Attribution of Variations in Maximum Temperature Records 1932 – 2010 Central United Kingdom, with Implications for Global Warming

D.Proctor, P.Geol.
Calgary, Alberta; December 30, 2011

Abstract

Previously sourced and plotted data for averaged annual maximum temperature and hours of bright sunshine covering the period 1932 to 2010 for the Central United Kingdom were analyzed. Changes in the two relative to a stable period (1962 – 1973) amounted to increases of 0.98C and 108 hours in 2010.

Three factors were found to be associated with all temperature changes:

1. The duration of bright sunshine, such that C = 9.27E-3C X Sunshine hour – 0.10C. This factor was constant with time, but the changes in bright sunshine hours followed (with time) a quasi-sinusoidal pattern with indeterminate amplitude, but a peak-to-peak cycle of 62 years.
2. A quasi-sinusoidal (with time) Pacific Decadal Oscillation-Atlantic Multidecadal Occillation-like variation, with a cycle length of 56 years and amplitude of 0.31C.
3. A linear (with time), consistent increase of temperature, such that C = 9.53E-4 (Yr-1873) – 0.1425 C.

The majority of temperature change was due to the sunshine duration factor. The PDO-AMO-like varying factor contributed the second most significant portion of the temperature change record, sometimes adding and sometimes subtracting from the temperature changes associated with increased/decreased bright sunshine. The third factor was tied to the PDO-AMO-like factor as a long-term warming, but added only a minor amount, 0.095C/century.

 

The datum period 1962 – 1973 recorded a stable period of 1315.9 hours, i.e. a daytime cloudiness of 70.0%. From 1932 to 1948, and from 1980 to 2010, the Central United Kingdom experienced increased bright sunshine of about 42 and 108 hours, respectively. This is a bright sunshine increase of 3.2% gross and 0.96% net more sunshine for the earlier period, and 8.2% gross, and 2.5% net additional sunshine for the most recent period. Stated in the reverse, in the 1932-1948 periods when temperature rose 0.32C, there was 0.96% less cloudiness; in the 1980 – 2010 period, when the average maximum temperature rose 0.98C, 2.5% less cloudiness.

The PDO-AMO –like temperature changes did not match perfectly either the timing or amount of temperature change associated with heat release and storage for either the PDO or the AMO events as individual events. The changes appear more of a non-equal combination of both, though the combination was not determined within this study.

It is concluded that changes in the Central United Kingdom Maximum temperature history of the past 70 years is fundamentally a response to changes in the amount of sunshine (i.e., cloudiness) in association with rises and falls in temperature resulting from natural heat storage and release of the from the planet’s two largest oceans. The remaining, small portion of temperature rises seen in the Central UK may as well be attributed to land-use changes or inappropriate adjustments in the temperature records as it could to CO2-related changes in heat retention. Regardless of cause, this minor temperature rise, at 0.1C/century is of no consequence to the local biosphere.

Although the UK area studied is a small portion of an island mass with its own peculiar weather, the strong similarity in patterns, i.e. its climate patterns, to various GISTemp regional and whole-globe average temperature profiles suggests that the Central UK is a good proxy for what has happened across the planet and comes from common causes. Extended to 2060, it is proposed that an increase of cloudiness of about 2.5% and a decrease of temperature of about 1.0C will occur in the Central United Kingdom by 2040. Globally, cloudiness and temperature are expected to +1.7% and -0.70C, respectively.

Suggestions are offered as areas of similar study of the sunshine-PDO/AMO correlation and, hence, causation, of temperature variations of the near-past and probable near-future.

Introduction

The study of the recent phenomena called “Global Warming” is widely perceived as a study suitable only for experts in the atmospheric sciences. High-level computers and the ability to perform statistical gymnastics are said to be necessary to comprehend the temperature changes of the 20^th century. That one, with a simple working background in the natural sciences and familiarity with scientific inquiries, can contribute to the ongoing debate, armed only with a keen eye, sharp pencil and the ability to recognize a forest regardless of the types of trees present (including bristlecone), is anathema to the current anti-CO2 narratives. Though the most fundamental, revolutionary explanations of the world came from such non-technological researchers as Aristotle, Galileo and Darwin, currently only government-funded, PhD, peer-reviewed researchers are believed to have worthwhile opinions on the most significant environmental and social issue in humanity’s history. This study was undertaken, in part, to confront that conceit.

