The coming
cooling: usefully accurate climate
forecasting for policy makers.
Norman J. Page
Houston, Texas
Dr. Norman J.
Page
Email: norpag@att.netDOI: 10.1177/0958305X16686488
Energy & Environment
0(0) 1–18
(C )The Author(s) 2017
Reprints
and permissions:
sagepub.co.uk/journalsPermissions.nav
DOI:
10.1177/0958305X16686488
journals.sagepub.com/home/eae
ABSTRACTThis paper argues that the methods used by the establishment climate science community are not fit for purpose and that a new forecasting paradigm should be adopted. Earth's climate is the result of resonances and beats between various quasi-cyclic processes of varying wavelengths. It is not possible to forecast the future unless we have a good understanding of where the earth is in time in relation to the current phases of those different interacting natural quasi periodicities. Evidence is presented specifying the timing and amplitude of the natural 60+/- year and, more importantly, 1,000 year periodicities (observed emergent behaviors) that are so obvious in the temperature record. Data related to the solar climate driver is discussed and the solar cycle 22 low in the neutron count (high solar activity) in 1991 is identified as a solar activity millennial peak and correlated with the millennial peak -inversion point - in the RSS temperature trend in about 2003. The cyclic trends are projected forward and predict a probable general temperature decline in the coming decades and centuries. Estimates of the timing and amplitude of the coming cooling are made. If the real climate outcomes follow a trend which approaches the near term forecasts of this working hypothesis, the divergence between the IPCC forecasts and those projected by this paper will be so large by 2021 as to make the current, supposedly actionable, level of confidence in the IPCC forecasts untenable.
1.
The Problems with the IPCC - GCM Climate Forecasting methods.
Climate forecasts are
made by the IPCC using analytic numerical models called General Circulation Models
(GCMs) which attempt to describe the climate dynamics using sets of
differential equations.
This modelling approach is of limited value for predicting future temperature
with any calculable certainty because of the difficulty of sampling or
specifying the initial conditions of a sufficiently fine grained
spatio-temporal grid of a large number of variables with sufficient precision. In
addition, Essex 2013 (1) proved that models with the number of
variables in the GCMs are simply incomputable.
The
IPCC AR5 WG1 SPM report Fig 5 shows how the models are structured and the
latest IPCC estimates of Radiative Forcing by emissions and drivers (2).
Fig. 1 Radiative Forcing by Emissions and
Drivers Fig SPM-05 (2)
Fig.
1 shows Very High Confidence for the C02 forcing. The SPM (2), page 17, states:
“Greenhouse gases contributed a global mean surface warming likely
to be in the range of 0.5°C to 1.3°C over the period 1951 to 2010, with the
contributions from other anthropogenic forcings, including the cooling effect
of aerosols, likely to be in the range of -0.6°C to 0.1°C. The contribution
from natural forcings is likely to be in the range of -0.1°C to 0.1°C, and from
natural internal variability is likely to be in the range of -0.1°C to 0.1°C.“
Collins et al 2006 (3) discus the implications for interpreting variations in forcing
and response across the multi-model ensemble of coupled
Atmosphere-Ocean General Circulation Models (AOGCMs) used in the IPCC AR4
report. Because of the complexity of the processes included in
these models, it is necessary to parameterize or simplify these processes. The
lack of observational or theoretical constraints has resulted in a diversity of
parameterizations for many forcing components of the climate system. Different
AOGCMs have different atmospheric profiles. The calculations in the Collins
paper omit the effects of stratospheric thermal adjustment to forcing derived
by using fixed dynamical heating. The inter-comparison is based upon calculations
of the instantaneous changes in clear-sky fluxes when concentrations of the well
mixed greenhouse gasses are perturbed. While the relevant quantity for climate
change is all-sky forcing, the introduction of clouds greatly complicates the
inter-comparison exercise and therefore clouds are omitted from Collins RTMIP (The Radiative Transfer Model Inter-comparison Project) study. Collins states: “in many cases, there are substantial discrepancies
among the AOGCMs and between the AOGCMs and LBL codes.” Collins concludes: “The reasonable accuracy of AOGCM forcings at
Top of Model and the significant biases at the surface together imply that the effects
of increased WMGHGs on the radiative convergence of the atmosphere are not
accurately simulated.”
