1. Actual carbon as a % of atmospheric gases
2. Carbon produced by water vapor.
3. Carbon produced from human and animal
exhaleing in the normal course of living.
4. Carbon produced from wetland biodegredation.
5. Carbon produced from avrage annual volcanoe eruptions.
6. Carbon produced by burning of fossil fuels.
a. a subset of USA carbon production
from burning of fossil fuels as a
% to world man made carbon production.
7. An analysis of the overall reduction of
man made carbon dioxide if the USA cut its
carbon production by 50%.
7. The cost to the USA economy in lowered
GDP if the 50% carbon reduction was mandated.
You might be interested in reading this fellow’s experience with the Australian Carbon Office:
http://ncwatch.typepad.com/media/files/D-Evans2007.pdf
He’s a professional modeler like you, and among other things he describes the “gravy train” of AGW science. Six-figure salaries for scientists… not a bad gig, you don’t really want to mess that up, especially when it would mess up your friends as well. Check it out.
]]>This reminds me of the now infamous Mann “Hockey Stick” chart. Somebody (can’t recall who…Steve McIntyre?) took the model and showed that it would take random data and generate a hockey stick curve. The transfer function was essentially “anything in, hockey stick out”.
How convenient.
Having taken a lot of math as an undergrad on the way to an engineering degree, including a fair bit of control and modeling theory, it makes me wonder about the mathematical fluency of the people building these models. They may know climate science, but they seem to be absolutely abysmal at building models. And even worse at validating those models. You know, little things like running more than one data set through the model to see how it performs. Or trying it out on historical data before trying to give us an extrapolation out to the year 2100.
I work in the process industries where adaptive model-based control is used along with advanced models for plant optimization…things like running the plant in real time to minimize fuel costs, product quality variation, maximize output, etc. Maybe these climate scientists ought to get familiar with people whose models actually have to work in the real world, and work good enough to make money. Or understand the concept of model validation using historical data before claiming it is ready for prime time. But then again, these faulty models the climate scientists keep giving us are probably part of the full-employment act whereby continual fearmongering becomes its own industry. Models that don’t predict catastrophy probably don’t bring in grant monies or make for good headlines and soundbites.
Oh, and the NYT failing to get it right? That’s about as predictable as the sun rising tomorrow.
]]>I believe the inability of the climate models to accurately predict historical temperatures based on earlier, historical temperatures was a feature of a court case tried a few years ago. A citizen watchdog organization called the Center for Regulator Effectiveness objected to the use of the National Assessment on Climate Change largely because the two data models on which it was based could not outperform a random number generator in predicting US ground temperatures.
If I recall correctly, the case was settled out of court with some modifications and disclaimers to the NACC.
By the way, do you recall the huge flap about a Bush administration flunky “rewriting scientific reports?” That was this case. The Bush administration was OBEYING A COURT ORDER, something the New York Times article mentioned only in a quote from one of the authors of the NACC, and only in a dismissive tone, as though it was an excuse.
]]>While I fully appreciate the problems inherent in just measuring surface temperature accurately, and in properly accounting for all the variables that have biased these readings both episodically and systematically, the one topic that still seems elusive is the details of the climate models themselves.
Some questions:
- How many variables are input to these models? 5? 20? More?
- How sensitive is the output (I presume it is just a single variable – T) to small variations in input variables?
- What type of error bars exist around these model outputs based on uncertainties in the input variables?
- How non-linear are the various contributions of the input variables and how accurately are the inputs known, let alone their relationships to one another?
- How senstive are these models to boundary conditions? To initial conditions?
- How have these models been validated? Can they reproduce various historical data with a high degree of accuracy? For instance, if we load these models with data from 1950-1980, are they able to accurately “predict” temperatures in the period 1980-2000?
The models used are the entire basis for the “fact” of Global Warming and its relationship to increasing levels of CO2, yet those that wield the models are like some kind of secret order of priests that will not let the “laymen” understand what’s inside the black box.
I work in a company that makes mathematical models for process control so I have a cursory understanding of these matters. I also majored in Electrical Engineering; control theory and mathematical modeling of processes aren’t entirely foreign to me. With something as obviously complex as the climate, I am wondering just what makes us think that we have models that are even partially approaching robust and have any reliable predictive value whatsoever.
We have posited that the temperature is being driven predominantly by increased CO2, but how reliable are the models that purport to show this? Can they even model the historical relationship between CO2 and temperature accurately? What contribution to other input variables have?
My list of questions grows by the day. Anybody who can chime in and talk to me about these models and their veracity would be most welcome. But I’m becoming increasingly frustrated with “it’s complicated…you wouldn’t understand…leave it to the experts” hand waving.
]]>1) Who are you?
Just a layman observer. Going by real name. Not a scientist. That’s the point, really. I think it is very important for laymen to understand these issues. Unlike a lot of scientists involved in this, I do not think it beyond the ability for a layman to understand them.
