Global Mean Temperature

[palerider]

I'm sorry I misread what you wanted. From the graph and references, I thought you could figure it out from there. The error for each red point is about +/- .2 degrees C. For the 5 year moving average on the blue curve, the error would be about +/-.05.

I figured this out long ago. I asked the question in an effort to help you and others figure it out as well.

To answer your OP, the surface temperature over a ten year average is 14.51 deg C for the 2000's. The ten year average for the 1980's is 14.18 deg C. (Goddard Institute for Space Studies) Errors for the later two measurements are about +/- .1 deg C. That translates to 58.12 deg F now and 57.52 in the 1980's. I know you asked for the temperature for the "present", but nobody could possibly know that.

+/- .1 degrees C. Now that is interesting also. You are apparently a fount of interestingness. I can only guess that 14.5 degrees is your personal favorite and that is why you used it, but if you look around the world at the various agencies, it seems that not everyone agrees. For example:

• On German public TV in 2009, Hans Schellnhuber stated that the global mean was 15.3 degrees.
• Stefan Rahmstorf states that it is 15.5 degrees.
• The IPCC 2007 4AR says 14.5 degrees.

Just a bit of research yields claims from 14 degrees to nearly 16 degrees. You state confidently that it is 14.5 with a margin of error of .1 degree when with little effort, one can find a two degree variance depending on which data base one cares to look at and I won't even go into the rampant data tampering.

The point is that while you may believe that the global mean is this or that for this period of time or that period of time and the margin of error is a tenth of a degree, you are kidding yourself because the fact is that we don't really know what the global mean is within a whole degree. Contrast that with claims of warming of tenths of a degree per decade and a thinking person can't help but see some real problems with claims of warming or cooling.

The anomoly chart you provided is no better. The baseline for calculating anomolies is the global mean and the spread there is, again, two degrees or so. It is laughable to me for a group of people to be claiming fractions of a degree of change per decade or century when there is a two degree spread depending on which set of data one cares to start with.

How does one claim fractions of degrees of change with a straight face and a clear conscience when the margin of error is orders of magnituded greater than the claimed change?

 

[palerider]

Oops, the errors I gave above are from the HadCRUT data set and are only one aspect of the error. The NASA data set is clearer, and the errors are +/- .5 C annually. (See http://data.giss.nasa.gov/gistemp/graphs_v3/)[/quote]

Since you mention giss, I suppose I should mention data tampering after all.

Here is the US temperature anomoly according to giss from 1880 to 1999 as stated in 1999:

Here is what the same US temperature anomoly according to giss looked like in 2011. The alterations are obvious and call into question any claims based on the giss database.

Here are a few more examples of the degree to which the data set has been tampered with:

And it isn't just US temperatures:

So how confident are you that the giss data set is clearer? And the data tampering isn't limited to nasa and giss. The fact is that the entire data set world wide has been so corrupted by tampering that I doubt that more than a handfull of scientists have any idea what the actual temperatures are, or more importantly, were.

 

[Lagboltz]

I'm not surprised that the assessment of the current global temperature has a one degree spread. It depends on sparse sampling (i.e. A few hundred temperature points around the world) and a various types of modeling.

Here is a little story that shows how to assess something like global warming. My swimming pool seemed to be leaking, so I made a float apparatus to measure the water level of the pool. It was hard to measure because of slow undulations, so I took several readings each day and averaged. On a graph of the daily level, the leak seemed to be about 2 mm a day, and looked similar to the GW graphs – a noisy but definite trend. I then wondered about evaporation and filled a non-leaking pan and measured it for evaporation. That correction turned out to be .5 mm a day.

I had absolutely no idea what the absolute depth of the pool was. It wasn't important in determining that there was a leak. The important factor was consistency in the way the daily levels were measured, and the evaporation correction I made after the fact. (Data tampering??!).

In the same way, the important thing in measuring changing temperature of the planet is consistency of the measurement concept. And yes, science makes corrections and improves accuracy as newer models come about.

Your focus on the absolute measurement of temperature at some particular point of time is quite misguided in the same way that an accurate measure of the absolute depth of my pool was not a factor.

 

[palerider]

Here is a little story that shows how to assess something like global warming. My swimming pool seemed to be leaking, so I made a float apparatus to measure the water level of the pool. It was hard to measure because of slow undulations, so I took several readings each day and averaged. On a graph of the daily level, the leak seemed to be about 2 mm a day, and looked similar to the GW graphs – a noisy but definite trend. I then wondered about evaporation and filled a non-leaking pan and measured it for evaporation. That correction turned out to be .5 mm a day.

