Sunday, June 8, 2014

See http://agwunveiled.blogspot.com/ for assessment since 1610
6/11/13
Updated 7/22/14
Mistakes made by the Consensus

CO2 increase from 1800 to 2001 was 89.5 ppmv (parts per million by volume). The atmospheric carbon dioxide level has now (through June, 2014) increased since 2001 by 27.7 ppmv (an amount equal to 31.0% of the increase that took place from 1800 to 2001) (1800, 281.6 ppmv; 2001, 371.13 ppmv; June, 2014, 398.83 ppmv) while the average global temperature trend has been flat 1. This is outside of the ‘limits’ asserted by the ‘Consensus’ of the Climate Science Community 2.

So how did the Consensus get it so wrong?

The scientists in the Consensus apparently don’t understand some of the science very well, stubbornly refuse to acknowledge some science or may not even be aware of some relevant science.

Here are some of the issues:

1. Global Climate Models
Climate Scientists use huge mathematic models that are intended to simulate climate over the entire globe. The mathematic models are very computationally intensive and are run on powerful computers. These so-called Global Climate Models (GCMs) divide the atmosphere into about 100,000 or more contiguous blocks which may also be called elements. For example, the Hadley Center model named HadAM3 is a 73 by 96 grid with 19 levels for a total of 133,152 elements. HadGEM1 has four times as many. The number of elements is limited by the practical consideration of computer run time. The programs are somewhat compromised because they necessarily use strategies such as parameterization of some phenomena and also use algorithms to suppress numerical computational artifacts such as aliasing and computational instability.

The known laws of physics and some approximations are applied to calculate energy interchange between the elements. Once everything balances at a particular time, the program advances by a specific time interval and repeats the process. This works great if you know exactly where you started (initial conditions) and have perfectly determined what causes change. Neither of these is true for the GCMs.

To be true for initial conditions, all properties at every point in the atmosphere would need to be specified which is clearly impractical. Instead, properties must be interpolated and extrapolated from the comparatively few known measurements and then smeared over the elements. Wikipedia has an extensive discussion of climate models at http://en.wikipedia.org/wiki/Global_climate_model . Caution is advised when consulting Wikipedia on controversial subjects because article content can be controlled by administrators and might exhibit their biases.

To perfectly determine how things change, requires exact application of the physical laws to each of the elements. Again this is impractical (e.g. some of the phenomena take place in less volume than an element) so some of the phenomena, such as convection and cloud behavior, are approximated using human-determined parameterization. One of the acknowledged sources of greatest uncertainty in GCMs is the parameterization of cloud behavior. The high sensitivity of average global temperature (AGT) to tiny cloud changes is readily demonstrated 10.

Climate Scientists are apparently unaware of the inherent limitations of the climate models that they use. Inherent in this type of modeling (approximation of initial conditions and time-step progression with approximate application of physical laws) is that the longer the program runs, the greater the uncertainty in the results. Although the GCMs are pretty good at predicting weather for up to a few days they are useless for predicting climate for years. This has been demonstrated in the total failure of GCMs to predict the apparently flat and even declining AGT trend since about 2001.

Thus the so-called global climate models are effectively global weather models. It is woefully naïve to assume that all that is needed to turn a global weather model into a global climate model is to run it longer.

The GCMs were expanded in an attempt to account for the influence of oceans in Atmospheric/Oceanic Global Climate Models (AOGCM). However, these models have suffered from poor definition of initial conditions especially temperature distribution in the oceans and a paucity of attention to the various natural ocean oscillations.

Both the GCMs and AOGCMs fail to incorporate thermalization and also their results are distorted by artificial enhancement by the users of the influence of increased atmospheric carbon dioxide.

2. Thermalization
Absorption and emission spectral lines reveal that gases can absorb and emit electromagnetic radiation (EMR) only at certain specific wavelengths. IR absorption for CO2 is in the range 14-16 microns. EMR from the planet is mostly in the range 5-50 microns.

