm. All important aspects of the Earth system
330
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need to be represented instead of having the atmospheric/or ocean-atmospheric
GCMs drive the other components of the Earth system.
THE EARTH SYSTEM HEAT BUDGET
It is useful to consider the Earth’s heat budget as part of a coupled system
instead of focusing on “global warming” as primarily an atmospheric focus.
Conceptually, the Earth system’s heat budget, when averaged for annual time
scales and longer, can be considered with respect to the following bins.
Atmosphere: the troposphere predominately, since about 80% of the mass of
the atmosphere resides in this layer.
Oceans: the ocean and larger, deep lakes can be considered as containing two
layers; above the thermocline and the remainder (the deep ocean).
Sea ice: heat energy is stored when sea water is frozen, and heat is released
when it melts. The thickness of the ice and its areal coverage describe this heat
store.
Continental glaciers: heat energy is stored when snow accumulates over time
and is converted into fresh water ice masses. The heat is released into the
atmosphere when melting and evaporation, or sublimation occurs, and into the
oceans when icebergs calf into the sea and melt.
Land: heat energy is stored in the ground when soil becomes frozen (permafrost
develops), and heat is released when the permafrost melts. The amount of heat
storage is a function of the wetness of the soil when it freezes, the heat capacity
and density of the soil, and the depth of freezing. Heat is also stored when the soil
and underlying rocks warm, and heat is released when the soil and subsurface
rocks cool. The amount of heat storage depends on the wetness of the soil, and the
soil heat capacity and density.
Lost to space: heat energy is continuously lost to space. With an increase in
heat input within the Earth system, it is inadequately known how the heat lost to
space would change.
By averaging over annual time periods, shorter-term variability in heat
storage (such as winter snows over non-glaciated regions which disappear in the
spring) do not affect this budget. Similarly, Antarctic sea ice is predominantly
melted and refrozen each year (Gloersen et al., 1992). External to the Earth’s
climate system, are the irradiance of the Sun and volcanic eruptions.
Human disturbances, such as the effect of an increase in downwelling longwave electromagnetic energy due to CO2 and other anthropogenic greenhouse
gases, have their initial effect in the atmosphere. However, interfacial fluxes can
transfer this heat energy input into one or more of these heat storage bins.
Similarly, landuse change can directly alter the net electromagnetic radiation
received at the surface with resultant changes in the heat storage in the atmosphere
and in the ground.
The transfer between the bins of the heat energy needs to be assessed for
different time scales. Existing evidence, such as illustrated on pages 319–328,
suggests these transfers are highly nonlinear and illustrate the chaotic dynamical
nature of the Earth system.
Earth System Modeling—An Integrated Assessment Tool
331
Examples of the assessment of these terms include Levitus et al. (2000) who
reported an approximate 2 × 1023 Joules added heat content to the world oceans
from 1948 to 1998, the NRC (2000) study which found no statistically lower
tropospheric temperature change up to the present, and Nagurnyi et al. (1999)
who reported on a 3% change of average ice thickness in the Arctic Ocean basin
between 1972–1992. Levi (2000), in contrast, report that the Arctic ice cover is
40% thinner than it was 20 to 40 years ago. Permafrost depth has no trend (http:
//www.geography.uc.edu/CALM). Changes of solar luminosity of almost 3 watts
per meter squared from 1979 to 1991 have been observed associated with sunspot
activity (Parker, 2000).
A consequence of considering the Earth’s heat budget in the context of heat
pools is that fluxes of heat can occur between different components of the system
over a variety of time scales. Heat could be inserted into the atmosphere through
enhanced greenhouse gas downwelling, but stored in the deeper ocean. Only
when a threshold is passed might the consequences of the heat immediately
reappear from the ocean into the atmosphere. Such a potential nonlinear response
is quite distinct from the nearly monotonic increase in tropospheric temperature
predicted by the IPCC. Such a storage of heat deeper in the ocean was found in
the Levitus et al. (2000) study who reported on substantial changes in heat content
in the 300 meter to 1000 meter layers of each ocean, and in depths greater than
10000 meters of the North Atlantic. If this is a continuing trend, the deep ocean
could provide a long-term storage for heat energy; an effect which has not been
represented realistically in any ocean-atmosphere GCM on this time scale.
