A few days ago, I commented on the surprisingly large negative urban adjustments made by NASA at several Peruvian stations. I’ve now calculated the maximum negative and maximum positive urban adjustments at all NASA stations – something that I was able to do only because of my scraping of NASA data from their website (something that caused some controversy last year.) The results of these calculations have been added into the giss.info.dat (ASCII) data set at CA/giss data directory (The core of this is taken from the GISS station.inv file.)
While Hansen et al 1999 notes in passing that urban adjustments can be “of either sign”, neither it nor any other survey actually carries out a simple inventory. Indeed, a notable feature of the Hansen urban adjustment a a statistical method is that its efficacy is not actually demonstrated on a statistical data set of known properties, but is merely asserted and then implemented in an important practical setting – a practice that we’ve seen elsewhere in climate articles.
I’ve classified the 7364 stations in the GISS network into the following categories (with a cross-stratification to U.S. and ROW):
1. Positive urban adjustment (this is the Phoenix, Tokyo “expected” case, illustrated in Hansen et al 1999)
2. Negative urban adjustments (this is the counterintuitive case encountered at Puerto Maldonado and discussed recently)
3. “Bipolar” adjustments (this is an adjustment which is negative in one part and positive in another. These arise from the operation of the two-legged adjustment, but the interpretation of such cases is not discussed in Hansen et al 1999, 2001. In the inventory below, these occur in both the Positive and Negative stations and are deducted in making a total.)
4. Not used. (These are stations in the data base for which no adjusted version is calculated. In the U.S., their exclusion seems to result from the record being too short (under 20 years). I haven’t canvassed ROW exclusions in detail, but, in a quick look, shortness seems to be the predominant factor, but the methodology also provides for exclusion if there are not 3 rural comparanda – this doesn’t seem to be a big hurdle since 1000 km is a large radius and “rural” is, to say the least, loosely interpreted as discussed elsewhere.)
5. Unadjusted. (These are stations that are Code 1 in the U.S. and R in the ROW.)
I remind readers that there is a noticeable difference between U.S. and ROW temperature histories, which received much publicity last summer in connection with Hansen’s “Y2K” error – in which Hansen and NASA observed that the U.S. accounted for only 2.5% of the earth’s surface and re-ordering or errors in the U.S. data were immaterial to the world results.
First, here is a table showing the inventories – take a quick look at the table and I’ll comment below.
|Negative||740 (39%)||1108 (20%)||1848 (25%)|
|Positive||1003 (52%)||1233 (23%)||2236 (30%)|
|“Bipolar”l||324 (17%)||335 (6%)||659 (9%)|
|Subtotal: Adjusted||1419 (74%)||2006 (37%)||3425 (47%)|
|No Adjustment||353 (18%)||2220 (41%)||2573 (35%)|
|Not Used||149 (8%)||1217 (22%)||1366 (19%)|
|Total||1921 (100%)||5443 (100%)||7364 (100%)|
There are many striking aspects to the adjustment inventory.
First, 74% of all U.S. stations are adjusted, while only 37% of ROW stations are adjusted. This is a statistically significant difference by any measure. Is this because the ROW stations are, on average, located in more rural settings than in the US? Or is it because of a difference in methodology (or metadata)? While no one to my knowledge has carried out the engineering-quality investigations necessary to resolve the matter, my impression is that the US has made a fairly concerted effort to maintain weather stations in rural settings (Orland, Miles City etc.) and that many ROW stations are in cities and small towns (especially airports). Using a consistent apples-and-apples population classification, I would be very surprised if this very large difference between U.S. and ROW classifications held up.
Second, negative urban adjustments are not an exotic situation. In the ROW, there are almost the same number of negative adjustments as positive adjustments. In the U.S., there are about 50% more positive adjustments as negative adjustments – again a noticeable difference to the ROW. Some commenters on my Peruvian post seemed to think that negative urban adjustments were an oddball and very anomalous situation. In fact, that’s not the case, negative adjustments are nearly as common as positive adjustments. As such, extreme cases (such as Puerto Maldonado) need to be analyzed and explained.
In my next post, I’ll discuss some very interesting obiter dicta in Hansen et al 1999 about negative urban adjustments.
In the meantime, I’ll illustrate the above tables by showing the locations of several classes of stations on a world map (apologies to Hu McCulloch for not implementing a Mollweide projection on this occasion.)
First, here is a location map for all GISS stations with negative urban adjustments. The color coding is: red: greater than 2 deg C; salmon – 1 to 2 deg C; pink – 0 to 1 deg C. Peru definitely has a noticeable number of extreme examples (Pucallpa, Puerto Maldonado, Cuzco plues Piura, which I’d not mentioned), so I guess I do sometimes have sharp eyes for picking anomalies out of large data sets. Other ROW cities with negative urban adjustments exceeding 2 deg C are : Asmara, Nouadhibou, Darbhanga, Krasnovodsk, Sancti Spiritu, Praha/Ruzyne (which will no doubt intrigue Luboš Motl) and Beirut/Beyrouth Airport.
Third, here is a corresponding map for all (used) stations with no adjustments. This is an important map to consider, because readers need to keep squarely in mind that there are a LOT of stations which aren’t adjusted and that, even if you didn’t use any of the adjusted stations, there still would be a lot of stations in the index. Some readers have wondered why Hansen doesn’t simply use the “good” stations in his index – and I must say that this question certainly crosses my mind as well.
Here is a histogram of maximum positive and maximum negative adjustments for all adjusted ROW stations, showing somewhat similar distributions, though there is a very slight balance towards positive adjustments.
In discussions last summer about comparing the 1930s to the 2000s, NASA made the unarguable observation that the U.S. was only 2,5% of the world’s surface (6% land surface). I thought that it would be interesting to illustrate the population of stations which were present in 1930 and 2000 or later, stratifying the results by whether the stations were positive, negative and unadjusted. First here are the stations with negative adjustments:
Next here are the long stations with positive urban adjustments.
Obviously, while the U.S. is only a small fraction of the world’s land surface, it provides the vast majority of the long station records.