What will happen during a new Maunder Minimum?


The Ministry of Infrastructure and the Environment stopped financing Climate Dialogue, at which point both PBL Netherlands Environmental Assessment Agency and the Royal Netherlands Meteorological Institute (KNMI) stopped their involvement in this project. The blog ClimateDialogue will remain online until 1 January 2016 and Marcel Crok will take on the responsibility for its continuation. Neither PBL nor KNMI will be responsible for any text written after 1 January 2015.


According to the latest IPCC report, AR5, the influence of the sun on our climate since pre-industrial times, in terms of radiative forcing, is very small compared to the effect of greenhouse gases.

According to some more skeptical scientists such a small solar influence is counterintuitive. The Little Ice Age, the period roughly from 1350 to 1850, in which winters on the Northern Hemisphere could be severe and glaciers advanced, coincided with the so-called Maunder Minimum, a period of supposedly low solar activity. In their eyes, the sun therefore still is a serious candidate to also explain a substantial part of the warming since pre-industrial times.

Sunspot records since 1600 suggest there has been a considerable increase in solar activity in the 20th century leading to a Grand Solar Maximum or Modern Maximum. However recently these sunspot records have come under increasing scrutiny and newer reconstructions show a much ‘flatter’ sunspot history. This challenges the idea of a Modern Maximum.

The current solar cycle 24 is the lowest sunspot cycle in 100 years and the third in a trend of diminishing sunspot cycles. Solar physicists expect cycle 25 to be even smaller than Cycle 24 and expect the sun to move into a new minimum, comparable with the Dalton or even the Maunder Minimum. Studying such a minimum with modern instruments could potentially answer a lot of the questions surrounding the influence of the sun on our climate.

We are very pleased that no fewer than five (solar) scientists have agreed to participate in this exciting new Climate Dialogue: Mike Lockwood (UK), Nicola Scafetta (US), Jan-Erik Solheim (NO), José Vaquero (ES) and Ilya Usoskin (FI).

The introduction and guest posts can be read online below. For convenience we also provide pdf’s:

Introduction What will happen during a new Maunder Minimum
Guest blog Mike Lockwood
Guest blog Nicola Scafetta
Guest blog Jan-Erik Solheim
Guest blog Ilya Usoskin
Guest blog José Vaquero

Introduction What will happen during a new Maunder Minimum?

What will happen during a new Maunder Minimum?

According to the latest IPCC report, AR5, the influence of the sun on our climate since pre-industrial times, in terms of radiative forcing, is very small compared to the effect of greenhouse gases:

Figure 1: Radiative forcing estimates in 2011 relative to 1750 and aggregated uncertainties for the main drivers of climate change. Replicated from figure SPM.5 in AR5.

The estimated increase in radiative forcing due to the sun since 1750 is only 0.05 W/m2 compared to a total increase that is mainly caused by greenhouse gases of 2.29 W/m2. This almost negligible influence is even smaller than the estimation in the fourth assessment report which was 0.12 W/m2 on a total of 1.66 W/m2. The reduction since AR4 has partly to do with the decreased solar activity in the current solar cycle 24.

Such a small solar influence might seem counterintuitive. The Little Ice Age, the period roughly from 1350 to 1850, in which especially winters on the Northern Hemisphere were severe, coincided with several solar minima (Wolf, Spörer, Maunder and Dalton Minimum, see figure 2).

Figure 2, replicated from Feynman (2007)[i]. The radiocarbon proxy for the level of solar variability (Stuiver and Braziunas, 1988)[ii]. Higher Δ14C corresponds to lower solar activity. The Δ14C, which can be interpreted as roughly inversely proportional to the decadal running average of solar activity, changes on time scales of centuries. Several distinct periods when the solar activity was low (Δ14C high) have been given the names shown on the figure. Note the repeated periods of low solar activity between 1300 AD and 1750 AD, i.e. during the Wolf, Spoerer, and Maunder Minimums. The Maunder Minimum corresponds to the period 1645–1715 when there were almost no sunspots (Eddy, 1976)[iii].

Figure 2 also suggests that during the so-called Medieval Warm Period (the IPCC in AR5 uses the term Medieval Climate Anomaly) solar activity was relatively high. How cold it was globally in the Little Ice Age and how warm it was in the Medieval Warm Period is itself a matter of debate.

AR5 has this to say about it:

New paleoclimate reconstruction efforts since AR4 (Figure 5.7; Table 5.4; Appendix 5.A.1) have provided further insights into the characteristics of the Medieval Climate Anomaly (MCA; Table 5.1) and the Little Ice Age (LIA; Table 5.1). The timing and spatial structure of the MCA and LIA are complex (see Box 6.4 in AR4 and Diaz et al., 2011; and Section 5.5), with different reconstructions exhibiting warm and cold conditions at different times for different regions and seasons. The median of the NH temperature reconstructions (Figure 5.7) indicates mostly warm conditions from about 950 to about 1250 and colder conditions from about 1450 to about 1850; these time intervals are chosen here to represent the MCA and the LIA, respectively.

Below we show figure 5.7a from AR5 which presents many different temperature reconstructions for the Northern Hemisphere. These reconstructions are based on many different proxies (tree rings, corals, ice cores, ocean/lake sediments etc.). Proxy data are scarce in the Southern Hemisphere and therefore most reconstructions focus on the Northern Hemisphere. Some reconstructions show quite a large temperature difference between the Medieval Warm Period and the Little Ice Age of almost two degrees Celsius. Other reconstructions are much flatter.

Figure 3: Reconstructed (a) Northern Hemisphere annual temperatures during the last 2000 years. Replication of figure 5.7a from AR5. Individual reconstructions (see Appendix 5.A.1 in AR5 for further information about each one) are shown as indicated in the legends, grouped by colour according to their spatial representation (red: land-only all latitudes; orange: land-only extra-tropical latitudes; light blue: land and sea extra-tropical latitudes; dark blue: land and sea all latitudes) and instrumental temperatures shown in black (Hadley Centre/ Climatic Research Unit (CRU) gridded surface temperature-4 data set (HadCRUT4) land and sea, and CRU Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over Land version 4 (CRUTEM4) land-only; Morice et al., 2012). All series represent anomalies (°C) from the 1881–1980 mean (horizontal dashed line) and have been smoothed with a filter that reduces variations on time scales less than about 50 years.

Total Solar Irradiance
Direct temperature measurements are available since 1850 or so. However, direct measurements of the sun by satellite started only in 1978. There is still considerable debate about the exact absolute values of the Total Solar Irradiance (TSI) in this period. The difficulty is that one has to merge data from different satellites together. The most recent SORCE/TIM measurements are probably our best shot and the background level of TSI therefore seems to be around 1361 W/m2 .

There are three groups (PMOD, ACRIM and RMIB) in the world who prepared a composite from all the TSI data. AR5 presented their results together with the more recent TIM measurements:

Figure 4: direct TSI measurements by satellite since 1978. Replication of AR5 figure 8.10.

The sun goes through cycles of approximately 11 years in which the activity goes up and down. The differences in TSI between the maxima and minima are very small however, on the order of 0.1%. Two of the datasets (PMOD and ACRIM) suggest that the most recent solar minimum in 2008 (the start of solar cycle 24) was somewhat lower than the TSI during the two preceding minima. In Box 10.2 of AR5 the IPCC concludes that “it is very likely that there has been a small decrease in solar forcing of –0.04 [–0.08 to 0.00] W/m2 over a period with direct satellite measurements of solar output from 1986 to 2008” and therefore that “there is high confidence that changes in total solar irradiance have not contributed to global warming during that period”.

TSI Reconstructions
Before 1978 there are no direct measurements and scientists have to rely on proxies for solar activity. Ultimately reconstructions of the TSI hundreds of years back in time always have to rely on either the sunspot record or on cosmogenic isotopes like Beryllium-10 and Carbon-14.[iv] Chapter 5 of the AR5 report presents the following figure with TSI reconstructions for the last millennium:

Figure 5: changes in TSI during the last millennium. Replication of figure 5.1b in AR5. The blue reconstruction is one published by Lean et al. 1995.[v] Since the 90-ies reconstructions of the TSI have become considerably flatter suggesting that the influence of the sun through time is relatively small. See AR5 for all the references.

Hoyt and Schatten (1994)[vi], based on the so-called group sunspot number, estimated the increase in TSI since the Maunder Minimum to be around 4 W/m2 which translates into an increase in radiative forcing of around 0.7 W/m2,[vii] much larger than the increase in AR5. Lean et al 1995 (the blue curve in figure 5 above) also suggests a difference in TSI of several W/m2 since 1700. However more recent TSI reconstructions found a much smaller difference in TSI between the Maunder Minimum and the present (see figure 5).

Shapiro (2011)[viii] using a new approach found a much larger difference in TSI between the Maunder Minimum and present, of about 6 W/m2. They assume that the minimum state of the quiet sun (say during a Maunder Minimum) corresponds to the observed quietest area on the present sun.

Figure 6, replicated from Shapiro (2011). The red reconstruction is based on 10Be isotopes from an ice core drilled on the South Pole, the cyan curve is based on Greenland 10Be measurements.

AR5 did not show the above reconstruction of Shapiro, but it discussed the paper shortly in chapter 5, saying:

The larger range of past TSI variability in Shapiro et al. (2011) is not supported by studies of magnetic field indicators that suggest smaller changes over the 19th and 20th centuries (Svalgaard and Cliver, 2010[ix]; Lockwood and Owens, 2011[x]).

Schrijver (2011)[xi], not shown in the above figure either, concluded that the very low solar activity in 2008-2009, when the sun went into a long ‘sleep’ could be representative for earlier minima like the Maunder Minimum. They hypothesize therefore that the TSI during the Maunder Minimum was similar to the TSI in 2008-2009.

If indeed TSI during the Maunder Minimum was similar to levels today this raises lots of new interesting questions. If TSI can’t explain the Little Ice Age, what else can? Some scientists think volcanism played a role. Or maybe the Little Ice Age was not a global phenomenon and therefore globally it wasn’t that much colder so there is little need for other climate influences?

Sunspots are probably the most well-known proxy for the sun. Sunspots - darks spots on the sun caused by intense magnetic activity - are counted since 1610 (see figure 3 below). Although the dark sunspots are cooler areas at the surface of the sun, the surrounding margins of sunspots are brighter than the average. Overall, more sunspots increase the Sun's solar brightness. Sunspots were rarely observed during the Maunder Minimum in the second part of the 17th century (approximately from 1645 to 1715) for reasons which are not yet fully understood.


Figure 7: 400 years of direct sunspot observations. Source: Wikimedia Commons/Global Warming Art.

Sunspot numbers rise and fall on an irregular cycle of 11 years. The 11-year solar cycles are numbered sequentially, starting with the observations made in the 1750s. We are currently at or near the maximum of solar cycle 24.

Figure 8: Sunspot numbers during solar cycles 23 and 24, updated in May 2014. Source: David Hathaway, Nasa. http://solarscience.msfc.nasa.gov/predict.shtml

Several papers in the last decade have claimed that solar activity in the second part of the 20th century was higher than any time in the past 10.000 years.[xii][xiii] Some solar physicists call the recent period of high solar activity the Grand Solar Maximum or the Modern Maximum. This would suggest that at least part of the warming since pre-industrial times could be attributed to increased solar activity.

However in recent years the concept of the Grand Solar Maximum has come under increasing scrutiny. Two methods for counting sunspots have been developed, the group sunspot number and the Wolf sunspot number. On top of that there are inhomogeneities in the series. A team of solar scientists led by Leif Svalgaard, organised a series of workshops, to correct the sunspot record.[xiv] Their corrected figure (the yellow line in figure 5 below) suggests there was no Grand Solar Maximum in the second part of the 20th century. However, before 1700 direct sunspot observations are not very reliable so it remains to be seen how many sunspots there were during the Maunder Minimum.

Figure 9: The group sunspot numbers (in blue) suggest a steady increase in solar activity since the Maunder Minimum. The corrected Wolf sunspot numbers however show periods of high solar activity in the 18th and 19th century that are similar to what is called the Modern Maximum. Source: Brian Owens, Spot of bother: have we been getting solar activity wrong?, New Scientist, 13 September 2013

To conclude, both temperature and TSI reconstructions of the past millennium are still pretty uncertain. In very general terms one could say that the amplitude of temperature reconstructions has become larger in recent studies while the amplitude of solar reconstructions has become smaller. This does not plea for a large solar contribution on the climate. Still the sun is a popular alternative for CO2 amongst climate sceptics. In this Climate Dialogue we want to explore why some climate scientists are still in favour of a large solar influence on the climate, even in the 20th century.

Amplification mechanisms
Studies that conclude a possible large solar effect on the climate, are often based on the suggestion there are large correlations between solar proxies and climate in the past[xv][xvi], which implies that some amplification mechanism must be in play to explain how relatively small changes in the solar output can have relatively large effects on the climate. Shaviv (2008)[xvii] found that in the last 50 years the oceans have stored 5-7 times more solar energy during a solar cycle than the radiative forcing implies.

Several mechanisms for solar amplification have been proposed. The most famous one is the cosmic rays hypothesis which assumes that variations in cosmic rays which are related to solar activity cause changes in cloud cover. A second mechanism focuses on changes in UV radiation during a solar cycle which are much larger than the very minor change (0.1%) in TSI. The changes in UV influence the temperature of the stratosphere and these changes might influence the temperature of the troposphere. However most models indicate that on a global scale the influence is quite small.

Other studies focus on a role for the oceans. Van Loon and Meehl[xviii] published several papers in which they show a solar influence in the Pacific Ocean. Soon (2009[xix], 2013[xx]) suggested that solar influences can explain a large part of the temperature variations in the Arctic.

A new Maunder Minimum?
Many solar physicists expect the sun to move into a new minimum, comparable with the Dalton or even the Maunder Minimum. The current solar cycle 24 is the smallest sunspot cycle in 100 years and the third in a trend of diminishing sunspot cycles. Solar physicists expect cycle 25 to be even smaller than Cycle 24. There is, however a notion that the solar dynamo invoking solar activity is unpredictable. Methods are merely based on comparing the shape of a solar cycle with those in the past.

The current consensus among climate scientists seems to be that even when the sun enters a new Maunder Minimum this will not have a large effect on the global temperature, which will be dominated by the increase in greenhouse forcing because of its much larger magnitude.[xxi][xxii] Feulner (2010) found that a new Maunder Minimum would lead to a cooling of 0.3°C in the year 2100 at most – relative to an expected anthropogenic warming of around 4°C. Recently Schürer (2014)[xxiii] even took the large solar effect of the Shapiro (2011) paper into account and concluded that even that has only a small effect.

As IPCC puts it in AR5[xxiv]:

Nevertheless, even if there is such decrease in the solar activity, there is a high confidence that the TSI RF variations will be much smaller in magnitude than the projected increased forcing due to GHG.

The next 20 to 30 years will be very interesting and probably a lot will become clear about the role of the sun by then.

Questions for this Climate Dialogue:

1) What is according to you the “best” solar reconstruction since 1600 (or even 1000) in terms of Total Solar Irradiance?

2) Was there a Grand Solar Maximum in the 20th century?

3) What is your preferred temperature reconstruction for the same period? How much colder was the Little Ice Age than the current warm period?

4) What is the evidence for a correlation between global temperature and solar activity?

5) How much of the warming since pre-industrial would you attribute to the sun?

6) Is the Total Solar Irradiance (TSI) of the sun all that matters for the Earth’s climate? If not, what amplification processes are important and what is the evidence these play a role?

7) what is the sun likely going to do in the next few decades and what influence will it have on the climate? Is there consensus on the predictability of solar variability?

[i] Feynman, J., 2007. Has solar variability caused climate change that affected human culture? Feynman, J., 2007.

Advances in Space Research 40, 1173–1180.

[ii] Stuiver, M., Braziunas, T.F. The solar component of the atmospheric 14 C record, in: Stephenson, F.R., Wolfendale, A.W. (Eds.), Secular Solar and Geomagnetic Variations in the Last 10,000 Years. Kluwer publs, Dordrecht, The Netherlands, p. 245, 1988.

[iii] Eddy, J.A. The Maunder minimum. Science 192, 1189–1202, 1976.