Similarly, the melting of sea ice and/or permafrost could provide a buffer to
tropospheric temperature changes until most of the ice melted.
Alternatively, the excess heat associated with anthropogenically elevated
greenhouse gases could be radiated out into space. Gray (2000, personal
communication), for example, proposes that if the hydrologic cycle were
invigorated, cumulus cloud updrafts would be more concentrated such that the
lower relative humidity in the subsiding air between the clouds would promote
increased long-wave radiative loss to space.
The understanding of the Earth system including the interactions among the
component systems is not understood. However, it is clear that no one component
(such as the atmosphere) can be considered, within the context of climate, as
separate and distinct entities. This is a major reason why Earth system models are
needed as an assessment tool in climate.
CONCLUSION
This paper briefly overviews the complexity of the Earth’s climate system.
Direct anthropogenic and natural effects, and feedbacks within the climate
system, occur across the spectrum of time and space scales. The carbon cycle is
an important component of the system, but is not the only important driver of
long-term climate change.
There are important consequences associated with the need to consider the
Earth’s climate as a complex, coupled nonlinear dynamical system. First, there
332
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are several human perturbations to the Earth system in addition to the radiative
effect of increased carbon dioxide and other anthropogenic gases, which are equal
or larger in their regional and global effect. These perturbations include landuse
change, the biogeochemical effect of enhanced carbon dioxide, and the
microphysical effect of pollutant aerosols.
Second, since the land, oceans and lakes, and sea ice interacts with the
atmosphere through complex, nonlinear feedbacks, deterministic prediction
beyond some time period is unattainable. At present this limit appears to be the
seasonal lead time (e.g., see Landsea and Knaff, 2000). For the periods of decades
and longer, the uncertainty associated with human deliberate and inadvertent
interaction (such as associated with as of yet undiscovered technology) within the
climate system assures that the future system will not be predictable.
As a result of this limit on prediction, a broader assessment of risk to climate
and other environmental stresses is proposed (Pielke and Guenni, 1999; Kabat,
2001). This vulnerability-sensitivity assessment methodology would permit
evaluations of what component of society is most at risk to perturbations in the
environment. Policymakers can determine where resources should be placed to
minimize the risk, or to insure against loss. Vorosmarty et al. (2000), for example,
demonstrates that the greatest threat to drinkable water over the next 50 years is
not climate change as produced by any of the GCMs, but population growth.
The development of an integrated Earth system model by the Frontier
Research System for Global Change would be an effective tool to assess risks that
society faces in the 21st Century.
Acknowledgments—This paper was completed with support from NSF Grants ATM9910857 and DEB-9632852, NASA Grant No. NAG8-1511, and NOAA Grant No.
NA67RJ0152. Dallas Staley and Allison Edwards very ably completed the typing and
editing of the manuscript. Fredi Boston significantly assisted in obtaining several of the
references.
REFERENCES
Amiro, B. D., J. I. MacPherson, and R. L. Desjardins, 1999: BOREAS flight measurements of forestfire effects on carbon dioxide and energy fluxes. Agric. Forest Meteor., 96, 199–208.
Anthes, R. A. and T. T. Warner, 1978: Development of hydrodynamic models suitable for air
pollution and other mesometeorological studies. Mon. Wea. Rev., 106, 1045–1078.
Atmosphere, 1999: Newsletter of CSIRO, Issue 7, for more information contact Kevin Walsh,
[email protected].
Bard, E., G. Raisbeck, F. Yiou, and J. Jouzel, 2000: Solar irradiance during the last 1200 years based
on cosmogenic nuclides. Tellus, 52B, 985–992.
Baron, J. S., M. D. Hartman, T. G. F. Kittel, L. E. Band, D. S. Ojima, and R. B. Lammers, 1998:
Effects of land cover, water redistribution, and temperature on ecosystem processes in the South
Platte basin. Ecol. Appl., 8, 1037–1051.
Bengtsson, L., K. Arpe, E. Roeckner, and U. Schulzweida, 1996: Climate predictability experiments
with a general circulation model. Clim. Dynamics, 12, 261–278.
Bonan, G. B., 1997: Effects of land use on the climate of the United States. Climatic Change, 37,
449–486.
