California temperatures, GISS USHCN, E.M. Smith and Anthony Watts

This is the day that I am going to look at the temperature records for California. It was the truncation of the number of stations in the GISS analysis that led E.M. Smith to his post, that started me off into taking a significant look into the temperature records. His concern was initiated by the discovery that the number of stations being used by GISS to monitor CA temperatures had been cut to four, all located near the coast. So does this have any meaning? The task begins, as I outlined at the beginning, by getting the data from the 50 USHCN stations and also inputting the data from the GISS stations.

There will be a slight pause while I do this. And after loading in the data from the 54 USHCN stations there are a few observations. Firstly the data from Death Valley is missing three data points (1896, 1897 and 1899). So noting that 1896 was 0.29 degrees above the state average, and that Death Valley is on average 16.62 degrees above the state average, suggests that in 1896 the temp there would have been 75.92 deg. And so we enter that and do the same for 1897, and 1899. And in passing I note that there are a couple of stations (Death Valley and Indio) that are below sea level. Wonder how that will work out. There don’t seem to be that many in the heights, but we’ll see how the graphs plot out. This is the correction that I explained in more detail when I was looking at the data from Colorado and found some values missing.

Now I get the four station data from GISS that Chiefio lists, which are San Francisco, Santa Maria, Los Angeles, and San Diego, which I download from the GISS site and at first none of these are on the GISS list. So I go back to the station locator and type in San Francisco and I get four stations and checking with Chiefio’s list by grid reference the top one is the one he cites. (and it is the one that has data from 1880 to 2010). So the next one on his list is Santa Maria, try that through the station locator and there are two locations, but neither has a full set of data!! In this case it is the lower of the two, which gets me information from 1948 on. Los Angeles data is all there, as is San Diego’s, though again one has to choose the longer history site from the four available. Phew!

OK so what have we got? With the varying conditions in the state (and particularly since we are coming down from the mountains to the sea) I will expect that there will be some influence of longitude, but given the concentration of data along the coast and at low elevations, I am not sure how it will end up. And then there are a lot more towns with larger populations that we have seen in the states we have looked at until now. Checking populations Cedarville was too rural for the usual city-data site, so I got the population from neighborhoodlink . Cuyamaca is a State Park with zero inhabitants (I put down 1). Electra also appears to be on none of the lists – (So looking at the one site with info, I put down 10). Lake Spaulding is a fishing camp (no data – suggest 5).

And having put in all the data (using the elevations of the airports for the GISS stations) one finds some interesting results. Firstly how do the GISS stations compare with the USHCN data?

While the GISS stations are on average 1.6 degrees warmer than the USHCN stations, the difference between the two is increasing:


(Note however that the small number of GISS stations relative to the number of USHCN stations means that when one does a total average for the state the differences induced are quite small.) Even without the GISS station contribution, the temperature in the state has been increasing, though the rate seems relatively constant since about 1900.


The state is a relatively long one, and there remains a strong influence of latitude:


I had expected, since the mountains are on the East and the sea is on the West, that there would also be an influence of longitude.


And that, at any significant level is apparently a wrong assumption.

Hmm! Well how about height, that has been fairly consistent.


And so it is again, though note that those below sea level seem to be even hotter than predicted.

Now one thing that we also can check on, given that there are significantly more stations in this state, is as to whether the scatter in the data is getting worse. This is something that would be suggested by Anthony Watts survey of stations. The premise has been that as the maintenance on the stations declines so the scatter in the results would get worse. This would be reflected in an increasing standard deviation across the stations in the state.


And while there was no such trend in other states, it is very clear and significant in California.

And with respect to the influence of population, I am still seeing that logarithmic fit, with the knee of the curve being at around 10,000 folk. Thus if GISS is cutting off all the influence of towns below that size, and just calling them rural, the evidence continues to suggest that this is a significant error.


Well I also suppose that this is one of the states where there are enough larger cities in the data bank that we can plot this on a log scale:


Correcting the individual temperatures as though the stations were at the center of the state (i.e. adjusting for latitude, as I did last time), one gets:


And then if one looks at the effect of elevation with latitude taken out of the data, one gets:


Again a clear correlation, except that there is a considerable scatter as one gets down to the range below about 100 m. Since this brings in the possible effects of the nearby ocean, and I am not sure how to isolate that at this time, we’ll leave that part of the analysis until we have more information.

The average elevation of California, by the way, is 884 m, and if you look at the plot above you can see that there are only 9 stations (out of 58) that are above that height. So if you do just an average of the data (which is what I have been doing) it will be weighted by the stations below the average elevation, and this will bias the results. By how much? Well we’re going to have to find more data before we can answer than question, and I suspect that it has to do with making adjustments for being close to the ocean.