While BEST is making a public relations train wreck for themselves by touting preliminary conclusions with no data analysis paper in place yet to support their announcements, there have been other things going on in the world of surface data integrity and uncertainty. This particular paper is something I have been assisting with (by providing metadata and images, plus being part of the review team) since May of last year. Regular WUWT readers may recognize the photo on the cover as being the USHCN station sited over a tombstone in Hanksville, UT photographed by volunteer Juan Slayton.
What is interesting about it is that the author is the president of a private company, WeatherSource who describes themselves as:
Weather Source is the premier provider of high quality historical and real-time digital weather information for 10,000’s of locations across the US and around the world.
What is unique about this company is that they provide weather data to high profile, high risk clients that use weather data in determining risk, processes, and likely outcomes. These clients turn to this company because they provide a superior product; surface data cleaned and deburred of many of the problems that exist in the NCDC data set.
I saw a presentation from them last year, and it was the first time we had ever met. It was quite some meeting, because for the first time, after seeing our station photographs, they had visual confirmation of the problems they surmised from examining data. It was a meeting of the minds, data and metadata.
Here’s what they say about their cleaned weather data:
Weather Source maintains a one-of-a-kind database of weather information built from a combination of multiple datasets merged into one comprehensive “super” database of weather information unlike anything else on the market today. By merging together numerous different datasets, voids in one dataset are often filled by one of the other datasets, resulting in database that has higher data densities and is more complete than any of the original datasets. In addition all candidate data are quality controlled and corrected where possible before being accepted into the Weather Source database. The end result is a “super” database that is more complete and more accurate than any other weather database in existence – period!
It is this “super” database that is at the core of all Weather Service products and services. You can rest assured knowing that the products and services obtained through Weather Source are the best available.
They say this about historical data:
Comprehensive and robust are the two words which best describe the Weather Source Historical Observations Database.
Comprehensive because our database is composed of base data from numerous sources. From these we create merged datasets that are more complete than any single source. This allows us to provide you with virtually any weather information you may need.
Robust because our system of data quality control and cleaning methods ensure that the data you need is accurate and of the highest quality.
As an example, here is a difference comparison of their cleaned data set with a GHCN station:
The processes they use are not unlike that of BEST; the emphasis is on solving problems with the historical data gathered by the NOAA volunteer operated Cooperative Observer Network (COOP). The COOP network also has a smaller subset, the U.S. Historical Climatology Network (USHCN) which is a hand picked set of stations from researchers at the National Climatic Data Center (NCDC) based on station record length and moves:
USHCN stations were chosen using a number of criteria including length of record, percent of missing data, number of station moves and other station changes that may affect data homogeneity, and resulting network spatial coverage.
The problem is that until the surfacestations.org project came along, they never actually looked at the measurement environment of the stations, nor did they even bother to tell the volunteer operators of those stations that they were special, so that they would perform an extra measure of due diligence in data gathering and ensuring that the stations met the most basic of siting rules, such as the NOAA 100 foot rule:
Temperature sensor siting: The sensor should be mounted 5 feet +/- 1 foot above the ground. The ground over which the shelter [radiation] is located should be typical of the surrounding area. A level, open clearing is desirable so the thermometers are freely ventilated by air flow. Do not install the sensor on a steep slope or in a sheltered hollow unless it is typical of the area or unless data from that type of site are desired. When possible, the shelter should be no closer than four times the height of any obstruction (tree, fence, building, etc.). The sensor should be at least 100 feet from any paved or concrete surface.
As many know and NOAA concedes in their own publications on the issue, about 1 in 10 weather stations in the USHCN meet the 100 foot rule. That is the basis of the criticisms I have been making for sometime – that siting has introduced unaccounted for biases in the data.
WeatherSource is a private company catering to business using a special data cleaning process that removes many, but not all of the data integrity issues and biases that have been noted. I’ve seen a detailed flowchart and input/output comparisons on their processes and have confidence that they are addressing issues in a way that provides a superior data set. However, being a business, they have to keep their advantage close, and I have agreed not to disclose their processes which are their legal intellectual property.