[iv] Cosmic rays produce 10Be and 14C high in the atmosphere by collisions with other molecules. When solar activity is high, less cosmic rays enter our atmosphere so less 10Be and 14C is produced. There is therefore an inverse relationship between these radioactive isotopes and solar activity.

[v] Lean, J., J. Beer, and R. Bradley, 1995a: Reconstruction of solar irradiance since 1610: implications for climate change. Geophys. Res. Lett., 22, 3195–3198.

[vi] Hoyt, D. V., K. H. Schatten, and E. Nesme-Ribes (1994), The one hundredth year of Rudolf Wolf’s death: Do we have the correct reconstruction of solar activity? Geophys. Res. Lett., 21 (18), 2067, doi:10.1029/94GL01698

[vii] The solar radiative forcing is TSI in Watts per square meter (W/m2) divided by 4 to account for spherical geometry, and multiplied by 0.7 to account for planetary albedo (Meehl 2002). The albedo factor is due to the fact that the planet reflects approximately 30% of the incoming solar radiation: ∆F = 0.7 * ∆TSI/4.

[viii] Shapiro, A. I. et al. A new approach to the long-term reconstruction of the solar irradiance leads to large historical solar forcing. Astron. Astrophys. 529, A67 (2011).

[ix] Svalgaard, L., and E. W. Cliver, 2010: Heliospheric magnetic field 1835–2009. J. Geophys. Res., 115, A09111.

[x] Lockwood, M., and M. J. Owens, 2011: Centennial changes in the heliospheric magnetic field and open solar flux: the consensus view from geomagnetic data and cosmogenic isotopes and its implications. J. Geophys. Res., 116, A04109.

[xi] Schrijver, C. J., W. C. Livingston, T. N. Woods, and R. A. Mewaldt (2011), The minimal solar activity in 2008–2009 and its implications for long-term climate modeling, Geophys. Res. Lett., 38, L06701, doi:10.1029/2011GL046658.

[xii] S.K. Solanki et al., Unusual activity of the Sun during recent decades compared to the previous 11,000 years. Nature431, 1084-1087 (2004).

[xiii] Usoskin, I.G. (2008) A history of solar activity over millennia. Living Reviews in Solar Physics 5:7–88.

[xiv] An overview of recent issues can be found in this 2012 talk of Svalgaard: http://www.leif.org/research/TIEMS-Oslo-2012-Svalgaard.pdf. The SSN workshops page is: http://ssnworkshop.wikia.com/wiki/Home.

[xv] Bond, G., Kromer, B., Beer, J., Muscheler, R., Evans, M.N., Showers, W., Hoffmann, S., Lotti-Bond, R., Hajdas, I., and Bonani, G. 2001. Persistent solar influence on North Atlantic climate during the Holocene. Science 294: 2130–2136.

[xvi] Neff, U., Burns, S.J., Mangini, A., Mudelsee, M., Fleitmann, D., and Matter, A. 2001. Strong coherence between solar variability and the monsoon in Oman between 9 and 6 kyr ago. Nature 411: 290–293.

[xvii] Shaviv, N.J., 2008. Using the oceans as a calorimeter to quantify the solar radiative forcing. Journal of Geophysical Research 113, A11101 , doi:10.1029/2007JA012989.

[xviii] van Loon, H. and Meehl, G.A. 2012. The Indian summer monsoon during peaks in the 11 year sunspot cycle.

Geophysical Research Letters 39: L13701, doi:10.1029/2012GL051977; van Loon, H., and G. A. Meehl (2011), The average influence of decadal solar forcing on the atmosphere in the South Pacific region, Geophys. Res. Lett., 38, L12804, doi:10.1029/2011GL047794.

[xix] Soon, W., 2009. Solar Arctic-mediated climate variation on multidecadal to centennial timescales: empirical evidence, mechanistic explanation, and testable consequences, Phys. Geogr. 30, 144–184.

[xx] Soon W, Legates DR (2013) Solar irradiance modulation of Equator-to-Pole (Arctic) temperature gradients: Empirical evidence for climate variation on multi-decadal timescales. J Atmos Sol Terr Phys 93(1):45–56

[xxi] Jones, G. S., M. Lockwood, and P. A. Stott (2012), What influence will future solar activity changes over the 21st century have on projected global near-surface temperature changes?, J. Geophys. Res., 117, D05103, doi:10.1029/2011JD017013, Jones (2012).

[xxii] Feulner, G., and S. Rahmstorf (2010), On the effect of a new grand minimum of solar activity on the future climate on Earth, Geophys. Res. Lett., 37, L05707, doi:10.1029/2010GL042710.

[xxiii] Small influence of solar variability on climate over the past millennium, Schurer, A. P., Tett, S. F. B. & Hegerl, G. C. Feb 2014 In: Nature Geoscience. 7, 2, p. 104-108 5 p.

[xxiv] AR5 section 8.4.1.

Guest blog Mike Lockwood

The sun plays only a very minor role

Before discussing any detail, there are a few general points that I do feel need to be made. Please forgive me if writing these down appears patronising for, indeed, these points are well known and I have never seen them contested in any rational argument - which makes it all the more surprising (and alarming) how often one or more of the following fundamental considerations is overlooked.

1. The difference between global mean climate and regional-and-seasonal climates is significant. Both data and models show that there are many locations and seasons for which there can be sustained trends that are quite different from the trend in the global mean whatever the cause in the latter. Thus taking temperature data or proxies from restricted locations and a certain time of year (or with a seasonal bias) can be misleading.

2. Following on from point (1), it becomes entirely possible for there to exist genuine solar signals in regional and seasonal climates that do not have any significance for the behaviour of global average climate.

3. Detection of trends and cycles requires long data sequences (such that the effect of the trend exceeds the noise or several cycles are detected: how many cycles depends on the application of a detection of seemingly cyclic behaviour).

4. Our data series on both climate and the Sun are not homogeneous, particularly the longer ones. Instrument location (or distribution of locations for networks), sensitivity, measurement technique, calibration, noise levels, time resolution all change over time. Too often a composite time series is used because it is the best available, but the effects of its limitations are rarely investigated.

5. Significance tests of apparent correlations and relationships are absolutely vital – and they must be done properly and thoroughly. Because natural internal variability of the climate system causes noise in any data series, coincidences do and will occur and the onus is on anybody proposing any connection to provide evidence that the relationship did not occur by chance. This requires comparison against a suitable noise model to show that the probability that it was not a chance occurrence was low. In precise laboratory sciences like high energy physics they generally work to the “3-sigma” level for which this probability must be below 0.3%. For areas like geophysics, where it is not generally possible to separate variables, and natural variability always gives noise, a strong result is at the “2-sigma” level for which this probability is below 5.5%. Many reported connections do not even pass at the “1-sigma” level for which the probability limit is 32%. Multivariate fits can often look convincing, but drilling down can reveal that it has been arrived at by an unphysical or unlikely combination of the input parameters. This is referred to as “over fitting” and features that are, in reality, due to the internal variability of the climate system can be incorrectly fitted by a feature, or combination of features, in the inputs to the fit. Proper significance tests to check this has not occurred must allow for the length of the data series, the persistence in both data series and the numbers of degrees of freedom employed in making in the fit. How data gaps are handled is also important and checks are needed that they have not had an effect. But even when a significance test is passed (at a certain level), the connection may still be a “selection effect”, whereby it works with one data series but others that do not work are dismissed with hand-waving arguments or, more commonly, just ignored. Science has to fit all the known facts, so such selective reporting is, quite simply, bad science.

6. Solar radiative forcing FRS and change in total solar irradiance dS are different things and the difference needs to be clear when putting TSI changes into context. The two are related by FRS = dS *(1-A)/4 where A is Earth’s albedo and the factor 4 arises because TSI is power per unit area of the disc presented to the Sun by the Earth and radiative forcing in per unit surface area of Earth. The albedo is not known well (let alone its variation with time) and is impossible to measure properly because every point of the Earth’s surface/atmosphere reflects and scatters the incoming sunlight incident upon it in every direction back into space. Our best estimate of A is somewhere near 1/3, which means FRSdS /6. The largest estimates of dS since 1750 are around 6 Wm-2 [Shapiro et al., 2011] which therefore corresponds to FRS ≈ 1 Wm-2 . However, most recent estimates of dS are in the range 0.6-0.9 Wm-2 which corresponds to FRS between about 0.1 and 0.15 Wm-2. In contrast the radiative forcing caused by changes in well-mixed greenhouse gasses over the same interval is relatively well known at 3.2 ± 1.2 Wm-2. Thus the largest estimate in TSI change is about 30% of the known greenhouse radiative forcing, the mean of a basket of recent estimates is about 4%.

7. Logic based on the name given to a phenomenon, interval or feature is bad science, because the name is often inadequate and misleading.

Having made those general points, let me go on to discuss a couple of specifics in relation to solar influences on global and regional climates.

TSI changes and their effects
The one fact that everyone can agree upon is that, if there were sufficiently large changes in Total Solar Irradiance (TSI), then there would be changes in the global terrestrial climate. The problem is that changes in TSI are limited by the massive thermal time-constant of the solar convection zone. The question is how limited are they on centennial timescales?

Until towards the end of solar cycle 23, our TSI observations only covered 3 solar cycles that were rather similar in amplitude and were all part of a grand solar maximum [Lockwood et al., 2009]: this meant there were great uncertainties in what the TSI level would be when solar activity was weaker. Subsequently, solar activity has declined rapidly, through the so-called “exceptional” (it is only exceptional for the space age) solar minimum 23-24 and the weak current solar cycle 24. For the first time, this has given us some dynamic range in the data and starts to constrain the centennial TSI reconstructions.

I will here use the composite of TSI measurements constructed by WRC/PMOD [Fröhlich, 2000]. Before continuing, I must acknowledge that this is not the only such composite. The main difference between the various composites arises from a difference in philosophy. Because absolute radiometry from space is an extremely difficult measurement to make [Fröhlich, 2003], the PMOD composite is based on the concept that continuous corrections and adjustments are required. An alternative philosophy, adopted in particular in the construction of the ACRIM composite [Willson, 1997] , is to trust the instruments to remain constant in calibration and sensitivity. This is clearly dangerous but, to be fair to the ACRIM composite, there is also a danger that with too many and/or invalid adjustments, the trend in any data sequence could be made into anything one wanted.

That having been said, the vast majority of the difference between ACRIM and PMOD comes down to just one event: a known pointing direction glitch by the oldest satellite deployed (Nimbus7) which has to be re-used in an interval known as the “ACRIM gap” after the loss of the ACRIM1 instrument on SMM and the start of operation of ACRIM2 on the UARS satellite [Lockwood and Fröhlich, 2008]. Despite some published claims to the contrary, modelling of TSI using data from solar magnetographs strongly supports the PMOD composite [Krivova et al., 2009] and only the PMOD composite has a consistent variation with other solar activity indicators such as sunspot number and cosmic ray fluxes [Lockwood and Fröhlich, 2008].

Hence I regard the PMOD philosophy of constant checking and re-calibration of data as the right one to adopt and I regard the PMOD composite as by far the best and so employ it here.

Figure 1: The PMOD composite of total solar irradiance (TSI). Daily values are shown in grey, 11-year running means in blue. Data were downloaded from the WRC/PMOD website on 6th September 2014 (http://www.pmodwrc.ch/pmod.php?topic=tsi/composite/SolarConstant).

Figure 1 shows daily values of TSI from the PMOD composite (in grey) along with their 11-year running (“boxcar”) means (in blue). The last available value is for the 11-year period centred on decimal year 2009.03 and is 1365.77 Wm-2, which is 0.38 Wm-2 lower than the peak running mean value of 1366.15 Wm-2 which was observed at decimal year 1979.9.

Figure 2: Corrected sunspot number, RC. This is a composite of international, Zurich, Wolf and group sunspot numbers with implementations of corrections as discussed by Lockwood et al. [2014]. The grey shows annual means, the blue 11-year running means. The horizontal dashed line shows the last available value of these 11-year means (for 2002-2013, inclusive) and yellow dots show when average sunspot activity was at this level in the past. The Data available from http://onlinelibrary.wiley.com/store/10.1002/2014JA019972/asset/supinfo/JGR_Lockwood_OSF2_supplementarydata.txt?v=1&s=9889b7655c22cc249a9f6e2f43aa76305125cbdd

Because TSI is undoubtedly related to sunspot number, it is instructive to do the same analysis for sunspot numbers. Figure 2 shows annual means (in grey) of extended and corrected sunspot number, RC. This sequence is very similar to others and its precise derivation is discussed in Lockwood et al. [2014]. The blue line shows the 11-year running means of these sunspot data. The value for 2009 is 32.4, and the last time we saw such low average levels of sunspot number was 1900. This value was also seen in the fall into, and recovery out of, the Dalton minimum and also applies around 1750.

In general, we cannot say that TSI is a function of sunspot number only; however, that is an assumption (implicit or explicit) we are forced to make if we use sunspot numbers alone to reconstruct TSI back to the Maunder minimum. This being the case, we would expect the average TSI around 1900 and around 1750 to be very similar to the 2009 value, i.e. 1365.77 Wm-2, which gives a change in TSI between 1750 and 1980 (the peak of the running-mean of the TSI observations) of (1366.15 -1365.77) = 0.38 Wm-2.

We can compare this expectation to the reconstructions. The largest drift in TSI is the Shapiro et al. [2011] reconstruction for which the average TSI around 1900 is 1362.5 Wm-2, i.e. the change in TSI between 1900 and 1980 is predicted to be (1366.15 -1362.5) = 3.65 Wm-2. Hence comparison of figures 1 and 2 indicates that the long-term drift in the Shapiro [2011] reconstruction since 1900 is overestimated by a factor of almost 10.

Looking at 1750, the difference is not quite so great: the Shapiro et al. reconstruction gives around 1364. 5 Wm-2 which is 1.3 Wm-2 lower than we would estimate from the recent TSI data. Hence the Shapiro et al. reconstruction appears to overestimate both the long term trend in TSI after 1900 and the fluctuation level before 1900. The value of TSI for 1750 and 1900 of 1365.77 Wm-2 inferred here does agree very well with many other recent reconstructions of TSI [for example, Wang et al., 2005; Foukal et al., 2006; Krivova et al., 2011; Schrijver et al., 2011].

Referring back to general point 6, the change in TSI between 1750 and 1980 of dS = 0.38 Wm-2 is a radiative forcing of FRS ≈ 0.06 Wm-2. Hence the recent downturn in solar activity is giving us direct evidence that the contribution of TSI is small, of order a few percent of the effect of well mixed greenhouse gases. This agrees with modelling studies that have predicted that even a return to Maunder minimum TSI values would only give a minor slowing of greenhouse gas driven warming [Feulner and Rahmstorf , 2010; Jones et al., 2012]. The inferred radiative forcing of FRS ≈ 0.06 Wm-2 is very similar to the IPCC estimate and the main change to the IPCC consensus values that I would suggest is needed is that the level of confidence in the value should, in the light of the recent data, be changed from “medium” to “high”.

There are many other observations consistent with the above conclusions because the nature of the warming is not consistent with a rise in TSI. For example, a TSI rise would mean that the stratosphere, like the troposphere below it, would have warmed. This is not what is observed: the stratosphere has cooled which is a characteristic signature of enhanced greenhouse trapping in the troposphere. In addition, a rise in TSI would give a rise in average day-night temperature differences which has not been observed and summer temperatures would have risen more than winter ones – in fact the opposite is true and winter temperatures have risen considerably more than summer ones.

Multiple Regression fits to Recent Decades
Thus latest evidence strongly points to some role of TSI variability on global climate change, but only a very minor one. The question then one asks is: what about other sun-climate interaction possibilities? The most well-known of these is cosmic-ray modulation of cloud cover, but some others are mentioned in the next section.

I have long been a supporter (and actually a member) of the CLOUD experiment at CERN: not because I think it will revolutionise our view of radiative climate forcing, but because I am sure it will make really valuable contributions to our understanding of cloud microphysics. I do think there are some really interesting indications of solar influences (via cosmic rays and other energetic particles) on atmospheric electricity phenomena and consequent effects on cloud edges and polar tropospheric pressures. This is a vibrant area of research and I will do it no favours by making a hurried review of it here. However, I do have reasons for thinking that the net effect on the terrestrial global radiation budget will be small, and I discuss those reasons here.

