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SOLAR WIND

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

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.

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 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. The Third Assessment Report charts them in Figure 7.9 after subtracting the average for the first 100 years. [Rev. 7/10/07] 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.


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

Solar Wind Index (“aa”) data start January, 1868. ftp.ngdc.noaa.gov/STP/SOLAR_DATA/RELATED_INDICES/AA_INDEX/ [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.

Posted on July 6, 2007 6:42 AM |

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Comments (14)



Murray duffin wrote:

Dr. Glassman,

Your solar wind comments are intriguing. We have several imperfect solar correlations with climate change. The roughly 11 year cycle is clearly correlated. Long term irradiance seems to be correlated, but is taken to have a very weak forcing effect. The solar magnetic field and solar wind modulation of incoming cosmic rays seems to be a fair correlation, but with some obvious recent problems. Svensmark's 1998 paper shows a pretty good correlation of sunspot cycle length with temperature up to about 1970, that has been contested for recent decades. Rise time from sunspot min to max also seems to correlate - shorter rise times of successive cycles = more warming. (same as cycle length??). The recent decline in sunspot max and cycle lengthening (cycles 22 and 23) do not seem to correlate well with temp. for the last 2 decades, but solar prominences and CMEs remained very active at least through 2005 (related to Atlantic hurricanes??). Is it possible to come up with a "solar index" that combines these effects with appropriate weighting that gives an undeniable correlation? I lack the analytic talent to undertake such an analysis, but it might be right up your alley.

The theory of solar activity driven by planetary motion seems highly likely, and the 60 to 80 year Gleissberg cycle is evident in almost all climate records. (It may well be that the sun also drives ENSO/SOI). However there may have to be a solar index composed of sunspot level, cycle length, solar wind, CME activity, etc that gives a best correlation, and that accounts for all or most of global warming/cooling.

Regards, Murray Duffin

[RSJ:Correlation is a mathematical measure of the similarity between two data streams. The quality of being perfect or imperfect has no meaning. The values measured depend largely on the signal to noise ratio in the data streams, which is typically quite low in climatology. Often a useful scientific model follows from discovery of a barely detectable peak in the correlation function compared to the background noise. Svensmark's and Gleissberg's contributions are examples.

[Be aware, though, that the IPCC mentions neither Gleissberg nor his cycle. While Gleissberg phenomena may be relevant to a climate model someday, they need not concern us here because they are not relevant to the anthropogenic global warming conjecture.

[You note that the "11 year cycles [are] clearly correlated", but you don't say to what. Some sources for your views about "long term irradiance" and the solar magnetic field would have been helpful in promoting a dialog. Nonetheless, the Consensus on Climate seems at odds with what you report. It says,

The absolute value of the spectrally integrated total solar irradiance (TSI) incident on the Earth is not known to better than about 4 W/m^2, but satellite observations since the late 1970s show relative variations over the past two solar 11-year activity cycles of about 0.1%, which is equivalent to a variation in radiative forcing of about 0.2 W/m^2. Prior to these satellite observations, reliable direct measurements of solar irradiance are not available. Variations over longer periods may have been larger, but the techniques used to reconstruct historical values of TSI from proxy observations (e.g., sunspots) have not been adequately verified. Bold added, IPCC, Third Assessment Report (TAR), Technical Summary, p. 45.

[You need to read Section 6.11.2.2, Cosmic rays and clouds, TAR, pp. 384-385. This section, except for two speculatively citations in one paragraph on p. 709, contains the entire IPCC discussion of Svensmark, and of his model relating surface temperature to cloudiness through Galactic Cosmic Rays. It expressly cites Svensmark, H., Influence of Cosmic Rays on Earth's Climate, Physical Review Letters, Vol. 81, No. 22, pp. 5027-5030, 11/18/98, but for a different proposition than yours:

[Svensmark (1998) showed that, at least for the limited period of this study, total cloud varies more closely with GCRs than with the 10.7 cm solar activity index over the past solar cycle. Id.

[In that paper, Svensmark says,

[clouds are clearly linked with cosmic rays rather than with other solar phenomena such as sunspots or the emission of visible light, ultraviolet and X-rays. Id., unpaginated.

[which overall doesn't support your "pretty good correlation of sunspot cycle length with temperature". Moreover, Svensmark's 1998 paper say nothing with respect to 1970.

[The Consensus makes clear that it is looking to cloudiness for the greenhouse effect:

[Even if high cloud[s] did respond to cosmic rays, it is not clear that this would cause global cooling as for thin high cloud the long-wave warming effects dominate the short-wave cooling effect. … Thus the evidence for a cosmic ray impact on cloudiness remains unproven. Id., p. 384.

[And lastly, dismisses Svensmark:

[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. Id., p. 385.

[The Consensus here errs twice. First, it should have been looking not to the greenhouse effects of clouds but to the cloud albedo effect. Second, it should have checked the correlation not between solar activity and cloud cover, but between solar activity and global temperature. This paper, Solar Wind, El Niño/Southern Oscillation, & Global Temperature: Events & Correlations, addresses the latter using the aa geomagnetic index (see TAR, ¶6.11.1.2, p. 381-382), but not the sunspot number. The other error will be addressed in an upcoming paper now in review.]