The data cleaning techniques of the BEST team made known to me when I met with them in February 2011 were similar, but they promised to use publicly available data and to make the process fully transparent and replicable, as shown on their web page:
So, knowing that BEST had independently identified surface data issues that needed attention, and because there was some overlap in the ideas pioneered by the privately held intellectual properties of WeatherSource with the open source promise of BEST, you can imagine my excitement at the prospect.
Unfortunately, on March 31st in full public view before Congress BEST fumbled the ball, and in football parlance there was a turnover to the opposing “team”. BEST released some potentially buggy and admittedly incomplete preliminary results (which I’ve seen the basis for) with no transparency or replicability whatsoever. BEST doesn’t even have a paper submitted yet.
However, the WeatherSource team has produced a paper, and they have it online today. While it has not peer reviewed by a journal, it has been reviewed by a number of professional people, and of course with this introduction on WUWT, is about to be reviewed worldwide in the largest review venue it could possibly get.
It should be noted, that the data analysis was done entirely by Mr. Gibbas of WeatherSource. My role was limited to providing metadata, photographs from our surfacestation project, and editing support.
Here’s the full paper:
An Investigation of Temperature Trends from weather station observations representing various locations across Utah (21.5mb PDF) by Mark Gibbas
Bear in mind, because this is a private company, and because small companies must hold their IP rights close to maintain competitive advantages, the data cleaning method can be discussed in general terms, but they cannot provide the code for review without compromising their IP rights. However, the source data used in this paper can be made available on request, as WeatherSource has tentatively offered access to it provided that the end user is doing research, does not use it for commercial gain, and does not republish it in its original form. Use the WUWT contact page if you would like to request a copy of the WeatherSource cleaned data after first agreeing to the caveats listed. Anonymous requests will be ignored. The metadata list of stations is available here: Utah-stations-metadata (PDF) Utah-stations-metadata (Excel .xls)
Here are some excerpts:
BACKGROUND
However, in this study done on a much smaller scale, our goal was to examine the long-term observational temperature records for weather stations scattered across the state of Utah, and attempt to identify local influences acting upon them. For instance, it is well known that urban areas can exhibit a sharp warming trend due to the influence of expanding urban infrastructure. Less well known are other influences upon temperature such as those associated with modern agriculture.
It is not until these local-scale influences are properly quantified that one can begin to infer fluctuations or trends that may be occurring on larger-scales, such as state, region, country, or global. This investigation is an important step towards that ultimate goal—but more importantly, shows that this goal is still difficult to divine given the uncertainty and methodology currently employed to extract a climate change signal from the surface temperature record.
…
APPROACH
To better understand the various influences on the observed temperature record, this study identified and examined numerous weather stations with long observational histories from urban, agricultural and natural wilderness or low impact locations (a low impact location is one that has experienced minimal alterations to its natural state) from around the state of Utah.
Initially, weather data from the National Climatic Data Center (NCDC) U.S. Historical Climatology Network version 2 (USHCNv2 2009)6 were to be used for this study. However, numerous data issues were discovered within the USHCN and as a result this data set was abandoned. Instead, the study used data from the original NCDC U.S. Cooperative Summary of the Day Data (referred to as the DSI-3200 dataset), that was cleaned via a comprehensive regression based data cleaning methodology. A brief explanation of the issues found within the USHCN will follow.
Within the cleaned DSI-3200 dataset, the study identified weather stations within Utah that had at least 50 years of data. Using tools such as Google Earth and demographic information based on the US census, these weather station locations were
ranked in terms of how urban and how agricultural they were. The ranking was done on a scale of 1 to 5 with 5 being the highest. Low impact stations were identified as locations that had low urban and low agricultural rankings.
Based on the urban and agricultural indices, the weather stations were divided into three groups representing urban, agricultural and low impact locations.
