Posted by drj | Filed under Uncategorized
In an earlier post I describe the trials and tribulations of tracking down some station data from Environment Canada’s website.
The obvious question to ask is, how does this affect the ccc-gistemp analysis?
For starters, how much extra data do we get, once we’ve merged all the duplicates and rejected short records and so on? Here’s the station count by year for the GHCN stations (dark lines), and the extra Environment Canada stations (lighter lines):
This count is made after ccc-gistemp Step 2 processing, so duplicate records for the same station have been merged, short records have been discarded, and urban stations that could not be adjusted have been dropped (the log tells me there are 18 such stations). New Environment Canada stations (recall that some of the Environment Canada data is for stations that are not in GHCN) do not get any brightness information in the v2.inv file; it so happens that in ccc-gistemp this means they get marked as rural, more by accident by design. I should probably fix this (by calculating brightnesses for the new stations, and rejecting stations with no brightness data), but this will certainly do for a preliminary analysis.
The 1990s still don’t reach the dizzying peaks of the 1960s (in terms of station count), but the Environment Canada data is certainly a welcome contribution. More than doubling the number of stations for recent decades.
The first thing to note if you haven’t seen one of these before, is the scale. The swings in this zone are much larger than the global average (this zone is 5% of the Earth’s surface); the recent warming in this zone is over 5 °C per century! The remaining points of note are the slight differences here and there in the very recent period. That large dip in the 2000s is 2004, and the new analysis has the anomaly some 0.16 °C colder (+0.57 versus +0.73). A warm spike is 1995 is 0.09 °C warmer. The same blips are also just about visibly different on the Northern Hemisphere analysis, but the differences smaller.
The additional Environment Canada is welcome, and does affect the result just enough to be visible, but the trends and any conclusion one could derive are not affected at all.
The data are available here, but you don’t need to download that if you’re using ccc-gistemp. Run «python tool/cascrape.py» to download the data, and then run «python tool/camatch.py» to generate a mapping table. «python tool/run.py -d ‘data_sources=ghcn ushcn scar hohenpeissenberg ca.v2′» will then run the analysis.