phrases elicit so much controversy today. But is our climate truly
changing? And if it is, do we know why it is changing?
At the United Nations, the
Intergovernmental Panel on Climate Change (IPCC) certainly thinks
the world is getting warmer and puts much of the blame on human
activity. In its 2001 Third Assessment Report, the IPCC projects
that average global temperature will increase by 1.6° to 6°C
The report indicates that,
globally, the 1990s were the warmest decade on record, with 1998
the single warmest year. Accompanying this global-scale temperature
increase were changes in other climate variables, such as precipitation,
snow cover, glacier extent, and sea level. The changes in these
variables are broadly consistent with the IPCCs estimate that
Earths surface warmed by roughly 0.6°C over the 20th century.
The 2001 IPCC report concluded that there is new and stronger
evidence that most of the warming observed over the last 50 years
is attributable to human activity.
Atmospheric carbon dioxide
and other trace gases help keep our planet warm by absorbing some
of the Suns heat that the Earth would otherwise emit back
into space. This natural greenhouse effect makes Earths surface
about 34°C warmer than it would be without greenhouse gases.
But human activities, such as the burning of fossil fuels, have
added greenhouse gases to the atmosphere. Atmospheric carbon dioxide
levels, for example, have increased by about 30 percent since the
beginning of the Industrial Revolution. This human-caused enhancement
of the natural greenhouse effect has contributed to the warming
of the planet over the last century.
Climate change can occur
even in the absence of human activities. The climate system is like
a bell that rings in a certain way. One form of ringing
is the ocean warming phenomenon known as El Niño or its cooling
sister, La Niña. Such changes are thought to be due to the
internal variability of the climate system. But external events
can also cause natural climate changes. Large volcanic eruptions
can pump massive quantities of dust into the upper atmosphere (the
stratosphere). The dust may remain in the stratosphere for years,
cooling Earths surface by absorbing and reflecting some of
the incoming sunlight. Natural changes in the Suns energy
output and slow changes in Earths orbit can also influence
Carbon dioxide and other
greenhouse gases get the most press, but there are other human influences
as well. Changes in land use can be a concern. For example, Livermore
scientists recently showed that human-caused changes in land-use
patterns (especially conversion of forests to farm land) may have
caused a gradual global cooling of approximately 0.25°C, mostly
before the 20th century.
averaged temperatures have changed at different levels in
Earths atmosphere. This profile is from close to Earths
surface through to the stratosphere. Temperatures are in the
form of departures (anomalies) from long-term monthly means
computed from 1979 to 1999 and are in degrees Celsius. The
stratospheric warming caused by the El Chichón and
Mount Pinatubo volcanic eruptions is clearly evident, as is
the cooling of the lower atmosphere after Pinatubo. Results
are from the so-called reanalysis project jointly performed
by the National Center for Environmental Prediction and the
National Center for Atmospheric Research.
Large-scale burning of rain
forests sends particulate matter into the lower atmosphere, warming
us. At the same time, with fewer trees, less carbon dioxide can
be absorbed from the atmosphere, which warms us further. Land surface
changes also affect Earths reflectivity, or albedo.
If Earth is getting warmer,
is it possible to expose individual factors causing climate change?
And what will global warming mean on a regional level? Two Livermore
research teams are searching forand findinganswers.
Atmospheric scientist Ben
Santer, a 1998 John A. and Catherine T. MacArthur Foundation Fellow,
has used sophisticated climate models to separate the effects of
recent major volcanic eruptions and El Niños from other causes
of climate change. The motivation for this research was to shed
light on one of the outstanding puzzles in climate science: why
Earths surface has apparently warmed faster than the lower
At the same time, a team
led by physicist Philip Duffy has brought the highest resolution
yet to global climate modeling, revealing a wealth of regional effects
for the first time. Instead of a 300-kilometer gridthe previous
state of the artDuffys team has been able to perform
global simulations using models with grid cell sizes of 75 and even
50 kilometers. These are the finest-resolution global climate simulations
performed to date. The figure below compares these resolutions.
