Lucia at Rank Exploits poses this thought:
“I always think it’s best to ask yourself: Do I really think a particular method of looking at data is meaningful? Would I still believe this if the answers turned out “wrong” from my POV? Or, will I eventually find myself explaining my own method gives uncertainty bounds that are “too small”, when my method suddenly gives “wrong” (in my eyes) answers?
So, in this regard, I need to ask Tilo: Why throw out GISS Temp”?
Thanks, Lucia, for taking a different look at this. It is true that one can make arguments for and against GISTEMP as a valid/not valid data set.
My view is that this is a lot like voting for a company wide policy change on a company board of directors.
Let’s say this issue was on a company board of directors decision to choose to make changes to policy related to employee comfort. Some employees complain that the work environment is too hot and they have been suffering a long term effect. The board decides to hire four consultants with the mandate: “tell us if we should expend the money to replace all of our a/c units company wide in all of our world locations. The cost will be huge, so we need to know before we make a policy change to do this.”
One of the consultants to members of the board who strongly advocates the policy change also has been lobbying company staff worldwide and other board members with the data he has collected and collated that shows that the trend is shifting in the direction that he advocates. As consultant, he is also the creator of one of the datasets used to evaluate the policy change.
Now when the time comes to make the decision, the board brings in all the data sets from consultants. They look at each one and see that the majority of them have no change in the last 11 years that supports the policy change to put in new a/c units. Yet the one consultant that has been pushing this policy change gives an impassioned speech that his data set tells a story that the others do not. Some of the board members who are skeptical of this person and his data that supports the policy change do some research of their own. They discover that the dataset created by the consultant who advocates the policy change has been adjusted at many data points, almost without exception in favor of the policy change. Some board members also learn of some math errors in the data, point out the math errors, and also some of the questionable ways individual data points have been adjusted. the consultant shrugs and retorts “you’re just a bunch of court jesters”.
Meanwhile, it has been discovered that one of the business friends of the consultant who has been lobbying board members and staff has a company that trades in air conditioner systems. That person has been traveling to all of the worldwide offices of the company and lobbying the employees to tell them that their work environment is indeed getting hotter, and that the data from his friend the consultant proves it beyond the shadow of a doubt. He tells them that his friend the consultant uses special techniques to find the “real” trend in the data and that the other datasets aren’t as valuable as this one. He urges the employees to form pacts and unions to lobby the corporate board to make a change. The employees do just that.
The board looks at the data, they listen to the impassioned pleas of the employees, and they also listen to the one consultant who calls them “court jesters”, and his friend the a/c salesman, who says “the time is now, you must act now’. But a few employees that are concerned that the expense the company may be about to undertake is unwarranted, they think the work environment is just fine, and the “solution” may hurt the company more than help it.
One of the employees finds that in the largest company facility, 78% of the temperature sensors in used to collect environmental data in that facility have been installed incorrectly, and shows that they are too close to equipment that produces waste heat. They also discover that two of the consultants use IR sensors to get the data, but that the other two consultants are using the direct measure environmental sensors, 78% percent of which in the largest company facility are installed incorrectly. These few employees that discover this also lobby the board by pointing out some of these issues with the datasets.
So it is time for the board to vote. The one consultant who has lobbied the board most heavily says “don’t worry about that 78% of the problematic environmental sensors in the biggest facility, I can adjust for that.”
But then one of the other skeptical board members says: “The employee that found this says “How can you adjust for these if you’ve never seen or visited them? How can you know they are all equally biased or not?”. And, isn’t it true that in some of the data you presented, there were no sensors present, and some of the data was interpolated by you, particularly at the far ends of the building?”
The consultant says: “I stand by my data and methods, and if you don’t do something soon, your facility may reach a tipping point where you can no longer keep it cool enough to work in, your company productivity will tank.”
One of the board members says, “Ok lets stop and look at this differently”. “What if we simply ignore the dataset from the consultant who calls us “court jesters” and has the buddy who’s the air conditioner salesman?. Look, now there’s no trend in the last 11 years”.