On the technical level, the study sprang from a recent, 2011 blog post of Tallbloke’s Talkshop [http://tallbloke.wordpress.com/2011/08/30/comparing-sunshine-hours-and-max-annual-temperature-in-the-uk/] In this post, a pair of profiles from the Central United Kingdom (1932 to 2010) meteorological database – the longest, most complete in the world – was presented. The annualized, maximum daily temperatures and the annualized, daily recorded number of bright sunshine hours were plotted against time. A 5-year running average had been added on each. The blog author noted that both the sunshine hours and maximum temperature profile showed similar cycles but the cycles were not quite synchronized; he speculated there might be something of interest to be found in its study. What follows is the result of following up on that speculation.

Methodology

The fundamental data captured from the referenced blog post is shown in Figure 1. “Maximum Temperature” refers to the grouped, annual average of the greatest temperature reached for each day. “Sunshine hours” has been defined as:

Average number of hours of bright sunshine each day in a calendar month or year, calculated over the period of record. Hours of bright sunshine is measured from midnight to midnight … bright sunshine has generally been recorded with a Campbell-Stokes recorder. This device only measures the duration of “bright” sunshine, which is less than the amount of “visible “sunshine.(http://www.bom.gov.au/climate/cdo/about/definitionsother.shtml)

When overlaid, the Maximum Temperature profile (Fig. 1a), shows strong pattern similarity to the GISTemp temperature anomaly profile for the Northern Latitudes (Fig. 2). The Central UK data, in pattern sense at least, is thus a reasonable proxy for regional (if not more) temperature profiles. What is learned from a study of the specific central United Kingdom data, therefore, is considered probably applicable to that of the general global data.

Figure 3 is an overlay of both sunshine hours (Fig. 1a) and maximum temperatures of the Central UK (Fig. 1b) area on a common time-axis. Both show a relatively stable period of about 1962 to 1973. This period, in which sunshine hours were about 1315.9 hours, and the average maximum temperature, 11.87*C, was selected as a reference datum for comparison purposed. It is noted that the reference datum of 1315.9 hours represents only 30.02% of potential bright sunshine hours per year, indicating, in other words, that the Central United Kingdom is cloudy 70% of the time (Combined with an average annual average temperature of less than 12*C, the two points explain why so many UK citizens take their annual holidays in southern Spain.)

The differences between the datum and the five-year running value of each parameter were measured on an enlarged version of Figure 3 (Fig. 16: pencil-and-ruler methods are crude compared to computer-held data points, but they appear to have been sufficient). Time intervals of 5 years were used initially, but additional points were measured as required to better plot the changes. These variations, i.e. “temperature anomalies” as known within standard global warming analyses, were dropped into an Excel spreadsheet and plotted (Fig. 4).

At first appearance the pattern appears rough, though within it a linear trend of increasing temperature with sunshine hours is apparent. When the dates of data points are added, however, it is clear that the pattern is not rough at all (Fig. 4d).

From 1932 until about 1948, temperatures and sunshine hours both increased, after which, until 1968, they decreased. The pattern is semi-elliptical, with a clear axial trend. This semi-ellipse describes a period of time here called “Cycle A”. From 1968 to 2010 both temperature and sunshine hours increase again, though in greater amounts, in a way similar to the first part of Cycle A. This second time period, referred here as “Cycle B”, can be fitted on the same axial trend line as Cycle A if the year 2010 is viewed – as it appears to be – the point of maximum sunshine-hours and maximum temperature of the cycle, analogous to the 1932 to 1948 period.

Two points are to be noted. The first is that Cycle A, shown in its (hypothesized) entirety, is not a true ellipse: the first part, the warming & more sunshine portion, is not a mirror image of the second part, the cooling & less sunshine part. The second point is that a negative temperature of -0.08C results from an extension of the axial trend line backward to the point of zero sunshine hours. Intuitively this does not make sense: when there are no additional hours of sunshine, the temperature should be equal to the datum, not less than the datum. This situation is considered an artefact of either the (crude) measurement method with small divergences from the datum, an overly aggressive smoothing function of the 5-year running average algorithm, or spurious adjustments in the base data. (The problem requires “b” values of – 0.10C and – 0.14C to be added to two mathematical relationships of the general y = mx + b type found in this study. Regardless of the cause, the situation/error does not detract from the general conclusions made later.)