Duffys work would not
be possible without Livermores massively parallel supercomputers,
which can quickly perform the computationally demanding calculations
inherent in global climate modeling. The first simulations using
the 50-kilometer grid ran on the Advanced Simulation and Computing
(ASCI) White computer during its initial, unclassified testing period
in December 2000. Because the ASCI White computer is now used exclusively
for classified computations, models used by Duffys group are
being run on other supercomputers at Livermore and at Lawrence Berkeley
A 1-year simulation of global
climate using the 300-kilometer grid can now be accomplished in
4 or 5 hours. Five years ago, it would have taken over a day to
complete a comparable simulation. For the 50-kilometer grid, At
best, we can do about a month of simulated climate in a day,
says Duffy. A 50-kilometer grid for climate modeling was the stuff
of dreams 5 years ago.
topography of California and Nevada is simulated in models
with (a) 300-kilometer and (b) 50-kilometer grids. Models
that use the 300-kilometer grid have been the state of the
art, but Livermore has developed a 50-kilometer-grid model.
Even with 50-kilometer grids, the topography of California
and Nevada is not represented. The Coast Range mountains are
not visible in (b), and the data smoothing process lowers
the elevation of the Sierra Nevada mountains.
of the controversy about global warming results from two apparent
contradictions. One relates to observed temperature data and the
other to the issue of how well computer models of the global climate
system can represent such observations.
While Earths surface
has warmed by about 0.15° to 0.2°C per decade since 1979,
temperatures in the troposphere (the layer of the atmosphere extending
from Earths surface to 8 to 16 kilometers above it) have shown
little warming, and even a slight cooling.
The apparent lack of tropospheric
warming from 1979 to the present has been used to cast doubt on
the reality of strong surface warming. It is important to understand
whether this difference between surface and tropospheric warming
rates is real or is an artifact of data problems. If this difference
is real, what factors might be causing it?
The second puzzle relates
to the inability of many climate models to simulate the apparent
difference in surface and tropospheric warming rates. This inconsistency
is sometimes used to bolster arguments that models are inappropriate
tools for making projections of future climate change.
Recent work by Santer and
his colleagues has addressed both of these puzzles. They have learned
that at least some of the differential warming of Earths surface
and lower troposphere is real and attributable to the combined effects
of stratospheric ozone depletion, volcanic eruptions, and natural
climate variability. Differences in the geographic regions sampled
by the surface thermometer network and the satellite-based tropospheric
temperature measurements also explain some of the divergent temperature
changes of the surface and troposphere.
But, Santer concedes,
accounting for these effects still does not fully explain
the different rates of temperature change. Nor does it explain why
models dont reproduce this differential behavior accurately.
patterns of annually averaged temperature anomalies in (a) the
lower troposphere and (b) at Earths surface. Tropospheric
temperature measurements are from polar-orbiting satellites,
and surface measurements were made by thermometers. White areas
denote missing data. Although the satellites have near-global
coverage, the surface data have large gaps. Comparing satellite
and surface data over areas of common coverage helps to explain
some of the differential warming of the surface and troposphere.
Anomalies are expressed relative to annual mean temperatures
averaged over 1979 to 1998.
A Search for Resolution
several years, Santer has been working with other investigators
at Livermore and research institutions around the world to reconcile
the apparent contradictions in actual data and global climate models.
In one study of climate between 1979 and 1998, they discovered that
a model including anthropogenic (human-caused) factors and volcanic
aerosols produced surfacetroposphere temperature differences
that were the closest yet to actual observed data.
As a follow-up, they wanted
to examine the influence of volcanoes alone. But, says Santer, We
had a bit of bad luck. Nature made our lives difficult. There was
a major El Niño in 1982, at the same time as the eruption
of El Chichón in Mexico. A smaller El Niño coincided
with the 1991 eruption of Mount Pinatubo in the Philippines. This
made it tough to disentangle the effects that volcanoes and El Niños
had on surface and tropospheric temperatures.