Posted by Murray duffin | October 13, 2007 7:56 PM



Dan Pangburn wrote:

I am convinced that CO2 is not a significant factor in the planet getting warmer. This is apparent by plotting the carbon level and temperature level vs time since 1860. http://cdiac.ornl.gov/trends/emis/tre_glob.htm

[RSJ: This chart shows the estimate for global carbon emissions since 1750 in metric tons. A search for this type of chart in the TAR and 4AR was not productive. Cumulative global emissions since 1850 is cited as 156 GtC (4AR, p. 139), which is about 20 times the maximum scale of the CDIAC chart! (1 Pg = 103 Tg = 103 Mt = 1 Gt, peta = 1015, tera=1012, giga=109, mega=106. For more on relevant units, see TAR, p. 869.) Global emissions rate in GtC/yr is in Figure 2.3(b), 4AR, p. 138. Atmospheric concentrations of CO2 in ppm from year 0 to 2000 are shown in FAQ 2.1, Figure 1 (unpaginated). Similar concentrations for the past 10,000 years are in Figure SPM.1, p. 3, with insets for the period since 1750. The caption says the Consensus attributes the increases since about 1750 "to human activities in the industrial era."]

and http://www.ncdc.noaa.gov/oa/climate/research/anomalies/anomalies.html

[RSJ: This chart from the National Climatic Data Center is a slight revision and seven year update of data presented by the IPCC in TAR, p. 107, Figure 2.1(a). The NCDC provides links to the data in tabular form, including monthly beginning January, 1880 and current through September, 2007.]

Also from 440 mya when temperature was the same as now but CO2 level was 10 times the present http://mysite.verizon.net/mhieb/WVFossils/Carboniferous_climate.html.

[RSJ: This page, dating from 1996 and last updated 9/19/06 was posted by AGW skeptic Monte Hieb, Chief Engineer, West Virginia Office of Miners Health Safety and Training. Monte Hieb has been well excoriated by the AGW folks. When someone criticized an article by Ray Pierrehumbert wearing his RealClimate hat, suggesting the group might read Hieb, Gavin Schmidt replied,

[[Response: Far be it for me to start the whole engineers vs. scientists thing again, but Monte Hieb's calculations are, to quote Senator Dirksen, "hogwash" and are almost as wrong as it is possible to be! - see: http://www.realclimate.org/index.php/archives/2006/01/calculating-the-greenhouse-effect/ . I hope that the rigour he applies to his mine safety calculations is significantly higher.].

[Pat II: What Angstrom didn't know, Comment 77. http://www.realclimate.org/index.php/archives/2007/06/a-saturated-gassy-argument-part-ii/. The paper, Calculating the greenhouse effect, is by Schmidt to criticize "an Australian climate 'contrarian'", perhaps the late John L. Daly, Still Waiting for Greenhouse, http://www.john-daly.com/, a valuable site. Schmidt's reference says nothing about Hieb or his writings.

[Pointing to a merely different calculation in no way demonstrates a challenged calculation is wrong. Whichever calculation proves correct, the error is RealClimate's for failure to engage in a rational argument.

[Similarly, the first rounds of the debate against Monte Hieb are won by the defendant. He is accused of being an engineer and not a climatologist, of being biased and a tool of the coal industry, of being uncredentialed, of not publishing in peer reviewed journals, and of getting (unspecified) facts wrong. Except for the one blank, it's a firing squad with the man at the post, not his arguments.

[Back to Pangburn, what he cites is a graph by Monte Hieb containing two traces, Atmospheric CO2 and Ave. Global Temp, over a range of the last 600 million years. Hieb attributes his data to fully qualified climatologists, or better. His AGW prosecutors say nothing about the reliability of any of Hieb's data, much less these two records. For the purposes here, we must accept the data as valid, and Pangburn's datum extract is correct, even a bit understated.]

Correlation does not prove causation but lack of correlation proves lack of causation. The irradiance data is not completely satisfactory. This analysis helps.

[RSJ: Pangburn's observation at 440 mya coincides with the minor ice age called the Andean-Saharan. Between about 330 and 260 mya, the Karoo Ice Age, Hieb shows atmospheric CO2 was also comparable to the modern levels. However, Hieb's data show a hot climate, about eight degrees hotter than the present for the first quarter of the epoch, even while CO2 concentration was falling. If the CO2 concentration were predictive of the temperature changes, the lags were in the millions of years.

[The disconnect between the Consensus and responsible critics lies in just such simple observations. What is not brought to the surface is that the GCMs, once meaning Global Climate Models, and now Global Circulation Models, cannot predict climate, and cannot even reproduce the climate over the 430,000 to 600,000 years of the Vostok record or the 600 million years of Hieb's sources. The models are initialized in an equilibrium state for modern climate, and then tipped over by the addition of some anthropogenic CO2. They're still GCMs -- Global Catastrophe Models.

