Climate is generally defined as average weather, and as such, climate change and weather
are intertwined. Observations can show that there have been changes in weather, and it is
the statistics of changes in weather over time that identify climate change. While weather
and climate are closely related, there are important differences. A common confusion between
weather and climate arises when scientists are asked how they can predict climate 50 years
from now when they cannot predict the weather a few weeks from now. The chaotic nature of
weather makes it unpredictable beyond a few days. Projecting changes in climate (i.e.,
long-term average weather) due to changes in atmospheric composition or other factors is a
very different and much more manageable issue. As an analogy, while it is impossible to predict
the age at which any particular man will die, we can say with high confidence that the average
age of death for men in industrialised countries is about 75. Another common
confusion of these issues is thinking that a cold winter or a cooling spot on the globe is
evidence against global warming. There are
always extremes of hot and cold, although their frequency and intensity change as climate changes.
But when weather is averaged over space and time, the fact that the globe is warming emerges
clearly from the data.
Meteorologists put a great deal of effort into observing, understanding and predicting the day-to-day
evolution of weather systems. Using physics-based concepts that govern how the atmosphere moves, warms,
cools, rains, snows, and evaporates water, meteorologists are typically able to predict the weather
successfully several days into the future. A major limiting factor to the predictability of weather
beyond several days is a fundamental dynamical property of the atmosphere. In the 1960s, meteorologist
Edward Lorenz discovered that very slight differences in initial conditions can produce very different forecast results. This is the so-called butterfly effect:
a butterfly flapping its wings (or some other small
phenomenon) in one place can, in principle, alter the subsequent weather pattern in a distant
place. At the core of this effect is chaos theory, which deals with how small changes in certain
variables can cause apparent randomness in complex systems.
Nevertheless, chaos theory does not imply a total lack of order. For example, slightly different
conditions early in its history might alter the day a storm system would arrive or the exact path
it would take, but the average temperature and precipitation (that is, climate) would still be
about the same for that region and that period of time. Because a significant problem facing weather
forecasting is knowing all the conditions at the start of the forecast period, it can be useful to
think of climate as dealing with the background conditions for weather. More precisely, climate can
be viewed as concerning the status of the entire Earth system, including the atmosphere, land, oceans,
snow, ice and living things (see Figure 1) that serve as the global background conditions that
determine weather patterns. An example of this would be an El Niño affecting the weather in
coastal Peru. The El Niño sets limits on the probable evolution of weather patterns that
random effects can produce. A La Niña would set different limits.
Another example is found in the familiar contrast between summer and winter. The march of the seasons
is due to changes in the geographical patterns of energy absorbed and radiated away by the Earth system.
Likewise, projections of future climate are shaped by fundamental changes in heat energy in
the Earth system, in particular the increasing
intensity of the greenhouse effect that traps heat near Earth’s surface, determined by the amount
of carbon dioxide and other greenhouse gases in the atmosphere. Projecting changes in climate due
to changes in greenhouse gases 50 years from now is a very different and much more easily solved
problem than forecasting weather patterns just weeks from now. To put it another way, long-term
variations brought about by changes in the composition of the atmosphere are much more predictable
than individual weather events. As an example, while we cannot predict the outcome of a single coin
toss or roll of the dice, we can predict the statistical behaviour of a large number of such trials.
While many factors continue to influence climate, scientists have determined that human activities
have become a dominant force, and are responsible for most of the warming observed over the past 50
years. Human-caused climate change has resulted primarily from changes in the amounts of greenhouse
gases in the atmosphere, but also from changes in small particles (aerosols), as well as from changes
in land use, for example. As climate changes, the probabilities of certain types of weather events
are affected. For example, as Earth’s average temperature has increased, some weather phenomena have
become more frequent and intense (e.g., heat waves and heavy downpours), while others have become
less frequent and intense (e.g., extreme cold events).