Multiple regression fits of global mean temperature have been made using known inputs into the atmosphere at earth’s surface. Similarly global multiple regression maps of the air surface temperature have been generated, revealing influences on regional climates. These have been carried out using a number of different procedures and datasets [Lockwood, 2008; Lean and Rind, 2008; Folland et al., 2013 ; Kaufmann et al., 2011], but the conclusions are always very similar [Imbers et al., 2013 ] that contributions to global mean air surface temperature changes are in very close proportion to the estimated radiative forcings (see figure 3).

It is important to bear in mind that these are multiple regression fits and so can lack statistical significance for the reasons presented in general point 5 – in particular they are prone to “over fitting”. Although tests had been applied in the above studies, the most rigorous testing was carried out by Imbers et al. [2013]. These authors showed that the key conclusions of these fits were robust to the inclusion of additional signals and also to the characterization of internal variability using both a short memory AR1 (“auto-regressive 1”) process or a long memory FD (“fractionally differenced process” or “Fractional ARIMA model”) noise models. In addition, these authors extended the sensitivity analysis to show that 20- and 60-year oscillations that have been proposed to explain recent apparent changes in rates of warming [e.g. Loehle and Scafetta, 2011] do not change the conclusions.

The key point is that these multiple regression detection/attribution fits all show that the influences are broadly proportional to their estimated radiative forcings, in the case of solar variability that is estimated from TSI observations (as discussed above), which leaves little room for any other solar factor or mechanism. Furthermore, any proposed mechanism must explain all – and I stress all - the data, not just the global means air surface temperature: such constraints include the latitudinal profile (why the Arctic has warmed most), coherent longitudinal variations, the altitude profile (the cooling in the stratosphere), the seasonality (why the warming is greater in winter), the lack of a diurnal variation increase. All these features are well explained by the observed rise in well-mixed greenhouse gases and so to be considered a serious alternative, any proposed mechanism must also explain all these observations.

Figure 3: (top) The observed global mean air surface temperature anomaly fromHadCRUT3 (in grey) line) and the best multivariate fits using the methods of Lean and Rind [2008] (blue), Lockwood [2008] (red), Folland et al. [2011] (green), and Kaufmann et al. [2011] (orange). Note that in the case of Folland et al. data are plotted with 94.4% confidence interval based on the ensemble standard deviation. The remaining panels show the individual temperature contributions from multiple regression fits to the data in the top panel : from ENSO (second panel); volcanoes (third panel); solar variability (fourth panel); anthropogenic contribution (fifth panel); and other factors (sixth panel) that include the AMO for Folland et al. [2011] and minor annual, semi-annual, and 17.5 year cycle identified in the residuals of Lean and Rind’s [2008] model. In the case of Folland’s data, the figure shows the mean ˙1 standard deviation of the 120 time series ensemble. For the Lockwood [2008] fits, solar variability was quantified using cosmic ray flux measurements and each input was convolved with a response time-constant which was a fit variable in each case. From Imbers et al. [2013].

Solar Influences on Regional/Seasonal Climates
This is an area of some academic debate, but there is a growing body of evidence that there are effects of solar changes on some regional and seasonal climates [see reviews by Gray et al., 2010, Lockwood et al., 2012]. The mechanisms largely involve modulation of the jet stream (particularly in the northern hemisphere which is different from that in the south because it passes over much more complex ground orography than the southern jet stream which is largely over ocean).

The jet stream straddles the boundary between the troposphere, responsible for weather and climate, and the much less dense stratosphere above it. The modulation appears to be driven by changes in the stratosphere caused by solar UV irradiance changes and/or by the catalytic destruction of high-latitude stratospheric ozone by energetic particle fluxes that are modulated by solar activity. These effects matter much more in winter when the driver of the jet stream, the equator-to-pole gradient in lower stratospheric temperatures is much greater.

Both data and models indicate a “quadrupole pattern” whereby lower solar activity drives lower winter temperatures in central and northern Eurasia and the central and northern USA, but higher winter temperatures around the Mediterranean and over Greenland and northern Canada. These ideas are increasingly supported by numerical models that extend up into the stratosphere and so can capture stratospheric dynamics and temperatures [e.g. Ineson et al., 2011]. Thus far models have no self-consistent treatment of the ozone abundance and this is likely to yield an amplification of the predicted effect. Models that do more than employ “fixed ozone” by including the chemistry and the dynamics will be needed to evaluate the relative effects of UV and energetic particles.

An example of results for the winter North Atlantic, and which can be associated with solar modulation of jet stream “blocking events” is shown in figure 4. Therefore, in global average temperatures these tend to cancel out and there’s no evidence that they don’t cancel out completely. However this effect is still relevant to long-term analysis of global climate change because the distribution of observations is not homogeneous and has changed over time.

In particular as we go back in time, observations become increasingly dominated by central Europe. Many pieces of evidence for solar influence on climate relate to observations made in Europe in winter from before the mid nineteenth century. Treating this evidence as indicative of global temperature variations is therefore a major error. The situation is better with some paleoclimate data when one has a wide variety of locations from what to take observations, such as from tree ring data. However note these data are often biased to the growing season, i.e. summer.

Figure 4: (a). Composite difference maps of winter (December/January/February) means of mean surface level pressure (MSLP, contours 1 hPa apart) and 2-m temperature (colour map) between low- and high-solar conditions defined by the lower and upper terciles of the open solar flux (FS). (b). Scaled anomalies associated with the NAO for comparison, in this case the two subsets are for the lower and upper terciles of the NAO index. Data are from the European Centre for Medium-range Weather Forecasting ERA-40 reanalysis data set (Uppala et al. 2005) extended to cover 44 complete winters (1957/1958 to 2000/2001) using operational data. A bootstrap test was employed, resampling the sets of winters in order to estimate the sampling uncertainty, to test whether the solar pattern in a was different from that associated with the NAO in b: this found that the two patterns are significantly different at the 93% level [from Woollings et al. 2010].

The “Little Ice Age”
I dislike this name as it has been used to build arguments that rely on the name which, as mentioned in point 7, is inherently bad science. Many paleoclimate records show an interval of lower temperatures but the start and end dates are not well agreed. For example it has been claimed to correspond to the Maunder minimum (i.e. 1645-1715) , such that in many arguments the terms “Maunder minimum” and “Little Ice Age” are even taken to be synonymous! This is undoubtedly invalid as most paleoclimate records indicate that the lower temperatures began at least 50 years before the start of the Maunder minimum.

But I also dislike the name as it is inherently misleading: this period was not an ice age in any shape or form. For example, the Central England Temperature (CET) is the longest series of temperature measurements in existence, starting in 1659 and continuing to the present day. The CET data for the Maunder minimum show that there was a higher occurrence frequency of cold winters in this interval. However, the term “little ice age” implies that they were unremittingly cold. This is not the case. For example, the coldest winter in the CET record is 1683-4, right in the middle of the Maunder minimum; but the winter just two years later was the fifth hottest in the whole 350-year CET record. Furthermore, there are many examples of hot summers during the Maunder minimum. Hence, in central England at least, it was not unremittingly cold – far from it!

Prof. Mike Lockwood is a professor of space environment physics with the Department of Meteorology, University of Reading, UK. He has received five international academic awards and in 2006 was elected as a Fellow of the Royal Society of London.
Prof. Lockwood studies variations in the Sun on all timescales up to millennia and their effects on near-Earth space, Earth's atmosphere and climate. His quantification of the variation in the open solar magnetic flux using geomagnetic activity observations provided a constraint which allowed physics-based reconstruction of total solar irradiance over the past 10,000 years.


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Guest blog Nicola Scafetta

The Sun has a significant influence on the climate

According to the latest IPCC report, AR5, the influence of the Sun on our climate since pre-industrial times, in terms of radiative forcing, is very small compared to the variation of radiative forcing due to added anthropogenic greenhouse gases: 0.05 [0.00 to 0.10] W/m2 vs. 2.29 [1.13 to 3.33] W/m2. Thus, the IPCC message is that changes in solar activity are nearly negligible compared to anthropogenic ones. Can this interpretation be trusted?

In a famous lecture Feynman reminded us that scientific models must predict physical observations. If this crucial condition is not fulfilled, a physical model cannot be considered valid or complete, and the science cannot be considered “settled.” Indeed, it has been demonstrated that there are serious discrepancies between the general circulation climate model predictions and the data (e.g. Scafetta, 2013b). Thus, it is legitimate to question the science behind the IPCC interpretation and investigate alternative interpretations of climate changes.

Here I summarize how the scientific literature alternative to the thesis that the Sun contributes little to climate change has addressed the issue. Indeed, solar and global surface temperature records appear to be quite related to each other at both short and long time scales once the appropriate methodologies and solar models are adopted. It is necessary, however, to clarify a few concepts because no consensus on the solar contribution to climate changes exists. I believe that many people are somehow confused on this topic.

Understanding the data
The global surface temperature patters are evidently not determined exclusively by solar inputs. On time scales up to the millennial one, global climate averages are mostly regulated by volcano eruptions, anthropogenic forcings and numerous natural oscillations, which include solar, astronomical and lunar tidal oscillations. To avoid misleading conclusions, the different physical attributions need to be taken into account. In addition, the quality of solar and climatic records varies. Instrumental measurements are often processed through complex physical and statistical models and if direct measurements are missing, a low-quality solar and climate proxy reconstructions are adopted.

Many empirical studies (e.g.: Bond et al., 2001; Douglass and Clader, 2002; Eichler et al., 2009; Friis-Christensen and Lassen, 1991; Hoyt and Schatten, 1993; Hoyt and Schatten, 1997; Kerr, 2001; Kirkby, 2007; Loehle and Scafetta, 2011;Scafetta, 2012b; Scafetta, 2013a; Scafetta, 2013b; Scafetta, 2014; Scafetta and West, 2007; Scafetta and West, 2008; Shaviv, 2008; Soon, 2005; Soon, 2009; Steinhilber et al., 2012; White et al., 1997) have found a strong but complex solar signature in the climate system at multiple timescales once that specific models and records have been used. Some of these studies have claimed that the Sun could have contributed at least ∼ 50% of the post 1850 global warming. This conclusion contradicts the current analytical climate models, such as the general circulation models (GCMs) adopted by the IPCC that predict only a 5% or lower solar contribution to the warming observed during the same period (e.g. see the IPCC (2013)).

For example, Douglass and Clader (2002); Lean and Rind (2009); van Loon and Labitzke (2000); Scafetta (2009); Scafetta (2013c) evaluated the signature of the 11-year solar cycle on the temperature by simultaneously filtering off the volcano signature, the anthropogenic signature and the ENSO oscillations. These authors found that during the period from 1980 to 2000, which experienced very large 11-year solar oscillations, the 11-year solar cycle signature on the global surface temperature had an amplitude of about 0.1 K. At higher altitudes, however, the amplitude of the 11-year solar signature increases up to ∼ 0.4 K (e.g.: Scafetta, 2013c; van Loon and Labitzke, 2000; Svensmark and Friis-Christensen, 2007).

On longer time scales the solar influence on climate becomes clearer once appropriate solar proxy models are used (e.g.: Eddy, 1976; Hoyt and Schatten, 1997; Kirkby, 2007). Steinhilber et al. (2012) found an excellent correlation between a 9,400-year cosmic ray proxy model of solar activity from ice cores and tree rings and the Holocene Asian climate as determined from stalagmites in the Dongge cave, China. In particular, data show a strong millennial oscillation common to both solar and temperature records (e.g.: Bond et al., 2001; Kerr, 2001) that must have contributed significantly to the warming observed since 1850.

In fact, Christiansen and Ljungqvist (2012) showed that the extra tropical surface temperature of the northern hemisphere experienced significant warm periods during the Roman Optimum (100 B.C. - 300 A.D.), and during the Medieval Warm Period (900-1400 A.D.) and significant cool periods during the Dark Age (400-800 A.D.) and the Little Ice Age (1400-1800 A.D.) (Christiansen and Ljungqvist, 2012).

Thus, following this millennial cycle, since 1800 the temperature had to increase naturally: the millennial climatic maximum induced by the millennial solar maximum had to occur in the 21th century and could have contribute about 50% or more of the warming observed since 1850 (e.g.: Humlum et al., 2011; Scafetta, 2012a; Scafetta, 2013b). Numerous other climatic oscillations at the decadal, bi-decadal, 60-year and secular scales that could be solar-astronomically induced are also typically observed in a large number of data (e.g.: Scafetta, 2010; Scafetta, 2013b; Scafetta, 2014).

Empirical studies versus climate model studies
Thus, there is an apparent incompatibility between the empirical and analytical studies. This is likely due to (1) the different philosophical approaches used to address the problem and (2) to the current lack of scientific understanding of microscopic physical mechanisms regulating climate change. Let us understand the reason.

The empirical/holistic approach focuses on the macroscopic characteristics of the data that are interpreted using detailed cross-correlation pattern recognition methodologies. It does not require the microscopic identification of all physical microscopic mechanisms to recognize macroscopic patterns such as cycles, which can be directly modelled.

On the contrary, the analytical GCM approach focuses on the microscopic modelling of the individual physical mechanisms and their coupling: it uses Navier-Stokes equations, thermodynamics of phase changes of atmospheric water, detailed radiation budget of the Earth and atmosphere and ocean dynamics, specific radiative forcing functions as inputs of the model, etc. The GCMs depend on very numerous internal variables and are characterized by serious uncertainties such as those related to the cloud formation (IPCC, 2013), which regulate the important albedo index.

It is evident that the analytical models need to be physically complete to be meaningful. On the contrary, there are several reasons suggesting that the current analytical climate models are severely incomplete. This lack of detailed knowledge is manifest mostly in the large error bar that characterizes the climatic sensitivity to CO2 doubling that, according to the IPCC, varies between 1.5 and 4.5oC . Works suggesting a strong solar effect on climate would imply a climatic sensitivity to CO2 doubling of about 1.5oC . Note that this low climatic sensitivity to radiative forcing implies that the total solar irradiance varied significantly more than what currently used as total solar irradiance forcing in the current climate models and/or that solar forcings alternative to the radiative one are influencing the climate. Thus, the models may have used a wrong total solar irradiance input and/or they oversimplify the solar influence on climate.

Let us briefly summarize some of the arguments proposed in the referenced literature.

(1) The analytical models such as the CMIP5 GCMs adopted by the IPCC (2013) have used a solar forcing function deduced only from a specific total solar irradiance proxy record that shows only a very small secular variability (e.g. Wang et al. (2005)), while alternative total solar irradiance proxy models showing a far greater secular variability and different details in the patters also exist (Hoyt and Schatten, 1997; Shapiro et al., 2011). These alternative solar models better correlate with the temperature patterns on multiple scales and reconstruct a large fraction of the warming observed since 1850 (Scafetta, 2013b; Hoyt and Schatten, 1997; Soon, 2009; Soon, 2005; Soon and Legates, 2013).

(2) The analytical models still assume that solar-climate interaction is limited to TSI forcing alone. However, other solar-climate mechanisms likely exist although still poorly understood. For example, the climate system may be particularly sensitive to specific radiations (e.g. ultraviolet light) and to cosmic ray or solar wind variations that could significantly modulate the cloud cover system (Kirkby, 2007). Other still unknown space weather and gravitational mechanisms may exist.

(3) The climatic records are characterized by numerous natural oscillations from the decadal to the millennial timescales that have been demonstrated to be not reproduced by the analytical models, but are present in specific solar, lunar and astronomical records (Scafetta, 2012b; Scafetta, 2013b; Scafetta, 2013a; Scafetta, 2010; Scafetta, 2012a). These oscillations, including the millennial cycle, stress the importance of solar and astronomical effects on the Earth's climate (Scafetta,2013b; Steinhilber et al., 2012).

In general, analytical models may theoretically be considered the best way to exploit the confirmatory analysis. However, the exploratory analysis - which is needed in order to envisage the primary physical drivers of phenomena - is a completely different gnoseologic concern. One cannot substitute the crucial stage of the exploratory analysis with any kind of complex confirmatory mathematics. Both stages are needed and, in general, to describe a complex system usually empirical/holistic approaches may be more satisfactory than an analytical ones. In the analytical modelling, mistakes can also be easily made when the original set of primary drivers and forcing functions is speculated.

