Foreword: The focus of this essay is strictly altitude placement/change of GHCN stations. While challenge and debate of the topic is encouraged, please don’t let the discussion drift into other side issues. As noted in the conclusion, there remain two significant issues that have not been fully addressed in GHCN. I believe a focus on those issues (particularly UHI) will best serve to advance the science and understanding of what GHCN in its current form is measuring and presenting, post processing. – Anthony
By Steven Mosher, Zeke Hausfather, and Nick Stokes
Recently on WUWT Dr. McKitrick raised several issues with regard to the quality of the GHCN temperature database. However, McKitrick does note that the methods of computing a global anomaly average are sound. That is essentially what Zeke Hausfather and I showed in our last WUWT post. Several independent researchers are able to calculate the Global Anomaly Average with very little differences between them.
GISS, NCDC, CRU, JeffId/RomanM, Tamino, ClearClimateCode, Zeke Hausfather, Chad Herman, Ron Broberg, Residual Analysis, and MoshTemp all generally agree. Given the GHCN data, the answer one gets about the pace of global warming is not in serious dispute. Whether one extrapolates as GISS does or not, whether one uses a least squares approach or a spatial averaging approach, whether one selects a 2 degree bin or a 5 degree bin, whether one uses an anomaly period of 1961-90 or 1953-1982, the answer is the same for virtually all practical purposes. Debates about methodology are either a distraction from the global warming issues at hand or they are specialist questions that entertain a few of us. Those specialist discussions may refine the answer or express our confidence in the result more explicitly, but the methods all work and agree to a high degree.
As we noted before, the discussion should therefore turn and remain focused on the data issues. How good is GHCN as a database and how serious are its shortcomings? As with any dataset, those of us who analyze data for a living look for several things. We look for errors, we look for bias, we look at the sampling characteristics, and we look at adjustments. Dr. McKitrick’s recent paper covers several topics relative to the make up and changes in GHCN temperature data. In particular he covers changes over time in the sampling of GHCN stations. He repeats a familiar note: over time the stations representing the temperature data set have changed. There is, as most people know, a fall off in stations reporting shortly after 1990 and then again in 2005. To be sure there are other issues that he raises as well. Those issues, such as UHI, will not be addressed here. Instead, the focus will be on one particular issue: altitude. We confine our discussion to that narrow point in order to remove misunderstandings and refocus the issue where it rightly belongs.
McKitrick writes:
Figure 1-8 shows the mean altitude above sea level in the GHCN record. The steady increase is consistent with a move inland of the network coverage, and also increased sampling in mountainous locations. The sample collapse in 1990 is clearly visible as a drop not only in numbers but also in altitude, implying the remote high-altitude sites tended to be lost in favour of sites in valley and coastal locations. This happened a second time in 2005. Since low-altitude sites tend to be more influenced by agriculture, urbanization and other land surface modification, the failure to maintain consistent altitude of the sample detracts from its statistical continuity.
There are several claims here.
- The increase in altitude is consistent with a move inland and out of valleys
- The increase in altitude is consistent with more sampling in mountainous locations.
- Low level sites tend to be influenced by agriculture, urbanization and other land use modifications
A simple study of the metadata available in the GHCN database shows that the stations that were dropped do not have the characteristics that McKitrick supposes. As Nick Stokes documents, the process of dropping stations is more related to dropping coverage in certain countries rather than a direct effort to drop high altitude stations . McKitrick also get the topography specifics wrong. He supposes that the drop in thermometers shifts the data out of mountainous inland areas into the valleys and low level coastal areas, areas dominated by urbanization and land use changes. That supposition is not entirely accurate as a cursory look at the metadata shows.
There are two significant periods when stations are dropped; Post 1990 and again in 2005. As Stokes show below.
FIGURE 1: Station drop and average altitude of stations.
The decrease in altitude is not caused by a move into valleys, lowland and coastal areas. As the following figures show, the percentage of coastal stations is stable, mountainous stations are still represented and the altitude loss more likely comes from the move out of mountainous valleys .
A simple summary of the total inventory shows this
| ALL STATIONS | Count | Total | Percent |
| Coastal | 2180 | 7280 | 29.95 |
| Lake | 443 | 7280 | 6.09 |
| Inland | 4657 | 7280 | 63.97 |
TABLE 1: Count of Coastal Stations
The greatest drop in stations occurs in the 1990-1995 period and the 2005 period, as shown above McKitrick supposes that the drop in altitude means a heavier weighting for coastal stations. The data do not support this
| Dropped Stations 90-95 | Count | Total | Percent |
| Coastal | 487 | 1609 | 30.27 |
| Lake | 86 | 1609 | 5.34 |
| Inland | 1036 | 1609 | 64.39 |
| Dropped in 2005-06 | |||
| Coastal | 104 | 1109 | 9.38 |
| Lake | 77 | 1109 | 6.94 |
| Inland | 928 | 1109 | 83.68 |
TABLE 2: Count of Coastal Stations dropped
The great march of the thermometers was not a trip to the beach. Neither was the drop in altitude the result of losing a higher percentage of “mountainous” stations.
