Putting the Meteors back in Meteorology by Dr Chris Barnes, Bangor Scientific and Educational Consultants, E-mail doctor.barnes@yahoo.co.uk   

Author Website http://drchrisbarnes.co.uk



The combined effects of meteor showers, solar flux and GCR on the interdecadal climate in the UK.  At lease recently, these are seen to be in control with no evidence of warming.   The UK temperature anomaly can be accounted for by a simply algorithm.


1.            In the UK in the inter-decadal period 2005-2011 annual rainfall is most strongly correlated with cosmic ray flux. The much higher correlation coefficient for Cosmic Rays is supportive of the notion of a stronger, real physical effect and is also supportive of the work of Svensmark.     Alternatively, and/or additionally meteoric debris does provide the nucleation material for rainfall but cosmic rays provide the correct atmospheric electricity conditions, see Tinsley (2000) and Carslaw and Harrison (2002).

2.            Annual temperatures can be correlated with a simple linear algorithm (SFCM) involving cosmic ray flux (C), solar flux (SF) and radio meteor flux (M) according to equation (1). 

Delta Temp = -.707 + 2.916* SFCM …………………………………………………………………(1)

Where SFCM =   {(SF-C) +M}    P<.023 so statistically significant







In ancient history, the term meteorology literally meant the study of anything that fell from the sky.  Meteors from outer space were called "fire meteors". Rain was called "hydro-meteors", and frozen precipitation, such as hail and snow was referred to as "ice meteors".


The Question posed here is quite simply; do space meteors have an influence on our weather and climate and if so by how much.  Also,   are there other extra-terrestrial sources besides our sun which do the same?


There is also recent and substantially growing interest in Space Weather influence on Earth’s weather and climate as either a dominant alternative or a modulating adjunct to anthropogenic climate change. 


As early as 1956, Bowen discussed the relation between meteors and rainfall and he found, at least for the month of January   that there was a tendency for more rain to fall on certain calendar dates than on others. There was also a close correspondence between the dates of the rainfall maxima in both the northern and southern hemispheres and he concluded this was difficult to explain on a climatological basis, thus that the effect might, however, be due to an extra-terrestrial influence.   Bowen observed that the rainfall peaks occurred approximately 30 days after prominent meteor showers, and suggested that this was because of the nucleating effect of meteoritic dust falling into cloud systems in the lower atmosphere, the time difference being accounted for by the rate of fall of the material through the atmosphere.  Bowen’s hypothesis was tested for a specific meteor stream, namely the Bielids, which is known to have a 6.5-year period. The rainfall 30 days after this meteor shower was found to have a similar period. Furthermore, the phase of the rainfall periodicity was almost identical with that of the meteor shower.


Whipple and Hawkins (1956) further commented on the correlations of both heavy rainfall and of noctilucent clouds with meteor streams. The latter being caused by suspensions of meteoric debris in the upper atmosphere. They further suggested a mechanism for removal depending on the gradual disintegration of micrometeorites under the influence of corpuscular radiation from the sun.


As an alternative to visual meteor observing which does not detect daytime showers or micrometeorites, radio and radar methods of meteor detection are available (refs). Signals are scattered both forward and backwards by meteors but forward scatter of continuous wave (CW) signals is the preferred method of detection.   Radio amateurs have featured heavily in the development of this technology and the same type of system used by Andy  Smith G7IZU ( ref )  was  also adopted for use at Jodrell Bank Radio telescope  http://www.jb.man.ac.uk/meteor/.  Unfortunately, VHF TV which was the main source of radio carrier for this type of meteor detection has almost exclusively ceased to operate in Europe since 2011 and so the hunt is on for new sources of suitable radio emission.  It is hoped that this paper will be the catalyst for someone somewhere to develop more dedicated sources and detectors.    It is known that both meteor rates measured by both scintillation and radio methods show height and number variations according to the solar cycle, see  Bumba (1949), Hughes (1974) , Lindblad (1976), Ellyett (1977), Simek and Pecina (2002), Lindblad (2003) and Dubietis and Arlt (2007).  


The appearance of summer Northern Hemisphere noctilucent clouds affects meteor decay times, see Singer et al (2007) (Advances in Meteoroid and Meteor Science

 By Frans J.M. Rietmeijer, D. Janches, J. M. Trigo-Rodriguez, J.M. Trigo-Rodriguez, J. Llorca, Jordi Llorca).  This is most likely because ablation of meteors at mesospheric E-layer heights 88-110km results in the formation of this phenomenon because of   the effect of ice particles on the mesospheric potassium layer, see Raizada et al (2006).  Gumbel and Megner (2009) have shown that meteoric smoke needs to be electrically charged in order to bring about sufficient mesospheric ice nucleation.   


