when we need to study something in
science that is too large or complex to
easily work with we'll often make a
model of it in order to recreate it in a
simpler way you might not realize it
well we actually use models all the time
in our everyday lives they can be static
and representational like the New York
City subway map or they can be used to
make predictions of dynamic systems like
a weather forecast or the ups and downs
of the stock market even our
understanding of climate change a
phenomenon on the tip of everyone's
tongue is based on predictions made by
climate models global climate models
otherwise known as gc-ms are gigantic
computer codes of mathematical
representations of our Earth's climate
system and they are generated based on
the laws of physics let's take a look at
how it works
energy enters the atmosphere as solar
radiation which then transfers and
circulates within the Earth's system to
track how the energy flows through this
system GCMs try to simulate the Earth's
climate conditions they account for
processes such as solar inputs the
heating and reflection from the land and
water the movement of air masses and ice
albedo responds GCMs divide the earth
into thousands of gridded 3d cells each
grid box holds a single value of the
atmospheric variables such as winds
temperature pressure humidity cloud
cover and cold ice that is being
predicted in the past 30 years due to
advancement in computational power the
size of the gridded boxes has been
reduced and the mathematical equation
as the earth is divided into discrete
cells so too is time divided into finite
intervals or time steps if we want to
compute quickly the global climate in
ten years we might be tempted to do it
in one go with a time step of ten years
such a large time step is impossible
though because the energy we are
accounting for within a cell would have
moved on into adjacent cells movement of
air for example is a factor that may
only need a small time step of 30
minutes or less so instead GCMs take an
initial climate state and calculate the
climate State one time step away after
which they repeat the calculation with
the resulting new climate State this is
repeated until the total number of time
steps reaches our desired target time of
10 years it might seem like there would
be lots of room for error it's so much
processing but think of it this way when
we flip a coin it is hard to predict
whether the result will be heads or
tails however over many flips we can
reliably say that the probability of
heads is 50% GCMs have time scales that
are decades long which means hundreds of
thousands of time steps or coin tosses
because GCMs simulate the Earth's
climate over large timescales
many consider GCN's to be robust in
comparison to shorter time scale models
to test the GCMs scientists have gone
back and used the model to recreate
climate history this way they can
compare the models predictions with
actual empirical evidence recorded in
the past they've started at 1900 and
attempted to predict the climate changes
over the next 100
plus years a century in which an
unprecedented spike in temperature
occurred they ran the model twice the
first time they only included natural
forces the second time they included
human factors in the equations
accounting for the increased addition of
greenhouse gases in the atmosphere
comparing the observed global and
regional climate data from over 100
years the first round of GCMs
predictions didn't match up but when the
models ran the second time accounting
for both natural and human factors
affecting climate the results were
incredible not only do the predictions
live within a 95% confidence interval of
the actual data but they are correct for
both of the continents and the oceans
individually and it wasn't only one GCM
that found this to be true the results
of every single one of the 15
independently run GCMs agreed this
allows scientists to attribute the
climate changes they have observed to
human factors such as increased
industrialization deforestation and
organizations which lead to an increase
in the concentration of greenhouse gases
if you're still skeptical let's take a
look at another example when the first
sophisticated GCMs were developed and
run in the 1990s the predictions they
generated didn't seem to make any sense
they indicated that there would be a
rapid heating of the upper layers of the
troposphere and a corresponding cooling
of the lower levels of the stratosphere
but when the recorded data from 1960
onwards was examined it turned out that
the GCMs predictions were verified the
cause was once again the effect of green
gases these calluses concentrated in the
troposphere we're trapping low-frequency
radiation causing the unexpected warming
of the troposphere and the corresponding
cooling of the stratosphere so even
though not all models make correct
predictions every single time the GCMs
have proven over and over again to be
reliable and sophisticated systems and
if the past is any indication of the
future we would be wise to heed its
predictions
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