[Observations like Pangburn's and Hieb's are telling, but the Consensus remains undeterred. Notwithstanding the imperatives of science, climatologists are not trying to replicate the time before the present. Their models only look forward. At least if they offered a defense other than ad hominem attacks, that's what they would say. They don't know what happened before the Big Bang, either.

[And so, the objective of the Rocket Scientist's Journal on climate is not to offer contesting theories to that of the Consensus. It is to expose the errors in the AGW conjecture as set forth in the IPCC Reports.

[Pangburn's closing observation about causation and correlation is often repeated, and true. Most often, correlation requires fine mathematical computations. In this case of the two records over-plotted by Hieb, the lack of correlation between CO2 concentration and global temperature is abundantly obvious. Clearly the AGW conjecture that CO2 drives the climate doesn't hold on the scale of the ice ages any more than it did on the scale of the glacial epochs. See The Acquittal of Carbon Dioxide.

[Pangburn's last two observation about irradiance and an analysis that helps seemed out of place and ambiguous. Perhaps he will elucidate.]

Posted by Dan Pangburn | November 12, 2007 3:28 PM



dan pangburn wrote:

Climate obviously has changed and will continue to change. The observation that ice is melting does not show that human activity is the cause. The assertion that humans have or ever can have a significant influence on climate by limiting the use of fossil fuel (a.k.a. limiting human production of carbon dioxide) is not supported by any historical record. Avoid the group-think and de facto censorship by Climate Scientists. Directly interrogate official government data that taxpayers have paid for from ORNL and NOAA as follows: If the carbon dioxide level from Law Dome, Antarctica http://cdiac.ornl.gov/ftp/trends/co2/lawdome.combined.dat is graphed on the same time scale as fossil fuel usage from http://cdiac.ornl.gov/trends/emis/tre_glob.htm it is discovered that the current carbon dioxide level increase started about 1750, a century before any significant fossil fuel use. If average earth temperature since 1880 from http://www.ncdc.noaa.gov/oa/climate/research/anomalies/anomalies.html is graphed on the same time scale as fossil fuel use it is discovered that there is no correlation between rising fossil fuel use and average global temperature to 1976. The asserted hypothesis that, since 1976, increasing carbon dioxide level has caused the temperature to rise is refuted by the carbon dioxide level from http://cdiac.ornl.gov/trends/co2/vostok.html and earth temperature from http://cdiac.ornl.gov/ftp/trends/temp/vostok/vostok.1999.temp.dat determined from the Vostok, Antarctica ice cores. If these are graphed on a higher resolution time scale it is discovered that the change in atmospheric carbon dioxide level lags earth temperature change by hundreds of years. If Law Dome carbon dioxide data and Vostok temperature are plotted on the same graph since 1000 AD (or before) it is observed that temperature oscillates up to ±1.5ºC (half pitch about 100 yr) while carbon dioxide level remains essentially unchanged (between 9000BC and 1750AD). The conclusion from all this is that carbon dioxide change does not cause significant climate change.

Regarding the irradiance data being not completely satisfactory. IMO irradiance, solar wind, cosmic rays, local relative humidity (propensity to form clouds) and possibly as yet undiscovered factors appear to all interact and have not yet been completely sorted out. The Glassman analysis is at least a start but most of temperature change remains uncorrelated.

[RSJ: 1. Instead, what ought to be demanded of the Federal government is that every paper supported by Federal money be made freely available to the public. In addition every paper or data source on which the IPCC relied for its reports should be made freely available, on line, personal computer-compatible before any decisions are made with respect to making the IPCC results any part of public policy.

[2. The technique of co-plotting records and by eye determining correlation and lead/lag relationships is a start, but the method is subjective and unscientific. To make matters worse, the records for plotting are regularly smoothed and partial. Smoothing even properly done falsely magnifies correlation. Leads and lags and correlation are mathematical relationships, and are calculable. Once those computations are made over the full range of the data, then science can discover and affirm cause and effect relationships in models.

[3. In the current paper, "Solar Wind Has Twice the Global Warming Effect of El Niño", you will find the following. The IPCC proclaims El Niño to have a "profound impact on humanity and society". And evidence that the Solar Wind has twice the power of El Niño in its likely effect on global surface temperature. You'll find that the IPCC is aware of the positive correlation between clouds and temperature, between cloud formation and galactic cosmic rays, and between cosmic rays and the solar wind. Nonetheless, the Consensus rejected the model that solar wind affects temperature for lack of evidence. A strong case exists for a model that cloud albedo regulates warming, minimizing any greenhouse effect, and modified by the sun, not through changes in its radiant power, but through the solar wind and dwarfing the El Niño effect.