And you don't believe there is a difference between the evaporative rate of a swimming pool and that of a pan?

I had absolutely no idea what the absolute depth of the pool was. It wasn't important in determining that there was a leak. The important factor was consistency in the way the daily levels were measured, and the evaporation correction I made after the fact. (Data tampering??!).

And there is where your problem began. In order to accurately determine whether or not you have a leak, you first need to know how much water you have. Further, you altered your data based on the evaporative rate from a pan vs a body of water that was many orders of magnitude larger. You have no idea what effect your data tampering had on the actual amount of water you were or were not losing to a leak. Perhaps you made a decision to act based on what you believed, but that decision wasn't based on anything like hard data.

The decision to call in a repairman or not is a relatively small financial decision and while it wouldn't be entirely rational to make it based on flawed methodolgy, the decision only affects you; but the financial decisions being made based on the terribly flawed hypothesis, methodology, and modelling of climate change are very large and stand to alter the world's economies.

In the same way, the important thing in measuring changing temperature of the planet is consistency of the measurement concept. And yes, science makes corrections and improves accuracy as newer models come about.

So now I know. For a very long time I have wondered how someone could look at obvious examples of an effort to lower the temperature of the past in order to make the present appear warmer; now I know. You apparently believe they are making the data base more accurate. I have to tell you guy, that is one of the most delusional statements anyone has ever made to me regarding the whole climate change issue.

The fact is that we don't know enough about the climate to even begin to have a clue as to what sort of adjustments might need to be made and the fact that more and more data collection stations are being taken off line around the world thus giving us an even poorer picture of what the climate is actually doing does nothing more than further blur what we believe we know.

Your focus on the absolute measurement of temperature at some particular point of time is quite misguided in the same way that an accurate measure of the absolute depth of my pool was not a factor.

But knowing the absolute amount of water in your pool was an important factor if you actually wanted to know what was happening. In the end, you made your decision based on an opinion that wasn't founded on anything like fact but just a suspicion, and although you probably won't admit it, your financial situation was a major factor in guiding that suspicion. Regardless, whatever you did in the end, it wasn't based on anything like actual science and the same holds true for decisions being made today based on the assessments of the field of climate science.

 

[palerider]

I'm not surprised that the assessment of the current global temperature has a one degree spread. It depends on sparse sampling (i.e. A few hundred temperature points around the world) and a various types of modeling.

The actual spread is more like 2 degrees but aside from that, what does the fact that you can confidently tell me the global mean temperature within a tenth of a degree and tell me that the margin of error is 5 tenths of a degree when you acknowledge that the spread for the basis of your anomoly chart is at least a full degree say about your critical thinking skills?

When someone tells me that they are accurate to a tenth of a degree and their margin of error is half a degree, and the spread for the basis of the temperature that they gave me is 4 times larger than that, I have to wonder whether they have been completely duped or whether they are trying to dupe me.

So which is it?

 

[Lagboltz]

And you don't believe there is a difference between the evaporative rate of a swimming pool and that of a pan?

And there is where your problem began. In order to accurately determine whether or not you have a leak, you first need to know how much water you have. Further, you altered your data based on the evaporative rate from a pan vs a body of water that was many orders of magnitude larger. You have no idea what effect your data tampering had on the actual amount of water you were or were not losing to a leak. Perhaps you made a decision to act based on what you believed, but that decision wasn't based on anything like hard data.

The pan was partly immersed in the pool to preserve the same temperature and wind conditions. This keeps the evaporation rate per square centimeter close to the same as the pool. So yes, I believe that was fairly accurate. So it didn't matter how large or deep the pan or pool was in measuring surface evaporation per square centimeter as long as the surface conditions are the same.

The decision to call in a repairman or not is a relatively small financial decision and while it wouldn't be entirely rational to make it based on flawed methodolgy, the decision only affects you; but the financial decisions being made based on the terribly flawed hypothesis, methodology, and modelling of climate change are very large and stand to alter the world's economies.

Financial considerations had nothing to do with my measurement procedure. As it was, the corrected pool level was dropping at 4.5 cm a month. I didn't want the ground water to cause a hidden erosion and I had the leak located and repaired.

So now I know. For a very long time I have wondered how someone could look at obvious examples of an effort to lower the temperature of the past in order to make the present appear warmer; now I know. You apparently believe they are making the data base more accurate. I have to tell you guy, that is one of the most delusional statements anyone has ever made to me regarding the whole climate change issue.

