Sunday, December 15, 2013

Calculated Mean Global Temperatures 1610-2012

Introduction
This monograph is a clarification and further refinement of Reference 10 (references are listed at the end of this paper) which also considers only average global temperature. It does not discuss weather, which is a complex study of energy moving about the planet. It does not even address local climate, which includes precipitation. It does, however, consider the issue of Global Warming and the mistaken perception that human activity has a significant influence on it.

The word ‘trend’ is used here for temperatures in two different contexts. To differentiate, α-trend applies to averaging-out the uncertainties in reported average global temperature measurements to produce the average global temperature oscillation resulting from the net ocean surface oscillation. The term β-trend applies to the slower average temperature change of the planet which is associated with change to the temperature of the bulk volume of the material (mostly water) involved.

The first paper to suggest the hypothesis that the sunspot number time-integral is a proxy for a substantial driver of average global temperature change was made public 6/1/2009. The discovery started with application of the first law of thermodynamics, conservation of energy, and the hypothesis that the energy acquired, above or below break-even (appropriately accounting for energy radiated from the planet), is proportional to the time-integral of sunspot numbers. The derived equation revealed a rapid and sustained global energy rise starting in about 1941. The true average global temperature anomaly change β-trend is proportional to global energy change.

Subsequent analysis revealed that the significant factor in calculating the β-trend is the sunspot number anomaly time-integral. The sunspot number anomaly is defined as the difference between the sunspot number in a specific year and an average sunspot number for several years.

Measured temperature anomaly α-trends oscillate above and below the temperature anomaly β-trend calculated using only the sunspot number anomaly time-integral.

The existence of ocean oscillations, especially the Pacific Decadal Oscillation, led to the perception that there must be an effective net surface temperature oscillation for the planet with all named and unnamed ocean oscillations as participants. Plots of measured average global temperatures indicate that the net surface temperature oscillation has a period of 64 years with the most recent maximum in 2005.

Combination of the effects results in the effect of the ocean surface temperature oscillation (α-trend) decline 1941-1973 being slightly stronger than the effect of the rapid rise from sunspots (β-trend) resulting in a slight decline of the trend of reported average global temperatures. The steep rise 1973-2005 occurred because the effects added. A high coefficient of determination, R2, demonstrates that the hypothesis is true.

Over the years, several refinements to this work (often resulting from other's comments which may or may not have been corroborative) slightly improved the accuracy and led to the equations and figures in this paper.

Prior work
The law of conservation of energy is applied effectively the same as described in Reference 2 in the development of a very similar equation that calculates temperature anomalies. The difference is that the variation in energy ‘OUT’ has been found to be adequately accounted for by variation of the sunspot number anomalies. Thus the influence of the factor [T(i)/Tavg]4 is eliminated.

Change to the level of atmospheric carbon dioxide has no significant effect on average global temperature. This was demonstrated in 2008 at Reference 6 and is corroborated at Reference 2 and again here.

As determined in Reference 3, reported average global temperature anomaly measurements have a random uncertainty with equivalent standard deviation ≈ 0.09 K. A substantial contributor to this variation appears to be the apparent random variation in magnitude and period of el Nino.

Global Warming ended more than a decade ago as shown here, and in Reference 4 and also Reference 2.

Average global temperature is very sensitive to cloud change as shown in Reference 5.

The value used for average sunspot number was 43.97 (average 1850-1940) in Ref. 1, 42 (average 1895-1940) in Ref. 9, and 40 (average 1610-2012) in Ref. 10. It is set at 34 (average 1610-1940) in this paper. The procession of values for average sunspot number produces slight but steady improvement in R2 for the period of measured temperatures and progressively greater credibility of average global temperature estimates for the period prior to direct measurements becoming available.

Initial work is presented in several papers at http://climaterealists.com/index.php?tid=145&linkbox=true

The sunspot number anomaly time-integral drives the temperature anomaly trend
It is axiomatic that change to the average temperature trend of the planet is due to change to the net energy retained by the planet.

