El Niño or the North Atlantic ocean circulation, while
the second tries to average over that variability to pre-
dict changes in the mean state.
Show and tell There are many different climate models, but they all make some common
predictions. One is that volcanic eruptions like that of Mount Pinatubo in 1991 always result in a
drop in global temperature due to the injection of aerosols into the stratosphere that block
sunlight from reaching the surface. The different colours in this simulation represent particles at
different heights two months after the eruption, from red (highest) to blue (lowest).
NASA’s Goddard Institute for Space Studies
For this second kind of prediction, you always need
a scenario for what might happen to the drivers of
climate change. Will carbon dioxide concentrations
continue to increase? Will air pollution continue to decrease in the developed world but increase in the
developing world? How fast will tropical deforestation
progress? These scenarios are highly dependent on
economics or political decisions and so qualify easily
for the “hard prediction” category. Nonetheless, economists do their best to make a range of reasonable estimates for plausible futures and calculate the resulting
changes in emissions.
ference that any particular cause might have, how can
we recognize its fingerprint in the real world? This is the
basis of the conclusion of the Intergovernmental Panel
on Climate Change (IPCC) that human activities are
behind the rise in global temperature in recent decades.
The other use is in helping chart the course of the
future. People are quick to dismiss model projections
of climate as being inferior to observations, but as Tom
Knutson and Robert Tuleya, from the Geophysical
Fluid Dynamics Laboratory at Princeton University
and Old Dominion University, Virginia, pointedly noted in 2005, “If we had observations of the future, we
obviously would trust them more than models, but
unfortunately observations of the future are not available at this time.”
But climate is complex. There are multiple causes,
giving rise to multiple effects such that the interactions
among the various components – like low-level ozone,
aerosols (airborne particles) and clouds – can get
hideously complicated. Ozone near the ground is
created from the soup of emissions from car exhausts,
factories and fires, and it is a public-health problem as
well as a greenhouse gas. Aerosols too can come from
multiple sources: sulphur-dioxide emissions from coal-burning power plants produce sulphate aerosols in the
air; black carbon (soot) and organic-carbon aerosols
come from incomplete combustion of biomass and
even from the complex organic molecules emitted by
plants. They all interact directly with the Sun’s radiation to either block it (for sulphates) or increase absorption (black carbon). They also have indirect effects
by changing how easy it is for clouds to form, or by
changing how reflective snow is (black carbon effectively makes the snow dirtier).
However, prediction is hard, particularly of the
future (to paraphrase Niels Bohr). For the climate,
there are two kinds of possible predictability. The first
is based on extrapolating seasonal and interannual
changes based on precise knowledge of today’s state of
the atmosphere and ocean combined with an understanding of how the various modes of variability in the
ocean might develop. Whether these efforts can provide useful information on regional climate on year-to-year and longer timescales is currently being explored.
The more usual source of predictability, however, is
considering the long-term changes related to increases
in greenhouse gases, a volcanic eruption or other
changes in the composition of the atmosphere. The
first relies on a thorough understanding of patterns like
Climate is complex – there are
multiple causes, giving rise to multiple
effects such that the interactions
among the various components can
get hideously complicated
Solving the puzzle
Science, however, has made tremendous progress by
trying to break things down into their component
parts. Thus, we have traditionally studied the impact
of carbon dioxide separately from the impact of sulphate aerosols and separately from the impacts of the
emissions that cause ozone (the “precursors”). Frequently, these studies are carried out by separate scientists, in separate institutions under separate grants
and with separate goals. While this has led to a great
deal of insight, it has also tended to divorce the science
from policy.
Let me give some examples. In the last IPCC report
there is an iconic figure that shows the magnitude of
the effects on climate for the 20th century. Carbon dioxide is the largest warming factor, followed by methane, nitrous oxide, low-level ozone and black carbon.
On the cooling side, there are sulphate and nitrate
aerosols and land-use changes. There is nothing wrong
with this picture, but how helpful is it in deciding what
to do about the power generation in China that produces carbon dioxide but also sulphates, or for assessments of mileage standards that will affect ozone
precursors, black carbon from diesel exhaust as well
as gasoline use? In each case, we have mixed results
for the climate and potential impacts on other public-policy issues as well (air quality for instance). Because
of scientists’ focus on single-factor experiments
(change carbon dioxide, or change black carbon, or
change sulphates), we have not historically provided
