In Part Nine – Data I – Ts vs OLR we looked at the monthly surface temperature (“skin temperature”) from NCAR vs OLR measured by CERES. The slope of the data was about 2 W/m² per 1K surface temperature change. Commentators pointed out that this was really the seasonal relationship – it probably didn’t indicate anything further.
In Part Ten we looked at anomaly data: first where monthly means were removed; and then where daily means were removed. Mostly the data appeared to be a big noisy scatter plot with no slope. The main reason that I could see for this lack of relationship was that anomaly data didn’t “keep going” in one direction for more than a few days. So it’s perhaps unreasonable to expect that we would find any relationship, given that most circulation changes take time.
We haven’t yet looked at regional versions of Ts vs OLR, the main reason is I can’t yet see what we can usefully plot. A large amount of heat is exported from the tropics to the poles and so without being able to itemize the amount of heat lost from a tropical region or the amount of heat gained by a mid-latitude or polar region, what could we deduce? One solution is to look at the whole globe in totality – which is what we have done.
In this article we’ll look at the mean global annual data. We only have CERES data for complete years from 2001 to 2013 (data wasn’t available to end of the 2014 when I downloaded it).
Here are the time-series plots for surface temperature and OLR:
Here is the scatter plot of the above data, along with the best-fit linear interpolation:
The calculated slope is similar to the results we obtained from the monthly data (which probably showed the seasonal relationship). This is definitely the year to year data, but also gives us a slope that indicates positive feedback. The correlation is not strong, as indicated by the R² value of 0.37, but it exists.
As explained in previous posts, a change of 3.6 W/m² per 1K is a “no feedback” relationship, where a uniform 1K change in surface & atmospheric temperature causes an OLR increase of 3.6 W/m² due to increased surface and atmospheric radiation – a greater increase in OLR would be negative feedback and a smaller increase would be positive feedback (e.g. see Part Eight with the plot of OLR changes vs latitude and height, which integrated globally gives 3.6 W/m²).
The problem of the”no feedback” calculation is perhaps a bit more complicated and I want to dig into this calculation at some stage.
I haven’t looked at whether the result is sensitive to the date of the start of year. Next, I want to look at the changes in humidity, especially upper tropospheric water vapor, which is a key area for radiative changes. This will be a bit of work, because AIRS data comes in big files (there is a lot of data).