CO2 Past Cause or Effect?
OK so I'm still trying to get back to blogging, but here is a cool new tool I couldn't resist trying. I compiled some of the CO2 and temperature data from the Carbon Dioxide Information Analysis Center (CDIAC) Trends Studies and uploaded it to a new collaborative data visualization project called Many Eyes. From there I produced the following visualization that clearly shows the correlation between CO2 and temperature deviations over a period of 420,000 years!
In addition I produced another graph showing how we are reaching record high levels of CO2 over the same period. One bug/issue is that the line graph didn't work for the larger data set so this one was produced by taking 20 year averages.
An interesting note in working with this data that is harder to see from these graphs is that in the past, increases in temperature mainly preceded the increases in CO2. Perhaps I misinterpreted the different year scales, but it does provoke some interesting questions. If CO2 is already rising before temperatures, does that mean that worst is yet to come? For example, I did a small model of what might happen if the CO2 currently trapped in permafrost was released by thawing and it produced some startling results. In summary, the CO2 released created a feedback via increased temperatures that accelerated the release rate. A similar dynamic may be true in the melting of polar ice that reflects sunlight back into space.
Anyway, it is clear that we have some challenging problems ahead of us, but I for one believe we are on the cusp of a technology and social revolution that will finally provide us with a means to solve them or at the very least mitigate the damage.
Update:
I made some corrections to how the the data was serialized. See the data set for the visualization below for a detailed description.
Anyway, it is clear that we have some challenging problems ahead of us, but I for one believe we are on the cusp of a technology and social revolution that will finally provide us with a means to solve them or at the very least mitigate the damage.
Update:
I made some corrections to how the the data was serialized. See the data set for the visualization below for a detailed description.
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