Table 1 in reference 2 shows the influence of atmospheric carbon dioxide (CO2) to be insignificant (tiny change in R2 if considering CO2 or not) so it can be removed from the equation by setting coefficient ‘C’ to zero. With ‘C’ set to zero, Equation 1 in Reference 2 calculates average global temperature anomalies (AGT) since 1895 with 89.82% accuracy (R2 = 0.898220).

The current analysis determined that 34, the approximate average of sunspot numbers from 1610-1940, provides a slightly better fit (in fact, the best fit) to the measured temperature data than did 43.97 and other values 9,10. The influence, of Stephan-Boltzmann radiation change due to AGT change, on energy change is adequately accounted for by the sunspot number anomaly time-integral. With these refinements to Equation (1) in Reference 2 the coefficients become A = 0.3588, B = 0.003461 and D = ‑ 0.4485.  R2 increases slightly to 0.904906 and the calculated anomaly in 2005 is 0.5045 K. Also with these refinements the equation calculates lower early temperature anomalies and projects slightly higher (0.3175 K vs. 0.269 K in 2020) future anomalies. The resulting equation for calculating the AGT anomaly for any year, 1895 or later, is then:

Anom(y) = (0.3588,y) + 0.003461/17 Σyi=1895 (s(i) – 34) – 0.4485

Where:
            Anom(y) = calculated temperature anomaly in year y, K
            (0.3588,y) = approximate contribution of ocean cycle effect to AGT in year y
            s(i) = average daily Brussels International sunspot number in year i

Measured temperature anomalies are from Figure 2 of Reference 3. The excellent match of the up and down trends since before 1900 of calculated and measured temperature anomalies, shown here in Figure 1, demonstrates the usefulness and validity of the calculations.

Projections until 2020 use the expected sunspot number trend for the remainder of solar cycle 24 as provided 11 by NASA. After 2020 the limiting cases are either assuming sunspots like from 1925 to 1941 or for the case of no sunspots which is similar to the Maunder Minimum.

Some noteworthy volcanoes and the year they occurred are also shown on Figure 1. No consistent AGT response is observed to be associated with these. Any global temperature perturbation that might have been caused by volcanoes of this size is lost in the temperature measurement uncertainty.

Much larger volcanoes can cause significant temporary global cooling from the added reflectivity of aerosols and airborne particulates. The Tambora eruption, which started on April 10, 1815 and continued to erupt for at least 6 months, was approximately ten times the magnitude of the next largest in recorded history and led to 1816 which has been referred to as ‘the year without a summer’. The cooling effect of that volcano exacerbated the already cool temperatures associated with the Dalton Minimum.
 
 Figure 1: Measured average global temperature anomalies with calculated prior and future trends using 34 as the average daily sunspot number. (Last update 12/15/13)

As discussed in Reference 2, ocean oscillations produce oscillations of the ocean surface temperature with no significant change to the average temperature of the bulk volume of water involved. The effect on AGT of the full range of surface temperature oscillation is given by the coefficient ‘A’. (A, B, C, and D are the coefficients in Equation 1 of Reference 2)

The influence of ocean surface temperature oscillations can be removed from the equation by setting ‘A’ to zero. To use all regularly recorded sunspot numbers, the integration starts in 1610. The offset, ‘D’ must be changed to -0.1993 to account for the different integration start point and setting ‘A’ to zero. Setting ‘A’ to zero requires that the anomaly in 2005 be 0.5045 - 0.3588/2 = 0.3251 K. The result, Equation (1) here, then calculates the trend 1610-2012 resulting from just the sunspot number anomaly time-integral.

Trend3anom(y) = 0.003461/17 * Σyi = 1610 [s(i)-34] – 0.1993                (1)

Where:
Trend3anom(y) = calculated temperature anomaly β-trend in year y, K degrees.
0.003461 = the proxy factor, B, W yr m-2.
17 = effective thermal capacitance of the planet, W Yr m-2 K-1
s(i) = average daily Brussels International sunspot number in year i
34 ≈ average sunspot number for 1610-1940.
-0.1993 is merely an offset that shifts the calculated trajectory vertically on the graph, without changing its shape, so that the calculated temperature anomaly in 2005 is 0.3251 K which is the calculated anomaly for 2005 if the ocean oscillation is not included.