When a molecule of a greenhouse gas (ghg) absorbs a photon of EMR (EMR can be considered to be in packets called photons) and then bumps in to another molecule before it has emitted a photon, its energy changes to a lower level and the probability that it will emit another photon is greatly reduced. The mechanism of bumping in to another molecule causes pressure and thermal conduction in the gas mixture. When a photon is absorbed but not emitted, the energy of the absorbed photon has been thermalized which warms the atmosphere. The practical observation of this is a greater temperature range night-to-day with lower absolute humidity.

After a photon (which has the necessary wavelength) has been absorbed by a carbon dioxide or other ghg molecule, but prior to it bumping in to another molecule, the ghg molecule is at an energy level where it can emit a photon. The photon that it emits (if it emits one) is the same wavelength as the photon that it absorbed. An observed characteristic of gases is that significant absorption of EMR takes place only at certain discreet wavelengths and significant emission takes place only at the same discrete wavelengths. (Theory and extremely fine measurements have revealed that the absorption and emission ‘lines’ are actually narrow statistical distributions)


It should be apparent that a time interval must pass between absorption and emission by any molecule. If that time interval were zero there would be no evidence that the photon had been absorbed and no ghg effect. The amount of time that passes between absorption and emission is very short (one assessment is 1-10 milliseconds, others as brief as less than a microsecond) but it must be more than zero. The amount of time that passes between contacts of molecules in a gas is also very brief. Kinematic theory of gases indicates less than approximately 0.1 microsecond. If a photon is absorbed by a carbon dioxide molecule just before it contacts a nitrogen (or other) molecule, then emission will not have taken place and the photon is thermalized. Thus part of the radiation that is absorbed by gas molecules is emitted as EMR (of the same wavelength that was absorbed) and the rest is thermalized. The fraction that is thermalized is determined by the statistical probability of which occurs first, the variable time to emit after absorption by a molecule or the variable time to make contact with another molecule. [All EMR energy in a microwave oven appears as heat but the physical process (which involves liquids and solids) is entirely different from that described here for gases.]

Failure to identify and account for thermalization is a major deficiency of the 1997 Kiehl & Trenberth chart which has been relied on heavily by the Intergovernmental Panel on Climate Change (IPCC). This chart is shown in the fourth IPCC report at AR4WG1, Chapter 1, page 96. A 2008 update to this chart can be seen at http://chriscolose.wordpress.com/2008/12/10/an-update-to-kiehl-and-trenberth-1997/ . It also fails to indicate thermalization.

The Kiehl and Trenberth (K & T) charts erroneously imply that all radiation, that was absorbed close to the ground and re-radiated, penetrates substantially to high altitude and also that all radiation from high altitude gets all the way to the ground. Both of these implications are misleading. With thermalization, EMR flux declines logarithmically with distance from the emitting surface.

A rough analysis 3 using an improved Kiehl and Trenberth type graphic indicates that, at sea level atmospheric pressure, a total of about 12% of the absorbed radiation (most of the IR that is absorbed is absorbed by water vapor) is thermalized (The large ‘notch’ in the EMR spectrum from earth indicates that CO2 thermalization should be much more than 12%). Thermalization of absorbed radiation is primarily what warms the lower atmosphere. (Thermalization is reduced at high altitude because the molecules are further apart.)

The three assessments of energy flow are presented below; all are in units of watts/m2.

Item
K & T 1997
K & T 2008
With 12% thermalization
Incoming solar radiation (insolation)
342
341
341
Reflected solar radiation
107
102
105
Energy radiated from earth’s surface
390
396
386
Surface radiation absorbed by the atmosphere
350
356

Back radiation from absorbed radiation near surface


306
Absorbed IR that is thermalized


40
IR from surface directly to space
40
40
40
IR emitted by atmosphere to space
165
169
121
IR emitted from clouds to space
30
30
75
Energy transported to atmosphere via convection
24
17
17
Energy transported to atmosphere via latent heat
78
80
80
Solar radiation directly absorbed by atmosphere
67
78

Solar radiation directly absorbed by clouds and atmosphere


75
Back radiation from clouds that reaches the ground


16
Solar radiation absorbed by surface
168
161
161

3. Feedback
A feature of GCM (and AOGCM) applications is feedback factor. In the phenomenon referred to as the ‘enhanced greenhouse effect’ increased temperature causes increased water vapor in the atmosphere which further increases temperature. Feedback factor relates to the ratio of temperature with feedback to temperature with no feedback. The result with positive feedback is a greater increase (or decrease) than would have occurred if there were no feedback.