Posted by dan pangburn | December 4, 2007 5:36 PM



Dan Pangburn wrote:

The ORNL and NOAA references appear to be unsmoothed data. However, since the conclusion is based on lack of correlation, any unidentified smoothing would make the conclusion more secure. The Vostok carbon dioxide data is at much greater time spacing than the temperature data. Graphics reveal locations where subjective interpretation may cause one to avoid certain data. For example, there is a point for carbon dioxide at 17695 ybp. A subjective assessment from observing the totality of information is that a point 17000 ybp would probably result in a significantly different 'curve' than a straight line from the point at 17695 ybp to the next point at 13989 ybp. Although a specific defined mathematic process will produce repeatable values, I found the subjective graphic determination of the definite lag of CO2 change to temperature change during transition from glacial to interglacial to be a compelling discovery. An objective, mathematically precise assessment would be welcome.

[RSJ: First, two points of order: By "the ORNL and NOAA" data, I presume you mean your references in your comment of 12/4/07 relative to Law Dome data vs. carbon emission history, and Vostok CO2 data vs. Vostok temperature. Also, by "the conclusion" you would mean your conclusions in that same comment.

[The subjective assessment is invaluable in science. It provides an intuitive check on any objective measurements and conclusions. However, the subjective has no part to play in the process model itself.

[Subjective conclusions can lead to a world of trouble and error. Climatologists were enthralled with their first look at the Vostok record. It appeared to corroborate the long held model of the greenhouse effect and the role of CO2. A little numerical analysis showed that conclusion be perversely wrong. CO2 follows temperature, not the reverse. At least it did on the paleontological record. No reason exists for the reverse to be true, yet that is exactly what the IPCC and the Consensus persist in doing with their GCMs. As their story goes, manmade CO2 in the future will cause temperatures to rise while the natural CO2, 16 times as intense, will have no effect.

[Mathematical tests exists to accomplish two important tasks. One is to test for smoothing in the data. Natural smoothing needs to be accommodated in the model. Data reduction smoothing needs to be discounted. The other task for the math is to investigate small signals and relationships buried in the noise. After all, climatology is about detecting small signals in extreme noise limited by the state of the art in measuring.

[Even though lack of correlation in measurements supports lack of correlation in models, that does not extend to lack of subjective correlation in the data. Science does more than just welcome the mathematical results. It requires that one do the math.

Posted by Dan Pangburn | December 16, 2007 3:55 PM



matt vooro wrote:

GLOBAL WARMING

Since 1990's there has been a continuous increase in global average temperatures .prime cause may not be greenhouse gases but rather the increase in Stratospheric joule heating caused by the increase in solar wind intensity. …

[RSJ: Matt Vooro brings up a real physical process, stratospheric joule heating coupled with the solar wind. But it is not part of the IPCC GCMs, and its strength is unquantified. It would not be, as he suggests, a competitor with greenhouse gases, which are not a source of energy. It would add to solar shortwave radiation. The addition, though, should prove negligible, because just a few percent change in solar radiation is sufficient to shift Earth's climate from ice age to arid.

[Vooro's lengthy post (about 1500 words) is a new subject, and neither a comment on any RSJ papers nor a contribution to the discussions. It is sorely in need of editing for typos and dangling references, and appears to be a disjointed conglomeration of several different writings. The RSJ policy is to accept all comments, and to provide thoughtful responses. This one was not junk, but too rough to post in its entirety.]

Posted by matt vooro | January 18, 2008 7:15 PM



matt vooro wrote:

Dear sir

I think you have completely misdescribed my material and electrical joule heatig. It is not just solar short wave radiation.

[RSJ: There is no connection made in our dialog between joule heating and shortwave radiation.]

Did you really read the reference papers at the back ?. I enclose the conclusions of that paper on what joule heating is about. Also the joule heating concept is not unquantified if you read the number of papers in the various scientific journals since 2004. Why it was omitted By IPCC has been questioned by others as well.

[RSJ: No, I didn't read your references. You shouldn't expect anyone to undertake such a task. You give references like a required reading list for a course. You should state all the needed facts in your argument, with citations to the page in your references for fact checking. That may be the only purpose of references in scientific writing.

[You have failed to quantify joule heating and to show how it compares to solar shortwave heating. Don't expect your readers to substantiate your qualitative claims by searching through vague "various scientific journals since 2004."

[You questioned the omission from the IPCC reports? Where is the authority for this claim?]

To only quote one paragraph of mine yet take two to find faults seems kind of one sided and clearly biased.Also my material although not agreeing with your views is realted to solar heating. Your very negative comments seem extreme and puzzling.Other bloggers have entered into an healthy dialoge with me including major scientific organizations .They may not agree with everything but they respect disenting opinions equally

[RSJ:Your writing was too burdensome to repair for posting. It contained misspellings, extra periods, run-on sentences, sentences without capital letters, references to "above" and "below" that couldn't be located, and citations not linked to your argument. You should use a spell checker and a grammar checker before submitting.

[Your material was not rejected for disagreeing with my papers. In fact, a major purpose of the blog is to seek such commentary. The web is the last refuge for legitimate peer review.

[Where might one find these "other bloggers" and what are the "major scientific organizations" that have responded to you? Where is the end to your last sentence above?]