Sunspot numbers back to 1610 are shown in Figure 2 of Reference 1.

Applying Equation (1) to the sunspot numbers of Figure 2 of Reference 1 produces the trace shown in Figure 2 below.

Figure 2: Anomaly trend from just the sunspot number anomaly time-integral using Equation (1).

Average global temperatures were not directly measured in 1610 (thermometers had not been invented yet). Recent estimates, using proxies, are few. The temperature anomaly trend that Equation (1) calculates for that time is roughly consistent with other estimates. The decline in the trace 1610-1700 on Figure 2 results from the low sunspot numbers for that period as shown on Figure 2 of Reference 1. 

How this phenomenon could take place
Although the connection between AGT and the sunspot number anomaly time-integral is demonstrated, the mechanism by which this takes place remains somewhat theoretical.

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

Henrik Svensmark, a Danish physicist, found that decreased galactic cosmic rays caused decreased low level (<3 km) clouds and planet warming. An abstract of his 2000 paper is at Reference 13. Marsden and Lingenfelter also report this in the summary of their 2003 paper 14 where they make the statement “…solar activity increases…providing more shielding…less low-level cloud cover… increase surface air temperature.”  These findings have been further corroborated by the cloud nucleation experiments 15 at CERN.

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

Simple analyses 5 indicate that either an increase of approximately 186 meters in average cloud altitude or a decrease of average albedo from 0.3 to the very slightly reduced value of 0.2928 would account for all of the 20th century increase in AGT of 0.74 °C. Because the cloud effects work together and part of the temperature change is due to ocean oscillation, substantially less cloud change is needed.


Combined Sunspot Effect and Ocean Oscillation Effect
As a possibility, the period and amplitude of oscillations attributed to ocean cycles demonstrated to be valid after 1895 are assumed to maintain back to 1610. Equation (1) is modified as shown in Equation (2) to account for including the effects of ocean oscillations. Since the expression for the oscillations calculates values from zero to the full range but oscillations must be centered on zero, it must be reduced by half the oscillation range.

Trend4anom(y) = (0.3588,y) – 0.1794 + 0.003461/17 * Σyi = 1610 [s(i)-34] – 0.1993   (2)

The ocean oscillation factor, (0.3588,y) – 0.1794, is applied to the period prior to the start of direct temperature measurements as a possibility. The effective sea surface temperature anomaly, (A,y), is defined in Reference 2.

Applying Equation (2) to the sunspot numbers from Figure 2 of Reference 1 produces the trend shown in Figure 3 next below. Available measured average global temperatures from Figure 2 in Reference 3 are superimposed on the calculated values.
 
 Figure 3: Calculated temperature anomalies from the sunspot number anomaly time-integral plus ocean oscillation using Equation (2) with superimposed available measured data from Reference 3 and range estimates determined by Loehle.

Figure 3 shows that temperature anomalies calculated using Equation (2) estimate possible trends since 1610 and actual trends of reported temperatures since they have been accurately measured world wide. The match from 1895 on has R2 = 0.9049 which means that 90.49% of average global temperature anomaly measurements are explained. All factors not explicitly considered (such as the 0.09 K s.d. random uncertainty in reported annual measured temperature anomalies, aerosols, CO2, other non-condensing ghg, volcanoes, ice change, etc.) must find room in that unexplained 9.51%. Note that a coefficient of determination, R2 = 0.9049 means a correlation coefficient of 0.95.

A survey 12 of non-tree-ring global temperature estimates was conducted by Loehle including some for a period after 1610. A simplification of the 95% limits found by Loehle are also shown on Figure 3. The spread between the upper and lower 95% limits are fixed, but, since the anomaly reference temperatures might be different, the limits are adjusted vertically to approximately bracket the values calculated using the equations. The fit appears reasonable considering the uncertainty in all values.

Calculated temperature anomalies look reasonable back to 1700 but indicate higher temperatures prior to that than most proxy estimates. They are, however, consistent with the low  sunspot numbers in that period. They qualitatively agree with Vostok, Antarctica ice core data but decidedly differ from Sargasso Sea estimates during that time (see the graph for the last 1000 years in Reference 6). Credible worldwide assessments of average global temperature that far back are sparse. Ocean oscillations might also have been different from assumed.