Santer and his Livermore
colleagues had been doing similar work for the past 10 years. For
the first half of that time, they were trying to identify human-caused
climate signals in observed temperature records. This involved using
both model and observational climate data to understand the characteristic
fingerprints of the many natural and anthropogenic influences on
climate. (See the figure below.)
Previous researchers had
attempted to remove the effects of explosive volcanic eruptions
and El Niños from surface and tropospheric temperatures so
they could obtain better estimates of the underlying human component
of climate change. But Santers team was the first to deal
fully with the correlation of volcanic eruptions and El Niños,
known in statistical problems as collinearity.
The teams observational
data were land and ocean surface temperatures compiled at the Climatic
Research Unit in Norwich, England, together with satellite-based
tropospheric temperature measurements. Their model data came from
a number of different sources: the Max Planck Institute for Meteorology
in Hamburg, Germany, the Goddard Institute for Space Studies in
New York, and the National Center for Atmospheric Research in Boulder,
Colorado. Researchers from all of these organizations participated
in the team. Other team members were with Livermores Program
for Climate Model Diagnosis and Intercomparison, which routinely
develops methods and tools for the diagnosis, validation, and intercomparison
of global climate models.
Atmospheric temperature changes predicted to occur in response
to a doubling of preindustrial levels of carbon dioxide. (b)
Projected temperature response to a 2-percent increase in the
Suns energy output. Each factor that influences our climate
has a characteristic fingerprint. Scientists typically
use computer models of the climate system to gain information
on these fingerprints. In a model, it is possible to study the
climatic effects of a single influence only, such as changes
in atmospheric carbon dioxide. This is not feasible in the real
world, where multiple factors that influence climate are changing
simultaneously. Both (a) and (b), which are clearly dissimilar,
show annual mean changes (in degrees Celsius) as a function
of latitude and altitude.
The team first dealt with
observed data. They found that removing El Niño and volcanic
effects always led to larger warming trends in the residual surface
and lower tropospheric data than in the raw observational data (where
these effects were left in). Although El Niños caused a small
net warming from 1979 to 1999, the El Chichón and Mount Pinatubo
volcanic eruptions caused a larger net cooling during the same period.
Removing both El Niños and volcanoes more clearly revealed
the underlying warming trend in surface and tropospheric temperatures.
It also helped to explain some of the differential warming of the
surface and troposphere.
Its clear that
if the Mount Pinatubo and El Chichón eruptions had not occurred,
the lower troposphere would have experienced more pronounced warming,
The team then removed volcanic
and El Niño effects from model output and compared the results
with observations. It is important to do this because even in a
model with perfect representation of El Niño
variability, the simulated El Niños would not necessarily
occur at the same time that they happened in the real world. Also,
some model experiments include the effects of well-observed volcanoes
(such as Mount Pinatubo) but exclude other eruptions where less
is known about the properties of the volcanic aerosols. Removing
volcano and El Niño effects from both models and observations
allows a fairer comparison of the underlying simulated and observed
responses to human-caused changes in greenhouse gases.
The general conclusion from
such comparisons was that removing volcano and El Niño effects
from atmospheric temperature data improves the correspondence of
the modeled and observed differential warming of the surface and
troposphere over the last several decades. It does not, however,
fully reconcile models and reality. The remaining differences are
probably caused by problems with the observational temperature data;
missing or inaccurately specified forcings in the climate
model experiments, such as the neglect of land use changes or aerosol
particles from biomass burning; and errors in the climate responses
that the models predict.
Santer and his colleagues
are actively investigating these possibilities. We hope weve
showed that this is a complex scientific issue, says Santer.