FIGURE 2: Distribution of Altitude for the entire GHCN Inventory
| Minimum | 1st Qu | Median | Mean | 3rd Qu | Max | NA |
| -224.0 | 38.0 | 192.0 | 419.9 | 533.0 | 4670 | 142 |
TABLE 3: descriptive statistics for Altitude of the entire dataset
We can assess the claim about the march of thermometers down the mountains in two ways. First, by looking at the actual distribution of dropped stations.
FIGURE 3 Distribution of altitude for stations dropped in 1990-95
| Minimum | 1st Qu | Median | Mean | 3rd Qu | Max | NA |
| -21.0 | 40.0 | 183.0 | 441 | 589.2 | 4613.0 | 29 |
TABLE 4: Descriptive statistics for the Altitude of dropped stations
The character of stations dropped in the 2005 time frame are slightly different. That distribution is depicted below
FIGURE 4 Distribution of altitude for stations dropped in 2005-06
| Minimum | 1st Qu | Median | Mean | 3rd Qu | Max | NA |
| –59 | 143.0 | 291.0 | 509.7 | 681.0 | 2763.0 | 0 |
TABLE 5: Descriptive statistics for the Altitude of dropped stations 2005-06
The mean of those dropped is slightly higher than the average station. That hardly supports the contention of thermometers marching out of the mountains. We can put this issue to rest with the following observation from the metadata. GHCN metadata captures the topography surrounding the stations. There are four classifications FL, HI, MT and MV: flat, hilly, mountain and mountain valley. The table below hints at what was unique about the dropout.
| Type | Entire Dataset | Dropped after90-95 | Dropped 2005-06 | Total of two major movements |
| Flat | 2779 | 455 (16%) | 504 (23%) | 959 (43%) |
| Hilly | 3006 | 688 (23%) | 447 (15%) | 1135 (38%) |
| Mountain | 61 | 15 (25%) | 3 (5%) | 18 (30%) |
| Mountain Valley | 1434 | 451(31%) | 155 (11%) | 606 (42%) |
TABLE 6 Station drop out by topography type
There wasn’t shift into valleys as McKitrick supposes, but rather mountain valley sites were dropped. Thermometers left the flatlands and the mountainous valleys. That resulted in a slight decrease in the overall altitude.
That brings us to McKitrick’s third critical claim. McKitrick claims that the dropping of thermometers over weights places more likely to suffer from urbanization and differential land use. “Low level sites tend to be influenced by agriculture, urbanization and other land use modifications.” The primary concern that Dr. McKitrick voices is that the statistical integrity of the data may have been compromised. That claim needs to be turned into a testable hypothesis. What exactly has been compromised? We can think of two possible concerns. The first concern is that by dropping higher altitude mountain valley stations one is dropping stations that are colder. Since temperature decreases with altitude this would seem to be a reasonable concern. However, it is not. Some people make this claim, but McKitrick does not. He doesn’t because he is aware that the anomaly method prevents this kind of bias. When we create a global anomaly we prevent this kind of bias from entering the calculation by scaling the measurements of station by the mean of that station. Thus, a station located at 4000m may be at -5C, but if that station is always at -5C its anomaly will be zero. Likewise, a station at sea level in Death Valley that is constantly 110F will also have an anomaly of zero. Anomaly captures the departure from the mean of that station.
What this means is that as long as high altitude stations warm or cool at the same rate as low altitude stations, removing them or adding them will not bias the result.
To answer the question of whether dropping or adding higher altitude stations impacts the trend we have several analytical approaches. First, we could add back in stations. But we can’t add back in GHCN stations that were discontinued. The alternative is to add stations from other databases. Those studies indicate that adding addition stations does not change the trends:
http://moyhu.blogspot.com/2010/07/using-templs-on-alternative-land.html
http://moyhu.blogspot.com/2010/07/arctic-trends-using-gsod-temperature.html
http://moyhu.blogspot.com/2010/07/revisiting-bolivia.html
http://moyhu.blogspot.com/2010/07/global-landocean-gsod-and-ghcn-data.html
The other approach is to randomly remove more stations from GHCN and measure the effect. If we fear that GHCN has biased the sample by dropping higher altitude stations, we can drop more stations and measure the effect. There are two ways to do this. A Monte Carlo approach and an approach that divides the existing data into subsets:
Nick Stokes has conducted the Monte Carlo experiments. In his approach stations are randomly removed and global averages are recomputed. Stations were removed based on a randomization approach that preferentially removed high altitude stations. This test gives us an estimate of the Standard Error as well.
| Period | Trend of All | Re-Sampled | s.d |
| 1900-2009 | 0.0731 | 0.0723 | 0.00179 |
| 1979-2009 | 0.2512 | 0.2462 | 0.00324 |
| Mean Altitude | 392m | 331m |
Table 7 Monte Carlo test of altitude sensitivity
This particular test consists of selecting all the stations whose series end after 1990. There are 4814 such stations. The sensitivity to altitude reduction was performed by randomly removing higher altitude stations. The results indicate little to no interaction between altitude and temperature trend in the very stations end after the 1990 period.