Numerical estimations presented in this study show that the energy of the solar wind could be transferred into the Earth atmosphere through the electric fields induced by the solar wind disturbances. This process could be effective up to stratospheric altitudes 20-25 km where the ionized layer produced by the galactic cosmic rays and by some other sources exists. The electric currents induced by the electric fields are able to heat the atmosphere at altitude 25 km up to 5*10^-2 K/h (1.2 K/day). It means that the stratospheric warming could be produced not only by dynamical factors but also by a local heating of the atmosphere by electric current. The latter phenomenon could be especially effective in high-latitude region. Several rough approximations have been made in the above calculations, which nevertheless did not prevent to get realistic results. The aim of the further studies is to elaborate more accurate values of the parameters involved in these calculations. £/div>

[RSJ:To what does "this study" and "further studies" refer? Is this a clipping from some article? Why are you writing in incomplete conditionals ("could be" if what?) instead of the definitive ("is")? Why do we care about the altitudes at which induction occurs and not care about the power induced? The last two sentences read like a poor translation from a foreign language, but regardless contain nothing of substance worth translating. The next two paragraphs suffer the same problems. They read like gobbledegook.]

The preliminary calculation of the global Joule heating fields allows to conclude that the proposed method of the Joule heating rate parameterization is physically correct and meaningful. The obtained magnitude of the Joule heating is comparable with atmospheric heating rate due to even solar UV radiation and should be taken into account in climate models.

The direct consequences of such warming could be the changes in dynamics of the stratospheric polar vortex, global ozone concentration and also climate/weather pattern. All these processes could only be accurately evaluated using a global scale 3D chemistry-climate model and the parameterization for the additional heating presented and explained here.

Posted by matt vooro | February 4, 2008 10:26 AM



matt vooro wrote:

Dear Sir I have removed all graphs from my previous material to make it read more clearly.It would appear that the graphs did not come through in my previous post. If it is too long , please remove the references If this material is still unsatisfactory, I would sincerely ask you to remove all my material from this web page.

matt

[RSJ: Your submission on 1/22/08 didn't contain graphs, nor did it appear to have missing graphs. The problem with your submissions is not length, but typography and relevance.

[I am posting your comments, annotated, and provisionally. I have deleted only your references because they are not linked to your comments, they include abstracts and without free links to the papers. They are merely additional clutter. The rest of your comments, warts and all, are posted in full plus criticism for the tutorial value on how to participate in scientific dialog.]

INCREASED HIGH VELOCITY SOLAR WIND DAYS- COULD THIS BE THE PRIME CAUSE OF GLOBAL WARMING?

Since 1990's there has been a continuous increase in global average temperatures .The prime cause may not be greenhouse gases but rather the increase in Stratospheric joule heating [electrical heating ]caused by the increase in solar wind intensity. "Sprites" and jets have been found to transfer electricity between the ionosphere and storm clouds. So electricity is known to be transferred 65 km directly to our atmosphere. This joule heating may extend all the way to our lower atmosphere and possibly to the very surface of the planet. Here we define the increased solar wind intensity as increased number of days per year with high solar wind velocity of 500 km/sec or more. .Quiet wind is closer to 275 -350 km/s.

[RSJ: Global average temperature has been flat for the last five years or so. Smoothed over a sufficient length of time, the data do show a long term rise. The cause is not greenhouse gas, so we need to look elsewhere. If joule heating is a candidate cause, then your argument needs to show as the first order of business how much energy it could couple into the atmosphere. Solar shortwave radiation is more than ample to account for the most extreme climates. Instead, your argument wanders disconnectly into the altitude at which joule induction occurs and the frequency of occurrence of high solar winds. ]

Solar wind dynamic pressure and the Joule heating are directly related. Solar dynamic pressure or pulse is directly proportional to the density of the wind times the wind velocity squared. Thus doubling the solar wind velocity [which is what happens during these solar wind peaks of 500-1000 km/s and more] increases the dynamic pressure pulse four fold, increases the electrical field-aligned currents, which then increases ionospheric Joule heating which contribute to global warming. The magnitude of electric field in the atmosphere is proportional to the nearness of Magnetopause to the Earth.

[RSJ: How many watts? What is your authority for the physics connecting solar wind velocity, solar wind dynamic pressure, and electric field currents? And for the physics of ionospheric joule heating and global warming? And for the physics connecting the electric field in the atmosphere and the distance to the magnetopause?]

Data supplied by the University of Maryland and SOHO shows a very recent solar wind velocity spike during December 2007. It is no wonder that the year 2007 was tied with1998 as the second warmest global year. There were 93 days of high solar wind during 2007, the 4th highest on record1995-2007]. Already during January 2008 we have had 14.5 high velocity solar wind days. The average number of solar wind days per month is 6.3

[RSJ: How many watts? Your treatment of solar wind and global temperature is not valid; it is subjective correlation and has no scientific basis. You have set arbitrary measures and thresholds, removed the noise, and compared your private view of the results to proclaim discoveries. Expressions like "It is no wonder" are purely subjective and have no place in scientific dialog.

