Some Oddities with cooling stations

Quick Note: Just so folks don’t get confused, I’m showing the short records, just to give you a brief look at why I move onto to long records. There is no take away from that except this: dont draw any conclusions from short records, So we look at them quickly to show the issue, and move on. Basically we are exploring the data, and in the end, I’ll add up some google earth code that makes the task of exploring much more fun. That’s basically the point of this blog: the tools,  the programming, not so much the debate. Plenty of other places for that.

Now, that  the whole analysis has been moved to raster, I took some time to play around with a question that has interested  a couple of people. Cool stations. A while back when I was looking at ways of bounding uncertainties in the record I went on a hunt for the station that cooled the most and the station that warmed the most. A few weeks later Verity and Tonyb did a post on cooling stations. So, I started down the path again basically to understand how prevalent the ‘cooling stations” are and if there is anything special or unique about them. Now, the simplistic way to think about this problem is that in a warming world every place has to get warmer. Well, that’s  just plain common sense. or is it? We can put this question differently. if the average  goes up by say .8C in 110 year period how can ANY site see a negative trend? And if we find them, what does that mean? That’s an open question. So I started out down that path, and what I found makes me scratch my head. One way of looking at what I found is this: UHI may play a role.  I’m just going to point out the issue or oddity I found and ponder on it. Its definitely not conclusive, but I did scratch my head and wonder about the significance of this.  So this is notebook scriblings.

We start by simply looking at the distribution of all trends. For this exercise I’m not correcting for any autocorrelation, I’m basically on a fishing expeditions for the highest and lowest trends. Looks like this

That’s a monthly slope. And without testing that distribution you should be able to see a couple things: it’s peaky with long tails and the mean is going to be positive. Also note that you have some very large warming trends and cooling trends. Since the ends can be revealing, I sorted all the stations by trend and looked through them all. some 5000 charts.

The coolest: 40678349000  SANCTI SPIRIT  21.93 -79.45

Not much of a mystery there. In fact, going through the extremes you will find that nearly all of the extremes are these short records.

one of the warmest: 40371881001  ROBB RSAL 53.23 -116.97

Now, there is a whole separate issue with these short records, leave that aside. So Instead, I went looking at long records. Record that have over 1080 months of data from 1900 to 2009. Why 90 years? No real justification, I’m just exploring. Anyway, as we can expect the distribution shifts. Below see a distribution of the decadal trends for all long stations. While eliminating short runs changes the shape of the distributions and shifts it positive we STILL see stations that cool. random chance? or is there something different about them. Think on that, More tommorrow