Rocket Scientist's Journal tag:www.rocketscientistsjournal.com,2010://1 2009-11-12T19:50:33Z Movable Type 3.31 IPCC'S FATAL ERRORS tag:www.rocketscientistsjournal.com,2009://1.58 2009-03-31T14:50:59Z 2009-11-12T19:50:33Z INTERNAL MODELING MISTAKES BY IPCC ARE SUFFICIENTTO REJECT ITS ANTHROPOGENIC GLOBAL WARMING CONJECTURE ALBEDO REGULATES CLIMATE, NOT THE GREENHOUSE EFFECT.CO2 HAS NO MEASURABLE EFFECT ON CLIMATE. ------------------------------------------------------------ FATAL ERRORS IN IPCC’S GLOBAL CLIMATE MODELS by Jeffrey A. Glassman, PhD... INTERNAL MODELING MISTAKES BY IPCC ARE SUFFICIENT
TO REJECT ITS ANTHROPOGENIC GLOBAL WARMING CONJECTURE

ALBEDO REGULATES CLIMATE, NOT THE GREENHOUSE EFFECT.
CO2 HAS NO MEASURABLE EFFECT ON CLIMATE.

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FATAL ERRORS IN IPCC’S GLOBAL CLIMATE MODELS

by Jeffrey A. Glassman, PhD

Revised 9/30/09.

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Some critics of the science of anthropogenic global warming (AGW) urge that its reliance on a consensus of scientists is false, while others simply point out that regardless, science is never decided by consensus. Some critics rely on fresh analyses of radiosonde and satellite data to conclude that water vapor feedback is negative, contrary to its representation in Global Climate Models (GCMs). Some argue that the AGW model must be false because the climate has cooled over the last decade while atmospheric CO2 continued its rise. Researchers discovered an error in the reduction of data, the widely publicized Hockey Stick Effect, that led to a false conclusion that the Little Ice Age was not global. Some argue that polar ice is not disappearing, that polar bears are thriving, and that sea level is not rising any significant amount.

To the public, these arguments cast a pall over AGW claims. But in a last analysis, they merely weigh indirectly against published positions, weigh against the art of data reduction, or rely on short-term data trends in a long-term forecast. Such charges cannot prevail against the weight of the United Nations International Panel on Climate Change (IPCC), and its network of associated specialists in the field, principally climatologists, should they ever choose to respond categorically. Moreover, these proponents can support their positions with hundreds running into thousands of published, peer-reviewed papers, plus the official IPCC publications, to weigh against tissue-paper-thin arguments, many published online with at best informal and on-going peer review.

On the other hand, what can carry the day are the errors and omissions included in the AGW model with respect to real and demonstrable processes that affect Earth’s climate. Here is a list of eight major modeling faults for which IPCC should be held to account.

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1. IPCC errs to add manmade effects to natural effects. In choosing radiative forcing to model climate, IPCC computes a manmade climate change, implicitly adding manmade effects to the natural background. Because IPCC models are admittedly nonlinear (Third Assessment Report, ¶1.3.2), the response of the models to the sum of manmade and background forces is not equal to the sum of the background response and the response to manmade forces.

A computer run, for example, that assumes the natural forces are in equilibrium, and then calculates the effects of a slug of manmade CO2 that dissolves over the years is not valid. The run needs to be made with the natural outgassing process and anthropogenic emissions entering the atmosphere simultaneously to be circulated and absorbed through the process of the solubility of CO2 in water.

2. IPCC errs to discard on-going natural processes at initialization. IPCC initializes its GCMs to year 1750 in an assumed state of equilibrium. At this time, Earth is warming and CO2, while lagging the warming, is increasing, both at near maximum rates. This initialization causes the models to attribute natural increases in temperature and CO2 to man. The error occurs not because the models fail to reproduce the on-going natural effects. It occurs because subsequent measurements of temperature and CO2 concentration, to which IPCC fits its modeled AGW response, necessarily include both natural and manmade effects.

Earth is currently about 2ºC to 4ºC below the historic peak in temperature seen in the Vostok record covering the four previous warm epochs. IPCC models turn off the natural warming, then calculate a rise attributed to man over the next century of 3.5ºC.

3. IPCC errs to model the surface layer of the ocean in equilibrium. IPCC models the surface layer of the ocean in equilibrium. It is not. It is thermally active, absorbing heat from the Sun and exchanging heat as well as water with the atmosphere. It is mixed with vertical and horizontal currents, stirred by winds and waves, roiling with entrained air, active in marine life, and undulating in depth.

This assumption of equilibrium in the surface layer leads IPCC to model CO2 as accumulating in the atmosphere in contradiction to Henry’s Law of solubility. This causes its model of ACO2 uptake by the ocean to slow to the rate of sequestration in deep water, with time constants ranging into many millennia. A consequence of Henry’s Law instead is that the surface ocean is a reservoir of molecular CO2 for atmospheric and ocean processes, and causes it to be in disequilibrium.

Assuming the surface layer to be in equilibrium leads IPCC to conclude that the measured increase in CO2 is from man’s emissions, without increases due to background effects or warming of the ocean. It also supports IPCC’s conclusion that atmospheric CO2 is well-mixed, contradicting its own observations of CO2 gradients in latitude and longitude. This false assumption allows IPCC to use the MLO record to represent global CO2, and falsely calibrate CO2 measurements from other sources to make them all agree.

4. IPCC errs to erase the global pattern of atmospheric CO2 concentration from its model. IPCC admits that East-West CO2 gradients are observable, and that North-South gradients are an order of magnitude greater. IPCC ignores that MLO lies in the high concentration plume from massive CO2 outgassing in the Eastern Equatorial Pacific. At the same time, IPCC ignores that ice core data are collected in low CO2 concentrations caused by the polar sinks where the ocean uptakes CO2. These features show that CO2 spirals around the globe, starting at the equator and heading toward the poles, and diminishing in concentration as the surface layer cools. The concentration of CO2 should be maximal at MLO, and minimal at the poles, but IPCC makes them contiguous or overlapping through arbitrary calibrations.

5. IPCC errs to model climate without the full dynamic exchange of CO2 between the atmosphere and the ocean. IPCC ignores the planetary flows of CO2 through the atmosphere and across and through the surface layer of the ocean, and then into and out of the Thermohaline Circulation. CO2 is absorbed near 0ºC at the poles, and returned about one millennium later to the atmosphere at the prevailing tropical temperature. IPCC does not model this temperature-dependent exchange of about 90 gigatons of carbon per year, even though it swamps the anthropogenic emission of about 6 gigatons per year.

The outgassing is a positive feedback that confounds the IPCC model for the carbon cycle.

6. IPCC errs to model different absorption rates for natural and manmade CO2 without justification. IPCC considers the ocean to absorb ACO2 at a few gigatons per year, half its emission rate. It reports natural CO2 outgassed from the ocean as being exchanged with the atmosphere at about 90 gigatons per year, 100% of the emission rate. IPCC offers no explanation for the accumulation of ACO2 but not natural CO2.

Thus IPCC models Earth’s carbon cycle differently according to its source, without its dynamic patterns in the atmosphere and the ocean, without its ready dissolution and accumulation in the surface ocean, and without the feedback of its dynamic outgassing from the ocean.

As a result, IPCC’s conclusions are wrong that CO2 is long-lived, that it is well-mixed, that it accumulates in the atmosphere, and that it is a forcing, meaning that it is not a feedback.

7. IPCC errs to model climate without its first order behavior. IPCC does not model Earth’s climate as it exists, alternating between two stable states, cold as in an ice age and warm much like the present, switched with some regularity by unexplained forces.

In the cold state, the atmosphere is dry, minimizing any greenhouse effect. Extensive ice and snow minimize the absorption of solar radiation, locking the surface at a temperature determined primarily by Earth’s internal heat.

In the warm state, the atmosphere is a humid, partially reflective blanket and Earth’s surface is on average dark and absorbent due primarily to the ocean. The Sun provides the dominant source of heat, with its insolation regulated by the negative feedback of cloud albedo, which varies with cloud cover and surface temperature.

As Earth’s atmosphere is a by-product of the ocean, Earth’s climate is regulated by albedo. These are hydrological processes, dynamic feedbacks not modeled by IPCC but producing the first order climate effects and the natural background which mask any effects due to man. IPCC global climate models do not model the hydrological cycle faithfully. They do reproduce neither dynamic specific humidity nor dynamic cloud cover. They are unable to predict climate reliably, nor to separate natural effects meaningfully from any conjectures about at most second order effects attributed to man.

8. IPCC errs to model climate as regulated by greenhouse gases instead of by albedo. IPCC rejects the published cosmic ray model for cloud cover, preferring to model cloud cover as constant. It does so in spite of the strong correlation of cloud cover to cosmic ray intensity, and the correlation of cosmic ray intensity to global surface temperature. Consequently, IPCC does not model the dominant regulator of Earth’s climate, the negative feedback of cloud albedo, powerful because it shutters the Sun.

By omitting dynamic cloud albedo, IPCC overestimates the greenhouse effect by about an order of magnitude (computation pending publication), and fails to understand that Earth’s climate today is regulated by cloud albedo and not the greenhouse effect, much less by CO2.

© 2009 JAGlassman. All rights reserved.

]]> SOLAR WIND tag:www.rocketscientistsjournal.com,2007://1.57 2007-07-06T13:42:20Z 2009-12-09T21:27:55Z SOLAR WIND HAS TWICE
THE GLOBAL WARMING EFFECT
OF EL NIÑO

THE CONSENSUS ON CLIMATE
MISTAKENLY ATTRIBUTES SOLAR WIND WARMING
TO MANMADE CARBON DIOXIDE

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SOLAR WIND, EL NIÑO/SOUTHERN OSCILLATION,
& GLOBAL TEMPERATURE:
EVENTS & CORRELATIONS

by Jeffrey A. Glassman, PhD

Revised 7/10/07

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ABSTRACT

Classical and advanced signal analysis techniques applied to the climate data of global temperature, solar wind, and El Niño/Southern Oscillation (ENSO) reveal new events and correlations in graphical form. The results include:

1.         Major state changes appear in the global temperature record around 1934.4 and 1979.5.

2.         A major state change occurred in the solar wind index around 1937 to 1939, and a secondary state change occurred in the 1970s.

3.         Major state changes occurred in the Southern Oscillation Index beginning about 1919.3 and 1979.4. A large state change occurred during the brief period of 1940.2 to 1942.0.

4.         The state changes are real in the records, but may be due either to data acquisition artifacts or to real physical phenomena.

5.         The Southern Oscillation Index has a weak cyclic behavior with a period of 3.38 years.

6.         Global temperature lags the Southern Oscillation Index by about 5 months.

7.         The global temperature record appears to suffer from excessive processing.

8.         High correlations found by other investigators may be the result of prior data smoothing.

9.         The low level of correlation between temperature and other parameters may be due to excessive noise, equivalently due to low signal to noise ratio. More importantly, it may be due to the closed loop gain of a mechanism in the climate, unknown to the Consensus on Climate, that regulates global surface temperature.

10.       Global temperature is weakly correlated with ENSO. The SOI could account for 4.6% of the measured variation in global temperature.

11.       Global temperature and the solar wind index are correlated. The solar wind index may contribute as much as 8.9% of the processed global temperature variations.

12.       Global temperature lags the solar wind index by about two to five years.

13.       ENSO and the Southern Oscillation affect the global surface temperature. The reverse, that temperature might affect either, is not true.

ENSO may, as the Consensus says, devastate, but it has only half the capacity of the solar wind to warm the planet. By omitting the solar wind, the Consensus underestimates the natural causes of global warming, simultaneously overestimating the anthropogenic sources by the equivalent of two ENSOs, assigning the error to carbon dioxide emissions.

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INTRODUCTION

Global surface temperature is the essence of global climate, and by definition the only sensible measure of Global Warming. Global temperature is influenced in part by the solar wind, according to a chain of connections admitted by the IPCC in its Third Assessment Report (TAR). The Consensus on Climate, which finds its voice in the IPCC reports, recognizes (1) a dependence of global temperature on clouds, (2) a positive correlation between clouds and galactic cosmic rays, and (3) the physics of cosmic ray flux modulation by the solar wind. Nonetheless, the Consensus refuses to adopt this three part model into its climate models for lack of evidence. Climate Change 2001, ¶6.11.2.2, pp. 384-385. More importantly, the TAR never uses the record of solar wind measurements, a record longer than the record of temperature from thermometers.

According to the Consensus, Earth’s surface temperature is strongly affected by the El Niño/Southern Oscillation (ENSO), so named because of the strong indication of El Niño events in the Southern Oscillation Index (SOI), a continuous variable. The Consensus is convinced that ENSO causes human suffering, and that on some time scales it is the cause of the strongest natural fluctuations in the climate. Climate Change 2001, Box 4, p. 52. However, the Global Circulation Models, formerly known as Global Climate Models but more aptly named Global Catastrophe Models, (GCMs), have yet to demonstrate sufficient accuracy in replicating ENSO to even answer the pointed question posed by the Consensus: whether anthropogenic greenhouse gases cause a positive feedback by precipitating warming El Niño events. Climate Change 2001, p. 151; ¶7.6.5, pp. 453-455. The SOI sample record is substantial, but the TAR uses it merely to remark on its variability and to label the El Niño events for qualitative discussion.

These issues are well-suited to ordinary signal analysis of the three time series because the method can recognize signals at the threshold of detectability. It can, among other things, locate events in the record, and measure the correlation and lead or lag between temperature and the other parameters, all key to model building. That analysis, not reported in the IPCC and apparently never addressed by the Consensus, is initiated here for what it reveals, and as background for upcoming climate studies.

DATA SOURCES

Records are available on-line of monthly measurements of the Temperature anomaly, the Southern Oscillation Index, and Solar Wind Index.

Temperature anomaly

The Temperature anomaly is the subject of several reductions by the IPCC (e.g., Fourth Assessment Report, Summary for Policy Makers, p. 14, Fig. 5; Climate Change 2001, Fig. 2.7c, p. 114. See also http://svs.gsfc.nasa.gov/vis/a000000/a001000/a001008/a001008_pre.jpg), including the infamous reduction for the Northern Hemisphere (Climate Change 2001, Fig. 2.20, p. 134) known derisively outside the IPCC reports as the Hockey Stick reconstruction. The phrase solar wind appears just once in the TAR, and that is in the title of a reference about cloud creation. However, the TAR uses not a single datum from the solar wind database. The Fourth Assessment Report also mentions the solar wind, but again just once, and that is to say that the effects of solar wind fluctuations are ambiguous. Id., ¶2.7.1.3, p. 192.

Global temperature data are available from January, 1880. http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts.txt. (see also ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/monthly.land_and_ocean.90S.90N.df_1901-2000mean.dat.) The global temperature as reported by the IPCC extracted from Figure 2.7(c) is shown in Figure 1. The report states without elaboration that the data are an “optimum average”.


Figure 2.7: …[C]ombined land-surface air and sea surface temperatures (ºC), 1861 to 2000, relative to 1961 to 1990, for … (c) Globe. …[S]hown are the unsmoothed optimum averages – red bars… . Climate Change 2001, p. 114. [Click figures to enlarge.]

Figure 1

IPCC Data Distribution Center

Although not stated in the Third or Fourth Assessment Report, the IPCC maintains a separate Data Distribution Center. It includes information on the Temperature anomaly but not on the solar wind.

The Temperature anomaly is an IPCC-calculated global figure based on a grid of temperature differences spanning the globe and recorded by the Climate Research Unit (CRU), School of Environmental Sciences, University of East Anglia (UEA). The CRU provides additional information on-line about the Temperature anomaly and its treatment by the IPCC, however its key records exceed the number of rows permitted in Microsoft Excel.

The CRU data are the differences between a calculated mean temperature for each grid section and its average from 1961 to 1990. The IPCC interpolates for missing data points, and its final temperature anomaly is a weighted average over the globe and over an unspecified time interval. In the processing, adjustments are included for station altitude, and to convert sea surface temperature to local atmospheric temperature.

In spite of these complications, the National Oceanic and Atmospheric Administration (NOAA) says,

By adding the long-term monthly mean temperature for the Earth to each anomaly value, one can create a time series that approximates the temperature of the Earth and how it has been changing through time. National Climatic Data Center, Global Surface Temperature Anomalies, 2/6/06. http://www.ncdc.noaa.gov/oa/climate/research/anomalies/anomalies.html

According to the Consensus, the long-term mean surface temperature is 14ºC. Climate Change 2001, p. 89.


Southern Oscillation Index

The available Southern Oscillation Index (SOI) data, designated “aa”, begin January, 1876. ftp://ftp.bom.gov.au/anon/home/ncc/www/sco/soi/soiplaintext.html. {Begin rev. 7/10/07} The Third Assessment Report charts them in Figure 7.9 after subtracting the average for the first 100 years. A copy is Figure 2, below. The offset by the average has little effect; it is trivial because by design, the Index is nominally zero. {End rev. 7/10/07}


Figure 7.9: Darwin Southern Oscillation Index (SOI) represented as monthly surface pressure anomalies in hPa. Data cover the period from January 1882 to December 1998. Base period climatology computed from the period January 1882 to December 1981. The step function fit is illustrative only, to highlight a possible shift around 1976 to 1977. Climate Change 2001, p. 455.

Figure 2


Solar Wind Index

{Begin rev. 7/10/07} Solar Wind Index (“aa”) data start January, 1868. ftp.ngdc.noaa.gov/STP/SOLAR_DATA/RELATED_INDICES/AA_INDEX/ [rev. 7/10/07].{End rev. 7/10/07} The Third Assessment Report refers to Tinsley, B.A., 1996, Correlations of atmospheric dynamics with solar wind-induced changes of air-earth current density into cloud top, J. Geophys. Res., 101, 29701-29714 ($9) in its discussion of a correlation between galactic cosmic rays and clouds. Climate Change 2001, ¶6.11.2.2, pp. 384-385. The Consensus summaries a mechanism proposed by Tinsley to link cosmic rays and clouds, but without mentioning the solar wind at that point or anywhere else in the Report.


SIGNAL ANALYSIS

Referring to these three records as raw data, the objective now is to mine them for correlations and abrupt changes in statistics. These changes delineate changes in state of the data. They might signify data acquisition artifacts, or actual climate events or patterns. The techniques rely on numerical analysis, witnessed by graphs, and employ ordinary techniques used in engineering signal analysis.

Data Reduction.

Data reduction may employ advanced techniques, but if any part of those techniques is kept hidden, the method is secret and beyond mere esotericism. An example is the application of Principle Component analysis where the principle components are not fully disclosed. Principle Component analysis is esoteric, not wrong. However discarding components or keeping the selection method secret violates scientific principles, and is sufficient to invalidate the results.

The objective of data reduction is to reveal features in the underlying physical processes, uncovering any artifacts caused by faulty data acquisition along the way. It is to unveil real events and patterns with which to perfect models, and from those models to make predictions.

Data reduction used to support a subjective conclusion is for the movies. Data reduction to make the data better looking, or trendy, or to create correlations in support of conjectures is for the advertising business.

In science, where a method fails to produce a useful result for a model, the application fails. The method is not discarded merely for being instantaneously inapplicable.

On the other hand, where a method does produce a useful result, it is successful even if the method may be deemed heuristic for lack of theoretical exposition. Here applied is a technique that reveals profound changes in the statistics of the data, changes which could be artifacts of the record preparation, but which are reproducible and which could not have been produced by the data reduction technique.

Temperature and the Solar Wind.

The signal analysis begins with a co-plot of the Solar Wind Index, aa, and the so-called global “Temperature anomaly”. Plotting both records along the same time scale creates a parametric view of the data, with time the parameter. This is Figure 3.


Figure 3

In this chart, the solar wind rises slowly over its history, marked by decadal clumping. Temperature has a lazy-w shape. The solar wind starts relatively well organized, and later diffuses. The temperature history starts disorganized and later coalesces. Stated another way, the variability of the Solar Wind Index increases over its record, and the variability of the Temperature anomaly decreases. No rationale is known for this organizational behavior, nor for its complementary appearance.

Solar Wind Trend

Filtering helps measure the rise in the solar wind record. Figure 4 shows four overlaid, low pass filter reductions, with time constants of one, two, three and five decades. At the shortest time constant, the solar wind reveals a step-ramp-step shape, with breaks around 1920 and 1960.


Figure 4

The long term, steady rise in both the Solar Wind Index and the Temperature anomaly suggest a correlation between the two traces.