The fact is that we don't know enough about the climate to even begin to have a clue as to what sort of adjustments might need to be made and the fact that more and more data collection stations are being taken off line around the world thus giving us an even poorer picture of what the climate is actually doing does nothing more than further blur what we believe we know.

My, my. A conspiracy theorist.

But knowing the absolute amount of water in your pool was an important factor if you actually wanted to know what was happening. In the end, you made your decision based on an opinion that wasn't founded on anything like fact but just a suspicion, and although you probably won't admit it, your financial situation was a major factor in guiding that suspicion. Regardless, whatever you did in the end, it wasn't based on anything like actual science and the same holds true for decisions being made today based on the assessments of the field of climate science.

You are guessing wrong; how can you possibly know what I was thinking financially. I thought you would understand the simple example. Here is a formula for you:
(Rate of water level drop per day in mm) x (total surface area in sq mm) = (total loss of water per day in cubic mm). What could be simpler than that?
Here is a question for you. If you were given the job of measuring the loss of water in a pool due to leakage, how would you do it?

The actual spread is more like 2 degrees but aside from that, what does the fact that you can confidently tell me the global mean temperature within a tenth of a degree and tell me that the margin of error is 5 tenths of a degree when you acknowledge that the spread for the basis of your anomoly chart is at least a full degree say about your critical thinking skills?

When someone tells me that they are accurate to a tenth of a degree and their margin of error is half a degree, and the spread for the basis of the temperature that they gave me is 4 times larger than that, I have to wonder whether they have been completely duped or whether they are trying to dupe me.

So which is it?

I gave you an example of how it makes sense to measure noisy differential changes and over a period of time find an accurate rate of change. If you can do math, you might look up "linear regression" in wikipedia. They have a graph similar to what we are discussing. Yes, pool levels are different from temperature levels, but the mathematical analysis is the same.

 

[palerider]

The pan was partly immersed in the pool to preserve the same temperature and wind conditions. This keeps the evaporation rate per square centimeter close to the same as the pool. So yes, I believe that was fairly accurate. So it didn't matter how large or deep the pan or pool was in measuring surface evaporation per square centimeter as long as the surface conditions are the same.

But being floated in the pool wouldn't preserve the same temperature or wind conditions. The experiment would be easy enough to do with a thermometer and some dry ice. Take your pan of water out and float it in your pool. Put a thermometer in it and the same sort of thermometer in the pool. They won't be the same temperature during the day because the bottom of the pan will absorb more radiation at its depth than the surrounding pool and will therefore cause the water in the pan to be warmer and therefore evaporate at a different rate. Then put a piece of dry ice in the pool and one in the pan. You will see entirely different wind variables in the pan than on the surface of the pool as a result of the sides of the pan above the pool's surface.

Your idea, while it may seem reasonable on the surface, is, in fact terribly flawed when you get down to the physics of it and the information you got from your experiment didn't give you anything like the information you were looking for. There was a 50/50 chance you would be right whichever decision you made but the information you gleaned from your experiment wasn't a valid basis for that decision.

My, my. A conspiracy theorist.

You view unemotional, rational thought as conspiracy thinking? Interesting.

You are guessing wrong; how can you possibly know what I was thinking financially. I thought you would understand the simple example.

It was a simple experiment that didn't give you valid information regardless of what you think because you didn't look deeply enough into the physics of the situation you were trying to assess. That is evident in the fact that you believed that floating the pan in the pool would equalize the temperatures and give you the same rate of evaporation. In the end, you made a guess based on something other than facts. Your guess was either right or wrong but the data you gathered was useless.

Here is a formula for you:
(Rate of water level drop per day in mm) x (total surface area in sq mm) = (total loss of water per day in cubic mm). What could be simpler than that?

Here is a question for you. If you were given the job of measuring the loss of water in a pool due to leakage, how would you do it?

Nothing could be simpler except perhaps an accurate means of telling whether or not you have a leak. Turn off your pump for a while to allow your water to get still. Mark the water level. Then put a 5 gallon bucket of water next to your pool (home depot plastic bucket). Mark the water level there as well. Wait 24 hours. If your pool loses more water than the bucket, you have a leak. Then repeat the "experiment" over the course of 2 more days. One day with your equipment running and one day with the equipment off. If the leak only occurs when your equipment is running, then you know that your leak is not actually in your pool, but in your plumbing. If it happens on both days, then you can be pretty sure that the leak is in the pool shell itself. By the way. your bucket should be full of pool water as heavily chlorinated water evaporates at a different rate than tap water.