It cant be reduced to a one-minute sound bite. This
issue is important, because it relates to our ability to evaluate
climate models and to determine whether these models are useful
tools for predicting climate change over the next century.
of the problems involved in removing the effects of El Niño
variability and explosive volcanic eruptions from tropospheric
temperature data. (a) In the original satellite-based temperature
data, the cooling signal of the 1983 El Chichón eruption
is masked by (b) one of the strongest El Niño events
of the 20th century. After using an iterative method to successively
refine estimates of El Niño and La Niña effects
on tropospheric temperatures, these effects are removed from
the original temperature data in (a). The cooling effects of
the El Chichón and Mount Pinatubo eruptions are now more
easily seen in (c). It is clear in (d) that removing both volcanoes
and El Niño effects yields a pronounced warming trend
that was not apparent in the original temperature data.
The IPCCs prediction
that mean global temperatures will increase from 1.6° to 6°C
by the end of this century isnt especially useful for farmers
and others whose livelihoods depend on the weather. They need more
specific information on temperature increases expected in their
area, whether it be Kansas or Kenya. They also need to know about
changes in temperature extremes and in other important quantities
such as precipitation. By providing improved simulations of climate
change on regional scales, Livermores high-resolution climate
simulations should allow for more accurate assessments of the effects
of climate change on society.
Grids of 50 kilometers and
less are already used in numerical weather prediction, which is
much less computationally intensive than climate modeling because
it requires much shorter forecasts (days rather than decades). For
long-term climate modeling with resolution this fine, scientists
had to await the arrival of huge computers with hundreds of processors
team is using the Community Climate Model 3, or CCM3, an atmospheric
model developed by the National Center for Atmospheric Research
(NCAR) in Boulder, Colorado. CCM3, the fourth-generation CCM model,
is used at coarse resolutions in climate modeling centers around
For every change in
horizontal resolution, theres the problem of retuning the
model, says Duffy. Several physical processes such as convection,
cloudiness, and precipitation are too small to be represented explicitly
in climate models and are therefore treated using semiempirical
For example, although clouds
may be too small to be represented directly in a grid cell, they
must be accounted for because cloud cover affects the flow of radiation
in the atmosphere. So we parameterize their effects by modifying
the optical properties of that layer of the atmosphere, says
Because these parameterizations
are not based on first-principles physics, they must be tuned carefully
at each resolution. Tuning is done by adjusting parameter values
to make the models results agree as closely as possible with
observations. The 300-kilometer model has already been carefully
tuned at NCAR to optimize results at that resolution. In collaboration
with researchers at NCAR, Livermore researchers retuned their 75-kilometer
model. Thus far, tuning done for the 75-kilometer model has also
worked reasonably well with the 50-kilometer grid.
The teams proof of
principle with the 50- and 75-kilometer models was to compare their
modeling results to observed data. Although, as Duffy notes, the
50-kilometer model actually has better resolution than most of our
observational data. Perhaps not surprisingly, simulations
using the 50-kilometer model agreed better with observed data than
either a 75- or
300-kilometer grid. In some cases, there were substantial improvements.
When the team examined results in more localized regions of interest,
the results were striking. The upper figure below shows simulated
precipitation over the U.S. in December, January, and February using
50-, 75-, and 300-kilometer grids and compares all three to observed
data. As the grid size shrinks, both small-scale and large-scale
simulated precipitation features converge toward observations. This
example shows that as spatial resolution becomes finer, not only
is fine-scale detail added to the model results, but the large-scale
aspects of the solution also become more realistic.
representation of December, January, and February precipitation
over the U.S. improves as the resolution increases. Simulations
using (a) 300-kilometer, (b) 75-kilometer, and (c) 50-kilometer
resolution are compared with (d) actual observed data. Both
fine- and large-scale aspects of the simulation improve as spatial
comparison of elevations in California, as represented in models
having (a) 300-kilometer, (b) 75-kilometer, and (c) 50-kilometer
resolution, with (d) actual elevations at 50-kilometer resolution.