Temperature & Solar Wind Events

Independent, best fit, three-step fits to Temperature and the Solar Wind, beginning in common from 1880, reveal no particular relationship. Figure 5.


Figure 5

In the legend, the numbers in parenthesis are one standard deviation error between the best fit and the data over the full record, expressed in the units of the ordinate. The digit preceding the standard deviation is the number of segments in the mathematical model. Best fit modeling characterizes the data objectively. The dates and step magnitudes are determined mathematically, once the investigator selects the number of segments. The fits are arithmetically best in that they minimize the sum squared error for the model type, and as is evident, not necessarily the best shape subjectively. For example, Temperature appears as though it would be better modeled with a step-step-ramp shape.


Cumulative Signal Analysis.

Numerical analysis of cumulative data reveals characteristics of the physics hidden by noise in the raw data. An illustration applied to the Temperature anomaly and the Solar Wind Index is in Figure 6.


Figure 6

The segmented, best fit curves comprise straight lines between the end points and the labeled graph markers on or near the cumulative curve. The best fit endpoints in this analysis lie on the cumulative data, and were not subject to optimization. The slope of each segment is the height of step in the corresponding best fit to the raw data. The step height approximates the mean value of the raw data in the segment interval span. The curves have the smallest Root Mean Square (RMS) error between the raw data, not the cumulative data, and the stairstep fits, as finally determined by the Excel 2004 solver routine, for the operator-selected number of segments.

The Temperature record contains a state change beginning at about 1934.4 and 1979.5, with second order state changes at about 1918.2, 1946.5, and 1997.4. Any of these may be data acquisition artifacts, as when instrumentation technology, standards, or the set of measuring stations changed. For example, new standards for thermometer measurements became effective circa 1920, and satellite measurements were added in the ’70s. No discrete, climate event appears in these data from 1880 to the present which suggests an anthropogenic source or event.

Masking Effects of Scaling

Cumulative analysis removes noise from the measurements, much as do its cousin, the running-average, pass band filters, and other classes of filters. Noise is the variation in the data from any source other than the parameter under examination. The cumulative technique has the advantage of lack of subjectivity, the bane of science. The analysis is immediately applicable to the Temperature anomaly because the original investigators reduced that record to approximately zero mean. If a constant 14ºC had not been subtracted from the Temperature, its cumulative graph would be the ramp in Figure 7.


Figure 7

The plot in Figure 7 comprises individual, unconnected dots for each data point. The state change information is still in the data, but it is no longer resolvable to the unaided eye. The state structure might be visible with a magnifying glass on a graph rendered at upwards of 600 dots per inch. Greater resolution would be required had the temperature been recorded in total degrees Kelvin. This demonstrates the masking effects of merely an unfortunate choice of units. The technique of offsetting a bias or mean is common in signal analysis, and, in some circumstances, necessary. It is the creation of an arbitrary zero point, as commonplace as using the Fahrenheit and centigrade scales. It causes no loss of information so long as the offset is stated. That is, the raw record can be restored exactly by calculating sample by sample differences in a cumulative history, and restoring the offset.

The Solar Wind Index retains a large average value. So in the cumulative, its signal changes ride masked atop a strong ramp. Figure 6. Another operation is necessary to prepare this raw record for cumulative analysis.

Raw Solar Wind vs. Cumulative Temperature anomaly

A comparison of Temperature with its seven-segment fit to the raw Solar Wind Index with its three-step fit is shown in Figure 8. The coincidental break around 1934 is suggestive of an actual climatic event. This coincidence is a form of correlation, and like correlation does not prove a cause and effect, but suggests where a causative event might lie. On the other hand, its absence is convincing evidence against a cause and effect.


Figure 8

The step models fitted to the Solar Wind Index and Temperature in Figures 5 and 8 could be fanciful. The fitting of a step function yields steps even if the record is a pure ramp or other kind of smooth curve. The fact that the steps are unequal may be due to noise, or to an acceleration in the record. No such choice occurs with the cumulative technique, next applied to the Solar Wind Index by offsetting the raw data by the full record mean.

Solar Wind Events

The Solar Wind Index contains changes, including one profound change, previously unknown at least to the Consensus on climate. A primary state change occurred around 1937-1939, and a secondary state change around 1980. These are revealed in Figures 9 through 12 with progressively increasing numbers of segments, 3, 4, 7, and 9, respectively. Since the RMS error to the raw data is already on these charts, the standard deviations referenced to the cumulative data are the RMS error between the segmented ramps and the cumulative curves.


Figure 9


Figure 10


Figure 11


Figure 12

Cumulative Solar Wind vs. Cumulative Temperature anomaly

The next three charts show a cumulative analysis of the offset Solar Wind Index parametrically with that of the Temperature anomaly. The best fits are independent, and for 3, 4, and 7 segments, with the RMS errors referenced to the raw data. Increasing the number of segments has a relatively minor effect on the accuracy (mathematically the accuracy cannot decrease), and quickly produces diminishing returns.


Figure 13


Figure 14


Figure 15

A reasonable conclusion is that the evident Solar Wind events did not precipitate the evident global temperature events.

Autocorrelation Functions of Solar Wind & Temperature anomaly

Next in Figure 16 are the autocorrelation functions for the Solar Wind Index and Global Temperature.


Figure 16

The well-known 11 year solar cycle appears in the Solar Wind Index, but not in the Temperature record. (Compare with “The surface temperature response to the 11-year cycle is found to be small (citations).” Climate Change 2001, p. 708.) The breadth and shape of the Temperature autocorrelation function suggests weighted filtering over a window of about 20 years. Such filtering can distort signal analysis, requiring special considerations. All the correlation function calculations here employ the tape-loop algorithm.

While global temperature is correlated with the solar wind, the cyclic behavior of the solar wind is not evident in the temperature. One reason might be heavy temporal smoothing of the Temperature anomaly record.

Cross Correlation of Solar Wind & Temperature anomaly

Next is the cross correlation function between Temperature and Solar Wind Index. Figure 17.


Figure 17

This curve is not sharp enough to yield a conclusive lead/lag relationship, perhaps again because of temperature processing. Physically, global temperature should not lead the Solar Wind Index. The data suggest that the Temperature lags the Solar Wind Index by about two to five years.

Temperature anomaly vs. Solar Wind Scatter Diagram

The most significant relationship between Temperature and the Solar Wind appears in the cross-plot scatter diagram with the linear fits. These are in Figure 18 with zero time offset between the traces.


Figure 18

The pair of lines are the result of simply changing which variable is assigned as the dependent variable, and it illustrates the centroid of the scatter, and the correlation between the variables. The legend includes the slope of each straight line fit, where the product of the slopes is the coefficient of determination, r2, the square of the correlation coefficient, r. The smaller the acute angle between the lines, the greater the correlation, and the lines cross at the means of the two variables. (If the lines are perpendicular, the traces are called orthogonal. Only if the lines are perpendicular and cross at (0,0), are they strictly called uncorrelated.) The line T(aa) is bold to emphasize the feasibility of temperature depending, in part, on the Solar Wind, while the reverse, aa(T), is not possible.

In signal analysis terms, the coefficient of determination is a measure of the mutual power between the normalized variables. In statistical terms, the coefficient represents the variability in a given observation due to an explanatory variable. The solar wind could account for 8.9% of global temperature in a linear regression.

On the Low Coefficient of Determination, r2.
Landscheidt

Another investigator came to quite different conclusions about the relationship between the solar wind and global temperature. This investigator, obviously outside the Consensus (the Consensus did not analyze the solar wind), wrote:

Abstract. Near-Earth variations in the solar wind, measured by the geomagnetic aa index since 1868, are closely correlated with global temperature (r = 0.96; P < 10-7). Geomagnetic activity leads temperature by 4 to 8 years. Allowing for this temperature lag, an outstanding aa peak around 1990 could explain the high global temperature in 1998. After 1990 the geomagnetic aa data show a steep decline comparable to the decrease between 1955 and 1967, followed by falling temperatures from 1961 through 1973 in spite of growing anthropogenic CO2 emissions. This points to decreasing global temperature during the next 10 years. Landscheidt, T., Solar wind near earth: indicator of variations in global temperature, Proceedings of 1st Solar & Space Weather Euro Conference, 9/29/00, p. 1. http://www.mitosyfraudes.org/Calen/SolarWind.html.

Smoothed yearly aa index [ordinate]. Smoothed yearly Northern Hemisphere temperature anomalies [abscissa]. Figure 1. Scatter plot of yearly means of the geomagnetic aa index and Northern Hemisphere land air and sea surface temperature anomalies 1868 -1998. The aa data are shifted to offset a 6-year lag of temperature. The slope of the regression line and the aggregation of the slightly smoothed data around the straight line fit indicate a close correlation (r = 0.75) which is highly significant ( P < 10-7). Id., p. 2.

A scatter plot of the raw yearly data shows a promising positive correlation between aa and temperature (r = 0.48). Id., p. 3. The data were subjected to three-point smoothing. The least-squares linear fit line indicates a strong correlation ( r = 0.75) which explains 56% of the variance. This correlation is highly significant. After the shift, the record of yearly means is reduced from 131 to 125 data points as the data lost by shifting cannot be replaced. [¶] Three-point smoothing, applied once, reduces the number of independent data to 42. Id., p. 3.

Regardless of statistical rationale, the yearly averaging at the outset produces additional correlation out of whole cloth, exacerbated by the additional three-point smoothing. Amplified correlation attained by smoothing is a mathematical recreation, but is less likely to lead to a physical model with predictive power than working from raw data.

Köhnlein

Still another investigator on a related subject followed a similar course, writing

A linear fit of daily solar wind parameters todaily sunspot numbers does not lead to very useful results. The residual scatter is simply too high. If, however, the plasma- and sunspot data are smoothed beforehand by an averaging procedure, then structures show up which are persistent in each of the last two sunspot cycles. This averaging procedure can either be accomplished by running means over, say, 3 years or by the lower terms of a Fourier analysis. Both versions lead to the same results. Köhnlein, W. Cross-correlation of solar wind parameters with sunspots (‘long-term variations’) at 1 AU during cycles 21 and 22, Astrophysics and Space Science, v. 245: 81-88. 11/13/96. p. 83. www.springerlink.com/index/N675G9N846K16722.pdf

Indeed, it is the so-called scatter (noise) that causes decorrelation. Smoothing removes noise, and thereby increases correlation mathematically, but it is no part of the underlying physics. Alternatively stated, smoothing is a regression process that progressively reduces data toward a curve. Two data streams similarly filtered appear correlated. Two straight lines are perfectly correlated, even though the sources were independent.

A strong r-squared, that is, one near unity, is excellent support for a cause and effect model between the dependent variable and the predictor, or explanatory, variable. Conversely, a small value may be due to noise alone, indicating that the independent variable has little predictive power, if any. A weak value might be a masking by noise. It can be an indication of a poor signal-to-noise ratio, a common challenge in communications, astronomy, and climatology. The noise can be an additive interference, contributions from multiple sources, or limitations in the instrumentation and data reduction. An example of a noisy source is the algorithm to reckon a global temperature from widely scattered measurements, over land and sea, and complicated by weather phenomena, altitude, seasonal and diurnal effects, measurement complexities, estimations and arbitrary weightings.

Open Loop Inferences from Closed Loop Measurements

Yet another cause of poor correlation, one not recognized by the Consensus, is the phenomenon of closed loop behavior. During the time that a separate phenomenon regulates the dependent variable, the closed loop response to a predictor variable can be sharply attenuated. The response is reduced by the closed loop gain. Earth’s global temperature is likely just such a variable. The mean temperature around 14ºC is not just some accidental, instantaneous value as the climate slowly wanders between the temperature of Venus and that of Neptune.

Current Global Catastrophe Models force greenhouse gas accumulation to drive the climate to end life-as-we-know-it – except for the dominant greenhouse gas, water vapor. And except for clouds, which have yet to be modeled successfully. And except for albedo, which the Consensus treats as a constant known to one significant figure.

Why Is Earth’s Climate so Stable?

The notion of a Delicate Blue Planet is romantic and juvenile. The idea of a tipping point is a manifestation of paranoia if believed, or mischief if not. Round boulders do not perch on the sides of hills, and cones are not found standing on their tips. Minute disturbances quickly produce a new, quasi-stable state. Global Climate Models are not designed with any stable state. They are as chaotic as the weather. They tip over in a random direction due to one disturbance or another, hence Global Catastrophe Models. At the start of a run, the GCM stands on its tip. The Consensus computes the average cause and effect from runs with a number of these unstable Global Conical Models. See especially Climate Change 2001, Chapter 8, Model Evaluation, pp. 471-512. Catastrophe is certain. The only questions by this paradigm is how fast, how far, which direction, and from which causes.

A rational approach to Earth’s climate begins with the observation that it is in a conditionally stable state, and the scientific challenge is first to model the variables that regulate that state. The global temperature does not move much with changes in the solar wind or ENSO. In part, that is because the temperature is regulated, and measurements can only be made in closed loop.

The next task for climatology is to determine the margin for closed loop control. The atmosphere has lots of room for more water vapor, more clouds, and a greater albedo, or the reverse, thanks to the immense reservoir of liquid water and its heat capacity.

Still, comparing cumulative temperature to the raw solar wind index as done for Figure 8 but with four segments for each trace instead of three, results in a weak and somewhat subjective support for a model for temperature dependence on the solar wind. It is shown in Figure 19.


Figure 19

The correlation in Figure 18 supports a dependence of global temperature on solar wind, and is an important conclusion of this signal analysis. Figure 19 suggests the dependence does not strongly arise from a state change in the solar wind. It shows the effects of constraining the breaks in the temperature model to coincide with those in the solar wind. The constraint increases the standard deviation of the error by more than 50% (4.8 to 7.5). Experimentation by lagging the temperature breaks might produce a better fit.

Regardless, the correlation need not be strong because the solar wind is not a direct source of warming. Instead the theory of cloud formation makes the solar wind a gate to admit greater solar radiation through reduction in cloud cover and hence reduction in Earth’s albedo.

Temperature and ENSO.

Third for signal analysis is the Southern Oscillation Index (SOI), a strong, climate measure over the South Pacific, well‑established as an indicator of Los Niños (the harmful El Niño and his amiable sister, La Niña) events. Sir Gilbert Walker, the discoverer, apparently was responsible for setting the Index to be neutral at zero, and negative toward an El Niño event. A sustained, strong excursion in negative or positive territory of the SOI invariably indicates such events, so climatologists give the phenomenon the name ENSO for El Niño/Southern Oscillation. See Figure 2, above.

ENSO Events

Raw SOI data are featureless compared to its colorful behavior in the cumulative. The comparison is shown in Figures 20 and 21 along with three- and five-piece linear fits, respectively. As before, the standard deviations given are for the respective fits.


Figure 20


Figure 21

The cumulative curves are surprising. Nothing in the cumulative signal analysis could have caused these statistical changes. The analysis proceeds mechanically without regard to the time coordinate.

When the Consensus analyzed the variability of ENSO, it divided the instrument record into four main epochs: the first 40 to 50 years, the period of 1920 to 1960, an intervening period, and the last 40 to 50 years, with special remarks for the period of low SOI from 1990 to 1995. Climate Change 2001, p. 151. The data are more precisely characterized by three first order epochs, separated at 1916 and 1977, with a brief, strong retrace in the middle epoch between 1940 and 1942.

ENSO vs. Temperature anomaly Events

Parametric comparisons of ENSO and the Temperature anomaly are in Figures 22 and 23, the cumulative first, yielding the dates demarking the first order events, and second in raw data form, providing the best fits.


Figure 22


Figure 23

At the opening of the record in 1880, SOI is slightly negative, during which time the climate is dominantly below average 0.3 ºC. This lasts until 1918 when ENSO enters a 60 year cooling state, interrupted for just two years beginning in 1940 by a sharp warming signal. Meanwhile, global temperature is within ± 0.1ºC of its long term average. In the middle of 1977, ENSO turned sharply toward El Niño by 4.2 units. During this time until the present, global temperature accelerated an average of over 0.4 ºC.

ENSO Effects

The Consensus claims that ENSO has global implications, and that its contribution to global temperature is probable.

Warm episodes of the El Niño-Southern Oscillation (ENSO) phenomenon (which consistently affects regional variations of precipitation and temperature over much of the tropics, sub-tropics and some mid-latitude areas) have been more frequent, persistent and intense since the mid-1970s, compared with the previous 100 years. Climate Change 2001, Summary for Policy Makers, p. 5.

Warm phase ENSO episodes have been relatively more frequent, persistent, or intense than the opposite cold phase during this period. [¶] This recent behavior of ENSO is related to variations in precipitation and temperature over much of the global tropics and subtropics and some mid-latitude areas. The overall effect is likely to have made a small contribution to the increase in global surface temperature during the last few decades. Climate Change 2001, p. 103.

Thus, cooler nutrient-rich waters upwell from below along the equator and western coasts of the Americas, favouring development of phytoplankton, zooplankton, and hence fish. Climate Change 2001, Technical Summary of the Working Group I Report, p. 52.

By cumulative analysis, the change attributed to the “mid-1970s” can be objectively set to the period of 1977.5 to 1979.4. The quantification of the ENSO signal to Los Niños events alone by the Consensus obscures indications of major shifts and trends in Pacific circulations.

In the upwelling discussion, the Technical Summary omits the coincident CO2 increases observed by Keeling and Revelle:

During “normal” years the partial pressure of carbon dioxide in surface ocean waters near the equator in the Eastern Tropical Pacific is 60 to 80 parts per million higher than in the atmosphere, and there is a flux of about 0.6 gigatons of carbon from the sea to the air. During these years the Southern Oscillation Index is positive, that is, the air pressure off the coast of South America is higher than in the far western Pacific. The excess CO2 is carried to the surface by water upwelling from depths of between 50 and 150 meters. These upwelling waters are also rich in plant nutrients, resulting in intense biological production and the settling out from the surface layers into deep waters of particulate organic matter containing nearly 1 gigaton of carbon.

During “El Nino” years, when the Southern Oscillation Index is negative, upwelling and biological productivity virtually cease, the surface waters are depleted in nutrients, and the carbon dioxide partial pressure in the sea is about the same as in the atmosphere. Consequently there is no appreciable flux of CO2 from tropical waters into the air. Bold added, Keeling, C.D. and R. Revelle, Effects of El Nino/Southern Oscillation on the Atmospheric Content of Carbon Dioxide, Meteoritics, Vol. 20, No.2, Part 2, June 30, 1985. P. 437.

These El Niño factors are discussed in the main body of the TAR, but in the context of “CO2 variability” and a “reduced upwelling of CO2-rich waters”. Climate Change 2001 in ¶3.5.2, pp. 208-209. Regardless, the Consensus concludes with a contrary observation:

In any case, the slowdown (of the early 1990s) proved to be temporary, and the El Niño of 1998 was marked by the highest rate of CO2 increase on record, 6.0 PgC/yr. Id., p. 210.

Signal analysis supports a different view of the relationship between ENSO and global temperature.

ENSO & Temperature anomaly Relationship

First is the autocorrelation function of the Southern Oscillation Index. Figure 24.


Figure 24

This figure shows that the well-known cyclic behavior of ENSO has a period of 3.38 years (“preferred period of about three to six years”, Climate Change 2001>, Technical Summary, p. 52), and again no evidence of the solar 11-year cycle.

Next is the cross correlation function between Temperature and the negative of the Southern Oscillation Index. Figure 25.


Figure 25

Because a negative going SOI is a warming trend, the cross-correlation calculation includes a sign change to preserve the usual orientation of the function. A well-defined peak in correlation of 0.46 occurs at a five month lag in the temperature record.

The TAR says,

Whether global warming is influencing El Niño… is a key question, especially as El Niño affects global temperature itself. Citations omitted, Climate Change 2001, p. 151.

The five month lag in temperature suggests the answer is no. Surface temperature is a weak, lagging indicator of ENSO, not a predictor.

Next is the scatter diagram of Temperature lagged by five months and the Southern Oscillation Index for the period of 1880 to 2007. Figure 26.


Figure 26

The chart shows the small but significant relationship that global temperature tends to increase with decreasing SOI. In a linear regression, the SOI could account for 4.6% of the variability (power) in the temperature anomaly.

The Measured Correlations Are Not Likely Due to Noise

Finally as a demonstration and validation of the conclusions, examine the correlation between the Solar Wind Index and the Southern Oscillation Index in a scatter diagram. The two should be orthogonal (r2=0). As shown in Figure 27, r2 = 0.0015.