For example, one of the reasons why the IPCC claims that the sun has not contributed to the warming observed since 1970s is because the adopted solar model (Wang et al., 2005) suggests that the average solar activity was quite constant or even decreased during this period. This interpretation follows the PMOD total solar irradiance satellite composite by Fröhlich (2006). However, Scafetta and Willson (2009); Scafetta and Willson (2014) have shown that PMOD used altered total solar irradiance satellite records based on hypotheses that appear contradictory. On the contrary, the unaltered total solar irradiance satellite records are combined in the ACRIM composite which suggests that solar activity increased from 1980 to 2000 and decreased afterward (Willson and Mordvinov, 2003). Even if the direct effect of the total solar irradiance may be small and the difference between ACRIM and the PMOD might be climatically negligible, the pattern shown by the ACRIM composite suggests a dynamics, for example a 60-year oscillation, regulated by astronomical forcings whose harmonics are found in the climate system as well (Scafetta, 2010; Scafetta, 2013b; Scafetta, 2012a; Scafetta,2014). See the difference between the ACRIM and the PMOD composite here http://acrim.com/TSI%20Monitoring.htm.

Significant correlation between solar-astronomical records and temperature records

Figure 1a and 1b

Figure 1A compares the sunspot number record since 1700 (blue curve) versus two alternative total solar irradiance reconstructions (Wang et al., 2005; Hoyt and Schatten, 1997). The figure highlights that while the sunspot number is relatively flat, solar proxy models present a more significant secular variability that, however, depends greatly on the chosen proxy model. Some solar model predicts a variability significantly larger than others. Figure 1B simply compares the Central England Temperature record (Parker et al., 1992) and the solar reconstruction proposed by Hoyt and Schatten (1997). A good correlation is noted for 300 years, which includes a significant portion of the warming observed since 1900.

Figure 2

Figure 2 shows examples of solar-climate correlations taken from Steinhilber et al. (2012); Svensmark and Friis-Christensen (2007); Soon and Legates (2013); Thejll and Lassen (2000); Eichler et al. (2009) and Kirkby (2007). A good correlation between solar-astronomical records and climate records is evident at short and long time scales. Figure 2A compares a reconstruction of solar activity and a reconstruction of Asian climate during the Holocene (last 9000 years) (Steinhilber et al., 2012).

Figure 2B shows that the radiosonde temperature anomalies, after an appropriate filtering of other climatic factors, reveals a clear signature of the 11-year solar cycle reconstructed by the cosmic ray record (Svensmark and Friis-Christensen, 2007). Figure 2C compares the instrumental global surface temperature record versus a SCL121 solar cycle length model (Thejll and Lassen, 2000). Figure 2D compares the annual-mean equator-to-pole gradient over the entire Northern Hemisphere versus the estimated total solar irradiance record (red) of Hoyt and Schatten (1997) (red, with updates by Scafetta and Willson (2014)) from 1850 to 2010 (Soon and Legates, 2013).

Figure 2E compare a Siberian temperature reconstruction with solar activity proxies for 750 years (Eichler et al., 2009). Figure 2F depicts a temperature reconstruction for the Central Alps over the last two millennia, obtained from a δ18O based temperature proxy model versus the variations of cosmic rays (14C) and CO2 over the same period (Kirkby, 2007). These empirical results clearly suggest that the Sun has a significant influence on the climate system.

Figure 3

Figure 3A shows the good performance of an empirical model for the global surface temperature proposed by Scafetta (2013b). This model has the peculiarity of attempting a reconstruction of the climate variability using 6 identified solar-astronomical oscillations at periods of 9.1, 10.4, 20, 60, 115 and 983 year. Other harmonics are likely present. These oscillations are able to model the natural decadal-to-millennial natural climatic oscillations. To this harmonic component it is necessary to add an estimate of the anthropogenic and volcano components made by properly attenuating the CMIP5 general circulation model ensemble mean simulation by a factor β ≈ 0.5 to simulate a climate sensitivity to CO2 doubling of about 1.5oC . Scafetta (2013b) showed that his model outperforms all CMIP5 general circulation models in reconstructing the global surface temperature record. Figure 3B shows a detail with an update of the semi-empirical astronomical model proposed by Scafetta (2012b) in 2011 against the HadCRUT3 global surface temperature record (Brohan, 2006). The red curve shows the original global surface temperature record published in Scafetta (2012b), which ended in October 2011. The blue curve shows the global surface temperature updated to the most current available month, which is May 2014. The back curve within the cyan 1-σ error area is the semi-empirical astronomical model forecast (which was modelled to start in 2000). The figure clearly shows that the proposed semi-empirical model outperforms the IPCC 2007 CMIP3 general circulation model projections (green area) and has successfully forecast the temperature trend from October 2011 to March 2014. Note that a simplified version of the same model was proposed by Scafetta since 2009 (Lorenzetto, 2009; Scafetta, 2010).

Figure 4

Finally Figure 4A compares the four CMIP5 climate model ensemble average projections versus the HadCRUT4 global surface temperature record. Figure 4B shows the solar–astronomical semi-empirical model against the HadCRUT4 GST record: a common 1900–2000 baseline is used. The figure highlights the better performance of the solar–astronomical semi-empirical model versus the CMIP5 models, which is particularly evident since 2000 as shown in the inserts.

As Figures 3 and 4 show, the proposed model has correctly predicted the observed continued standstill of the global surface temperature while the CMIP3 and CMIP5 general circulation models adopted by the IPCC in 2007 and 2013 predicted for the period 2000-2014 a strong warming of about 2oC/century, which has not been observed.

The solar–astronomical model projections for the 21st century look significantly less alarmist than those proposed by the IPCC. This is due to the fact that by taking into account the natural oscillations from the decadal to the millennial scales, the climate sensitivity to CO2 doubling must be about 1.5oC while the CMIP5 climate models predict a climate sensitivity of about 3oC . See Scafetta (2013b) for details.


Figures 1-4 provide a strictly alternative message to the one proposed by the IPCC. The Sun must have contributed significantly to climate changes and will continue to do so.

After having noted that not even CO2 and other greenhouse gases, either of natural or of anthropogenic origin, could be the cause, let alone the primary cause, of global climate changes, Quinn (2010) wrote: “Evidence indicates that global warming is closely related to a wide range of solar-terrestrial phenomenon, from the sun's magnetic storms and fluctuating solar wind all the way to the Earth's core motions. Changes in the Solar and Earth magnetic fields, changes in the Earth's orientation and rotation rate, as well as the gravitational effects associated with the relative barycenter motions of the Earth, Sun, Moon, and other planets, all play key roles. Clear one-to-one correspondence exists among these parameters and the Global Temperature Anomaly on three separate time scales.”


Nicola Scafetta graduated in Physics at the University of Pisa (Italy) and received his Ph.D. in statistical mechanics and complex systems at the University of North Texas (USA) in 2001. Since 2002 he moved to Duke University and collaborates with the Active Cavity Radiometer Irradiance Monitor (ACRIM) in several projects concerning solar dynamics and solar-climate interactions. He is currently proposing that the climate system is regulated by a significant natural component that appears to be regulated by solar and astronomical harmonics that the current climate models do not take into account.


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Guest blog Jan-Erik Solheim

Most of the warming in the 20th century is due to the sun


According to the latest IPCC report, AR5, the influence of the Sun on our climate since pre-industrial times, in terms of radiative forcing, is very small compared to the effect of greenhouse gases. Figure 1 in the introduction (SPM.5 in AR5) is quite misleading, since it compares the TSI at solar minimum around 1745 with TSI around minimum in 2008. They are apparently the same. This covers the fact that the Sun has changed quite a lot in the time between.

The cooling since the Holocene Maximum and the Modern Maximum

The cooling over Central Greenland since the Holocene maximum is very clear in the GISP2 record (Alley, 2000), which is shown below:

Figure 1: Central Greenland (GISP2) surface temperature the past 4000 years (blue line). Natural cycles modeled and forecasted shown by the green line. Only 3-cycles are included (2800, 1190 and 560 yr) in the model. This model shows a temperature increase for the period 1850-2100 which is called the Modern Warm Period (MWP) (From Humlum et al., 2011)

What we observe in the GISP2 temperature graph is a cooling since the Holocene maximum 4000 years ago, interrupted with temperature peaks about 1000 years apart. The temperature peaks can be identified with historic warm periods.

It should be emphasized that Central Greenland temperature changes are not identical to global temperature changes. However, they tend to reflect planetary temperature changes with a decadal-scale delay. The GISP2 record ends in 1855 before the onset of the 20th Century warming. A simple harmonic model based on wavelet analysis, which we may call a natural cycle model, with only 3 long periods (2800, 1190 and 560 yr) is used to forecast the observed warming until 2800 (Humlum et al., 2011). This indicates that the modern maximum will peak at some time this century. A more detailed harmonic analysis and a comparison with the HadCRUT4 temperature data set shows that we are close to the peak of this long period cycle, which will have a maximum around 2060 (Solheim, 2013a).

This means that an expected future Maunder Minimum will appear when a strong (low frequency) natural cycle provides a temperature peak, and the expected cooling may be less than in the Maunder Minimum, which appeared in a minimum of the low frequency natural cycle.

The “best” solar reconstruction

The reconstruction by D. Hoyt and K. Schatten (1993) updated with the ACRIM data (Scafetta, 2013) gives a remarkable good correlation with the Central England temperature back to 1700. This is shown in the figure below.


Figure 2: Total solar irradiance reconstruction by Hoyt and Schatten updated with the ACRIM record (since 1980) (the red curve) versus the updated Lean model used in the CMIP5 reconstructions (blue). The lower panel shows a comparison between the TSI model by Hoyt and Schatten and the Central England temperature record (From Scafetta, 2013, figure 15)­.

The TSI reconstruction of Hoyt and Schatten, also show close correlation with the variation of the surface temperature at three drastically different geographic regions with the respect to climate, as shown in Figure 3 below:

Figure 3: Three radically different climate regions compared with the Hoyt-Schatten TSI reconstruction: Top: Contiguous USA, Middle: Arctic (Soon, 2009) and bottom: China. (Soon et al., 2011)

Finally, the excellent relationship between the TSI and the Equator-to-Pole (Arctic) temperature gradient (EPTG) is displayed in figure 4. Increase in TSI is related to decrease in temperature gradient between the Equator and the Arctic. This may be explained as an increase in TSI results in an increase in both oceanic and atmospheric heat transport to the Arctic in the warm period since 1970.

Figure 4: Annual-mean EPTG over the entire Northern Hemisphere (°C/latitude; dotted blue line) and smoothed 10-yr running mean (dashed blue line) versus the estimated TSI of Hoyt and Schatten (Soon and Legates, 2013).

My conclusion is that the Hoyt-Schatten-ACRIM is the “best” TSI reconstruction

Grand Solar Maximum in the 20th century

Figure 5: The upper panel shows the timing of sunspot minima related to an average period of 11.07 yr, determined from estimated sunspot minima since 1610 (Solheim, 2013b). Positive values means that the time of minimum is delayed with respect to the expected time. O-C means Observed minus Computed (expected) time of minimum. In the same curve solar minima the last 1000 yrs are marked with a thick line. The lower panel shows reconstructed 10-yr averaged sunspot numbers (Usoskin et al. 2007) and Hoyt-Schatten group sunspot no after 1615 and updated until 2015 with modern scaled sunspot numbers from SILSO (World Data Center for the production, preservation and dissemination of the international sunspot number).

As shown in Figure 5, lower panel, the 10-yr average sunspot no has never been as high the last 1000 yrs as in the last part of the 20th century. In the same graph I have shown a first harmonic component, with period 190 years, of the timing of sunspot minima. This period apparently controls the timing of sunspot minima. When this curve is near minima or is ascending, sunspot periods become longer, and arrive delayed. This was for the first time demonstrated by Richards et al. (2009).

From Figure 5 l conclude that we have had a Grand Solar Maximum in the 20th Century and that we are due to have a Grand Minimum during the next decades.

The Little Ice Age

Figure 6: Estimates of extra-tropical Northern Hemisphere (90°-30°N) decadal mean temperature variations AD 1-1999 (dark grey line) with the mean variance adjusted CRUTEM3+HadSST2 mean values for the same area 1850-1999 (Ljungqvist 2010).

My preferred temperature reconstruction for the last 2000 years is Ljungqvist (2010) for the extra-tropical Northern Hemisphere as shown in Figure 6. This reconstruction utilizes many paleo-temperature proxies never before included in any large-scale temperature reconstruction. It shows clearly the Roman and Medieval warm periods. The difference between the warmest decade (950-959) and the coldest (1690-1699) is 0.9 °C in this reconstruction. The author admits there is a divergence problem also in his reconstruction, i.e. that the tree-ring growth shows lower temperature in the last decades of the 20th century than the thermometer measurements suggest. The author also concludes that the cooling during the Little Ice Age (LIA) probably is larger than his estimates, since the trees reflect summer temperatures, while other seasons may have cooled more.

Following Ljungqvist my conclusion is that the LYA NH extra-tropical temperature difference is more than one degree °C for a decadal average temperature compared with the last decade.

Correlation between global temperature and solar activity

Since the sunspot numbers are the longest series that relates to solar activity it has been used to correlate with the Earth’s temperature and other climate parameters.

One of the newer attempts to correlate solar activity with climate is done by Stauning (2014). He finds a relation

ΔTA = 0.009 (±0.002)×SSNA -0.70 °C, (1)

where SSNA is the average sunspot number in a sunspot period and ΔTA is the temperature anomaly for the same period shifted 3 yrs. The correlation calculated for cycles 10-21, excluding cycles 16-19 (which is the high level in the 20th century) is r = 0.975. If this relation is used to subtract the solar component of the temperature anomaly, he gets the following:

Figure 7: Cycle average global temperature anomalies (HadCRUT4gl) corrected for contributions from solar activity. (Stauning, 2014)

Stauning’s conclusion is that the solar activity relation can explain the temperature anomaly in the period 1860-1990, but from then on another components appears. He concludes that the reduction in solar activity disguises a global temperature rise that otherwise would have happened.

Global temperature variation - attribution to the Sun

If all the warming up to 1990 is due to the Sun as proposed by Stauning (2014) in equation (1), this means that we have experienced about 0.5°C warming by the Sun in the period 1900-1990. After 1990 the reduced solar activity related to sunspots has disguised a warming of about 0.6°C (Figure 7), making the observed warming between the decades 1985-95 to 2003-13 only 0.3 °C.

However, this does not include effects of the longer cycles in TSI which peak around 1940 and 1995 (Figs 3 and 4), and the longer period of about 200 yr (fig 5), which controls the length of solar cycles. The change from shorter to longer cycles happened about the year 2000, and if one assume that a longer cycle imply more time for cooling, then the effect will appear some decades ahead. This is the same delayed effect we observe in the yearly cycle of warming/cooling of the Earth. In the Northern Hemisphere we have the minimum solar insolation on Dec 21, but the average temperature minimum appears on Feb 11, which is about 13 per cent time delay with respect to the yearly cycle. Power spectrum analyses of global and hemispherical temperature series shows that 60 and 20 yrs quasi-periodic oscillations are much stronger than the 11 yr sunspot period, and can be attributed to periodic forcing from the mayor planets Jupiter and Saturn (Scafetta 2013). These periods may dominate in the coming decades when the effects of the 11-yr cycle is reduced.

A fair part of the warming after 1990 may be due to the cleaning of the stratosphere of aerosols after the Pinatubo eruption in 1992. Observation of earthshine during lunar eclipses shows that the atmosphere now is very clean (Keen 2008; 2013) The difference in volcanic Aerosol Optical Thickness (AOD) since 1996 is estimated to Δτ = -0.033 which corresponds to ΔT=0.13°C (no feedback calculation). This is based on the relation between aerosol forcing and optical depth Fa ≈ -21 Δτ, where Fa is the forcing in Wm-2 (Hansen et al. 2000).