So 3 of 4 datasets, each presented by independent consultants are in front of them and show no change in the past 11 years. The one that does show a trend has been heavily lobbied and has been shown to have errors in measurement by environmental sensors and questionable data adjustment methods applied. Plus the consultant who prepared it has insulted those members who dared to question his data and methods, and he is the only one of the four consultants who has links to the air conditioner salesman, as it was discovered that the air conditioner salesman invited the consultant to speak at one of his employee rallies.
How do you think the board of directors will vote on this policy change?
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Heh, the boards of directors of lots of companies have already voted. Did you see the article by Dar, from Right-Side News, at icecap.us a few days ago?
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Don’t forget that Hadley is re-doing its secret formula and will be releasing the New & Improved version soon.
Whose version do you expect this new temperature series to agree with given Hadley’s own record of advocacy?
Apparently nobody has told the board of directors that their investment can not be effective in reducing the temperature in their plants unless every other company in every other country around the globe also makes the same major investment in new equipment.
I suspect if the board members knew that, they would be far more reluctant to make the investment until they knew that all of the other companies would make the investment as well.
The key question I hear nobody asking is: “If you have correctly analyzed the issue, would the “solution” you are advocating solve the problem?” In the case of every “solution” offered by the AGW “affirmers”, the answer is resounding NO. This is even true of the $45 trillion “half-solution” announced last week by the IEA. Also, if IEA believes we have a “whole issue”, why propose a “half-solution”?
What if, in fact there was some extra heat there, though certainly nothing alarming, but in any case, it was completely beyond the company’s control? In fact, they could buy all the air conditioner systems in the world and it would not make one iota of difference, except maybe bankrupt the company. That is actually our situation with the whole AGW hoax.
[…] Read the whole thing on Watts Up With That. […]
The board of directors will ignore the three truthful reports, order the air conditioners and receive their kickbacks…
Most of the hot air is coming from the board, so they will buy the air conditioners, with a suitable kickback from the suppliers. The other consultants will be blacklisted. The board will pat themselves on the back in the next annual report and be re-elected.
The stockholders will get the shaft.
I’ve worked for several companies like this. Pay taxes to one now.
Watts the Shareholders Value in this?
And their market research tells them most of their British customers don’t believe in man-made climate change.
http://www.guardian.co.uk/environment/2008/jun/22/climatechange.carbonemissions
I vote to buy stock in the air conditioner manufacturers. I wonder when the bubble will burst. Bubbles always burst.
I agree there are problems with any individual temperature record. Also, it would have been better all around if none of the temperature records were supervised by someone who is heavily involved in developing and running predictive model which must be tested against data. Given the rather involved manipulations of past temperatures, it’s not surprising that more skeptical people end up suspecting there could be confirmation bias affecting the decisions about how to detect and correct data in the historical record.
That said, I think the individual do make a good faith effort to create accurate temperature records. Also, at a certain point, I think it would be difficult for someone to pervert the current record too much even if they wished to do so intentionally. For one thing, there are too many eyes watching. (I think the eyes are a good thing.)
So, I prefer to use all records. Until one record is quite obviously an outlyer, beyond the level consistent with measurement uncertainty, I’ll just attribute the deviation to measurement uncertainty.
That said, I have some blog visitors who insist I should scrap the satellites, others insist I should scrap GISS. So far, no one has complained specifically about NOAA/NCDC. But, no matter what choice, there is someone who doesn’t like a particular data set.
When people read quotes like this (below), how can we ever win over the average person?
“Scientists may one day retrieve carbon dioxide that wold otherwise pollute the atmosphere, using its carbon atoms to form hydrocarbons. These could then be used as vehicle fuel or as a feedstock to make plastics and other materials we now derive from oil. So, could the expanding clouds of carbon dioxide in our atmosphere really have a silver lining?”
Quoted from Reader’s Digest July 2008 edition page 20 attributed to Duncan Graham-Rowe, New Scientist.
An uphill battle is one thing, but stuff like this creates a mountain.
why not listen to the tiny guy at the very back of the table?
he asks, why you are not looking at the full dataset of 30 years?!?
and why you are using 11 years, among all posibilities?
and why those 11 years happen to start with the HIGHEST POINT in the entire dataset?!?
REPLY: Sorry SOD, wrong. The time period that Tilo uses in his graph starts in 1997, not 1998.
