Are Climate Models Realistic? (Now Includes at Least February Data)

Global mean temperature must decline in a statistically significant trend for a period of from thirty to fifty years, doing so under steadily rising CO2 concentrations, before the climate science community ever begins to question its central dogma.
In the meantime, everyone else will just have to decide what it is they do, or do not, want to believe about AGW, hopefully based upon their own evaluation of the various arguments and counter-arguments about it.

Very nice job. I’m an IT/Finance numbers guy (education: Ga Tech Physics), and a devote data bigot – the more we see this type of analysis, the easier and quicker the response to alarmist data manipulation (AKA Marcotting).
Question: what is the base period used to generate the temperature data anomaly for title chart: slopes vs calendar years (ie: warmer than what base period)?
I think I’m looking at “temperature anomaly” spreads in some of these allegedly “best practice” trend tracking data sets (uti/from: 2000.9/trend @ about 0.19 to gistemp/from: 2001.33/tend @ about 0.58 = 0.39 degree C) that appear to exceed 12 years of the predicted 3 degrees/century IPCC warming.
It’s laughable that educated people (?) take this stuff seriously, let alone propose to spend trillions “fixing” it…however, now that I think about it, the “fix” might be as simple as better basic science education.

The difference between skeptic and warming sites is that on the skeptic sites readers are asked to make their own opinion while on the warming sites readers are expected to run with what the “experts” and their admirers say.\
Irony: in the climate debate, only the Republican-claimed view is obtained democratically.

Susan, the Mauna Loa observatory measures global atmospheric concentration possibly with a slight NH lead. The squiggle just proves that ther is more temperate land in the NH, and that about half of biological carbon sequestration is terrestrial plants that take it up in summertime.
China, not the US, is the biggest CO2 emitter, and they are growing like crazy despite the US recession. The US ‘meeting’ Kyoto goals actually means only about a 5% reduction, which works out to about 1% of global anthropogenic CO2, which is of course rounding error invisible in the chart.
The fact that there has been no statistically significant change in temp for more than 15 years (or 17 in a different paper) falsifies present climate models. By itself it does not yet falsify the thesis that there could be some AGW. But for sure the models are now proven too sensitive, so existing (AR4) predictions for both rate and level are too alarmist and the urgency for immediate action is gone.

justthefactswuwt:
At April 8, 2013 at 11:06 am you answer the question of Susan at April 8, 2013 at 10:24 am which was

OK, so where does the CO2 indicated by Mauna Loa actually come from on the cycle measured?
Do we have any empirical measurements showing the source?

However, your answer only addresses the sources of CO2 from human activities.
Nature emits 34 molecules of CO2 for each molecule of CO2 emitted from all human activities.
Hence, I write to answer the major part of Susan’s question. And I provide information which can be found in one of our 2005 papers
(ref. Rorsch A, Courtney RS & Thoenes D, ‘The Interaction of Climate Change and the Carbon Dioxide Cycle’ E&E v16no2 (2005) )
I do this to provide information and do NOT want this answer to Susan’s question to deflect from its important subject which is “Are Climate Models Realistic?” in terms of their global temperature projections.
MECHANISMS OF THE CARBON CYCLE
Short-term processes
1. Consumption of CO2 by photosynthesis that takes place in green plants on land. CO2 from the air and water from the soil are coupled to form carbohydrates. Oxygen is liberated. This process takes place mostly in spring and summer. A rough distinction can be made:
1a. The formation of leaves that are short lived (less than a year).
1b. The formation of tree branches and trunks, that are long lived (decades).
2. Production of CO2 by the metabolism of animals, and by the decomposition of vegetable matter by micro-organisms including those in the intestines of animals, whereby oxygen is consumed and water and CO2 (and some carbon monoxide and methane that will eventually be oxidised to CO2) are liberated. Again distinctions can be made:
2a. The decomposition of leaves, that takes place in autumn and continues well into the next winter, spring and summer.
2b. The decomposition of branches, trunks, etc. that typically has a delay of some decades after their formation.
2c. The metabolism of animals that goes on throughout the year.
3. Consumption of CO2 by absorption in cold ocean waters. Part of this is consumed by marine vegetation through photosynthesis.
4. Production of CO2 by desorption from warm ocean waters. Part of this may be the result of decomposition of organic debris.
5. Circulation of ocean waters from warm to cold zones, and vice versa, thus promoting processes 3 and 4.
Longer-term processes
6. Formation of peat from dead leaves and branches (eventually leading to lignite and coal).
7. Erosion of silicate rocks, whereby carbonates are formed and silica is liberated.
8. Precipitation of calcium carbonate in the ocean, that sinks to the bottom, together with formation of corals and shells.
Natural processes that add CO2 to the system
9. Production of CO2 from volcanoes (by eruption and gas leakage).
10. Natural forest fires, coal seam fires and peat fires.
Anthropogenic processes that add CO2 to the system
11. Production of CO2 by burning of vegetation (“biomass”).
12. Production of CO2 by burning of fossil fuels (and by lime kilns).
Several of these processes are rate dependant and several of them interact.
At higher air temperatures, the rates of processes 1, 2, 4 and 5 will increase and the rate of process 3 will decrease. Process 1 is strongly dependent on temperature, so its rate will vary strongly (maybe by a factor of 10) throughout the changing seasons.
The rates of processes 1, 3 and 4 are dependent on the CO2 concentration in the atmosphere. The rates of processes 1 and 3 will increase with higher CO2 concentration, but the rate of process 4 will decrease.
The rate of process 1 has a complicated dependence on the atmospheric CO2 concentration. At higher concentrations at first there will be an increase that will probably be less than linear (with an “order” <1). But after some time, when more vegetation (more biomass) has been formed, the capacity for photosynthesis will have increased, resulting in a progressive increase of the consumption rate.
Processes 1 to 5 are obviously coupled by mass balances. Our paper assessed the steady-state situation to be an oversimplification because there are two factors that will never be “steady”:
I. The removal of CO2 from the system, or its addition to the system.
II. External factors that are not constant and may influence the process rates, such as varying solar activity.
Modeling this system is a difficult because so little is known concerning the rate equations. However, some things can be stated from the empirical data.
At present the yearly increase of the anthropogenic emissions is approximately 0.1 GtC/year. The natural fluctuation of the excess consumption (i.e. consumption processes 1 and 3 minus production processes 2 and 4) is at least 6 ppmv (which corresponds to 12 GtC) in 4 months. This is more than 100 times the yearly increase of human production, which strongly suggests that the dynamics of the natural processes here listed 1-5 can cope easily with the human production of CO2. A serious disruption of the system may be expected when the rate of increase of the anthropogenic emissions becomes larger than the natural variations of CO2. But the above data indicates this is not possible.
The accumulation rate of CO2 in the atmosphere (1.5 ppmv/year which corresponds to 3 GtC/year) is equal to almost half the human emission (6.5 GtC/year). However, this does not mean that half the human emission accumulates in the atmosphere, as is often stated. There are several other and much larger CO2 flows in and out of the atmosphere. The total CO2 flow into the atmosphere is at least 156.5 GtC/year with 150 GtC/year of this being from natural origin and 6.5 GtC/year from human origin. So, on the average, 3/156.5 = 2% of all emissions accumulate.
This is not surprising because few things in nature are so stable that they vary by as little as 2% p.a..
I stress that I hope this answer to Susan’s question will not lead to deflection of the thread onto the carbon cycle. The subject of this thread is “Are Climate Models Realistic?” in terms of their global temperature projections.
Richard