Lifetimes of meteoric material   in the mesosphere are not unlimited. It takes about a week for meteoric smoke to interact with stratospheric sulphuric acid at heights as low as 40 km (see Saunders et al 2012).  Proof that meteoric dust is partly responsible for rain cloud nucleation is found by studying radioactive Beryllium delivery to terrestrial soils; see Graly et al (2011).


 It may be expected therefore and a potential hypothesis investigated here that meteors will indeed affect weather and climate either as a barometer of the solar cycle or more directly by albedo and/or atmospheric chemistry effects at mesospheric, stratospheric and tropospheric heights.    Since Northern Hemisphere meteor rates are not constant, there ought to be seasonal effects evident   as a potential test of aspects of this hypothesis.


Solar association with weather and climate is universally accepted, although at least for some there has been pre-occupation with the effects of anthropogenic drivers. 


The only other extra-terrestrial and extra-solar system potential driver is Galactic Cosmic Rays or GCR.   Svensmark and Christensen (1997) have investigated the global cloud cover observed by satellites. They found that the observed variation of 3–4% of the global cloud cover during the recent solar cycle was strongly correlated with the cosmic ray flux. This, in turn, is inversely correlated with the solar activity. The effect is larger at higher latitudes in agreement with the shielding effect of the Earth's magnetic field on high-energy charged particles. The observed systematic variation in cloud cover was proposed to have a significant effect on the incoming solar radiation and may, therefore, provide a possible explanation of the tropospheric and stratospheric 10–12 year oscillations. They stated that the relation between cosmic ray flux and cloud cover ought to also be of importance in an explanation of the correlation between solar cycle length and global temperature.


The work of Svensmark is both controversial and topical as it has since led to the Cosmoclimatology theory of climate change in 2007. Following much criticism from the CO2 climate warming lobby,   Svensmark, Pepke and Pedersen published again in 2013 in Physics Letters A and showed, that there is in fact a correlation between cosmic rays and the formation of aerosols and clouds. According to the study, solar activity is responsible for c. 50 percent of temperature variation.


Thus the present study presented below is topical, timely and novel as it examines in detail correlation both annual and seasonal variation of weather and climate anomaly in the UK in terms of simple and complex functionality against all three above possible drivers.  


Experimental      Data Sets


The data sets used have been obtained as follows:


  1. UK weather data sets; temperature, rainfall and sunshine anomaly have been obtained from  the UK Met Office website at   http://www.metoffice.gov.uk/climate/uk/anomalygraphs/
  2. Cosmic Ray data has been obtained from the University of Oulu at http://cosmicrays.oulu.fi/
  3. The radio meteor data was taken from the Colorgramme  at  http://www.tvcomm.co.uk/radio/


The data from sets 1 and 2 were used without further processing. The data from source 3 was processed according to a colour/area to number estimation algorithm wherein black=0, blue =2, green =4, yellow =6, orange =8 and red =10. 


Experimental   Part 1:  Annual Correlations 


Data set 1 provides monthly anomaly data compared with three possible long term average data sets (LTAS).  Data set 3 only provides data for the years 2005-2011 inclusive so this limits the study as a whole.   The monthly data for each of these years for rainfall, temperature and sunshine has been totalised and averaged by the present author.    Correlations are sought with weather feature each potential climate driver in turn.  Where applicable more complex correlation algorithms have also been sought employing more than one driver.  


Testing the theory that meteor rates are influenced by the solar cycle.

The radio meteor data treated accordingly as described above was plotted against sunspot number for the years 2005-2011.  A near sinusoidal relationship is seen as plotted in Figure 1 below.



Results and Discussion:   Annual Correlations 


Annual rainfall has been correlated singly and separately with the three known possible extra-terrestrial drivers, see figure 2 below.     


At least in the UK in the period including the years 2005-2011 inclusive, annual rainfall would appear to most strongly correlate with Cosmic Ray Flux, although clearly there is some effect of meteors and to an even lesser extent the solar cycle.   One particular point appears to be anomalous in that it is by far and above the largest residual in all three linear correlations.  That is the point associated with the year 2010 which had only an aggregate 87 % of LTA rainfall.  Interestingly     March to October 2010 saw several eruptions of the Icelandic volcano  Eyjafjallajökull.   Other volcanic eruptions for example that of Mount Pinatubo are know to have had a dramatic effect on the immediate hydrological cycle in both time and space, see for example, Trenberth and Dai 2007   http://onlinelibrary.wiley.com/doi/10.1029/2007GL030524/full.