The other approach, dividing the sample, was approached in two different ways by Zeke Hausfather and Steven Mosher. Hausfather, approached the problem using a paired approach. Grid cells are selected for processing if the have stations both above and below 300m. This eliminates cells that are represented by a single station. Series are then constructed for the stations that lie above 300m and below 300m.
| Period | Elevation > 300m | Elevation <300m |
| 1900-2009 | .04 | .05 |
| 1960-2009 | .23 | .19 |
| 1978-2009 | .34 | .28 |
Table 8. Comparison of trend versus altitude for paired station testing
FIGURE 5: Comparison of temperature Anomaly for above mean and below mean stations
This test indicates that higher elevation stations tend to see higher rates of warming rather than lower rates of warming. Thus, dropping them, does not bias the temperature record upward. The concern lies in the other direction. If anything the evidence points to this: dropping higher altitude stations post 1990 has lead to a small underestimation of the warming trend.
Finally, Mosher, extending the work of Broberg tested the sensitivity of altitude by dividing the existing sample in the following way, by raw altitude and by topography.
- A series containing all stations.
- A series of lower altitude stations Altitude < 200m
- A series of higher altitude stations Altitude >300m
- All Stations in Mountain Valleys
- A series of stations at very high altitude. Altitude >400m
The results of that test are shown below
FIGURE 6 Global anomaly. Smoothing performed for display purpose only with a 21 point binomial filter
The purple series is the highest altitude stations. The red series lower elevation series. Green is the mountain valley stations. A cursory look at the “trend” indicates that the higher elevation stations warm slightly faster than the lower elevation, confirming Hausfather. Dropping higher elevation stations, if it has any effect whatsoever works to lower the average. Stations at lower altitudes tend to warm less rapidly than stations at higher elevations. So quite the opposite of what people assume, the dropping of higher altitude stations is more likely to underestimate the warming rather than over estimate the warming.
Conclusion:
The distribution of altitude does change with time in GHCN v2.mean data. That change does not signal a march of thermometers to places with higher rates of warming. The decrease in altitude is not associated with a move toward or away from coasts. The decrease is not clearly associated with a move away mountainous regions and into valleys, but rather a movement out of mountain valley and flatland regions. Yet, mountain valleys do not warm or cool in any differential manner. Changing altitude does not bias the final trends in any appreciable way.
Regardless of the differential characteristics associated with higher elevation, changes in temperature trends is not clearly or demonstrably one of them. For now, we have no evidence whatsoever that marching thermometers up and down hills makes any contribution to a overestimation of the warming trend.
Dr. McKitrick presented a series of concerns with GHCN. We have eliminated the concern over changes in the distribution of altitude. That merits a correction to his paper. The concerns he raised about latitude, and airports and UHI will be addressed in forthcoming pieces. Given the preliminary work done on airports. (and here) and latitude to date, we can confidently say that the entire debate will come down to two basic issues: UHI and adjustments, the issues over latitude changes and sampling at airports will fold into those discussions. So, here is where the debate stands. The concerns that people have had about methodology have been addressed. As McKitrick notes, the various independent methods get the same answers. The concern about altitude bias has been addressed. As we’ve argued before, the real issue with temperature series is the metadata, its related microsite and UHI issues and adjustments made prior to entry in the GHCN database.
Special thanks to Ron Broberg for editorial support.
References:
A Critical Review of Global Surface Temperature Data Products. Ross McKitrick, Ph.D. July 26, 2010
Readjustment of column 5 (for policy makers)
The table below hints at what
Type Entire Dataset Dropped after90-95 Dropped 2005-06 Total of two major movements
Flat 2779 455 (16%) 504 (23%) 959 (43%)
Hilly 3006 688 (23%) 447 (15%) 1135 (38%)
Mountain 61 15 (25%) 3 (5%) 18 (30%)
Mountain Valley 1434 451(31%) 155 (11%) 606 (42%)
TABLE 6 Station drop out by topography type
Type Entire Dataset Dropped after90-95 Dropped 2005-06 Total of two major movements
Flat 2779 455 (16%) 504 (22%) 959 (38%)
Hilly 3006 688 (23%) 447 (19%) 1135 (42%)
Mountain 61 15 (25%) 3 (5%) 18 (30%)
Mountain Valley 1434 451(31%) 155 (16%) 606 (47%)
TABLE 6 Station drop out by topography type (readjusted)
“”” Rex from NZ says:
August 19, 2010 at 11:26 am
Can someone clarify two things for me: (1) How many times a day is
the temperature recorded for the stations (in general), and what has
been used to determine the frequency and choice of time, and (2) How
is it known, or established, or agreed, as to what area of land is represented
by each station. Surely this latter is critical, because the area the station
represents needs to be known so that the proper weight can be applied
when working out the mean. It is the mean temperature per sq kilometre
that is surely the important thing, not just the mean per se.