Positive feedback is a part of the Global Warming Theory which posits that a small temperature increase from increased carbon dioxide is amplified by the increase in water vapor that the small temperature rise causes.

Warren Meyer in the form of a video clip at http://www.climate-skeptic.com/tag/feedback shows projected scenarios that have been calibrated by past measurements with various assumptions of feedback factor and demonstrates that feedback can not possibly be as high as must be assumed for GCMs to predict significant global warming.

Proxy data from ice cores show temperature trend direction changes. Apparently some climate scientists do not fully understand Feedback Control Theory. It dictates that temperature trend direction changes are not possible if NET feedback from average global temperature is significantly positive. GCMs do not predict significant Global Warming unless feedback is assumed to be strongly positive.

Without human-caused global warming there can be no significant human-caused global climate change.

4. Safe Carbon Dioxide Level
Included in the erroneous scary stories that have been circulated (and not refuted by Climate Scientists) are concerns about health and also about fears of the atmospheric carbon dioxide level reaching a ‘tipping point’ leading to runaway warming. The federal standard for the allowable level of carbon dioxide in the air that we breathe is that it should not be more than 5000 parts per million by volume (ppmv). Another study, found using the Google search link http://www.logico2.com/Documents/ACGIH%20recommendations%20for%20CO2.pdf corroborates this as a safe conservative level. Still another reference reports that performance of normal healthy males (e.g. in a submarine) is not degraded at levels up to 20,000 ppmv. Some greenhouses artificially increase the carbon dioxide level to about 1500 ppmv to enhance plant growth. The seasonally corrected atmospheric level in May, 2013 was 396.59 ppmv 4.

Runaway warming at elevated atmospheric carbon dioxide levels is refuted by the determination that during the late Ordovician period the planet plunged into the Andean-Saharan ice age when the atmospheric carbon dioxide level was over ten times the present.

5. Lack of Correlation
The complete lack of correlation between temperature and CO2 level for over 500 million years is evident in a graph 5 available on the web. Note also from this graph that, for nearly all of the past, the atmospheric carbon dioxide level has been higher, usually several times higher, than it is at present.

Many Climate Scientists have either not noticed, ignored or bizarrely rationalized climate determinations from the past and even measurements made since the thermometer was invented. During the last and previous glaciations, atmospheric CO2 increase often lagged temperature increase by hundreds of years. CO2 decrease also often lagged temperature decrease by hundreds of years. The lag is apparent in data determined from ice cores. Graphs of the reported data are shown in a March, 2008 paper 6. An event can not have been caused by another event that FOLLOWED it.

As shown on the first graph 6, average global temperatures for over a century have trended down, then up then down, then up then down, while average annual atmospheric CO2 levels have always risen since 1800.

Lack of correlation implies lack of causation. (Not proven because unidentified modulating factors might exist.)

6. Measured Data.
Thermometer measurements of average global temperature are reported as far back as 1850 by four agencies. Reported values are the differences between some fixed value, usually an average between predetermined dates, and the measured value. The differences are called anomalies. The actual temperature is the anomaly added to the fixed reference value. Numerical data and graphs of the anomalies are widely available on the web.

The normalized average of four reporting agencies is graphed in Figure 1 of a paper 7 made public 5/8/13. Billions of dollars have been spent in failed attempts to write GCMs and train them to calculate the measured data for extended time periods.