[You need to measure the crosscorrelation and the coefficient of determination over the entire records. This has been done for you on this site, above, using the geomagnetic aa index. If you don't like the aa index, then the paper is a model for what you need to do with your record of choice. You might want to crosscorrelate the aa index with the short SOHO proton flux record to test the surrogate value of the aa index that extends back well into the 19th Century.]

The high velocity solar wind days are analogous to the number of days in a year that our planet's furnace and fan were on and set on HIGH. The global temperature anomaly is what your thermostat reads as the actual temperature.

The average number of high solar wind days per year for the three years [1995-1997] around the last solar minimum was 42. The average number of high solar wind days 10 years later at the current solar minimum for the three years [2005-2007] is 92 , more than double .This may account for why temperature anomalies have also gone up from those around 0 .30 to 0.60 due to joule heating.

Solar wind data only goes back to November 1994 otherwise I would go back earlier to the 1980's when the so called "manmade global warming" started. I continue to state that in my personal opinion, the prime cause [80%] of global warming is the sun. Man made greenhouse gases contribute to the warming but only about 20%. £/div>

[RSJ: The geomagnetic aa index is a reasonable surrogate for the solar wind. "[T]he aa-index is highly controlled by the solar wind velocity". http://www.dsri.dk/showpage.php3?id=191. That cite then reports on an examination of empirical probability densities [a far better practice is to estimate probability distributions] of the aa index and the solar wind velocity. The reader might want to Google for combinations of various geomagnetic indices, e.g., aa index, Ap index, Kp index, PC index, Dst index, AE index, with or without the solar wind.

[The aa index starts in 1868. The IPCC uses 1750 as a baseline year to begin the industrial era and man's influence. Why would a reader care about your personal opinion? Science is not about beliefs, it's about models with predictive power, and your model of greenhouse gases fails. GHGs are passive; they insulate. They store heat acquired from the sun and Earth, which like all warm bodies they lose by the usual methods of heat transfer.]

Some scientists have commented that there has been no change to the sun since the 1950's. This may not be true as per the quote below taken from a 1999 technical paper noted at the end of this article. As the magnetic fields of the sun increase, so do the electrical fields and associated joule heating.

[RSJ: You need to supply the comments and not leave it to your readers to search for what you found worthwhile. Name the sources on which you rely or wish to criticize. Quote them and cite to the page where the quote can be found.]

Here we report that the measurements of the near-Earth interplanetary magnetic field reveal that the total magnetic field leaving the sun has risen by a factor 1.4 since 1964. Using surrogate interplanetary measurements, we find that the rise since 1901 is by a factor of 2.3. This change may be related to chaotic changes in the dynamo that generates the solar magnetic field. We do not yet know quantitatively how such changes will influence the global environment.

[RSJ: Is this a clipping from an abstract? It jumps in disconnected fashion from concept to concept. In the end "we" can't quantify the effects. That is the transcendent problem with your discussion. Solar radiation imparts 8.2 million quads per year of energy on Earth. Man consumes just under 500 quads per year. According to Vooro, joule heating contributes an unknown number of quads per year, which may or may not be part of the 8.2 million quads from the sun.

[Next you provide three rambling, useless paragraphs on alternative conjectures for global warming.]

If the magnetic fields leaving the sun increased 40% during the period 1964 to 1999, then by 2008 the increase could be up by another 10%, assuming the same rate of change.

There may also be another source of global warming since about 1988, namely weather modification experiments and techniques as explained as per the following web page. http://www.globalresearch.ca/index.php?context=va&aid=7561

The unusual hot weather of 1998 could come from these weather modification sources as there were no unusual solar events or sudden rise in greenhouse gas levels in the atmosphere. An El Nino existed but past similar strong El Nino's like 1972/1973 and again 1982/1983 showed no such temperature increases. The writer has no other evidence to confirm whether and when any weather modifications took place.

I applaud and support all the efforts to reduce the use of fossil fuels and when this is not possible, the use of latest and cleanest fossil fuel technologies capture] to clean up the pollution associated with greenhouse gases. £/div>

[RSJ: Why would your readers care about your personal likes and support? Are you in a position to award grants?]

It is very odd and very noticeable that the issue of pollution as opposed to global warming is rarely now mentioned by all the scientific bodies engaged in the climate change debate. If everyone is focused on CO2 reductions only and climate change, cleaning up the pollution is conveniently forgotten. £/div>

[RSJ: The Supreme Court recently ruled that the EPA may treat CO2 emissions as a pollutant. That, too, has zero scientific validity. CO2 is benign and beneficial. It is an optimum effluent. It is a greening agent. One man's meat is another man's poison.]

I merely wish to point out that manmade greenhouse gases may not be the prime cause behind global climate change. The facts of this will become apparent in the near future. The sun and our planet are changing due to causes that are not all of man's making but we can reduce the pollution that we are causing.