Or am I way off the track here? “””
Well probably they are read just once per day but apparently with a max/min thermometer, which gives you two numbers per day; but evidently at no particular time. That could be ok if the diurnal temperature cycle is a pure sinusoid; then the max/min readings would occur exactly 12 hours apart; and the average of those two numbers would be the correct daily average Temperature.
BUT, if the diurnal cycle is not sinusoidal, then it at least must contain some second harmonic component witha period of 12 hours, and so you would now need four readings per day at a maximum of six hours interval in order to get the correct daily average.
But if there are clouds blowing through; so the temeprature goes up and down in a random fashion, then even four measurements per day will be incorrect, so you can’t recover even the correct daily average.
And who cares how much area you assign to each thermometer. Hansen seems to think that the temperature stays the same out to 1200 km away from the thermometer, so that’s pushing a million square km area for each thermometer. And there aren’t too many of those thermometers placed out in the ocean so they could be even further apart that 1200 km.
But so far nobody has let that stop them from averaging all those thermometers. Well of course they dopn’t actually average the temperatures; just the differneces between the thermometer reading, and some other fixed value that they can’t determine accurately either.
“”The warming actually starts around 1975, change point analysis””
Then you and I do not agree on that either.
The 1975-1990’s saw a slight increase.
After 1990 temps could have just as easily flat lined or gone down.
That would be right in line with the past.
You’re really only seeing less than 1/2 a degree.
“”Drop all the stations that were dropped in 1990. take ONLY the stations that have continuous records from 1990 to the end. Answer. NO DIFFERENCE.””
No difference in trend is exactly what I’m talking about.
Dropping that many stations and not showing a difference in trend, does not pass the sniff test.
“”Nick did that. Look at his study””
I’ll go look again, but I didn’t see where each individual dropped station was looked at.
Fewer stations means you don’t have to record as many innacurate numbers.
Makes a person feel that much better about it. They’ve lied fewer times.
Andrew
REPLY: Doubtful. The people that record the data, especially airports, would be caught out if they lied. – Anthony
Fascinating disection. In the post climategate era I’ve seen a lot of very good disection of what IS VALID with AGW theory by skeptics. This is driving the debate from “an inert gas can’t change temperature” and “it’s all an evil scheme made up by people haters with malice” to a far more specific and thus relevent and honest debate.
Each time something is rigorously verified as valid, the focus of brain power and other resources can be applied to an ever shrinking set of criteria to investigate.
Perhaps we need a “state of the debate” executive summary that shows what we have seen shown to be credible, what is clearly not credible, and what still needs to be flogged.
Off the top of my head, seems we have the following to ascertain if there IS a valid AGW problem and the extent of the problem:
Historic record:
Data quality
Data modification methodology
Averaging methodology
Trend choices (years, High to High vs. mid to mid, etc.)
for both surface T and ppm GHG
Predictive Modeling:
Estimations of GHG increase due to natural and man made causes
Feedback/Forcing weightings, relationships, and missing pieces
Accuracy when run in reverse as compared to historic record and why there are discrepancies
Effects and changes to the world we can expect as result of a strong warming signal if there is one:
all the doomsday hypothesises [sic lol] (though this seems the most work for the least return on investment at the core of the debate)
The real questions:
Is it really getting warmer?
Will this pose a threat to the world?
How soon? to what degree?
What is it caused by?
What could we realistically do to prevent this?
How could we prepare for this?
some require a previous question to be true to even be necessary but you see what I’m saying.
My skepticism started when I saw hugely authoritarian and decisive statments being made by scientists about things which seemed beyond our current level of scientific ability to know with such extreme levels of certainty. It got a big boost when I started seeing the proposed solutions. But my “skepticism” is no emotional-religious-political driven belief one way or another. Which is why WUWT is oen of the few climate sites I truly enjoy reading every day.
A couple of points.
1. It appears you have set up straw men from McKitrick’s quote.
He says: “The sample collapse in 1990 is clearly visible as a drop not only in numbers but also in altitude, implying the remote high-altitude sites tended to be lost in favour of sites in valley and coastal locations. This happened a second time in 2005.”
Your first two points address his suppositions about the increase in stations over the many years before the 1990 and 2005 station reductions, and that doesn’t seem relevant to me. Interestingly, you agree that the average altitude has gone down, as when you say, “The decrease in altitude is not caused by a move into valleys, lowland and coastal areas.”.