1997 was not the hottest year on record. his comparison did not start with the highest point on the dataset as you incorrectly assert.
Go look:
http://wattsupwiththat.wordpress.com/2008/06/20/warming-on-11-year-hiatus/
He chose that to eliminate precisely your type of criticism. Sure, the 29 years of sat data show a rise, no dispute there, but the point is, in recent history it has not followed the model predictions. That is what makes it significant.
If you are suggesting you are the “the tiny guy at the very back of the table” I can assure you that as long as you hide behind the phantom handle, nobody takes you seriously.
Of course that won’t stop you from ranting, carry on.
During the debate about global warming, various projections were made about the effects and consequences of increased carbon dioxide emissions due to human activities.
People, whose primary concern is to preserve the environment, want to see a reduction of carbon dioxide emissions.
They would be surprised to learn that man’s activity of burning fossil fuels has a very important long-term environmental benefit for life on earth.
Life on earth is supported by the carbon cycle. The carbon cycle starts with growing plantlife using photosynthesis to extract carbon from the carbon dioxide in the air. The biomass thus generated creates a foodchain that supports all life on earth. The carbon incorporated in living matter will eventually revert to carbon dioxide by decay and burning. The carbon dioxide returns to the atmosphere and completes the cycle.
This carbon cycle however only takes place on dry land where there is plenty of oxygen available to complete breakdown of organic matter into carbon dioxide.
In water – and 75%, or more of the earth surface is covered by oceans – this cycle is only partially completed. The photosynthesis still takes place and biomass is still generated to support aquatic life. The decay however is incomplete as a significant portion of the organic matter sinks to the bottom of the oceans. In the bottom of oceans there is no sufficient oxygen available to complete the cycle and decompose organic matter into carbon dioxide. This carbon-containing residue accumulates at the bottom of the oceans and through sedimentation and other geological changes becomes carbon containing fossil fuel. Since there is no natural mechanism for this fossilized carbon to convert to carbon dioxide, the oceans act as very long-term sinks for atmospheric carbon dioxide. Atmospheric carbon dioxide concentrations in the past must have been significantly higher than today, since all fossil fuel carbon that we have, or ever had, must have existed at one time as atmospheric carbon dioxide.
This action of carbon dioxide removal by the oceans is continuing today and will eventually deplete carbon dioxide from the atmosphere totally, making the earth incapable of supporting life, as we know it.
Fortunately, this disaster scenario is prevented by man’s activity of burning fossil fuel.
So if you really want to save life on earth, burn fossil fuel!
As soon as it was reported that Pluto’s temperatures were also rising it was decided it could no longer be a planet. Poor Pluto…
Interesting analogy Anthony, and there’s another analogy that uses a company approach that I like to apply.
Suppose the market buzz about a particular company was that sales were flat. This company starts issuing press releases that sales in it’s Reading store reached record levels on one weekend, and that sales of a particular item have increased. Another press release forecasts record growth next year.
When the forecast growth doesn’t occur and sales start to decline, the business media would probably start asking some hard questions and investors would start asking for changes at the top, but the AGW folks seem to get a pretty free ride in the media. The press still uncritically publish the AGW releases about record temperatures and more ‘extreme weather events’, as evidenced by the whole AGW causes volcanoes nonsense last week.
We have currently 4 “global” temperature measurements plus a set of sea temperature measurements.
One of them, GISS, is widely different. Another employs mystical techniques. Two are direct satellite measurements. Those who require global warming are trying hard to tweak the satellite and sea temp measurement to fit the model.
The models give wildly different results, depending upon how parameters are tweaked. It is illegitemate to take the average of all these and state that the outcome of the real world falls within statistically meaningful bounds of the models.
Example: I have a thousand model results and they say heads or tails. When I toss a coin and it is either head or tail, then it falls within the model bounds! And if it fell on the edge, would that mean the models perfectly predicted the outcome? Clearly not.
But we are be played these games all the time by the realclimate boys. Don’t US Citizens have the right to demand accountability from federal government agencies?
Ok, lets say the board goes for it and they get their new a/c units and change the environment back to what is considered acceptable. CO2 levels are now in the optimal range. What can we expect.
* 01/14/1882 – Southern California’s greatest snow occurred on this date. Fifteen inches blanketed San Bernardino, and even San Diego reported a trace of snow.