If we want to compare insolation today with 1750, we have also to consider the effect of perihelion change.We are in the (lucky) situation that the Earth at present is in the perihelion position around Jan 4th. The difference between the date of perihelion and solistice shifts with one day every 57 or 58 years. This has caused the solar flux on the Earth at the time of vernal spring to increase with 0.24 per cent since 1750. This is significant when compared with the apparent AGW contribution of 0.12 (AR4) or 0.17 per cent (AR5). The increased insolation results in earlier snow melting, which again changes the albedo such that more heat from the Sun is absorbed. This is a non-linear effect that adds heat to the Earth. Steele (2014) has shown that the insolation changes because of Earth orbit change used in climate models are wrongly modeled, and the result as he writes is “worse than useless”, and this needs to be corrected before a difference between the solar forcing around 1750 and now can be realistic estimated in climate models.

Amplification processes

The relations between solar variations and Earth climate are many and complicated. Most of them work locally and regionally, and many are non-linear. The following chart (Figure 8) gives an idea of some of the interactions. In particular I find that the planetary gravity beats are interesting since we may detect periods that are related to orbits of the moon and planets, as demonstrated by Scafetta (2013) and others (Solheim, 2013a). Scafetta (2012) has proposed a physical mechanism for amplification of tiny gravitational interactions by modulation of nuclear reactions in the solar interior. This may generate gravity waves which are transmitted to the solar surface. Such signals are observed in TSI variations (Scafetta and Willson, 2013) and may be imbedded in the solar wind that interacts with cosmic rays that hits the Earth. Variations in cosmic rays may again contribute to cloud formation as proposed by Svensmark (2007) and collaborators. Even if the route from planets to Earth is long and crooked, we are extremely good in detecting periodic signals that may point to a connection.

Figure 8: The interaction between planetary beat and solar variability, and changes in climate and environments on the planet Earth (Mörner, 2012)

A new Maunder Minimum?

In the previous sections I have listed a few facts that may help us estimate the solar influence on climate in the next decades:

· We are close to a peak in the 1000yr temperature cycle, which has given the Roman and Medieval warm periods (Figs 1 and 3).

· The modern maximum of the millennium quasi-periodic cycle takes place around 2060. This makes a solar minimum less dramatic than in the Maunder Minimum period, which appeared in a minimum of the millennium cycle.

· A non-solar component of the order 0.1-0.2°C has to be included (Fig .7).

· Since the stratosphere now is clean, and volcanic eruptions that affect the stratosphere happens a few times per century, are unpredictable, we may expect volcanic cooling of the order 0.1-0.2°C in the future.

· The quasi period of about 60 yr will be in cooling direction the next 30 yrs.

· The sunspot cycle will be longer in 21th Century, indicating a cooler climate (Fig 5).

· The pattern of minima the last 1000 yr (Fig. 5-upper panel) indicates that a new minimum is to be expected the coming decades.

Evaluating these parameters, I arrive at the conclusion that the global temperature may during the next solar deep minimum fall to a level slightly higher than around 1900 which means -0.6 ±0.2 °C relative to the last decade.

Jan-Erik Solheim is a retired professor from University of Tromsø, Norway. He received his Cand. Real. degree in Astrophysics from University of Oslo in 1964. He has worked with cosmological models and observational astrophysics during several visits to University of Texas, Department of Astronomy, the McDonald Observatory and other institutes. From 1971 to 2002 he had a position at the Institute for Physics and Technology at the University of Tromsø, where he became interested in fast photometry and detection of signals from pulsating and interacting stars. He participated in establishing the Nordic Optical Telescope at La Palma, Spain and participated in coordinated world wide observing campaigns (Whole Earth Telescope) from 1987. After retirement he has worked as an independent scientist on some aspects of relations between the Sun and Earth and the possibility of detecting signals from planets in solar and climate variations.


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Ljungqvist, F. C., A new reconstruction of temperature variability in the extra-tropical Northern Hemisphere during the last two millennia, Geogra. Ann. 92A (3), 339-351, 2010

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Scafetta, N. Does the Sun work as a nuclear fusion amplifier of planetary tidal forcing? A proposal for a physical mechanism based on the mass-luminosity relation, Journal of Atmospheric and Solar–Terrestrial Physics, 81—82, 27-40, 2012

Scafetta, N. Solar and Planetary oscillation control on climate change: Hind-cast, forecast and a comparison with the CMIP5 GCMS, Energy and Environment, 24, 456-396, 2013

Scafetta, N. and Willson, R. C. Empirical evidences for a planetary modulation of total solar irradiance and the TSI signature of the 1.09-year Earth-Jupiter conjunction cycle, Astrophysics Space Sci DOI 10.1007/s10509-013-1558-3, 2013

Solheim, J.-E. Signals from the planets, via the Sun to the Earth, Pattern Recogn. Phys. 1, 177-184, 2013a

Solheim, J.-E. The sunspot cycle length – modulated by plants?, Pattern Recogn. Phys. 1, 159-164, 2013

Soon, W. Solar Arctic-mediated climate variation on multidecadal to centennial timescales: Empirical evidence, mechanistic explanation, and testable consequences. Physical Geography, 30, 144-184, 2009

Soon, W. Dutta, K., Legates D. R. Velasco, V. and Zhang, W. Variation in surface air temperature of China during the 20th Century, Journal of Atmospheric and Solar-Terrestrial Physics, 73, 2331-2344, 2011

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Stauning, P. Reduced Solar Activity Disguises Global Temperature Rise, Atmospheric and Climate Sciences, 4, 60-63, 2014

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Svensmark, H. Cosmoclimatology: A new theory emerges, Astronomy and Geophysics, 48, 1.18-1.24, 2007

Usoskin, I. G., Solanki, S. A., Kovaltsov, G. A. Grand minima and maxima of solar activity: new observational constraints, Astron. Astrophys., 471, 301-309, 2007

Guest blog Ilya Usoskin

The Grand Maximum was not a unique event

The “best” TSI reconstruction

Unfortunately, there is no known imprint of Total Solar Irradiance (TSI) in the past. Here I mean that we have no terrestrial record which would be influenced by TSI so that its variability would be primarily defined by TSI. For example, the width of tree rings in a tree at mid-latitudes is an imprint of the local climate (the warmer is the summer, the more tree grows, and the wider is the ring), sunspot number is an imprint of solar activity, etc. If we could find something (e.g. a substance in the Earth’s environment) that would respond to changes in TSI without being essentially affected by other factors, we could use it as an imprint for TSI. However, such a proxy is missing.

Sometimes, indices of solar activity, such as the sunspot number, are called TSI proxies, but that’s not correct. They are used as input parameters to calculate TSI in some models but cannot serve as a real proxy, simply because we don’t know how they are related. Most of the models use a linear regression between solar indices and TSI, based on an empirical correlation. This is very risky, as correlations never give a real relation for the periods outside when it was established. An example is a correlation between cloudiness and the temperature somewhere. It looks obvious than cloudy days are colder, but not for the high-latitude winter days, where cloudy days are warmer than sunny days. Thus, an extension of a correlation outside the range of its validity may easily lead to spurious results (is we used temperature as a proxy for cloudiness, we would get wrong for winters).

Since all the TSI reconstructions in the past are based on extrapolations and cannot be directly verified via imprints or proxy records. Thus, no OBJECTIVELY “best” reconstructions can be defined. Most of the TSI models use a simple regression between TSI and other indices (e.g. the modulation potential, which is a measure of cosmic ray variability; or sunspot numbers) extrapolated backwards in time. Only a few models (e.g. the one developed by a group at the Max-Planck Institute for Solar System Research in Göttingen, which pioneered a physical way to model TSI, see Vieira et al. 2011) use a physics-based numerical model rather than a regression. In this model they try to use a physical model to account for all known physical processes that lead to changes in solar irradiation – dark sunspots, bright structures, background radiation. This makes an “absolute” basis for TSI reconstructions.

On the other hand, the regression-based approaches are crucially dependent on the choice of the reference period and dataset and need to be ad-hoc “calibrated” without any guarantee that it works in the past. E.g. the model by Shapiro et al. uses the quite sun model which leads to a very large variability of TSI at centennial scale. But this cannot be checked. To conclude I would slightly favour the model of the Max-Planck Institute MPS (VSK in Figure 1 below), for the reason that it is based on a physics-based approach which, supposed the physics behind it is correct, should catch the non-linearity of the relations.

Figure 1: changes in TSI during the last millennium. Replication of figure 5.1b in IPCC’s AR5 report. The blue reconstruction is one published by Lean et al. 1995. Since the 90-ies reconstructions of the TSI have become considerably flatter suggesting that the influence of the sun through time is relatively small. See AR5 for all the references.

The Grand Maximum in the 20th century

Before discussing further, I would like to correct the statement in the Introduction article for this Climate Dialogue after Fig. 8 that “Several papers in the last decade have claimed that solar activity in the second half of the 20th century was higher than [at] any time in the past 10000 years”. The fact is that the recent Grand Maximum is rare but not unique on the time scale of the Holocene. Several similar Grand Maxima (about 20) took place over the last 11 millennia (Usoskin et al. 2007), thus one per about 500 years. Therefore, the Grand Maximum in the 20th century is not a unique event but a rare event.

The very existence of the Grand Maximum is not questioned by others I think. As e.g. stated by Clette et al. (Space Sci. Rev., 2014 in press): “The recalibrated series may thus indicate that a Grand Maximum needs to be redefined as a tight repetition/clustering of strong cycles over several decades, without requiring exceptionally high amplitudes for those cycles compared to other periods.” This indicates that even in the “corrected” sunspot series the Grand Maximum is observed as a period of clustering of several consecutive high cycles, even though the height of the cycles is not unique.

A special word can be said about the “corrected” sunspot number series shown in Fig. 9 of the Introduction.

It is not a result of the scientific consensus, not even mainstream yet, as still many uncertainties remain, and the proposed series is yet a working hypothesis based on unpublished work. Other corrections and justifications (e.g. Leussu et al., 2013; Lockwood et al., 2014) also exist and are being discussed in the community. Anyway, Fig. 9 is picked up from a popular article rather than from a scientific paper and represents the personal view of an author (B. Owens who presented it in the New Scientist magazine) rather than a scientific paradigm.

The preferred temperature reconstruction.

I don't have any preferred reconstruction as it is beyond my field of expertise.

The correlation between global temperature and solar activity

There are many indirect results suggesting such a relation on long-term scale (centuries to millennia). Just a few examples can be the correlation between solar activity and the extent of icebergs in the North Atlantic (Bond et al., 2001) or a coincidence between solar activity minima and cold/wet spells in Europe (Usoskin & Kovaltsov, 2008), both at millennial time scales. Each of those is weak and not very convincing along, since it is based on a statistical correlation which can be disputed. However, in the aggregate they imply that there is a link between paleoclimatic and solar activity reconstructions.

Attribution of warming to the sun

Although the present knowledge remains poor, in particular since most of the climate models consider only the direct TSI effect which is indeed quite small, I would intuitively and subjectively say that the solar influence was an important player until mid-20th century, but presently other factors play the dominant role. However, such time-delaying processes as e.g. ocean heating, are not straightforwardly considered.

Amplification processes

The most famous mechanism for an indirect influence of the Sun on climate is the cosmic-ray-cloud connection, aka Svensmark's mechanism, which suggests induced/mediated nucleation of cloud condensation nuclei by atmospheric ionization caused by cosmic rays. This mechanism, called also the clear-sky mechanism, has been extensively studied recently, in vitro, in the dedicated laboratory experiments CLOUD and SKY. It was shown that the effect does exist but is too small to play a notable role in the climate. Thus, this mechanism is hardly a strong player.

On the other hand, another mechanism, related to the global electric circuit, may operate in already existing clouds (see e.g. Tinsley, 2008; Harrison et al., 2011). This may operate via the electro-scavenging or electro-freezing of condensation nuclei in the presence of electric field, which is induced by the global electric current between the ground and the ionosphere. Cosmic rays, which control the conductivity of air via ionization, and geomagnetic activity, which affects the ionospheric potential, both affect this current and thus may slightly modulate the properties of clouds. However, quantitative models are still missing. Other possible positive feedback (amplification) mechanisms include top-down and bottom-up ones involving atmospheric chemistry and large scale air-mass and ocean dynamics. In a very simplified way the “top-down” mechanism may be related to the ozone depletion in the stratosphere by charged energetic particles of solar or magnetospheric origin, that leads to enhanced UV irradiation of the ground, and also changes the wind pattern in the stratosphere, affecting the heat redistribution. It can be completed by the “bottom-up” mechanism related to the large-scale atmosphere-ocean interaction, which provides a positive feed-back (see a review by Gray et al., 2010).

The predictability of solar variability.

Although there are different long-term “predictions” of solar activity, they are not predictions in a strict sense. It is more correct to call them “possible scenarios”. Such scenarios are based typically on multi-harmonic (or neural network or other) mathematical extrapolations of the existing series. This would work if the time series were stationary (in the sense that a sufficiently long subset of data contains the main statistical features of the entire series). However, the solar activity data sets have been shown to be essentially non-stationary thus making true predictions hardly possible because of an essential stochastic/chaotic component. E.g. with an equal success one can predict the behaviour of the financial market and become rich.

The issue is that no one knows when the new Grand Minimum occurs and no one really knows what would happen then. I call such extended minima of suppressed solar activity Grand Minima, since the Maunder Minimum (lasting from 1645 till about 1700 or 1712) is only one of those. Later minima, such as the Dalton Minimum (ca. 1800 AD) and modern (ca. 1900 AD) ones were not really Grand Minima, in neither depth or duration.

Figure 2: Solar activity (sunspot number) reconstructed from 14C data for the last 11 millennia (Usoskin et al., 2007). Blue/red color indicate the Grand Minima/Maxima of solar activity.

Indeed, we are certain that there will be a Grand Minimum sooner or later (there were 27 ones during the last 11 millennia, see Figure 2) but their occurrence is unpredictable. The 27 minima during 11 millennia imply that Grand Minima appear roughly every 400 years, but they are spread very unevenly, with intervals between them being from a hundred years to a few thousand years. No regularity was found in their occurrence (except for the ~200-yr repetition appearing sporadically), but rigorous statistical studies suggest that they occur randomly. Thus, no definite prediction of a future Grand Minimum is possible, but a probabilistic forecast can be made, e.g. Barnard et al., (2011) said: “There is an 8% chance of the Sun falling into a grand minimum during the next 40 years”. This is not a prediction but a probabilistic forecast or also called a possible scenario.

Concerning the influence on climate, I think we are unable at the moment to make a realistic assessment to what will be the consequence, since many processes are still poorly understood and modelled.


Prof. Ilya Usoskin works at the University of Oulu (Finland). He is vice-Director of the Finnish National Centre of Excellence in Research on Solar Long-term Variability and Effects (ReSoLVE). He focuses his research on Solar and Solar-terrestrial physics as well as in Cosmic Ray physics. He is a member of numerous scientific commissions and panels, reviewer and member of editorial boards for a number of professional journals, and an organizer of scientific conferences and symposia, including a series of biennial International Symposia in Space Climate. He is an author of more than 200 scientific publications, including 150 peer-review ones, among those a dozen of invited reviews and book chapters.


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Leussu, R., et al., Inconsistency of the Wolf sunspot number series around 1848, Astron. Astrophys., 559, A28, 2013.

Lockwood, M., Owens, M. J., Barnard, L., Centennial variations in sunspot number, open solar flux, and streamer belt width: 1. Correction of the sunspot number record since 1874, J. Geophys. Res., 119, 5172, 2014.

Semeniuk, K., et al., Middle atmosphere response to the solar cycle in irradiance and ionizing particle precipitation, Atmos. Chem. Phys., 11, 5045, 2011.

Shapiro, A. et al., A new approach to the long-term reconstruction of the solar irradiance leads to large historical solar forcing, Astron. Astrophys., 529, 67, 2011.

Tinsley, B., The Global Atmospheric Electric Circuit and its Effects on Cloud Microphysics, Rep. Progr. Phys., 71, 066801, 2008.

Usoskin I.G., and G.A. Kovaltsov, Cosmic rays and climate of Earth: Possible connection, Compt. Rend. Geosci., 340. 441-450, 2008.

Usoskin, I.G., S.K. Solanki, G.A. Kovaltsov, Grand minima and maxima of solar activity: new observational constraints, Astron. Astrophys., 471, 301-309, 2007.

Vieira L. et al., Evolution of the solar irradiance during the Holocene, Astron. astrophys., 531, 6, 2011.