Burke, E. J., W. J. Shuttleworth, and Z.-L. Yang, 2000: Heterogeneous vegetation affects GCMmodeled climate. GEWEX News, 10, 7–8.
Earth System Modeling—An Integrated Assessment Tool
333
Castro, C. L., T. B. McKee, and R. A. Pielke, Sr., 2001: The relationship of the North American
monsoon to tropical and North Pacific sea surface temperatures as revealed by observational
analyses. J. Climate (submitted).
Changnon, D., T. B. McKee, and N. J. Doesken, 1993: Annual snowpack patterns across the Rockies:
Long-term trends and associated 500-mb synoptic patterns. Mon. Wea. Rev., 121, 633–647.
Chase, T. N., R. A. Pielke, T. G. F. Kittel, R. Nemani, and S. W. Running, 1996: The sensitivity of
a general circulation model to global changes in leaf area index. J. Geophys. Res., 101, 7393–
7408.
Chase, T. N., R. A. Pielke, T. G. F. Kittel, R. R. Nemani, and S. W. Running, 2000a: Simulated
impacts of historical land cover changes on global climate in northern winter. Climate Dynamics,
16, 93–105.
Chase, T. N., R. A. Pielke, J. A. Knaff, T. G. F. Kittel, and J. L. Eastman, 2000b: A comparison of
regional trends in 1979–1997 depth-averaged tropospheric temperatures. Int. J. Climatology,
20, 503–518.
Claussen, M., 1994: Coupling global biome models with climate models. Climate Research, 4, 203–
221.
Claussen, M., 1998: On multiple solutions of the atmosphere-vegetation system in present-day
climate. Global Change Biology, 5, 549–559.
Claussen, M., C. Kubatzki, V. Bovkin, A. Ganopolski, P. Hoelzmann, and H. J. Pachur, 1999:
Simulation of an abrupt change in Saharan vegetation at the end of the mid-Holocene. Geophys.
Res. Lett., 26, 2037–2040.
Claussen, M., V. Brovkin, and A. Ganopolski, 2001: Biogeophysical versus biogeochemical
feedbacks of large-scale land cover change. Geophysical Research Letters (in press).
Climate Impacts LINK, 1997: Newsletter of the U.K. Department of the Environment, Transport and
the Region’s Climate Impacts LINK Project, Climatic Research Unit, University of East Anglia,
United Kingdom, Issue No. 15, December 1997.
Climate Impacts LINK, 1999: Newsletter of the U.K. Department of the Environment, Transport and
the Region’s Climate Impacts LINK Project, Climatic Research Unit, University of East Anglia,
United Kingdom, Issue No. 17, August 1999.
Cotton, W. R. and R. A. Pielke, 1995: Human Impacts on Weather and Climate, Cambridge
University Press, New York, 288 pp.
Crowley, T. J., 2000: Causes of climate change over the past 1000 years. Science, 289, 270–277.
Cubasch, U., B. D. Santer, A. Hellbach, G. Hegerl, H. Hock, E. Maier-Reimer, U. Mikolajewicz, A.
Stossel, and R. Voss, 1994: Monte Carlo climate change forecasts with a global coupled oceanatmosphere model. Climate Dynamics, 10, 1–19.
de Noblet-Ducoudré, N., M. Claussen, and C. Prentice, 2000: Mid-Holocene greening of the Sahara:
first results of the GAIM 6000 year BP experiment with two asynchronously coupled atmosphere/
biome models. Climate Dynamics, 16, 643–659.
Delworth, T. L. and T. R. Knutson, 2000: Simulation of early 20th Century global warming. Science,
287, 2246–2250.
Doherty, R., J. Kutzbach, J. Foley, and D. Pollard, 2000: Fully coupled climate/dynamical vegetation
model simulations over Northern Africa during the mid-Holocene. Climate Dynamics, 16, 561–
573.
Easterling, D. R., T. C. Peterson, and T. R. Karl, 1996: On the development and use of homogenized
climate datasets. J. Climate., 9, 1429–1434.
Eastman, J. L., M. B. Coughenour, and R. A. Pielke, 2001: The effects of CO2 and landscape change
using a coupled plant and meteorological model. Global Change Biology (in press).
Ewing, M. and W. L. Donn, 1956: A theory of ice ages. Science, 123, 1061–1066.
