In 2011 Professor Murray Salby showed CO2 follows temperature and not as is usually claimed rising CO2 causing rising temperatures. However he did not show his method or reveal his data sources. In this post I reproduce some of his work showing both data and method, so you will be able to reproduce his results too.
The CO2 data came originally from www.esrl.noaa.gov/gmd/ccgg/trends/
The RSS data came originally from http://www.remss.com/missions/amsu
Here is the python script I used to create plots and numbers for this post.
If you don’t like python here is a excel worksheet which does almost everything the python script does. The python script does a fit to determine two numbers but the worksheet doesn’t.
In this post I show slides and describe a talk given by Professor Murray Salby on 24 July 2012 at Sydney Institute. The post has a link to a youtube video of the talk, which I suggest you watch as it’s really interesting.
In brief Professor Salby showed
a plot of temperature anomaly versus CO2 which clearly showed temperature anomalies did not follow the rise in CO2.
a plot of net changes in level of CO2 which clearly did follow temperature anomalies closely. From this Professor Salby deduced levels of CO2 are determined by temperatures. It’s kind of hard to think of any other explanation.
- a graph showing observed and calculated levels of CO2 where the calculated CO2 levels agreed closely with observed levels.
So for lack of a better idea this is what I’m going to do.
The next figure shows both observed and generated (i.e. calculated) monthly co2 levels. The agreement is so close the generated values obscure the line for observed values most of the time.
The correlation between observed and generated values is 0.9995
I reckon that’s pretty good.
So how were the values generated.
First the temperature data is monthly anomalies. This means (as far as I know) the relationship between different months is unknown. If you know differently please let me know – jeremy at jeremyshiers.com.
Observed values were taken for the first 12 months – you’ve got to start somewhere.
For each subsequent year, the co2 level for each month was generated
CO2 this month this year = a + b × Temp this month this year + CO2 this month last year
Hang on a minute you might be thinking – Ein minuten bitte
Is this right? It’s certainly wacky!
Well I agree, but there didn’t seem to be much choice given the lack of absolute temperature data.
- If you think about it what this means is for every month in a year CO2 levels are going up each year an amount a
- In addition each months level changes an amount b times current months temperature anomaly from the CO2 level in the same month the previous year. So there is also variation in CO2 level for each month in different years based on the current temperature anomaly for that month and year (apart from first year).
- As the generated values are ‘seeded’ with the first years monthly observed values, these set the overall shape for each subsequent year. Though each years shape can be stretched or shrunk based on current temperatures.
Perhaps you will agree with me that it is striking, to put in mildly, that the CO2 levels for the 34 years from 1980 to 2013 can be determined from the 1979 levels with the aid of just temperature anomalies and 2 numbers.
By the way the numbers are
a = 1.59812
b = 1.52506
In the face of this (obviously inspired by Murray Salby) it seems impossible to accept CO2 causes temperatures to rise?
What do you think?
If there is anything I feel uncomfortable with it is the constant annual step increase in CO2 (a = 1.59812 ).
It seems almost certain to me there is some other mechanism (maybe it’s not currently active) which will eventually lower CO2 levels at some point in the future. An ice age?
[update 20 Jan 2014]
On rereading the post I feel I should have discussed the annual variation in CO2 emissions more.
These are attributed to plant growth and as most of earths land is in northern hemisphere the effect of plants CO2 emissions, due to photosynthesis, follows the northern hemisphere timetable (i.e. rising in spring, highest in summer then falling back to a low in winter).
It might be possible to model this using absolute temperatures but not with temperature anomalies. In any case the overall shapes is roughly the same each year so taking the first years ‘shape’ and adjusting it by temperature anomalies seems reasonable to me.