Guest blog José Vaquero

“I am fairly skeptical about a new Maunder Minimum”

The influence of the Sun on the Earth's climate is a fascinating subject. Everyone understands that the sun is the main energy source for the climate system. However, the detection of clear variations of climatic parameters due to solar activity is very difficult because the climate system is extremely noisy. Therefore, time scales have a fundamental role in these studies.

“Solar Activity” is a rather vague term. Thanks to cosmogenic isotopes we have some “visions” of “solar activity” in recent millennia (Usoskin, 2013). The most striking of these “visions” of solar activity is that the Sun may be in a “normal” state, or in two states named grand solar maximum or minimum (Usoskin et al., 2014). Obviously, we think that if the Sun is in a state of grand maximum (minimum), then more (less) energy arrives on Earth and, therefore, the temperature of our atmosphere will increase (decrease). This simple idea agrees with the fact that the Little Ice Age coincided with the Maunder Minimum (MM) and the Medieval Climate Anomaly (MCA) coincided with the Solar Medieval Maximum. But in the end, things are a bit more complicated.

For example, My colleague Ricardo Trigo and I obtained recently a result that supports the view that internal variability of the coupled ocean-atmosphere system was the main driver of the Medieval Climate Anomaly and, therefore, solar activity had a minor role in this climatic episode. Note that new reconstructions of solar activity based on cosmogenic radionuclides do not sufficiently constrain the Total Solar Irradiance (TSI) range during the Medieval Climate Anomaly (see black lines in Figure 1). Therefore, we made other approximations of the problem. We reconstructed the average solar cycle length (SCL) during the Medieval Climate Anomaly using historical data from naked-eye sunspot observations and aurora sightings. We used information recorded in historical sources because they have a high time-resolution. We conclude that the average duration of the solar cycle was about 10.72 ± 0.20 years. This value for the solar cycle length corresponds to a solar activity that was probably not exceptionally intense, supporting the hypothesis that internal variability of the climatic system is the main driver of the Medieval Climate Anomaly.

These results are based on very old observations that were made without telescopes. Using these early historical data it is easier to detect solar maxima like the medieval maximum than solar minima like the Maunder Minimum. It is very difficult to detect solar minima using naked-eye sunspot observations and aurora sightings. Therefore, historical sunspot observations using astronomical telescopes are a key to understand the Maunder Minimum.

Figure 1. Different solar activity proxies during the period 1050–1300: TSI reconstructed by Steinhilber, Beer, and Fröhlich (2009) (dashed black line), TSI reconstructed by Vieira et al. (2011) (continuous black line), annual number of naked-eye observations of sunspots (Vaquero, Gallego, and García, 2002) (blue line), and annual number of auroral nights (Krivský and Pejml, 1988) (orange line). Black arrows are evenly spaced and correspond to the estimated maxima of solar cycle derived from Vaquero and Trigo (2012). Blue arrows correspond to estimated maxima of solar cycle using naked-eye observations the orange arrows are based on auroral nights sightings. Inset shows, using the same color code, a histogram of the delays (in years) between the fitted and estimated maxima of solar cycle. Oort and Wolf refers to two periods of lower solar activity.

Maunder Minimum
The classical reconstruction of solar activity based on the Wolf number started in 1700, 1749 and 1818 at annual, monthly and daily scales respectively (International Sunspot Number, ISN, see Clette, 2011). Therefore, it does not cover the Maunder Minimum. However a reconstruction by Hoyt and Schatten (the so-called Group Sunspot Number, GSN) published in 1998 starts in 1610 and, therefore, is an extremely useful tool to investigate the Maunder Minimum. The Group Sunspot Number is based only on the number of sunspot groups that are observed in the solar disc. However the International Sunspot Number is computed from the number of sunspot groups and the number of individual spots that are observed in the solar disc. GSN and ISN are pretty similar from approximately 1880 onwards. However they are very different in the 18th century and much of the 19th century. GSN values show a strong upward trend since the end of the Maunder Minimum up to now while values of ISN show no significant trend.

The Hoyt and Schatten series (GSN) is the best reconstruction of solar activity of the last four centuries available today. But we now also know there are some major problems with this series. All reconstructions of solar activity or the Total Solar Irradiance of the last centuries or millennia used this series, directly or indirectly, at some point in their derivation. Thus, the GSN is of great significance.

Unfortunately, Clette et al (2014), of which I am coauthor, has revealed several problems in the current GSN. The most important is that the reference series (obtained from the solar program at the Royal Greenwich Observatory, RGO, started in 1874) is not homogeneous in its early years. I presented Figure 2 in the 2nd Sunspot Number Workshop (Brussels, May 2012, see Cliver et al., 2013). This figure shows the ratio between the sunspot group counts of RGO and the main sunspot observers in the last quarter of the 19th century. All ratios present a positive trend. Therefore, sunspot counts by RGO are low in the first years of this Figure 2 and high in the last years in comparison with the rest of observers. Therefore, RGO counts are not homogeneous in these times. However, RGO was chosen by Hoyt and Schatten (1998) as the prime observer due to the prestigiousness of its solar program. This causes lower GSN values before 1880 because the calibration constant of the observers were adjusted using these first years and the error was propagated back.

Figure 2. Smoothed ratio between the sunspot group counts of Royal Greenwich Observatory and the main sunspot observers in the last quarter of the 19th century.

Furthermore, Leussu et al. (2013) found the ISN series decreased by roughly 20% around 1848 comparing with the data from original sunspot drawings by Schwabe. However, it was not found by Clette et al (2014) using the data provided by Hoyt and Schatten (1998). According to Leussu et al (2013), this breakpoint around 1848 was caused by the change of the primary observer from Schwabe to Wolf and, probably, an inappropriate individual correction factor used for Schwabe in the ISN. Therefore, we must check both historical sources to detect exactly the origin of these discrepancies.

This leads directly to the controversy over the existence of the Modern Grand Maximum in the 20th century. The results obtained by Clette et al. (2014) suggest there could be no Grand Solar Maximum in the second part of the 20th century. Figure 3 shows the trends for different versions of the sunspot number. Only the GSN produced by Hoyt and Schatten (1998) has a significant trend, allowing a high solar activity in the 20th century in comparison with the other centuries.

Figure 3. Trends different versions of the sunspot number: (yellow) ISN original, (blue) GSN original, (red) ISN corrected by Clette et al. (2014) and (green) GSN corrected by Clette et al. (2014).

In any case, note that the results presented by Clette et al. (2014) are preliminary and the authors indicate that “although recent cycles do not reach unprecedented amplitudes anymore, the repetition of five strong cycles over the last 60 years (cycles 17 to 22, with the exception of cycle 20) still marks a unique episode in the whole 400-year record” and they add that “this unique character is also illustrated when considering another sunspot byproduct, i.e. the number of spotless days over each sunspot cycle minimum”. Figure 4 shows the total number of spotless days from 1818 (red line). Unfortunately, we have no reliable data of this indicator for earlier years.

Figure 4. Cycle-to-cycle variation of the total number of spotless days from cycle 6 to 24, for which daily sunspot numbers are available (red curve). The ISN series is over-plotted with a reversed scale to highlight the strong anti-correlation between this indicator and the amplitude of the solar cycle.

My personal opinion is that we don’t have a clear definition of Grand Episodes (Maxima and Minima), including the Maunder Minimum. Note in Figure 5 a new definition of the limits dates of the Maunder Minimum(Vaquero and Trigo, 2015) that illustrate the problem with the exact definitions of Grand Episodes. The common limits dates for the MM are 1645-1715, proposed by Eddy (1976). However, we have proposed a redefinition of the Maunder Minimum period with the core “Deep Maunder Minimum” spanning from 1645 to 1700 (that corresponds to the Grand Minimum state) and a wider “Extended Maunder Minimum” for the longer period 1618–1723 that includes the transition periods. The origin of this new “definition” was the discovery of sunspot observations made by G. Marcgraf in 1637. A new analysis of this data (and other revised historical observations) indicated a possibly gradual onset of the minimum with reduced activity starting two cycles earlier (Vaquero et al., 2011).

Figure 5. Solar activity indexes for the period 1550-1800: (green)decadal sunspot numbers, reconstructed by Usoskin et al. (2014), (orange) 25-year moving average of the number of aurorae observed from Hungary (Rethly and Berkes 1963), and (blue) Group Sunspot Number (Hoyt and Schatten 1998, Vaquero et al. 2011).

Certainly, understanding the Maunder Minimum is key for our understanding of a lot of things about the Sun and the climate of the Earth because it is a unique Grand Minimum observed using telescopes. However, our knowledge about it is quite limited. I would like to present one example. Figure 6 shows one slide of my presentation in the last Sunspot Workshop (Locarno, 2014). It shows a fragment of a table containing observations of the solar meridian altitude. Observations of the solar meridian altitude (the angle between the Sun and the horizon just when the Sun is crossing the meridian of a place) are relevant because Hoyt and Schatten (1998) used them to check if the Sun had been observed during the Maunder Minimum (see Vaquero and Gallego (2014) for details).

These observations (Figure 6) were made by Hevelius, one of the main sunspot observers during the Maunder Minimum. Note how, in this table, comments (in Latin) about sunspots are not exactly associated with the reported solar meridian altitude. Thus, Hoyt and Schatten (1998) computed “zero” sunspot numbers using all the observation in this table if no information on sunspot was available. Therefore, the number of days with “zero” sunspots is very large because there are few days with direct information about the presence/absence of sunspots.

Figure 6. A fragment of the table of solar meridian altitudes published by Hevelius in his Machinae Coelestis, showing records of sunspots during the Maunder Minimum. Comments about sunspots are not exactly associated with the reported solar meridian altitude.

I have computed the average sunspot number using only the direct information about sunspots that appear in this historical source. My results indicate an average value of GSN equal to 3 and 4 for the periods 1659-1661 and 1653-1663, respectively. In contrast, Hoyt and Schatten (1998) obtained values equal to 0.9 and 0.5 for the same periods. Therefore, the estimations of GSN from Hevelius’ direct observations are 3-8 times higher than the values obtained by Hoyt and Schatten. Obviously, the values remain very low if we compare them with modern values (see Figure 3). But this result indicates that there are probably many erroneous values equal to zero in the database by Hoyt and Schatten (1998). And, our knowledge of the Maunder Minimum, based now on the original GSN series, is not correct in some important details.

In any case, with better or worse series of temperature and solar activity, today we have the best tools to explore the relationship between the Sun and the Earth's climate: Earth System Models. I hope that the next few years a lot of results will be published offering explanations of the mechanisms that produce local changes in the Earth's climate due to changes in solar activity, since some steps are being given. These models are the appropriate tools to explore the impact of a possible new “Maunder Minimum” in the 21th century.

I am in fact pretty skeptical about the possibility of a new great episode. I think we do not know enough about how the solar dynamo works and, therefore, it is not possible to offer predictions. In fact, the studies that suggest a Grand sunspot minimum are based on observational aspects and not on the physics of the Sun.

In any case, I can point out that we showed recently that the transition from a normal to a grand minimum state occurred gradually during the Maunder Minimum (Vaquero et al., 2011). Therefore, if a new Grand minimum occurs in this century, I expect that the transition will be gradual (not abrupt) again.

Some authors have begun to explore the impact of a possible solar grand minimum on the climate of the Earth (Feulner and Rahmstorf, 2010; Jones et al., 2012; Anet et al., 2013; Meehl et al., 2013). Early results suggest that this grand minimum will not stop global warming caused by CO2 and other greenhouse gases. However, locally, the signal of this possible Grand minimum could be more intense, for example, in the European winter (Barriopedro et al., 2008; Lockwood et al., 2010).

José Manuel Vaquero (Badajoz, Spain, 1973) is a physicist interested in the reconstruction of solar activity and Earth’s climate during the last centuries from documentary sources. He is currently lecturer in Physics of the Earth (Centro Universitario de Mérida, University of Extremadura, Spain). Dr. Vaquero has published over 110 papers in peer-reviewed journals and a book (co-authored by Manuel Vázquez) entitled “The Sun Recorded Through History” (Springer, Astrophysics and Space Science Library, Vol. 361, 2009).


Anet, J., et al. (2013), Impact of a potential 21st century grand solar minimum on surface temperatures and stratospheric ozone, Geophys. Res. Lett., 40(16), 4420–4425.

Barriopedro D., García-Herrera R., Huth R. (2008): Solar modulation of Northern Hemisphere winter blocking, J. Geophys. Res., 113, D14, D14118

Clette, F. (2011) Past and future sunspot indices: New goals for SoTerIA. Journal of Atmospheric and Solar-Terrestrial Physics 73:182–186, 2011.

Clette, F., L. Svalgaard, J.M. Vaquero and E. W. Cliver (2014) “Revisiting the Sunspot Number. A 400-year perspective on the solar cycle” Space Science Reviews (accepted).

Cliver, E.W., F. Clette, L. Svalgaard (2013) Recalibrating the Sunspot Number (SSN): The SSN workshops. Cent. Eur. Astrophys. Bull. 37(2), 401–416.

Eddy, J.A. (1976) The Maunder Minimum. Science 192, 1189.

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Krivský, L., Pejml, K.: 1988, Publ. Astron. Inst. Czech. Acad. Sciences 75

Jones, G. S., M. Lockwood, and P. A. Stott (2012),What influence will future solar activity changes over the 21st century have on projected global near-surface temperature changes?, J. Geophys. Res., 117(D5).

Leussu, R., Usoskin, I.G., Arlt, R., Mursula, K. (2013) Inconsistency of the Wolf sunspot number series around 1848. A&A 559:A28.

Lockwood, M. et al., M. (2010) Are cold winters in Europe associated with low solar activity? , Environ. Res. Lett. 5, 024001.

Meehl, G. A., J. M. Arblaster, and D. R. Marsh (2013), Could a future grand solar minimum like the maunder minimum stop global warming?, Geophys. Res. Lett., 40(9), 1789–1793.

Rethly, A., Berkes, Z., 1963. Nordlicht beobachtungen in Ungarn, 1523–1960. Verlag der Ungarischen Akademieder Wissenschaften, Budapest.

Steinhilber, F., Beer, J., Fröhlich, C.: 2009, Geophys. Res. Lett. 36, L19704.

Usoskin, I.G., A History of Solar Activity over Millennia, Living. Rev. Solar Phys., 10, 1, 2013.

Usoskin, I.G., G. Hulot, Y. Gallet, R. Roth, A. Licht, F. Joos, G. A. Kovaltsov, E. Thebault and A. Khokhlov (2014) Evidence for distinct modes of solar activity, Astron. Astrophys., 562, L10.

Vaquero, J.M., Gallego, M.C., García, J.A.: 2002, Geophys. Res. Lett. 29, 1997.

Vaquero, J.M., M. C. Gallego. I. G. Usoskin, G. A. Kovaltsov (2011) “Revisited sunspot data: A new scenario for the onset of the Maunder minimum” The Astrophysical Journal Letters 731, L24.

Vaquero, J.M. and Trigo, R.M. (2012) “A note on solar cycle length during the Medieval Climate Anomaly” Solar Physics 279, 289-294.

Vaquero, J.M., Gallego, M.C (2014) “Reconstructing Past Solar Activity using Meridian Solar Observations: the Case of the Royal Observatory of the Spanish Navy (1833-1840)” Advances in Space Research 53, 1162-1168.

Vaquero, J.M. and Trigo, R.M. (2015) “Redefining the limit dates for the Maunder Minimum” New Astronomy 34, 120-122.

Vieira, L.E.A., Solanki, S.K., Krivova, N.A., Usoskin, I.G.: 2011, Astron. Astrophys. 531, A6.

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  • Marcel Crok

    Let’s start the dialogue with our first question: What is according to you the “best” solar reconstruction since 1600 (or even 1000) in terms of Total Solar Irradiance?

    Lockwood in his guest blog uses an interesting two step approach to this question. First he uses the PMOD dataset to get an impression of how Total Solar Irradiance decreased in the recent “low” solar activity cycle 24. The difference with earlier cycles in the PMOD dataset is rather small (0.38 W/m2). See his figure 1:

    In step 2 he correlates sunspots with TSI. In 1900 solar activity based on the sunspot record was similar as today. So TSI should be as well. See his figure 2:

    Conclusion: if the correlation between sunspots and TSI (as determined in the PMOD period since 1979) is pretty good, then TSI must have been similar then and now. It also means TSI reconstructions since 1700 are rather flat.