Furthermore, it is laymen who must (and will) determine the policy, not the experts. The experts inform us, oh, yes. But they do NOT decide for us (and perish the thought)!
2) You say, “And there DOES have to be a heat increase in order for a heat sink to “kick in” and contribute. Is this true? Isn’t the temperature of the asphalt a factor of the temperature of the radiant source (e.g. the sun), rather than of the ambient air? Can’t it therefore get hotter than the ambient air temp? If you’ve ever replaced a roof during the summer, you’ve experienced how hot the asphalt gets, and unless I’m mistaken, it’s as much as 20 or 30 degrees F hotter than the ambient air.
In a word, yes. Such radiant heat is a direct offset as you describe. When a heat sink first appears on the scene, there is a direct bias a greater or lesser degree, the same as with waste heat.
And yes, this one-time offset appears in the record and biases the record to that extent. That is–supposed to be–adjusted for, whether it is a result of a site move or a change of site environment (i.e., they build the parking lot near the station). Usually it isn’t.
But a heat sink has a far more insidious effect. Namely that (after the direct offset) it increases the TREND of any subsequent temperature change. (Even if the offset has been adjusted out.)
Take the yucky scenario you describe. If the “real” temperature rises X degrees, the thermometer will go up MORE than X degrees. Likewise, if the temperature drops by X degrees, the temperature will go down by more than X degrees (but a certain chunk of the initial warming offset remains in any event).
This is not only because of the pumping up of the T-Max (which you describe), but also because when it gets cold at night, the accumulated joules come out of the asphalt and warm the air. LaDochy et al. (Dec. 2007) point out that T-Min is affected even more than T-Max insofar as urban heat Island effect is concerned. (T-Max and T-Min are averaged to get the daily temperature.)
I will therefore clarify my earlier statement:
AFTER THE HEAT SINK IS ALREADY IN PLACE (and we hope against hope its one-time effect is adjusted for), there must be a temperature increase past that point for a heat sink to exaggerate that trend.
I emphasize that what makes a heat sink such a vile monster is that it not only produces a one-time offset, but it exaggerates the TREND ITSELF.
3) Can you explain briefly how the satellite temp readings from UAH and RSS are done? What, if anything, do they have to do with the ground temp stations Watts is examining?
Fear not. Mr. Watts is not “going ASAT”. He is only measuring ground stations.
What is interesting about satellite records is that they do not measure temperature directly, they use a microwave proxy. Sometimes interferences occur and adjustments must be applied. (Orbital decay also intrudes.) Snow areas are also hard to measure for various reasons.
But they get a much smoother “overall looksee” than the scattered, poorly-distributed, homogenized (and, we suspect, pasteurized), long-suffering surface networks.
The fly in the ointment is that (according to Christy, if I understand him correctly) the troposphere has a bit of a heat sink about it: namely that the satellite TRENDS [sic] are said to be 1.2 times greater than the ground-level trends (and 1.4 in the tropics).
This takes us hideously into the realm of calculus: It is not enough to divide the satellite temperature by 1.2 (or 1.4), oh, no. One must actually do this to the slope of the trend.
On top of that, the AMOUNT the trend ITSELF varies depending on the baseline temperature, the rate of change increasing as the base temperature increases. As I pointed out before, the tropics use a 1.4 figure, not a the 1.2 for temperate zones.
Then throw in the humidity factor (which changes the density of the troposphere and screws with the equation. So deserts skew one way and jungles another.
At or before this point, I stand well off and radio for brain support. The only weaponry at my foot-slogging disposal is a lousy Masters in History. But what is important for the layman to know is that satellite data trends [sic! sic! SIC!] are NOT the same as surface temperature trends. So it is important to know if the satellite data has been adjusted to reflect this if you want to “compare” satellite and surface records.
There is also more than a little suspicion that when satellite measurements are “off”, they are adjusted to conform with surface data. Considering the variance in how much the trend should be adjusted (as per Christy), this should come as no surprise.
The only true antidote for this would be absolute openness in regard to data and adjustment methods so that the real scientists on both sides of the debate can have a crack at it. All Data, Algorithms, Code, And Operating Manuals, PLEASE! NASA, unfortunately, is notoriously reluctant to disclose (and is not alone in this).
]]>1) Who are you?
2) You say, “And there DOES have to be a heat increase in order for a heat sink to “kick in” and contribute.”
Is this true? Consider a site where a black asphalt roof is placed 10 feet from a sensor. Isn’t the temperature of the asphalt a factor of the temperature of the radiant source (e.g. the sun), rather than of the ambient air? Can’t it therefore get hotter than the ambient air temp? If you’ve ever replaced a roof during the summer, you’ve experienced how hot the asphalt gets, and unless I’m mistaken, it’s as much as 20 or 30 degrees F hotter than the ambient air.
3) Can you explain briefly how the satellite temp readings from UAH and RSS are done? What, if anything, do they have to do with the ground temp stations Watts is examining?
Thanks for all your interaction here.
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