For the atmosphere as a whole therefore
cloud processes, including convection and its interaction with boundary layer
and larger-scale circulation, remain major sources of uncertainty, which propagate
through the coupled climate system.
Various approaches to improve the precision of multi-model
projections have been explored, but there is still no agreed strategy for
weighting the projections from different models based on their historical
performance so that there is no direct means of translating quantitative
measures of past performance into confident statements about fidelity of future
climate projections.The use of a
multi-model ensemble in the IPCC assessment reports is an attempt to
characterize the impact of parameterization uncertainty on climate change
predictions. The shortcomings in
the modeling methods, and in the resulting estimates of confidence levels, make
no allowance for these uncertainties in the models. In fact, the average of a
multi-model ensemble has no physical correlate in the real world.
The
IPCC AR4 SPM report section 8.6 deals with forcing, feedbacks and climate
sensitivity. It recognizes the shortcomings of the models. Section 8.6.4
concludes in paragraph 4 (4): “Moreover
it is not yet clear which tests are critical for constraining the future
projections, consequently a set of model metrics that might be used to narrow
the range of plausible climate change feedbacks and climate sensitivity has yet
to be developed”
What
could be clearer? The IPCC itself said in 2007 that it doesn’t even know what
metrics to put into the models to test their reliability. That is, it doesn’t
know what future temperatures will be and therefore can’t calculate the climate
sensitivity to CO2. This also begs a further question of what erroneous
assumptions (e.g., that CO2 is the main climate driver) went into the
“plausible” models to be tested any way. The IPCC itself has now recognized
this uncertainty in estimating CS – the AR5 SPM says in Footnote 16 page 16 (5): “No best estimate for equilibrium climate
sensitivity can now be given because of a lack of agreement on values across
assessed lines of evidence and studies.” Paradoxically the claim is still made
that the UNFCCC Agenda 21 actions can dial up a desired temperature by
controlling CO2 levels. This is cognitive dissonance so extreme as to be
irrational. There is no empirical evidence which requires that anthropogenic
CO2 has any significant effect on global temperatures.
The
climate model forecasts, on which the entire Catastrophic Anthropogenic Global
Warming meme rests, are structured with no regard to the natural 60+/- year
and, more importantly, 1,000 year periodicities that are so obvious in the
temperature record. The modelers approach is simply a scientific disaster and
lacks even average commonsense. It is exactly like taking the temperature trend
from, say, February to July and projecting it ahead linearly for 20 years beyond
an inversion point. The models are generally back-tuned for less than 150 years
when the relevant time scale is millennial. The radiative forcings shown in Fig. 1 reflect
the past assumptions. The IPCC future temperature
projections depend in addition on the Representative Concentration Pathways (RCPs)
chosen for analysis. The RCPs depend on highly speculative scenarios,
principally population and energy source and price forecasts, dreamt up by
sundry sources. The cost/benefit analysis of actions taken to limit CO2 levels
depends on the discount rate used and allowances made, if any, for the positive
future positive economic effects of CO2 production on agriculture and of fossil
fuel based energy production. The structural uncertainties inherent in this
phase of the temperature projections are clearly so large, especially when
added to the uncertainties of the science already discussed, that the outcomes
provide no basis for action or even rational discussion by government
policymakers. The IPCC range of ECS estimates reflects
merely the predilections of the modellers - a classic case of “Weapons
of Math Destruction” (6).
Harrison
and Stainforth 2009 say (7): “Reductionism
argues that deterministic approaches to science and positivist views of
causation are the appropriate methodologies for exploring complex, multivariate
systems where the behavior of a complex system can be deduced from the
fundamental reductionist understanding. Rather, large complex systems may be
better understood, and perhaps only understood, in terms of observed, emergent
behavior. The practical implication is that there exist system behaviors and
structures that are not amenable to explanation or prediction by reductionist
methodologies. The search for objective
constraints with which to reduce the uncertainty in regional predictions has
proven elusive. The problem of equifinality ……. that different model structures
and different parameter sets of a model can produce similar observed behavior
of the system under study - has rarely been addressed.” A
new forecasting paradigm is required.