Your pool analogy isn't very applicable with regard to accurately assessing the climate. One involves a relatively predictable set of variables and the other is mostly chaos with very little predictablity at all.

I gave you an example of how it makes sense to measure noisy differential changes and over a period of time find an accurate rate of change. If you can do math, you might look up "linear regression" in wikipedia. They have a graph similar to what we are discussing. Yes, pool levels are different from temperature levels, but the mathematical analysis is the same.

You gave a good example of believing in data that was gathered via a flawed hypothesis followed by less than effective methodology.

Tell me, where in linear regression is it acceptable to alter past data in order to change the appearance of data being gathered in the present. Another important thing to remember with regard to linear progression as a tool for predictive modelling is that if the predictions generated by the models don't match observations, then the models are useless and a rational individual would scrap them and begin to look in another direction for cause. Present modelling.....all present modelling assumes CO2 to be an important driver in the climate. Till that assumption is dropped, models will continue to be a waste of money and a useless waste of time.

 

[Lagboltz]

etc.

The cake pan was wide so that edge effects were minimized. There wasn't much wind anyway. The pan was in intimate thermal contact with the pool surface, which was the important factor. Furthermore, even if the evaporation rate was 25% in error (certainly an overestimate), that small correction would have amounted to a pool level drop of 1.5 +/- .08 mm per day -- a small error. I used pool water in the pan, of course. I don't consider any of that "terribly flawed."

I checked the pool level before the pump went on and after the pump went off (7 hours pump time). I checked to see if the before and after levels had different rates and averaged them over a week. There was no measurable plumbing leakage.

Your scheme of putting a bucket outside the pool looses the integrity of thermal contact and introduces different wind conditions. I consider that flawed. Your suggestion of 3 days was not nearly enough considering the undulating pool level. You make no mention of multiple pool measurements to minimize that error. You simply did not look deeply into the physics of the situation, and have a poor sense of experimental design.

Now, back to your OP. You adamantly stated that you must know the absolute climate temperature (and in my example of the pool, you said, "you first need to know how much water you have"). You did not directly comment on the validity of the water loss equation,

(Drop of water level per day in mm) x (total surface area in sq. mm) = (total loss of water per day in cubic mm).

You seem to implicitly accept it, but you digressed again about a flawed hypothesis. Suppose we have an accurate measurement of pool depth as a function of time. Do you disagree that the above equation is valid?

Finally, linear regression on a data set results in two numbers: the y-intercept and the slope. The word "warming" implies a change independent of absolute value. It could be "warming" after a winter in Siberia or Miami; i.e. warming implies a rate of change, independent of the y-intercept. So you still have not given a reason why the absolute temperature at some point in time (the y-intercept) or the absolute amount of water in a pool is relevant in computing the slope in the global warming data (or water loss in a pool).

 

[palerider]

The pan was in intimate thermal contact with the pool surface, which was the important factor.

Like I said, put your pan of water in the pool and measure its temperature vs the temperature of the pool and tell me that you have the same evaporative rate.

Furthermore, even if the evaporation rate was 25% in error (certainly an overestimate), that small correction would have amounted to a pool level drop of 1.5 +/- .08 mm per day -- a small error. I used pool water in the pan, of course. I don't consider any of that "terribly flawed."

Of course you don't, but then you beleive that altering past temperature data down in order to make the present appear warmer isn't terribly flawed either.

Your scheme of putting a bucket outside the pool looses the integrity of thermal contact and introduces different wind conditions. I consider that flawed. Your suggestion of 3 days was not nearly enough considering the undulating pool level. You make no mention of multiple pool measurements to minimize that error. You simply did not look deeply into the physics of the situation, and have a poor sense of experimental design.

More measurements are not necessary because as I stated, you are dealing with a contained system with predictable variables. If, in reality, you had a leak, you wouild know in the first day using my method. The other two days are simply to determine whether the leak is in the plumbing or the shell. Overcomplicating a simple system is as bad as oversimplyfing a complicated system.

Now, back to your OP. You adamantly stated that you must know the absolute climate temperature (and in my example of the pool, you said, "you first need to know how much water you have"). You did not directly comment on the validity of the water loss equation,

Of what value is a graph of temperature anomolies claimed to be accurate to tenths of a degree if one doesn't know the actual temperature to within 2 degrees? Can you say mental masturbation?