Figure 27

An elementary simulation shows the probability of r2 being at least this large due to noise alone is a weak but not improbable 10%. Accepting the hypothesis that the traces are orthogonal is bolstered by the facts that r-squared for temperature and solar wind is 60 times larger, and for temperature and the southern oscillation it is 31 times larger.

These results support a model in which global temperature does not affect ENSO. Because the solar wind affects global temperature, if the temperature in turn influenced ENSO, the solar wind and ENSO would be correlated. They are not measurably correlated. This conclusion is supported by the fact that temperature lags ENSO.

CONCLUSIONS

According to the Consensus, “ENSO … play[s] a fundamental role in global climate” (Climate Change 2001, Technical Summary, p. 51) The Consensus on Climate said of its destructive power,

Changes associated with ENSO produce large variations in weather and climate around the world from year to year. These often have a profound impact on humanity and society because of associated droughts, floods, heat waves and other changes that can severely disrupt agriculture, fisheries, the environment, health, energy demand, air quality and also change the risks of fire. ENSO also plays a prominent role in modulating exchanges of CO2 with the atmosphere. The normal upwelling of cold nutrient-rich and CO2-rich waters in the tropical Pacific is suppressed during El Niño. Id., p. 52.

although the ENSO correlation with carbon dioxide proved fleeting:

In any case, the slowdown (of the early 1990s) proved to be temporary, and the El Niño of 1998 was marked by the highest rate of CO2 increase on record, 6.0 PgC/yr. Climate Change 2001, p. 210.

The Consensus accounts for the various natural sources for climate variability, and the remainder it must attribute to man:

Any human-induced changes in climate will be embedded in a background of natural climatic variations that occur on a whole range of time- and space-scales. Climate variability can occur as a result of natural changes in the forcing of the climate system, for example variations in the strength of the incoming solar radiation and changes in the concentrations of aerosols arising from volcanic eruptions. Natural climate variations can also occur in the absence of a change in external forcing, as a result of complex interactions between components of the climate system, such as the coupling between the atmosphere and ocean. The El Niño-Southern Oscillation (ENSO) phenomenon is an example of such natural “internal” variability on interannual time-scales. To distinguish anthropogenic climate changes from natural variations, it is necessary to identify the anthropogenic “signal” against the background “noise” of natural climate variability. Climate Change 2001, Technical Summary, p. 25.

In this accounting, the Consensus places ENSO third on its list after solar radiation and volcanoes. It excluded the solar wind, arguing

We conclude that mechanisms for the amplification of solar forcing are not well established. … At present there is insufficient evidence to confirm that cloud cover responds to solar variability.Climate Change 2001, ¶6.11.2.2 Cosmic Rays and Clouds, p. 385

The evidence has been hidden in the climate records for decades. It was not just that the solar activity was linked to cloud cover, but that it was linked to global surface temperature. The solar wind could account for 8.9% of the variation in the Temperature anomaly, 1.93 times the power of ENSO, which accounts for 4.6% of the surface temperature.

Climate signal analysis establishes the following:

1.         Major state changes appear in the global temperature record around 1934.4 and 1979.5.

2.         A major state change occurred in the solar wind index around 1937 to 1939, and a secondary state change occurred in the 1970s.

3.         Major state changes occurred in the Southern Oscillation Index beginning about 1919.3 and 1979.4. A large state change occurred during the brief period of 1940.2 to 1942.0.

4.         The state changes are real in the records, but may be due either to data acquisition artifacts or to real physical phenomena.

5.         The Southern Oscillation Index has a weak cyclic behavior with a period of 3.38 years.

6.         Global temperature lags the Southern Oscillation Index by about 5 months.

7.         The global temperature record appears to suffer from excessive processing.

8.         High correlations found by other investigators may be the result of prior data smoothing.

9.         The low level of correlation between temperature and other parameters may be due to excessive noise, equivalently due to low signal to noise ratio. More importantly, it may be due to the closed loop gain of a mechanism in the climate, unknown to the Consensus, that regulates global surface temperature.

10.       Global temperature is weakly correlated with ENSO. The SOI could account for 4.6% of the measured variation in global temperature.

11.       Global temperature and the solar wind index are correlated. The solar wind index may contribute as much as 8.9% of the processed global temperature variations.

12.       Global temperature lags the solar wind index by about two to five years.

13.       ENSO and the Southern Oscillation affect the global surface temperature. The reverse, that temperature might affect either, is not true.

ENSO may devastate, but it has only half the capacity of the solar wind to warm the planet. By omitting the solar wind, the Consensus underestimates the natural causes of global warming, simultaneously overestimating the anthropogenic sources by the equivalent of two ENSOs, assigning the error to carbon dioxide emissions.

BIBLIOGRAPHY

IPCC, Fourth Assessment Report (4AR), 2007

IPCC, Third Assessment Report (TAR), Climate Change 2001

Keeling, C.D. and R. Revelle, Effects of El Nino/Southern Oscillation on the Atmospheric Content of Carbon Dioxide, Meteoritics, Vol. 20, No.2, Part 2, June 30, 1985

Köhnlein, W. Cross-correlation of solar wind parameters with sunspots ('long-term variations') at 1 AU during cycles 21 and 22, Astrophysics and Space Science, v. 245: 81-88. 11/13/96

Landscheidt, T., Solar wind near earth: indicator of variations in global temperature, Proceedings of 1st Solar & Space Weather Euro Conference, 9/29/00

National Oceanic and Atmospheric Administration (NOAA), National Climatic Data Center, Global Surface Temperature Anomalies, 2/6/06

National Oceanic and Atmospheric Administration (NOAA), National Climatic Data Center, Southern Oscillation Index (aa)

Tinsley, B.A., 1996, Correlations of atmospheric dynamics with solar wind-induced changes of air-earth current density into cloud top, J. Geophys. Res., 101, 29701-29714 ($9)

© 2007 JAGlassman. All rights reserved.

]]> CO2: "WHY ME?" tag:www.rocketscientistsjournal.com,2007://1.56 2007-06-11T19:43:55Z 2010-03-08T16:33:04Z ON WHY CO2 IS KNOWN NOT TO HAVE ACCUMULATED IN THE ATMOSPHERE & WHAT IS HAPPENING WITH CO2 IN THE MODERN ERA by Jeffrey A. Glassman, PhD Revised 12/30/09. - Myles Goodman at Drexel posted the following question as...
ON WHY CO2 IS KNOWN
NOT TO HAVE ACCUMULATED IN THE ATMOSPHERE &
WHAT IS HAPPENING WITH CO2 IN THE MODERN ERA

by Jeffrey A. Glassman, PhD

Revised 12/30/09.

-

Myles Goodman at Drexel posted the following question as a comment to the Acquittal of Carbon Dioxide:

You posit that CO2 does NOT accumulate in the atmosphere. How do you explain atmospheric concentrations of CO2 increasing over the last 100 years?

The Acquittal shows that carbon dioxide did not accumulate in the atmosphere during the paleo era of the Vostok ice cores. If it had, the fit of the complement of the solubility curve might have been improved by the addition of a constant. It was not. And because the CO2 presumably still follows the complement of the solubility curve, it should be increasing during the modern era of global warming in recovery from Earth's various ice epochs. These conclusions find support in a number of points in the IPCC reports.

So the answer to the post begins with supporting background on why CO2 is known not to accumulate in the atmosphere, and then goes on to other aspects of the model that global warming causes increases in CO2, which accounts for the last 100 years or so.

]]>
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… UNDER CONSTRUCTION …

1. Estimates vary, but climatologists in the Consensus say that the atmosphere contains 730 GTons (PgC) of carbon and the uptake to the oceans alone is at least 90 GTons/year. It's a ninth grade algebra problem to calculate how long it takes to empty a bucket with 730 units at the rate of 90 units per year. If you throw in uptake by photosynthesis at 120 GTons/year and perhaps leaf water at the IPCC figure of 270 GTons/year, thus including everything in the IPCC's Third Assessment Report, 480 GTons a year is pouring out of the bucket.

{Begin rev. 6/5/09a}

Turnover time (T) (also called global atmospheric lifetime) is the ratio of the mass M of a reservoir (e.g., a gaseous compound in the atmosphere) and the total rate of removal S from the reservoir: T = M / S. For each removal process, separate turnover times can be defined. In soil carbon biology, this is referred to as Mean Residence Time. AR4, Glossary, p. 948.{End rev. 6/5/09a}

Now throw in approximately 100% replenishment, and you have an eleventh grade physics or chemistry problem where the level in the bucket is only slowly changed but the solution is quickly diluted. {Begin rev. 6/5/09b} This is a different question from residence time, elevated to a mass balance problem. {End rev. 6/5/09b}

Regardless of which way one poses the problem, the existing CO2 in the atmosphere has a mean residence time of 1.5 years using IPCC data, 3.2 years using University of Colorado data, or 4.9 years using Texas A&M data. The half lives are 0.65 years, 1.83 years, and 3.0 years, respectively. This is not “decades to centuries” as proclaimed by the Consensus. Climate Change 2001, Technical Summary of the Working Group I Report, p. 25.

See The Carbon Cycle: past and present, http://www.colorado.edu/GeolSci/courses/GEOL3520/Topic16/Topic16.html & Introduction to Biogeochemical Cycles Chapter 4, http://www.colorado.edu/GeolSci/courses/GEOL1070/chap04/chapter4.html, UColo Biogeochem cycles.pdf; The Carbon Cycle, the Ocean, and the Iron Hypothesis, http://oceanworld.tamu.edu/resources/oceanography-book/carboncycle.htm

2. In 1985, Keeling provided two estimates of the residence time (the reciprocal of his global air-sea transfer coefficient) and uptake of CO2 in the entire oceans, based on different methods from different locale. They were 7.9 years for 2 GTons/year and 5.2 years for 4.35 GTons/year. Keeling, C.D. and R. Revelle, Effects of El Nino/Southern Oscillation on the Atmospheric Content of Carbon Dioxide, Meteoritics, Vol. 20, No.2, Part 2, June 30, 1985. No one today uses such small numbers for the uptake, so the residence time must be much less than Keeling suspected.

3. There are no separate, physical paths to pipe natural CO2 and anthropogenic CO2 in the atmosphere or to segregate them in other reservoirs. There is a theory of plant isotopic preference, and a hypothesis of isotopic bias in the dissolution of natural and manmade CO2, but the Consensus has not posited such an effect in the carbon cycle exchange between the atmosphere and the reservoirs. In fact, the Consensus accounts for the difference in the concentrations in carbon isotopes in the atmosphere and the ocean not by selective solubility but by selective photosynthesis in the ocean. Climate Change 2001, Box 3.6, p. 207. Natural and anthropogenic are indiscriminately mixed in the atmosphere, and undergo similar if not identical residence times.

4. Sidebar: By losing its long residence time assumption, the Consensus finds its well-mixed conjecture invalidated. The admission in the TAR of CO2 gradients over the globe also contradicts its well-mixed claims. Independently, gradients must exist because of the highly concentrated outgassing of CO2 from equatorial waters, and the balancing concentrated polar uptakes. Consequently, the concentration of CO2 depends on where it is measured. Keeling himself warned not to mix CO2 measurements without regard to sinks and sources. He used calibration techniques to mix records.

5. The TAR says,

CO2 naturally cycles rapidly among the atmosphere, oceans and land. However, the removal of the CO2 perturbation added by human activities from the atmosphere takes far longer. This is because of processes that limit the rate at which ocean and terrestrial carbon stocks can increase. Anthropogenic CO2 is taken up by the ocean because of its high solubility (caused by the nature of carbonate chemistry), but the rate of uptake is limited by the finite speed of vertical mixing. Climate Change 2001, Technical Summary of the Working Group I Report, p. 51.

The first sentence is semantic gamesmanship to imply that CO2 cycles rapidly only if the CO2 is natural. That conjecture is made specific in the next sentence. The rest is fraught with error.

The well-established physics of gas solubility in water should not be changed to suit the AGW conjecture. {Begin rev. 6/5/09c} Henry’s Law states that at equilibrium the solubility of a gas, here CO2, in a solvent, here sea water, is proportional to the partial pressure of the gas adjacent to the solvent. The constant of proportionality is an empirical number, maximum at zero ºC or less, strongly and exponentially dependent on temperature and, for sea water, weakly dependent on salinity. Salinity reduces solubility about 10% at the cold end (3.41 to 3.28 g/kg at 0ºC), and about 1% (1.20 to 1.17 at 28ºC) at the hot end. DoE, Handbook of Methods for the Analysis of the Various Parameters of the Carbon Dioxide System in Sea Water, v. 2.11, 9/29/97; Lefevre, N., Functions to calculate equilibrium constants and solubility of carbon dioxide in sea water, 4/5/95, http://www.ipsl.jussieu.fr/OCMIP/phase1/distrib/funcchem.f. {End rev. 6/5/09c} The partial pressure of CO2 in sea water is a variable taken to mean, and computed from, the partial pressure of CO2 in an atmosphere in equilibrium with the sea water. See for example, Takahashi, T., et al., Method of underway pCO2 Measurements in Surface waters and the atmosphere during the AESOPS expeditions, 1996-1998 in the Pacific sector of the Southern Ocean and the Ross Sea, 4/25/00. http://usjgofs.whoi.edu/PI-NOTES/southern/Takahashi-pco2.html. The concept of the partial pressure of CO2 in sea water is a consequence of Henry’s Law.

Because solubility theory by definition applies at equilibrium, it is a steady state model, and hence static. While IPCC assumes the surface layer is equilibrium, it nevertheless recognizes the importance of kinetics in dissolution, a subject of extensive research over the years. IPCC says with regard to both the kinetic and static processes,

The air-sea exchange of CO2 is determined largely by the air-sea gradient in pCO2 between atmosphere and ocean. Equilibration of surface ocean and atmosphere occurs on a time scale of roughly one year. Gas exchange rates increase with wind speed (Wanninkhof and McGillis, 1999; Nightingale et al., 2000) and depend on other factors such as precipitation, heat flux, sea ice and surfactants. The magnitudes and uncertainties in local gas exchange rates are maximal at high wind speeds. In contrast, the equilibrium values for partitioning of CO2 between air and seawater and associated seawater pH values are well established [citations]. Bold added, AR4, ¶7.3.4.1 Overview of the Ocean Carbon Cycle, p. 528.

IPCC shows the result of the determining CO2 flux using the Wanninkhof gas exchange rate model in the beautiful Takahashi diagram. AR4, Figure 7.8, p. 523.
Why_me-1

[IPCC] Figure 7.8. Estimates (4° X 5°) of sea-to-air flux of CO2, computed using 940,000 measurements of surface water pCO2 collected since 1956 and averaged monthly, together with NCEP/NCAR 41-year mean monthly wind speeds and a (10-m wind speed)£sup§2£/sup§ dependence on the gas transfer rate (Wanninkhof, 1992). The fluxes were normalised to the year 1995 using techniques described in Takahashi et al. (2002), who used wind speeds taken at the 0.995 standard deviation level (about 40 m above the sea surface). The annual flux of CO2 for 1995 with 10-m winds is –1.6 GtC yr£sup§-1£/sup§, with an approximate uncertainty (see Footnote 1) of ±1 GtC yr£sup§-1£/sup§, mainly due to uncertainty in the gas exchange velocity and limited data coverage. This estimated global flux consists of an uptake of anthropogenic CO2 of –2.2 GtC yr£sup§-1£/sup§ (see text) plus an outgassing of 0.6 GtC yr£sup§-1£/sup§, corresponding primarily to oxidation of organic carbon borne by rivers (Figure 7.3). The monthly flux values with 10-m winds used here are available from T. Takahashi at http:// www.ldeo.columbia.edu/res/pi/CO2/carbondioxide/pages/air_sea_flux_rev1.html. Notes: Scale revised to make height proportional to width of range and to show number of cells by range and in total. Click on figures to enlarge.

Figure 1

At this point, IPCC conspicuously ignores its data that the ocean outgasses 90.6 GtC yr£sup§-1£/sup§ and absorbs 92.2 GtC yr£sup§-1£/sup§. AR4, Figure 7.3, p. 515. That’s a flux difference of exactly 90 GtC yr £sup§-1£/sup§, in each direction, apparently the additive, balanced, background of natural CO2 flux. IPCC asserts that Takahashi’s uptake is of anthropogenic CO2, and by computing the difference with the outgassing, implying that Takahashi measured and computed anthropogenic CO2. None of Takahashi’s measurements nor computational parameters discriminate between CO2 and ACO2, and none can. IPCC offers no explanation for how the 90 GtC yr £sup§-1£/sup§ might be distributed around the globe, how 90 GtC annually moves into and out of the atmosphere without its theoretically associated pCO2 disequilibrium, nor how Takahashi’s measurements failed to detect that flux.

IPCC does not provide the area of the Takahashi cells. However, a similar chart with supporting data files is available on-line from the Carbon Dioxide Research Group, Lamont-Doherty Earth Observatory of Columbia University. http://www.ldeo.columbia.edu/res/pi/CO2/carbondioxide/air_sea_flux/sumflux_2006.txt. This chart provides each cell area as well as the total flux per cell (called the Box flux). The total uptake is -2.42 GtC and the total outgas is 1.01 GtC, which within a few percent of IPCC’s claim for its Takahashi chart. Consequently, the only application IPCC makes of the kinetic theory of solubility, the Takahashi chart, fails to support its total flux estimates by a wide margin.

{Begin rev. 11/12/09} Takahashi’s results confirm the model of the role of the Thermohaline Circulation in the global distribution of CO2. See RSJ, The Acquittal of Carbon Dioxide, Figure 23. The density of Takahashi cells by type in Figure 1 show a gradual increase corresponding to a gradual uptake of CO2 across the surface of the ocean in the cooling, poleward circulations, and a precipitous drop corresponding to the bursts of CO2 outgassed dominantly (about 80%) in the Eastern Equatorial Pacific (EEP). As shown in the legend to Figure 1, Takahashi's cells have a width of 0.5 mol m£sup§-2£/sup§yr£sup§-1£/sup§, except his maximum uptake cell is eight times as wide (4 mol m-2yr-1), dominantly placed in the polar regions, and his maximum outgassing cell is seven times as wide (3.5 mol m-2yr-1), and are dominantly clustered in the EEP.

However, the Takahashi cells need to be recalibrated to account for the full volume of outgassing (90.6 GTC/yr) and uptake (92.2 GTC/yr) as reported by IPCC. AR4, Figure 7.3, p. 515. For the minimum values of each Takahashi cell, the total outgassing, uptake, and net would be (-4.11, 6.64, -3.44) GgmC/yr, at average they are (-3.32, 1.20, -2.12) GgmC/yr, and at maximum, (-2.54, 1.73, -8.05) GgmC/yr. {Begin rev. 12/30/09}The values should be on the order of 90 PetagmC/yr, an unresolved discrepancy of 10^7. A possible recalibration of the Takahashi diagram that agees with IPCC data is shown in Figure 1A:

Why_me-1

An example of the Takahashi chart with the scale recalibrated to preserve the general shape of cell values, but with the total outgassing set to 90.6 GtC/yr and the total uptake set to 92.2 GtC per IPCC AR4 Figure 7.3, p. 515.

Figure 1A

This recalibration extends the central uniform width of cells through the polar regions, no good reason being known for accelerated CO2 solubility there. Takahashi's polarity of the flux is maintained in each cell, so that the zero point is unchanged. The uniform height of 13.2 mol m£sup§-2£/sup§yr£sup§-1£/sup§ sets the total uptake to IPCC's estimate of -92.2 GtC/yr. The maximum outgassing of 944.6 mol m£sup§-2£/sup§yr£sup§-1£/sup§ causes the total outgassing to equal IPCC's 90.6. {End rev. 12/30/09.}

In March, 2007, Martin Durkin, a documentarian, produced a most controversial film titled The Great Global Warming Swindle in which he claims everything that the public has been told about CO2 causing global warming is a lie, leaving the Sun as the only climate driver. His film drew from about 34 individuals, mostly scientists, about 20 of whom appeared on camera. Included on camera was Professor Carl Wunsch, who, on the heels of the film's release, sided with IPCC and other believers in AGW to file a complaint with UK’s Office for Communications (Ofcom) for breaches of the Ofcom Broadcasting Code and the British Communications Act. They claimed that they did not have adequate notice of the nature of the production, and that it mislead the public by presenting misinformation. Ofcom found merit to some of the complaints, parts dealing with inadequate notice. In its decision, Ofcom noted that complainants referred to its opposition as “global warming deniers”, an appropriate contrast with believers in matters of faith but not science, and that Professor Wunsch did

describe the ’conveyor’ as “a kind of fairy-tale for grownups”.