2. The Past is the Key to the Present and
Future. Finding then Forecasting the Natural Quasi-Periodicities Governing
Earth’s Climate - a Geological Approach.
2.1 General Principles.
The
core competency in the Geological Sciences is the ability to recognize and
correlate the changing patterns of events in time and space. This requires a
set of skills different from the reductionist and mathematical/statistical approach
to nature, but which is essential for investigating past climates and
forecasting future climate trends. It is necessary to build an understanding of
the patterns and a narrative of general trends from an integrated overview of
the actual individual local and regional time series of particular variables.
Earth’s climate is the result of resonances and beats between various
quasi-cyclic processes of varying wavelengths. It is not possible to forecast
the future unless we have a good empirical understanding of where the earth is
in time in relation to the current phases of those different interacting
natural quasi periodicities which include the principal components of the
observed emergent phenomena. When analyzing or comparing data time series geologists refer to
a stratigraphic
unit that serves as the standard of reference as a “type section”. In
climatology it is useful when illustrating hypotheses to talk in terms of “type
reconstructions”. Mann’s “Hockey Stick” is the iconic example. It
is necessary also to be cognizant of the fact that the emergent time series
will reflect turning points and threshold effects in the underlying physical
process interactions. Such turning points mark the major inflection points in
temperature and solar activity time series and serve as geologists would say as
“golden spikes” when analyzing and forecasting temperature and solar activity trends.
2.2 The
Present Warming in Relation to the Milankovitch and Millennial Cycles
Fig. 2 shows that Earth is past the warm peak
of the current Milankovitch interglacial and has been generally cooling for the
last 3,500 years.
Fig. 2 Greenland Ice core derived temperatures and CO2 from Humlum 2016 (8)
The millennial cycle peaks are obvious at
about 10,000, 9,000, 8,000, 7,000, 2,000, and 1,000 years before now as seen in
Fig. 2 (8) and at about 990 AD in Fig. 3 (9). It should be noted that
those believing that CO2 is the main driver should recognize that Fig. 2 would
indicate that from 8,000 to the Little Ice Age CO2 must have been acting as a coolant.
The following papers trace
the progressive development of the most relevant
reconstructions starting with the hockey stick: Mann et al 1999. Fig. 3 (10), Esper et al 2002
Fig. 3 (11), Mann’s later changes - Mann et al 2008 Fig. 3 (12), and Mann et al
2009 Fig. 1 (13). The
later 2012 Christiansen and Ljungqvist temperature time series of Fig. 3 is
here proposed as the most useful “type reconstruction” as a basis for climate
change discussion. For real world local climate impact estimates,
Fig 3 shows that the extremes of variability or the data envelopes are of more
significance than averages. Note also that the overall curve is not a simple
sine curve. The down trend is about 650 years and the uptrend about 364 years.
Forward projections made by mathematical curve fitting have no necessary
connection to reality, particularly if turning points picked from empirical data are
ignored.
Fig 4. RSS trends showing the millennial cycle
temperature peak at about 2003 (14)
Figure
4 illustrates the working hypothesis that for this RSS time series the peak of
the Millennial cycle, a very important “golden spike”, can be designated at
2003.
The
Hadcrut 4gl data trends are very similar to the UAH data trends with the
millennial peak at 2005.3 in Fig. 5 (15).
The
RSS cooling trend in Fig. 4 and the Hadcrut4gl cooling in Fig. 5 were truncated
at 2015.3 and 2014.2, respectively, because it makes no sense to start or end
the analysis of a time series in the middle of major ENSO events which create
ephemeral deviations from the longer term trends. By the end of August 2016, the
strong El Nino temperature anomaly had declined rapidly. The cooling trend is
likely to be fully restored by the end of 2019.
