Satellite Temperature Trend Also Halved by Simple Regridding
Posted by Jeff Id on February 20, 2009
From the work of several different people I have been able to recreate the reconstruction of the Satellite temperature for the Antarctic. From my previous work we learned that it was possible to recreate the results of the AWS trend by aligning only 3 pc’s calculated by RomanM and placed in a matrix where missing values are infilled by RegEM. I then used the same technique to recalculate trends by the re-gridded data as presented by Jeff C.
First the satellite Principal Component reconstruction.
Don’t let the terminology scare you, scientists sometimes count on that trepidation. PC’s are just curves multiplied times a coeffecient and added together to create a new trend. If you do it 55o9 times you can create a different trend for 5509 points describing the temperature of the antarctic. The process used above was to take the graphs on the right side and delete the pre-1982 data. The graphs were placed in a matrix with all of the manned surface station data as ‘originally’ presented by Dr. Steig. RegEM was run on line and the pre 1982 data was recreated looking almost exactly like the original 3 pc’s. This demonstrates a reasonable verification of the method, we can actually recreate the part of the satellite reconstruction we have data for.
I’m doing my best to ignore the unnatural unreasonable look of PC3, it’s not that easy for me.
Above is a plot of the various surface stations in the antarctic used to reconstruct the historic data for the AWS stations.
Jeff C, noting the heavy weighting of the stations in the tip of the peninsula which didn’t seem to me be reasonably addressed in the paper, came up with an idea to regrid the data on a triangular matrix which looks like this.
Temperature stations grouped into cells were turned into an individual high accuracy trend rather than reused a dozen times in the same reconstruction. The idea is to reduce the likelihood that a certain pattern would be overweighted in the trend. While the re-gridding is imperfect, IMO it is a serious improvement over the original paper.
I requested Jeff’s regridded data, which he politely provided and used it in RegEM to redo the satellite reconstruction of temp trends. The three pc’s are below.
The graphs on the left are the originals from Steig09 the ones on the right are the reconstructed pc’s using Jeff’s data. I don’t pretend to understand the reason for every detail of the reconstruction but it seems to me that the difference between the unnatural PC3 and the regridded much more reasonable PC3 is created by excessive correlation of a large percentage of peninsula series. Other than that the 6 pc’s are reasonably similar.
The original satellite data looks like this.
Now my reconstructed trend demonstrating again that I am able to recalculate Dr Steig’s work.
And the improved trend from Jeff C re-gridded results.
The slopes in the above graphs are in degrees C/year. The slope 0.007 is half the original trend and it happens to match the AWS surface data reconstruction. It seems like the regridded data which is more in filled has a more stable result than the original paper. It also is verification that station positioning (density of peninsula measurements) affects the satellite total reconstruction as well.
This graph shows the original trend distribution. Code by JeffC.
The next graph shows my own RegEM reconstructed version of the original.
Here is a graph of the regridded trends.
The final result shows a slightly different distribution of trend but a much lower overall trend indicating the sensitivity of RegEM to the weighting and density of temperature stations. These effects were apparently ignored and left un-investigated by the authors, however they are real an create significant differences in the reconstruction. After demonstrating that I can reproduce the results from the mid level pc analyzed satellite data (still not the original satellite data) and viewing the same halving of trend that Jeff C got on the AWS data by simple re-gridding, substantially more focus needs to be directed toward quality control of station weighting in the final trend.