Finally, linear regression on a data set results in two numbers: the y-intercept and the slope. The word "warming" implies a change independent of absolute value. It could be "warming" after a winter in Siberia or Miami; i.e. warming implies a rate of change, independent of the y-intercept. So you still have not given a reason why the absolute temperature at some point in time (the y-intercept) or the absolute amount of water in a pool is relevant in computing the slope in the global warming data (or water loss in a pool).

Because if you don't know the absolute temperature within tenths of a degree, you have no basis to claim warming or cooling to within tenths of a degree. If your margin of error is 2 degrees, it is rediculous to believe you can make accurate statements regarding anomolies. Again, mental masturbation. There is a reason that the models are so poor at either reflecting present reality or predicting future conditions. You are describing the thinking that goes into present modelling and you are also describing why present modelling is such an abject failure.

 

[Lagboltz]

Like I said, put your pan of water in the pool and measure its temperature vs the temperature of the pool and tell me that you have the same evaporative rate.

Yep. Same temperature; same configuration; same evaporative rate. Your big bucket idea fails that and is ludicrous.

Of course you don't, but then you beleive that altering past temperature data down in order to make the present appear warmer isn't terribly flawed either.

Irrelevant to the pool example. You digress. A red herring.

More measurements are not necessary because as I stated, you are dealing with a contained system with predictable variables. If, in reality, you had a leak, you wouild know in the first day using my method. The other two days are simply to determine whether the leak is in the plumbing or the shell. Overcomplicating a simple system is as bad as oversimplyfing a complicated system.

The leak was too small to see in one day in an undulating pool. Your technique would totally fail.

Of what value is a graph of temperature anomolies claimed to be accurate to tenths of a degree if one doesn't know the actual temperature to within 2 degrees? Can you say mental masturbation?

Can you say "I don't understand science?" Or more specifically you could say, "I don't understand linear regression." But that's OK, not many people do.

Because if you don't know the absolute temperature within tenths of a degree, you have no basis to claim warming or cooling to within tenths of a degree. If your margin of error is 2 degrees, it is rediculous to believe you can make accurate statements regarding anomolies. Again, mental masturbation. There is a reason that the models are so poor at either reflecting present reality or predicting future conditions. You are describing the thinking that goes into present modelling and you are also describing why present modelling is such an abject failure.

You continually seem to want to argue the pool example. You give an inane way of handling the same problem. And, in the end, you reverse your original argument and agree with the crux of my pool example: that you don't need to know the amount of water in the pool. It's just the change in surface level that's important.

I will try to make it really really simple for you.

1. Suppose you have a good thermometer. You go around the house at noon and make measurements at many specific locations to roughly determine the average house temperature. Those measurements give you an average as a baseline value, but do not measure the true average, which would require an infinite number of measurements at an infinite number of locations.

2. Five hours later you go to those same specific locations and measure the temperatures again. You find the average temperature has risen by one degree.

3. You also have a friend double check, but he chooses different locations. His temperature measurements are generally two degrees higher than yours. Perhaps his locations were too close to a hot ceiling or other warm spots. However, the average he computes at noon and 5 hours later also gives a one degree rise.

4. The averages disagree because of sampling locations, but the differentials give the same warming.

5. Would you claim the there is no evidence of the house warming?
I hope you understand this example better than you did the pool analogy.

 

[palerider]

Yep. Same temperature; same configuration; same evaporative rate. Your big bucket idea fails that and is ludicrous.

Clearly you didn't actually do it or you woudn't be claiming the same temp.

As to my "big bucket" idea....clearly you never researched that either.

http://www.poolcenter.com/leaks.htm
http://www.ehow.com/how_4963967_detect-swimming-pool-air-leaks.html
http://www.albertgrouplandscaping.com/our-blog/bid/53041/How-to-detect-a-leak-in-your-swimming-pool
http://poolandpatio.about.com/od/maintainingyourpool/tp/LeakTests.htm

Irrelevant to the pool example. You digress. A red herring.

Your pool example is irrelevant to global temperature data so pot, meet kettle.

The leak was too small to see in one day in an undulating pool. Your technique would totally fail.

Except that my technique is recommended by professional pool maintenence firms while yours would provide you no useful data at all.

Can you say "I don't understand science?" Or more specifically you could say, "I don't understand linear regression." But that's OK, not many people do.