* 05/23/1882 – An unusual late season snow blanketed eastern Iowa, with four to six inches reported around Washington.
* 06/17/1882 – A tornado traveled more than 200 miles across the state of Iowa killing 130 persons. The tornado touched down about ninety miles west of Grinnell, and struck the town and college around sunset, killing sixty persons, and causing more than half a million dollars damage. Traveling at nearly 60 mph, the tornado hit Mount Pleasant about 11 PM causing another half a million dollars damage.
* 07/09/1882 – Ice formed on the streets of Cheyenne, WY, during a rare summer freeze.
* 08/04/1882 – A vivid aurora was visible from Oregon to Maine, down the east coast as far as Mayport FL, and inland as far as Wellington KS. Observers at Louisville KY noted “merry dancers” across the sky, and observers at Saint Vincent, MN, noted it was probably the most brilliant ever seen at that location.
* 08/08/1882 – An August snowstorm was reported by a ship on Lake Michigan. A thick cloud reportedly burst on the decks covering them with snow and slush six inches deep. Snow showers were observed at shore points that day.
* 08/10/1882 – Sandusky OH noted a four minute snow squall during the morning, frost was reported in the suburbs of Chicago, and a killing frost was reported at Cresco IA.
* 09/12/1882 – Hot and dry winds caused tree foliage in eastern Kansas to wither and crumble.
* 10/02/1882 – An early season windstorm over Oregon and northern California blew down thousands of trees and caused great crop damage in the Sacramento Valley.
* 12/12/1882 – Portland, OR, was drenched with 7.66 inches of rain, a record 24 hour total for that location. (12th-13th)
Or, how about this.
* 01/02/1910 – A great flood in Utah and Nevada washed out 100 miles of railroad between Salt Lake City UT and Los Angeles CA causing seven million dollars damage.
* 02/26/1910 – Parts of Washington State were in the midst of a storm which produced 129 inches of snow at Laconia between the 24th and the 26th, a single storm record for the state. A series of storms, which began on the 23rd, led to a deadly avalanche on the first of March. By late on the 28th, the snow had changed to rain, setting the stage for disaster.
* 03/01/1910 – The deadliest avalanche of record in the U.S. thundered down the mountains near Wellington Station WA sweeping three huge locomotive train engines and some passenger cars, snowbound on the grade leading to Stevens Pass, over the side and into a canyon, and burying them under tons of snow. The avalanche claimed the lives of more than 100 people. The station house at Wellington was also swept away.
* 04/23/1910 – The temperature at the Civic Center in Los Angeles, CA, hit 100 degrees to establish an April record for the city.
* 04/25/1910 – Chicago, IL, was blanketed with 2.5 inches of snow, and a total of 6.5 inches between the 22nd and the 26th. It was the latest significant snow of record for the city.
* 04/29/1910 – The temperature at Kansas City, MO, soared to 95 degrees to establish a record for the month of April. Four days earlier the afternoon high in Kansas City was 44 degrees, following a record cold morning low of 34 degrees.
* 08/20/1910 – The “big blow up” of forest fires finally came to an end in Idaho. A record dry August fueled 1736 fires which burned three million acres destroying six billion board feet of timber. The fires claimed the lives of 85 persons, 78 of which were fire fighters, and consumed the entire town of Wallace. The smoke spread a third of the way around the world producing some dark days in the U.S. and Canada. The forest fires prompted federal fire protection laws.
* 09/15/1910 – Rains of .27 inch on the 14th and .73 inch on the 15th were the earliest and heaviest of record for Fresno CA, which, along with much of California, experiences a “rainy season” in the winter.
* 10/17/1910 – A hurricane made a loop off the southwest coast of Florida. Winds above 100 mph were reported at Fort Myers FL, and the barometric pressure at Sand Key reached 28.40 inches.
* 10/18/1910 – Northeasterly winds as high as 70 mph (from a hurricane moving northward up the Florida peninsula) carried water out of Tampa Bay and the Hillsboro River. The water level lowered to nine feet below mean low water. Forty ships were grounded.
Or, possibly this.
* 01/19/1933 – Giant Forest CA received 60 inches of snow in just 24 hours, a state record, and the second highest 24 hour total of record for the U.S.