Ofcom Broadcast Bulletin, Issue 114, 21 July 2008, p. 75 of 86. Professor Wunsch was not a contributor to IPCC’s Third or Fourth Assessment Reports, although IPCC did cite several articles he authored or co-authored. Wunsch is a professor of oceanography at MIT, a visiting professor in oceanography at Harvard and University College London, a senior visiting fellow in mathematics and physics at Cambridge, and author of four text books on oceanography. His placing the word conveyor in quotation marks suggests a misnomer, but the word is used often in the TAR and AR4, with and without quotes, usually as conveyor belt. More than a few of IPCC's references include the word conveyor in the title, of course without the qualification of quotation marks.

Professor Wunsch’s fairy-tale remarks are ambiguous, but in context appear to be a reference to the Gulf Stream, and not specifically the conveyor belt associated with the THC. However, the Gulf Stream is the North Western Atlantic warm circulation that serves as a collector for CO2, eventually to feed the northern headwaters of the THC. Wunsch in the documentary and his writings refers to climate memory in the ocean, stating that for some phenomena it can be as large as 10,000 years. Nowhere does he recognize the THC or conveyor belt role in the uptake and outgassing of CO2, nor the associated well-known transport delay of about 1,000 years. As well as these things are known today, the one millennium transport delay is the dominant signal in ocean memory.

Nothing Wunsch has said in the documentary is ambiguous nor appears context sensitive, yet his remarks are supportive of the theme of the documentary. He seems to have suffered thespian’s remorse for his participation in an inconvenient exposè of a family dogma. As an oceanographic, his observations are surprising. The existence of the THC is firmly established, IPCC even publishing a graph of the volume of sea water that it carries according to nine different authorities. TAR, Figure 9.21, p. 563. The role of the THC in atmospheric CO2 presented in The Acquittal of CO2, and as partially validated by the Takahashi diagram have yet to be challenged.

In other articles on the Journal, IPCC has been faulted for its specific assumption that the surface layer of the ocean is in equilibrium. This assumption has many unfortunate consequences. IPCC uses it to cause Anthropogenic CO2 to accumulate in the atmosphere, but not natural CO2! This gives nCO2 and ACO2 measurably different solubility coefficients, a previously unknown property. Since the only difference known between the two species of the gas is their isotopic mix, IPCC gives sea water the previously unknown ability to fractionate. Another result from this assumption is that IPCC can invoke inappropriate chemical equilibrium equations to give the sequestering of sea water multiple simultaneous time constants, ranging from centuries to thousands in the IPCC reports, and up to 35,000 years in the papers of its key author, oceanographer David Archer, University of Chicago. The assumption is foolishness as shown by its consequences, but it tends to confirm oceanographer Wunsch’s 10,000 year memory claim. The science should have influenced Wunsch to distance himself from IPCC, neither joining with it in the lawsuit, nor identifying himself as a supporter of its conclusion, the existence of AGW. {End Rev. 11/12/09}

It is the kinetic theory that has failed. As shown by the Lamont-Doherty data, Takahashi has used the following equation for CO2 flux as proposed by Wanninkhof in 1999:

Why_me-2

(1)

Other forms of this equation are found in the literature, and Wanninkhof compares several forms for k, normalized to a Schmidt number of 660, in the Figure 2 next.

Why_me-3

[Wanninkhof] Figure 1. Gas exchange relationships for steady winds reported in the literature. They include the general relationships of Smethie et al. [1985], Liss and Merlivat [1986], Wanninkhof [1992], and the relationships including specific parameterization of bubble mediated processes of Asher and Wanninkhof [1998], Monahan and Spillane [1984], and Woolf [1997]. The thick solid line (k = 0.0283 u£sub§10£/sub§£sup§3£/sup§) is the deconvolved cubic relationship using the global mean gas transfer rate determined from £sup§14£/sup§C. Where applicable, a drag coefficient of 1.1 x 10£sup§3£/sup§ was used and all data were normalized to Sc = 660.

Figure 2

All of the representations of the gas exchange coefficient, except the Monahan curve which Takahashi did not use, are asymptotic to zero as the wind speed goes to zero. For these, as shown by Equation (1), the CO2 flux is zero for no wind, and the no wind flux is not included as a background for the wind enhanced flux. The dependence of gas exchange on the other factors cited by IPCC (in bold at p. 528, above) is not represented in Equation (1) with any of the coefficients, except perhaps Monahan’s, and it is not represented in the Takahashi diagram. Under the same proviso, the flux of Equation (1) and in Tagahashi’s analysis relied on by IPCC is an additive flux enhancement due to the wind, and not the total flux. {End rev. 6/5/09c}

{Rev. 6/10/09} Man’s CO2 emissions of about 6 GtC/yr might be lost just in IPCC’s errors in estimating the massive natural emissions: 90 GtC/yr from the ocean, 120 GtC/yr from the land, not including 270 GtC/yr from leaf water. To show a danger from ACO2, IPCC adopted the Revelle conjecture about a bottleneck in the atmosphere-ocean CO2 exchange. It suppressed established solubility physics and resuscitating the Revelle buffer factor, the troubled anthropogenic conjecture in, and the essence of, Revelle and Suess’s 1957 pitch for an International Geophysical Year grant. Revelle, R., H. E. Suess, Carbon Dioxide Exchange between Atmosphere and Ocean and the Question of an Increase of Atmospheric CO2 during the Past Decades, Tellus, 9, 1957, pp. 18-27. Regardless, IPCC embraces the “Revelle factor (or buffer factor)” as if it had been validated. AR4, ¶7.3.4.2 Carbon Cycle Feedbacks to Changes in Atmospheric Carbon Dioxide, p. 531. IPCC illustrates its conclusion with this duplex figure:

Why_me-4

[IPCC] Figure 7.11. (a) The Revelle factor (or buffer factor) as a function of CO2 partial pressure (for temperature 25°C, salinity 35 psu, and total alkalinity 2,300 µmol kg–1) (Zeebe and Wolf-Gladrow, 2001, page 73; reprinted with permission, copyright 2001 Elsevier). (b) The geographical distribution of the buffer factor in ocean surface waters in 1994 (Sabine et al., 2004a; reprinted with permission, copyright 2004 American Association for the Advancement of Science). High values indicate a low buffer capacity of the surface waters.

Figure 3

IPCC defines the Revelle factor by its Equation 7.3,

Why_me-5

(2)

“relating the fractional change in seawater pCO2 to the fractional change in total DIC after re-equilibration (Revelle and Suess, 1957; Zeebe and Wolf-Gladrow, 2001)”. The rectangular brackets, [], designate “concentration of”, and DIC stands for Dissolved Inorganic Carbon, which is equal to [CO2]+[HCO£sub§3£/sub§£sup§–£/sup§]+[CO£sub§3£/sub§£sup§--£/sup§]. IPCC does not provide symbols to distinguish gaseous from aqueous CO2, such as CO2(g) and CO2(aq). Wolf-Gladrow uses CO2(aq) and “CO2 in air”. In this section, IPCC mixes the parameters, at one point referring to the hybrid “gaseous seawater CO2 concentration”.

Revelle & Suess introduce their buffer factor by saying,

Because of the peculiar buffer mechanism of sea water, however, the increase in the partial CO2 pressure is about 10 times higher than the increase in the total CO2 concentration of sea water when CO2 is added and the alkalinity remains constant, so that under equilibrium conditions at a given alkalinity

Why_me-6

(3)

γ being a numerical factor of the order of 10 for r and s small compared to A£sub§0£/sub§ and S£sub§0£/sub§ respectively. Bold added, R&S, id., pp. 24-25.

Thus,

Why_me-7

(4)

The authors define their variables as follows:

S£sub§0£/sub§:       Total carbon of the marine carbon reservoir at equilibrium condition, at time zero.
A£sub§0£/sub§:      Atmospheric CO2 carbon at time zero.
i:      Annual amount of industrial CO2 added to the atmosphere.
t:      Time in years.
s=S£sub§t£/sub§–S£sub§0£/sub§:      Amount of CO2 derived from industrial fuel combustion in the sea at time t.
r=it-s:       Amount of CO2 derived from industrial fuel combustion in the atmosphere at time t.

R&S discovered and reported that they could not set the parameters for the peculiar exchange they sought without causing a “too fast exchange rate” or “unexpectedly short mixing times for the ocean”. R&S’s problem was pure speculation at the outset, having never observed an ocean in equilibrium. The Revelle factor was a phantom.

IPCC’s version of the Revelle buffer factor is functionally different than the original. At equilibrium pCO2(g) equals pCO2(aq) by definition, and the ratio of pCO2(g) to [CO2(g)] approximates the ideal gas relationship, being nearly equal to the product of the gas constant and the absolute temperature, RT. But no such simple relationship exists between [CO2(g)] and [CO2(aq)]. Furthermore, the model for the CO2 flux arises out of the disequilibrium all across the ocean, where pCO2(g) ≠ pCO2(aq). Wanninkhof, 1999.

In its Second–Order Draft, the illustration comprised three graphs:

Why_me-8

[IPCC] Figure 7.3.10. The Revelle factor (or buffer factor) as a function of seawater temperature (S=35, pCO£sub§2£/sub§=230 μatm, TAlk=2300 μmol kg£sup§–1£/sup§) (a), as a function of pCO£sub§2£/sub§ (T£sub§C£/sub§ = 25º, S = 35, TAlk = 2300 μmol kg£sup§–1£/sup§) (b), and with its geographical distribution for year 1994 (c). With increasing partial pressure of CO£sub§2£/sub§ and decreasing temperature, the Revelle factor increases and thus the buffering capacity of the seawater decreases. Source: (a) and (b) from Zeebe and Wolf-Gladrow (2001), (c) from Sabine et al. (2004a).

Figure 4

Dieter Wolf-Gladrow reproduced this omitted inset in a presentation available online. D. Wolf-Gladrow, CO2 in Seawater: Equilibrium, Kinetics, Isotopes, 6/24/06, Chart 58, T dependence of Revelle factor. It is identical to Figure 7.3.10(a) except that he added the symbol RF0 to the ordinate, “Revelle or buffer factor”. He also provided inset (b) (Wolf-Gladrow, id., Chart 59), and both are attributed to Zeebe and Wolf Gladrow, 2001. Earlier in the presentation, Wolf-Gladrow provides this next illustration on the subject of solubility:

Why_me-9

Figure 5

Wolf-Gladrow, D., id., Chart 11, faithfully reproduced. The curves are identical, with the Revelle factor being a simple linear transformation of Henry’s coefficient, K£sub§0£/sub§: RF0 = 3.86 + 183.9*K£sub§0£/sub§. Climatologists set about to measure the Revelle factor, but what they measured was solubility.

In response to a reviewer’s comment to the draft Fourth Assessment Report, IPCC provides the explanation for removing the solubility-like curve.

Comment: "buffer factor decreases with rising seawater temperature…" This is a common misconception. The buffer factor itself has almost no temperature sensitivity (in an isochemical situation). In contrast, the buffer factor strongly depends on the DIC to Alk ratio. The reason why there is an apparent temperature sensitivity is because of the temperature dependent solubility of total DIC (note that (a) is not isochemical, it is done with a constant pCO2, i.e. DIC will decrease with increasing temperature). In the ocean, surface ocean DIC and Alk are controlled by a myriad of processes, including temperature, so it is wrong to suggest that the spatial distribution of the buffer factor shown in Figure 7.3.10c is driven by temperature. [Nicolas Gruber (Reviewer’s comment ID #: 307-70)]

[Editor’s] Notes: Taken into account. The buffer factor has a considerable T dependency (see Zeebe and Wolf-Gladrow, 2001). However, it is right that in the real ocean, this T dependency is overridden often by other processes such as pCO2 changes, TAlk changes and others. The diagram showing the T dependency of the buffer factor was omitted now in order not to confuse the reader. The text was changed.

Bold added, AR4, Expert and Government Review Comments on the Second-Order Draft, Chapter 7, 6/15/06, p. 70 of 132. Isochemical means the chemical products are not varying, which would be one of the prerequisites for equilibrium.

So the editor contradicts a criticism of one of IPCC’s senior, contributing author’s to this chapter, and leaves the matter unsettled. The final resolution is to delete old figure (a) “in order not to confuse the reader.”

IPCC leaves the reader with empirical evidence, directly and through references, that the Revelle buffer is a linear transformation of solubility, and conceals part of that evidence. No evidence exists that the Revelle buffer factor is anything but solubility and Henry’s law, nor that it should be peculiar to ACO2 instead of all CO2. IPCC relies on a conjecture inherited from the original authors that the Revelle factor buffers against anthropogenic CO2(g), but not natural CO2(g), and IPCC’s Third and Fourth Assessment Reports ignore Henry’s law, which denies IPCC a method to discriminate between the two species of CO2.

Moreover, Henry’s law, coupled with fundamentals of system science, dictate that ACO2 and natural CO2 may not be modeled as additive and be faithful to physics. CO2 emissions, presumably lighter weight, add to the local, existing CO2 in the atmosphere to create a new isotopic mixture. Thereafter the two gases share a combined partial pressure, pCO2(g), to effect absorption proportional to pCO2(g) and outgassing inversely proportional to pCO2(g), a nonlinear phenomenon as required by Henry’s law. A linear fit to the nonlinear phenomenon might suit some special application, but any such parametrization needs to be justified against the full model. In general, being nonlinear, a natural carbon cycle may not be reliably added to an anthropogenic carbon cycle as IPCC has done and as its radiative forcing paradigm necessitates. {End rev. 6/10/09}

{Rev. 6/11/09} So who won the argument, Gruber or the editor? It’s a draw. Whether IPCC’s formulation (Eq. 2) or Revelle’s (Eq. 3), the Revelle factor is a relationship between measurable or estimable parameters. It has an existence as an empirical formula, but not one derivable as a consequence of an à priori model. But to call it a buffer is to add another ambiguity. Buffer can be used in the sense of storage or an accumulator, referring here to the accumulation of either CO2(g) or CO2(aq). IPCC said as the Revelle factor increases “the buffering factor of the seawater decreases.” Caption, Figure 7.3.10; Figure 4, above. IPCC puts it this way: “The lower the Revelle factor, the larger the buffer capacity of seawater.” AR4, ¶7.3.4.2, Carbon Cycle Feedbacks to Changes in Atmospheric Carbon Dioxide, p. 531.

The Revelle factor increases as the water temperature decreases or as pCO2 increases, just as solubility does, and as it increases, the concentration of CO2 in seawater increases. Consequently buffer in the context of the Revelle buffer factor means to buffer against the storage of CO2, to act as a barrier to the uptake of CO2, and hence to cause CO2, specifically ACO2, to accumulate in the atmosphere. The phrase “buffer capacity” seems self-contradictory.

Revelle & Suess’s “peculiar mechanism of sea water” had no basis in physics, and didn’t work out numerically. IPCC has found a physical basis for the Revelle buffer factor: marine carbon chemistry. It says,

The ocean will become less alkaline (seawater pH will decrease) due to CO2 uptake from the atmosphere (see Box 7.3). The ocean’s capacity to buffer increasing atmospheric CO2 will decline in the future as ocean surface pCO2 increases (Figure 7.11a). This anticipated change is certain, with potentially severe consequences. Bold added, AR4, ¶7.3.4.2 Carbon Cycle Feedbacks to Changes in Atmospheric Carbon Dioxide, p. 531.

IPCC’s Box 7.3 contains equations 7.1 and 7.2 for the marine carbon chemistry and discusses how the acidity level and buffering capacity change due to added CO2. AR4, p. 529. Wolf-Gladrow provides a more complete set of equations, including the important stoichiometric equilbrium constants for each reaction. Wolf-Gladrow, id., Chart 3. These equations are readily solved, and the solution diagrammed in the Bjerrum plot. Wolf-Gladrow provides several examples in his presentation, including the following:

Why_me-10

Wolf-Gladrow Bjerrum plot, Chart 7, showing DIC by carbonate fraction, temperature, and salinity.

Figure 8

Why_me-11

Wolf-Gladrow Bjerrum plot, Chart 21, comparing fresh water and seawater, proton levels, and pH. Notes: zlp refers to the “zero level of protons” at the top of the chart; the pK£sub§i£/sub§ are the base 10 logarithms of the two equilibrium constants in the reactions producing the two carbonate ions.

Figure 9

IPCC doesn’t use the Bjerrum plot in either its Third or Fourth Assessment Report, however the plot illustrates IPCC’s discussion of the marine carbon chemistry. AR4, ¶7.3.4 Ocean Carbon Cycle Processes and Feedbacks to Climate, pp. 527-532. The plot provides the carbonate fraction determined by the independent variable, pH. If the CO2 fraction rises, it must be accompanied by a decrease in pH at equilibrium. IPCC’s conclusions (1) that the ocean buffers against ACO2 absorption because the absorption causes the ocean to become less alkaline, (2) that the buffering is measured by the Revelle factor, and (3) that the addition of ACO2 causes potentially harmful acidification (AR4, Box 7.3, p. 529) all rest on assumed equilibrium chemistry in the surface layer, also known as the mixed layer.

IPCC models climate from equilibrium point to equilibrium point, but computes CO2 flux according to a disequilibrium between pCO2(g) and pCO2(aq). IPCC assumes that the mixed layer is in equilibrium at some fractional distribution according to a Bjerrum plot, when that can never be the case. The Bjerrum model only applies to a dead, stagnant, and isolated body of water. The mixed layer exchanges heat with its environment through short wave radiation from the Sun and long wave radiation to space. Every point on the surface is fed by horizontal and vertical currents. Wind, wave action, and entrained air, along with living flora and fauna, add to the dynamics. No model exists for marine chemistry of a real surface layer, and no reason exists to accept equilibrium chemistry as even approximating the real ocean.

Equilibrium is not a continuous measure like pressure, temperature, and concentration. It is a state of a system, as is linear in models. Expressions like “highly nonlinear” or concepts of near equilibrium have no objective meaning. In equilibrium, no work, no heat exchange is being done with the environment. A system is either in equilibrium, or it is not. It is an idealization.

Science dictates that IPCC should abandon its model of additive natural CO2 and anthropogenic CO2 cycles, and abandon its reliance on equilibrium. It needs to abandon its model that the mixed layer must have any specific fraction of carbonate products, and allow the CO2 molecular concentration to vary freely and as necessary to satisfy the laws of solubility. It needs to scrap the Revelle factor and apply Henry’s law. It needs to model the carbon cycle using mass balance calculations applied to the ever-changing mixture of natural and anthropogenic CO2, and according to Henry’s law. IPCC’s global circulation models, formerly known as global climate models, have the carbon cycle wrong. {End rev. 6/11/09}

{Begin rev. 11/12/09} Next in computing the greenhouse effect of CO2, IPCC needs to abandon its assumption that the radiative forcing effects are logarithmic with respect to the gas concentration. For example, a claim with respect to CO2 at AR4, ¶2.3.1 Atmospheric Carbon Dioxide, p. 140; an approximation with respect to water vapor at AR4, Box 8.1: Upper-Tropospheric Humidity and Water Vapour Feedback, p. 631, and at ¶8.6.3.1 Water Vapour and Lapse Rate, p. 633; and previously a claim with respect ot water vapor at TAR, ¶7.2.1.2 Representation of water vapour in models, p. 426. The physics of absorption are governed by the Beer-Lambert Law, nowhere used by IPCC. This law applied to the radiative forcing yields the following equation, including a decaying exponential: RF = RF0 + ΔRF*(1-e-kx), where x is the normalized concentration (or depth). In a small region, this equation can be approximated by a logarithmic function. But the logarithmic function goes on forever. As IPCC says,

It has been suggested that the absorption by CO2 is already saturated so that an increase would have no effect. This, however, is not the case. Carbon dioxide absorbs infrared radiation in the middle of its 15 mm band to the extent that radiation in the middle of this band cannot escape unimpeded: this absorption is saturated. This, however, is not the case for the band's wings. It is because of these effects of partial saturation that the radiative forcing is not proportional to the increase in the carbon dioxide concentration but shows a logarithmic dependence. Every further doubling adds an additional 4 Wm-2 to the radiative forcing.