From
Figures 3 and 4 the period of the latest Millennial cycle is from 990 to 2003 -
1,013 years. This is remarkably consistent with the 1,024-year periodicity seen
in the solar activity wavelet analysis in Fig. 4 from Steinhilber et al 2012 (16).Fairbridge
and Sanders 1987 (17) p 452 provide the commensurability relationships of
planetary and lap periodicities as a basis for future analysis of the sun-climate
connection. Their reported Uranus Saturn Jupiter Lap time periodicity of 953
years is pertinent. here. Scafetta
2013 (18) compares the GCMs with a semi-empirical harmonic climate model based chiefly
on astronomical oscillations. The model is constructed from six astronomically deduced
harmonics with periods of 9.1, 10.4, 20, 60, 115 and 983 years. Scafetta’s
abstract also states: “In particular,
from 2000 to 2013.5 a Global Surface Temperature plateau is observed while the
GCMs predicted a warming rate of about 2 C/century. In contrast, the hypothesis
that the climate is regulated by specific natural oscillations more accurately fits
the GST records at multiple time scales.”
2.3 The Quasi-Millennial Temperature Cycle –
Amplitude.
An
estimate of the average amplitude of the NH temperature Millennial cycle can be
made from the 50-year moving average curve (red) of Fig. 3 above. It is about
1.7 degrees C from the 990 peak to the LIA minimum at about 1640. This is entirely
consistent with the Northern Hemisphere estimates of Shindell (21), and with
the Arctic amplitude reported by Mckay et al 2014 (22).
(a) Reconstruction
calculated using the original (black) and updated database presented here
(red). (b) Scatter plot illustrating the influence of the revisions; 1:1 line
shown in red. (c) Time-series of the differences in reconstructed temperature
(revised—original); no change shown as red line. (d) Comparison between Kaufman
et al. 7 Arctic—wide temperature reconstruction and the revised PAGES 2k Arctic
reconstruction (averaged to decadal values). Note the factor-of-two difference
in the temperature scales.
It is
important to note that in Fig. 7d the end 20th century warming peaks
at about the same temperature as the MWP peak at about 1,000 AD, contrary to
the expectations of the CAGW establishment.
2.4 The sixty year +/- cycle
Over
the last 135 years an approximate 60 year periodicity is clearly present in the
temperature data.
Fig. 8
HadSST3 Temperature Anomaly (23)
The global
SST data shows cooling from 1880 to 1910, warming from 1910 to 1944, cooling
from 1944 to 1974, warming from 1974 to 2004 and
cooling since then. This 60-year +/- periodicity in Fig. 8 modulates the underlying
longer wave 1,000-year periodicity seen in Figs. 3, 6 and 7 above by about 0.5
degrees per 60-year cycle. This 60-year cycle is also well documented in Figs.1
and 3 in the Scaffetta paper referenced previously (18) and in Fig. 2a and b Gervais 2016 (24), which paper also suggests a
TCR of 0.6 and questions the entire dangerous warming paradigm.
2.5
The Solar Driver.
The
most useful proxies for solar “activity” are the 10Be data and the Neutron
count. The general increase in solar activity since the Little Ice Age is
obvious in the decrease in the NGRIP and Dye -3 ice core 10 Be flux data
between about 1700 and the late twentieth century.
Fig. 9 Berggren et al 2009 A 600-year annual 10Be record from the NGRIP ice core
Greenland (25)
Steinhilber Figure 3 BCD
(26) shows the correlation of the various climate minima within the last 1,000
years to 10Be cosmic ray intensities. Temperature
drives CO2, water vapor concentrations, and evaporative and convective cooling
independently. The whole CAGW - GHG scare is based on the obvious fallacy of
putting the effect before the cause. Unless the range and causes of natural
variation, as seen in the natural temperature quasi-periodicities, are known
within reasonably narrow limits it is simply not possible to even begin to estimate
the effect of anthropogenic CO2 on climate. Given the lack of any empirical CO2-climate connection
reviewed earlier and the inverse relationship between CO2 and temperature seen
in Fig. 2, and for the years 2003– 2015.3 in Fig. 4, during which CO2 rose 20
ppm, the simplest and
most rational working hypothesis is that the solar “activity” increase is the
chief driver of the global temperature increase since the LIA.