…
CAUSES OF TEMPERATURE OBSERVATION VARIANCE
One would assume that if you had two weather stations that were about 30 miles apart in nearly identical elevation and terrain and both recorded temperature consistently for fifty years, the time-series of these stations should correlate nearly perfectly and that both stations would show the same general trend. However, in practice we find that such stations often have significant differences in their observations even though they were both subject to the same large-scale weather patterns. But how could this be? The answer most often is related to changes that occur at the weather stations sites that influence the temperatures that are recorded.
…
Having established that station changes can artificially compromise the capture of real
weather trends, an obvious question has to be: So what good are all the station data if each station is subject to false trends due to station changes? When considering very small numbers of stations this is indeed a problem, but when more stations are averaged as a group, the effects of random station changes largely cancel out. The reason for this is related to the statistical principal that random actions (station changes) average out to zero whereas non-random actions (urban growth) do not.
Thus, to more accurately determine the trend of the urban, agricultural, and low impact
groups, we must average the members into a composite trend for each group. Chart 5 provides a view of the average trends from each group. JJA refers to the summer season months of June, July and August. DJF refers to the winter season months of December, January, and February. TMax refers to the trend of the daily maximum temperature and TMin refers to the same for the daily minimum temperature. The vertical axis is the trend in degrees per year.
In studying chart 5, we note the following:
- The trend in the minimum temperature is more significant than that of the maximum temperature. A thorough discussion as to why this is the case would be quite involved, so for now we’ll simply state that the dominance of the minimum temperature trend is as expected.
- The urban group of stations exhibits the strongest trend, followed by the agricultural group, then the low impact group.
- The difference between the agricultural group to that of the low impact group is more distinct in the summer than in the winter.
- The trend of the low impact minimum temperature is nearly the same in both the summer and winter seasons, whereas the trend of minimum temperature for the urban and agricultural groups is stronger in the summer.
Chart 6 provides a view of the aggregated summer and winter trend for the three groups in degrees Fahrenheit per decade. The urban value of 0.42F is on par with the published estimates of 0.36F to 0.44F per decade for current global warming.
The net trend for the agricultural group is 0.27F per decade which is 37% less than the urban group. The low impact group has trend of 0.20F per decade which is 52% lower than the urban group.
CONCLUSIONS
This study has established that there are significant differences in temperature trends
between weather stations sited at urban, agricultural, and low impact locations. In addition, it has been shown that urban stations exhibit significantly higher trends, which are created by numerous urban factors that are not related to global CO2 levels. Evidence has also been provided that agricultural locations experience a warming trend related to increases in humidity. Given that both urban and agricultural environments likely have warming trend factors that are unrelated to CO2 based warming, it makes sense that to get a more accurate determination of CO2 based global warming, station observations from low impact locations need to be given much greater weight. Currently that is not the case since the vast majority of weather observations originate at airports, which by design are near urban areas.
…
DAILY TEMPERATURE CLEAN METHOD
The method to clean the DSI-3200 data used in this study utilizes regression estimates from neighboring stations to validate, correct and fill missing values as needed to produce a high quality continuous time-series of daily temperature data. The cleaning method does not attempt to correct for urban warming, discontinuities or any other external influences upon the recorded temperature. Key features of the cleaning method include:
- Adjustment of morning, afternoon and midnight reporting times to produce working time-series arrays that are consistent across all stations used in the cleaning process.
- All regressions are based on seasonal time windows.
- Two types of regressions are done for each neighbor to target pair; a seasonal regression based on three years of data centered on a date of interest, and a seasonal regression based on all data. If the three year regression is stable, this value is preferred; otherwise the long term seasonal regression value is used.
- Regression estimates from all neighbors are collect into a distribution, which is analyzed and compared to the original value from the target station. If the target station original value is confirmed based on statistical tests, it is retained. Otherwise if it is found to be suspect it is replaced with the regression estimate from the station with the greatest correlation to the target station. In all cases, missing values within the target time-series are filled using the estimates from the highest correlating neighboring station.