TAR, ¶1.2.3 Extreme Events, p. 93. Thus IPCC attributes a logarithmic effect to the emergence of weaker absorption regions for CO2 in the longwave band. The argument is unnecessary and fallacious. The absorption grows in any subband according to the RF equation above. As formulated by IPCC, CO2 can absorb more LW radiation than exists in its band as if its effect spread across the band, adding 4 Wm-2 for every doubling to infinity. The logarithmic function never saturates, and as a result IPCC doesn’t have to determine an operating point for CO2 as a greenhouse gas. Instead the laws of physics provide for saturation, and an operating point is essential. The absorption of any GHG in a particular band can be no greater than the relative width of the band, and is further reduced by the relative blackbody radiation in that band. As the critics IPCC acknowledged, CO2 appears to be well into saturation. IPCC needs to compute the marginal effects of additional CO2 instead of adding 4 Wm-2 for every doubling. IPCC needs to respect the Beer-Lambert Law. {End rev. 11/12/09}

Nor does solubility favor natural CO2 over anthropogenic CO2 based on the rate of vertical mixing. It is the same for both. There is no centrifuge effect to segregate heavy CO2 from light CO2.

One would expect no chemical reaction between ions in the ocean and molecular CO2 in the atmosphere. Solubility should be a purely kinetic process bringing CO2 into solution where it can dissociate first and then participate in the chemical reactions.

6. The IPCC provides the following data in Climate Change 2001:

Parameter Value  Page
Fossil fuel CO2 uptake to emissions ratio [u/e]    50%  187
Ocean CO2 uptake, PgC/yr    90  188
Land CO2 uptake, PgC/yr    120  188
Calculated total uptake, nominal    210        
Fossil fuel emissions, 1980-1989, PgC/yr    5.4  185
Fossil Fuel emissions, 1990-1999, PgC/yr    6.3  185
Calculated Fossil Fuel emissions, average    5.8      
Ratio CO2 total increase to Fossil Fuel emissions, 50%/yr    2.9  187
Total ACO2 = Fossil Fuel emissions/(3/4)     7.8  185
El Niño reduced emissions, min, PgC/yr    0.2  185
El Niño reduced emissions, max, PgC/yr    1  185
Estimated El Niño incidence    50%      
Calculated El Niño reduced emissions, weighted average, PgC/yr    .3        
Net gain in CO2, PgC/yr    3.3  185

Note: 1 Petagram (Pg) = 1 Gigaton (Gton)

So the natural

u/e = 210/207.8 = 101.06%,

a net uptake of CO2 from the atmosphere. Adding ACO2 emissions and crediting the El Niño reduction in natural emissions,

u/e = 211.9/215.2 = 98.41%,

is a net addition (denominator - numerator) of 3.3 PgC/yr.

Next the IPCC segregates anthropogenic parts in both the numerator and denominator

u/e = (2.9 + 209.2)/(5.8 + 209.4)

but converts it into

2.9/5.8 + 209.2/209.4 =

50% explicit fossil fuel CO2 emission reduction +

99.80% implicit natural CO2 emission reduction.

Only in IPCC algebra does

(a+b)/(c+d) = a/c + b/d.

This is the result of assigning net transactions, whether fluxes or radiative forcings, to individual components in the transaction without physical justification. Consensus physics here is no better than its algebra. Because measured increases in CO2 concentration appear to be correlated with reasonable estimates for the growth of anthropogenic emissions, the Consensus assumes it has established a cause and effect relationship. Assuming correlation implies cause and effect is the same error the Consensus made in assuming that the increase in CO2 caused the increase in temperature in the Vostok ice core reductions.

Until the Consensus can show that the solubility of CO2 in water depends on the carbon isotope or some other as yet unknown property differing between natural and manmade CO2, the IPCC data support the conclusion that all CO2 is reduced by the same number, approaching 98.41% per year.

7. The Consensus says,

Although there is sufficient uptake capacity in the ocean to incorporate 70 to 80% of foreseeable anthropogenic CO2 emissions to the atmosphere, this process takes centuries due to the rate of ocean mixing. As a result, even several centuries after emissions occurred, about a quarter of the increase in concentration caused by these emissions is still present in the atmosphere. To maintain constant CO2 concentration beyond 2300 requires emissions to drop to match the rate of carbon sinks at that time. Climate Change 2001, Technical Summary of the Working Group I Report, p. 75.

This argument, at the crux of the Anthropogenic Global Warming alarm, is contradicted by the Consensus' own data. Considering the uptake to the ocean alone, which is at least 90 GTons/year, the Mean Residence Time of the 730 GTons of atmospheric CO2 is 8.1 years, and the half life is 5.3 years. The CO2 concentration is down to one quarter in twice the half-life, or 10.6 years, more than an order of magnitude less than several centuries.

Thus the Consensus bases its key argument on its fallacious 50% calculation, which it then applies to its scenarios of accelerating anthropogenic CO2. That calculation nakedly assumes different residence times for anthropogenic and natural CO2. Instead of centuries, the Mean Residence Time of both kinds of CO2 is 8.1 years, based on elementary mathematics applied to the Consensus' own data on the oceanic uptake alone. Relative to the Consensus model, CO2 does not accumulate in atmosphere.

8. The Consensus says in the same section of the TAR,

Before the Industrial Era, circa 1750, atmospheric carbon dioxide (CO2) concentration was 280 ±10 ppm for several thousand years. It has risen continuously since then, reaching 367 ppm in 1999.

The present atmospheric CO2 concentration has not been exceeded during the past 420,000 years, and likely not during the past 20 million years. Climate Change 2001, p. 185.

The 420,000 year figure was the greatest age of the Vostok CO2 data, which achieved peaks of about 300 ppm. See IPCC Figure 3.2(d) on page 201. A rough straight line fit to the Mauna Loa data (Figure 3.2(a), page 201) shows the measurements have exceeded 300 ppm for roughly 50 years.

Check the Vostok data. ftp://ftp.ncdc.noaa.gov/pub/data/paleo/icecore/antarctica/vostok/co2nat.txt {Err. 12/8/09}.

The CO2 samples number 283, covering 414,085 years. The average spacing is 1463 years. The chances of sampling an epoch like the present 50 year record, if it existed, is about 50/1463 or 3.4%.

That translates into a 3.4% confidence level for the statement that the present CO2 trend was unprecedented in the last 420 Kyears. That confidence level does not begin to rise to an acceptable standard for a scientific conclusion.

This unprecedented claim is a mantra of the Consensus. It made a normal scatter plot of the Vostok data, but then seduced itself by connecting the dots!

9. The Vostok record (Figure 3.2(d)) shows five peaks in 420,000 years. What are the chance that the peaks shown are below the true maximum given that the average sample interval is a millennium and a half? A. The chances are about 96.6%. Thus the confidence is greater than 95% that any measured maximum is more than 50 years from the peak CO2.

10. So the question about the CO2 record for the last century asks for an explanation of an implicit exaggeration of an exaggeration. The measured record is actually only 50 years old, and it may show a high rate of CO2 growth. The elements of a model to fit the record should accommodate all the following.

11. The CO2 growth rate at Mauna Loa is unprecedented because no comparable measurements exist.

12. The CO2 level at Mauna Loa is substantially higher than the calculations from Vostok ice cores. Because Mauna Loa sits in the plume of the massive CO2 outgassing from the Eastern Equatorial Pacific, and because Vostok sits inside one of the great polar CO2 sinks, Mauna Loa should be higher than Vostok records for the same average, global CO2 concentration. How much higher is for further study by climatologists. The origin of the CO2 at Mauna Loa is dominated by Eastern Equatorial Pacific outgassing.

13. As shown in the Acquittal of Carbon Dioxide, the CO2 concentration lags global warming and is shaped like the complement of the solubility curve. The current epoch of global warming is just one more such epoch shown several times in the Vostok data, and the increase in CO2 concentration is similar to the paleo record, within the resolution of that record.

The Consensus had no explanation for the increase in CO2 it alleged caused the historical ice epoch recoveries. Once the Consensus accepts those new results from the Acquittal of Carbon Dioxide, it will have an explanation for the CO2 but no satisfactory explanation for the global warming at any time.

14. Also, small changes in ocean or atmospheric currents could have an additional profound effect on the CO2 measured at Mauna Loa. The center of the CO2 plume may now be moving toward Hawaii, causing an increase in CO2 concentration there. This could also account for the seasonal effects evident in the Keeling curve.

15. The CO2 rich atmosphere rises near the equator and splits into north and south plumes. As it rises in each hemisphere, it enters a Hadley Cell, carrying it first poleward, and then down into and to feed the westerly trade winds. The trade winds carry the CO2-rich atmosphere across Hawaii. However, the trade winds are also seasonal, varying cyclically in direction and magnitude. The seasonal fluctuations Keeling attributed to the biosphere growing seasons might be better correlated with the trade wind vector at Hawaii.

16. Anthropogenic CO2 may be an additional component of the 3.3 PgC/yr seen at Mauna Loa. It is at most 7.8 parts in 90, or less than 9%. The 3.3 PgC/yr is not unabsorbed ACO2.

To the extent that the record at Mauna Loa is influenced by the venting of CO2 from the Thermohaline Circulation, the changes in plume intensity may be due to events a millennium old.

18. The Consensus assumes anthropogenic changes act in a state of climate equilibrium. It assumes that both the CO2 and the global temperature are in equilibrium but for man. Instead, climate change forecasts must operate in the state of Earth's on-going, triple recovery from the last ice age, the last glacial epoch, and the Little Ice Age, whatever the causes might be. Any valid forecast must first account for that natural warming.

Kyoto-like arrangements can have no measurable effects on the rise in CO2. To stop the rise in CO2, man must stop global warming.

The Consensus weaves a tangled web.

]]> Gavin Schmidt on the Acquittal of CO2 tag:www.rocketscientistsjournal.com,2006://1.55 2006-11-09T21:56:28Z 2009-06-29T23:03:52Z GAVIN SCHMIDT'S RESPONSE TO THE ACQUITTAL OF CO2SHOULD SOUND THE DEATH KNELL FOR AGW by Jeffrey A. Glassman, PhD Revised 6/29/09. Gavin A. Schmidt is a well-placed leader of the Anthropogenic Global Warming movement. He is a climate modeler...
GAVIN SCHMIDT'S RESPONSE TO THE ACQUITTAL OF CO2
SHOULD SOUND THE DEATH KNELL FOR AGW

by Jeffrey A. Glassman, PhD

Revised 6/29/09.

Gavin A. Schmidt is a well-placed leader of the Anthropogenic Global Warming movement. He is a climate modeler at NASA. While London trained as a mathematician, he was an NOAA Postdoctoral Fellow in “Climate and Global Change Research”. He is an editor for the Journal of Climate. He is the principal of an authoritative blog called RealClimate.org.

As he has admitted and has been shown to be true, he usually doesn't respond to outside criticism. E.g., re newspapers see http://www.realclimate.org/index.php/archives/2004/12/. However, he deigned to answer the Acquittal of Carbon Dioxide. See the discussion of the Acquittal at website for CrossFit, Comment #48, 10/31/06, www.crossfit.com. This is what he has to say:

“[Response: That's pretty confused. He neither understands the physics of CO2, nor the implications of the Vostok record, nor the concept of positive feedback. We've discussed each of these issues before, and I would refer you there. - gavin]

“Dec 2004. What does the lag of CO2 behind temperature in ice cores tell us about global warming? http://www.realclimate.org/index.php/archives/2004/12/how-do-we-know-that-recent-cosub2sub-increases-are-due-to-human-activities-updated/

“22 Dec 2004. How do we know that recent CO2 increases are due to human activities? http://www.realclimate.org/index.php/archives/2004/12/co2-in-ice-cores/

“5 Jul 2006. Runaway tipping points of no return. http://www.realclimate.org/index.php/archives/2006/07/runaway-tipping-points-of-no-return/Gavin Schmidt on Physics”

RSJ dissects Dr. Schmidt's reply categorically.

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GAVIN SCHMIDT ON PHYSICS

Schmidt's opening, “[Response: That's pretty confused. He neither understands the physics of CO2, … “, doesn't tell the reader what physics is relevant, what misunderstanding Schmidt might have perceived, or who or what is confused.

Relevant physics. According to the Intergovernmental Panel on Climate Change (IPCC), jointly established by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) in 1988, the physics of CO2 includes an annual gross exchange of about 90 PgC (90 Gigatons of carbon) per year, that determines the fate of CO2 in the atmosphere, it must be calculated in a numerical model, and it depends on the solubility of CO2 in water, and in particular, on the solubility pump.

The Acquittal of Carbon Dioxide extensively addresses the solubility of CO2 in water. So what Gavin cannot reveal as my misunderstanding of CO2 physics must be an error of commission, not omission.

What does Gavin say about solubility and CO2 on his website, www.realclimate.org? First, Google says: Your search - “solubility pump” site:realclimate.org - did not match any documents. So apparently he completely neglects the CO2 physics of the solubility pump!

If you Google for solubility and “carbon dioxide” at realclimate.org, you'll find the following six unique citations.

(1) Schmidt, 16 May 2006, Current volcanic activity and climate?

“There is an obvious spike in the rate of CO2 increase at that time -- in the other direction!

“I've been all over the CO2 record from Mauna Loa, and I can tell you that just after the Mt. Pinatubo explosion, the growth rate of atmospheric CO2 decreased. From about 1977 to 1990, atmospheric CO2 was increasing at about 1.5 ppmv/yr, but from 1991-1992 the rate dropped to about 0.9 ppmv/yr. It rebounded quickly, and by 1994 was above 1.5 ppmv/yr.

“Gavin? Could the Mt. Pinatubo explosion have caused a decrease in atmospheric CO2 increase rate? Or is the dip due to some other cause?

“[Response: A small dip is completely understandable in terms of the carbon cycle response to the cooling. It could be a solubility effect (cooler oceans take up more CO2), but it's more likely a biosphere effect - reduced soil respiration maybe. I'd have to look up the relevant literature to be more precise... - gavin].” Comment #6. www.realclimate.org/index.php/archives/ 2006/05/current-volcanic-activity-and-climate/. www.realclimate.org/index.php/archives/ 2006/05/positive-feedbacks-from-the-carbon-cycle/

Gavin admits his confusion. Could solubility be playing a role, he asks!

(2) Archer of RealClimate.org, 27 May 2006, Positive feedbacks from the carbon cycle.

“The oceans are presently taking up about 2 Gton C per year, a significant dent in our emissions of 7 Gton C per year. This could slow in the future, as overturning becomes inhibited by stratification, as the buffer loses its capacity due to acidification. Eventually, the fluxes could reverse as with a decrease in CO2 solubility due to ocean warming.” www.realclimate.org/index.php/archives/ 2006/05/positive-feedbacks-from-the-carbon-cycle/

Here the author admits only to the possibility that CO2 solubility could play a role. However, another aspect of this citation is far more important. Climatologists calculate that annually the oceans dissolve between 92 and 107 Gigatons of carbon, and emit variously between 90 and 103 Gigatons back into the atmosphere, accuracy unknown. (Google for 90 PgC/yr for an idea of the extent of this calculation.) The anthropogenic crowd presume that the 90 GT figure is natural equilibrium and that the excess uptake of 2 GT is associated with anthropogenic CO2. Of course, this is nonsense. The 2 GT figure is merely the difference between two large, uncertain estimates. The sources for both the 90 GT and 92 GT figures and the 103 GT and 107 GT remain a mystery, concealing the method of computation, its probable accuracy, and the dependence on conditions and assumptions, especially but not exclusively global temperature (climate).

These observations point to one of several persistent scientific flaws in the AGW conjecture. In this case, the GCMs do not work in the most significant figures, but they claim validity in the region of the middle most significant figures. Now restricting the domain of a scientific model is a routine practice, and is quite acceptable. If the climatologists wish to restrict the domain of their GCMs to the present post glacial period, or even to the time since the Little Ice Age, they should so state this restriction. If they do so, however, they are bound to express their models against a doubly warming background: the post glacial period of about 60 to 80 thousand years, or to the post LIA period of a hundred and fifty years or so.

Instead, the climatologists glue the dubious CO2 measurements of the last fifty years onto the record of several centuries or the record of several hundred millennia, thus to proclaim an unprecedented level of CO2 due to man. They fail to account for the differences in measurement methods and the problems that implies with regard to calibration. They fail to account for the global temperature and the effects that has on the CO2 concentration. They fail to account for the difference in sampling intervals. They ignore that some technicians making the modern measurements reported that they waited until the wind was from the sea because that lead to higher CO2 levels. [Gray, Vincent R. “Atmospheric Carbon Dioxide”, letter 3/1/99, Re: paper on pre-industrial carbon dioxide. http://www.john-daly.com/bull120.htm.] They ignore the hint in that admission that maybe the CO2 was fresh outgassing and not manmade at all.

(3) Schmidt, 8 Mar 2006, Art and climate, no response to Comment # 17:

“Similarly is there a graph somewhere giving the solubility of CO2 versus temperature in sea water?” www.realclimate.org/index.php/ archives/2006/03/art-and-climate/

Gavin never answered. The graph is in the Acquittal of Carbon Dioxide.

(4) Schmidt, 5 Dec 2005, Debate over the Early Anthropogenic Hypothesis, by response to Comment #36.

“It seems that increases in ocean temperature and CO2 outgassing go hand in hand. [Citations]

“'The total amount of carbon in the ocean is about 50 times greater than the amount in the atmosphere, and is exchanged with the atmosphere on a time-scale of several hundred years. Dissolution in the oceans provides a large sink for anthropogenic CO2, due in part to its high solubility, but above all because of its dissociation into ions and interactions with sea water constituents'

“'CO2 solubility is temperature dependent, hence air-sea heat transfer contributes to seasonal and regional patterns of air-sea CO2 transfer (Watson et al., 1995). Net cooling of surface waters tends to drive CO2 uptake; net warming drives outgassing.'” www.realclimate.org/index.php/archives/ 2005/12/early-anthropocene-hyppothesis/

Gavin never took the hint. The evidence of the CO2 solubility affecting the CO2 concentration in the atmosphere is in the Vostok data, reported and measured in the Acquittal of Carbon Dioxide.

Actually, climatologists' calculations indicate a little more than one eighth of the CO2 in the atmosphere is exchanged annually. The record indicates that CO2 stream has a transport lag of about one millennium, but the exchange is perpetual.

(5) RealClimate.org, 7 Jun 2005. How much of the recent CO2 increase is due to human activities?

“Why are the ocean and land taking up carbon, when we know that warming of the oceans reduces the solubility of CO2 and warming of the land accelerates bacterial degradation of the soils? The answer is that warming is not the only process that influences the oceans and land biosphere. The dominant process in the oceans is the response to increasing atmospheric CO2 itself. If the oceans had not warmed, they might have taken up even more carbon, although we cannot say for sure because warming may have other impacts, for example on marine biota. On land, bacterial degradation of the soils may have increased in response to warming, but for the moment this effect is smaller than the land response to other processes (for example fertilization by CO2 and nitrogen, changes in precipitation, etc).” www.realclimate.org/index.php?p=160

This is all qualitative, with no references to computations. It is dismissive of the effect on solubility of warming, which is, after all, the principal independent variable of the solubility curve (solubility as a function of temperature, with a parameter of partial pressure.)

Once the climatologists re-integrate ocean models with their Global Circulation Models, they are likely to discover that the uptake of CO2 is relatively constant (at least until the entire sea surface freezes), and is limited by exposure time, partial pressure, wind velocities, and area. However, the outgassing is likely to be the more important parameter with respect to CO2 concentrations. At the same time, the greenhouse effect will be minor, mostly dependent on water vapor, and relatively constant.

(6) Schmidt, 21 Jan 2006. Calculating the greenhouse effect, no response to Comment #24 by Dave Nicosia (NOAA):

“Also, in the ice core data, CO2 appears to passively follow the inferred temperature trends through the millennia suggesting its concentration follows the glacial to interglacial periods and the increased solubility of the oceans at lower temperatures.” www.realclimate.org/index.php/archives/ 2006/01/calculating-the-greenhouse-effect/

Gavin did not answer. As the Acquittal of Carbon Dioxide shows, Dave Nicosia was insightful.

GAVIN SCHMIDT ON THE IMPLICATIONS OF VOSTOK

Schmidt says of the Acquittal of Carbon Dioxide, “nor [does the author understand] the implications of the Vostok record … .”

The Acquittal says that the shape of the Vostok relationship between CO2 concentration and the temperature traces is fully represented by the solubility of CO2 in water, with no constant offset (no “forcing” component), and is confirmed by the CO2 lag, which the article quantifies, and which is consistent with the transport lag caused by the oceanic thermohaline circulation. Has anything written by or for Schmidt contradicted these new findings in the Acquittal?