Based on Fig. 9 and the Oulu neutron count in Fig. 10 (27) and the
evidence for the temperature peak from Figures 3, 4, and 5, it is reasonable to
conclude that the solar activity millennial maximum peaked with a solar
activity “Golden Spike” in Cycle 22 at about 1991.
Fig. 10 Oulu Neutron Monitor data (27)
The
connection between solar “activity” and climate is poorly understood and highly
controversial. Solar “activity” encompasses changes in solar magnetic field
strength, IMF, GCRs, TSI, EUV, solar wind density and velocity, CMEs, proton
events, etc. The idea of using the neutron count and the 10Be record as the
most useful proxy for changing solar activity and temperature forecasting is
agnostic as to the physical mechanisms involved. Having said that, however, it
seems likely that the three main solar activity related climate drivers are the
changing GCR flux - via the changes in cloud cover and natural aerosols
(optical depth), the changing EUV radiation producing top down effects via the
Ozone layer, and the changing TSI - especially on millennial and centennial
scales. The effect on observed emergent behaviors i.e. global temperature
trends of the combination of these solar drivers will vary non-linearly
depending on the particular phases of the eccentricity, obliquity and
precession orbital cycles at any particular time convolved with the phases of
the millennial, centennial and decadal solar activity cycles and changes in the
earth’s magnetic field. Because of the thermal
inertia of the oceans there is a varying lag between the solar activity peak and
the corresponding peak in the different climate metrics. There is a 13+/- year
delay between the solar activity “Golden Spike” 1991 peak and the millennial
cyclic “Golden Spike” temperature peak seen in the RSS data at 2003 in Fig. 4.
It has been independently estimated that there is about a 12-year lag between
the cosmic ray flux and the temperature data - Fig. 3 in Usoskin (28).
Fig.11
Tropical cloud cover and global
air temperature (29)
The
global millennial temperature rising trend seen in Fig11 (29) from 1984 to the
peak and trend inversion point in the Hadcrut3 data at 2003/4 is the inverse
correlative of the Tropical Cloud Cover fall from 1984 to the Millennial trend
change at 2002. The lags in these trends
from the solar activity peak at 1991-Fig 10 - are 12 and 11 years respectively.
These correlations suggest possible teleconnections between the GCR flux,
clouds and global temperatures.
By
contrast, the lag between the solar activity peak at 1991 and the Arctic sea ice
volume minimum is 21 years (30). It is simple and natural to correlate the cycle
22 low in the neutron count (high solar activity) in 1991 with the millennial temperature
peak and trend inversion in the RSS in 2003 with the solar activity 1991 Golden
Spike, and to project forward a probable general temperature decline for the
coming decades and centuries. Lags differ between data sets because of the real
geographical area differences, proxy data point selection differences and
instrumental differences between different proxy time series.
3. Future Temperature Trends
To summarize, the forecasts which follow rely on four basic working
hypotheses. First, the solar millennial activity cycle peaked in 1991+/- as
seen in Fig 10 in the Oulu neutron count. Second, the corresponding millennial
temperature cycle peaked in the RSS data at about 2003-Fig. 4.Third, the 60
year temperature cycle peaked at about the same time and fourth, Ockham’s razor
would suggest that the simplest working hypothesis currently available, based
on the weight of all the data, is that the trends from the 990 Millennial peak to
the 2003 Millennial cycle peak seen in Figs 3 and 4 will, in general, repeat
from 2003 to 3004.
3.1 Long Term.
The depths of the next LIA will likely occur about 2640 +/-.
In the real world no pattern repeats exactly because other things are never
equal. Look for example at the short-term annual variability about the 50-year
moving average in Fig. 3. The actual future pattern will incorporate other
solar periodicities in addition to the 60-year and millennial cycles, and will also
reflect extraneous events such as volcanism. However, these two most obvious
cycles should capture the principal components of the general trends with an
accuracy high enough, and probability likely enough, to guide policy. Forward projections made by mathematical curve fitting alone
have no necessary connection to reality if turning points picked from empirical
data in Figs 4 and 10 are ignored.
3.2 Medium Term Forecast to 2100
Fig.
12. Comparative Temperature Forecasts to 2100.
Fig. 12 compares the IPCC forecast with the Akasofu (31) forecast
(red harmonic) and with the simple and most reasonable working hypothesis of
this paper (green line) that the “Golden Spike” temperature peak at about 2003
is the most recent peak in the millennial cycle. Akasofu forecasts a further
temperature
increase to 2100 to be 0.5°C ± 0.2C, rather than 4.0 C +/- 2.0C predicted by the
IPCC. but this interpretation ignores the Millennial inflexion
point at 2004. Fig. 12 shows that the well documented 60-year temperature cycle
coincidentally also peaks at about 2003.Looking at the shorter 60+/- year
wavelength modulation of the millennial trend, the most straightforward
hypothesis is that the cooling trends from 2003 forward will simply be a mirror
image of the recent rising trends. This is illustrated by the green curve in
Fig. 12, which shows cooling until 2038, slight warming to 2073 and then cooling
to the end of the century, by which time almost all of the 20th century warming
will have been reversed. Easterbrook 2015 (32) based his 2100 forecasts on the
warming/cooling, mainly PDO, cycles of the last century. These are similar to Akasofu’s
because Easterbrook’s Fig 5 also fails to recognize the 2004 Millennial peak
and inversion. Scaffetta’s 2000-2100 projected warming forecast (18)
ranged between 0.3 C and 1.6 C which is significantly lower than the IPCC GCM ensemble
mean projected warming of 1.1C to 4.1 C. The difference between Scaffetta’s
paper and the current paper is that his Fig.30 B also ignores the Millennial
temperature trend inversion here picked at 2003 and he allows for the
possibility of a more significant anthropogenic CO2 warming contribution.
3.3 Current Trends
The cooling trend from the Millennial peak at 2003 is
illustrated in blue in Fig. 4. From 2015 on, the decadal cooling trend is temporally
obscured in the RSS temperature data by the recent El Nino. The El Nino peaked
in February 2016. Thereafter to the end of 2019 we might reasonably expect a
cooling at least as great as that seen during the 1998 El Nino decline in Fig.
4, or about 0.9 C. It is worth noting that the increase in the neutron count in
2007-9 seen in Fig. 10 indicates a possible solar regime change, which might
produce an unexpectedly sharp decline in RSS temperatures 12 years later from 2019-21
to levels significantly below the blue cooling trend line in Figs. 4 and 5. This
suggestion was also made in Easterbrook’s conclusions. (32)
4. Conclusions.
Establishment climate model forecast outcomes included two
serious errors of scientific judgment in the method of approach to climate
forecasting and thus in the subsequent advice to policy makers in successive
SPMs. First, as previously discussed, the analyses were based on inherently
untestable, incomputable and specifically structurally flawed models, which
included many unlikely assumptions. Second, the natural solar-driven,
millennial and multi-decadal cycles plainly visible in the data were totally
ignored. Unless we know where the earth is with regard to, and then
incorporate, the phase of the millennial and 60-year cycles in particular,
useful forecasting is simply impossible. I would, in contrast, contend that by
adopting the appropriate time scale and method for analysis, a commonsense
working hypothesis with sufficient likely accuracy and chances of success to
guide policy has been formulated here. The UNEP, IPCC and UNFCCC rely heavily
on the “precautionary principle” to motivate their agendas and action plans. The
working hypothesis proposed here provides a broad overview of future climate
trends for the N H This could be the basis for a more realistic and useful
application of the principle. In reality, there is very substantial climate variability
between the earth’s different geographical regions. It would be prudent to
designate regional Type Reconstructions and Solar and temperature Golden Spikes
and then build regional narratives of climate trends for the past 2000 years. In order to increase the accuracy, precision and
practical value of forecasts, the earth might then be usefully divided up into
the following climate “Plates”:
1 Northern Hemisphere
2 Southern Hemisphere
3 East Pacific -North America –W
Atlantic
4 East Atlantic - Western Europe
5 Russia
6 China
7 India and SE Asia
8 Australasia and Indonesia
9 South America
10 N. Africa
11 Sub-Saharan Africa
12 The Arctic
13 The Antarctic
14 The Intra-tropical Pacific Ocean (detailed analysis of the energy
exchanges and processes at the ocean /atmosphere interface in this area is
especially vital because its energy budget provides the key to the earth’s
thermostat)
If the real climate
outcomes follow trends which even approach the near-term forecasts in paragraph
3.3 above, the divergence between the IPCC forecasts and those projected by
this paper in Fig. 12(green line) will be so large by 2021 as to make the current
confidence level in the establishment IPCC forecasts untenable. The
economically destructive counterproductive climate and energy policies
associated with such forecasts will be seen to be scientifically and publically
insupportable. In the Novum Organum (32), Francis Bacon classified the
intellectual fallacies of his time under four headings which he called idols.
The fourth of these were described as: “Idols
of the Theater are those which are due to sophistry and false learning. These
idols are built up in the field of theology, philosophy, and science, and
because they are defended by learned groups are accepted without question by
the masses. When false philosophies have been cultivated and have attained a
wide sphere of dominion in the world of the intellect they are no longer
questioned. False superstructures are raised on false foundations, and in the
end systems barren of merit parade their grandeur on the stage of the world.”
Acknowledgements
The author would like to acknowledge all those in the climate science community who have contributed to the massive accumulation of the basic instrumental and proxy climate data that has taken place in the last thirty years, without which empirical climate science would have no foundation. I also appreciate the very apposite comments and suggestions made by one of the anonymous reviewers and the assistance of my wife Hilary in the adaptation of a number of the figures for the Journal publication.
References
| 1. Essex. Believing six impossible things before breakfast, climate models, www.youtube.com/watch?v=hvhipLNeda4 (2013, accessed 16 December 2016). | |
| 2 . | IPCC. Summary for policymakers. p. 12 [Figure 5]. In: Stocker TF, Qin D, Plattner G-K, et al (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change]. Cambridge: Cambridge University Press, 2013. |
| 3. | Collins WD, Ramaswamy V, Schwarzkopf MD, . Radiative forcing by well-mixed greenhouse gases: estimates from climate models in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). J Geophys Res 2006; 111: D14317. Google Scholar CrossRef |
| 4. | Solomon S, Qin D, Manning Z, et al (eds). Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press, 2007, para 8.6.4. |
| 5. | IPCC. Summary for policy makers [note 16, p.16]. In: Stocker TF, Qin D, Plattner G-K, et al. (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press, 2013. |
| 6. | O’Neil C. Weapons of math destruction. 1st ed. New York: Crown Publishers, 2016, p. 3, 12. |
| 7. | Harrison S and Stainforth D. Predicting climate change, p. 111, http://onlinelibrary.wiley.com/doi/10.1029/2009EO130004/pdf (2009, accessed 16 December 2016). |
| 8. | Humlum O. An overview to get things into perspective, Figure 3, www.climate4you.com/ (2016, accessed 16 December 2016). |
| 9. | Christiansen B and Ljungqvist FC. Clim Past 2012; 8: 765–786, www.clim-past.net/8/765/2012/ (accessed 16 December 2016). |
| 10. | Mann ME, Bradley RS, Hughes MK. Northern hemisphere temperatures during the past millennium: inferences, uncertainties, and limitations [Figure 3, p. 361]. Geophys Res Lett 1999; 26: 759–762, www.meteo.psu.edu/holocene/public_html/shared/articles/MBH1999.pdf. |
| 11. | Esper J, Cook ER, Schweingruber FH. [Figure 3] Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability. Science 2002; 295: 2252–2253, www.sciencemag.org. |
| 12. | Mann ME, Zhang Z, Hughes MK, . Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia. [Figure 3]. Proc Natl Acad Sci U S A 2008; 105: 13252–13257. Google Scholar CrossRef, Medline |
| 13. | Mann ME, Zhang S, Rutherford S, et al. Global signatures and dynamical origins of the little ice age and medieval climate anomaly [Figure 1, p. 1257]. Science 2009; 326: www.meteo.psu.edu/holocene/public_html/shared/articles/MannetalScience09.pdf. |
| 14. | RSS data. using Wood for Trees, ). |
| 15. | Hadcrut4gl.1980 – Present, using Wood for Trees, www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.5.0.0.monthly_ns_avg.txt (accessed 16 December 2016). |
| 16. | Steinhilber F, Abreu JA, Beer J, et al. 9,400 years of cosmic radiation and solar activity [Figure 4], https://epic.awi.de/30297/1/PNAS-2012-Steinhilber-1118965109.pdf at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1118965109/-/DCSupplemental (2012 accessed 16 December 2016). |
| 17. | Rampino M, Sanders JE, Newman WS, et al. Long range forecasts of solar cycles and climate change [chapter 25]. In: Landscheit T (ed.) and The suns orbit 750–2050 [Chapter 26]. In: Fairbridge R (ed.). New York: Climate. Von Nostrand Rheinhold Co., 1987, pp. 421–541. |
| 18. | Scafetta N. Discussion on climate oscillations: CMIP5 general circulation models versus a semi-empirical harmonic model based on astronomical cycles. Earth Sci Rev 2013; 126: 321–357. Google Scholar CrossRef |
| 19. | Kern AK, Harzhauser M, Piller WE, et al. Strong evidence for the influence of solar cycles on a Late Miocene lake system revealed by biotic and abiotic proxies, 2012, pp. 124–136, http://dx.doi.org/10.1016/j.palaeo.2012.02.023. |
| 20. | Scaffetta N, Milani F, Bianchini A, . On the astronomical origin of the Hallstatt oscillation found in radiocarbon and climate records throughout the Holocene. Earth Sci Rev 2016; 162: 24–43. Google Scholar CrossRef |
| 21. | Shindell DT, Schmidt GA, Mann ME, . Solar forcing of regional climate change during the maunder minimum. Science 2001; 294: 2149–2152. Google Scholar CrossRef, Medline |
| 22. | McKay NP and Kaufman DS. An extended Arctic proxy temperature database for the past 2,000 years, www.nature.com/articles/sdata201426, http://dx.doi.org/10.1038/sdata.2014.26 (2014, accessed 16 December 2016). |
| 23. | Had SST 3 data, http://www.cru.uea.ac.uk/cru/data/temperature/HadSST3.pdf (accessed 16 December 2016). |
| 24. | Gervais F. Anthropogenic CO2 warming challenged by 60-year cycle. Earth Sci Rev 2016; 155: 129–135. Google Scholar CrossRef |
| 25. | Berggren AM, Beer J, Possnert G, et al. A 600-year annual 10Be record from the NGRIP ice core Greenland, onlinelibrary.wiley.com/doi/10.1029/2009GL038004/full (2009, accessed 16 December 2016). |
| 26. | Steinhilber F, Abreu JA, Beer J, et al. 9,400 years of cosmic radiation and solar activity from ice cores and tree rings, www.pnas.org/cgi/doi/10.1073/pnas.1118965109 (2012, accessed 16 December 2016). |
| 27. | Oulu Neutron Monitor data, http://cosmicrays.oulu.fi/#database (accessed 16 December 2016). |
| 28. | Usoskin I, Schussler M, Solanki SK, et al. Solar activity over the last 1150 years, https://www.researchgate.net/publication/41624745_Solar_activity_over_the_last_1150_years (2005, accessed 16 December 2016). |
| 29. | Humlum O. Climate4you graph. Tropical cloud cover and global air temperature, www.climate4you.com/ (2011, accessed 16 December 2016). |
| 30. | Polar Science Center: Arctic sea ice volume and trend from PIOMAS, http://psc.apl.washington.edu/wordpress/wp-content/uploads/schweiger/ice_volume/BPIOMASIceVolumeAnomalyCurrentV2.1.png (accessed 16 December 2016). |
| 31. | Akasofu S. On the recovery from the little ice age. Nat Sci 2010; 2: 1211–1224. http://dx.doi.org/10.4236/ns.210.11149. Google Scholar |
| 32. | Easterbrook. Evidence for predicting global cooling for the next three decades, www.globalresearch.ca/global-cooling-is-here/10783 (2015, accessed 16 December 2016). |
| 33. | The four idols of Francis Bacon, www.sirbacon.org/links/4idols.html (accessed 16 December 2016 |