The method outlined above has been found to produce values that are most consistent with the original time-series. This has been determined by qualifying accurate values then artificially removing these values and running the degraded time-series through the clean method. The resulting filled values are then compared to the original qualified values that were removed. These tests have routinely produced replacement values that when compared to the original values, have an R2 or 0.985 or greater.
– end of excerpts
One issue that was noted was how a formerly remote and rural USHCN station, the Snake Creek Powerhouse, had become surrounded by an Olympic sports center and later an irrigated golf course. It was made a special case study #2.
From case#2
The next plot provides the addition of the NCDC USHCN adjusted data. Unfortunately, the adjustment does not correct the issues with the Snake Creek site and in fact degrades the quality of the data further. While the trend for the Snake Creek station is suspect to begin with, the Clean DSI-3200 data set shows an upward trend of 0.246 or roughly a quarter degree Fahrenheit per decade. In contrast the USHCN adjusted data show a cooling trend of -0.05 or roughly -0.5 degrees Fahrenheit per decade.
Clearly this adjustment is wrong; otherwise this location in UT would be experiencing some of the most extreme cooling on the planet.
The adjustment applied to Snake Creek can only be regarded as alarming, and results in the following conclusions:
1. It is suspected that the adjustments are the result of a simple automated method
that applies a linear adjustment to the data such that the end portions of the linear
adjustment can have results that are unrealistic.
2. It is apparent that there is a lack of review and quality checking of the results.
…
end excerpt
While Utah is most certainly not the world, this paper does demonstrate what can be learned by doing a detailed look at station metadata in concert with temperature data cleaning and analysis. As noted in the paper, like BEST’s early presentation, it has not tried to correct for UHI or for humidity (moist enthalpy) which tends to effect night time temperatures the most, particularly in irrigated agricultural areas. Human experience knows this effect in southwest USA dry deserts, which can be very cold at night due to low humidity versus the Southeast USA, where humidity makes for warm summer nights. The question is how much are night-time temperatures being elevated by the addition of humidity from irrigation?
A study of California’s central valley in 2006 by Dr. John Christy of UAH did in fact do a small scale study in the California Central Valley where he took extreme care to hand code in reams of metadata in order to discover this effect. See:
Christy on irrigation and regional temperature effects
Until we get a method in place to look at issues like this and UHI, the surface record is still filled with uncertainties.
However, I find it encouraging that a private company that provides weather data where risk management is involved, where being right provides economic reward and being wrong means economic failure, has seen and dealt with the issues of NOAA COOP station biases, at least partially. Much more is needed.
The mere fact that company exists for the express purpose to create and sell corrected and cleaned data from our government surface temperature record, and they they have clients that need it, speaks volumes.
Details on this paper from Mark Gibbas is here at SPPI with a full download available
Looking at Fig. 5, the smallest change in the three areas looked at (urban, agricultural and low impact) is in Dif-Tmax. This ,too me, is an indication that Tmax is the best signal for warming/cooling. If we attempting to measure changes to the incoming signal (solar heat gain) then Tmax seems to be the best metric. Late afternoon signals and Tmin are a measure of heat retention with or without the benefit of moisture and are just an after affect of heat gain.
Tmax only is a much clearer signal.
Reply to
JAE said:
April 4, 2011 at 5:07 pm
Great article. But the “cold desert night” myth always bugs me:
I have lived in North Carolina, and now live in Idaho. I haven’t been here long, but I am convinced that desert nights are in fact much colder. To check, I found two stations at about the same elevation, both rural, in NC and ID. I averaged the day high and night lows and found the difference. For Meridian, Idaho, the average delta for August 2010 was 38.35 Deg. F. For the same month, in Little Switzerland, NC, it was 21.1 Deg. F. Quite a difference. Elevations were 2572 (ID) and 2560 (NC).
If you were comparing day and night temps in city environments, like Atlanta and Phoenix, you were probably only observing the importance of UHI.