    Now this contrasts with the TSI reconstruction that Scafetta and Solheim prefer, the one of Hoyt and Schatten 1993 (in combination with the ACRIM dataset, in which Scafetta is involved). See e.g. Solheim’s figure 2A:

    Now if we look at the sunspot record that Solheim shows in his figure 5, this agrees with Lockwood’s record showing similar levels in 1900:

    This suggests the difference in the TSI reconstructions is mainly based on using PMOD or ACRIM. Is that correct? Does that explain all the differences between your TSI reconstructions?

    These questions are first of all posed to Lockwood, Scafetta and Solheim, but Usoskin and Vaquero of course are more than welcome to weigh in.

  • Jan-Erik Solheim

    Expected temperature decrease in the coming Maunder Minimum (an update)

    Since I wrote my first blog, new TSI reconstructions have been published by Velasco Herrera et al. (2015). In this paper they present a new method, which they call Least Squares Support Vector Machines, to estimate the TSI index from year 1000 to 2100 AD. They use the PMOD and ACCRIM composites for calibration, and they have generously given me their data sets to investigate.

    Below are the two estimates, which I have compared with the Central England temperature measurements since 1659:

    In both cases the Central England temperature (CET) is drawn in black, while the TSI estimates are in red. CET follows both quite well after 1850, but V-ACC (TSI estimate based on ACCRIM) correspond better in the minimum around 1970. The big difference is before 1850. Here CET follows V-ACC quite well, while it is systematically higher than the V-PMOD estimates.

    Based on this comparison, I conclude that the CET, which is a good proxy for the global temperature, follows V-ACC better, and I select this as the better choice for estimating the global temperature change for the coming minimum.

    The two reconstructions give slightly different dates and length for the coming minimum: TSI(PMOD) has an estimated minimum 2004-2075 with a TSI-reduction of 0.09 W/m2. The estimate based on ACCRIM has minimum 2002-2063 and a TSI-reduction of 0.18 W/m2.

    To convert the TSI to global temperature, I use the results just published by Harde (2014). He uses an advanced two-layer climate model to calculate the influence of an increasing CO2-consentration and a varying solar activity on global warming. His simulations including an increased solar activity the last century give a CO2 initiated warming of 0.2°C and a solar influence of 0.54°C during this period. The CO2 climate sensitivity (for doubling) becomes 0.6°C, and the solar sensitivity is 1.15°C for 0.1% TSI(V-PMOD) or 0.9°C for 0.1% TSI (V-ACC) increase.

    For the two TSI reconstructions and a CO2 increase of 2 ppm per year, I have calculated that the global temperature change in the coming minimum will be -0.23°C based on TSI(V-PMOD) and -0.80°C based on the TSI(V-ACC) estimates.

    Since the Central England Temperature followed TSI(V-ACC) best I conclude that a GST estimate based on TSI(V-ACC), giving -0.8 °C relative last decade, is most likely for the coming minimum..

    H. Harde, Advanced Two-Layer Climate Model for the Assessment of Global Warming by CO2, Open Journal of Atmospheric and Climate Change, ISSN(print): 2374-3794 ISSN(Online): 2374-3808, 2014 In Press

    V.M. Velasco Herrera, B. Mendoza and G. Velasco Herrera, Reconstruction and prediction of the total solar irradiance: From the Medieval Warm Period to the 21st Century, New Astronomy, 24, 221-244, 2015

  • Jan-Erik Solheim

    Reply to Jos Hagelaars’ comment:

    I fail to see the correlation with the Ljungqvist reconstruction (and even more so with the last decade added) and sunspot numbers in the bottom panel in figure 5 of the post of Prof. Solheim. For instance the high number of sunspots around 1600 or 1750 and the low temperature in the NH extra tropics in that period? Would Prof. Solheim be so kind to explain this lack of correlation?

    I agree there is a jump in the Northern hemisphere temperature from the 1980ies to the period after 2000. It will be interesting to see how trees react to this jump, since most of the changes have been related to milder winters and warmer spring and fall in the North. It is always a danger to combine proxy temperatures from trees, which may not respond fast, to instrument temperatures. The graph below shows the difference between the North, Equatorial and South in satellite tropospheric temperature, and we find a large temperature jump in the North, a small in the South, and none in Equator region:

    Temperature observations by satellites as interpreted by Drs Roy Spencer and John Christy, at University of Alabama (graph from climate4you.com)

    When it comes to the lack of correlation around 1600 or 1750, it may be that the proxies are too slow to react to these short periods of increased solar activity. If we assume that the sunspot no is proportional to the heat the Earth receives, we have to compare areas under the average sunspot curve, which is much larger in the period 1780-2000. If we instead look at the TSI reconstruction (figure 2 in my second blog), we see quite nice correlation with CET – also around 1750.

  • Jan-Erik Solheim

    Reply to Greg Goodman:

    Thanks – a very helpful comment. The sudden jumps in temperature need to be explained. But can you explain why the jumps are bigger in the Northern Hemisphere temperature records?

  • Mike Lockwood

    The plots that Jan-Erik Solheim shows between CET [Central England Temperature] and TSI reconstructions are really interesting but must now be evaluated with proper statistical testing against the null hypothesis. Agreements of the kind he presents, and indeed seemingly even closer ones, do often happen by chance and it is vital to test if there is any significance in the similarities or not. I will not here pre-judge what I think those tests are likely show, but they must be done.

    I have carried out some statistical significance testing of winter Central England temperature (CET) against solar activity and did find a weak signal there that was indeed statistically robust in that there were more cold winters when solar activity was low [Lockwood et al., 2010]. (Winter here means December-January-February (DJF) averages).

    I did also find a tendency for more hot summers when solar activity was low, which I was very excited about because it would have been consistent with our interpretation of the winter data in terms of enhanced jet stream blocking in the eastern Atlantic when solar activity is low. However, this summer relation failed the statistical tests, yielding a very high probability that it was just a chance occurrence. Thus there is no evidence that there is a corresponding effect on summer blocking events. This is, in fact, not surprising because the equator-to-pole temperature gradient in the lower stratosphere/upper thermosphere which drives the jet stream is very much weaker in summer than in winter and modelling of the solar activity effect strongly suggests it is a predominantly a winter phenomenon.

    But the statement I would take issue with immediately is “I conclude that the CET, which is a good proxy for the global temperature, ….”. This is a major assumption that is not supported by the data. The figure below shows global regression maps by Lean [2010].

    These are maps of the detected surface temperature responses in data for 1980-2008 to input data series of El-Niño (ENSO), volcanic forcing, solar variability, and anthropogenic forcing. The fit residuals at each point (not shown) are attributed to natural internal variability. This is multiple regression analysis, exactly like that shown in figure 3 of my blog except that the regression has been carried out for the temperature in each pixel of the map, not on the global mean temperature.

    To avoid the relative scaling of the plots reflecting the relative radiative forcings of each input, each response map has been scaled to the response for a known event: these are (from top to bottom): the ‘‘super El Niño’’ event of 1998, the Pinatubo volcanic eruption, solar cycle 23 and the net anthropogenic forcing over the period 1980–2006. These are very similar in form to the maps of the regression coefficients presented by Lean and Rind (2008) that did use the radiative forcings. Note that these are not proper detection-attribution studies because the internal variability has not been modelled and so it is possible noise features have been “over-fitted”.

    As discussed in my blog, we know that the effect of over-fitting was small in the studies of the global mean temperature but it remains a potential factor in these regression maps. However, looking at the responses to El-Niño, volcanic and anthropogenic forcing, the results are explicable and consistent with climate modelling suggesting over-fitting is not an issue here either. Looking at the top panel, as expected, the El-Niño response is dominated by the equatorial Pacific, but with significant (so-called) “teleconnection” responses elsewhere, particularly North America. The cooling effect of the volcanic forcing is seen in the second panel to be global, but there are some regions where the opposite response is seen.

    Moving to the bottom panel the response to anthropogenic forcing is very much as predicted by models, with the greatest response over land and hence in the land-dominated northern hemisphere. The response is the expected temperature rise at most locations over the globe but there are some small regions where the opposite is seen.

    Lastly the third panel shows the response to solar activity. It can be seen to be primarily a Eurasian phenomenon (under the northern hemisphere jet stream after it has hit land after crossing the Atlantic), and central England lies at the western end, but inside, this region. Note that our studies with the ERA-40 re-analysis data during the satellite era reveal that low solar activity gives colder winters in Eurasia but this is accompanied by warmer winters in Greenland/Arctic Canada region [Woollings et al, 2010]: this is not seen in the figure because there were insufficient data in this region. This highlights how easy it is for biases to be introduced by regional and seasonal effects because the Greenland/Arctic Canada changes in the Woollings et al. [2010] study largely counterbalance the Eurasian effect, but that counterbalancing is missing in the dataset used in the Lean [2010] study because of the missing Arctic data.

    The key point is that CET is certainly not representative of a global response to solar forcing. This is precisely the points about the crucial difference between regional and global mean responses that I was making in points (1) and (2) of my blog.

    The regionality of the response to solar forcing seen in the figure is extremely significant because as we go back in time our surface temperature observations become more and more weighted to precisely the Eurasion region where we find this response (which is, in fact, largely a winter response). Care must be taken with even paleoclimate data to ensure that they are taken from sites which avoid a bias being introduced by this regionality.

    Lockwood M, Harrison RG, Woollings T, Solanki SK (2010) Are cold winters in Europe associated with low solar activity? Environ Res Lett 5:024001. doi:10.1088/1748-9326/5/2/024001
    Lean J (2010) Cycles and trends in solar irradiance and climate. Wiley Interdiscip Rev Clim Change 1(1):111–122. doi:10.1002/wcc.018
    Lean JL, Rind DH (2008) How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophys Res Lett 35:L1870. doi:10.1029/2008GL034864
    Woollings T, Lockwood M, Masato G, Bell C, Gray L (2010) Enhanced signature of solar variability in Eurasian winter climate. Geophys Res Lett 37:L20805. doi:10.1029/2010GL044601

  • Ilya Usoskin

    Unfortunately, I see no real way to define “the best” TSI reconstruction in the past. The problem is that we have no independent way to “validate” such a reconstruction outside the training period.

    Let me explain. A normal way of reconstructing a quantity X from a proxy data Y, using a mathematical/statistical/empirical relation in the absence of a full physical model relating the reconstructed quantity X and the proxy Y, consists of several steps:

    (1) selection of a “training” period when both X and Y are known with good accuracy;

    (2) building a model (let us call it a regression) relating Y to X and optimizing it;

    (3) selecting a “validation” period when both X and Y are known, but were NOT used to build the regression; it is crucial that data from the “validation” period are not used in the training of regression.

    (4) for the “validation” period, values of X’, the reconstructed from Y values, are compared with the real values of X to validate the regression.

    Ideally, “validation” period should be at least as long as the “training” period or better longer. However, this procedure cannot be done for TSI reconstructions for the obvious reason. The measured TSI series (X) is too short to split it in parts. Moreover, until the 2000s it was totally dominated by the 11-yr cycle without a secular change. Only after 2006 there is a hint for the secular change but it is too short to make a reliable regression. Thus, any extrapolation of the regression between TSI and a proxy is not “validated” in the strict sense, and may fail, and what is worst, this failure cannot be controlled.

    Another problem is that the centennial variability cannot be in any way controlled since the “training” period is too short for that.

    Figure 1. A regression between group sunspot numbers (GSN) and 10Be concentration in an ice core for the period 1700-2010. Small red dots depict the annual values, while big yellow dots – the 11-yr averaged values.
    Different regressions are shown: yellow line corresponds to the regression built using 11-yr averaged data for the entire period, while the green line – annual values for the period 1950-2010 [Modified after Usoskin & Kovaltsov, 2004]. One can see that a regression built using annual values for 1950-2010 leads to wrong results when used on a centennial time scale.

    We have made a clear case of such a failure using 10Be data (Usoskin & Kovaltsov, Solar Phys., 224, 37, 2004) – see the plot above. Let us assume we have annual data of 10Be and GSN only for the period 1950-2010 (as shown by the green oval), and we build a regression for that period (as shown by the blue line). Then we happily extrapolate the regression (blue line) backwards down to 1700 AD, and… get wrong results. Fortunately, we do have both 10Be and GSN data since 1700 to validate the relation. It is obvious that a regression built for the period 1950-2010 doe NOT reproduce the centennial trend adequately. The same situation may happen for TSI reconstruction but since we do not have a mean to validated TSI values in the past, we do not know that.
    All this is explained simply by the strong non-linearity of the relation which make the regression to depend on time scale, averaging, etc.

    In such a situation, there is no way to select a “best” reconstruction.

    In this sense, the most favorable is a reconstruction by the German group (Krivova, Solanki) who use not a regression but a physics-based model. Still it has one parameter whose value is found by fitting the data, but at least it intrinsically includes possible non-linearities.

    And I don’t believe that solar activity and hence TSI can be predicted in the future in a deterministic sense. Only in the probabilistic one.

  • Jan-Erik Solheim

    Comments to Mike Lockwood :

    I agree that CET is not global, but still it may be one of the best series to investigate long time changes. In particular since it covers a significant part of the Maunder Minimum, which is a topic in this discussion

    A reason for using data from one location is the better control of the data. The global temperature is a composite of many thousand recordings, and the number of stations and weighting scheme changes with time. Observations, done with care during centuries, from a well preserved site, may be a better for detecting long time changes as explained by Butler (1994) for Armagh Observatory. He finds a good correlation between Armagh and NH temperatures. However, the Armagh series reach back only to 1844.

    The global regression maps for Pinatuba shows the same global cooling as calculated by Greg Goodman (2014-10-18) of 0.2 °C

    C. J. Butler, Solar Physics, 152, 35, 1994

    Question to Mike Lockwood:

    I find your multiple regressions quite impressive, but which of the parameters used in your Fig 3 explains the obvious correlation between cloud cover and global temperature as shown below?

  • Jan-Erik Solheim

    Comment to Mike Lockwood and Ilya Usoskin:

    The CET data set covers 355 years. To investigate the relations between CET and TSI I have split the data set in two parts, each 177 yrs long, and compared with the two TSI reconstructions by Velasco Hererra et al. as shown in my previous comment.

    A: Central England Temperature versus TSI calibrated with PMOD:

    The first 177 yrs are shown with blue dots, and the last with red dots. I find the same slope for the two parts. A CET average temperature of 9.4 °C can be estimated for their grand minimum period 2004-75 based on the linear relation based on all data 1659-2013.

    B. Central England Temperature versus TSI calibrated with ACCRIM.

    With the TSI-ACCRIM calibration the temperature gradient is the same for the two subsets, but the last 177 yrs (red) is shifted up 0.1 °C. Estimated CET average temperature for the grand minimum period 2002-2063 based on the relation 1835-2013 is 9.2 °C.

    Temperature change to the next minimum
    The average CET temperature 1994-2013 is 10.2 °C. Temperature decrease based on TSI with PMOD will be 0.8 °C for the period 2004-75. Based on TSI-ACCRIM it will be 1.0 °C for the period 2002-63. Since the temperature gradient from Equator to Pole also changes with TSI (Soon and Legates, 2013), the change in global temperature will probably be less.

    W. Soon and D. L. Legates, Solar irradiance modulation of Equator-to-Pole (Arctic) temperature gradients: Empirical evidence for climate variation on multi-decadal timescales, Journal of Atmospheric and Solar-Terrestrial Physics, 93, 45-56, 2013

    The estimated cooling – already here?
    One may wonder if the cooling already has started as indicated by the TSI drop
    Since most of the heat goes into the oceans, it is interesting to notice that the average temperature 0-800m in the North Atlantic along 59N parallel has decreased 0.7 °C from the peak in 2006/2007, and it is still decreasing.

  • José Vaquero

    Dear colleagues,
    My opinions are very close to the exposed ideas by Mike Lockwood and Ilya Usoskin.
    I also think that we have a way to select a “best” reconstruction and, in any case, we would choose the reconstructions made using a physics-based model. A similar situation occurs with respect to the prediction of the solar activity (and TSI). The deterministic models fails and we can only find probabilistic solutions (see , for example, Solanki and Krivova, 2011).
    Solanki, SK; Krivova, NA (2011) Analyzing Solar Cycles, Science 334, Issue 6058, p. 916.

