Predicting Carbon Dioxide Concentration using Gaussian Processes
Back in 2005 I used
a Gaussian Process model
to predict the atmospheric concentration of carbon dioxide over a
twenty year horizon, as described
in Gaussian Processes
for Machine Learning
(with Chris
Williams). Now (2011) that a little more data has become available,
I thought it would be fun to see how well the predictions have stood
the test of time.
The data used to be available
at http://cdiac.esd.ornl.gov/ftp/trends/co2/maunaloa.co2,
but now it seems that more up-to-date data can be found
at ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt. The
data at these two sources seem to differ slightly - below I use the
second source.
I used the
matlab/octave gpml
toolbox and the following script to make
the predictions. The original predictions used an older version of the
software, but the model is the same.
Below are the training data (in blue), the predicted 95% confidence
region (in grey) and the more recent observations (in red). The actual
CO2 concentration grew at close to the fastest rate deemed
likely by the model.
Here is another plot, focusing on more recent times and the future.
By Carl Edward
Rasmussen, April 20th 2011.