Contrary to the ‘Consensus’ approach, an analysis was started with the measured data. An equation was derived using the first law of thermodynamics and some logic. A seminal discovery was that the time-integral of sunspot numbers, properly reduced by radiation from the planet, resulted in a graph with a shape similar to the observed temperature run-up that has been called Global Warming.

Analysis of the PDO and ENSO combined with thermal capacitance calculations showed that the energy stored in the oceans is many times that stored in the atmosphere. This revealed that the oceans must be accounted for in any rational assessment. The up-trends and down-trends of the PDO are each about 32 years long. The up-trends and down-trends of all the oceans are included in a factor called ESSTA, for Effective Sea Surface Temperature Anomaly, with 32-year-long trends and amplitude to be determined. The amplitude of the ESSTA has turned out to be about 2/5 of a degree C.

Using the first law of thermodynamics (conservation of energy), observations on the sunspot number time-integral and ESSTA, an equation was derived that calculates AGT for the period of accurate global temperature measurements. A refined version of the equation, included in a paper 7, calculates measured temperatures with an excellent coefficient of determination of 0.90 irrespective of whether the influence of atmospheric CO2 is included or not. A refinement 11 of this paper extends the calculations back to 1610 and slightly increases accuracy.

7. Sunspot number effect
Total Solar Irradiation, TSI, varies slightly with sunspot number. The amount of variation is tiny (only about 0.1%) which has caused many climate researchers, who have mistakenly only looked at TSI, to discount sunspots as having anything to do with earth’s climate.

Various papers have been written that indicate how the solar magnetic field associated with sunspots can influence climate on earth. Decreased sunspots are associated with decreased solar magnetic field which decreases the deflection of and therefore increases the flow of galactic cosmic rays 8 on earth.

Henrik Svensmark, a Danish physicist, found that increased galactic cosmic rays caused increased low level (<3 km) clouds. An abstract of his 2000 paper is at http://prl.aps.org/abstract/PRL/v85/i23/p5004_1 . Marsden and Lingenfelter also report this in the summary of their 2003 paper 9 where they make the statement “…solar activity increases…providing more shielding…less low-level cloud cover… increase surface air temperature.” This has been further corroborated by the CLOUD experiments at CERN.

These papers associated the increased low-level clouds with increased albedo leading to lower temperatures. Increased low clouds would also result in lower AVERAGE cloud altitude and therefore higher average cloud temperature. Although clouds are commonly acknowledged to increase albedo, they also radiate energy to space so increasing their temperature increases radiation to space which would cause the planet to cool. Increased albedo reduces the energy received by the planet and increased radiation to space reduces the energy of the planet. Thus the effects work together to change the AGT of the planet.

Simple analyses 10 indicate that either an increase of approximately 186 meters in average cloud altitude or a decrease of average albedo from 0.3 to the very slightly reduced value of 0.2928 would account for all of the 20th century increase in AGT of 0.74 °C.

The mechanism sequence for the influence of sunspots on earth’s AGT appears to be: Fewer sunspots; reduced solar magnetic shielding; increased galactic cosmic rays penetrating the atmosphere; increased low-level clouds. Increased low-level clouds result in increased albedo and lower average cloud altitude; higher average cloud temperature; increased cloud-to-space radiation. Increased albedo and higher average cloud temperature both cause planet cooling. The opposite, more sunspots, produces higher AGT. TSI is complementary but is a much smaller contributor.

Others have looked at just amplitude or just time factors for sunspots and got poor correlations with AGT. The good correlation comes by combining the two, which is what the time-integral does. Note that a low but broad solar cycle may have just as much cumulative influence on AGT as a high but brief one. Both magnitude and duration are accounted for by using the time-integral of sunspot numbers.

The energy leaving the planet is accounted for by the average of sunspot numbers. Thus the net effect on AGT is the time-integral of the sunspot number anomaly. The sunspot number anomaly is defined as the difference between the sunspot number in a specific year and an average sunspot number for several years.

References:
9. Marsden & Lingenfelter 2003, Journal of the Atmospheric Sciences 60: 626-636 http://www.co2science.org/articles/V6/N16/C1.php