Foley, J. A., 1994: The sensitivity of the terrestrial biosphere to climate change: a simulation of the
middle Holocene. Global Biogeochemical Cycles, 8, 505–525.
Gedney, N. and P. J. Valdes, 2000: The effect of Amazonian deforestation on the northern
hemisphere circulation and climate. Geophysical Research Letters, 27, 3053–3056.
Gloersen, P., W. J. Campbell, D. J. Cavalieri, J. C. Comiso, C. L. Parkinson, and J. J. Zwally, 1992:
Arctic and Antarctic sea ice, 1978–1987: Satellite passive-microwave observations and analysis.
334
R. A. PIELKE , Sr.
NASA SP-511, NASA Scientific Technical Information Program, Washington, D.C., 290 pp.
Grasso, L., 1996: Numerical simulation of the May 15 and April 26, 1991 tornadic thunderstorms.
Ph.D. Dissertation, Atmospheric Science Paper 596, Colorado State University, Dept. of
Atmospheric Science, Fort Collins, CO 80523, 151 pp.
Gray, W. L., J. D. Sheaffer, and C. W. Landsea, 1997: Climate trends associated with multidecadal
variability of Atlantic hurricane activity. Chapter 2 in: Hurricanes, H. F. Diaz and R. S.
Pulwarty, eds., Springer Verlag, Germany, 15–54.
Hayden, B. P., 1998: Ecosystem feedbacks on climate at the landscape scale. Phil. Trans. R. Soc.
London B, 353, 5–18.
Herrick, C. and D. Sarewitz, 2000: Ex post evaluation: A more effective role for scientific
assessments in environmental policy. Science Technology and Human Values, 25, 309–331.
Houghton, R. A., J. D. Unruh, and P. A. Lefebvre, 1993: Current land cover in the tropics and its
potential for sequestering carbon. Global Biogeochemical Cycles, 7, 305–320.
Hurrell, J. W. and K. E. Trenberth, 2001: Satellite versus surface estimates of air temperature since
1979. J. Climate (in press); IGBP, Sweden (in preparation).
IGACtivities 2000: Newsletter of the International Global Atmospheric Chemistry Project, 21,
23 pp.
IPCC, 1992: Climate Change 1992, The Supplementary Report to the IPCC Scientific Assessment,
J. T. Houghton, B. A. Callander, and S. K. Varney, eds., University of Cambridge Press, 200 pp.
IPCC, 1996: Climate Change 1995, The Science of Climate Change. J. T. Houghton, L. G. Meira
Filho, B. A. Callander, N. Harris, A. Kattenberg, and K. Maskell, eds., University of Cambridge
Press, 572 pp.
James, L. D., 2000: Multiple frontiers in hydrology unite for greater good. EOS, 81, 310–316.
Kabat, P. (ed.), 2001: Biospheric feedbacks in the climate system and the hydrological cycle. BAHC
Synthesis Book, Stockholm Sweden, IGBP (in press).
Kaneyasu, N., K. Takeuchi, M. Hayashi, S. Fujita, I. Uno, and H. Sasaki, 2000: Outflow patterns of
pollutants from East Asia to the North Pacific in the winter monsoon. J. Geophys. Res., 105,
17,361–17,377.
Klein, G. K., 2001: Estimating global land use change over the past 300 years: The hyde 2.0 database.
Global Biogeochemical Cycles (in press).
Landsea, C. W. and J. A. Knaff, 2000: How much skill was there in forecasting the very strong 1997–
98 El Niño? Bull. Amer. Meteor. Soc., 81, 2107–2119.
Larow, T. E. and T. N. Krishnamurti, 1998: Initial conditions and ENSO prediction using a coupled
ocean-atmosphere model. Tellus, 50A, 76–94.
Lauenroth, W. K., I. C. Burke, and M. P. Gutmann, 1999: The structure and function of ecosystems
in the central North American grassland region. Great Plains Res., 9, 223–259.
Leemans, R., 1999: Land-use change and the terrestrial carbon cycle. IGBP Global Change
Newsletter, 37, 24–26.
Levi, B. G., 2000: The decreasing Arctic ice cover. Physics Today, January, 19–20.
Levine, J. S., T. Bobbe, N. Ray, R. G. Witt, and A. Singh, 1999: Wildland fires and the environment:
A global synthesis. Environment Information and Assessment Technical Report, UNEP/
DEIA&EW/TR.99-1, 46 pp.