Remember that, as a rule, night lows cannot be lower than the dew point. If the humidity is high, the temp cannot drop as much as it would in a dry area. The enthalpy of the moisture in the air going from gas to liquid is too much to easily overcome.
So the cold desert night is a physical fact, not a myth.
Agree w/others, Tmax is the best measurement for trends. Inversions (or lack of) make nighttime lows problematic & very dependent on local winds, elevation of the sensor, surrounding impediments, etc. Pielke Sr has documented this. Sunlight during the day causes thermals & winds that disrupt the inversions & reliably mix the air near the ground (except in the polar regions where sunlight is absent).
Whatever analysis Anthony eventually comes up with should separate out the daytime highs/nighttime lows & compare the resulting trends. I realize this could be very difficult from a data viewpoint, but otherwise, the combined trends are not really useful.
The suggestions to cut NOAA’s budgets for the next decade are very appropriate. Reorganization and downsizing (RIF – Reduction In Force) should also procede without regard to seniority. Every federal department should also go through the same process. We’re NOT broke, we’ve just got too many boot-licking in-laws on the payroll.
Re: Cold desert nights
Depends on the desert. The low deserts of California, Arizona & Sonora, for example, stay pretty damn hot late into the summer nights, urban OR rural. By low, I mean elevations of 1,000 ft (300 m.) or less.
But hotter longer in bigger cities, most notably Phoenix, with midnight temps of 100F pretty common in the hotter months. IMS this is also true in the Persian Gulf, and also Bagdad & vicinity.
RE: UHI stuff
An interesting research project for amateur (or professional) climatologists would be to study urban microclimates using private weather stations (PWS). Here, for exmple, is <a href="“>WUG’s map of Phoenix, showing (by eyeball) 35 or more weather stations in the metro area, including some out in (maybe) open desert, for comparison. Maybe Anthony can speak to the accuracy and reliability of these little stations, but I’d guess they’re no worse than many official stations. WUG keeps archives, indefinitely?
Anyway, interesting project for someone. I’m sure most PWS owners would be happy to have their instruments calibration checked.
Thanks to all for the supportive comments. Also a BIG THANKS to SPPI (http://scienceandpublicpolicy.org/) for providing funding for much of this work.
A little bit about me…
I am neither a global warming advocate or global warming denier. My only bias (so-to-speak) is I have an unquenchable passion for truth. Also I’m an avid outdoorsman, I enjoy skiing, hiking, kayaking and everything else where I can be in the wilderness. Consistent with this lifestyle, I am a fierce advocate for protecting our environment. And I am a meteorologist/mathematician/programmer. I guess this grouping of characteristics is a bit odd, but I believe they serve me well, at least I hope.
A few short years ago, after having reviewed the many studies on global warming from NASA, IPCC, and others, I did indeed lean towards the idea that carbon based global warming (CBGW) theory was valid. From my perspective back then, the evidence was compelling. There was indeed a documented rise in temperatures coinciding with a documented rise in CO2. This together with the fact that CO2 is a ‘greenhouse’ gas seemed to sway the evidence in favor of CBGW theory.
However sometimes it turns out that things are not so simple, and in recent years I have viewed other evidence that suggests CBGW theory may not be entirely accurate. Specifically there are four pieces of evidence:
1) It has come to light that the oceans are a (hugely) significant sink and source of CO2. Warm ocean waters emit CO2 into the atmosphere while cold ocean water absorbs CO2 from the atmosphere. Over the past many years we have all seen the charts showing the correlation of CO2 and temperature. CO2 goes up and temperatures rise. CO2 goes down and temperatures fall. In all this, it has been implied that CO2 is what is driving temperature trends. But the fact that warm oceans emit CO2 forces us to challenge this assertion. Correlation is not causation after all, so it is quite possible (and perhaps likely) that increases in ocean temperature have been the cause of increased CO2. In other words, global warming may cause CO2 increase.
2) Studies by NASA have suggested that the net radiative balance between CO2 warming and the affect of subsequent increased cloud cover limits or negates CO2 warming. In other words the net effect of increased clouds serves to mitigate any warming CO2 may produce.