[RSJ: "References" deleted. They included links to web pages and reproduced abstracts.]

Posted by matt vooro | February 4, 2008 11:30 AM



Barry Cooper wrote:

Jeff,

Please let me know if this is accurate: since the relative importance of CO2 as a forcing increases as you go up in the atmosphere, then logically if CO2 is a principle forcing in our current time, the temperature increases will be measurably higher in the upper troposphere and stratosphere. Christopher Monckton made this point, and it appears to me to be a predictive hypothesis that could bring the process of falsification into the short term.

Is this accurate? Strategically, it seems to me, there is a pronounced need to force them into specific predictions that can be verified and this one seems relatively easy.

[RSJ: Barry, your question touches on a few important and complicated facets of climate modeling.

[Point of Order. Forcing is a nickname for radiative forcing, the modeling paradigm for global climate models (in the broadest sense), selected and defended by the IPCC. That puts your question in the domain of the IPCC and its GCMs. Regardless, the only models worth falsifying, as you would like, are those models. That is because no reasonable climate crisis or threat would exist but for the IPCC. And with regard to forcing, the IPCC says,

In the context of climate change, the term forcing is restricted to changes in the radiation balance of the surface-troposphere system imposed by external factors, with no changes stratospheric dynamics, without any surface and tropospheric feedbacks in operation (i.e., no secondary effects induced because of changes in tropospheric motions or its thermodynamic state), and with no dynamically-induced changes in the amount and distribution of atmospheric water (vapour, liquid, and solid forms). Bold added, Third Assessment Report (TAR), ¶6.1 Radiative Forcing (¶6.1.1 Definition), p. 353.

[That definition of forcing might resolve your question because forcing doesn't apply in the stratosphere, and the stratosphere is not allowed to change dynamically. But that is a narrow, legalistic answer I won't make.

[The IPCC defines forcings to be external factors, so the natural CO2 circulating through the ocean and atmosphere, 60 times that produced by man, is not a forcing. While the TAR (2001) defined radiative forcing to exclude any feedbacks, especially of water vapor (as quoted above), by the Fourth Assessment Report (AR4) (2007) GCMs included water vapor feedback. Nevertheless, the IPCC didn't bother changing its definition. Except to interpret your question to be about extra CO2 added to the climate, the following response does not rely on the technical scope of forcing.

[Is Lord Monckton your source for the notion that "the relative importance of CO2 as a forcing increases as you go up in the atmosphere"? You could be referring to his August 2007 paper, "Greenhouse Warming, What Greenhouse Warming". http://scienceandpublicpolicy.org/images/stories/papers/monckton/whatgreenhouse/moncktongreenhousewarming.pdf. Regardless, your "relative importance" model does not appear to be an IPCC concept.

[To the IPCC, by far the dominant effect of CO2 on the climate is its greenhouse effect, and the greenhouse effect is a phenomenon of the troposphere. Moreover the IPCC treats CO2 as well-mixed throughout the troposphere. While that is not true on a small scale, particularly where the IPCC can resolve CO2 measurements, the well-mixed assumption should be satisfactory on the scale of the atmospheric layers, i.e., troposphere, stratosphere, mesosphere, and thermosphere. Therefore, the relative importance of CO2 doesn't change through the troposphere, which is up to about 11 km, the bottom of the stratosphere.

[Point of Order: "Temperature increases" can mean warming such as caused by forcings, or it can mean the changes in temperature with altitude caused by gas and radiative thermodynamics. Technically, the latter sense is known as the temperature lapse rate, which varies with altitude. So by asking whether the "temperature increases will be measurably higher in the upper troposphere and stratosphere" you might mean higher than temperature increases at the surface, or higher than normal in that region. Viewed either way, the answer is the same and it rests on an understanding of lapse rate, which is in a state of great confusion within the IPCC.

[Regardless that small amounts of CO2 are found in the stratosphere, the stratosphere exhibits no blanket or insulating effect. The IPCC now recognizes that the greenhouse effect is a blanket effect (AR4, FAQ 1.1, p. 97, but the word blanket is not found in the TAR), and nowhere does the IPCC model or calculate a blanket effect. The blanket model may be incompatible with the radiative forcing paradigm.

[A blanket supports a temperature drop through its interior proportional to the heat flowing through it. The blanket or greenhouse resistance is proportional to the ratio of the lapse rate to the heat through the blanket.

[In the US Standard Atmosphere the tropospheric lapse rate is -6.5ºC/km. For insight into temperature lapse rate, look at a graph of the temperature of a Standard Atmosphere, such as the US Standard Atmosphere at http://www.aerospaceweb.org/question/atmosphere/q0112.shtml. (In case you have relied on Lord Monckton's paper cited above, he explains his model with a pair of lapse rate profile charts across the troposphere and into the lower stratosphere.) The Standard is a piecewise linear model, discontinuous in lapse rate (the slope), which is most obvious in its tabular form. See, for example, http://mtp.jpl.nasa.gov/notes/altitude/StdAtmos1976.html . There is also an International Standard Atmosphere which differs only slightly from the US. These Standards are idealized averages representing the globe for a middling moisture content, and use a geopotential height, that is, a pressure altitude, instead of actual altitude for the independent variable to make the data latitude-independent. Regional or local lapse rates will be different, and they change with moisture content, convection, and much more. The GCMs appear to start with a Standard Atmosphere and then calculate a new lapse rate in search of a radiation equilibrium cell by cell.

