They are attempting to average the value of a scalar field (temperature), which is continuous over the entire surface of the earth, based on discrete measurements of that field. It seems to me that the best approach would be to use one of the many available algorithms that interpolate a continuous field based on discrete measurements, and then use the interpolated field to calculate the average value of the field. This would quit naturally and automatically correct for sparse and concentrated data points, by weighting them appropriately in the interpolation step.

So instead of arguing over ground-based weather station and urban heat islands… I just want to know why here in San Diego my gas bill last winter showed I used more “therms” than I have ever used in the past 20 years, it snowed in Baghdad for the first ever known, China had the worst snow storms, the snow pack melt and water content to feed Lake Meade is still running up 34% from the the “all-years” average and the upper Midwest had some of the longest, coldest temperatures ever recorded. I also went snow skiing in the local mountains in June…that hasn’t happened in 20 years.

So, like the stock market…I want to get a few more years of data, especially in light of the much lower than average solar radiance the past couple years… Anybody want to bet me $100 that solar activity versus CO2 is the real culprit in the past few years of temperature changes?

And by the way… I always post my real name…rather than trying to be anonymous behind a screen name. My credentials as a 30 year engineer are mine and I stand by them. I wouldn’t mind engaging any of you if you reveal your credentials and emails.

Mike

Keith – stations are weighed by distance from the point for which the anomaly is being computed. I do not know what you are trying to say about the US temperature measurements. Are you saying they should be given greater weight, or that they are given greater weight but shouldn’t be? They are not given greater weight in measuring global average temperatures, and nor should they be.

Does the temperature in St. Louis really effect the temperature in Atlanta, and vice versa – why don’t you do some investigating yourself? I always find it astonishing how often I see people here asking questions like this which they can investigate themselves in a matter of minutes. Get the GISS data for St. Louis, and for Atlanta, and compare them.

That average temperature anomaly is computed from the temperature stations within that grid box, and also any within 1200 kilometers. To give a graphic perspective so people can visualize this, let’s say my grid box is centered in St. Louis, Missouri. My local grid average is determined by not only the stations within my grid (roughly within 375 kilometers of me), but also from stations in Pittsburgh, Atlanta, Dallas, and Minneapolis, to name just a few. Basically, the radius of effect means that my one grid box is not determined from the 63,759 square kilometers within it, but from the 4.52 million square kilometers around it, an area 71 times as large.

Based upon this computation design, the United States should be represented by roughly two grid boxes if you are looking at total area involved in determining the average temperature anomaly compared to total area of the US (9,826,630 square kilometers total area of the United States divided by the 4,521,600 square kilometers derived from the 1200 kilometer radius of effect). But the US grid sample is still based upon the 63,759 square kilometer grid box determination, so the total US grid boxes are 154. That would seem to me to heavily weight the US sample.

Why the 1200 kilometer area of effect? Doesn’t this mean that certain stations get counted multiple times? Based upon my math above, it would suggest many stations in the United States get counted over 77 times. Does the temperature in St. Louis really effect the temperature in Atlanta, and vice versa? Surely, we could just use the temperatures provided by the stations within the grid boxes to determine that grid box’s average anomaly and work out a good global average from that.

And I don’t need to read the paper since the explanation of what they mean is given in the quote. What I asked about was the significance.

“They are not talking about urban heat islands at all:”

Did I say they were? Who’s misrepresenting here?

And what is the significance of the word ‘classical’?