Analyses

It is intuitive that, should the number of sunshine hours increase, maximum temperatures reached during that time would increase. It is also intuitive the effect on the temperature would be the same in a more-hours and less-hours scenario, though in an opposite way. The pattern would be the same “up” and “down”: there would be only one line (or curve). The pattern of Cycle A does not show this, however. Either the maximum temperature achieved is not a consistent function of the number of sunshine hours as the value of sunshine changes, i.e. the insolation power changes in time, or there is at least one other factor other than sunshine duration responsible for the maximum temperature recorded in the Central UK. It is well established that the top of atmosphere (TOA) solar insolation has been virtually constant on an annual basis for hundreds of years. Other factors than this must have been/are involved to create the Cycle A (and presumably the beginning of Cycle B) pattern.

Figure 5 shows a series of models considered from first principles to show the result of both linear and non-linear patterns that might be involved in the identified Maximum Temperature and Sunshine Hours cycle(s). Only one combination results in a pattern similar to Cycle A (and the beginning of Cycle B). Shown in Figure 6, two factors, one linear, the other non-linear, but stepping out of phase with each other over time, i.e. at times both warm, and at others, one warms while one cools, appear to be in play.

(A combination of several linear patterns creates one linear pattern; the same is true of multiple non-linear/sinusoidal patterns. For the purposes of satisfying the principle of Occam’s Razor, continued analyses followed from looking for one linear and one non-linear factor. As it turned out, this was appropriate.)

Breakdown of Factors

Measurements of the axial trend line seen initially in Figure 4c and deviations from the trend line (Fig. 7/7b) at each data point are plotted in Figures 7c and 7d.

The linear factor:

Crude measurements account for the scatter in 7C, the trend has been corrected as an idealized trend line with a relationship of:

T = 9.27E-3 (Hrs) – 0.10 C*

This reveals that a certain amount of the temperature changes recorded is completely related to the number of sunshine hours received – a not unexpected connection. Not all of the total temperature record is attributable to increases and decreases in sunshine hours, however.

The nonlinear factor &minor associated linear warming factors:

The portion of the temperature changes here discussed is that of the raw temperature change less that of the axial trend. The difference, plotted in Figures 7d and 8a, displays a quasi-sinusoidal pattern of temperature changes with time. Both Cycle A and, for the first portion present of Cycle B, show the same pattern but have different amplitudes. However, a consideration was made that only one quasi-sinusoidal factor attached to a linear temperature rise would account for the revealed pattern. This linear relationship (Figure 4d) was measured and calculated as an idealized function as:

T = 9.53E-4 (Yr – 1871) – 0.1425 C* (Fig. 8b), amounting to 0.095C/century.

With the linear function removed, Figure 8c shows the resultant de-trended, quasi-sinusoidal temperature pattern. The pattern has a peak-to-peak cycle length of 56 years and an amplitude of 0.31C. (A simple extension forward and backward from the nearest portion of the cycle was used to extend the graph backward and forward to 1860 and 2060, respectively, as discussed further in this report.)

The sunshine-temperature trend-line

As noted, although the relationship between the number of sunshine hours experienced and the maximum temperature reached (Fig. 7c and 9/9a) is clear, how this relationship shows in the records is also a function of the way the changes of bright sunshine amount occurs through time (Fig. 9d). Plotting the converted sunshine-induced heat against time (Fig. 9c) reveals another quasi-sinusoidal pattern, one similar to, but offset in time from, that of the first quasi-sinusoidal temperature variable discussed above. Peak-to-peak cycle time, at 62 years, is 11% longer from the other (of 56) and the “highs” and “lows” are somewhat different.

(Using the same concept of projecting forward and back as used for the non-sunshine related temperature changes, Figure 9d shows both data and expectations from principle over the period 1860 to 2060. The rationale for doing so is that whatever the cause, human or natural though it may be, it is likely that the pattern 50 years either way is not very different from what it was closest to those times. This is, in fact, the basis of the IPCC/Global Warming “scenarios”, except that the IPCC considers what happened pre-1970 was “natural”, and what happened(s) post-1970 principally of human origin. Orthodox climate interpretations are based on this idea; here the same principles are applied.)