2. You appear to be misunderstanding your own data from Tables 1 and 2. The percent of Coastal stations dropped in 2005 is 9.4% vs 34% of the total, while the percent of Inland stations dropped is 83.7% vs 64% of the total. Since Coastal by definition is near sea level altitude, while Inland is at a higher altitude, my read of your numbers is that higher altitude stations in fact got the axe at a higher rate in the 2005 culling.
3. Your argument on item 3 appears to say that items 1 and 2 are irrelevant. The fact that you decided to address the first two items suggests to me that your motive in dealing with them was merely to set up your theoretical superiority over McKitrick’s expertise. And then, when you run your analysis using 300m and 400m altitudes, it raises more flags for me – 300m is not quite even a mountain by international standards, while your Figure 6 does not display the results for 400m. Why didn’t you use a substantial high altitude test, say 1000m?
As a layman, I have to look beyond the “proof” offered and look at how it was put forth. While you could be 100% correct, your style makes me not want to trust you.
Frankly this representation does not show enough information. What is “mountainous”? Hills and mountains? Is a mountain valley average at, below or above 300m? What about flats and hills?
Where did 300m come from anyway? There are states that have their lowest point above 300m. In fact in half the states in the US, the average elevation is above 300m. So in your methodology, half the states in the US are high altitude. That doesn’t seem right. How many urban areas are above and below 300m? By what standard is 300m and above considered high altidude.
Your conclusion is that mountain valley and not mountain moved to flatland . Yet you do not show this. Since you did not state it, I would assume that the 61 mountain readings are the ones represented by an altitude above around 3,000 (thousand) meters. What are the rest of the above 300 but below 3,000 catagorized as? Hill, mountain valley, some of both, a lot of both? Any flatlands above 300m? How many?
Not saying your conclusions are wrong, just that from what you presented, I cannot tell if you are actually doing what you said you are doing.
You split hairs by saying it is not mountainous but mountain valley doing the moving, yet I do not see a plot of around 3000m or greater altitude trend compared to the rest. Barring that an actual plot of the 4 groups.
Frankly if you say you are going to see what “mountainous” regions compare with the others then you need to define what is “mountainous” like saying hills, mountains and mountain valleys above 300m and then plot it. You cant claim to see what flats, valleys, hills and mountains trend just by plotting the altitude if the altitude you pick has a different ratio of groups in them.
You may be able to say “high altitude”, but you cannot say mountain or “mountainous” if you did not bother to seperate them from the calculation.
REPLY: Doubtful. The people that record the data, especially airports, would be caught out if they lied. – Anthony
Not to nitpick you Anthony, I love WUWT but…
Your response is just an assertion. Like just about everything else in climate science.
And I’m still mad that I got snipped a couple weeks back due to inconsistent moderation. 😉
Andrew
REPLY: No, it is more than assertion, it is firsthand knowledge as a former pilot on how airports operate and airport data is used . If they fudge the temperature at the airport, then the density altitude calculation gets screwed up, and planes trying to take off on a hot humid day end off the end of the runway.
Try flying out of Leadville, Colorado on a hot humid day with bunged up density altitude calculation, you won’t make that mistake twice.
That being said, airports are a terrible place to measure climate, because the data comes from the runway areas. Great for aviation safety, the primary mission, not so great for climate, not part of the airport mission at all. – Anthony
MikeD, as long as we’re setting up climate questions to be asked, one that occurred to me recently is: Why is Greenland covered with glaciers? Take a look at this snow and ice map for middle of August 2010
http://www.natice.noaa.gov/pub/ims/ims_gif/ARCHIVE/NHem/2010/ims2010230.gif
Notice how Greenland is now a giant peninsula of white sticking far south into latitudes where nowhere else around the pole is there any glaciers or snow. It can’t be mountains because surely the northern Rockies in Canada and Alaska are more or less as high. To my mind this is a massive mystery and should be explained before we start trying to control the world’s climate.
Ken Harvey says:
August 19, 2010 at 10:49 am
Can we identify one data set from one single station that covers many years continuous to the present, that is widely regarded as accurate to a degree beyond reproach and is free of outside bias and adjustment? …
____________________________________________________________________________
For a large selection of mostly non-urban weather stations in both hemispheres there is this: http://www.john-daly.com/stations/stations.htm
Another useful data set is this: http://pages.science-skeptical.de/MWP/MedievalWarmPeriod.html
Graphs of oldest temp records: http://i47.tinypic.com/2zgt4ly.jpg
We are asked to confine discussion to altitude changes in measuring stations, but then we get this thrust at us:
“Given the GHCN data, the answer one gets about the pace of global warming is not in serious dispute.”
Apologies for going off topic, but this statement has to be addressed.
Apart from the fact that global temperature, even according to the dubious statistics under study here, has been pretty flat for 12 years, ocean heat content has (according to its own set of dubious statistics) been falling since 2003. So no matter what the atmospheric temp or SST is up to while the excess heat burps out of the ocean, that means some there is some ‘cooling in the pipeline’.