Fischer et al 2007  have studied tropical volcanic eruptions over the last half millennium, and even these are shown to produce somewhat drier conditions over Europe for up to 2 years  after each eruption. http://onlinelibrary.wiley.com/doi/10.1029/2006GL027992/full.   It is logical to suppose therefore that 2010 in the UK may be an anomalous dry year  because of Eyjafjallajökull particularly as it was predominantly under a South Easterly Jetstream, see Gudmundsson et al (2010)   http://adsabs.harvard.edu/abs/2010AGUFM.V53F..01G    and given it was an explosive-mixed eruption of unusually long duration, see Gudmundsson et al (2011). http://meetingorganizer.copernicus.org/EGU2011/EGU2011-12542.pdf.


Following the above if one removes 2010 from the data set and re-examines the correlations, significant changes occur, see Figure 3 below.    






   Figure 3



Although the result for cosmic rays gives by far the best correlation coefficient, the reality is possibly that all three extra-terrestrial phenomena have a concerted interplay which gives rise to rainfall as we know it. Using 130 years’ worth of data Hiremath (2006) has reached a similar conclusion regarding rainfall over India.   It is further interesting to note that Hiremath (2006) noted that his correlations were disturbed by intermittent volcanic eruptions.    By value of correlation coefficient and behaviours of the residuals alone, it is feasible that the behaviour of radio meteors is on an annual basis acting merely as a mirror of solar activity, as outlined above.  


The results for the annual temperature correlations are shown in Figure 4 below:




The regression coefficient for temperature versus radio meteors alone is particularly low, possibly indicating there are competing processes at work.   The temperature anomalies versus Cosmic Ray and Solar Flux regressions have similar coefficients with a negative coefficient of temperature against % comic ray compared with LTA and a positive slope for Solar Flux.  These data suggest that either the Sun controls climate directly or indirectly by deflecting cosmic rays at periods of high solar activity as would be in line with the theory of Svensmark.    At least for the UK and taken with the rainfall result there is very strong support for Svensmark.    Close inspection of the data also tends to suggest that 2010 was the coldest year in the data set and the Icelandic volcano Eyjafjallajökull could be to blame.


Since the  cosmic ray and solar flux regressions are of opposite slope but with a subtly different ‘non opposite’ pattern with regard to residuals, it was decided  to try an create a combine algorithm by simple normalisation and subtraction.  Furthermore   since meteor showers  are a Northern Hemisphere seasonal phenomenon known to be associated with the formation of noctilucent or Polar Mesospheric   Cloud   also a barometer of the solar cycle but with a surprising teleconnection to the Winter Stratosphere, see Karlsson et al (2009) so perhaps  relevant to LTA temperatures.  There are many references to Noctilucent clouds as a barometer of climate change but since they are effectively mainly seeded by meteoric material, thus an attempt was made at a combined parameter linear algorithm which turned out to be surprisingly simple and to produce striking results, see  Figure 5 below:




The algorithm used to fit the data in Figure 5 is very simple, see equation (1). 

Delta Temp = -.707 + 2.916* SFCM …………………………………………………………………(1)

Where SFCM =   {(SF-C) +M}


Whereas the data due to 2010 remains one of the largest residuals   it is perhaps somewhat surprisingly closer to the regression line than expected.  Presumably this is due to the above teleconnection phenomenon and the way in which volcanic emissions interact with the stratosphere. 


The conclusion is that at least for British Temperature anomaly over the short period from 2005-2011 a simple algorithm including just solar  flux and extra-terrestrial sources of meteor and GCR flux appears to adequately be able to account for the observed changes. 



Attempts have been made to linearly correlate the LTA sunshine anomaly data for the period with the three potential drivers, see Figure 6. 


Figure 6


The regression factors for Radio Meteors and for Cosmic Rays improve substantially to   R= .40 and .44 without 2010.  The data were all also explored for higher order, polynomial fits.   Only solar flux produced an improved regression, figure 7. 

Linear correlation regressions against   all three parameters are not surprisingly poor.  The maximum regression coefficient was 0.52 for Solar Flux with Cosmic Ray Flux and radio meteor flux lagging at 0.44 and 0.40 respectively.  The regressions were all anti-correlated with sunshine.


For sunshine to be maximised there would need to be a lack of both high and low cloud and aerosol and as all three will have different seasonal behaviours in the UK and the Northern Hemisphere in general it is not surprising therefore that a single good linear correlation against any of the controlling potential extra-terrestrial factors does not follow in an annual versus LTA analysis.  Ezekwe et al (1981) has examined the seasonal variation of dust haze and sunshine and solar irradiance in Nigeria, for example.  Sear et al (1981) has found that sunshine can only be predicted on a monthly basis for Davis, California with regression coefficients from .81 -.91.   However, here an excellent correlation between sunshine and solar flux can be obtained by using a cubic fit giving a regression coefficient of .99.       