* 02/09/1933 – The temperature at Moran, WY, located next to Teton National Park, plunged to 63 degrees below zero to establish a state record. The temperature at the Riverside Ranger Station in Montana dipped to 66 below zero to establish a record for the state, and a record for the nation which stood until 1954.
* 04/04/1933 – Pigeon River Bridge, MN, reported 28 inches of snow, which established the state 24 hour snowfall record. (4th-5th)
* 04/16/1933 – Franklin Lake, NH, was buried under 35 inches of snow.
* 05/06/1933 – Charleston, SC, was deluged with 10.57 inches of rain, an all- time 24 hour record for that location.
* 08/12/1933 – The temperature at Greenland Ranch in Death Valley, CA, hit 127 degrees to establish a U.S. record for the month of August.
* 08/23/1933 – The Chesapeake-Potomac hurricane moved over Norfolk VA and Washington D.C. A tide seven feet above normal flooded businesses in Norfolk, and damage in Maryland was estimated at seventeen million dollars.
* 09/05/1933 – A hurricane hit Brownsville, TX, killing forty persons and causing 12 million dollars damage.
* 11/13/1933 – The first dust storm of the great dust bowl era of the 1930s occurred. The dust storm, which had spread from Montana to the Ohio Valley the day before, prevailed from Georgia to Maine resulting in a black rain over New York and a brown snow in Vermont. Parts of South Dakota, Minnesota and Iowa reported zero visibility on the 12th. On the 13th, dust reduced the visibility to half a mile in Tennessee.
* 12/30/1933 – The temperature reached 50 degrees below zero at Bloomfield, VT. It was the coldest reading in modern records for New England. The temperature at Pittsburgh NH reached 44 degrees below zero.
* 12/31/1933 – A 24 hour rainfall of 7.36 inches set the stage for the worst flood in Los Angeles history. Flooding claimed 44 lives.
Brutal cold, blistering heat, floods, droughts, severe storms, hurricanes, etc. If we’re responsible for the extremes of our time, who then is responsible for all of these events, for these years and the many like them, during lower levels of CO2?
At what point does ridiculous become so ridiculous, that it is called insane?
If you want more history, go here.
http://www.weatherforyou.com/history/
dinner is served
The eggs have been scrambled.
The crow has been stewed.
The guests are arriving
to get their just due.
On reason I started getting so interested in looking at the temperature data is because I am an actuary. My job is to look at data and trends, and use professional judgment to determine what the most appropriate information is to use to most accurately price and reserve insurance products. Using outliers in data presents competitive and profit risks, but completely excluding outliers presents its own sort of risks. Knowing which trend lines to look at is more art than science, but plays in important part in determining appropriate short-term, mid-term, and long-term pricing depending on the nature of the product.
I’ve tried to look at the temperature data as an actuary, acknowledging a number of complicating factors in simply looking at trend analysis: the trend lines continually change, there are longer-term cycles afoot, and so on. But all my analyses really boil down very simply to a few basic things: (1) There has been an underlying warming trend in the area of 0.5 – 0.6 degrees Celsius per Century for 120-150 years; (2) Mid-term trends shoot above and below this underlying trend line in a cyclical fashion; (3) Short-term projections using the most recent trends would have to be in favor of stable or declining temperatures, bringing the mid-term trends down to or below the underlying trend lying (thus corroborating that line). I am certain that anyone completely distanced from the politics and emotion of the issue, presented with the trends, would come to the same general conclusion.
However, part of an actuary’s job is to question the bias in your data, and assess data quality. It seems clear that the data with the least potential bias and error (satellite data), presents the least convincing argument for sustained warming, and taking this into account, most actuaries would weight such data much more strongly and probably determine that a proper trend is something south of the long-term indicated trend in the SST data.
Whenever I look at the new data and put my charts together and comment on them, I always try to do it with the frame of mind not so much as a skeptic (or proponent, if that would be the case), but instead to appraoch it as if I were being asked to give an honest and objective analysis as an actuary.
A month ago, I had posted on my blog my predicted anomalies for the next 12 months based on an actuarial based model. I had predicted a GISS anomaly of about 45. My predicted NCDC anomaly was about 0.486. Actual GISS came in at 36 and NCDC was .43 or so.