Reconstructing the Central United Kingdom Maximum Temperature Record

The preceding suggests the maximum temperature profile for the central United Kingdom between 1932 and 1948, and by extension from 1948 to 2010, could be attributed to

1. a heating proportional to the amount of bright sunshine received, the amount of bright sunshine changing through time in a quasi-sinusoidal manner with a cycle time of 62 years,
2. a quasi-sinusoidal heating and cooling with a 56 year peak-to-peak frequency, and
3. a minor, long-term, consistent heating of about 0.10C/century.

These three factors were isolated from the temperature/sunshine records, idealized and expressed separately (Fig. 10). To determine whether the deconstruction/idealization process introduced errors, the isolated portions were re-integrated and compared to the original data profile (Fig. 11). The fit is good.

The fit of original and reconstructed profiles is good does not, of course, show more than that the mathematical deconstruction was internally consistent. A way to check that the hypotheses behind the deconstruction are correct (or at least consistent with observation) is to extend the observed data backward to a period in which data not included in the study can be used as a comparison. Such data for the Central UK area was not available to this author at the time of writing, but data for the Northern Latitudes, for which the Central UK data was considered a good proxy, was available.

In Figure 12 the reconstructed temperature data with extensions is overlain on GISTemp profiles of the Northern and other Latitudes groupings, as well as the Global Meteorological Land Stations [http://data.giss.nasa.gov/gistemp/graphs/. ]The period 1860 to 1932 for all profiles matches reasonably well with both reconstruction and hindcast extensions. The prior period of Southern Latitudes also matches reasonably well (though the modern period of 1960 – 2000 does not).

The hypotheses that

1) the three identified patterns are sufficient to explain the temperature rise of the Central UK (and other regions), and

2) that the recent past can be used to hindcast the further past,

both appear confirmed.

The Non-Linear Factors

The attribution of much of the temperature rise in the Central UK to increased amounts of sunshine requires little explanation. Each time the sun “moves from behind” clouds and we, the air and the environment around us warms, is observable proof that temperatures rise with more sunshine. The mathematical relationship between added sunshine and increased temperature, as shown to be consistent and linear, fits in with general observations that the top of atmosphere (i.e. TOA) solar insolation has not changed over the last 150 years. The quasi-sinusoidal temperature portion of the record and the quasi-sinusoidal variation in time of the number of sunshine hours require further explanation.

The explanation offered here is recognized as not directly providing the causations: the patterns show up because, in the natural world, the same patterns show up elsewhere, have done so for centuries, and are unrelated to man or his activities in the world. These are the patterns are those of the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) (Fig. 13 & 14) (http://en.wikipedia.org/wiki/Pacific_decadal_oscillation &http://en.wikipedia.org/wiki/Atlantic_multidecadal_oscillation) . The PDO and AMO cycles are sufficiently studied and understood, however, that attribution of part of the Central UK temperature profile to the mechanisms of the PDO-AMO cycles is legitimately more of an explanation than a rationalization.

The PDO and the AMO reflect periodic release and storage of oceanic heat with cycles of 20 or 30 years for the Pacific Ocean, and 60 years for the Atlantic. Each of the oceanic basins shows a warming and cooling pattern with a similar, one-half or one-third cycle period of the observed quasi-sinusoidal temperature factor (Fig. 14a, b & c). Neither one nor the other, however, quite matches time-wise the highs and lows of the reconstructed Central UK record (Fig. 14d, e & g). A non-proportional combination of the PDO and AMO, although not done here, appear likely to fit the reconstructed record enough to suggest that the observed heating pattern in the Central UK is derived from the oceanic heating and cooling cycles. It is also noteworthy that the Southern Latitude profile, an area dominated by the Southern Ocean/Antarctica, matches the UK record least: if the oceans are responsible for much of the Central UK temperature patterns, the Atlantic, and then the Pacific, would reasonably be considered more of an influence than the Southern.

By invoking the PDO and the AMO as an explanation for the temperature changes, the mechanisms by this occurs have not, of course, been explained, but the source of the mechanisms is said to be found. As well, the PDO and AMO events are well known and studied. Whatever the causes for the PDO/AMO events, they are invoked here as the primary cause of the patterns showing up in the Central UK temperature record.

At the same time, the quasi-sinusoidal variation of sunshine hours has no intuitive foundation in the PDO/AMO cycles. It is possible that the amount of sunshine hours, i.e. cloudiness, is influenced by but not created by, those factors that induce such patterns in the global oceans. The cause for change of cloudiness, not its expression, lies elsewhere.