E. Pallé* and  C.J. Butler (2001) have emphasised the cosmic ray link to sunshine in the obvious way it is anti-correlated with cloudiness in the period    1984 and 1991 but it was found the correlation did not hold good  in the period 1991–1994.

A significant Volcanic Eruption happened in 1990 in Redoubt, Alaska, see  Casadevall (1994) and in June 1991 Mount Pinatubo erupted, see  Bluth and Doirin (1992) whose measurements indicated that Mount Pinatubo has produced a much larger and perhaps longer-lasting SO2 cloud and climatic implications than those  El Chichón.


There has of course been an Icelandic volcanic eruption in 2010 during the period of this short term study and one would expect this to have influenced the form of the above fit.


Experimental   Part 2: Monthly and Seasonal Effects  


It was instructive to examine the monthly radio meteor distribution for each year in the study.  Fourth order polynomial regressions were found to produce the best fits to the numerical data and have been shown for each year in the study and as an average, see Figure 8, below.





Keay (1963) was perhaps the first to conclude that meteor rates were greater during a 6 month period of each year.  This conclusion is also approximately borne out by the present study.  Meteor distribution has also been shown to vary with the lunar cycle (ref) as well as the solar cycle and this to some extent may account for the shifting of the spring and autumn troughs as seen above.


Investigation into meteors and sunshine on a monthly basis for years of study 2005-2011


It has been shown above that sunshine is either uncorrelated or very weakly negatively correlated with meteor rate on an annual basis.   However, since most meteors arrive in an approximately 6 month window and since the above distributions are rather non uniform it was decided to make a month by month investigation.  Furthermore as meteoric debris diffuses from the mesosphere into the stratosphere and troposphere it may, conceivably, have very different effects. To test this meteor rates were correlated against sunshine for   both the month of entry based on radio detection and the subsequent month to allow for atmospheric diffusion.    The results are only presented graphically for months in which there was any reasonable correlation, see Figure 9.




The January regression factor is strong .76 and positive. The May /June regression factors are of the order of .44.  To a lesser extent October showed a positive correlation with sunshine and meteor rates whereas the month of November showed a negative correlation when its sunshine was plotted against October’s meteor rates.   As early as 1957 Bowen studied meteor rates and found a   relationship with 30-31 days delay between significant meteor showers and enhanced rainfall.   A negative correlation with sunshine is tantamount to increased cloudiness and possibly the advent of rainfall.   Perhaps somewhat counter intuitively the present data set strongest effects seem to be when meteor rates are close to troughs on the above distribution curves rather than peaks.  Presumably at the peak there is so much meteoric material that a saturation effect regarding rain nucleation may take place.



Investigation into meteors and the QBO (30 mb Equatorial Zonal Wind Index): January to July


Data is shown below, figure 10.


                                           Figure 10


It is evident that when the zonal wind index changes sign and amplitude in a roughly symmetric manner   and in either direction in the period January to July, for example   2006 and 2009, maximum positive values of QBO correspond with roughly symmetric maximum positive or negative deviations in meteor rate whereas negative values of QBO correspond with close to average meteor rates.  On the other hand when the QBO remains wholly negative, for example 2005 meteor rates are also mainly less than average. 


The exception is 2010 which has a wholly negative QBO but with very large   apparent deviations in radio meteor rate.  Of course 2010 coincided with the Eyjafjallajökull eruption. Ellyett (1977) has commented on the possibility that an apparent meteor density gradient change in 1963 may have been due to volcanic dust.   Indeed there were several very relevant volcanic eruptions in 1962 and 1963 including those of Tokati, Hokkaido, Japan, June 1962, Kilauea volcano December 1962 (ref http://onlinelibrary.wiley.com/doi/10.1029/JZ069i010p02033/abstract) , Miyake-jima, Japan 1962, and Gunung Agung in February and March 1963. It is thus possibly this same sort of phenomena   which distorts the radio meteor rates in the 2010 plot above.


Radio Meteor rates measured in the UK only seemed to have a very strong correlation with the zonal wind between the months of January and July. Statistically the zonal winds seem to change most in sign and magnitude round about July.    The QBO has been shown to influence Atlantic Storminess and Jet Stream tracks so on that basis it was decided to reinvestigate the monthly effect of meteors on rainfall. 


Monthly rainfall effects.


It has been suggested in the past that meteors are highly correlated with rainfall, particularly regarding a 31 day delay after major showers (refs).     The same data sets referred to above have been employed.  Direct month to month linear correlations have been examined as have correlations between radio meteor rates in an initial month, followed by the subsequent month’s rainfall anomaly compared to the LTA.     