So quit it with ‘the pace of global warming’ rhetoric please.
Thanks.
I don’t see that the biggest difference between low & high altitude stations is addressed yet:
The diurnal, as in the difference between the high & low temps as a function of altitude.
A greater percentage of UHI night warming is injected into the data set by removing higher altitude stations, for the simple reason that thinner atmosphere cools quicker and further when the sun is down.
Recomputing GISS by restoring the station selection should remove some of the warming attributed to anthropogenic causes.
So, can airports be hotter. Sure. How much? when? where? how long? all testable. I prefer to focus on testable things. And, the final thing is this. The biggest issue is the potential step change when an airport starts. its the trend baby. The farther you get away from that step change, the LESS it matter….
POssibly but then possibly not. It depends on how the airport and its use changes over the years. Will a small municipal airport still record the same temperature if it eventually expands or sees an increase in traffic.
BillD says;
“One problem with your conclusion is that green house gases are expected to reduce night-time cooling and to have a greater effect on the min rather than the max temperature.”
****
*night time minimum and winter temps have been moderating througout the oldest temperatures records i.e CET since 1660 (not just since 1975).
* Medieval castles were built in the way they were as night time temperatures were warmer than in previous or subsequent periods.
* Frosts were a rarity during much of the MWP and during warm periods of the LIA.
In other words night time and winter temperatures have varied throughout the record without any help from co2 and in the past have been as warm or warmer than the modern era.
tonyb
Whenever someone says “move along, nothing to see here” one is reminded of the old New Yorker cartoon in which a street crowd is watching a man fighting off what looks like a giant squid and a passerby says, “It doesn’t take much to draw a crowd in New York”. No doubt the geomorphologists among us will be weighing in on the reductionist approach to topographic forms and their environmental contexts that is applied in this article.
E.M.Smith says:
August 19, 2010 at 10:52 am
…
So we have a bunch of folks doing number games for amusement and claiming to find truth. They aren’t.
…
So, IMHO, we have crappy data and get crappy results from it. Admiring the uniformity of the crappiness does not yield much comfort.
I have to agree bigtime with The Musings From The Chiefio..
The Chiefio has done a lot of meaningful work looking at the Altitude issue… for example: NCDC GHCN Africa By Altitude
http://chiefio.wordpress.com/2009/12/01/ncdc-ghcn-africa-by-altitude/
which includes the following table:
[chiefio@tubularbells Alts]$ more Therm.by.Alt1.Dec.ALT Year -MSL 20 50 100 200 300 400 500 1000 2000 Space DAltPct: 1849 0.0100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 DAltPct: 1859 29.0 19.4 25.8 25.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 DAltPct: 1869 51.3 20.5 20.5 7.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 DAltPct: 1879 36.9 36.9 4.8 1.2 2.4 0.0 0.0 6.0 11.9 0.0 0.0 DAltPct: 1889 24.7 19.9 17.8 4.1 2.1 0.0 1.4 12.7 17.5 0.0 0.0 DAltPct: 1899 24.2 22.0 15.2 4.4 0.9 1.4 3.0 10.9 17.8 0.2 0.0 DAltPct: 1909 21.4 18.3 14.0 6.2 1.3 2.6 2.9 10.8 21.5 0.9 0.0 DAltPct: 1919 21.0 15.7 12.7 7.2 2.3 6.2 4.8 9.5 20.0 0.6 0.0 DAltPct: 1929 16.8 15.6 11.6 6.8 2.9 6.8 5.0 10.6 23.0 0.8 0.0 DAltPct: 1939 15.5 13.5 10.4 7.1 2.7 6.9 4.5 11.0 27.8 0.6 0.0 DAltPct: 1949 14.8 11.0 8.5 6.5 4.9 9.2 5.2 11.9 27.3 0.7 0.0 DAltPct: 1959 16.5 10.3 8.7 5.0 7.8 13.3 9.0 14.4 14.6 0.7 0.0 DAltPct: 1969 17.3 9.2 8.4 5.9 7.8 12.5 8.8 12.8 16.6 0.8 0.0 DAltPct: 1979 17.9 9.9 8.7 6.4 8.5 12.0 8.6 11.3 15.7 1.0 0.0 DAltPct: 1989 15.8 9.5 8.8 6.8 9.2 12.5 9.0 11.8 15.7 1.0 0.0 DAltPct: 1999 14.9 8.9 9.1 7.8 11.3 14.1 9.6 10.2 13.2 1.0 0.0 DAltPct: 2009 13.7 8.0 9.1 8.2 12.3 13.9 9.6 11.1 13.1 1.1 0.0If your paper was built upon this level of detail I might begin to take it seriously…
If your paper addressed any of the Latitude issues identified in the Africa By Altitude posting I might begin to take it seriously.
When I look at your Figure 1 my BS detectors are activated big time.