Levitus, S., J. I. Antonov, T. P. Boyer, and C. Stephens, 2000: Warming of the world ocean. Science,
287, 2225–2229.
Lewis, T., 1998: The effect of deforestation on grown surface temperatures. Global Planetary
Change, 18, 1–13.
Lindzen, R. S., 1990: Some coolness concerning global warming. Bull. Amer. Meteor. Soc., 71, 288–
299.
Liston, G. E. and R. A. Pielke, 2000: A climate version of the Regional Atmospheric Modeling
System. Theor. Appl. Climatology, 66, 29–47.
Liston, G. E. and M. Sturm, 1998: A snow-transport model for complex terrain. J. Glaciology, 44,
498–516.
Liston, G. E., J. P. McFadden, M. Sturm, and R. A. Pielke Sr., 2001: Modeled changes in arctic tundra
snow, energy, and moisture fluxes due to increased shrubs. Global Change Biology (in press).
Earth System Modeling—An Integrated Assessment Tool
335
Lohmann, U. and J. Feichter, 2001: Can the direct and semi-direct aerosol effect compete with the
indirect effect on a global scale? Geophysical Research Letters, 28, 159–161.
Lohmann, U., J. Feichter, J. Penner, and R. Leaitch, 2000: Indirect effect of sulfate and carbonaceous
aerosols: A mechanistic treatment. J. Geophysical Research, 105, 12,193–12,206.
Lorenz, E. N., 1979: Forced and free variations of weather and climate. J. Atmos. Sci., 36, 1367–
1376.
Lu, L., R. A. Pielke, G. E. Liston, W. J. Parton, D. Ojima, and M. Hartman, 2001: Implementation
of a two-way interactive atmospheric and ecological model and its application to the central
United States. J. Climate, 14, 900–919.
Mölders, N., 2000: Similarity of Microclimate as simulated in response to landscapes of the 1930s
and the 1980s. J. Hydrometeorology, 1, 330–352.
Nagurnyi, A. P., V. G. Korostelev, and V. V. Ivanov, 1999: Long-term variability of sea ice thickness
in the Arctic basin calculated from measurements of elastic-gravity waves on the ice surface.
Russian Meteor. Hydrol., 3, 54–59.
Neilson, R. P. and D. Marks, 1998: A global perspective of regional vegetation and hydrologic
sensitivities from climatic change. J. Vegetation Sci., 5, 715–730.
NRC, 2000: Reconciling Observations of Global Temperature Change, National Academy Press,
104 pp.
O’Brien, K. L., 2000: Upscaling tropical deforestation: Implications for climate change. Climatic
Change, 44, 311–329.
Parker, E. N., 2000: The physics of the sun and the gateway to the stars. Physics Today, June, 26–
31.
Parkinson, C. L., 1995: Recent sea-ice advances in Baffin Bay/Davis Strait and retreats in the
Bellingshausen Sea. Ann. Glaciology, 21, 348–351.
Pielke, R. A., 1984: Earth sciences: Atmospheric sciences—1983, Encyclopaedia Britannica
Yearbook of Science and the Future, 279–282.
Pielke, R. A., 1998: Climate prediction as an initial value problem. Bull. Amer. Meteor. Soc., 79,
2743–2746.
Pielke, R. A., 2000: Overlooked issues in the U.S. National Climate and IPCC assessments.
Preprints, 11th Symposium on Global Change Studies, 80th AMS Annual Meeting, Long Beach,
CA, January 9–14, 2000, 32–35.
Pielke, R. A., P. L. Vidale, J. L. Eastman, G. E. Liston, R. L. Walko, G. Dalu, A. Barr, and C. Ziegler,
1997: Influence of landscape on boundary layer atmospheric processes. Symposium on the
Boundary Layers and Turbulence (Land-Surface). 77th AMS Annual Meeting, 2–7 February
1997, Long Beach, California, 10–14.
Pielke, R. A., J. Eastman, T. N. Chase, and T. G. F. Kittel, 1998a: 1973–1996 trends in depthaveraged tropospheric temperature. J. Geophys. Res., 103, 16,927–16,933.