My prediction for June for GISS is about 48. I am almost done with NCDC. These projections are based on an 11-year monthly weighting scheme of second differences in trend changes. It’s completely objective. This should tell us one of two things: if the projections are generally fairly accurate and the errors are distributed both high and low, then the no major changes in the cyclical trends would seem to be occurring. However, if, as time goes on, the projections are consistently high or low, then it indicates something is occurring not accounted for in the weighting scheme (ie, something new). I’ve only done this for two months, but both projections have been higher than the actual anomaly. Only time will tell. Eventually, I wish to extend this to all the temperature measures.
OK, admittedly, I think I strayed from the point. Just mainly wanted to share that I have been trying to take a somewhat business-like approach to my analyses. The results may not be perfectly clear, but there is basically no scenario at all where I can fathom any trend that, if I were looking at insurance trend lines, would lead me to believe the sky is falling.
Joe Tritz
http://digitaldiatribes.wordpress.com
The trouble with the arguments against GISS is they also apply to HadCRUD. The difference is HadCRUT does not release any methods or datasources which makes it impossible for external auditors to check for biases. GISS algorithms and code may be impossible to replicate but they are at least available to the public.
The same arguments could be used by alarmists to disqualify the satellite data.
So I think Lucia is one the right track: use an average of the 5 major datasets and don’t try to pass judgement on any particular dataset. A single dataset with a bias will only have a small effect on the average.
Robert Wood wrote: “…But we are be played these games all the time by the realclimate boys. Don’t US Citizens have the right to demand accountability from federal government agencies?”
Unfortunately, we have one of the most incompetent Administrations in the history of the United States. And at that low level, there is no accountability!
Jack Koenig, Editor
The Mysterious Climate Project
http://www.climateclinic.com
How would I vote? Having recently downloaded A LOT of Smith and Reynolds SST data from NOAA’s NOMADS system, (Easy to do once you read the instructions, BTW), I’d vote to terminate any relationship the board has with GISS. The data GISS presents is contrived. It’s fundamentally HadCRUT on steroids, inflated by 1200km smoothing. Stations appear and disappear for no apparent reason. Hansen, the godfather of GCM-manufactured AGW, continues to perpetuate a faulty hypothesis that was initiated without accounting for natural oceanic oscillations caused by thermohaline circulation and ENSO.
Anthony, the ERSST data holds a remarkable amount of amazing information. I’ve put together a dozen or so posts over at my blogspot, for anyone who’s interested.
http://bobtisdale.blogspot.com/2008/06/smith-and-reynolds-sst-posts.html
Here’s the teaser. This is a graph of the Mid-Latitude Pacific SST Anomaly (20 to 65N, same latitudes as the PDO) MINUS Global SST Anomaly. Basically, it’s the same calculation as the AMO but using data from the North Pacific. It sure doesn’t resemble the PDO. It’s the same order of magnitude as the AMO in recent times, but it has a major kick in early years.
http://i28.tinypic.com/jrwjk6.jpg
Note: The second link in the attached post contains the Smith and Reynold’s instructions for retrieving their data though NOMADS. Happy downloading.
Regards
<b<Tom Klein, I liked your analysis [probably because I agree. I might pick a nit about ocean coverage, but that’s about it].
Subsidence along the tectonic plates beneath the ocean pulls sediment down [the carbon based organisms you referred to] on average at about the same rate as your fingernails grow.
That doesn’t sound like much, but over tens or hundreds of millions of years, an enormous amount of carbon must have been pulled down into the Earth through undersea subsidence.
No one knows for certain, but this could have provided the bulk of the petroleum we use today.
The atmosphere does need more CO2. A lot more. In the past, atmospheric carbon dioxide was as high as thousands of ppm, with no ‘runaway global warming.’ Today plants are starved of CO2. They’ve learned to adapt, but much more CO2 would be much better. When CO2 levels were high, plant and animal life flourished.
Finally, I did my daily click on Anthony’s link of the Sun, and got all excited for a second when I spotted the first sunspot! Turned out to be only a smudge on my computer screen, though. Dang.
Well, at least I can post one of the first pictures of water on Mars.