The results have only been indicated herein below for the cases in which there was any reasonable correlation.


For the first three months, figure 11, it can be seen that there is a positive and diminishing linear correlation of rainfall with radio meteor concentration. One possible conclusion   is that at least some of the meteoric material’ residence time may be considerably less than 31 days in the winter and early spring.    This is borne out in that no sensible correlations were observed for January’s meteor input predicting February’s rainfall or for February’s meteors as a possible predictor of March rainfall.    It is interesting to note that these positive slope correlations all only take place when the time averaged meteor input rate is falling, see Figure 8.   




Also  March meteor input  predicts an anti-correlation for April rainfall as meteor input starts to rise again.  The anticorrelation even on a month to month basis continues as the meteor input rate rises until it  levels out in May, see Figure 8.   This is strongly inidcative of two competing processes involving  meteor input and output rates to different parts of the atmosphere. Since meteoric material has to diffuse from the mesophere to lower levels of the atmosphere to be effective in combination with galactic cosmic ray flux in cloud seeding processes there is clearly a competing process, at least in springtime,  either in the mesophere or at least higher in the stratosphere which either prevents cloud seeding or scavenges moisture.  Such processes have been noted for the effects of aircraft contrails, for example.


 From May to July the regression factors fall to the point where they are insignifcant.  It is concluded therfore that at least in the UK and in these months, meteors do not control rainfall.  These are the months when mainly noctilucent of PMCS (Polar Mesospheric Cloud) is observed.  A typical PMC season begins approximately 20 days before summer solstice at 80° latitude, rises rapidly in occurrence frequency to 80–90%, and remains at that level until 50–60 days after solstice. Both occurrence frequency and brightness are latitude dependent, with higher values observed toward the poles. PMCs are normally observed at altitudes of 82–83 km, with higher altitudes at the start and end of each season, see DeLand et al (2006).  It may be that meteoric input is tied up with these and that they either limit release  of nucleating  material to lower altitudes and/or change  the chemistry and/or electrical properties of the atmosphere in a way in which meteors are no longer able to contribute to rainfall.   This idea is strongly supported by the proposal of  Bellan (2008) who propose noctilucent clouds to be composed of  tiny cold electrically charged ice grains located at about 85 km altitude, exhibit anomalously high radar reflectivity. They  shown that this observed high radar reflectivity is explained by assuming the ice grains are coated by a thin metal film because  substantial evidence exists indicating that such a film exists and is caused by the deposition of iron and sodium atoms on the ice grain from iron and sodium layers located immediately above the noctilucent cloud layer. The number of conduction electrons in the thin metal film coating an ice grain is very large. When averaged over the volume occupied by a large number of ice grains, the quivering of these metal film electrons provides a much larger contribution to radar reflectivity than does the much smaller number of dusty plasma electrons or electron holes. Using observations indicating that noctilucent clouds are the dominant sink for the summer-time iron and sodium layers, it is shown that a sufficiently thick metal layer should form on a typical ice grain in a few hours to a few days.   Sodium and iron are of course components of meteoric material and hence this shows how NLCS could retain such material and delay its diffcusion to lower heights in the atmosphere. 


There is a very slight fall in radio meteor rates on average from July to August reflected in increased rainfall consistent with the hypothesis developed above, see Figure 13.   


Figure 13

This is followed by an abrupt rise taking in August to September and presumably including the prolific and reliable August Perseids. This would seem to give rise to a fall in September rainfall, again consistent with the hypothesis developed above, see Figure 14 below.   




In October and November the correlations appear to weaken consistent with the time averaged flattening of meteor rates again, see Figure 8.

However in November to December, meteor rates seem to rise yet this time the rise producing increased rather than decreased rainfall, see Figure 15.  ( check if anything in refs)     






The improved month on month  ( November to December) correlation could also be significant  as it would seem to suggest that meteoric material  injected into the mesosphere in wintertime have a longer residency than those injected in springtime.   ( check for refs) and this observation is more like that observed by …. 1956.



The slope of the rainfall anomaly percentages divided by meteor rates versus month number can be best fitted to a quadratic equation which shows some symmetry around the summer solstice.   It is further very instructive to plot the slope of rainfall in every subsequent month divided by the prior month’s meteor rate which shows a sensible and finite regression coefficient. When this is done a skewed   distribution is seen in favour of the late autumn months.  This seems to confirm that meteoric material entering the mesosphere at this time of year takes longer to diffuse down through to the stratosphere and troposphere, see Figure 17. 