In support of his accusations, Schmidt refers us to three papers he authored, all on his website. None of these citations says that the Vostok record demonstrates the essential fact that atmospheric CO2 and any temperature are correlated!

Schmidt's first citation is “What does the lag of CO2 behind temperature in ice cores tell us about global warming?”, 12/3/04, http://www.realclimate.org/index.php/archives/2004/12/how-do-we-know-that-recent-cosub2sub-increases-are-due-to-human-activities-updated/. With regard to the Vostok record, this paper says only what is NOT significant. That insignificant fact, Schmidt asserts, is the lag of the CO2 concentration trace behind temperature trace. If that fact were insignificant, it would weaken the discoveries in the Acquittal, depriving its thesis of the lag that tends to confirm the model of the ocean controlling the atmospheric CO2 content.

In reality, the lag is an inconvenient fact to AGW. Enthusiasts must discredit this lag because their AGW conjecture rests on manmade CO2 causing global warming. That causal conjecture is severely damaged by the reversed timing: temperature changes precede CO2 concentration changes!

The AGW advocates postulate a rehabilitating theory: CO2 amplifies global warming. But this residual amplification conjecture is equally bizarre. This model states that the amplified warming somehow releases more CO2, and hence the amplification is a positive feedback.

First, to the extent that this amplification could be so, the instability should soon cause the CO2 record to lead temperature. It never has. Schmidt has no data to confirm his amplification suggestion. Also see Schmidt's reliance on feedback, below.

Schmidt's second citation, How do we know that recent CO2 increases are due to human activities? http://www.realclimate.org/index.php/archives/2004/12/co2-in-ice-cores/, 12/22/04, includes only the following with respect to Vostok:

“In addition to the data from tree rings, there are also of measurements of the 13C/12C ratio in the CO2 trapped in ice cores. The tree ring and ice core data both show that the total change in the 13C/12C ratio of the atmosphere since 1850 is about 0.15%. This sounds very small but is actually very large relative to natural variability. The results show that the full glacial-to-interglacial change in 13C/12C of the atmosphere -- which took many thousand years -- was about 0.03%, or about 5 times less than that observed in the last 150 years.”

plus

“CO2 levels are currently higher than for any time when we have direct measurements (directly, from 1950; before that, from air trapped in ice cores), which amounts to the last 780,000 years (see, e.g., a picture here for the last 400 kyr). Various considerations suggest that in the far past CO2 levels were considerably higher. From memory, the last time CO2 levels exceeded present was about 40 million years ago/” Response to Comment #4.

This modern increase in CO2 is often found supported by a graph showing the Vostok CO2 concentration in time, with rapidly rising modern data linked added to the end of the Vostok record. Nowhere do the climatologists justify the method linking data taken by different methods, in different locations, and with grossly different granularity.

Data show that carbon dioxide levels are rising, they are now 30% higher than at any time during at least the past 650,000 years, and likely even the past several million years. 31 Mar 2006, Bush on “The Fundamental Debate”, by the group, www.realclimate.org/index.php/archives/2006/03/bush-on-the-debate/

The Unprecedented Assumption

The absence of evidence is not evidence of the absence, even when abundant samples have proved negative. A tenet of the AGW conjecture is that the present levels of CO2 are unprecedented, going back four million years. The Vostok data goes back 420,000 to 780,00 years, but the sample interval is one to two millennia. The current surge in CO2 is known by a different method, and is only 150 years long, a seventh of a millennium. If there had been just one similar epoch to the present during the period covered by Vostok, the odds are strong that it would not have been detected.

The AGW advocates suffer from the same problem as creationists. Transition species are missing from the paleontology record, but fossilization is an infrequent event, much longer than the periods of speciation. The chances of discovering a brief epoch in gas concentration, like the chances for discovering transition species, is quite small. Neither proof of God's hand nor of man's lies in such sparse data.

The 12/22/04 paper also says,

“If we see CO2 increasing in the atmosphere, and humans emitting enough CO2 to account for that rise, then you have to go through some odd contortions to avoid a connection. You would have to postulate a suddenly increased natural sink (to remove the human CO2) and then a suddenly increased natural source (to increase the atmospheric CO2)”. Response to Comment #6.

To the contrary, to make the connection is to rely on correlation instead of a cause and effect model. This false reliance has blinded the AGW advocates to the on-going physics. Suddenly developed sinks and sources are not needed at all. The cold ocean is a sink to all atmospheric CO2, manmade, accidental, or ocean emitted. The cold sink is limited not by capacity, but by exposure, pressure, and winds. But for the ocean outgassing, the cold waters would scrub the atmosphere of all CO2 in less than a decade by the climatologists own uncertain carbon and flux estimates. The natural source is dominantly the oceans. CO2 concentration has increased because the oceans are warming, and have been since the Little Ice Age and since the last glacial period.

The Residence Time Assumption

Another AGW tenet is that the buildup of CO2 in the Mauna Loa Record is an accumulation of CO2 throughout the brunt of the industrial era. To make that accumulation viable, AGW advocates claim the duration of CO2 in the atmosphere is a century or more.

To the extent that the mean residence time of CO2 is less than a century, the build up must be from other sources. Others have claimed the residence time is the order of five years or less, and that appears to be supported by their calculations of the total concentration of CO2 in the atmosphere and the flux into the biosphere and especially the ocean. With such short CO2 persistence, the AGW conjecture needs shoring up with a new rationale for the 150 year growth in CO2.

The Keeling Curve Assumptions

The unprecedented rise in CO2, correlated to an extent with the calculated production of anthropogenic CO2, was discovered and analyzed by Charles David Keeling of the Scripps Institution of Oceanography, UCSD, from measurements made at the Mauna Loa observatory in Hawaii. The data comprise a seasonal oscillation on top of a steadily rising and even accelerating, non-seasonal base. Keeling warned that the data were a reliable indicator in a localized space, the middle, troposphere layers in the region. Keeling, C.D. & T. P. Whorf, Atmospheric carbon dioxide record from Mauna Loa, 1958-2004. www.cmdl.noaa.gov/ccgg/trends/. Reportedly he also said that his data reflected natural events, including perhaps an unprecedented process. Keeling, C.D. & T. P. Whorf, Atmospheric carbon dioxide record from Mauna Loa, 1958-2004. www.cmdl.noaa.gov/ccgg/trends/. He came to conclude that the seasonal portion was natural, and due to seasonal changes in the biosphere. See Kaufmann, Robert K. SEASONAL CHANGES IN ATMOSPHERIC CARBON DIOXIDE: LONGER GROWINGS SEASONS VERSUS GREENER SUMMERS, Boston University, Center for Energy & Environmental Studies. www.bu.edu/cees/people/faculty/kaufmann/documents/carbon_dioxide.pdf. However, he concluded that the non-seasonal part was anthropogenic. See Scripps CO2 Program, Charles David Keeling Biography. http://scrippsco2.ucsd.edu/program_history/charles_david_keeling_biography.html.

Later A.C. Manning in association with Keeling's son, Ralph, also a PhD oceanographer at Scripps, concluded that variations in north-south transport were likely a particularly important cause of the short-term variability of atmospheric CO2 at Mauna Loa. Manning, A.C. & R. F. Keeling, Scripps Institution of Oceanography, UCSD, Correlations in Short-Term Variations in Atmospheric Oxygen and Carbon Dioxide at Mauna Loa Observatory. www.cmdl.noaa.gov/publications/annrpt22/MANNING.pdf . These results raise the question whether the seasonal variations may be due simply to the variations in prevailing or trade winds at Mauna Loa. Did Charles Keeling consider wind as the cause, and subsequently rule it out?

Likewise, what evidence did Charles Keeling have for his conclusion that the rapid, non-seasonal increase observed at Mauna Loa was manmade?

The possibility that weather causes the seasonal fluctuations at Mauna Loa suggests that the non-seasonal part might be related to climate, too. Perhaps Mauna Loa is down-current and downwind from undersea volcanic activity. Or, perhaps Mauna Loa is located in the seasonally shifting stream of ordinary CO2 outgassing from the oceans.

Regardless, implicit in the climatologists use of the data are the unlikely assumptions that the MLO atmosphere is already well-mixed and globally representative. These were not Charles Keeling's original findings.

Schmidt's third and last citation, Runaway tipping points of no return, http://www.realclimate.org/index.php/archives/2006/07/runaway-tipping-points-of-no-return/, 7/5/06, helps little in gathering Schmidt's take on the relevance of the Vostok data. Just as the first paper established that the CO2 lag in the Vostok record is not helpful, the third paper says the CO2 concentration record is not helpful in understanding the present record! Here in a response to Comment #4, Gavin says

“So if CO2 levels are so much higher now (26% higher than any point since 420K years before the industrial era), why aren't temperatures lining up?

“[Response: The orbital configuration was different, with warmer [Northern Hemisphere] summers than today (or even the early Holocene). That seasonal change may have been crucial for the ice sheets. See the recent Overpeck et al and Otto-Bleisner et al papers in Science for what climate differences one would expect. With respect to today's CO2, that is not being changed by anything like the same mechanism, and so simply correlations with past data are not going to help. -gavin]” Bold added.

So Gavin urges the mechanisms affecting CO2 concentrations in the Vostok record are nothing like the mechanisms today. Therefore, he says, one should not attempt to correlate the two records. To the contrary, that is exactly what climatologists do when they tack the Mauna Loa measurements onto the Vostok record, or when they make proclamations about the unprecedented CO2 levels in the post-industrialization period. Gavin seems to recognize a difference in geophysical processes, but not in the measurement methods. The first is a conjecture. The second is a certainty.

GAVIN SCHMIDT ON POSITIVE FEEDBACK

“[N]or [does the author understand] the concept of positive feedback”,

urges Gavin. For this position, Schmidt refers readers to his 7/5/06 tome, “Runaway tipping points of no return“. http://www.realclimate.org/index.php/archives/2006/07/runaway-tipping-points-of-no-return/. Here he says,

“The idea is that in many non-linear systems (of which the climate is certainly one), a small push away from one state only has small effects at first but at some 'tipping point' the system can flip and go rapidly into another state. This is fundamentally tied to the existence of positive feedbacks … . However, [tipping point] is currently being used interchangeably a number of potentially confusing ways and so I thought I'd try and make it a little clearer.”

This introductory paragraph suggests the romantic, grammar school notion of the “Delicate Blue Planet”. This model has been so successful that it has distorted the thinking of climatologists, environmentalists, and chaos theorists. It holds that natural objects and systems are found in a delicate balance, and that the most minimal disturbance by man could upset that balance, and so produce a catastrophe. These delicate systems and objects are always invisible, of course. But if the model were to extend to the observable, nature would contain cones standing on their tips, and rounded boulders perched on slopes.

Climatologists need to rid themselves of this Delicate Blue Planet misapprehension. They need to abandon their pursuit, however attractive, of knife edges over catastrophes. They should reconfigure their models to account for stable states, past and present, of all the parameters in their domain. These include not just global climate, but gas mixtures, the water cycle, the carbon cycle, and the ozone layer. They should be modeling these phenomena in their natural, closed-loop states to discover the controlling parameters and their dynamic ranges.

Where does the ozone come from? What controls its concentration? What controls the thickness of the ozone layer, and why is it as thick as it is?

Next under the heading “Positive feedback”, Gavin begins,

“A positive feedback occurs when a change in one component of the climate occurs, leading to other changes that eventually 'feeds back' on the original change to amplify it.”

Leaving room for some climate jargon, this is almost a valid and workable definition. But it omits, among other things, the necessary control system context, and the concepts of gain, ordinary feedback, open loop and closed loop. Regardless, Gavin next attempts to explain what he thinks positive feedback means:

“A simple example leads to a geometric series for instance; i.e. if an initial change to a parameter is D, and the feedback results in an additional rD then the final change will be the sum of D+rD+r2D...etc.”

This explanation and Schmidt's understanding of positive feedback, or even feedback, are fatally flawed.

First, Schmidt's equation expresses a change in the parameter value, not the value of the amplified, closed loop output in his definition, or anything related to it. By reference to “in an additional rD”, and by the plus signs in the series, Gavin reveals that he is thinking of the change as an additive amount. By his definition, the feedback is to cause an amplification of the original parameter. Amplification is multiplicative, not additive. If the original parameter value was P, after positive feedback the value would be rP, with r greater than one, or it might be written as (1+g)P, where g is now the feedback gain, and is greater than zero for positive feedback. Schmidt's algebraic example does not represent his verbal definition.

Second, Schmidt's change formulation converges to a constant, D/(1-r), for |r| less than 1. This he urges is to demonstrate a positive feedback which does not run away, or in scientific terms, diverge. In fact, what he has demonstrated is a kind of feedback which converges to zero as the number of iterations increases. What he calls feedback is only a transient, and its steady state value is neither positive nor negative, but is arbitrarily close to zero. This example briefly resembles a positive feedback, but in the long run Schmidt's example has no feedback at all.

In its Glossary, RealClimate.org provides the following definition dated 28 Nov 2004:

“Forcings in the climate sense are external boundary conditions or inputs to a climate model. Obviously changes to the sun's radiation are external, and so that is always a forcing. The same is true for changes to the Earth's orbit (“Milankovitch cycles”). Things get a little more ambiguous as you get closer to the surface. In models that do not contain a carbon cycle (and that is most of them), the level of CO2 is set externally, and so that can be considered a forcing too. However, in models that contain a carbon cycle, changes in CO2 concentrations will occur as a function of the climate itself and in changes in emissions from industrial activity. In that case, CO2 levels will be a feedback, and not a forcing. Almost all of the elements that make up the atmosphere can be considered feedbacks on some timescale, and so defining the forcing is really a function of what feedbacks you allow in the model and for what purpose you are using it. A good discussion of recent forcings can be found in Hansen et al (2002) and in Schmidt et al (2004).” http://www.realclimate.org/index.php/archives/2004/11/forcings/

From this, a feedback is any model parameter which is not a forcing, that is, which is not a boundary condition. This is not the definition of feedback in control system theory, and it makes the concept of positive feedback at least awkward to define.

Schmidt uses other definitions anyway. In a posting on 4/6/05, “Water vapour: feedback or forcing?”, Schmidt wrote,

“While water vapour is indeed the most important greenhouse gas, the issue that makes it a feedback (rather than a forcing) is the relatively short residence time for water in the atmosphere (around 10 days).”

Now either a positive feedback is not a subset of feedback, or Schmidt's tutorial definition of positive feedback omitted the criterion of atmospheric residence time. According to RealClimate's Glossary,

“Water vapour act as a powerful greenhouse gas absorbing long-wave radiation. If the atmospheric water vapour concentration increases as a result of a global warming, then it is expected that it will enhance the greenhouse effect further. It is well known that the rate of evaporation is affected by the temperature and that higher temperatures increase the (saturated) vapour pressure (the Clausius-Clapeyron equation). This process is known as the water vapour feedback.” http://www.realclimate.org/index.php/archives/2004/11/water-vapour-feedback/.

So warming increases water vapor, which causes warming. Consequently, water vapor amplifies global temperatures. This fits Schmidt's definition of a positive feedback without reliance on Schmidt's essential criterion of residence time. Schmidt contributes to the confusion he set out to settle.

And why is water vapor a feedback and CO2 not? Because, goes the AGW scenario, the persistence of CO2 in the atmosphere is greater than 100 years. “What If … the 'Hockey Stick' Were Wrong?”, http://www.realclimate.org/index.php?p=114, Gavin's response to Comment #20. The IPCC puts the residence time of CO2 at “5 to 200 yr” with the note, “No single lifetime can be defined for CO2 because of the different rates of uptake by different removal processes.” IPCC, Climate Change 2001, ¶C.1, p. 38.

Gat et al in “Environmental Isotopes in the Hydrological Cycle, Principles and Applications”, Volume II: Atmospheric Water, Chapter 1, The Atmosphere, p. 1, place the residence time of CO2 at 4 years. See http://www.iaea.org/programmes/ripc/ih/volumes/volume2.htm . Would a four year persistence for CO2 qualify it as a feedback? Gavin's analysis doesn't answer that question. Regardless, as the Acquittal of Carbon Dioxide teaches, CO2 must not be treated as a forcing so that the GCM can reproduce a CO2 output consistent with the Vostok record.

Schmidt's tutorial concludes where he has the title, “points of no return”. Now he waxes literary, as if some being, human or spiritual, were in control and desirous of some outcome. He anthropomorphizes his science.

Will the earth return to a glacial state? Most assuredly. Will it recover with similar flora and fauna and climate as the present. That's not so certain.

At this point in his paper, Schmidt abruptly applies his errant tutorial to model based AGW via hypothesized ACO2 accumulation in the atmosphere. This is under the heading “10 years?”, a reference to his opening citation of Jim Hansen's doomsday prediction. If this part is to have any validity, it must overcome the fallacy of the manmade CO2 increases in his citations.

Schmidt's switch from his tutorial is itself a tipping point -- a linguistic or literary tipping point.

Nowhere does Schmidt suggest that the models on which he relies to frighten the public might have been validated. He relies instead on an incompetent tutorial to support the AGW conjecture.

The Acquittal of Carbon Dioxide survives Gavin Schmidt's opening salvo. The burden remains on the GCM operators advocates to revise their models. They need to abandon CO2 as a forcing, and instead make the atmospheric CO2 concentration respond to global temperature as dictated by the solubility of CO2 in water. This should be a fatal blow to anthropogenic global warming.

A Meta View

Transcending physics, scientific standards, understandable by a literate layman, must be satisfied. The AGW supporters are not meeting them. They need to divulge the domain in which their models meet all available data. They need to use their models to make significant and novel predictions which can be subject to experiment. (This is a modern reformulation of the Popper falsification criterion.) They need to discard their reliance on consensus, ad hominem attacks, and beliefs, and instead rely solely on the technical fidelity of their models to the climate record. Until their models are validated, they should disavow any public policy consequences of their work. Any of their work destined for public consumption must rely on nothing of significance that is available only for a fee.

On a higher technical plane are additional, widely understandable standards to be met. Information sources should be readily discoverable for all parameter values and models. All data values, raw or reduced, must be discoverable, or accompanied by a statement of the method, the accuracy, or better, an error analysis.

Global Climate on the Margin

In the final analysis of the AGW conjecture, proponents model their chosen phenomena on the margins and without justification. They admit the overwhelming greenhouse gas is water vapor, probably 30 to 50 times more important than CO2. They admit the CO2 attributed to man is minuscule, about 6 to 7 PgC/yr (calculated) into an atmospheric reservoir variously estimated between 720 and 760 PgC. That's around 1% of 2.5%, or 0.025% of GHG.

They estimate the uptake of CO2 by the ocean from 92 to 107 PgC/yr, an error of about ±7 PgC/yr, approximately equal to the anthropogenic total. They estimate the outgassing of CO2 from 90 to 103 PgC/yr, an error of roughly another ±7 PgC/yr. Without putting too fine a point on the method, and in consideration of the range of values by other, undiscovered authorities and the sources and methods employed by any of them, the net difference between uptake and outgassing estimates is about 3PgC/yr, ±14 PgC/yr. Nonetheless, the climatologist use a figure of 2 PgC/yr as their estimate of the oceanic uptake of the manmade CO2 of 7 PgC/yr. Regardless, they then proclaim that CO2 persists in the atmosphere of 50 to 500 years.

Water vapor is not only dominant among the greenhouse gases, it, like CO2, increases with increasing temperatures. Increase in water vapor should bring increases in cloud cover, decreasing solar radiation, and shutting down the warming effect.

CONCLUSION

Categorically, each of Gavin Schmidt's criticisms of the Acquittal of Carbon Dioxide missed its mark.

++++++++++++++++++++++++++++++++++++++++++++++++++++++

EPILOG

6/29/09.

Today is the end of June, 2009, and no climatologist of any sort who supports the AGW conjecture has countered this answer to Gavin Schmidt’s off-handed dismissal, nor refuted any of the other advances made here in climate science. These include, but are not limited to, the following. Atmospheric carbon dioxide is imprinted with the pattern of Henry’s Law for the solubility of CO2 in water, showing that it needs to be modeled not just as an anthropogenic forcing but also as a natural feedback. The Acquittal of Carbon Dioxide. CO2 does not accumulate in the atmosphere, shown by multiple types of evidence. See On Why CO2 Is Known Not To Have Accumulated in the Atmosphere & What Is Happening With CO2 in the Modern Era, updated and augmented this month. The solar wind is more strongly correlated with climate than is El Nino, which IPCC says has catastrophic effects, while ignoring the solar wind and its effects on clouds for lack of evidence. Solar Wind, El Niño/Southern Oscillation, & Global Temperature: Events & Correlations. And IPCC’s GCMs violate prerequisites of modeling and are inconsistent with climate physics, including omitting the most powerful and stabilizing feedback in Earth’s climate. Fatal Errors in IPCC’s Global Climate Models.