But if you use ‘one of the world’s best climate models…’ you can make these sort of claims:
http://www.bbc.co.uk/news/science-environment-22063340 (Plane flights ‘to get more turbulent’)
Never let the facts stand in the way of a good story.

Susan says:
April 8, 2013 at 11:28 am
If the hypothesis is correct, Mauna Loa should have shown something significant
in 2008-2009.
Since it didn’t where did the CO2 come from and what are the characteristics of
that source?
+++++++
The world slowed down, but China and emerging growth countries are GROWING and putting out more CO2 than other countries have cut. Even Germany is building a dozen new Coal Burning plants now while they are not growing.

greymouser70 says:
April 9, 2013 at 12:01 am
jorgekafkazar says
If I read Werner Brozek’s graph at the top of this post correctly; the sawtooth line is the plot of CO2 concentration in ppm(v) over time. It has nothing to do with the temperature. It does not need to have a zero point. What I think is causing the confusion is that there is no concentration scale on the graph. It would have been better to have put a concentration scale on the right side of the graph and making note that the trend lines and the concentration are not related.
The confusion is deliberate, only the CO2 data is normalized and the scale adjusted to fill the scale and plotted vs trends to give the impression that there’s a huge mismatch between CO2 and T. An honest way to present the data would be to normalize it all and plot the values (not the trends for T) on the same scale or plot ln(CO2) instead. If you realize that it’s the T trends that are plotted then what the graph is showing is that T is increasing linearly with a fairly constant trend (the CO2 is increasing with a fairly linear trend too, of ~0.5%/year).