Monthly temperature Effects


In much the same way as rainfall effects can be studied, so can temperature.  






Very interestingly and almost intriguingly, figure 18 shows that  from January to May the slope of the regression alternates negative to positive on a monthly basis.  Meteor rates are known to be correlated with the lunar cycle(ref) so possibly this is the connection here and such a connection might be the origin of the old fashioned weather lore statement ‘change in the month, change in the weather’.  The  regression factors are however not that strong, varying between .17 and .46.  This is followed by two months, namely; June and July where the coefficient of temperature with meteor rate is negative and indeed in June is strongly negative and highly correlated, see Figure 19. 


The oscillating trend then commences again with the exception of December which still has a positive coefficient, see Figure 20.



Figure 20


     It is instructive to compare the  overall slope of the meteor temperature effect plotted against month number with the average meteor rate    across the entire period per month.   Both sets of data fit well to a fourth   order polynomial.

The meteor temperature function  can best be described by comparison as being by visual inspection related to the meteor  rate  by ‘corner to corner mirror inversion’, see Figure 21



Figure 21

Of particular relevance is that the positive and negative areas under the ‘slope’ curve appear to cancel each other out.    This is suggestive that at least in the UK in the period considered there is no significant climate warming.   The   approximate midsummer period wherein cooling appears proportionate with meteor input coincides with the summer formation of Polar Mesospheric Cloud (noctilucent cloud) and the peak of the radio Sporadic E propagation season.  


This raises the prospect of an intriguing and exciting climate negative feedback system which could be protecting our planet.  Whether it is the PMCS themselves are that feedback system or merely spectators or Harbingers of it, this is open to question.   PMCS have been reported at mid-latitudes    more recently and with more brightness. The theory is that their water component increases as methane pollution becomes oxidised (see DeLand et al 2003) and can also trap lofted particulate sulphate pollution.  The meteor theory suggests PMCS or some related process cools the planet in June/July.  So presumably if we have more PMCS we have more cooling.  Volcanic eruptions are a source of natural sulphate discussed by Mills and Toon (2005).  They found that the record of the number of NLC sightings in response to large volcanic eruptions is inconsistent. However, injections of water vapor and particles may result in positive, negative or neutral response in the visual brightness of NLC, depending on the magnitude of sulfur, water vapor, and particulate injections.  They also calculated that variation in rates of   meteoric debris should make no more than 8% difference to the total numbers of PMCS.  McKay and Thomas (1982) discuss formation of NLCS by a large stellar impact and the dramatic climate cooling which would follow.  Thomas (1996)   has discussed NLC/PMCS in terms of a ‘miner’s canary for global change’, wherein reduced levels of CO2 and CH4 are found to confine the model NLC existence region to within the perpetually-sunlit polar cap region, where the clouds would no longer be visible to a ground observer. A doubling of CO2 and CH4 could extend the NLC region to mid-latitudes, where they would be observable by a large fraction of the world's population.  Thomas (1995) was one of the first to document that both solar and secular changes effect PMCS.  PMCS scatter solar light at the poles 100% of the time in the PMCS season detectable by satellite and appear to be more dominant and about 1km lower in height in the Northern Hemisphere than the Southern hemisphere, see Perot et al (2010).  Short period AGWS can be detected in PMCS, see Chandran     et al (2012).  Increased aviation and increased use of power systems and renewable energy of all sources of anthropogenic AGWS  which may, conceivably, impact on PMCS in as yet unforeseen ways.  PMCS may   correlate the QBO . Wave forcing of the QBO is certainly thought to come from gravity waves and it is essential to fit these in climate simulation models to produce a QBO, see Mengle et al (1995) and Scaife et al (2000).   Brighter cloud tops can cool the climate by increased albedo effects.  The appearance of PMSC is known to occur on average about 20 days before summer solstice and to extend to about 40-60 days thereafter.  This is roughly the same as a related meteor controlled phenomena of so called sporadic E or Es anomalous radio reflection propagation.  Es  has recently been shown by the present author to be correlated with the QBO (REF) and with earth tides and planetary waves.   The author expects PMSC/NLC to be similarly correlated. QBO is a barometer of both the stratosphere and the ocean atmosphere interaction (Gioergeta et al 1999) and possibly longer term solar effects (Holton 1994).  It may amplify solar cycle influence, Mayr (2006).  A  QBO has recently been shown in both stratospheric Ozone and NO2 (     Zawodny  and  McCormick 1991).   Joseph M. ZawodnyM. Patrick McCormick).   There is now inconvertible evidence of 2 way coupling between the stratosphere and troposphere  via waves, radiation and climate electricity, see for example  Mohanakumar  2008  and Barnes (     ).      Additional NO2 from aviation and deep convection could enter the mesosphere and have effects. 



