Differentiating between hypotheses for the cause of climate change
Written by Dr Kelvin Duncan
There is general agreement that the earth has been warming since the end of the Little Ice Age in about 1850, but there is less agreement as to the cause(s) of this increase. Two kinds of explanatory hypotheses have been proposed, but neither has been proven.
The most popular is Anthropogenic Global Warming (AGW) which proposes that greenhouse gasses, principally carbon dioxide released into the atmosphere by human activities, are the “drivers” of climate change.
The alternative hypothesis postulates that global warming is principally caused by natural means, as all previous warming periods have been, with the main driver of natural warming being increases in and interactions between solar radiation, cosmic rays, the magnetosphere, and water in the atmosphere. I abbreviate this latter hypothesis as NCC (Natural Climate Change).
The investigation of the influence of the sun on climate is still in its infancy and is still too early to make conclusions. However, NCC has received considerable support recently, especially with the work by Vitaliy Rusov published in Physics World who showed that this hypothesis correlates very well with global temperatures from 700,000 BP to the present day.
The AGW hypothesis proposes that atmospheric CO2 has a blanket effect, which both slows down the escape of radiant energy emitted from the earth’s surface to space, and re-radiates some of this energy back down to earth. It follows that if the CO2 content is increased by (largely) human action through burning hydrocarbon fuels, the earth will get warmer.
This hypothesis is extraordinarily popular and influential and is probably the hypothesis favoured by the majority of climatologists. Evidence in favour includes the rise since about 1850 in both atmospheric carbon dioxide concentration and average temperatures. It is also claimed that the temperature of the ionosphere is decreasing thus proving that the sun’s emissions are not the cause of global warming.
But it suffers from a number of scientific difficulties. It arose from the reasonable but not perfect correlation between the rise in the amount of carbon dioxide in the atmosphere and the rise in global temperatures. But correlations do not necessary indicate causation. False or co-correlations are very frequent and are often misinterpreted.
The famous example is the relationship between the importation of green apples into England and the English divorce rate in the early Twentieth Century which lead to an attempt to limit the importation of apples by the introduction of a Parliamentary Bill. The Bill was withdrawn when the correlation was shown to be spurious, both being dependent on population growth.
Another problem is that it has been shown by spectroscopists since about 1860 that CO2 is a relatively weak optical gas and because of this and the fact that it is only a very minor component of the atmosphere (about 0.043 %), it can exert only a very minor effect on global temperatures. Its optical effects are about 1/4000 that of the water in the atmosphere. It is also only active in the comparatively low energy infra-red region of the electro-magnetic spectrum.
Another difficulty is that in previous warming episodes in our present interglacial period (Minoan, Roman and Mediaeval) CO2 concentration followed the temperature rise and did not lead the increases in temperature. So it could not have been a driver of climate change in these warm periods.
Yet another problem is that the 103 or so AGW climate models that attempt to model the effect of carbon dioxide as a driver of climate change are grossly inaccurate in both their forward and backward predictions. They usually over-estimate the temperature rise by 300% or so.
A similar gross error was made by Arrhenius, who in 1896 postulated that a doubling of the amount of carbon dioxide in the atmosphere would raise global temperatures by 5 degrees. Since then we have experienced a doubling of carbon dioxide but the earth’s surface temperature has risen by about 0.95oC, not by the amount he predicted.
Furthermore, it may not be valid to claim that because the ionosphere is cooling it cannot be the sun that is driving the increase in global temperatures. As Dr Tony Phillips states: “Finally, please be aware that the thermosphere is very far above our heads – more than 100 km high. Just because the rarefied air up there is cooling off, it doesn’t mean the surface of the Earth is getting colder ( https://spaceweatherarchive.com/2018/).