If you believe that linear regression is useful in modelling and predicting global temperatures, then show me the model that has been successful.

1. Suppose you have a good thermometer. You go around the house at noon and make measurements at many specific locations to roughly determine the average house temperature. Those measurements give you an average as a baseline value, but do not measure the true average, which would require an infinite number of measurements at an infinite number of locations.

2. Five hours later you go to those same specific locations and measure the temperatures again. You find the average temperature has risen by one degree.

The problem with your example is that you are assuming that the measurements taken 5 hours ago have not been altered. In the case of climate science, and the surface temperature database the numbers have been altered. And you are assuming that you want something like an accurate measurement of the temperature in your house so you go about taking temperature inside closets, and interior bathrooms rather than going about taking temperatures on south facing windosills, near heater vents, and behind the refrigerator. An important study has just been accepted for publishing that shows that most of the temperature stations gathering data upon which your anomoly charts depend are substandard and are recording a significant warm bias.

5. Would you claim the there is no evidence of the house warming?

If you knew that my temperature data base around my house consisted of mostly measurements from south facing windosills, near heater vents, behind the refrigerator, and near light fixtures, what sort of assesment could you possibly make regarding the actual temperature of my home and whether it was, in fact warming, or cooling?

I hope you understand this example better than you did the pool analogy.

I hope that at some point you come to understand that a fraudulent data base will give you nothing more than fraudulent information and nothing usefull can be done with fraudulent data no matter how much linear regression you perform as indicated by the abject failure of climate modelling to reflect anything like observed measurements.

 

[Lagboltz]

Clearly you didn't actually do it or you woudn't be claiming the same temp.

As to my "big bucket" idea....clearly you never researched that either.

http://www.poolcenter.com/leaks.htm
http://www.ehow.com/how_4963967_detect-swimming-pool-air-leaks.html
http://www.albertgrouplandscaping.com/our-blog/bid/53041/How-to-detect-a-leak-in-your-swimming-pool
http://poolandpatio.about.com/od/maintainingyourpool/tp/LeakTests.htm

The big bucket guys have an OK method for dryer climates, but mine was more accurate in the high humidity area I live.

Your pool example is irrelevant to global temperature data so pot, meet kettle.

Then why do you keep harping on it? I'm only answering your comments which ignored the particular problems I had. You stop harping and I will stop replying.

Except that my technique is recommended by professional pool maintenence firms while yours would provide you no useful data at all.

Nope. My method more accurately addressed wind and temperature conditions and still measured the water level same way a big bucket would. I have a suspicion that you forgot exactly how I measured the evaporation rate.

If you believe that linear regression is useful in modelling and predicting global temperatures, then show me the model that has been successful.

Linear regression is used in the data interpretation after the modeling. It's use is more for understanding current data, and less for short term future prediction.

The problem with your example is that you are assuming that the measurements taken 5 hours ago have not been altered. In the case of climate science, and the surface temperature database the numbers have been altered. And you are assuming that you want something like an accurate measurement of the temperature in your house so you go about taking temperature inside closets, and interior bathrooms rather than going about taking temperatures on south facing windosills, near heater vents, and behind the refrigerator. An important study has just been accepted for publishing that shows that most of the temperature stations gathering data upon which your anomoly charts depend are substandard and are recording a significant warm bias.

My example is not aimed at understanding the complexities of climate science modeling. It's only interested in the data analysis concept for a benign environment that has no modeling.

If you knew that my temperature data base around my house consisted of mostly measurements from south facing windosills, near heater vents, behind the refrigerator, and near light fixtures, what sort of assesment could you possibly make regarding the actual temperature of my home and whether it was, in fact warming, or cooling?

I certainly agree. I'm obviously not talking about those extremes otherwise the assay would be silly and the average temperature would be well above just 2 degrees. In my house there is more than a 2 degree difference from room to room without significant heat sources, so I'm assuming much more reasonable sample points.

Under the premises I gave, you still didn't answer the question, "Would you claim there is no evidence of the house warming?"

I hope that at some point you come to understand that a fraudulent data base will give you nothing more than fraudulent information and nothing usefull can be done with fraudulent data no matter how much linear regression you perform as indicated by the abject failure of climate modelling to reflect anything like observed measurements.

Of course what you say is correct. However you are assuming fraudulence without any proof. Theoretically there can be fraudulence in research organizations whether they are politically motivated to be warmers or anti-warmers.

Getting back to the question which has a fundamental bearing on the house example, "Would you claim there is no evidence of the house warming?"