3) It can be argued logically that the rise in temperature and atmospheric CO2 over the past 60 years was caused (to a large degree) by a lengthy cycle within the Pacific ocean where there occurred a high frequency of El Nino and positive PDO conditions. These combined conditions worked to increase global temperatures and increase CO2. Over the past decade, the Pacific has been in a state where La Nina and negative PDO condition persist and during this time global temperatures have declined.
4) Lastly, it has become obvious to me that the treatment of global weather observations, whether intentional or by happenstance, has lead to a view that the global temperature is warming at an incredible rate. The truth is that many specific locations are warming rapidly to due to other factors such as urbanization and agriculture, where as other low impact areas are not warming at all. One argument that this is happenstance is the fact that the vast majority of temperature observing sites tend to be near developing areas, and thus the aggregate of these stations vastly over shadows the near zero trend exhibited by low impact stations.
So, being a person who seeks truth, I had to dig into the details of global warming myself. Today after having reviewed lots of science on both sides, and having done my own research, I am currently of the opinion that CBGW is, at a minimum, being over hyped. Is it possible that CBGW is valid to some degree? Perhaps. At this point I can’t rule out that carbon emissions may have some influence on warming temperatures – but to answer this with any confidence we simply have to learn more (and unfortunately much of the research done up to now is questionable for various reasons). That said I do have considerable doubts that CO2 emissions will result in extreme global warming scenarios that have been proposed by some groups (at least with respect to current theory)
Recommendations for proving/disproving CBGW:
1) We need a more quantitative understanding of how the oceans affect global temperature and CO2 levels.
2) We need to quantify, as objectively as possible, the affect clouds play in CBGW theory.
3) We need a much more sound and robust weather/temperature dataset from which we can objectively track global temperature trends.
These three items really wouldn’t be that hard to address if sufficient (yet modest) funds were provided to a team of objective, qualified, non-political scientists.
Lastly, over the past many years, my knowledge of CBGW has evolved and along with this my stance has changed as well. But what is my stance? What action do I recommend? Well, here it is… For many reasons (other than CBGW) I believe we should be working aggressively to wean ourselves off of carbon based fuels. Decreased emissions, energy security and improved energy efficiency are just some of the reasons. In the long run, we’ll need to have alternative energy sources as carbon based fuels will eventually run out. And something about a world that is powered by simple things such as the wind, the sun, the tides, and hydrogen just sounds like the right way to go. For me it’s almost an occam’s razor kind of thing… The simplest solution is the best.
And to all my friends who see my ‘telling the truth’ as giving CBGW and Big Oil the green light, well, I’m sorry if I may have caused you to be concerned or angry. But the telling the truth and keeping science honest is VERY important. Otherwise we can be lead into a false future with a lie. If CBGW is real, the advocates of this theory are going to have to prove it using fair data that does not conveniently, yet falsely, bolster the case for CBGW.
Peace,
Mark
Mike Bromley says:
@Mike Bromley: Is it fair to say the data is unsuitable? The difficulty I have with that answering that question is the answer is not a simple yes or no. The data certainly has issues, but it is not totally useless. In addition, there are effective methods for correcting the data. Lastly, even with data that is known to have issues, statistical measures of uncertainty can be helpful in quantifying the results.
juanslayton says:
@juanslayton: Welcome the weather data head scratchers club! I remember puzzling over the data from Phoenix years ago. It was a frustrating, yet enlightening moment. Little did I know that this research would become central to my activities for the next 10+ years. Anyway, the lesson I learned is that while the station metadata can sometimes be helpful, in many cases station changes are simply not documented. As such, the only way to objectively quantify station changes is through an analysis where a stations time-series is recursively compared to the time-series of other nearby stations. This is confounded by the fact that neighboring stations can also be affected by changes of their own, so this needs to be managed in the analysis. In the end, if done properly, one can obtain a ‘station change signal’ that when applied to the original data, can effectively remove artificial inhomogeneities.
