Posts Tagged ‘arctic warming’
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.
Posted by drj | Filed under Uncategorized
[update: 2010-08-26: the “no arctic” masks were wrong, fixed now]
I added a land mask feature to ccc-gistemp. The land mask is used In Step 5, where land and ocean series are combined into zonal averages. For each cell, ocean data is used unless: there is very little ocean data (fewer than 240 months); or, there is a land station within 100 km. With the new land mask feature an external file (input/step5mask which is the same format as the newly output work/step5mask) specifies whether to use land or ocean data for each cell.
This enables me to do something new: a run of ccc-gistemp using only land-data but restricted to locations where there is no ocean data:
I don’t actually have a true land mask to hand (a list of cells that cover the Earth’s land surface), but I can do a normal run of ccc-gistemp that uses both land and ocean data and use the generated mask. That mask tells me which cells have no ocean data (and therefore land data is used, if there is any).
That’s the mask I use above. This reduces the effects of the 1200 km smoothing radius, by preventing land series from being interpolated over the ocean (unless there is no ocean data). This mask has 3077 cells (8000 in total), so clearly some ocean cells are still using land data. It looks like this:
Note that black is not land, but it is where land data is used if there is any. Note the arctic ocean has no ocean data so land data (generally interpolated) is used. Also some of southern ocean will pick up land data.
The fact that the land series when restricted to exclude cells with ocean data shows greater warming can be explained by considering coastal stations. In the usual ccc-gistemp land-only analysis every station has an influence that extends out to a disc of radius 1200 km. Coastal stations will have a disc of influence that extends into the ocean. When excluding cells that have ocean data that means that a coastal station has its disc chopped in half (roughly), and an island station is almost entirely excluded. The overall effect is to weight stations in the continental interior more strongly. And warming is greater in the continent interior.
[edit: Chad also shows a version of the same effect (scroll down the very long post until you reach the red and black graphs): de-emphasising coastal stations increases trend]
We can show the effect of excluding Arctic and Antarctic zones (everything north of 60N and south of 60S is excluded):
Only a small effect. The broken mask version had an even smaller effect, but I’m still a little surprised by how small this difference is.
Again we can exclude cells with ocean data, and again this shows increased warming. This more than cancels out the cooling by excluding the high latitude zones:
Anyone got a real land mask? [edit: Yes, Steven Mosher pointed me at one; so expect a post using a real land mask soon]