Phil.:
Your post at April 9, 2013 at 10:03 am displays complete ignorance of what confidence limits indicate.
To avoid others being misled by your post I copy a post I provided on another WUWT thread to here. Please note its part which concludes saying,
“And that is all the confidence limits say; nothing more.”
Richard
In the thread at
http://wattsupwiththat.com/2013/04/03/proxy-spikes-the-missed-message-in-marcott-et-al/
richardscourtney says:
April 6, 2013 at 6:44 am
Werner Brozek:
Thankyou for your post addressed to me at April 5, 2013 at 5:47 pm.
Following several attempts to answer your specific questions in different ways – and cognisant of the need to use language comprehensible to onlookers – I have decided to provide this general answer in hope that it adequately covers all the issues you raise.
A full and proper consideration of these issues requires reference to text books concerning the use of statistical procedures as part of the philosophy of science. This very brief answer is my attempt at an overall view of confidence limits.
Nothing is known with certainty, but some things can be inferred from a data set to determine probabilities of being ‘right’ and ‘wrong’. These probabilities are the ‘confidence’ which can be stated.
As illustration, consider a beach covered in pebbles.
There are millions of the pebbles. A random sample of, say, 100 pebbles is collected and each pebble is weighed. This provides 100 measurements each of the weight of an individual pebble. From this an average weight of a pebble can be calculated. One such average is the ‘mean’ and it is obtained by dividing the total weight of all the pebbles by the number of pebbles (in this case, dividing by 100).
The pebbles on the beach can then be said to have the deduced mean weight.
However, none of the pebbles in the sample may have a weight equal to the obtained average. In fact, none of the millions of pebbles on the beach may have a weight equal to that average. Any average – including a mean – is a statistical construct and is not reality.
(This difference of an average from reality is demonstrated by the average – i.e. mean – number of legs on people. On average people have less than two legs because nobody has more than two and a few people have less than two.)
In addition to considering the mean weight of pebbles in the sample, one can also determine the ‘distribution’ of weights in the sample. Every pebble may be within 1 gram of the mean weight. In this case, there is high probability that any pebble collected from the beach will have a weight within 1 gram of the obtained average. But that does NOT indicate there are no pebbles on the beach which are 10 grams heavier than the obtained average. (This leads to the wider discussions of sampling and randomness which I am ignoring.)
Importantly, there is likely to be a distribution of weights such that most pebbles in the sample each have a weight near the mean weight, and a few pebbles have weights much lower and much higher than that average. This may provide a uniform distribution of weights within the sample. However, the sample may not have a uniform distribution because no pebble can weigh less than nothing, but a few pebbles may be much, much heavier than the mean weight: in this case, the sample is said to be ‘skewed’.
Assuming the sample is uniform then it is equally likely that a pebble will be within a range of weights heavier or lighter than the mean weight. (If the sample is skewed in the suggested manner then the likely range of weights heavier than mean weight is greater than the likely range of weights lighter than that average.) These ranges are the + and – ‘errors’ of the average and have determined probabilities.
The probable error range of +/-X at 99% confidence says that 99 out of 100 pebbles will probably be within X of the mean.
The probable error range of +/-X at 95% confidence says that 95 out of 100 (i.e. 19 out of 20) pebbles will probably be within X of the mean.
The probable error range of +/-X at 90% confidence says that 90 out of 100 (i.e. 9 out of 10) pebbles will probably be within X of the mean.
etc.
And that is all the confidence limits say; nothing more.
Therefore, if the weights of two pebbles are within the probable error range then at the stated confidence they cannot be distinguished as being different from the sample mean. And if a ‘heavy’ pebble has a weight within the probable error then that pebble’s weight says nothing about the sample mean.
In the above example, the sample mean is a statistical construct obtained from individual measurements of pebbles. And it is meaningless in the absence of a probable error with stated confidence: such an absence removes any indication of what the weight of a pebble from the beach is likely to be. And every pebble has equal chance of being within the +/- range of the mean.
The linear trend of a time series is also a statistical construct obtained from individual measurements. It has confidence limits with stated probability and it, too, is meaningless without such confidence limits.
At stated confidence a trend is equally likely to have any value within its limits of probable error.
(This is the same as any pebble is equally likely to have any weight within its limits of probable error from the mean weight.)
Therefore, if the trend is 0.003 ±0.223 °C/decade at 95% confidence then there is a 19:1 probability that the trend is somewhere between
(0.003-0.223) = -0.220 °C/decade and (0.003+0.223) = 0.226 °C/decade.
And, in this example, -0.220 °C/decade is not discernibly different from 0.226 °C/decade or from any value between them. There is a range of values from -0.220 °C/decade to 0.226 °C/decade which are not discernibly different. And similar is true for all probable error ranges.
Importantly, this lack of discernible difference is not affected by whether or not the range straddles 0.000 °C/decade.
I hope this is adequately clear.
Richard

thelastdemocrat:
Thankyou for your comment at April 9, 2013 at 8:54 pm (and for its unjustifiable flattery) in response to my post at April 9, 2013 at 11:53 am.
I write to make clear to onlookers that I agree your post. It addresses the part of my post which said;

In addition to considering the mean weight of pebbles in the sample, one can also determine the ‘distribution’ of weights in the sample. Every pebble may be within 1 gram of the mean weight. In this case, there is high probability that any pebble collected from the beach will have a weight within 1 gram of the obtained average. But that does NOT indicate there are no pebbles on the beach which are 10 grams heavier than the obtained average. (This leads to the wider discussions of sampling and randomness which I am ignoring.)

emphasis added: RSC
Richard

OOps! I typed too fast.
Clearly I intended to write
But global temperature varies by 3.8 deg.C during 6 months of each year; i.e. a rate of change of 76.00 deg.C per decade.
Sorry
Richard

Werner Brozek:
re your good comment at April 11, 2013 at 1:56 pm.
I write to make clear to onlookers that I was NOT suggesting any rate of change should be extrapolated (I know you were not suggesting I was).
My point was that all these data are statistical constructs and not physically real.
Your ‘boiling oceans’ extrapolation demonstrates that very clearly. Thankyou.
Richard