 

[palerider]

The big bucket guys have an OK method for dryer climates, but mine was more accurate in the high humidity area I live.

One of the "big bucket guys" business is in DC, the other is in NY. There are a few places in the US that can claim that they have a slightly higher humidity than DC, but not enough to alter the results of the bucket test.

Linear regression is used in the data interpretation after the modeling. It's use is more for understanding current data, and less for short term future prediction.

Unless the data are so flawed as to be practically useless.

Under the premises I gave, you still didn't answer the question, "Would you claim there is no evidence of the house warming?"

If you have an honest set of data that does not inject a warm bias then one could claim evidence of warming. That, however, is not the case with the data collection practices with regard to surface temperatures.

Of course what you say is correct. However you are assuming fraudulence without any proof. Theoretically there can be fraudulence in research organizations whether they are politically motivated to be warmers or anti-warmers.

I gave you clear evidence of past temperatures being altered. Here is a link to the source of a paper that has just been accepted for publication that brings the warm bias being injected into the data into sharp relief. There is ample evidence of fraudulence; whether you choose to look at it and see it for what it is is your decision. You can lead a horse to water but you can't make him appreciate baroque chamber music.

http://www.surfacestations.org/

Getting back to the question which has a fundamental bearing on the house example, "Would you claim there is no evidence of the house warming?"

Again, if the data do not inject a warming bias, then they would indicate evidence of warming. That is not the case, however, with the surface temperature data being used to claim anthropogenic warming in the climate and, alas, the data from which your temperature anomoloy chart came. The science upon which the claims of man made climate change based is so shoddy, that frankly, if it weren't for the political power to be gained via the hoax, it would be laughed out of even elementary school science classes.

 

[Lagboltz]

One of the "big bucket guys" business is in DC, the other is in NY. There are a few places in the US that can claim that they have a slightly higher humidity than DC, but not enough to alter the results of the bucket test.

I think we are in agreement with the original thrust of the pool level example which is that the absolute amount of water in the pool has no relevance, only the level change over time is important.

Forget my pool example and lets use the pool authority's method that you cite instead. Briefly, they say check for plumbing leakage, measure the drop in pool level and make an evaporative correction (using a big bucket).

The above is contrary to what you said earlier in that you need to know how much water you have. You also said that the evaporative correction is "tampering".

In order to accurately determine whether or not you have a leak, you first need to know how much water you have. Further, you altered your data based on the evaporative rate from a pan vs a body of water that was many orders of magnitude larger. You have no idea what effect your data tampering had on the actual amount of water you were or were not losing to a leak.

Unless the data are so flawed as to be practically useless.

If you have an honest set of data that does not inject a warm bias then one could claim evidence of warming. That, however, is not the case with the data collection practices with regard to surface temperatures.

That's right. If data is flawed, the results are flawed.

I gave you clear evidence of past temperatures being altered. Here is a link to the source of a paper that has just been accepted for publication that brings the warm bias being injected into the data into sharp relief. There is ample evidence of fraudulence; whether you choose to look at it and see it for what it is is your decision. You can lead a horse to water but you can't make him appreciate baroque chamber music.

http://www.surfacestations.org/

I will read that paper you cite in the next few days.

Again, if the data do not inject a warming bias, then they would indicate evidence of warming.

We are in agreement here. My point in the house temperature example is similar to the pool level example in that measuring a rate of change of a variable does not depend on an absolute value of the data, but only on the relative differences of rationally chosen data points.

That is not the case, however, with the surface temperature data being used to claim anthropogenic warming in the climate and, alas, the data from which your temperature anomoloy chart came. The science upon which the claims of man made climate change based is so shoddy, that frankly, if it weren't for the political power to be gained via the hoax, it would be laughed out of even elementary school science classes.

The possibility of fraudulent data tampering has been covered in other threads. My only motive here is to address your OP and your arguments concerning the numerics of your assertion.

Relative to the OP, here are my major points, assuming the integrity of the data.
1. Assessment of a temperature trend depends on the relative differences in a temporal and spatial data set, and is invariant to the absolute values of temperatures.

2. Temperature trends over a long period of time can be accurately determined even though the data are highly noisy.

How can noisy data be reliable? Linear Regression, (LR) provides two numbers: y-intercept, and slope of the data points. The slope is the parameter for assessing warming. The y-intercept is not used. Other information that can be gleaned from LR are the statistics of the fit, and standard deviations of the fit of noisy data to the LR. According to the Central Limit Theorem, the data groups that have independent and identical distributions will obey Gaussian statistics. A property of Gaussian statistics, is that the standard deviation of the trend will decrease by the square root of the number of data points.