[Point of Order: The lapse rate in the Standard Atmosphere is negative through the troposphere, but the IPCC (and others) define lapse rate as the rate of decrease of temperature, thus reversing the sign. AR4, Glossary, p. 948. The IPCC also defines lapse rate in general, making it applicable not just to temperature, but to other variables, such as gas concentration. In its Reports, the IPCC often discusses lapse rate ambiguously, not specifying whether it is temperature. This response uses the Standard Atmosphere sign convention, and uses lapse rate only with respect to temperature. There is also a scaling problem, which this response ignores. Lapse rate might be defined locally, such as within a GCM grid; regionally, such as an average for the tropics; or globally.

[GCMs calculate a new lapse rate during runs using a process called parametrization (UK) or parameterization (US). Parameterization is a universal method, and it can produce valid equations when based on sound scientific theories or laws. However, in IPCC parlance, it is a mathematical model based on some degree of curve fitting, used for phenomena where the physics could not be simulated. Reasons for parameterizing include that the physics doesn't fit the GCM scale, is too complex to run in affordable time, or is unknown. Whether parameterized variables, like lapse rate, track the real world is a matter of luck or skill on the part of the modeler. Also a parameterized variable is limited by the quality of the underlying data.

[Radiosonde and satellite data are the two primary sources for lapse rate data, but the IPCC has not been able to reconcile them. As a result, a wide discrepancy exists between GCMs in what is called the lapse rate feedback. More about feedback below, but first the IPCC says,

[There remains substantial disagreement between different observational estimates of lapse rate changes over recent decades, but some of these are consistent with GCM simulations (see Sections 3.4.1 and 9.4.4). Bold added, 4AR, Box 8.1: Upper-Tropospheric Humidity and Water Vapour Feedback, p. 632.

Section 9.4.4 refers back to section 3.4.1, where the IPCC says,

Within the community that constructs and actively analyses satellite- and radiosonde-based temperature records there is agreement that the uncertainties about long-term change are substantial. Changes in instrumentation and protocols pervade both sonde and satellite records, obfuscating the modest long-term trends. Historically there is no reference network to anchor the record and establish the uncertainties arising from these changes - many of which are both barely documented and poorly understood. Therefore, investigators have to make seemingly reasonable choices of how to handle these sometimes known but often unknown influences. It is difficult to make quantitatively defensible judgments as to which, if any, of the multiple, independently derived estimates is closer to the true climate evolution. This reflects almost entirely upon the inadequacies of the historical observing network and points to the need for future network design that provides the reference sonde-based ground truth. Bold added, AR4, ¶3.4.1 Temperature of the Upper Air: Troposphere and Stratosphere, p. 265.

[Point of Order: The IPCC defines the term feedback well enough (see climate feedback, AR4, Glossary, p. 943), but uses it in two other unique and misleading ways. For the most part, and especially with regard to lapse rate feedback, the IPCC uses feedback to mean simply that the GCM calculates the parameter value at run time. As of the TAR, the feedback by its definition did not apply to radiative forcing by its definition. The definitions haven't been updated, but as of AR4 the IPCC has relaxed the restriction against feedback in its models.

[Parameterization likely accounts for the tendancy of GCMs to drift slowly from their initial conditions into unrealistic states, a situation the IPCC finds baffling. It also causes a great discrepancy to exist between the output of different or even identical GCMs, exacerbating a chronic problem of poor quality observations. As a result, the IPCC and its GCMs have not settled on a consistent, much less validated, model for temperature lapse rate.

[With increasing altitude, the Standard atmosphere temperature of the stratosphere is initially constant (isothermal), and then increases (a positive lapse rate). By the Standard, the stratosphere is a negative blanket; it cools by carrying heat away from the troposphere. Thus, the dominant effect of CO2, to be a greenhouse gas, is precluded in the Standard stratosphere, and, of course, its secondary effect as a greening agent for the biosphere doesn't apply above the surface.

[Climate effects of the stratosphere are dominantly its cooling, aerosol effects, ozone layer absorption, and ice clouds. These are all larger than any speculated stratospheric CO2 effect.

[So the dominant effect of CO2 is constant with altitude through the troposphere, and has no effect above that.

[Your hypothesis that "CO2 is a principal forcing in our current time" and your conclusion that "temperature increases will be measurably higher in the upper troposphere and stratosphere" form a false cause and effect relationship. First, the hypothesis is false. See The Acquittal of Carbon Dioxide on this blog. CO2 concentration growth is an effect of global warming, not a cause of it. So we might rephrase the conclusion to ask the whether CO2 increases are positively corre