Reading this paper also activates my BS detectors big time.
The annual juggling of Stations really stinks… and Altitude is part of that stinking mess… as is Latitude, Airports, Coasts, UHI etc etc etc.
Steven Mosher says:
August 19, 2010 at 12:42 pm
Latitude:
Provide a hypothesis to test WRT lower altitudes. You want a record that contains ONLY low altitude?
You got it. that record show that OVER TIME low altitude and high altitude warm at the same rate. Change over time=the same.
For grins I compared the lowest site (-224m) with the highest site (4000+meters)
Answer? same trend.”
Unless I’m missing some important context, this equates to only one station in the world needed to determine temperature trend over time. If single low alt locations trend the same as single high alt locations, they would all certainly trend the same, all low alts and all high alts. Just pick one for the “warm” at the same rate” data.
Your comparison with the lowest and highest site (no proximity or other variables apparently considered) do not lend me confidence in your methods or conclusions.
Finding a different trend between one low and high alt station would be disproof.
Interesting discussion. These 2 statements seem to be contradictory: “Given the GHCN data, the answer one gets about the pace of global warming is not in serious dispute.” And the paper’s conclusion: “The concern about altitude bias has been addressed. As we’ve argued before, the real issue with temperature series is the metadata, its related microsite and UHI issues and adjustments made prior to entry in the GHCN database.” The pace of global warming is in serious dispute if you haven’t resolved if the GHCN data is accurate. The adjustments issue and UHI issues have been debated here at WUWT many times. The microsite issue is, of course, something that Anthony has worked very hard on, with his Surface Stations project. His comment that you are premature to say that .15C adjustment is the most skeptics can hope for is probably accurate. You say previous studies support this. Please cite them. My guess is that at the end of the day the adjustment will be significantly higher, perhaps 3-5 times or more higher than you claim.
Why do I say this? I personally surveyed 3 sites in the USHCN in SW Colorado for the Surface Stations project-Telluride, Hermit and Del Norte. The Telluride and Hermit sites are both high altitude sites which had significant environmental structure issues which would bias the temperature record upwards. The Telluride site had a metal sided shed 5 feet to the west of the MMTS, in addition to other site structure issues. As soon as the personnel at the regional NWS office responsible for this site learned we had been out to survey the site, they ran down and pulled out the instruments at the site, including the MMTS. But, as far as I am aware, the data collected from this site was never pulled out of the data base. (There is quite a bit more to the back story here.) The Hermit site was on private property, and the MMTS was placed 11.5 feet south of a greenhouse directly attached to a large solar design house. I mention the direction, because the prevailing winds in the winter (and winter is a long season when you are at +9000 feet) tend to blow down the valley, from north to south. The MMTS was placed in the heat shadow of the solar house and greenhouse. This site also had a large sheet metal roofed dog house 4 feet away to the SE, and a 3 foot boulder at the base of the MMTS. At both these sites I could clearly feel warmer temperatures near the MMTS than in the surrounding environment.
Of course, this is anecdotal, several sites prove nothing. What of the other sites surveyed for the project? Less than 15% of the sites meet NOAA/NCDC’s own standards for placing these monitoring stations, based upon the surveys done by the Surface Station project volunteers. What does this mean? Stations with CRN1 or CRN2 ratings are considered acceptable, with errors of 1C. And more than 85% of the stations surveyed fall into the latter categories. Would the microsite biases tend to cancel each other out-some reading colder cancelling out the warmer biases? While there are certainly a few stations that had colder biases due to vegetation or water nearby, the vast majority appear to be biased in a warmer direction. Prior to the publication of Anthony’s paper on the project, you can review the site data collected by the project yourself by clicking on the link on the right sidebar to the Surface Stations project. You can even see the individual site surveys, including the three I surveyed in SW Colorado. It is my understanding that the USHCN is part of the GHCN. I’ve heard it claimed that while the US data is just a part of the data in the GHCN, it is the best, most accurate record. If that is the case, then I would respectfully submit that disputing the accuracy of the GHCN data is a quite reasonable position, especially when the alleged temperature increase over the last century is less than 1.5C.
Funny, I have come to believe that GHCN has always been biased towards CAGW since they all started getting a more correct monthly salary.
This sentence somehow dropped out of the 3rd paragraph when I posted it:
CNN3, CRN4 and CRN5 rated stations would have expected errors of greater than >1C.
Apologies!
TABLE 6 Station drop out by topography type
Table 6 states that the greatest cumulative percentage dropout was for stations in Flat areas.
However, from the information supplied by the author, the greatest percentage dropout was for stations in Mountain Valley areas (42%).
This is followed closely by stations in Hilly areas (42%).
Stations in Flat areas coming in a poor third (35%).
Stations in Mountain areas, of which there was only 61 to begin with, were culled by a massive 30%.