It is interesting to note that the period in which there appears to be a cooling effect with incoming meteors is also approximately the same  period in which their correlation with rainfall is minimised, see figure 16.  Thus as an alternative to a direct climate cooling effect of PMCS  their changes could be merely indicative of tropospheric changes which have their own   negative feedback proceses which maximise at the same time as PMSC.    One such possibly is aviation and other NOX.   If there is less rain in the PMCS season there is less chance of NOX been washed out.   Lawrence and Crutzen (1999) have discussed NOx  as follows: Emissions of nitrogen oxides (NOx, the sum of NO and NO2) from fossil-fuel burning dominate the NOx burden of the lower troposphere in many regions. These emissions increase tropospheric ozone and hydroxyl-radical concentrations over their natural 'background' levels, thereby increasing the oxidizing power of the atmosphere. Fossil-fuel emissions of NOx account for about half of the global NOx source to the atmosphere; other significant sources are from biomass burning, soil emissions, aircraft exhausts and lightning, all primarily continental. However, ocean-going ships burning fossil fuels may also contribute a significant fraction (>10%) to global NOx production9. Here we use NOx emission data and a high-resolution chemistry–transport model to estimate that ship NOx emissions result in a more than 100-fold increase in surface NOx concentrations in heavily traversed ocean regions. This enhancement has a notable effect on modelled surface ozone and hydroxyl-radical concentrations. In particular, a predicted fivefold increase in the July hydroxyl-radical burden over the northern Atlantic and Pacific oceans would be expected to reduce the atmospheric lifetimes of reactive greenhouse gases—such as methane—as well as to increase aerosol production rates and cloud reflectivities, therefore exerting a cooling influence on the climate.   For instance orthers have shown that aircraft NOx net IRFs are spatially variable, with July values over the remote Pacific approximately balancing the IRF associated with aviation CO2 emissions (28 mWm−2 yr (TgNO2)−1). The overall climate impact of global aviation is often represented by a simple multiplier for CO2 emissions.



Another system which cools the planet and wherein this cooling maximises in general are simple tropospheric clouds.    The NASA Earth Radiation Budget Experiment (ERBE), flying aboard multiple satellites, is providing new insights into the climate system. Monthly averaged clear-sky and cloudy sky flux data derived from the ERBE are used to assess the impact of clouds on the Earth's radiation balance. This impact is examined in terms of three quantities: longwave, shortwave, and net cloud forcing. Overall, clouds appear to cool the Earth-atmosphere system. The global mean cooling varied from 14 to 21 W m−2 between April 1985 and January 1986. Hemispherically, the longwave and shortwave cloud forcing nearly cancel each other in the winter hemisphere, while in the summer the negative shortwave cloud forcing is significantly lower than the longwave cloud forcing, producing a strong cooling.   The UK observations here concur.    The question is how do we square more cloud with less rainfall.  The answer once again is aircraft and aviation cirrus and aerosol.   Henderson and Sellers (1989) have assembled records of mean monthly total cloud amount from 143 locations in North America.. Generally the low and middle latitude station records extend over the period 1900–1984 (U.S.A.) and 1900–1982 (Canada) but few Arctic stations have records before 1930 and some begin recording cloud amount as late as the 1960s. The low and middle latitude station records show a tendency for total cloud amount to increase over this century. Only one of the 77 continental U.S.A. stations does not show an increase. The high latitude stations record increasing total cloud amount in the summer (June, July, August) season but not in the annual mean. The records show the largest increase between about 1930 and 1950. They are temporally consistent but do not exhibit significant spatial coherence. The history of observing and reporting practice has been carefully examined; if any significant effect were to be expected from the changes documented it would be a decrease at the time when the greatest recorded increase occurs. Other factors associated with increased population are possible “explanations”. The most likely cause of increased cloud amount (if the temporal trend is real) is anthropogenerated clouds in the form of jet aircraft condensation trails.



Sassen (1997) produced  evidence indicates that the direct radiative effects of contrails display the potential for regional climate change at many midlatitude locations, even though the sign of the climatic impact may be uncertain. However, new information suggests that the unusually small particles typical of many persisting contrails may favour the albedo cooling over the greenhouse warming effect, depending on such factors as the geographic distribution and patterns in day versus night aircraft usage.     This has been supported recently by Barnes (  ).



The third and final mechanism for global cooling is connected with deep convection.  Wang(  2013)  has noted that the global surface temperature has been relatively flat since about 2000 despite the still rising CO2 concentration in the atmosphere. He further notes this is most puzzling as most climate models predict clear rising temperature trend with increasing CO2. Wang’s theory is based on the dynamic link due to the coupling between the lower stratospheric (LS) water vapor and stratospheric ozone. First of all, time series analysis shows that there is a lagged correlation between the ozone and LS vapor series from 1980 to the present, with the cross-correlation function greater than -0.8 at L = 4. This indicates the possibility of close link between the two variables. The ozone level has been increasing since ~ 1995 and the LS vapor has been decreasing since ~ 1999. At the same time, the global surface temperature is fairly flat and not rising as most models have predicted. The theory he developed shows that the increasing ozone since mid-1990s results in a more stable stratosphere which then suppresses the injection of water vapor into the lower stratosphere by deep convective storms. More and more evidence show that the deep convective storms are likely the major source of water vapor in the LS. Thus when such injection is suppressed, the LS water vapor concentration decreases. The smaller LS vapor concentration allows more surface IR to radiate away and thus is a cooling factor. This cooling factor compensates for the heating factor due to the increasing CO2 level. The net result is that the surface temperature has remained relatively flat since ~ 2000.  Of course expanding and persistent contrails also scavenge water and this has not been considered by Wang but has been remarked on by Barnes elsewhere. 




  1. In the UK in the inter-decadal period 2005-2011 annual rainfall is most strongly correlated with cosmic ray flux. The much higher correlation coefficient for Cosmic Rays is supportive of the notion of a stronger, real physical effect and is also supportive of the work of Svensmark.     Alternatively and/or additionally meteoric debris does provide the nucleation material for rainfall but cosmic rays provide the correct atmospheric electricity conditions, see Tinsley (2000) and Carslaw and Harrison (2002).
  2. Annual temperatures can be correlated with a simple linear algorithm (SFCM) involving cosmic ray flux (C), solar flux (SF) and radio meteor flux (M) according to equation (1). 

Delta Temp = -.707 + 2.916* SFCM …………………………………………………………………(1)

Where SFCM =   {(SF-C) +M}

  1. Sunshine totals are weakly anti-correlated with all three potential extra-terrestrial drivers but strongly correlated according to a cubic function with solar flux.
  2. Average Meteor rate distributions (using the radio forward scatter method) are in approximate agreement with the findings of Keay (1963).
  3.  On a monthly basis, for sunshine, only January is strongly and positively correlated with radio meteor flux.
  4. Radio Meteor rates measured in the UK only seemed to have a very strong further order polynomial correlation with the zonal wind between the months of January and July only.
  5. The slope of the monthly rainfall anomaly percentages divided by meteor rates versus month number can be best fitted to a quadratic equation against meteor rates which shows some symmetry around the summer solstice.  
  6. The slope of rainfall in every subsequent month divided by the prior month’s meteor rate which shows a sensible and finite regression coefficient. When this is done a skewed   distribution is seen in favour of the late autumn months.  This seems to confirm that meteoric material entering the mesosphere at this time of year takes longer to diffuse down through to the stratosphere and troposphere, see Figure 17.  Thus for the UK,   only in the late autumn months is the work of Bowen confirmed. 
  7. For monthly meteor /temperature anomaly effects compared with the LTA,  from January to May the slope of the regression alternates negative to positive on a monthly basis, confirming a  possible lunar influence ( ref needed)
  8. In June and July where the coefficient of temperature with meteor rate is negative, in June it is strongly negative and highly correlated, see Figure 19. 
  9. Of particular relevance is that the positive and negative areas under the ‘slope’ curve appear to cancel each other out.    This is suggestive that at least in the UK in the period considered there is no significant climate warming. 
  10. A negative feedback process is thus indicated possibly with PMSC as either its harbinger or direct cause.
  11. Processes in PMSC will require much more investigation but under some instances lofted pollution could cause cooling.
  12. NOX is a possible contender since the cooling period coincided with lower rainfall.
  13. Aircraft   contrail cloud is another contender discussed by Barnes and others elsewhere.
  14. Smaller LS vapor concentration allows more surface IR to radiate away and thus is a cooling factor. This cooling factor compensates for the heating factor due to the increasing CO2 level.
  15.   Of course expanding and persistent contrails also scavenge water and this has not been considered by Wang but has been remarked on by Barnes elsewhere. 
  16. The Icelandic volcano  Eyjafjallajökul strongly perturbed the results in 2010 .
  17. Extra-terrestrial  inputs and volcanism would appear to remain by far the strongest climate drivers in inteter-decadal times in a polluted 21st Century Atmosphere where mother Earth would still appear to have ‘designer-like’ feedback   mechanisms protecting its inhabitants from enhanced greenhouse gas concentrations.    


Further work


Discuss why radio meteor work is not so easy to do now.