There's a hot time in Marysville or how not to RTFR
Eli, as many others has been blogging about the surface station record. The pin up picture is Marysville
which "obviously shows spurious warming due to the urban heat island effect". Well, maybe not. Tamino has put up the numbers, using the GISSTEMP adjusted data that compares Marysville to nearby Orland, a rural station and shows they have the same trend over the last 30 years.
There has been lots of comment on these two stations, including Climate Audit from which the Orland figure was taken with a constant stream of stuff like this from another blog about Marysville
I can tell you with certainty, the temperature data from this station is useless. Look at the pictures to see why, and is it any wonder the trend for temperature is upward?There are also micro mistakes
But let’s say that they got all their adjustments exactly right. What does that say about the quantum of UHI? Here’s a town of 12,000 which qualifies as “rural” in all the UHI studies.sadly no. Steve blew that one, Marysville is NOT qualified as rural in GISS, and GISS is very clear about how rural stations are picked, by examining satellite data to find unlit areas where there are weather stations. The downside of this is that only ~250 rural stations are found in the US. Data for all the other stations is adjusted by
The urban adjustment in the current GISS analysis is a similar two-legged adjustment, but the date of the hinge point is no longer fixed at 1950, the maximum distance used for rural neighbors is 500 km provided that sufficient stations are available, and “small-town” (population 10,000 to 50,000) stations are also adjusted. The hinge date is now also chosen to minimize the difference between the adjusted urban record and the mean of its neighbors. In the United States (and nearby Canada and Mexico regions) the rural stations are now those that are “unlit” in satellite data, but in the rest of the world, rural stations are still defined to be places with a population less than 10,000. The added flexibility in the hinge point allows more realistic local adjustments, as the initiation of significant urban growth occurred at different times in different parts of the world.
Hansen, J.E., R. Ruedy, Mki. Sato, M. Imhoff, W. Lawrence, D. Easterling, T. Peterson, and T. Karl, 2001: A closer look at United States and global surface temperature change. J. Geophys. Res., 106, 23947-23963, doi:10.1029/2001JD000354ONLY RURAL STATIONS CONTRIBUTE TO THE TREND IN GISSTEMP. Marysville has NO EFFECT on the long term trend in the GISSTEMP record.
You have to RTFR to understand what is happening. Let us look at the USHCN adjusted and the GISS adjusted data
The RAW data is below the adjusted. That means that the nearby RURAL stations are warming faster than that so called hot spot Marysville. We can also look at the simple differences to see the hinged corrections.
RTFRs folks, Eli has enough aggro.
UPDATE: Stephen McIntrye asks where it is specifically stated that only the 250 or so rural US stations are used by GISS to estimate trends. Referring to the 2001 GISS paper linked above we see in the introduction (Eli quoted this before)
Only 214 of the USHCN and 256 of the GHCN stations within the United States are in “unlit” areas. Fortunately, because of the large number of meteorological stations in the United States, it is still possible to define area-averaged temperature rather well using only the unlit stations. This is not necessarily true in much of the rest of the world.There is also an interesting comment re land use (e.g. why not adjust for land use effects as well as UHI in Section 4.2
We provide one explanatory comment here about the rationale for trying to remove anthropogenic urban effects but not trying to remove regional effects of land use or atmospheric aerosols. Urban warming at a single station, if it were not removed, would influence our estimated temperature out to distances of about 1000 km, i.e., 1 million square kilometers, which is clearly undesirable. This is independent of the method of averaging over area, as even 5000 stations globally would require that each station represent an area of the order of 100,000 square kilometers, an area much larger than the local urban influence. On the other hand, anthropogenic land use and aerosols are regional scale phenomena. We do not want to remove their influence, because it is part of the largescale climate.which should amuse Prof. Pielke a. D. but back to the points that SM asked about while torturing surface stations (Eli has the feeling he is not being treated well over their also). From Section 4.2.2
Indeed, in the global analysis we find that the homogeneity adjustment changes the urban record to a cooler trend in only 58% of the cases, while it yields a warmer trend in the other 42% of the urban stations. This implies that even though a few stations, such as Tokyo and Phoenix, have large urban warming, in the typical case, the urban effect is less than the combination of regional variability of temperature trends, measurement errors, and inhomogeneity of station records.The bottom line is that since the urban and periurban stations have their temperatures adjusted so that the trends match those of the "nearby (500 km)" rural stations, the long term trend is only determined by the rural stations. The point raised in an earlier post, "Does GISSTEMP overcount rural stations"
The bottom line is that the ONLY stations which contribute to the overall trend are the RURAL stations. Moreover, rural stations near heavily settled areas will be more strongly overcounted because the trends in the few rural stations in such an area will dominate all of the stations in the area and the nearby points on the grid to which the temperature data is fit.To put it simply, the grid point temperature anomalies may be an average over all stations in the neighborhood, but the data in the non-rural stations has been previously constrained to match the trends of the rural stations. Thus the rural trends are being added multiple times to the average. What is left is shorter time variations that average to zero over the hinged UHI spline adjustments.
In the long run it does make a difference over 100 years, but not such a large difference that it would swamp warming from forcings such as greenhouse gases, solar, etc..
The primary difference between the USHCN and the current GISS adjustments, given that the GISS analysis now adapts the USHCN time of observation and station history adjustments, is the urban adjustment. The GISS urban adjustment, as summarized in Plate 2, yields an urban correction averaged over the United States of about -0.15°C over 100 years, compared with a USHCN urban adjustment of -0.06°C. When only urban stations are adjusted the impact of our adjustment is about -0.1°C on either the USHCN stations (Plate 2j) or on the GHCN stations (Plate 2k) in the United States. When both urban and periurban stations are adjusted, the impact is about - 0.15°C.
The magnitude of the adjustment at the urban and periurban stations themselves, rather than the impact of these adjustments on the total data set, is shown in Plate 2l. The adjustment is about -0.3°C at the urban stations and -0.1°C at the periurban stations. In both cases these refer to the changes over 100 years that are determined by adjusting to neighboring “unlit” stations. The adjustments to the periurban stations have a noticeable effect on the U.S. mean temperature because of the large number of periurban stations, as summarized in Table 1.