The Sun is entering a solar minimum (Figure 11), but what effect that will have on global surface temperatures has yet to be determined. However, some biological evidence is becoming apparent. Some agricultural agencies are convinced we are entering a Grand Solar Minimum which will have a profound and largely negative effect on humans. The present colder winters and reduced and delayed crops over the past few years have prompted the Oil Seed Crops organisation to state on its web page: “Our Sun is repeating a 400 year cycle which is already reducing global crop yields. Is your family Grand Solar Minimum Ready?”
The USDA has a wealth of data on agriculture in the USA that casts light on the question of global warming. Figure 1 shows the crop progress and condition of corn which shows climate sensitivity. Not only has the condition of corn crops deteriorated over that last four years, as shown in the top panel, the bottom panel shows that all stages of growth and production are being delayed indicating slower and poorer growth due to lower temperatures.
Figure 1. USDA records of corn condition and time of growth phases in the years from 2015-16 to 2019
The USDA has similar analyses for all important agricultural crops grown in the USA. Studying these data it seems that plants that are sensitive to cooler conditions, in the sense that they thrive poorly and produce less when the growing season is cooler, include species such as corn, soybeans, peanuts, and cotton, all of which are showing serious and progressive decline in yield over the last few years.
Other crops are not so sensitive to cooling temperatures. These include oats, pasture and range, rice, spring wheat, winter wheat, and sorghum. But even these show delayed life cycle stages and later harvesting, due to cooler temperatures. Early frosts, a feature of the last growing season, could damage even these otherwise temperature insensitive crops and further reduce yields.
In the United Kingdom yields per hectare are down for all crops listed except for “Minor Cereals”, as shown by the following table taken from Farming Statistics, Provisional crop areas, yields and livestock populations at June 2019 – United Kingdom (Department of Environment Food and Rural Affairs).
In New Zealand no detailed information appears to be available, but it has been announced that the 2020 grain harvest has been delayed by two weeks in spite of the growing conditions being excellent with good rainfall.
It appears we may be entering what at least one growers’ association in the USA are warning about – a cold period. Severe cold periods have occurred before and the conditions for humans were terrible. During the Little Ice Age, a period when the long-term solar cycle was at a minimum, people died in the streets from hunger and cold. Diseases were rampant and deadly. During the reign of Queen Elizabeth the First conditions for the poor became so bad that the first social welfare measures were introduced. The desperately poor and starving could petition crown agencies for an allowance – usually about 12 pence per week. Little enough though this was, it was sufficient to stave off absolute starvation.
Agriculture in Germany and Britain adjusted to the cold conditions of the Little Ice Age reasonably successfully by growing temperature-insensitive crops, such as oats, turnips and rye. But the French persisted with wheat, which did not flourish in the cold, wet conditions. Starvation and poverty became rampant and lead to the French Revolution. Marie Antoinette would have been better to advise her subjects to eat rye bread rather than cake when they clamoured for bread!
The NCC hypothesis suggests that increases in the earth’s orbital and axial variations coupled with variations in the type and amount solar energy and spatial particulate matter (cosmic rays) reaching the earth will cause global temperatures to rise through their interaction with the magnetosphere and by inducing changes in atmospheric water content. A reduction in the intensity of these fluxes, such as is occurring now, will cause cooling, but only after a lag period due to hysteresis caused by heat storage principally in the oceans.
Some AGW proponents claim that the flux from the sun and outer space has remained constant over time. This objection is simply wrong (See Figure 11) and ignores the fact that the electro-magnetic spectrum and particulate matter (protons and fragments of atoms) received by earth from the sun and space do vary and could, indeed must, have an large effect on climate. Sunspots vary, flares occur, and overall emissions vary on a long-termed cyclical basis. The world appears to be entering a long-term solar minimum and this is being reflected in diminished agricultural production, no matter what non-integrative thermometers record.