The three factors as revealed in this study – cloudiness, PDO-AMO heat release and storage, and a minor long-term heating factor – appear sufficient to explain local, regional and global heating and cooling patterns without recourse to the CO2-villain of the Global Warming story. CO2 growth has been approximately linear for the last 50 years (Fig. 15, http://www.esrl.noaa.gov/gmd/ccgg/trends/), a pattern that matches none of those determined here. Further, if these natural, cycling factors are the principal determinants of temperatures, then the forecast (Fig. 10d) is for a planet cooling in the years ahead. For the Central UK, this entails a drop in maximum temperatures of approximately 0.96C by 2040, with an increase in cloudiness of about 2.5% – a return to the conditions of 1970. Comparing the changes of the Central UK to that of the world, it appears the global change would be a drop of about 0.7C, and an increase in cloudiness of about 1.8% (by a quantitative comparison of specific temperature changes of the Central UK and the globe).

The amount of CO2 is increasing in the atmosphere each day. Based on observations made here of the previous 80 years, however, both regional and global temperatures are not expected to have any discernible, let alone catastrophic, impact in the near future.

Summary & Conclusions:

The Maximum Temperatures of the Central United Kingdom area are entirely determined by

a) the amount of sunshine received, i.e. changes in cloudiness,

b) a cycling input and output of heat related to changes in energy storage and release of the Atlantic and Pacific Ocean, and

c) a very minor, long-term increase in overall temperatures.

The amount of sunshine received has a cyclic pattern similar to, but not in lockstep with, the oceanic heating and cooling cycles of the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation.

The very minor, long-term increase in temperatures, at 0.1C/century may be related to anthropogenically produced CO2, but other factors, including artefacts of data manipulation and adjustments might be equally considered. Regardless, the amount of heating not attributable to additional sunshine or oceanic influences is minor to the point of invisibility.

CO2 as a threat to the biosphere is hereby repudiated. Nature, not man, is in charge of the current global, regional and local climate.

The coming period of 2010 to 2040 is predicted by the factors discussed here to be a time of local and global cooling and, without being dramatic, cloudiness. Temperatures are expected to drop by 0.7 to 1.0C during this period, and the amount of cloud cover increase between 1.5% and 2.5%.

In addition to determining that CO2 is not responsible for the warming of the world over the last century, the results of this study also demonstrate that “citizen-scientists” do, indeed, have the abilities to determine and reasonably comment upon matters of scientific, if not social, concern.

End Comment:

Political pollsters such as Gallup are able to create accurate pictures of the voting public because they recognize that a careful study of a small subset of the population gives a practical understanding of the positions of the whole population. If surveying everyone were necessary, the work would never be done. This analysis of the Central United Kingdom temperature, time and sunshine hours is obviously limited, but as only one individual, if representative of the nation, may consistently reflect the behaviour of the voting nation, so this study, in theory, may accurately reflect what is happening in the global temperature events of the near-past and near-future. Additional such studies obviously should be done, however.

It is suggested that a number of subsets, rather than large, merged data, be subjected to the analysis done here, i.e. at a national, rather than global/subglobal level. The reason is that the more data is averaged, the more significant patterns offset in time rather than cause, may confuse the general pattern. Plus, any diverse group of information, when combined and averaged, gives up “universals” as a mathematical construct without necessarily revealing anything about actual patterns within the data reflective of the universals. For example, the “global” temperature may have no more meaning than an average height of 5’ 6” to describe a room filled half with giants and half with dwarfs. In this example, a description of the separate characteristics of the height –enhanced and height-challenged would be more useful. The same may apply to the world’s climate.

The Central United Kingdom has, by the data reviewed, a non-bright sunshine history of 70%; Constable and Turner showed us in the 19^th century that much of this was due to sustained low and middle altitude clouds. A bright sunshine and maximum temperature dataset from, say, the Arctic, where upper clouds are more prevalent would be interesting. One from a seasonally cloudy area, like Vancouver or San Francisco, where the Pacific Decadal Oscillation would be more significant, would be also informative. Australia, with its boom-and-bust cycle of flooding and droughts, would be another good candidate.

Again: if globally distributed CO2 changes the dynamics of the atmosphere, the results are everywhere. The small will reflect the large. If the small doesn’t reflect the large, then the “large” is an artefact, not an observation.