Possible lower values for average sunspot number
Possible lower assumed values for average sunspot number, with coefficients adjusted to maximize R2, result in noticeably lower estimates of early (prior to direct measurement) temperatures with only a tiny decrease in R2. Calculated temperature anomalies resulting from using an average sunspot number value of 26 are shown in Figure 4. The projected temperature anomaly trend decline is slightly less steep (0.018 K warmer in 2020) than was shown in Figure 1.

Figure 4: Calculated temperature anomalies from the sunspot number anomaly time-integral plus ocean oscillation using 26 as the average sunspot number with superimposed available measured data from Reference 3 and range estimates determined by Loehle.

Carbon dioxide change has no significant influence
The influence that CO2 has on AGT can be calculated by including ‘C’ in Equation (1) of Reference 2 as a coefficient to be determined. The tiny increase in R2 demonstrates that consideration of change to the CO2 level has no significant influence on AGT. The coefficients and resulting R2 are given in Table 1.

Table 1: A, B, C, D, refer to coefficients in Equation 1 in Reference 2
Average daily SSN
ocean oscillation A
sunspots B
CO2 C
Offset
D
Coefficient of determination R2
% cause of 1909-2005 AGT change
Sunspots
Ocean oscillation
CO2 change
26
0.3416
0.002787
0
-0.4746
0.903488
63.8
36.2
0
32
0.3537
0.003265
0
-0.4562
0.904779
62.7
37.3
0
34
0.3588
0.003461
0
-0.4485
0.904906
62.2
37.8
0
36
0.3642
0.003680
0
-0.4395
0.904765
61.7
38.3
0
34
0.3368
0.002898
0.214
-0.4393
0.906070
52.3
35.6
12.1

Science explains why CO2 change has no significant effect on climate. (Added 10/2/14)
1) Firmly acknowledge the established fact that gas molecules can absorb/emit only at specific discreet wavelengths (with some slight broadening from pressure, etc.). Full spectrum Stephan-Boltzmann (S-B) radiation applies to liquids and solids, not to gases.
2) Realize from gas kinetics that the time between atmospheric molecule collisions is extremely short (The Hyperphysics calculator calculates approximately 0.1 nanosecond at sea level pressure and temperature).
3) The elapsed time between absorption and emission of a photon by a CO2 gas molecule is approximately 10 microseconds. http://rabett.blogspot.com/2013/04/this-is-where-eli-came-in.html
4) Thus it takes about 100,000 times as long for a ghg molecule to emit a photon (molecule ‘relaxation’) as it does to transfer energy to other molecules by impact. The process of absorbing a photon and transferring (thermal conduction in the gas) the added energy to other molecules is thermalization. A practical observation of thermalization by way of water vapor is that nights cool faster when absolute water vapor content is lower.
5) Thermalized energy carries no identity of the molecule that absorbed it.
6) Jostling between the molecules sometimes causes reverse-thermalization. Because, for terrestrial radiation wavelengths (nearly all in the range 5-50 microns), there are hundreds of absorption lines for water vapor compared to the single one for CO2 , EMR emission stimulated by reverse-thermalization is essentially all by way of water vapor.
7) The thermalized radiation warms the air, reducing its density, causing updrafts which are exploited by soaring birds, sailplanes, and occasionally hail. Updrafts are matched by downdrafts elsewhere, usually spread out but sometimes recognized by pilots and passengers as ‘air pockets’ and micro bursts.
8) The population gradient of ghg molecules, (especially water vapor above about 4 km, declining with increased altitude) favors radiation to space. Ghg molecules are ‘recharged’ by reverse-thermalization.
9) Clouds (average emissivity about 0.5) consist of solid and/or liquid water particles (each containing millions of molecules) that radiate according to S-B law. Low amount of water vapor above clouds and widening molecule spacing allows substantial radiation directly to space.
10) The tiny increase in ghg from increased CO2 causes absorption/thermalization to occur at slightly lower altitude which slightly increases the convection rate.

11) In the wavelength range of significant terrestrial radiation, water vapor at approximately 15,000 ppmv has 465 absorption lines (absorption opportunities) per molecule in the range 5-13 microns compared to 1 at 15 microns for CO2. The tiny increase in absorption lines due to a 100 ppmv CO2 increase, about 1 in 70,000, has no significant effect on climate.

Further discussion of ocean cycles (Added 6/23/14)
The temperature contribution to AGT of ocean cycles is approximated by a function that has a saw-tooth trajectory profile. It is represented in Equation (1) of Reference 2 by (A,y) where A is the total amplitude and y is the year. The uptrends and down trends are each determined to be 32 years long for a total period of 64 years. The total amplitude resulting from ocean oscillations was found here to be 0.3588 K (case highlighted in Table 1).

Thus, for an ocean cycle surface temperature uptrend, the contribution of ocean oscillations to AGT is approximated by adding (to the value calculated from the sunspot number anomaly time-integral) 0.3588 multiplied by the fraction of the 32 year period that has elapsed since a low. For an ocean cycle surface temperature down trend, the contribution is calculated by adding 0.3588 minus 0.3588 multiplied by the fraction of the 32 year period that has elapsed since a high. The lows were found to be in 1909 and 1973 and the highs in 1941 and 2005. The resulting trajectory, offset by half the amplitude, is shown as ‘approximation’ in Figure 5.

Temperature data is available for three named cycles: PDO, ENSO 3.4 and AMO. Successful accounting for oscillations is achieved for PDO and ENSO when considering these as forcings (with appropriate proxy factors) instead of direct measurements. As forcings, their influence accumulates with time. The proxy factors must be determined separately for each forcing. The measurements are available since 1900 for PDO 16 and ENSO3.4 17. This PDO data set has the PDO temperature measurements reduced by the average SST measurements for the planet.

The contribution of PDO and ENSO3.4 to AGT is calculated by:
PDO_NINO = Σyi=1900 (0.017*PDO(i) + 0.009 * ENSO34(i))        (3)

Where:
            PDO(i) = PDO index 16 in year i
            ENSO34(i) = ENSO 3.4 index 17 in year i


How this calculation compares to the idealized approximation used in Equation (2) is shown in Figure 5. The high coefficient of determination in Table 1 and the comparison in Figure 5 corroborate the assumption that the saw-tooth profile provides an adequate approximation of the influence of all named and unnamed ocean cycles in the calculated AGT anomalies.
Figure 5: Comparison of idealized approximation of ocean cycle effect and the calculated effect from PDO and ENSO.

Conclusions
Others that have looked at only amplitude or only duration factors for solar cycles got poor correlations with average global temperature. The good correlation comes by combining the two, which is what the time-integral of sunspot number anomalies does. As shown in Figure 2, the temperature anomaly trend determined using the sunspot number anomaly time-integral has experienced substantial change over the recorded period. Prediction of future sunspot numbers more than a decade or so into the future has not yet been confidently done although assessments using planetary synodic periods appear to be relevant 7,8.

As displayed in Figure 2, the time-integral of sunspot number anomalies alone appears to show the estimated true average global temperature trend (the net average global energy trend) during the planet warm up from the depths of the Little Ice Age.

The net effect of ocean oscillations is to cause the surface temperature trend to oscillate above and below the trend calculated using only the sunspot number anomaly time-integral. Equation (2) accounts for both and also, because it matches measurements so well, shows that rational change to the level of atmospheric carbon dioxide can have no significant influence.

Long term prediction of average global temperatures depends primarily on long term prediction of sunspot numbers.


References:
11. Graphical sunspot number prediction for the remainder of solar cycle 24 http://solarscience.msfc.nasa.gov/predict.shtml
12. http://www.econ.ohio-state.edu/jhm/AGW/Loehle/Loehle_McC_E&E_2008.pdf
13.   Svensmark paper, Phys. Rev. Lett. 85, 5004–5007 (2000)  http://prl.aps.org/abstract/PRL/v85/i23/p5004_1
14.  Marsden & Lingenfelter 2003, Journal of the Atmospheric Sciences 60: 626-636  http://www.co2science.org/articles/V6/N16/C1.php
15. CLOUD experiment at CERN http://indico.cern.ch/event/197799/session/9/contribution/42/material/slides/0.pdf
        16. PDO index http://jisao.washington.edu/pdo/PDO.latest
        (Linked from http://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/ )


20 comments:

  1. Your Reference 10 note is unhelpful to one without context. I suggest a link. And while I came to this article via a comment thread, the idea of it lying fallow without added commentary and updates seems a waste. Do you have a Disqus link to add ?

    ReplyDelete
  2. The Reference list with links is at the end of the article.

    I am unfamiliar with 'Disqus link'. Please clue me in through email link accessed through 'View my complete profile' in upper right corner of article. The email link is displayed on the left side just below where my picture isn't. Thanks.

    ReplyDelete
  3. Thanks Dan!
    This is the kind of information that we all need to read. I am totally frustrated with the Global Warming religion and their zealotry... I have resorted to cynicism on all my blogs. Thanks for your post! Keep up the good work!

    ReplyDelete
    Replies
    1. Thanks for the comments.

      The warmers lose cred as the CO2 continues to go up and average global temperature doesn't.

      Delete
  4. 1816? The year without a summer? Man, that must have sucked.

    ReplyDelete
  5. Replies
    1. Certainly a detailed piece. I just cannot bring myself to believe in man-made global warming. Wrote a satirical post some time back you might enjoy Paleo Indians and Global Warming. Almost forgot, thanks for your recent visit to one of my blogs (Right Wing Humor)>

      Delete
  6. I have only started reading this after reading your comment on WUWT so I haven't much to add except a little criticism about the axis titles. I guess for blogs the units should be (%deg;C) or divide by K (and not K degrees as you have in one graph). I know its difficult to choose the right option for the common reader but mixing things up is not a good idea.

    ReplyDelete
  7. The recommended symbol for degrees Kelvin is K with no degree symbol. Since a Celcius degree is the same size as a Kelvin degree that could have been used but requires also the degree sign. Practice in science work is to use K for either the temperature or the temperature difference which is what anomalies are. All of the graphs have the ordinate (anomaly) in degrees Kelvin using the symbol K. I added 'degrees' on the first graph in case someone might think that the K stood for 1000.

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  8. Dan - your method produces results very similar to mine which is based simply on the working hypothesis that the recent peak in temperatures is a result of a synchronous peak in the 60 year and 1000 year quasi- periodicities in the temperature data and the use of the neutron count (and 10Be) as the most useful proxy for solar activity over the long term. For several posts on this subject and for forecasts of the timing and amount of the coming cooling see
    http://climatesense-norpag.blogspot.com
    Here are the conclusions of the last post on the link
    "In earlier posts on this site http://climatesense-norpag.blogspot.com at 4/02/13 and 1/22/13
    I have combined the PDO, ,Millennial cycle and neutron trends to estimate the timing and extent of the coming cooling in both the Northern Hemisphere and Globally.
    Here are the conclusions of those posts.
    1/22/13 (NH)
    1) The millennial peak is sharp - perhaps 18 years +/-. We have now had 16 years since 1997 with no net warming - and so might expect a sharp drop in a year or two - 2014/16 -with a net cooling by 2035 of about 0.35.Within that time frame however there could well be some exceptional years with NH temperatures +/- 0.25 degrees colder than that.
    2) The cooling gradient might be fairly steep down to the Oort minimum equivalent which would occur about 2100. (about 1100 on Fig 5) ( Fig 3 here) with a total cooling in 2100 from the present estimated at about 1.2 +/-
    3) From 2100 on through the Wolf and Sporer minima equivalents with intervening highs to the Maunder Minimum equivalent which could occur from about 2600 - 2700 a further net cooling of about 0.7 degrees could occur for a total drop of 1.9 +/- degrees
    4)The time frame for the significant cooling in 2014 - 16 is strengthened by recent developments already seen in solar activity. With a time lag of about 12 years between the solar driver proxy and climate we should see the effects of the sharp drop in the Ap Index which took place in 2004/5 in 2016-17.
    4/02/13 ( Global)
    1 Significant temperature drop at about 2016-17
    2 Possible unusual cold snap 2021-22
    3 Built in cooling trend until at least 2024
    4 Temperature Hadsst3 moving average anomaly 2035 - 0.15
    5 Temperature Hadsst3 moving average anomaly 2100 - 0.5
    6 General Conclusion - by 2100 all the 20th century temperature rise will have been reversed,
    7 By 2650 earth could possibly be back to the depths of the little ice age.
    8 The effect of increasing CO2 emissions will be minor but beneficial - they may slightly ameliorate the forecast cooling and help maintain crop yields .
    9 Warning !! There are some signs in the Livingston and Penn Solar data that a sudden drop to the Maunder Minimum Little Ice Age temperatures could be imminent - with a much more rapid and economically disruptive cooling than that forecast above which may turn out to be a best case scenario.

    How confident should one be in these above predictions? The pattern method doesn't lend itself easily to statistical measures. However statistical calculations only provide an apparent rigor for the uninitiated and in relation to the IPCC climate models are entirely misleading because they make no allowance for the structural uncertainties in the model set up.This is where scientific judgment comes in - some people are better at pattern recognition and meaningful correlation than others. A past record of successful forecasting such as indicated above is a useful but not infallible measure. In this case I am reasonably sure - say 65/35 for about 20 years ahead. Beyond that certainty drops rapidly. I am sure, however, that it will prove closer to reality than anything put out by the IPCC, Met Office or the NASA group. In any case this is a Bayesian type forecast- in that it can easily be amended on an ongoing basis as the Temperature and Solar data accumulate. If there is not a 0.15 - 0.20. drop in Global SSTs by 2018 -20 I would need to re-evaluate.

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  9. Dr. Page,
    My assessment is an extrapolation of the past. I demonstrated that CO2 change had no significant effect in a paper made public in 2008 at http://www.middlebury.net/op-ed/pangburn.html. The determination of the two climate drivers appears solid. Prediction beyond 2037 depends on prediction of sunspot numbers and ocean oscillations, neither of which have been confidently predicted very far into the future.

    I don't like long moving averages because they tend to obscure trend peaks. Long moving averages can be misleading, particularly in noisy data.

    Although we both predict future declines, it looks like my decline prediction for 2035 is about twice what you came up with. I haven't looked beyond 2037 but suspect sunspots may be related to planetary synodic periods and ocean oscillations may be influenced by some beat frequency with the lunar cycles.

    I believe that future-cast must also work as hind-cast and simple is better than complex. Predictions must be physics based.

    I agree that what the IPCC is doing has little to do with climate.

    ReplyDelete
  10. Dan, I don't know if you do the Facebook thing or not, but here's an interesting thing about Bill Nye and Global Warming:

    Facebook Bill Nye Global Warming

    ReplyDelete
  11. JB - Thanks for the link. I added a comment.

    ReplyDelete
  12. I saw that. There was a guy named Christopher Robert that said this to you:

    Quote" I'll give you a hint: if it ends in .com and it's not a real journal (nature.com, sciencedaily.com, etc.), it's not a source, and no real scientist will ever even click your links. Richard Feynman would roll in his grave if he knew crazies like you were trying to cite him. Citing a blogspot to try to argue science....sheesh."Quote

    ReplyDelete
  13. Wow, some very straight forward info for a change. Instead of the blah blah blah global warming because blah blah blah.

    ReplyDelete
    Replies
    1. Never knew the point on c02 either, as temps don't change. All $$$$

      Delete
  14. The CO2 data that was used (and change to which was shown to have no significant effect) is from Law Dome Antarctica 1180-1958 and Mauna Loa Hawaii after 1958. A graph is on page 11 at http://climaterealists.com/attachments/ftp/Verification%20Dan%20P.pdf
    Several other early (most superseded) papers are at http://climaterealists.com/index.php?tid=145&linkbox=true

    ReplyDelete
  15. Thanks Dan,

    Best article I've read in weeks. Nice to see real mathematics.

    ReplyDelete
  16. Thanks for the feedback.

    Here is more. The physics of thermalization explains why non-condensing ghg change has no significant effect on climate.

    ReplyDelete