Unlike RealClimate.org where he and so many others practice before the public, this blog remains open and vital indefinitely. AGW supporters routinely lambaste critics of the AGW conjecture from behind their duck blinds on three grounds, none of which is substantive: (1) ad hominem (i.e., the critics are skeptics or deniers), (2) the criticism is not sanctified (i.e., not published in peer review journals), and (3) the criticism has been debunked (i.e., click “here”, then “here”, then “here”, ad nauseum). Gavin Schmidt’s original criticism, above, comprised two of these tactics, and nothing more.

Never mind that skepticism is a virtue among real scientists, nor that real scientists have a duty to answer any doubts from the lay public, especially when they are campaigning for public action. Never mind that in peer-reviewed journals one may not criticize the conventional model, nor fail to show respect to the AGW pioneers. These journals have evolved to defend professional rice bowls. Never mind the hypocrisy: RealClimate.org is not peer-reviewed. And, never mind what should be a professional embarrassment of refusing to answer fully and completely, simple, civil, technical inquiries.

As recently as 3/31/09, someone who signs as “KSW” wrote RealClimate.org asking for a civil response to the Journal. Here is his request, and Gavin Schmidt’s refusal:

Gavin, your pithy comments have made you my favorite contributor to RC. Thanks so much; I was surprised to see you take the time to so thoroughly rebut the rehashed comments made in #32 above.

Some time ago you dismissed a paper found at rocketscientistsjournal.com with a single line ‘that seems pretty confused’; well RSJ is back with a new criticism of the IPCC, I wonder if you could apply the same red pen technique to this new entry that you provided to #32.

Cheers.

[Response: I can’t really better my first judgment: That seems pretty confused. - gavin]

That does sum it up. Schmidt’s original criticism had no substance. He made no technical point. Then as now, he urged that The Acquittal was confused. But he cannot find a single example. Not one AGW supporter will come out of hiding to defend their conjecture on points, except where the group safely controls the agenda. This is not science.

÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷

]]> CO2 ACQUITTAL tag:www.rocketscientistsjournal.com,2006://1.54 2006-10-24T13:06:19Z 2010-01-22T14:25:31Z Rocket Scientist&#8217s Journal … UNDER CONSTRUCTION … THE ACQUITTAL OF CARBON DIOXIDE by Jeffrey A. Glassman, PhD Revised 11/16/09. - ABSTRACT Carbon dioxide in the atmosphere is the product of oceanic respiration due to the well&#8209;known but under&#8209;appreciated solubility...


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Rocket Scientist’s Journal

… UNDER CONSTRUCTION …

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THE ACQUITTAL OF CARBON DIOXIDE

by Jeffrey A. Glassman, PhD

Revised 11/16/09.

-

ABSTRACT

Carbon dioxide in the atmosphere is the product of oceanic respiration due to the well‑known but under‑appreciated solubility pump. Carbon dioxide rises out of warm ocean waters where it is added to the atmosphere. There it is mixed with residual and accidental CO2, and circulated, to be absorbed into the sink of the cold ocean waters. Next the thermohaline circulation carries the CO2‑rich sea water deep into the ocean. A millennium later it appears at the surface in warm waters, saturated by lower pressure and higher temperature, to be exhausted back into the atmosphere.

Throughout the past 420 millennia, comprising four interglacial periods, the Vostok record of atmospheric carbon dioxide concentration is imprinted with, and fully characterized by, the physics of the solubility of CO2 in water, along with the lag in the deep ocean circulation. Notwithstanding that carbon dioxide is a greenhouse gas, atmospheric carbon dioxide has neither caused nor amplified global temperature increases. Increased carbon dioxide has been an effect of global warming, not a cause. Technically, carbon dioxide is a lagging proxy for ocean temperatures. When global temperature, and along with it, ocean temperature rises, the physics of solubility causes atmospheric CO2 to increase. If increases in carbon dioxide, or any other greenhouse gas, could have in turn raised global temperatures, the positive feedback would have been catastrophic. While the conditions for such a catastrophe were present in the Vostok record from natural causes, the runaway event did not occur. Carbon dioxide does not accumulate in the atmosphere.

CO2 Rule
]]> I. INTRODUCTION

Carbon dioxide, a benign gas, is now the hyper–volatile fuel of public policy, media hype, and world politics. Climatologists, undeterred by their inability to predict even the dominant features of the earth’s climate record – the ice ages and the glacial periods – have nonetheless scored a political coup by cobbling together three selected bits of science into a cataclysmic prediction: man is on the verge of destroying life on the planet.

The three cobblestones are (1) a smattering of greenhouse gas physics, (2) half a million years worth of data from Vostok ice cores and (3) half a century of data from Mauna Loa atmospheric CO2 monitoring. Presented here are new results from analysis of the second, the Vostok data, reductions which have a profound effect on the other two legs of the global warming stool, on the role of carbon dioxide, and ultimately on public policy.

{Begin rev. 6/29/08.} The IPCC said,

One family of hypotheses to explain glacial/inter-glacial variations of atmospheric CO2 relies on physical mechanisms that could change the dissolution and outgassing of CO2 in the ocean. The solubility of CO2 is increased at low temperature, but reduced at high salinity. These effects nearly cancel out over the glacial/inter-glacial cycle, so simple solubility changes are not the answer.

IPCC, Third Assessment Report (TAR), Box 3.4, Causes of glacial/inter-glacial changes in atmospheric CO2, p. 202. Contrary to the IPCC conclusion, “changes in solubility” and second order effects of salinity are irrelevant. Changes in CO2 concentration due to classical temperature effects on solubility between ice age epochs account for the measured variations. These are intra-epoch effects, and whether they “nearly cancel out” on a larger scale is immaterial. {End rev. 6/28/08.}

CONTENTS
ABSTRACT
I. INTRODUCTION
II. VOSTOK DATA
A. CLIMATOLOGISTS’ VIEW OF VOSTOK DATA
B. VOSTOK REMAPPED
III. MODELING VOSTOK CO2 CONCENTRATION
A. CLIMATOLOGISTS CAN’T ACCOUNT FOR ATMOSPHERIC CO2
B. SCIENTIFIC OBSERVATION: SOLUBILITY PHYSICS APPEARS TO ACCOUNT FOR ATMOSPHERIC CO2 CONCENTRATION
C. FITTING SOLUBILITY PHYSICS TO VOSTOK MEASUREMENTS
D. THE OTHER STRAIGHT LINE FIT AND CORRELATION
E. MEASURING AND MODELING THE LAG IN THE CO2 DATA
F. LAG-COMPENSATED CO2 RECORD
G. FINDING THE OPERATING POINT FOR THE VOSTOK CO2 RECORD ON THE SOLUBILITY CURVE
H. THE CO2 CONCENTRATION IN THE VOSTOK ICE CORE DATA IS IMPRINTED BY THE PHYSICS OF THE SOLUBILITY OF CO2 IN WATER
I. ERROR ANALYSIS SHOWS THE PHYSICS OF CO2 SOLUBILITY IN WATER REPRESENTS VOSTOK DATA BETTER THAN ANY POLYNOMIAL
IV. CONCLUSIONS
A. A NEW MODEL FOR ATMOSPHERIC CARBON DIOXIDE
B. CARBON DIOXIDE SHOULD NO LONGER DRIVE PUBLIC POLICY
C. GREENHOUSE CATASTROPHE MODELS (GCMs)
D. WHAT CLIMATOLOGISTS NEED TO DO
BIBLIOGRAPHY

II. VOSTOK DATA
A. CLIMATOLOGISTS’ VIEW OF VOSTOK DATA
-> Contents …
CO2-01
Figure 1
"CO2, temperature, and dust concentration
measured from the Vostok, Antarctica ice
core as reported by Petit et al., 1999."
[Dust record deleted.] http://en.wikipedia.org
/wiki/Image: Vostok-ice-core-petit.png#file.
CO2-02
Figure 2
Extraneous traces deleted, http://www.realclimate.
org/index.php?p=221">.

Climatologists show the Vostok ice core data of temperature and carbon dioxide graphically on a frequently reproduced and well‑known chart like that in Figure 1. These data reveal a compelling correlation between the concentration of CO2 and temperature.

An aside: Recently published, new ice core data extend the carbon dioxide trace back an additional 200,000 years. Figure 2. This extended record cannot contribute to this analysis until someone reduces and publishes corresponding temperature data.

The author of Figure 1 employs a bit of marginally acceptable, subjective chartsmanship to underscore a point. He selected scale factors and data ranges to emphasize the correlation between carbon dioxide and temperature. The peak to peak swings in the chart traces are arbitrarily made to look alike. This is subjective and artificial, but harmless here.

What is not harmless, though, is climatologists seizing on the lock-step rising and falling of temperature and carbon dioxide as evidence, if not proof, of their greenhouse gas theory: increased CO2 allegedly causes increased temperatures. (A tacit assumption is that the ice core temperature swings represent the global swings, an assumption adopted for this analysis, too.)







{Begin rev. 11/16/09.}

The first deep ice cores from Vostok in Antarctica (Barnola et al., 1987; Jouzel et al., 1987, 1993) provided additional evidence of the role of astronomical forcing. They also revealed a highly correlated evolution of temperature changes and atmospheric composition, which was subsequently confirmed over the past 400 kyr (Petit et al., 1999) and now extends to almost 1 Myr. This discovery drove research to understand the causal links between greenhouse gases and climate change. AR4, ¶1.4.2 Past Climate Observations, Astronomical Theory and Abrupt Climate Changes, p.106.

What that causal link was, IPCC implies by predetermination upon its founding in 1988 and to its ultimate determination today that CO2 causes (then) or amplifies (now) a rise in temperature. A decade later, this early causal relationship is made explicit, along with a hint of its invalidation, in a paper not cited by IPCC and not freely available to the public:

Abstract. Ice-core measurements of carbon dioxide and the deuterium palaeothermometer reveal significant covariation of temperature and atmospheric CO2 concentrations throughout the climate cycles of the past ice ages. This covariation provides compelling evidence that CO2 is an important forcing factor for climate. But this interpretation is challenged by some substantial mismatches of the CO2 and deuterium records, especially during the onset of the last glaciation, about 120 kyr ago. Here we incorporate measurements of deuterium excess from Vostok in the temperature reconstruction and show that much of the mismatch is an artefact caused by variations of climate in the water vapour source regions. Using a model that corrects for this effect, we derive a new estimate for the covariation of CO2 and temperature, of r2 = 0.89 for the past 150 kyr and r2 = 0.84 for the period 350–150 kyr ago. Given the complexity of the biogeochemical systems involved, this close relationship strongly supports the importance of carbon dioxide as a forcing factor of climate. Our results also suggest that the mechanisms responsible for the drawdown of CO2 may be more responsive to temperature than previously thought. Bold added, Cuffey, K.M., and F. Vimeux, Covariation of carbon dioxide and temperature from the Vostok ice core after deuterium-excess correction, Nature 412, 523-527, 8/2/01.

The error arose when Cuffey et al., IPCC, and others relied on the point statistics of the correlation coefficient and the covariance instead of the full correlation function, which depends on the lag, but includes the point statistics at a lag of zero. If the cause goes away and the temperature continues to rise, then IPCC has modeled the climate as unstable, triggered by a transient orbital forcing event, but destined to heat until the seas run out of CO2. Or, does IPCC contend the orbital forcing is still present?! {End rev. 11/16/09.} When other analysts examined the data, they found that the CO2 trace lagged the temperature curve by about a millennium. This confounds the greenhouse theory prediction. CO2 couldn’t be the cause of past global temperature increases!

The climatologists were quick with an offense and a defense. They labeled the discoverers of the lag as contrarians. And Carbon dioxide while not initiating the temperature rise surely amplified it:

CO2 changes parallel Antarctic temperature changes during deglaciations (citations). This is consistent with a significant contribution of these greenhouse gases to the glacial–interglacial changes by amplifying the initial orbital forcing (citation). Bold added, TAR, ¶2.4 How Rapidly did Climate Change in the Distant Past?, ¶2.4.1 Background, p. 137. http://pame.arctic-council.org/climate/ipcc_tar/ wg1/072.htm.

That was a close call for the catastrophists!

B. VOSTOK REMAPPED
->Contents …
CO2-03
Figure 3
Vostok CO2-temperature pairs.
CO2-04
Figure 4
Vostok CO2/Temperature
Constellation.

The familiar graph of the Vostok data (Fig. 1), shows temperature and CO2 as functions of time. An alternative is to graph temperature as a function of CO2, or vice versa. An example is Figure 3.

In Figure 3, each pair of simultaneous readings of temperature and CO2 concentration is a dot on the graph, connected in sequence just to show that the time relationship is not lost. For example, the graph has labels for the ages of the first and last points. Without the paths, the dots form a constellation of data, as shown in Figure 4.

This analysis has no further call for the start and end marks. The graphs are just for human visualization of the data. At its roots, the information in the data is arithmetical.

III. MODELING VOSTOK CO2 CONCENTRATION
-> Contents …
Another observer of current climatology examined Vostok data in a similar coordinate system. He is Ferdinand Engelbeen, a gadfly and regular commenter to RealClimate.org, a major public outlet for the climatologists.

CO2-05
Figure 5
Best fit mathematical lines to the Vostok data.
Zero temperature refers to the current
global temperature. http://www.
ferdinand-engelbeen.be/klimaat/
correlation.html.

Engelbeen’s result is shown in Figure 5. He shows a best linear fit and a best quadratic fit, also known as the first and second order fits, respectively. Mathematics guarantees that increasing the order of the fit improves (or at least can’t worsen) the fit.

Mr. Engelbeen found this important Vostok relationship “surprisingly linear”. (Comment #2, http://www.realclimate.org/index.php?p=13#comment-69.) More importantly, his analysis confirms that the curvature in the data is not an optical illusion.

Curves like Engelbeen’s are purely mathematical fits. They indicate correlation, a mathematical relationship, but he gives them no connection to physics. The goal here is to uncover the physical relationship between the historic CO2 concentration and temperature. What causes the concentration effect to be curved as it is? In other words, can a cause and effect model be developed which might account for the correlation seen in the Vostok data?

A. CLIMATOLOGISTS CAN’T ACCOUNT FOR ATMOSPHERIC CO2
-> Contents …

According to at least one report, climatologists are at a loss to explain the source of the CO2:

Where did the carbon dioxide come from? “This is one of the grand unsolved puzzles in climate research,” said Thomas Stocker, a climate modeler at the Physics Institute of the University of Bern. Schoen [1999].

Moreover and to the contrary, climatologists dismiss the oceans as the source. Gavin A. Schmidt (NASA Goddard Institute for Space Studies (GISS), New York, New York; and Department of Earth and Environmental Sciences, Columbia University, New York, New York.) and his blog group at RealClimate believe …

The oceans cannot be a source of carbon to the atmosphere, because we observe them to be a sink of carbon from the atmosphere.

RealClimate, the Group, http://www.realclimate.org/index.php? p=160. Instead, this new analysis establishes that there is no contradiction in the oceans being simultaneously both a source and a sink.

The International Panel on Climate Change (IPCC) seems to agree with RealClimate:

[T]he observed increase in CO2 is predominately due to the oxidation of organic carbon by fossil-fuel combustion and deforestation.

IPCC [2001], C.1 Observed Changes in Globally Well-Mixed Greenhouse Gas Concentrations and Radiative Forcing. http://pame.arctic-council.org/climate/ipcc_tar/wg1/016.htm . But predominantly means not completely. So IPCC concedes:

Thus, the terrestrial biosphere does not cause the difference in atmospheric CO2 between glacial and interglacial periods. The cause must lie in the ocean, and indeed the amount of atmospheric change to be accounted for must be augmented to account for a fraction of the carbon transferred between the land and ocean.

IPCC [2001], 3.3 Palaeo CO2 and Natural Changes in the Carbon Cycle, 3.3.1 Geological History of Atmospheric CO2. http://www.grida.no/climate/ipcc_tar/wg1/107.htm. That fraction Stocker estimates is about half:

“About 50% of the 80-ppm glacial-to-interglacial increase can be explained by a change in the solubility of carbon dioxide.”

Schoen [1999], above, continuing her Stocker quotation. The phrase “change in the solubility” can be read several ways. Regardless, the analysis here shows that the well–known, fixed and constant physics of the temperature–dependent solubility of CO2 in water accounts for all the Vostok CO2 concentration measurements.

B. SCIENTIFIC OBSERVATION: SOLUBILITY PHYSICS APPEARS TO ACCOUNT FOR ATMOSPHERIC CO2 CONCENTRATION
-> Contents …

The solubility of CO2 in water is available from many handbooks, as shown in Figure 6. Solubility, labeled X_1 in the curve by tradition, is the saturated load of CO2 in water at the temperature indicated. It is relative, and dimensionless, being in grams of solute per 100 grams of solvent.

CO2-06
Figure 6
Solubility, X_1, of CO2 in water.
Handbook of Chemistry & Physics,
34th ed., 1953, Solubility of Gases
in Water, p. 1532. The curve is the
best–fit, fifth order by the author.
CO2-07
Figure 7
Vostok CO2 concentration
appears to be imprinted by
the solubility pump.
CO2-08
Figure 8
Straight line fit to Vostok
constellation of relative CO2
concentration and temperature
data pairs.
CO2-09
Figure 9
Vostok data represented
by alternative straight lines.

The complement of solubility, 1-X_1, represents the relative amount remaining in the air. (More precisely, the amount remaining in the atmosphere would be C-X_1, where C is an arbitrary constant. The constant C is immaterial to the slope of the curve, so does not enter into the fitting to the Vostok data. Therefore without loss of generality, C is shown as 1.)

As chartsmanship underscored the correlation between Vostok data traces, chartsmanship can make clear the correlation between the Vostok CO2 samples and CO2 solubility in water. Correlation is the key observation underlying this analysis. It is shown in Figure 7 by artful plotting of the complement of the solubility curve atop the Vostok data.

C. FITTING SOLUBILITY PHYSICS TO VOSTOK MEASUREMENTS
-> Contents …

To measure this apparent effect of the solubility pump, the concentration of CO2 may be expressed in relative terms, too. In the following, where relative CO2 concentration is shown, it is in percent of the midpoint of the Vostok concentration, and gets the new label CO2r. Also for convenience, the temperature difference gets the popular nickname “Del T”, short for the conventional “Delta T”.

The straight line fit to the constellation of data in relative CO2 concentration is shown in Figure 8.

Correlation and straight line fits share some important properties. The straight line is the unique line that minimizes the total (sum square) error between itself and, in this case, the CO2 concentration ratio samples. That straight line has a slope of 3.42% per degree Centigrade. As shown below, this result places the Vostok data squarely on the solubility curve, showing a physically meaningful operating point.

D. THE OTHER STRAIGHT LINE FIT AND CORRELATION
-> Contents …

The analysis could as easily have found the best fit straight line that minimizes the error between the fit and the temperature samples instead of the CO2 concentration. Conventionally, the independent variable is graphed on the x-axis, called the abscissa. But to this point, determining which of the variables might be independent and which dependent, is an objective of the analysis.

The choice of which is the dependent and which is the independent variable is often subjective, reflective of a presumed cause and effect model. Climatologists by their Greenhouse Catastrophe Model assume, and attempt to prove, that temperature is the dependent variable. The straight line fit corresponding to dependent temperature is shown alongside that for independent temperature in the next chart, Figure 9.

The catastrophe model has a slope of 21.6 degrees Centigrade per 100 percent change in CO2 concentration, or 0.216ºC/%.

The product of the two slopes is the mathematical “coefficient of determination”, conventionally labeled r2, with r being the “correlation coefficient”.

This dual line–fitting method unmasks some of the mystery of correlation. The smaller the angle between the lines, the stronger the correlation between the two variables. Here the product of the slopes is 0.740. Since the maximum is one, it is subjectively a fairly strong correlation (r = 0.860).

Others, however, have reported a lag in the CO2 data with respect to the temperature. Equivalently, temperature events lead or precede CO2 concentration changes. Good analytical techniques require quantification of that lead or lag, and offsetting the data traces to an optimum.