3. You cite three references for your question, what is the "temperature now."

For example:
On German public TV in 2009, Hans Schellnhuber stated that the global mean was 15.3 degrees.
Stefan Rahmstorf states that it is 15.5 degrees.
The IPCC 2007 4AR says 14.5 degrees.

What is the meaning of "now"? Are they all referring to the same year? The same time span of averaging? An LR projection? I would think the latter would be the most accurate.

Those sources are all professional "warmers." The point is that although they may differ in their technique of defining "temperature now" (a y-intercept) they do not differ on the nature of the slope of the temperature data.

 

[palerider]

I think we are in agreement with the original thrust of the pool level example which is that the absolute amount of water in the pool has no relevance, only the level change over time is important.

Actually we are not. You have altered my original statements as badly as hansen et al have altered the surface temperature database. If you were going to perform a linear regression study of your pool, which was your original statement, then as I pointed out, you would need to know how much water you began with in order to have a data set that was worth the time it took you to do it.

Then you asked me how I would do it and as I said, it is just as bad to overcomplicate a simple system as it is to oversimplify a complicated system. Your method was overcomplicated and wouldn't produce anything like actual useable information. My method was simple and would tell you whether or not you had a leak in 24 hours and

The above is contrary to what you said earlier in that you need to know how much water you have. You also said that the evaporative correction is "tampering".

Again, you are mischaracterizing my statements and taking them out of context. I said that if you were going to use your system, and get anything like useful data, you would need to know how much water you had in your pool. As in the house example, a data set that wasn't accurate or injected a bias would be useless in determining whether or not the house was warming or cooling, a data set that doesn't involve the absolute amount of water in the pool at any given time would produce results that would be of no value in determining whether or not you had a leak. A linear regression study on useless data is going to produce nothing but useless output. Of course, you could certainly act on that data and maybe be right or maybe be wrong, but your actions would not be accurately directed by the output of your study.

That's right. If data is flawed, the results are flawed.

You acknowledge that you couldn't rely on temperature data in the home if accurate temperatures were not taken. By the same token, you couldn't rely on the data gathered from your swimming pool unless you could be reasonably sure that you know how much water is in the pool. You chose a complicated method and as such, gathering meaningful data is going to be complicated.

We are in agreement here. My point in the house temperature example is similar to the pool level example in that measuring a rate of change of a variable does not depend on an absolute value of the data, but only on the relative differences of rationally chosen data points. {/quote]

If the output of the linear regression on the temperature data in the house is to mean anything, then of course you need to know what the actual temperature is. If you want to know whether or not water is leaking from your pool, using the same sort of methodology, you need to first know the actual amount of water in the pool.

The possibility of fraudulent data tampering has been covered in other threads. My only motive here is to address your OP and your arguments concerning the numerics of your assertion.

My original assertion that at this point, we don't know if warming or cooling is happening stands because the data have been so corrupted that any output from them is meaningless.

1. Assessment of a temperature trend depends on the relative differences in a temporal and spatial data set, and is invariant to the absolute values of temperatures.

Assuming that you have an accurate data set to work with.

2. Temperature trends over a long period of time can be accurately determined even though the data are highly noisy.

Not if the gathered data is injecting a signifigant bias, or early data has been deliberately altered to give a false impression of later data....or both.

How can noisy data be reliable?

We aren't talking about noise, we are talking about inept data gathering, and subsequent alteration of the ineptly gathered data.

3. You cite three references for your question, what is the "temperature now."

What is the meaning of "now"? Are they all referring to the same year? The same time span of averaging? An LR projection? I would think the latter would be the most accurate.

Take your pick...present year, present decade, present day... It doesn't matter because the data set is so flawed that whichever "now" you pick, there is going to be a margin of error that is multiple orders of magnitude greater than the claimed accuracy of the claimed temperature. Any temperature set that claims to be accurate to within a tenth of a degree is by defintion fraudulent as there is at least a two degree margin of error for the global mean.

Those sources are all professional "warmers." The point is that although they may differ in their technique of defining "temperature now" (a y-intercept) they do not differ on the nature of the slope of the temperature data.

And they do not differ in the use of a biased, deliberately altered surface temperature data set. No meaningful study can be made if it is based on flawed, biased, altered data.