Pielke, R. A., J. Eastman, T. N. Chase, J. Knaff, and T. G. F. Kittel, 1998b: Correction to “1973–1996
trends in depth-averaged tropospheric temperature”. J. Geophys. Res., 103, 28,909–28,911.
Pielke, R. A., R. L. Walko, L. Steyaert, P. L. Vidale, G. E. Liston, and W. A. Lyons, 1999: The
influence of anthropogenic landscape changes on weather in south Florida. Mon. Wea. Rev., 127,
1663–1673.
Pielke, R. A., T. Stohlgren, W. Parton, J. Moeny, N. Doesken, L. Schell, and K. Redmond, 2000:
Spatial representativeness of temperature measurements from a single site. Bull. Amer. Meteor.
Soc., 81, 826–830.
Pielke, R. A., Jr., 1998: Rethinking the role of adaptation in climate policy. Global Environment
Change, 8, 159–170.
Pielke, R. A., Sr. and L. Guenni, 1999: Vulnerability assessment of water resources to changing
environmental conditions. IGBP Newsletter, 39, 21–23.
Pinty, B., M. M. Verstraete, and N. Gobron, 2000: Do man-made fires affect earth’s surface
reflectance at continental scales? EOS, 81, 381–389.
Pitman, A. J. and M. Zhao, 2000: The relative impact of observed change in land cover and carbon
dioxide as simulated by a climate model. Geophys. Res. Lett., 27, 1267.
336
R. A. PIELKE , Sr.
Pitman, A., R. Pielke, Sr., R. Avissar, M. Claussen, J. Gash, and H. Dolman, 1999: The role of the
land surface in weather and climate: does the land surface matter? IGBP Newsletter, 39, 4–9.
Rotstayn, L. D., 1999: Indirect forcing by anthropogenic aerosols: global climate model calculation
of the effective-radius and cloud-lifetime effects. J. Geophys. Res., 104, 9369–9380.
Rotstayn, L. D., B. F. Ryan, and J. E. Penner, 2000: Precipitation changes in a GCM resulting from
the indirect effects of anthropogenic aerosols. Geophysical Research Letters, 27, 3045–3048.
Running, S. W., 2000: Why the Earth Observing System matters to all of us. Global Scanner, 1,
1–3.
Sarewitz, D., R. A. Pielke, Jr., and R. Byerly, Jr., 2000: Prediction, Science, Decision Making and
the Future of Nature. Island Press, Washington, D.C., 405 pp.
Schuster, P. F., D. E. White, D. L. Naftz, and L. D. Cecil, 2000: Chronological refinement of an ice
core record at Upper Fremont Glacier in south central North America. J. Geophys. Res., 105,
4657–4666.
Schwing, F. and C. Moore, 2000: A year without summer for California, or a harbinger of a climate
shift? EOS, 27, 301–305.
Segal, M., R. Avissar, M. C. McCumber, and R. A. Pielke, 1988: Evaluation of vegetation effects
on the generation and modification of mesoscale circulations. J. Atmos. Sci., 45, 2268–2292.
Segal, M., W. Schreiber, G. Kallos, J. R. Garratt, A. Rodi, J. Weaver, and R. A. Pielke, 1989: The
impact of crop areas in northeast Colorado on midsummer mesoscale thermal circulations. Mon.
Wea. Rev., 117, 809–825.
Sellers, P. J., M. D. Heiser, and F. G. Hall, 1992: Relations between surface conductance and spectral
vegetation indices at intermediate (100 m2 to 15 km2) length scales. J. Geophys. Res., 97,
19,033–19,059.
Shaw, B. L., R. A. Pielke, and C. L. Ziegler, 1997: A three-dimensional numerical simulation of a
Great Plains dryline. Mon. Wea. Rev., 125, 1489–1506.
Shepherd, T., 2000: On the role of the stratosphere in the climate system. SPARC Newsletter,
January, 7–10.
Shine, K. P. and P. M. de. F. Forster, 1999: The effect of human activity on radiative forcing of
climate change: A review of recent developments. Global Planetary Change, 20, 205–225.
Showstack, R., 2000: Update on U.S. Carbon Cycle Science Initiative. EOS, 81, 289.