The problem with this table was first referred to by Randy says:
August 19, 2010 at 10:18 am
and I have pointed it out twice already.
The author states: “The table below hints at what was unique about the dropout.”
Does it require a qualified statistician to pronounce what’s unique about this table? or are we supposed to “move along”.
It’s GMT where I am, and time for bed.
If I am wrong I apologise in advance, but i would appreciate clarification.
BillD: ” In the arctic and the in the mountains, the green house effect seems a more likely explanation than UHI for stronger increases in the min compared to the max temperature.”
I’ll give you an example. The last time I looked the GHCN station list for Canada dropped from 500 stations in 1975 reporting to less than 40 in the 2000’s. And sometimes less than 20.
Do I think GHCN has a lot of mountain and arctic stations still active? No.
“Give us back the RAW DATA and openly show all analysis and then we can talk. Until then, Garbage In, Garbage Out. The current “climate” data has no scientific use since it has not been gathered scientifically. And no claims that using anomalies wipes out significant problems are valid. In my opinion, politeness is turning into cover for lying.”
Anomalies dont remove problems. They prevent problems. Lucia has a wonderful explanation of this over on her sight. In the past people have misunderstood the process of creating an anomaly and what it actually does. Once you understand it, you slap your forehead and say.. Doh! But still I run into people who say silly things about base periods ( not an issue) and anomalies ( not an issue) In my mind as a critic of climate sciene, I do well to keep my criticism and barbs focused on the REAL issue.
Now WRT raw data. First there is no such thing as raw data. All data is processed. Second, I too would like to get my hands on the records made of the actual instrument readings. Until such time, we have what we have. So every claim has to be conditioned by that understanding. On the best evidence we have it is warming. How much? wow. I only know one way to START to answer that question and that is with analysis tools. Start by building the tools and work your way through the whole problem. Of course you can choose to throw you hands up and say we will never know and you can say we will never know until we get the raw data. Was there an LIA?
“(By the way, when has there been a serious dispute that the climate has warmed since the Little Ice Age?)”
I would not that on many occassion I get people arguing 2 things:
1. We cant say that its warmer today.
and later they say things like..
2. “Well we are coming out of a LIA.” a period for which there is very little measurement, Yet, Yet, no skeptic questions it. I find that ironic.
“When I understand the issues on Climate Audit (only sometimes) and I read the comments by Stephen Mosher, I am impressed. Not so today by any of the current arguments. (Hope this posts properly; I hit a key and the formatting changed)”
That’s my evil twin. I just go where the argument takes me. People( me TOO) said that the methods of calculating an average were flawed. I looked at that. they (I) were wrong. people (ME TOO! see CA threads) said that losing high altitude stations was an issue. Err they (I) were wrong.
Do I think there are issues with adjustements? yup. UHI yup? lack of proper metadata? yup. lack of access to the “least processed” data? yup. can I CONCLUDE anything from this? nope. I cannot conclude that it is garbage or not garbage. That would REQUIRE access to the whole process. since I try not to claim without evidence I hesitate to call it pure or garbage or just kinda dirty. But if I HAD access to the “raw” data and I compared raw to processed, THEN I could say whether it was garbage. Until then, I have my doubts both ways.
In furtherance of that what KINDS of issues would you look for between “raw” and “processed”
Glenn:
“For grins I compared the lowest site (-224m) with the highest site (4000+meters)
Answer? same trend.”
Unless I’m missing some important context, this equates to only one station in the world needed to determine temperature trend over time.”
NOTICE.. I SAID for GRINS. In The final analysis we of course looked at all the data. did you not read the paper. For one cut at the data I looked at the highest 100 stations and the lowest 100. highest 200 and lowest 200. Highest 50, lowest 50. Highest 1 lowest 1. Highest 1000, lowest 1000. Those above the mean, below the mean. Above the median, below the median.
Simply. The average station has gone up about 1C in the past century. Whether it was HIGH or LOW. the RATE of increase across long spans of time is similar. physics should tell you that.
I applaud this work but have to agree with many of the less appreciative comments. Taken as a whole I am not surprised that the data set stands up to scruitiny of this kind. Looking at the length and breadth of the data, by station, by country, by region tells me that the homogenisation is such that very large changes at local level are averaged out at the global level, such is the influence of adjusting stations in certain places.
For example the change of station characterisation method by GISS in February made very little difference overall: http://diggingintheclay.wordpress.com/2010/07/10/gistemp-plus-ca-change-plus-cest-la-meme-chose/ but at a regional level there are some huge changes. Is it a coincidence or by design that these cancel each other out at the lattitude and global levels? As a scientist I have to say I think it is the former. However all my instincts tell me that there is something being overlooked when this is worked up to global level and that there could be unintentional but nonetheless inherent bias in the data. Commonly you don’t see such biases, when you keep looking at something from the same angle. It is great to be able to replicate processing like this, but we may never see the biases if we keep on treating the data in the same manner.