This post is also published at the International Surface Temperatures Initiative blog.
ccc-gistemp is Climate Code Foundation‘s rewrite of the NASA GISS Surface Temperature Analysis GISTEMP. It produces exactly the same result, but is written in clear Python.
I’ve recently modified ccc-gistemp so that it can use the dataset recently released by the International Surface Temperature Initiative. Normally ccc-gistemp uses GHCN-M, but the ISTI dataset is much larger. Since ISTI publish the Stage 3 dataset in the same format as GHCN-M v3 the required changes were relatively minor, and Climate Code Foundation appreciates the fact that ISTI is published in several formats, including GHCN-M v3.
The ISTI dataset is not quality controlled, so, after re-reading section 3.3 of Lawrimore et al 2011, I implemented an extremely simple quality control scheme, MADQC. In MADQC a data value is rejected if its distance from the median (for its station’s named month) exceeds 5 times the median absolute deviation (MAD, hence MADQC); any series with fewer than 20 values (for each named month) is rejected.
So far I’ve found MADQC to be reasonable at rejecting the grossest non climatic errors.
Let’s compare the ccc-gistemp analysis using the ISTI Stage 3 dataset versus using the GHCN-M QCU dataset. The analysis for each hemisphere:
For both hemispheres the agreement is generally good and certainly within the published error bounds.
Zooming in on the recent period:
Now we can see the agreement in the northern hemisphere is excellent. In the southern hemisphere agreement is very good. The trend is slightly higher for the ISTI dataset.
The additional data that ISTI has gathered is most welcome, and this analysis shows that the warming trend in both hemispheres was not due to choosing a particular set of stations for GHCN-M. The much more comprehensive station network of ISTI shows the same trends.