Sisk, T. D. (ed.), 1999: Perspectives on the land use history of North America: A context for
understanding our changing environment. Biological Science Report USGS/BRD/BSR-19980003 (Rev. 1999), U.S. Department of the Interior, U.S. Geological Survey, 104 pp.
Sivillo, J. K., J. E. Ahlquist, and Z. Toth, 1997: An ensemble forecasting primer. Wea. Forecasting,
12, 809–818.
Skinner, W. R. and J. A. Majorowicz, 1999: Regional climatic warming and associated twentieth
century land-cover changes in north-western North America. Climate Res., 12, 39–52.
Song, J., 2000: Changes in dryness/wetness in China during the last 529 years. Int. J. Climatol., 20,
1003–1015.
Stahle, D. W., E. R. Cook, M. K. Cleaveland, M. D. Therrell, D. M. Meko, H. D. Grissino-Mayer,
E. Watson, and B. H. Luckman, 2000: Tree-ring data document 16th Century megadrought over
North America. EOS, 81, 124–125.
Stohlgren, T. J., T. N. Chase, R. A. Pielke, T. G. F. Kittel, and J. Baron, 1998: Evidence that local
land use practices influence regional climate and vegetation patterns in adjacent natural areas.
Global Change Biology, 4, 495–504.
Takata, K. and M. Kimoto, 2000: A numerical study on the impact of soil freezing on the continentalscale seasonal cycle. J. Meteorological Society of Japan, 78, 199–221.
Tenhunen, J. D. and P. Kabat (eds.), 1999: Integrating hydrology, ecosystem dynamics, and
biogeochemistry in complex landscapes. Dahlem Workshop Report, John Wiley & Sons, New
York, U.S.A.
Texier, D., N. de Noblet, S. P. Harrison, A. Haxeltine, D. Jolly, S. Joussaume, F. Laarif, I. C.
Prentice, and P. Tarasov, 1997: Quantifying the role of biosphere-atmosphere feedbacks in
climate change: Coupled model simulations for 6000 years BP and comparison with paleodata
for northern Eurasia and northern Africa. Climate Dynamics, 13, 865–882.
Earth System Modeling—An Integrated Assessment Tool
337
Trenberth, K. E., 1999: Global climate project shows early promise. EOS, 80, 269–275.
Unruh, J. D., 1995: Agroforestry, reforestry, and the carbon problem. The role of land and tree
tenure. Interdisciplinary Science Revs., 20, 215–227.
Vitousek, P. M., H. A. Mooney, J. Lubchenko, and J. M. Melillo, 1997: Human domination of Earth’s
ecosystems. Science, 277, 494–499.
Vorosmarty, C. J., P. Green, J. Salisbury, and R. B. Lammers, 2000: Global water resources:
Vulnerability from climate change acid population growth. Science, 289, 284–288.
Wang, B. and M. Allard, 1995: Recent climatic trend and thermal response of permafrost in Salluit,
northern Quebec, Canada. Permafrost Periglacial Proc., 6, 221–233.
Wang, G. and E. A. B. Eltahir, 2000a: Ecosystem dynamics and the Sahel drought. Geophys. Res.
Lett., 27, 795–798.
Wang, G. and E. A. B. Eltahir, 2000b: Role of vegetation in enhancing the low-frequency variability
of the Sahel rainfall. Water Resources Res., 36, 1013–1021.
Wang, G. and E. A. B. Eltahir, 2000c: Biosphere-atmosphere interactions over West Africa. II:
Multiple climate equilibria. Q. J. Roy. Meteor. Soc., 126, 1261–1280.
Weaver, J., A. Rodi, and J. Wilson, 1989: The impact of crop areas in northeast Colorado on
midsummer mesoscale thermal circulations. Mon. Wea. Rev., 117, 809–825.
Yonetani, T. and G. J. McCabe, Jr., 1994: Abrupt changes in regional temperature in the conterminous
United States, 1895–1989. Climate Res., 4, 13–23.
Zeng, X., R. A. Pielke, and R. Eykholt, 1993: Chaos theory and its applications to the atmosphere.
Bull. Amer. Meteor. Soc., 74, 631–644.
Ziegler, C. L., T. J. Lee, and R. A. Pielke, 1997: Convective initiation at the dryline: A modeling
study. Mon. Wea. Rev., 125, 1001–1026.
R. A. Pielke, Sr. (e-mail: [email protected])