The adjustment is readily made because the graphing steps above preserve the information in the Vostok records. The offset has no effect on the conclusions reached, but does provide a small increase in accuracy.

E. MEASURING AND MODELING THE LAG IN THE CO2 DATA
-> Contents …
CO2-10
Figure 10
Cross-correlation function
for all Vostok data.
CO2-11
Figure 11
Cross-correlation function
for most recent 4,000 years
of Vostok data.

CO2-12
Figure 12
Vostok sample record with CO2
offset to maximize its correlation
with the temperature record.
CO2-13
Figure 13
Vostok offset, relative CO2
and temperature pair constellation.
CO2-14
Figure 14
Best linear fit to Vostok data
pairs of offset, relative CO2
and temperature.
CO2-15
Figure 15
Best linear fit pair to Vostok
data pairs of offset,
relative CO2 and temperature.
CO2-16
Figure 16
The first order Vostok CO2
concentration varies with
temperature according to the
solubility curve at
0.247 g/100 g water,
corresponding to a temperature
of 8.26ºC.
CO2-17
Figure 17
The 3.49%/ºC slope of the Vostok
CO2 concentration fits the slope
of the solubility curve at 8.26ºC
for 3rd, 4th, or 5th order fits.
CO2-18
Figure 18
The Vostok CO2 concentration
best fits the solubility curve
in the domain of 0ºC to 14ºC.
CO2-19
Figure 19
Vostok CO2 concentration varies
according to the physics
of the solubility of CO2 in water.

By convention, the Greek tau (t for time) stands for lag. The relation between correlation and tau is the correlation function. Auto–correlation is correlation of a record with itself, and cross–correlation is the correlation between two different records. Figure 10 contains the cross–correlation function of CO2 and temperature for the entire Vostok record of 400,000 years. (The graph is more dense on the left because of an intentional computational artifact. Sample intervals increase exponentially to simplify the computation load. The correlation method wraps the data on itself, analogous to a 420,000–year long tape loop.)

Zooming in by a factor of 100 shows the fine structure in the near term. This is Figure 11.

Three or four nearly equivalent peaks appear where carbon dioxide has the greatest correlation with temperature. The fact that the correlation is relatively poor at zero temperature offset emphasizes that the lag is real, and that any model should account for the lag. Subsequent analysis is offset to the nearest local peak in the correlation at 1073 years. As already stated, the correlation shift has no effect on the qualitative result, namely that CO2 is not responsible for but is a response to global temperature. Applying the lag to the model does improve the accuracy of the results by a few percent.

F. LAG–COMPENSATED CO2 RECORD
-> Contents …

Offsetting the CO2 trace by 1073 years has the scientifically desirable effect of sharpening or flattening the constellation of data. This is an improvement in signal to noise ratio. It makes the curvature more apparent, as shown in Figure 12.

Again dropping the sample paths and representing the CO2 concentration in percentage produces the new constellation of ice core data, offset for maximum correlation, shown in Figure 13.

The best fit straight line through these points shows that the average variation of CO2 concentration is 3.49% per degree Centigrade, shown in Figure 14. The complementary, catastrophe straight line fit is 21.8ºC per 100% change in CO2 concentration, or 0.218ºC/%, included in Figure 15.

The offset for lag increased the slope from 3.42%/ºC to 3.49%/ºC with temperature as the independent variable, and the catastrophe slope from 0.216 ºC/% to 0.218 ºC/% CO2 with the greenhouse gas as the independent variable. The 1073 year offset slightly changes the operating point on the solubility curve. The product of the two slopes, r^2, is 0.7609, and r is thus increased from 0.860 to 0.872. (Computation of correlation by the straight line fit method does not involved data wrapping.)

For several reasons, the catastrophic fit can be put to rest. Carbon dioxide is dependent on temperature, and not the reverse. The reason is not just the fact that concentration lags temperature changes, but because it is a physical consequence of the ocean temperature distribution.

G. FINDING THE OPERATING POINT FOR THE VOSTOK CO2 RECORD ON THE SOLUBILITY CURVE
-> Contents …

The slope of the solubility curve is 3.49%/ºC at 8.26ºC. This is where the straight line fit to the lag–adjusted Vostok CO2 concentration is tangent to the solubility curve. It occurs at the solubility level of 0.247 g/100g water, as shown in Figure 16.

Locating the first order operating point on the original solubility data is made difficult by the granularity of the solubility data. The final point comes from analysis of the slope of the solubility curve in various polynomial representations, as shown in Figure 17.

The Vostok CO2 data occur over a relative temperature region, which mathematicians call the domain, of 14ºC. The best fit of the solubility curve to the Vostok data occurs in the region of 0ºC to 14ºC, the segment of the solubility curve shown in Figure 18.

H. THE CO2 CONCENTRATION IN THE VOSTOK ICE CORE DATA IS IMPRINTED BY THE PHYSICS OF THE SOLUBILITY OF CO2 IN WATER
-> Contents …

The operating region from the solubility curve transforms into a curve representing the Vostok CO2 concentration, as shown in Figure 19.

This segment of the solubility curve fit to the Vostok CO2 data accounts for all the Vostok CO2 data. That is, there is no additional concentration of CO2 in the Vostok record which is not imprinted with the solubility data. Additional, long term CO2 not involved in the solubility process would reduce the percentage variations, moving the operating point to hotter and physically meaningless temperatures, or even off the solubility curve altogether.

I. ERROR ANALYSIS SHOWS THE PHYSICS OF CO2 SOLUBILITY IN WATER REPRESENTS VOSTOK DATA BETTER THAN CAN ANY POLYNOMIAL
-> Contents …

What remains is assessment of the goodness of the solubility fit and the consequences of the analysis.

First, the solubility curve lies comfortably within the one standard deviation bands of the best linear fit. That fit is shown in Figure 20.

The CO2 solubility model even fits well within the catastrophe trend, as shown in Figure 21.

In fact, the CO2 solubility representation of the relationship between the CO2 concentration data and temperature records at Vostok is superior to any reasonable polynomial fit, as shown by Figure 22.

Superimposed in Figure 22 are every polynomial fit to the Vostok data, from the first to the tenth degree, with temperature the independent variable. Unlike the polynomials, the solubility fit has well behaved end effects. At high orders, the polynomials chase measurement errors, including transient effects like volcano eruptions or forest fires, a weakness that worsens as the order increases. The solubility curve chases neither measurement errors nor transients.

The solubility fit is accurate to within a fraction of a percent of the least error, that of the highest order polynomial. The polynomials are slightly superior at error reduction because they have the effect of reducing measurement errors along with representing the physical process. Polynomials are malleable, mathematically guaranteed to fit the data of the underlying process along with the errors and disturbances, but physically meaningless. The solubility model shape is fixed by the underlying physics, and fits according to whether those physics are applicable. Lastly, the solubility model is insensitive to measurement errors or transient events.

CO2-20
Figure 20
The solubility physics represents
the Vostok CO2 data within
one standard deviation
of the trend line.
CO2-21
Figure 21
The solubility reaction fits
well both linear trend lines
for the Vostok CO2
concentration data.
CO2-22
Figure 22
The physics of CO2 solubility
in water is better suited
to represent the Vostok records
than is any polynomial.


CO2 Rule


IV. CONCLUSIONS
A. A NEW MODEL FOR ATMOSPHERIC CARBON DIOXIDE
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Science is about models of the real world that, first of all, fit all the data. This analysis is a first step in postulating a scientific model for the CO2 observations. The short term objective here is to characterize the observed concentration that science demands future models reproduce, and to assess the consequences.

Looking beyond that characterizing of the Vostok data, the pattern in the data suggests a model for CO2 such as shown in the sketch of Figure 23.

CO2-23

Figure 23

{Rev. 11/12/09} The shaded area represents the interface of the ocean surface layer with the atmosphere. The ocean has circulation components that carry light weight water poleward in the surface layer, cooling along the way and thus absorbing more CO2 as Henry’s Law requires. It becomes more dense as it cools and is freshened from land runoff in the classical model. But as shown here, it also increases in density as it loads with CO2. It’s the surface component of a ThermoHaline Carbon Circulation, THCC. The subsurface component is labeled as the Conveyor Belt. The THCC headwaters are at the poles, where it has a CO2 concentration corresponding to a perpetual temperature of 0ºC to 4ºC, and proportional to the existing CO2 concentration in the atmosphere. The THCC emerges at the surface approximately one millennium later to outgas according to Henry’s Law in proportion to the CO2 concentration and surface temperature at the time and place of discharge. The bulk of this outgassing, perhaps 80%, occurs in the Eastern Equatorial Pacific. Thus the hypothesis is that the volume of CO2 outgassed by the ocean is proportional to the CO2 content then, a millennium ago, and the sea surface temperature now. {End Rev. 11/12/09}

Several processes are simultaneously underway in the Carbon Dioxide Stream of Figure 23. Superimposed on a latitude–temperature graph is the solubility curve (shown without its ordinate axis). Solubility gets a shaded thickness to suggest the temperature dependent potential to absorb or release CO2 everywhere.

The atmosphere is a cloud to portray the global mixing of atmospheric gases by the winds. The CO2 exchange should occur to some extent distributed over the surface of the ocean. It should also occur focused by the ocean’s meridional overturning circulation, also known as the thermohaline circulation, and popularly called a conveyor belt. The circulation descends at the poles and rises to touch the surface dominantly in the Indian Ocean and the Eastern Pacific. When the belt rises to the surface, the current is saturated with CO2 because of the rising temperature and falling pressure. It is ripe to release the gas.

Insofar as the thermohaline circulation governs the rate at which deep waters are exposed to the surface, it may also play an important role in determining the concentration of carbon dioxide in the atmosphere.

Wikipedia, Thermohaline Circulation. The Wikipedia entry also gives 1200 years as the period of the circulation, which is quite close to the observed lag, supplying additional corroboration for the model. See Figure 11, above. This source supplies no hint of the accuracy of the period, or of the probable geographic locations for the release of the CO2. See also http://www.grida.no/climate/vital/32.htm for a nice diagram of the circulation. For a recent revelation that integration of the ocean patterns into the GCMs was still a decade away, see IPCC [2001], Ch. 14 Advancing Our Understanding, ¶14.2.3.2 Thermohaline circulation. http://www.grida.no/climate/ipcc_tar/wg1/508.htm.

The distribution of evaporation and precipitation over the ocean (its hydrologic cycle) is one of the least understood elements of the climate system. However, it is now considered one of the most important, especially for ocean circulation changes on decadal to millennial time-scales.

The Ocean Component of the Global Water Cycle, Raymond W. Schmitt, Department of Physical Oceanography, Woods Hole Oceanographic Institution, [2002]. http://www.earthscape.org/t1/scr01/scr01a.html.

The atmosphere only holds a few centimeters of liquid water, or 0.001% of the total.

heating one part in 100,000 of the water, he seems to attribute to the Man Behind the Curtain that

[i]n a stronger CO2 greenhouse climate it is hypothesized that the hydrologic cycle will intensify.

Id. The cause and effect perversely get reversed. Intensification of the hydrological cycle through heating of the ocean should increase the concentration of CO2 in the atmosphere, where it will have a minute effect on atmospheric temperature.

Along the distributed path, the solubility effect observed in the Vostok data could represent a global average. Alternatively, in the conveyor belt path, the Vostok data could represent the release of CO2 at its focused contacts with the surface. Geometric modeling and calculations would help resolve the better model or a mix of the two mechanisms. The lag in the CO2 record suggests that the conveyor belt is the dominant flow.

B. CARBON DIOXIDE SHOULD NO LONGER DRIVE PUBLIC POLICY
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The discovery that the Vostok CO2 record is an effect of the oceanic solubility pump has profound effects on the science and on public policy.

Over those 420,000 years, warm ocean water has regulated the concentration of CO2 by release of this gas into the atmosphere. Because there is no trace of build–up of CO2 from forest fires, volcanoes, or the oceans themselves, cold waters must be scrubbing CO2 out of the air. Since there is no difference between manmade and natural CO2, anthropogenic CO2 is sure to meet the same fate.

To the extent that the analyst’s Vostok temperature trace represents a global atmosphere temperature, so does the concentration of CO2. Thus, CO2 is a proxy for global temperature, and attempting to control global temperatures by regulating anthropogenic CO2 is unfounded, futile, and wasteful.

C. GREENHOUSE CATASTROPHE MODELS (GCMs)
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Since the industrial revolution, man has been dumping CO2 into the atmosphere at an accelerating rate. However the measured increase in the atmosphere amounts to only about half of that manmade CO2. This is what National Geographic called, “The Case of the Missing Carbon”. Appenzeller [2004].

Climatologists claim that the increases in CO2 are manmade, notwithstanding the accounting problems. Relying on their greenhouse gas theory, they convinced themselves, and the vulnerable public, that the CO2 causes global warming. What they did next was revise their own embryonic global climate models, previously called GCMs, converting them into greenhouse gas, catastrophe models. The revised GCMs were less able to replicate global climate, but by manual adjustments could show manmade CO2 causing global warming within a few degrees and a fraction!

The history of this commandeering is documented in scores of peer-reviewed journal articles and numerous press releases by the sanctified authors. Three documents are sufficient for the observations here, though reading them is rocket science. (An extensive bibliography on climate, complete with downloadable documents, covering the peer-reviewed literature and companion articles by peer-published authors is available on line from NASA at http://pubs.giss.nasa.gov/.) The three are Hansen, et al., [1997], Hansen, et al., [2002], and Hansen, et al., [2005]. Among Hansen’s many co-authors is NASA’s Gavin Schmidt, above. He is a frequent contributor to the peer–reviewed literature, and he is responsible for a readable and revealing blog unabashedly promoting AGW. http://www.realclimate.org/.

The three peer-reviewed articles show that the Global Climate Models weren’t able to predict climate in 1997. They show that in the next five years, the operators decoupled their models from the ocean and the sun, and converted them into models to support the greenhouse gas catastrophe. They have since restored some solar and ocean effects, but it is a token and a concession to their critics. The GCMs still can’t account for even the little ice age, much less the interglacial warming.

All by themselves, the titles of the documents are revealing. The domain of the models has been changed from the climate in general to the “interannual and decadal climate”. In this way Hansen et al. placed the little ice age anomaly outside the domain of their GCMs. Thus the little ice age anomaly was no longer a counterexample, a disproof. The word “forcing” appears in each document title. This is a reference to an external condition Hansen et al. impose on the GCMs, and to which the GCMs must respond. The key forcing is a steadily growing and historically unprecedented increase in atmospheric CO2. “Efficacy” is a word coined by the authors to indicate how well the GCMs reproduce the greenhouse effect they want.

In the articles, Hansen et al. show the recent name change from Global Climate Models to Global Circulation Models, a revision appropriate to their abandonment of the goal to predict global climate. The climatologists are still engaged in the daunting and heroic task of making the GCMs replicate just one reasonable, static climate condition, a condition they can then perturb with a load of manmade CO2. The accuracy and sensitivity of their models is no longer how well the models fit earth’s climate, but how well the dozens of GCM versions track one another to reproduce a certain, preconceived level of Anthropogenic Global Warming. This suggests that the models may still be called GCMs, but now standing for Greenhouse Catastrophe Models.

In these GCMs, the CO2 concentration is not just a forcing, a boundary condition to which the GCM reacts, but exclusively so. In the GCMs, no part of the CO2 concentration is a “feedback”, a consequence of other variables. The GCMs appear to have no provision for the respiration of CO2 by the oceans. They neither account for the uptake of CO2 in the cold waters, nor the exhaust of CO2 from the warmed and CO2–saturated waters, nor the circulation by which the oceans scrub CO2 from the air. Because the GCMs have been split into loosely–coupled atmospheric models and primitive ocean models, they have no mechanism by which to reproduce the temperature dependency of CO2 on water temperature evident in the Vostok data.

GCMs have a long history. They contain solid, well-developed sub-models from physics. These are the bricks in the GCM structure. Unfortunately, the mortar won’t set. The operators have adjusted and tuned many of the physical relationships to reproduce a preconceived, desired climate scenario. There is no mechanism left in the models by which to change CO2 from a forcing to a feedback.

Just as the presence of measurable global warming does not prove anthropogenic global warming, the inclusion of some good physics does not validate the GCMs. They are no better than the underlying conjecture, and may not be used responsibly to demonstrate runaway greenhouse effects. Science and ethics demand validation before prediction. That criterion was not met before the climatologists used their models to influence public opinion and public policy.

The conversion of the climate models into greenhouse catastrophe models was exceptionally poor science. It is also evidence of the failure of the vaunted peer review process to protect the scientific process.

D. WHAT CLIMATOLOGISTS NEED TO DO
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The GCMs need to be revamped. They need to have the primary thermodynamic loop restored. This is the chain of dynamic events from solar radiation, through the shading and reflection of clouds responding to temperature changes, absorption primarily in the ocean, and the transport and exchanges of heat and gases by which the oceans create and regulate the earth’s climate and atmosphere. The models need to reflect the mechanisms which make the earth’s climate not vulnerable, but stable.

The CO2 concentration is a response to the proxy temperature in the Vostok ice core data, not a cause. This does not contradict that CO2 is a greenhouse gas, but it does contradict the conjecture that the presence of a greenhouse gas has any destabilizing effect on global climate. Other forces overwhelm the conjecture of a runaway greenhouse effect. The concentration of CO2 is dynamic, controlled by the solubility pump. Global temperature is controlled first by the primary thermodynamic loop.

The Vostok data support an entirely new model. Atmospheric CO2 is absorbed by the oceans. Fires, volcanoes, and now man deposit CO2 into the atmosphere, but those effects are transient. What exists in steady state is CO2 perpetually pumped into the atmosphere by the oceans. Atmospheric CO2 is a dynamic stream, from the warm ocean and back into the cool ocean.

Public policy represented by the Kyoto Accords and the efforts to reduce CO2 emissions should be scrapped as wasteful, unjustified, and futile.

CO2 Rule
BIBLIOGRAPHY
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Appenzeller, Tim, National Geographic Magazine, Feb. 2004, The Case of the Missing Carbon. http://magma.nationalgeographic.com/ngm/0402/feature5/.

Hansen, J., et al., 1997. Forcings and chaos in interannual to decadal climate change. J. Geophys. Res. 102, 25679-25720, doi:10.1029/97JD01495.

Hansen, J., et al., 2002. Climate forcings in Goddard Institute for Space Studies SI2000 simulations. J. Geophys. Res. 107, no. D18, 4347, doi:10.1029/2001JD001143.

Hansen, J., et al., 2005. Efficacy of climate forcings. J. Geophys. Res. 110, D18104, doi:10.1029/2005JD005776.

International Panel on Climate Change (IPCC), Climate Change 2001: Working Group I: The Scientific Basis.

Schoen, Deborah, Learning from Polar Ice Core Research, Environmental Science & Technology, April 1, 1999 / Volume 33, Issue 7 / pp. 160 A-163 A. http://pubs.acs.org/hotartcl/est/99/apr/learn.html.

Schmitt, R. W., Department of Physical Oceanography, Woods Hole Oceanographic Institution, [2002], Columbia Earthscape, “an online resource on the global environment”, The Ocean Component of the Global Water Cycle. http://www.earthscape.org/t1/scr01/scr01a.html.

CO2 Rule

Dr. Glassman has a BS, MS, and PhD from the UCLA Engineering Department of Systems Science, specializing in electronics, applied mathematics, applied physics, communication and information theory. For more than half of three decades at Hughes Aircraft Company he was Division Chief Scientist for Missile Development and Microelectronics Systems Divisions, responsible for engineering, product line planning, and IR&D. Since retiring from Hughes, he has consulted in various high tech fields, including expert witness on communication satellite anomalies for the defense in Astrium v. TRW, et al, and CDMA instructor at Qualcomm. Lecturer, Math and Science Institutes, UCI. Member, Science Education Advisory Board. Author of Evolution in Science, Hollowbrook, New Hampshire, 1992, ISDN 0-89341-707-6. He is an expert modeler of diverse physical phenomena, including microwave and millimeter wave propagation in the atmosphere and in solids, ballistic reentry trajectories, missile guidance, solar radiation, thermal energy in avionics and in microcircuit devices, infrared communication, analog and digital signals, large scale fire control systems, diffusion, and electroencephalography. Inventor of a radar on-target detection device, and a stereo digital signal processor. Published A Generalization of the Fast Fourier Transform, IEEE Transactions on Computers, 1972. Previously taught detection and estimation theory, probability theory, digital signal processing.

CO2 Rule

© 2006 JAGlassman. All rights reserved.

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