Our datasets of detailed recordings of the solar flux, such as the European funded TOSCA programme, started only in 1978, so are too short to decide the issue. Another factor is that changes in the solar flux may have been too small to have been been measured by the relatively crude recording devices used in the past. That a relatively minute and previously unmeasurable change in solar emissions could cause a measurable response in global temperature is shown by the following argument: the present average global temperature is 288oK. If the sun did not shine then the temperature of the surface of the earth is estimated to be about 30oK. Thus a one degree rise in temperature requires a percentage increase in the radiation and particle flux of (289 – 30)/(288 – 30) *100, that is a percentage increase of less than 0.4%. Most instruments in the past could not measure to that level of accuracy.
Finally, the energy of the sun’s emissions and cosmic rays is much greater than the relatively weak infra-red re-radiation due to atmospheric carbon dioxide. Photon energy is directly proportional to wave frequency, so extreme ultraviolet has 124 eV per photon, but the energy content of re-radiated infrared in the CO2 band has about 12.54 meV. Thus the short wavelength photons have about 1,000 times the energy of an infrared photon re-radiated from carbon dioxide.
What is needed is a means of differentiating between the two hypothesis. This may be available using existing temperature data since AGW and NCC differ in their effects on temperatures when the sun isn’t shining, that is at night. The AGW hypothesis would predict that CO2 takes effect both during the day and the night since the carbon dioxide concentration is constant in the homosphere over the 24 hours of a day, and so will influence both the minimum temperatures (TMIN) that occur at night as well as the maximum day temperatures (TMAX). As atmospheric CO 2 concentration increases over time due to human activities, it would cause an increase in both TMAX and TMIN, but this will occur only if CO2 is the most important driver of temperature change.
The NCC hypothesis, however, would predict that the maximum (daytime) temperatures (TMAX) should increase when the sun’s emissions are increasing over time, but the minimum (night time) temperatures (TMIN) will not increase at the same rate since the sun does not shine at night. There will be some increase in TMIN over time due thermal storage, but the temperatures will not be as great as under the AGW hypothesis. Thus the investigations of night time temperatures could be productive in deciding between the two hypotheses. If the two regression curves for TMAX and TMIN yield significantly different regression coefficients (b values, or slopes) then the NCC hypothesis is the best explanation of global warming.
Records of both TMAX and TMIN are available in most national records of climate over time for very many recording stations and the data is readily available even though financial charges may apply. However, a major problem in climatology is that meteorological recording stations are not distributed at random, nor are they distributed systematically. This presents considerable problems statistically, particularly when the highly heterogeneous data are pooled to yield auto-correlated, cross-correlated and multiply derived variables.
Newton’s Law of Cooling can be used fruitfully in the investigation of night time temperatures. It states that the rate of change of the temperature of an object is proportional to the difference between its own temperature and the ambient temperature (i.e. the temperature of its surroundings). The equation is:
where: T2 is the final temperature,T1 is the initial temperature, T0 is the constant temperature of the surroundings, Δt is the time difference between T2 and T1, and k is a constant to be found.
Figure 2 shows the rate of cooling of surface temperatures under Newton’s Law after the sun sets. The blue line shows the slower rate of cooling if carbon dioxide does provide a warming blanket effect, which is to be expected under the AGW hypothesis, while the orange line shows the cooling curve if carbon dioxide does not have an effect, which is the expectation under the NCC hypothesis. These two predictions can be investigated using available data to distinguish which of the two hypotheses fits the observations best.
As global warming occurs both T0 and T1 will increase over time as the temperature of the surface of the earth increases, but under the AGW hypothesis TMIN will increase more rapidly than under the NCC hypothesis.
In climatology it has been customary to measure the maximum daily temperature (TMAX) and minimum daily temperature (TMIN). Global warming is usually detected by “averaging” the daily TMAX and TMIN of the series of recordings, calculating the yearly average temperature, plotting these averages against year, and conducting linear regression analyses on the plots. But this procedure does not provide a test to distinguish between the two causal hypotheses. It also suffers from the creation of derived variables – the averages (both daily and yearly and the pooling of many sites) – thus making statistical analysis more suspect. The analysis of TMAX and TMIN separately avoids some of these problems and provides a means of judging between the two hypotheses.