Elevations in the models are lightly smoothedevened outto
prevent sudden changes that cause numerical noise and contaminate
the results. Even at 50-kilometer resolution, Californias
Coast Range mountains and the Central Valley are not well represented.
Simulations of California
climate are a real test of climate models because of the great variability
in climate that occurs within the states relatively small
area. Much of this variability results directly or indirectly from
the states major topographic features: the Coast Range, the
Central Valley, and the Sierra Nevada. The figure above compares
actual elevations at 50-kilometer resolution with topography as
represented in models having 300-, 75-, and 50-kilometer resolutions.
Although the topography is more realistic as the model resolution
becomes finer, neither the coastal mountains nor the Central Valley
are adequately represented in even the 50-kilometer model.
In part because of improved
representations of topography, the models ability to simulate
precipitation in California improves dramatically as the resolution
becomes finer. Nonetheless, 50-kilometer resolution is still not
adequate to represent the states Coast Range and Central Valley;
even at this resolution, the simulation of precipitation differs
noticeably from observations.
Simulations of Arctic climate
similarly improve dramatically with finer resolution, but further
improvements are nonetheless needed. Most coarse-resolution oceanatmospheresea
ice climate models produce poor simulations of the pattern of sea-level
pressure in the Arctic region. Poor data for sea-level pressure
result in unrealistic simulated atmospheric circulation, which in
turn produces unrealistic distributions of sea ice thickness and
concentrations and other problems. Accurate predictions of sea ice
and of changes in sea ice because of global warming are essential.
Sea ice strongly affects the climate not only in polar regions but
also in far-flung regions through influences on the large-scale
ocean circulation and on Earths radiation balance.
In addition to these simulations
of the present climate, Duffys team has simulated the effects
of increased greenhouse gases (that is, global warming) with the
75-kilometer-resolution model. This is the finest-resolution simulation
of global warming performed to date and shows very different results
from comparable simulations performed at coarser resolutions. Although
the globally averaged responses of temperature and other variables
to increased greenhouse gases are quite similar in the 75-kilometer
model and in coarser-resolution models, the regional responses can
be very different. For example, the figure at the bottom of the
page shows predicted wintertime temperature changes between 2000
and 2100 in the U.S. The finer-resolution model shows regions of
strong warming in the western U.S. and southeastern Canada, which
are not predicted by the coarser-resolution model. In at least some
cases, it seems clear that the results of the finer-resolution model
are more believable.
Duffys group has already
fielded inquiries from experts interested in the effects of localized
climate change on crop diseases, human health, water resources,
and the like. Although the finer-resolution models are far from
perfect, they may represent the best tools available today for assessing
the regional effects of global warming.
comparison of precipitation over California, as represented
in models at (a) 300-kilometer, (b) 75-kilometer, and (c) 50-kilometer
resolution, with (d) actual precipitation at 50-kilometer resolution.
temperature increases from 2000 to 2100 for December, January,
and February at resolutions of (a) 300 kilometers and (b) 75
kilometers. The predicted data from the model with finer resolution
are much more specific and useful.
A few months ago, a chunk
of ice larger than Rhode Island collapsed on the east side of Antarctica.
It was the largest single event in a series of ice shelf retreats
there extending back 30 years. Temperatures at the Antarctic Peninsula
have increased by 2.5°C over the last 50 years, much faster
than the global average. Getting Arctic and Antarctic models right
is crucial for determining what may happen to sea levels around
the world as temperatures continue to rise.
Closing in on how much humans
are responsible for the changes in our planets climate is
equally important. Getting it right matters to us all.
Key Words: climate
modeling, Community Climate Model 3 (CCM3), global warming, National
Center for Atmospheric Research (NCAR).
information ontact Ben Santer (925) 422-7638 (firstname.lastname@example.org)
or Philip Duffy (925) 422-3722 (email@example.com).
about the Intergovernmental Panel on Climate Change:
about Livermores Program for Climate Model Diagnosis and Intercomparison: