NIWA issued a response statement regarding the charges leveled by The NZ Climate Science Coalition here:
http://www.niwa.co.nz/our-science/climate/news/all/niwa-confirms-temperature-rise
They say:
Warming over New Zealand through the past century is unequivocal.
NIWA’s analysis of measured temperatures uses internationally accepted techniques, including making adjustments for changes such as movement of measurement sites. For example, in Wellington, early temperature measurements were made near sea level, but in 1928 the measurement site was moved from Thorndon (3 metres above sea level) to Kelburn (125 m above sea level). The Kelburn site is on average 0.8°C cooler than Thorndon, because of the extra height above sea level.
I’m not too impressed, especially when you see where the weather station for National Institute of Water and Atmosphere (NIWA) is, right on the rooftop next to the air conditioners:
Here is the station survey: NIWA_station_survey (PDF) and the Google Earth KML file
Thanks to: Dieuwe de Boer who did a good portion of station surveys in New Zealand last year.
The NZ Climate Science Coalition responds:
NIWA’s explanation raises major new questions
The NIWA climate controversy took a new twist tonight with the release of new data from the government run climate agency.
Reeling from claims that it has massaged data to show a 150 year warming trend where there isn’t one, NIWA’s chief climate scientist David Wratt, an IPCC vice-chair on the 2007 AR4 report, issued a news release stating adjustments had been made to compensate for changes in sensor locations over the years.
While such an adjustment is valid, it needs to be fully explained so other scientists can test the reasonableness of the adjustment.
Wratt is refusing to release data his organisation claims to have justifying adjustments on other weather stations, meaning the science cannot be reviewed. However, he has released information relating to Wellington temperature readings, and they make for interesting reading.
Here’s the rub. Up until 1927, temperatures for Wellington had been taken at Thorndon, only 3 m above sea level and an inner-city suburb. That station closed and, as I suspected in my earlier post, there is no overlap data allowing a comparison between Thorndon and Kelburn, where the gauge moved, at an altitude of 135 metres.
With no overlap of continuous temperature readings from both sites, there is no way to truly know how temperatures should be properly adjusted to compensate for the location shift.
Wratt told Investigate earlier there was international agreement on how to make temperature adjustments, and in the news release tonight he elaborates on that:
“Thus, if one measurement station is closed (or data missing for a period), it is acceptable to replace it with another nearby site provided an adjustment is made to the average temperature difference between the sites.”
Except, except, it all hinges on the quality of the reasoning that goes into making that adjustment. If it were me, I would have slung up a temperature station in the disused location again and worked out over a year the average offset between Thorndon and Kelburn. It’s not perfect, after all we are talking about a switch in 1928, but it would be something. But NIWA didn’t do that.
Instead, as their news release records, they simply guessed that the readings taken at Wellington Airport would be similar to Thorndon, simply because both sites are only a few metres above sea level.
Airport records temps about 0.79C above Kelburn on average, so NIWA simply said to themselves, “that’ll do” and made the Airport/Kelburn offset the official offset for Thorndon/Kelburn as well, even though no comparison study of the latter scenario has ever been done.
Here’s the raw data, from NIWA tonight, illustrating temp readings at their three Wellington locations since 1900:
What’s interesting is that if you leave Kelburn out of the equation, Thorndon in 1910 is not far below Airport 2010. Perhaps that gave NIWA some confidence that the two locations were equivalent, but I’m betting Thorndon a hundred years ago was very different from an international airport now.
Nonetheless, NIWA took its one-size-fits all “adjustment and altered Thordon and the Airport to match Kelburn for the sake of the data on their website and for official climate purposes.
In their own words, NIWA describe their logic thus.
- Where there is an overlap in time between two records (such as Wellington Airport and Kelburn), it is a simple matter to calculate the average offset and adjust one site relative to the other.
- Wellington Airport is +0.79°C warmer than Kelburn, which matches well with measurements in many parts of the world for how rapidly temperature decreases with altitude.
- Thorndon (closed 31 Dec 1927) has no overlap with Kelburn (opened 1 Jan 1928). For the purpose of illustration, we have applied the same offset to Thorndon as was calculated for the Airport.
- The final “adjusted” temperature curve is used to draw inferences about Wellington temperature change over the 20th century. The records must be adjusted for the change to a different Wellington location
Now, it may be that there was a good and obvious reason to adjust Wellington temps. My question remains, however: is applying a temperature example from 15km away in a different climate zone a valid way of rearranging historical data?
And my other question to David Wratt also remains: we’d all like to see the metholdology and reasoning behind adjustments on all the other sites as well.
But if you just measure the slopes, then it doesn’t matter if there is an overlap.
What’s the link for the raw data?
Nick
“Your plotted graph still shows a gap of a year between Throndon and Kelburn.
If there is no gap in data, there would be no data missing for a year.”
You are looking at it wrong.
My plotted graph shows a disconnect because I plotted two different series, without joining them into one. One ended in 1927, so there is an end of a line there. The next started in 1928, so there is the start of a line there. The “gap” of a year is not missing data, it is a visual illusion caused by my not joining the two series with a line. If you look at the data points (marked with an X, you will see that they are all separated by the same amount.
I’ll respond to your other posts after about 90 mins… gotta go do something.
The raw data is by (free) subscription from their data server. If I didn’t put in the link above (sorry, no time even to look right now), you can find it with google. They don’t allow me to post the raw data (as far as I can tell from a quick look at their user agreement) so you have to go get it. Otherwise, I’d upload it (in a form a little easier for excel than what they provide – had to write a script to put each month on a separate line).
John Moore (14:33:50) :
“Glen writes: “Another example of altitude adjustments not always working:
That’s a straw man argument. Anyone who claims altitude adjustments always work is as far out of touch as someone who denies the 1.2C/CO2-doubling value from the 1-D radiative balance model.”
It wasn’t an argument, John. It was another example in the area.
“Of course altitude adjustments don’t always work. We don’t even need to go looking for it – we know that UHI is but one of many factors that can cause errors.”
Take for instance a rural location early 1900s compared to an airport in the late 1900s. Airport could be influenced more by UHI than Kelburn was, or Kelburn influenced recently more than was Thorndon.
“The argument is that altitude adjustments are the most reasonable thing to do with two stations of different altitudes, IN THE ABSENCE OF OTHER INFORMATION. Our discussion on Thorndon/Kelburn fals into that category. That argument also means that the use of an altitude adjustment in that circumstance is not an argument for malfeasance or even sloppiness.”
That would be in the absence of ADEQUATE information. Sticking your hand into one of many boxs that may or may not contain a rattler is not a “reasonable” decision, if you have no other information which provides you with a reasonable assumption that there is no rattler in that particular box. I do not agree that this arbitrary use of lapse rate is not a good candidate for accusations of scientific misconduct, subject to specific methodologies and information that NIWA has decided for one reason or the other not to make public and available. In the absence of that, it is entirely reasonable to suspect that these adjustments were nothing more than ad hoc, and that other considerations that are reasonable could have been applied to the various stations which would have produced a different overall trend.
Adjusting by this arbitrary amount without explanation invites and legitimizes this scorn, especially in light of the fact that nearby stations that do not obey IS “other information”.
John: “I thiink we are still talking past each other somehow, but I don’t know what the missing link is.”
I think we can agree on that. Our main point of contention seems to be the validity of adjusting for altitude, and it perplexes me that you regard the data to be sloppy, but the decision to adjust for altitude based on no other information as reasonable.
I can’t set “sloppy” and “reasonable” together and make it look like a good meal.
But the only thing relevant to the gap issue is that the adjustment created it, not time or temperature, before and/or after. Were the adjusted graph just a record of one station, there would be no gap. The endpoint of Thorndon and the startpoint of Kelburn would overlap, right on top of one another.
They do not, not because of a real temperature difference, but because of the altitude adjustment itself.
This gap of a year on the NIWA graph is what interests me most about that portion of the graph. Why is it there? An artifact of a mapping program? What someone thought the uninitiated public would be able to see, to separate two station records artificially?
I don’t have reason to mistrust your graphs and figures. But I would like to see for myself the actual data coming out of the database, and how you succeed where I have failed to do so with regard to Thorndon and the first three years of Kelburn.
Perhaps we can share how we set the database up to search? What gets me is that I get Kelburn temp data from 1930 to present. Perhaps you are using a database type I have not tried? I pull maxmintemp and rainfall.
Oh, separating data in Excel is easy, just highlight a few rows from the first column of downloaded data, go to “Data” menu and select “Text to Columns” (next, next, finish). Then you can pick and choose data in their own columns, instead of pulling it out of merged data.
Glenn writes:
“The NIWA graph is good enough to see what the max adjusted annual difference is in the record between Kelburn and Airport, as I already mentioned, about .4C around 1970, one time in 45 years. That is what I meant by “virtually” no two averaged years of the magnitude of .6C difference. I was aware that the graph was plotted with yearly averages, and apologize for trying to hammer my point down by stressing how many months are included in over 40 years. But my point still stands.”
Hmmm… okay, we were talking about different things. I thought you were talking about one year differences between data in the same series. Now I see you are comparing two series – Kelburn and Airport.. But I apologize for my denseness, but I still don’t know what you mean. I look at Well Aero vs Kelburn (I don’t have the other aero stations) and see, for example, a .7C delta for 1995 (13.5 vs 12.8), and it looks like there are others. I would have to do some more excel fiddling (or better, change my script) to make it easy to compare stations on a month by month or year by year basis.
“I’m surprised to find you managed to find the raw temp data from a source on the Internet of Thorndon and 1928, 1929 and 1930 Kelburn stations. Perhaps I wasn’t registered with the adequate “subscription level” (argent) for the NIWA database, although I can access temp data for many stations, including Kelburn after 1930.”
“How were you able to actually download this data?”
I don’t have the raw data. I think this is “corrected” data (i.e. for a single station, changes at that station). They also have what appears to be raw daily and hourly data, but without knowing the station history, I can’t correct it myself, so I didn’t use it.
In other words, the data I am using, which is the same data that resulted in contention about the graph (the .6C correction), is already fiddled by NIWA, and I have to simply trust those adjustments (but the scope of the discussion was the height adjustment, not the station history corrections).
I simply asked for data for Thorndon and it gave me a series ending in 1927, and for Kelburn, a single series from 1928 to 2004. Below is what it the metadata at the top of the data (csv form). I assume Kelburn AWS is Kelburn with an automated station, whereas Kelburn (no suffix) is the manual station.
“,Station information:,,,,,,,,
,Name,Agent Number,Network Number,Latitude (dec.deg),Longitude (dec.deg),Height
(m),Posn_Precision,Observing Authority,
,Wellington Aero,3445,E14387,-41.322,174.804,4,G,N/A,
,Wellington, Kelburn Aws,25354,E1427P,-41.285,174.768,125,G,Metservice
,Wellington,Kelburn,3385,E14272,-41.286,174.767,125,G,Metservice
,Wellington,Thorndon,3391,E14278,-41.283,174.783,3,W,N/A
,”Note: Position precision types are: “”W”” = based on whole minutes”,” “”T”” =
estimated to tenth minute”,,,,,,,
,G = derived from gridref ,” “”E”” = error cases derived from gridref”,,,,,,,
,H = based on GPS readings (NZGD49),” “”D”” = by definition i.e. grid points.”,,
,,,,,
,Statistics codes in this query are:,,,,,,,,
,Code,Description,Units,,,,,,
,2,Mean Air Temperature,Celsius,,,,,,
,3,Mean Daily Maximum Air Temperature,Celsius,,,,,,
,4,Mean Daily Minimum Air Temperature,Celsius,,,,,,
,65,Mean Of 9am Temperature,Celsius,,,,,,,,,,,,,,,
,Note: Statistics calculations are based on Local Time.,,,,,,,,,,,,,,,,,
,Monthly extremes are recorded on the Local-Time day of the month.,,,,,,,,,,,,,,
,,,
,Annual extremes are recorded in the Local-Time month of the year.,,,,,,,,,,,,,,
,,,”
” Our main point of contention seems to be the validity of adjusting for altitude, and it perplexes me that you regard the data to be sloppy, but the decision to adjust for altitude based on no other information as reasonable.
I can’t set “sloppy” and “reasonable” together and make it look like a good meal.”
Okay, let’s use the word “noisy” rather than sloppy, because I think that’s more accurate.
If all you have is noisy data, you don’t have any choice but to use it if you want to do science about it. That’s a normal problem faced by scientists in many, many disciplines. They have noisy data, but they are still able to draw valuable insight from it. In fact, I’d say that noisy data is the norm, not the exception.
Now to using an altitude offset… as I have said before, if that’s all you’ve got, it may be better than nothing. But there are caveats associated with that statement. If you only have one case, it might be just plain wrong in that case. So while it may in general be correct, in specific it may be wrong. That means a couple of things:
1) you want to use averages – use a bunch of stations, and those errors will (hopefully) average out
2) you need to set your level of uncertainty appropriately. This is where I think the AGW alarmist paleoclimatologists are out to lunch. I’m sure they believe their conclusions, but it appears that they are way, way too assured of their results. That’s a pretty normal thing also – see the following aside.
——-
As an aside, if you aren’t familiar with Feynman’s exposition on the errors in the Milliken oil drop experiment, I strongly recommend it (of course, I recommend almost everything Feynman wrote, having been fortunate enough to attend some of his lectures at Hughes Malibu Research Center).:See http://www.lhup.edu/~DSIMANEK/cargocul.htm and search into it for “Millikan” if you don’t want to read the rest of this wonderful exposition.
John: “I simply asked for data for Thorndon and it gave me a series ending in 1927, and for Kelburn, a single series from 1928 to 2004. Below is what it the metadata at the top of the data (csv form).”
I don’t understand how you are getting temp data. I assume you are using the database query form http://cliflo.niwa.co.nz/pls/niwp/wgenf.genform1
What format are you using for download? I don’t recognize the format you posted.
Are you sure you’re getting temp data from “Wellington,Thorndon 3391”?
John: “If you only have one case, it might be just plain wrong in that case. So while it may in general be correct, in specific it may be wrong.”
I do not disagree, of course the reasonableness of averaging depends on the purpose of the application.
But doesn’t this contradict your claim in this Wellington case as being a reasonable technique? How is a level of uncertainty set for a single station with data that may be way off to begin wit, and can’t be compared with data from any other station?
You gotta have more than a simple claim that it is reasonable to adjust Thorndon by .8C. A 1.2C or .4C may be more accurate, but how in the dickens do we have any confidence in any??
“I don’t understand how you are getting temp data. I assume you are using the database query form http://cliflo.niwa.co.nz/pls/niwp/wgenf.genform1
What format are you using for download? I don’t recognize the format you posted.”
Yes to the site. I believe I asked for comma separated data (csv) for Excel. Then I ran it through a script that removed the extraneous comma in three of the station names (so the columns would line up). The download got data with one year per line, with the months spread across the line and the annual average at the end. My script produced two fies:
1) only annual average
2) monthly data, one month per line (this is why, on one of my graphs, the X axis looks weird – I used year*12+month-index as the value – so Excel could grok it easily.
“Are you sure you’re getting temp data from “Wellington,Thorndon 3391″?”
That’s what the metadata says – see my post above. Furthermore, the data matches the graph they posted in their explanation.
“But doesn’t this contradict your claim in this Wellington case as being a reasonable technique? How is a level of uncertainty set for a single station with data that may be way off to begin wit, and can’t be compared with data from any other station?
You gotta have more than a simple claim that it is reasonable to adjust Thorndon by .8C. A 1.2C or .4C may be more accurate, but how in the dickens do we have any confidence in any??”
If your purpose is simply to construct a Wellington time series, you can’t have much confidence in the adjustment.
If your purpose is to construct a time series for Wellington, that will then be used along with a whole bunch of other stations to get an average temperature time series of an area (say, all of New Zealand), your confidence would be higher. How high depends on all sorts of stuff, and I don’t know the answer to that for New Zealand, as an example, or some averaging grid that Wellington happens to fall into.
One question that has to come up is: why try to construct individual station time series at all, rather than using the areal averages to start with. My guess is that by constructing time series they can at least visually look for outliers or bugs in the method. It also gives them something to show the public (I’ll be they regret that now). Finally, it is how everyone tends to think about things, so they probably did it out of habit. And, perhaps they did it to alarm the public, in which case shame on them, but we don’t have evidence or even reasons for strong suspicion of that in this particular case.
One of the criticisms of the whole approach of reconstructing global temperature from surface temperature stations is the geographic sparsity of stations used in computing area averages – especially in the southern hemisphere. Others have noted that a single station in some areas can have an extraordinarily high impact on the overall global series, using the methods of CRU, and that’s a real big problem.
OK, now I’m getting something, changed the datatype to “Monthly/Annual Statistics”.
This what I get for Thorndon 1927 with csv format:
Station information:
Name,Agent Number,Network Number,Latitude (dec.deg),Longitude (dec.deg),Height (m),Posn_Precision,Observing Authority
Wellington,Thorndon,3391,E14278,-41.283,174.783,3,W,N/A
Note: Position precision types are: “W” = based on whole minutes, “T” = estimated to tenth minute,
“G” = derived from gridref , “E” = error cases derived from gridref,
“H” = based on GPS readings (NZGD49), “D” = by definition i.e. grid points.
Statistics codes in this query are:
Code,Description,Units
02,Mean Air Temperature,Celsius
03,Mean Daily Maximum Air Temperature,Celsius
06,Extreme Maximum Air Temperature,Celsius
43,Lowest Maximum Air Temperature,Celsius
61,Standard Deviation Of Daily Mean Temperature.,Celsius
62,Lowest Daily Mean Temperature,Celsius
63,Highest Daily Mean Temperature,Celsius
65,Mean Of 9am Temperature,Celsius
76,Days With Maximum Air Temperature > 25c,Day
Note: Statistics calculations are based on Local Time.
Monthly extremes are recorded on the Local-Time day of the month.
Annual extremes are recorded in the Local-Time month of the year.
Stats: Combined
Station,Year ,Stats_Code,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Annual
3391,1927,02,17.5,18.5,16.8,13.5,11.1,8.4,9.1,9.1,11.1,13.3,12.7,14.7,13.0
3391,1927,03,21.4,23.0,20.0,17.1,14.4,11.6,12.2,12.2,14.7,16.8,16.2,18.3,16.5
UserName is = glenn********
Total number of rows output = 24
Number of rows remaining in subscription = 1980150
Copyright NIWA 2009 Subject to NIWA’s Terms and Conditions
See: http://cliflo.niwa.co.nz/pls/niwp/doc/terms.html
Comments to: cliflo@niwa.co.nz
Yep, that’s the stuff. Notice how they put all of one year on a single line, making it a pain to do monthly plots in Excel. Fortunately, the annual is near the end, so it is easy to plot.
John
[REPLY – Just go to the Data tab and hit Text to Columns. ~ Evan]
John Moore (18:33:44) :
“That’s what the metadata says – see my post above. Furthermore, the data matches the graph they posted in their explanation.”
Thanks for the help. I finally got it, before I only searched for daily temps, and when I realized changing the datatype for monthly’s it worked.
I had no intention of questioning the data on the graph, although it may be worthwhile to try to determine what data may be pre-adjusted. But that’s another matter, my interest was to verify that data was actually available, and if there was a gap. No gap.
“If your purpose is to construct a time series for Wellington, that will then be used along with a whole bunch of other stations to get an average temperature time series of an area (say, all of New Zealand), your confidence would be higher.”
Not mine, and I don’t think that’s good science. Were a value known for the average altitude lapse rate within the coverage area of the associated station locations, I would. Then again, as I have said before, if that were known, only one station would be required for a large area, and a whole lot of data on specific lapse rates at different locations in the area. I just don’t believe in the blind application of an “average” lapse rate, without any further information specific to each location to be adjusted. Now it may be that the person responsible did compare variables such as rainfall, sunshine, humidity from the early 1900s to the late 1900s. That I could understand and have some confidence in.
Glad you found it. Now the fun begins 🙂
Well, we will have to disagree on whether the adjustment is or is not good science. I contend that it is not necessarily bad science, and that even without additional information, on average, such adjustments improve the information.
“[REPLY – Just go to the Data tab and hit Text to Columns. ~ Evan]”
Tried it, no go.
There are a whole bunch of lines, each of which has 12 months in it. I want to create a single series of monthly data from that.
Any tricks?
John Moore (19:35:55) :
“Well, we will have to disagree on whether the adjustment is or is not good science. I contend that it is not necessarily bad science, and that even without additional information, on average, such adjustments improve the information.”
I think you are right that we’ll have to let that “general rule” go. But then you seem to be less confident in applying such an adjustment to just one record, as is the case we are discussing here. So we may still have fuel for further discussion on such things as UHI effects on the complete Kelburn record, as well as at the airport station(s), and the confidence level of the altitude adjustment for the airport and Thorndon stations. (don’t know why NIWA wanted to bring the record up to the higher elevation, but I suppose that was to bring all the other stations in sync – another matter).
John Moore (19:41:11) :
“[REPLY – Just go to the Data tab and hit Text to Columns. ~ Evan]”
Tried it, no go.
There are a whole bunch of lines, each of which has 12 months in it. I want to create a single series of monthly data from that.
Any tricks?”
Format the download with HTML. Then copy and paste. Works great for the monthly data, no need to adjust columns.
I don’t see any way in excel to get what I want – tried open office also
Given data of the form
1962,5,6,7,8,9,8,7,6,5,4,3,4
1963,6,7,8,9,9,8,7,6,5,4,3,4
1964,4,5,6,7,8,9,8,7,6,5,4,3
Those represent 36 months, from 1962-1964 (bogus data). How would I plot that as a single 36 point long series?
John, perhaps you could put a confidence level on the Thorndon adjustment? 😎
Karori is 27 meters higher and 1.3 km from Kelburn. The two stations overlap in coverage from 1974 thru 1979, and is averages 0.98C lower than Kelburn for that period (12.58 – 11.6).
Karori is 8 km from the Airport, Kelburn is 6.3 km from the Airport.
Standard lapse rate for 27 meters is 0.18C. Karori is FAR cooler than a standard lapse rate from Kelburn to Karori, by 0.71C!
Would it be reasonable to adjust Kelburn as well as the Airport in that time period to compensate for a non-standard lapse rate of something more than .79C, and then to sort out the differences in absolute temps?
Or does Airport just HAVE to be dropped by a certain amount so that the absolute temps match as well as possible?
Or do any effects on temp such as UHI, local conditions, etc need to be considered before altitude adjustments are made? (Karori would)
Too bad there are no overlapping stations in the nearby hills to compare with Thorndon in the early 1900s.
It is one thing to prove that the earth is warming and another to prove that CO2 is causing run away green house effect.
Did anyone care to discount the effect (apart from geological influence already discussed by other readers above) of increase in the number of cars on the road, in the number of airconditioners and in population since the 1920s? While the scientists adjusted upwards shouldn’t they also adjust downwards to compensate for the said increase that has nothing to do with green house effect? This power consumption would inevitably produce heat as waste and release CO2 into the environment but the warmists are clouding the issue here and equate this waste heat as green house warming due to CO2.
Most of the historical stations has been more and more affected by heat island as a result of urbanisation. Until full detail on the stations and the pruning method can be made public and reviewed and discussed and debated, there is very little in way of credibility to the processed data used by the so call main stream climate scientists.
“Would it be reasonable to adjust Kelburn as well as the Airport in that time period to compensate for a non-standard lapse rate of something more than .79C, and then to sort out the differences in absolute temps?”
See below
“Or does Airport just HAVE to be dropped by a certain amount so that the absolute temps match as well as possible?”
No way. The drop should be based on best evidence.
“Or do any effects on temp such as UHI, local conditions, etc need to be considered before altitude adjustments are made? (Karori would)”
They should be part of the adjustment – see below.
“Too bad there are no overlapping stations in the nearby hills to compare with Thorndon in the early 1900s.”
Yes, it is.
…..as to how to do it better……
One thing to do would be to put stations in historical locations (like Thorndon) no longer present and record a few years of calibration data. That would help anywhere you didn’t have enough overlap.
Another would be to try to create a meteorological model of each station, and use it to “forecast” the past in some sense. Then one could use it to forecast an overlap period, and use that to help calibrate the differences. By “model” I mean something that accounts for things like elevation, exposure to various common weather conditions (katabatic, atabatic, trade winds, estimated cloud cover, etc). For stations near the ocean, in mountainous terrain, this might help a lot.
Another would be to try to measure average lapse rate for similar station differences in the same general area, and crank that in.
One could go on…
NIWA obviously didn’t try very many of these. If I were them, I’d use the adverse publicity to get funding to do more of this sort of thing, while at the same time making everything transparent.
If we are talking about lots of stations, then I agree that using deltas is fine (and probably more reasonable), for aggregates, than trying to construct continuous station time series – ASSUMING that nothing changes in any series over which you take the aggregate – such as time of observation, etc.
Exactly. That’s why I mentioned the TOB.
What it means is that if something changes, you have to treat it as a new series.
Now you need to have a strategy that copes with
1. 1 or more series
2. What to do if there is an overlap
3. What do do if there is no overlap.
Nick
Nick – Agreed, although again you can tie the series together what an adjustment – if you can do a good job of that. That’s what they are attempting on all these station series – like Darwin which has been much discussed.
I’ll come back on one thing. You can stitch them together with assumptions.
However, you only need to stitch them together if you need to know what the absolute temperature record has been in the past.
If you ask a different question, what’s been the trend in temperatures, I don’t think you need to stitch them together.
Since AGW is the claim, the world has warmed, knowing the trend us sufficient.
Nick