To apply the tests we analyse the available records of daily maximum temperatures (TMAX) and minimum temperatures (TMIN). Linear regressions are fitted to the annual averages of both TMAS and TMIN. The slopes of the regression equations (b, or regression coefficient), R2 values (Coefficients of Determination) , and standard errors were obtained and a variety of tests of significance were performed. The temperature data from a variety of meteorological stations were selected at random from a number of different geographical locations. The difference between TMAX and TMIN was also calculated to get TDIF. Note that this is not the same as the average temperature.
The most common pattern of these regressions is shown in the analysis of the temperature records for Bathurst, Australia in Figure 3.
Figure 3. Linear regressions fitted to the temperature data from Bathurst Agricultural Station, Australia
Note that each data point is the average temperature for five years instead of the more usual annual average temperature..
The regression coefficient of the TMAX line in Figure 3 (b.TMAX) shows an increase over time equivalent to about 1oC per century, which is consistent with there being an average global warming of about that magnitude. However, the regression coefficient for TMIN (b.TMIN) is negative, though not significantly different from zero, indicating that solar warming is much more likely to be the driver than carbon dioxide. The TDIF regression coefficient (b.TDIF) shows a widening gap between TMAX and TMIN as time progresses, which cannot be explained by the AGW hypothesis. But NCC explains the results since solar radiation warms the day, but not the night. As solar radiation increases over time TMAX will increase faster than TMIN.
A positive b.TMAX and a negative b.TMIN could be explained by the considerable reduction in rainfall that has happened at Port Moreton Lighthouse as shown in Figure 9. Less water in the atmosphere would have a desertification effect where TMAX increases and TMIN decreases – the days become warmer and the nights cooler. When there is less water in the atmosphere the clearer days allow more solar radiation through so TMAX increases, whereas the lack of a water blanket at night results in TMIN becoming colder, a phenomenon shown everywhere on clear nights, which are colder because of the relatively unimpeded radiation of heat to space compared to cloudy nights which are warmer. Clouds provide a thermal barrier reducing the loss of heat during the night. This could be a general phenomenon which is particularly marked in Australia, the second driest continent on earth.
This pattern where TMIN is not increasing as fast as would be predicted by the AGW hypothesis is shown by all the inland stations analysed. The countries or regions where inland stations were analysed include Australia, the USA, Canada, Africa and Asia. All showed the same pattern supporting the NCC hypothesis. A summary of samples of the regression coefficients and their significance is given in Table 1.
One or two stations were particularly interesting. Figure 4 shows the linear regression fitted to temperature data from Ouda, Morocco.
The Ouda data in Figure 4 shows TMAX has increased over time by about 1oC per century, which is consistent with global warming of about that magnitude. However, TMIN has not; its slope, b.TMIN, is negative, though not significantly different from zero, indicating that solar warming is much more likely to be the driver than carbon dioxide. The slope of TDIF, b.TDIF, shows a widening gap between TMAX and TMIN as time progresses, which cannot be explained by AGW. It is possible that for this station the increases in daytime temperature has reduced the amount of water in the local atmosphere thus lessening the blanket effect at night, resulting in cooler, not warmer, night-time temperatures. This is an increasing desertification effect whereby TMAX goes up and TMIN goes down. The days become warmer and the nights cooler over time.
The average temperature at this station does show a positive slope, and does correlate with increasing atmospheric carbon dioxide. But this is misleading since the increase in average temperature is solely due to the increasing maximum temperature and is not due to a general increase in temperature. The use of average temperatures in critical analyses should be deprecated.
Coastal stations often present a different picture, presumably due to local weather-influencing factors, such as changes in atmospheric water content during parts of the day. Fog, mist, and precipitation, particularly at night, can cause changes in temperature. Figure 5 shows the temperature record for Port Moreton Lighthouse, Australia (Station 040043), a station removed from any urban heat island effect, but with a temperature moderated by the ocean.
Here evidence of warming, and TDIF, which should be positive, shows no evidence for the direct action of AGW, but which may be explained by changes in the water content of the local atmosphere due to the proximity of the ocean.
Other temperature data sets from many other stations were investigated. Many of these datasets are incomplete with smaller or larger gaps in the records. However, they can still be analysed using my methods and criteria. Analysis of the TDIF data can be done even when data is missing or has suffered from disturbances, such as the gaps in recording, urban heat island effect, translocations of stations, equipment replacements, changes in land use, or changes in the expertise of personnel. In such cases both TMAX and TMIN are affected equally so that TDIF is not affected. There is no need for corrections.
My own country, New Zealand, provides an instructive example of a misinterpreted record. Lincoln township is a small rural village 14 kilometres from the city of Christchurch. Both Lincoln and Christchurch are located on an extensive flat plain and share the same weather. Lincoln University, a well respected agricultural university, is located in this village. The recording of temperatures there started very early on by New Zealand standards so the record is very useful. Since the village has much the same climate as Christchurch it has been used to investigate the urban heat island effect. Christchurch City shows a marked urban heat island effect while Lincoln is believed not to have been affected by any heat island effect since its population is small. It is one of the seven New Zealand stations favoured by the New Zealand National Institute of Water and Air (NIWA), a major advocate for the AGW hypothesis. The following figure shows the record of the average temperature for Lincoln since 1864.
Figure 6 seems to present strong evidence of global warming, but a closer inspection suggests a jump in baseline occurred in about 1960. Plotting the first half of the data shows that there has been no increase in temperature from 1864 to 1950. The graph for this period is shown in Figure 7.
The slope of the linear regression line in Figure 7 is not significantly different from zero, so there is no evidence of warming.
The remainder of the record, that for the period from 1951 to 2009 is shown in Figure 8.
Again, the slope in Figure 7 is not significantly different from zero, so there is no evidence of any warming. The mean temperature until 1950 was 10.90 degrees Celsius, whereas that since 1951 was 11.65 degrees Celsius. But this increase of 0.75oC since 1951 must have been caused by a rather short-termed event that caused a rise in the baseline judging by the data in the graphs. After this jump the temperature remained fairly constant.
A possible explanation is that in mid-century the University built some large buildings on campus near the recording station, so the most likely explanation for the jump in the baseline is a sudden “urban heat island effect” due to the new and much larger buildings storing heat and radiating heat to the recording station. Unfortunately, NIWA has made the incorrect conclusion that there was continual warming due to the AGW hypothesis.
Many of the National authorities charged with collecting and storing climate data believe it necessary to “correct” the data if the data is patchy or they suspect errors are present. NOAA explains on their web page in some detail why they believe corrections are necessary. But such corrections are normally not advisable in statistical analyses as they make sound conclusions much more difficult to make. Furthermore, “corrections” are not necessary if TDIF is used as the major focus for investigation. And as a matter of sound operational principle one should reject outliers only if you have good reason to do so external to the data sets themselves, and these reasons should not be based just on the “look” of the raw data.
The only data sets I have been able to access so far are the “corrected” databases held by the national authorities, but I hope the analysis presented here are robust against such corrections.
A more critical and analytic study of climate data requires some test criteria against which the various possible explanations may be tested and evaluated. To progress this some test criteria for the evaluation of the two main hypotheses are given in Table 2.
TABLE 2. Test criteria for evaluating the explanatory power of the two hypotheses for the cause of global warming: AGW – Anthropogenic Global Warming due mainly to human release of carbon dioxide, and NCC – Natural Climate Change due to changes in the intensity of natural phenomena that normally control climate.
In Table 2 all the criteria in Column 1 must hold if AGW is to be judged to be the best explanation, whereas the three criteria in the second column are all alternatives, so that any one being true discounts the AGW hypothesis and support the NCC hypothesis.
Local changes in atmospheric conditions must also have a considerable effect on local climate. The Port Moreton Lighthouse station (Australia) has had a dramatic change in rainfall over time as shown in Figure 9.
Atmospheric water in all its three phases is a very potent optical molecule. Changes of the magnitude shown in Figure 9 must have a profound effect on local climate. Its effects may be perhaps 4,000 times more powerful than carbon dioxide. So local increases in water content could moderate temperature differences between night and day, and could lead to a false impression of an increase in average temperature. Drivers could include influx of water into the atmosphere later in the day that may persist through the night. Fog, mist, cloud or any increase in atmospheric water at the site would act as a powerful blanket, keeping the night much warmer than if the sky were clear.
Reduction in atmospheric water would result in desertification phenomena where TMAX will increase due to the clarity of the air during the daytime and the absence of clouds, thus increasing the daytime temperatures. And yet TMIN will decrease as the desertification proceeds due to the reduction in the water thermal blanket. This is shown in both the Bathurst record and the Ouda record.
The term “desertification” is not intended to suggest full desert conditions would inevitably result, but rather that a stage of development would be reached where night and day temperatures diverge increasingly and start to show the same characteristics as do full blown deserts.
Some stations are anomalous. Agassiz, Ontario, Canada, is an inland station that has a different pattern of temperatures as shown in Figure 10.
In the Agassiz record shown in Figure 10 the TMAX line shows cooling, not warming, though TMIN does show some warming. This result cannot be due to carbon dioxide, if it were TMAX would show an increase. But it may be due to changes in the water content of the atmosphere. An urban heat island effect is unlikely. However, this station does show an important point: if the “averages” (actually the median between TMAX and TMIN) are plotted, as shown by the green line, you do get a false global warming of around 2.10C per century.
It is possible that the amount of atmospheric water has increased at Agassiz, which would have the effect of cooling TMAX but warming TMIN.
From the preceding examples it is obvious that analysing TMAX, TMIN and TDIF separately rather than the usual procedure of regressing an “average” of TMIN and TMAX, is a very useful way of testing which of the two hypotheses explains the data best. The regression coefficients for the examples given values are shown in Table 3. It is obvious that a number of stations show no significant increase in TMAX over time.
Another test is to calculate a statistic which gives the b.TMIN as a percentage of b.TMAX, then calculate the standard error and using Student’s test calculate its statistical significance. Table 3 gives the results for the stations analysed in this report.
TABLE 3. Regression coefficients for the fitted linear lines to the temperature data from various stations.
(b.TMAX is the slope for the maximum temperature regression, b.TMIN is the slope for the daily minimum temperature, and b.TDIF is the slope for the difference between these two.
In Table 3 two stations show a negative (declining) slope for the TMAX linear fitted line, two had slopes that were not significantly different from zero, indicating they neither warmed nor cooled, and four showed significant warming. However, the average slope was not significantly different from zero. This is not strong evidence for global warming, let alone AGW,
The TMIN regressions in Table 3 showed that all records had slopes that were very different than would have been expected under the AGW hypothesis, and two stations actually cooled.
Since AGW assume that the CO2 effect acts similarly both during the day and the night, the regression coefficients of the TMAX and TMIN data should be similar, that is b.TMIN / b.TMAX * 100 should be 100% or close to it. The column headed “b.TMIN as a % of b.TMAX” gives these calculated values and their significance levels from the expectation of 100 using t-test. Only one is within the range of the expectation of 100% (Abilene, USA) and then only just within the 95% confidence limits. All the other stations are significantly different showing that the data does not support the AGW hypothesis. And the average of all these values is significantly different to the expectation under AGW.
The increases in TMAX may be due to a number of phenomena besides increased solar radiation. Since 1850 there has been massive growth in the size of cities and town and in the size and types of material used for infrastructure such as roading. Modern dwellings are often in large apartment buildings rather than single houses with an relatively extensive garden. Roads used to be porous to water having been made of earth or gravel. Rainwater used to sink into the ground but now much is conducted away by roofs and drains besides sealed roads from where it used to land into streams and rivers. The water that has been diverted and concentrated into streams and rivers cannot evaporate as much as it would have done when the receptive surfaces were unmodified soils and trees that were wetted by rain and acted as large evaporative surfaces. The clearing of forests for other land uses and the loss of organic matter in farm soils would all contribute to this desertification, thus increasing b.TMAX and, often, diminishing b.TMIN . And the addition of a great number of radiative surfaces when tall buildings are built will change fundamentally the re-radiation fluxes from their original patterns. The clearance of plants will also reduce transpirational water from plants into the atmosphere.
These relatively recent changes will all have an effect on local temperatures with the average temperature increasing and the minimum temperature either increasing more slowly or staying the same or even declining. The average temperature would probably increase thus giving an impression of global warming. Enough instances of this would cause the global averages to be related to the total number of affected sites compared to the number of unaffected sites, which means there will not be a consistent global average. As the population of the world grows and cities expand the average temperature value will appear to go up even more than any natural drivers would cause.
And the great majority of meteorological recording stations are situated close to cities or regions of importance to human activity, such as airfields. Most of these recording stations will be affected by the local phenomena and so will give a false idea of the cause and extent of global warming when only mean temperatures are computed and the data is not examined critically.
No doubt changes in solar radiation are a much more important driver of climate change than the activities from increasing human activities. But changes in land use will have had an effect and seem a far more plausible driver than the AGW hypothesis, especially for local climates. And the data presented here supports this conclusion. The very inconsistency of temperature patterns from recording site to recording site is strong evidence against universal AGW, as is the cyclic temperatures since 1850. The temperature increases should have been very much smoother that they have been had the carbon dioxide concentration has been the main driver. For example, there were very hot temperatures in the 1930s, and cold temperatures in the 1980s.
It is my belief that climatologists should take plant growth into account as a measure of the energy flux to earth. Plants are integrative, so measure heat in as far more meaningful way than methods using two spot energy potential measures per 24 hours. Plants respond to energy capacity or amounts, not just energy potential (temperature) as do thermometers. And crops are spread over wide areas so are far more representative and reliable than the comparative low density and misleading locations of climate stations that we have at present. The available data sets of agricultural and forestry production are extremely comprehensive, and are readily available. They also have the great advantage that they are not intended to prove a point.
If my analysis is valid then the world is plunging into a very expensive, unnecessary, ineffective and possibly destructive programme in an attempt to stop climate change by reducing CO2 emissions. If it is the case, as I hoped I have proved, that climate change is not due to increased atmospheric CO2 but is mainly the result of natural causes as it has been in every previous episode of climatic change, then mitigation of any deleterious effects as they begin to occur is by far the more sensible and effective policy. Especially if we are about to enter a cooling period as the crop evidence suggest.
Figure 11. The solar flux cycle over the last 75 years. It is obviously an error to say the solar flux does not vary as many climatologists do. Indeed, we are currently entering a downturn (not shown) in the last few years that is even more severe than the trough in the 1980s which prompted climatologists at the time to declare the earth was entering an ice age.
About the author:
Dr Kelvin Duncan: Kelvin is a New Zealander with a PhD in biology, and post graduate qualifications in Organic Chemistry, and diplomas in Statistics and Spanish. Active in preserving and enhancing natural systems, Kelvin directed a programme for middle and upper management of companies on ecology and, sustainability, indigenous people’s concerns, and how to solve disputes between conflicting interest groups. He also lead three technology-transfer aid missions to certain Pacific Islands and was a Mercosur Scholar.
Dr Duncan taught ecology, physiology and the philosophy and history of science at various universities and was Dean of Science at the University of Canterbury (Sir Karl Popper’s refuge during WW2 where he had an immense effect) until 2002 when he left academia to pursue some of applied science topics including successful commercial developments resulted. His latest project is to replace glucose-based polymers in foodstuffs with fructose based polymers thus alleviating diabetes and obesity.
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