Huub Bakker says:
@Huub Bakker. I’ll second your position. I think it is important to remember that to optimize the advancement our knowledge, we all need to work together in a civil and constructive way.
From a distance, it seems to me that determining the change in the temperature of the atmosphere of the planet earth is much akin to determining the number of angels who may be dancing on the head of a pin, given that angels know how to dance.
A single number representing the change in the temperature of the earth’s atmosphere on an annual basis is a ridiculous number to attempt to generate. What is the real significance in question?
Whether we are a net radiator of solar energy or a net sink of solar energy over time.
Gathering data to record, manipulate, average, mean, median, mumbo-jumbo a bazillion measurements is a waste of time. They are a multitude of measurements, made willy-nilly, at differing times of the day, with different wind conditions, with so many other factors subject to subjective ‘correction’ that it is impossible, IMO, to attempt to reduce those multitudinous measurements to an accurate single number.
Gather by the gigaton, cut with a chainsaw, and measure with a nano-probe.
In actuality, the problem is determining actual solar {‘insolation’ (?), I don’t even attempt to know the terminology, so you can ignore me with full rectitude} radiation that is received, that which is radiated out to space, and that which is absorbed. If you cannot measure that, measuring all the temperature points on the planet is still a waste of time.
You may fire when ready, Gridley…
tom
Interesting point in “When considering very small numbers of stations this is indeed a problem, but when more stations are averaged as a group, the effects of random station changes largely cancel out. The reason for this is related to the statistical principal that random actions (station changes) average out to zero whereas non-random actions (urban growth) do not.”
I’d want to review the station changes. If weather measurers were responsible they’d be moving stations to sites less affected by things like A/C unit exhausts, and would ensure there are an adequate number of stations in remote areas such mountainous (where temperatures are cooler because of altitude).
(Noting however that a great many stations are at airports for a reason – airplane operations, so shouldn’t be moved. Instead more should be set up.)
John Goetz says: April 5, 2011 at 9:06 am “Because it is “rural” in the present day, GISS uses all of that station’s data to adjust nearby urban stations with “less pristine” data.”
Why bother using the urban stations as data to look for trends in? The urban stations are no longer independent.
“The record for Burlington, VT, also was affected by similar moves, originating in downtown, moving up toward the University, spending a period of time in farmland, and then finally landing at the airport.”
IMJ that station is useless for trend analysis.
“Same reason mining geologists criticise the data aggregation methods etc used by academia and state sponsored science to come up with the global temperature metric – if we get it wrong the mining engineers, not least the shareholders – tend to do serious things to us – apart from the humility of explaining to the corporate watch dogs that we got the ore -reserve slightly out.”
“Slightly” with tongue in cheek I expect. 😉
And sometimes investors fail to do the most basic checks – as in the Bre-X fraud? (Where an assay report described alluvial gold in samples supposedly crushed from rock.) They lost their investment.
Some of this climate alarmist stuff fails simple checks, gullible humans don’t check, so they will force the rest of us to reduce our quality of life.
Global warming is real. Just ask the the people of Tuvalu. Simply proofed. Whats worse though is how the atmosphere is becoming even dirtier. In fact this kind of thinking does not consider the current respiratory diseases that comes from open burning (including combustion engines). Nuclear energy at least keeps the atmosphere temporarily clean (until something bad happens and a Chernobyl occurs). Oh, and don’t rely on Governments to tell you the truth – it is after all the same as big business.
Huub Bakker says: April 5, 2011 at 12:20 am
“Hey, don’t diss all university staff. In normal scientific and engineering fields, if you get it wrong there goes your reputation. We aren’t all climate scientists! :-)”
Good point, but I ask if you are working to get university hierarchy to understand how much bad work or worse goes on in academia. (“Worse” is for example the academically incompetent racist demagogue employed by the University of British Columbia.)
Think how much more money might be available for your work if funding for such was eliminated.)