  • José Vaquero

    Reply to gymnosperm:

    If you believe the “little ice age” was caused by internal variability, do you believe a true ice age can be caused by internal variability? Not a stadial, the five to ten million year affairs with wide wings.

    I wrote that my results suggest that Medieval Climate Anomaly (Diaz et al., 2011) was driven by the internal variability of the coupled ocean-atmosphere system, with the solar activity playing a minor role in this climatic episode. Of course, the origin of a “true ice age” is not our Sun but a combination of elements of the Earth System, including a set of cyclic variations in the Earth’s orbit around the Sun (Milankovitch cycles).
    HF Diaz, RM Trigo, MK Hughes, ME Mann, E Xoplaki, and D Barriopedro (2011) Spatial and Temporal Characteristics of Climate in Medieval Times Revisited. Bull. Amer. Meteor. Soc. 92, 1487–1500.

  • I found the various posts quite interesting.

    Marcel Crok asked about the “best” solar reconstruction since 1600 (or even 1000) in terms of Total Solar Irradiance (TSI). I will try to respond in various ways because unfortunately this question cannot be unanswered. There is a need of a detailed discussion.

    All available TSI records are satellite measurements and they start in 1978. See the available TSI records at


    These records were combined to form composites. Two of the most common TSI composite were proposed by ACRIM and PMOD: see them at:

    As the reader can realize, even the TSI composites are ambiguous: the ACRIM one shows an increase from 1980 to 2000 and a decrease afterward and the PMOD composite, which is the one also showed by Marcel Crok in his first figure, shows a multidecadal decadal decreasing patter.

    It is important to remind that while the ACRIM composite uses the TSI measurements as published by the experimental teams, the PMOD composite uses altered records under a set of assumptions first proposed by Frohlich and Lean in 1998. The above reported ACRIM web-site explains in details the experimental issues.

    In any case, one of our recent publications

    Scafetta, N., and R. C. Willson, 2014. ACRIM total solar irradiance satellite composite validation versus TSI proxy models. Astrophysics and Space Science 350(2), 421-442.

    has likely fully demonstrated that Frohlich’s assumptions are questionable and self-contradictory. Thus, in my opinion, the ACRIM TSI composite is closer to the truth. The sun should have experienced a secular maximum around 2000 contributing the global warming observed from ~1970 to ~2000. Since 2000 the solar activity has gradually decreased. In our paper we have also proposed a tentative prediction for the next decade in Figure 15.

    I hope that the scientific community will fully understand the issues regarding the ACRIM and PMOD controversy. In my opinion, too many people have blindly believed PMOD without professionally checking the claims. Hopefully, our recent publication will clarify the issue. In fact, understanding solar activity during the last decades is necessary to understand the mechanisms driving the multidecadal and secular dynamics of solar activity.

    This brings us to the issue regarding the secular and millennial solar patterns asked by Marcel Crok.

    This issue is complicated by the fact that no TSI direct measurements exist before 1978. All TSI secular and millennial reconstructions are hypothetical models that are not even based on physical laws. They are proxy models! That is, they are based on hypothetical and unchecked relations among several solar related records.

    A typical example of how easy a proxy model can be proposed and how physically misleading it can be has been suggested by Lockwood in his figure 2. There the sunspot record is used as a “proxy” for TSI to which it would be linearly and positively correlated in its smoothed curve. Using this proxy model Lockwood argued that since 1700 the TSI has been 6 times at the current value simply because this is what that is observed in the used smoothed curve of the sunspot number record. The argument is, of course, very hypothetical and rudimentary. Indeed, it is unphysical.

    In fact, there are no robust physical evidences suggesting that the sunspot number record can be used to reconstruct TSI in the way Lockwood proposed. What is clearly known from physics is that sunspots reduce TSI emission: therefore, they contribute “negatively” to TSI variation. The physical reason why there appears to be a positive approximate correlation between TSI and the sunspot record is because solar faculae, that is, the bright spots also appear in grand number and extension during the sunspot maxima and their brightness usually, but not always, overcomes the darkness of the sunspots. Moreover, TSI is not modulated by sunspot and faculae alone. There is also the background radiation from the quite zone that is usually assumed to be “constant” without any solid physical evidence for it. Thus, it is evident that without knowing the dynamics of the faculae and of the background solar radiation any TSI proxy model based only on the sunspot number record is likely misleading.

    If since 1978 TSI did what the ACRIM TSI composite shows, the physical and empirical evidences are that there exists a very complex non-linear relation between sunspot, faculae and background radiation. TSI cannot be reconstructed by using simple linear proxy models and even less by using the sunspot number record alone.

    In figure 1A:

    Scafetta N., and R. C. Willson, 2013. Planetary harmonics in the historical Hungarian aurora record (1523–1960). Planetary and Space Science 78, 38-44.

    the sunspot number and a record of aurora observations since 1600 was compared. Although the two records are in general correlated, we also noted some significant discrepancy. Sometime, the two records have shifted patterns. For example, there appears to be a peak in the aurora record around 1940s while the sunspot record peaked around 1960. The issue is important because in the 1940s there was a maximum in the global surface temperature record. Thus, if one believes in the sunspot number record no correlation emerges between solar activity and global temperature. On the contrary, if the aurora record better mirrors solar activity and the TSI variation, then a good solar-climate correlation is observed.

    Indeed, one way to test a TSI model is to see how well it may match with both the solar records and the temperature records available for the past. Several examples are shown in this recent paper of mines:

    Scafetta N., 2014. Global temperatures and sunspot numbers. Are they related? Yes, but non linearly. A reply to Gil-Alana et al. (2014). Physica A 413, 329–342.

    These examples, as reproduced in my figures above, clearly suggest a strong solar-climate link once specifics proxy models are used. The correlation appears poorer when other reconstructions are used.

    To better address the issue there is a need to develop new solar proxy models that can at least include some physical properties of the dynamic of solar variation at the decadal to millennial scales. There is a need to understand what is physically causing this variability.

    In a number of papers that I have published recently, the proposed theory is that solar activity is partially modulated by gravitational and electromagnetic forcings caused by the planets. Because the planets are moving harmonically around the sun, the sun is forced by harmonic forcings that drive harmonic variations in solar activity. Of course, also in this case, evidently expected physical non-linearities make the patterns a little bit more chaotic. However, we and other scientists have produced a number of papers where the evidences for a planetary modulation of solar activity seems overwhelming. For example:

    Scafetta, N., 2014. The complex planetary synchronization structure of the solar system. In the Special Issue “Pattern in solar variability, their planetary origin and terrestrial impacts”, Eds: N.-A. Mörner, R. Tattersall, and J.-E. Solheim. Pattern Recognition in Physics 2, 1-19.

    Scafetta, N., and R. C. Willson, 2013. Multi-scale comparative spectral analysis of satellite total solar irradiance measurements from 2003 to 2013 reveals a non-linear planetary modulation of solar activity depending on the 11-year solar cycle. In the Special Issue “Pattern in solar variability, their planetary origin and terrestrial impacts”, Eds: N.-A. Mörner, R. Tattersall, and J.-E. Solheim. Pattern Recognition in Physics 1, 123-133.

    Scafetta N., and R. C. Willson, 2013. Empirical evidences for a planetary modulation of total solar irradiance and the TSI signature of the 1.09-year Earth-Jupiter conjunction cycle. Astrophysics and Space Science 348(1), 25-39.

    Scafetta N., 2012. Does the Sun work as a nuclear fusion amplifier of planetary tidal forcing? A proposal for a physical mechanism based on the mass-luminosity relation. Journal of Atmospheric and Solar-Terrestrial Physics 81-82, 27-40.

    Scafetta N., 2012. Multi-scale harmonic model for solar and climate cyclical variation throughout the Holocene based on Jupiter-Saturn tidal frequencies plus the 11-year solar dynamo cycle. Journal of Atmospheric and Solar-Terrestrial Physics 80, 296-311.

    Scafetta N., 2010. Empirical evidence for a celestial origin of the climate oscillations and its implications. Journal of Atmospheric and Solar-Terrestrial Physics 72, 951-970.

    In particular I would like to stress this last paper that summarizes and extends some of the findings of the previous ones and responds to the sole critiques until now proposed in the scientific literature:

    Scafetta, N., 2014. Discussion on the spectral coherence between planetary, solar and climate oscillations: a reply to some critiques. Astrophysics and Space Science 354, 275-299.

    The evidence collected until now is that to properly understand solar dynamics one needs to fully understand how solar activity works and how the sun is linked to the dynamics of the entire solar system. Proxy models based on planetary harmonics have been the only ones that have been showed to approximately hindcast both solar records and climate records throughout the Holocene until now. So, I think that future research will have to focus on these emerging issues and develop them.

    Right now, to answer Marcel Crok’s question I would say that nobody knows the right answer. However, numerous evidences suggest that the TSI proxy models proposed by Lean, Solanki, Lockwood and others are unsatisfactory because purely based on proxy associations without attempting to understand the underlaying physics of why solar activity varies on a decadal to millennial scales. If the ACRIM TSI composite is right, those proxy models are surely wrong because they better fit the PMOD composite, although in some contradictory way as explained in our paper (Scafetta and Willson, 2014). The TSI model proposed by Hoyt and Schatten is old but it has good correlation with some climate records. Preliminary solar models based on planetary harmonics has been proposed and they seem promising, as shown in my figures above. In my papers an interested reader can find references to many other works from other authors recently published on this topic.

    Now I would like to briefly answer a comment from Benestad made below on 2014-10-21 09:36:40. Here Benestad claims that “Much of the work on solar cycles presented here has been critizised in a collection of replications by Benestad (2014): Replication of a number of contrarian climate change studies. figshare. http://dx.doi.org/10.6084/m9.figshare.951971 and the discussion paper ‘Agnotology: learning from mistakes’:….”

    Benestad’s claims are quite curious. Apparently Benestad ignores that his own referenced paper has been “rejected” for publication. On the web-site of the journal it is written: “This discussion paper has been under review for the journal Earth System Dynamics (ESD). A final paper in ESD is not foreseen.”

    In fact, several authors have pointed out severe mathematical and physical errors in his paper. See here

    In particular see my own extended comment here:

    Section 3 of

    Scafetta N., 2013. Discussion on common errors in analyzing sea level accelerations, solar trends and global warming. Pattern Recognition in Physics, 1, 37–57.

    explains in details other math errors made by Benestad in another publication with Schmidt in 2009.

  • Jos Hagelaars

    Prof. Solheim shows a graph with the Ljungqvist reconstruction (figure 6 in his post) and states the following:
    “Following Ljungqvist my conclusion is that the LYA NH extra-tropical temperature difference is more than one degree °C for a decadal average temperature compared with the last decade.”

    The graph of Ljungqvist becomes more interesting when you add the decadal averages after the year 1999, which is the last point of the instrumental data in the original graph:

    The LIA NH extra-tropical temperature difference with the last decade increases to about 1.4 – 1.6 °C and the temperature difference of the MWP maximum with the last decade is about 0.5 – 0.7 °C.

    I fail to see the correlation with the Ljungqvist reconstruction (and even more so with the last decade added) and sunspot numbers in the bottom panel in figure 5 of the post of Prof. Solheim. For instance the high number of sunspots around 1600 or 1750 and the low temperature in the NH extra tropics in that period? Would Prof. Solheim be so kind to explain this lack of correlation?

  • Greg Goodman

    It is unrealistic to expect an instantaneous response to solar variation. Just the top ocean “mixed layer” has an enormous heat capacity. Also any change in SST will invoke the strongly negative Plank feedback, amongst others meaning SST will take tome to settle to a new dynamic equilibrium. The most simplistic way to model this kind of response is an exponential relaxation to equilibrium ( calculable by convolution with a suitable exponential decay ). This is in line with linearised IPCC thinking and the idea of a change in radiative forcing leading to a final change in surface temperature.

    As an illustration, here is a response with a 5 year time-constant applied to Svalgaard’s modified SSN compared to hadSST3 global average SST.


    The scaling was arbitrarily chosen to align the two series. The 0.6 deg C offset is freely chosen since the reference period for the SST “anomaly” is equally arbitrary.

    It is notable that the decadal variability in the two series only matches coincidentally in certain periods. There is a persistent phase drift showing that different periodicities dominate the two series on the decadal scale.

    Peaks from the more reliable post-WWII data strongly suggests a decadal lunar influence in SST. This is explained in detail in the text. This is in accord with much of N. Scafetta’s work.

    There is little convincing evidence of a circa 11 year signal in any climate data. Most such claims have relied on selective periods where the lunar signal in phase with SSN to arrive a spurious correlations.

    There is fair similarity on the inter-decadal scale for most of the SST record, if the significant “bias corrections” applied by Hadley Centre are correct. However, there is a divergence that starts in the late 1980s which raises a question of whether there is a correlation.

    A clue to this divergence is to be found in satellite measurements of the temperature of the lower stratosphere:

    The two step changes are clearly attributable to the two major eruptions. This step nature is now recognised by NCAR and AR5 but the implications for the lower climate are not followed through.

    A cooling in the TLS data implies a change in energy budget at the tropopause. Simply put cooling in TLS accompanies warming of the lower climate system.

    Both events left a clear, definitive change in the composition of the lower troposphere ( principally depletion of UV absorbing ozone ) and TLS. ERBE top of atmosphere energy budget data shows an additional 1.8 W/m2 of short-wave ( UV) energy entering the Earth climate system since Mt Pinatubo. Since the stratosphere shows cooling, this energy must end up in the lower climate.

    The change after the earlier El Chichon event would seem to be comparable.

    An extra 1.8 W/m2 due to a major stratospheric eruption is large enough and corresponds closely in timing to explain the recent divergence of SST and SSN.

    There is no single driver of climate variability. In order that attribute of refute solar influence it is necessary to recognise other factors that are present, otherwise there will be either false attributions or spurious rejection.

  • Greg Goodman

    Conclusion: if the correlation between sunspots and TSI (as determined in the PMOD period since 1979) is pretty good, then TSI must have been similar then and now. It also means TSI reconstructions since 1700 are rather flat.

    With respect, it’s really not that simple. You are implicitly assuming instantaneous equilibration of at least the 0-700m oceans there. You maybe need to explicitly state why you are assuming that.

    Activity over the last 60 years has been consistently higher than in the 60 years preceding the early 20th c. minimum. If there is a solar influence there will considerable lag due to thermal inertia of the oceans. Just comparing the current solar max to an earlier cycle is not sufficient to confirm or refute.

    Please refer to my other post here showing the need to consider other factors in attempting to discern whether there is a solar influence.

  • Greg Goodman

    Jan-Erik Solheim:

    A fair part of the warming after 1990 may be due to the cleaning of the stratosphere of aerosols after the Pinatubo eruption in 1992. Observation of earthshine during lunar eclipses shows that the atmosphere now is very clean (Keen 2008; 2013) The difference in volcanic Aerosol Optical Thickness (AOD) since 1996 is estimated to Δτ = -0.033 which corresponds to ΔT=0.13°C (no feedback calculation). This is based on the relation between aerosol forcing and optical depth Fa ≈ -21 Δτ, where Fa is the forcing in Wm-2 (Hansen et al. 2000).

    This is in agreement with what I presented earlier about TLS and ERBE TOA measurements. However, I think the current value for AOD is severely under-estimated.

    Lacis et al 1992 ( see article ) made detailed physically based calculations and came up with an estimation of 33 for the scaling of AOD to W/m2. This later got reduced to 21 on the basis that it better matched GCM model output. This is symptomatic of weight given to GCMs. In Hansen et al, ten years later, they chose to rescale the data rather than question the model. The two authors were involved in both papers.

    I used a study of ERBE data around Mt Pinatubo event to derive aerosol forcing and came very close to the original Lacis figure of 33.


    In which case ΔT=0.13°C becomes about 0.2°C

  • benestad

    It is important to include all the relevant facts and arguments in a discussion like this. Much of the work on solar cycles presented here has been critizised in a collection of replications by Benestad (2014): Replication of a number of contrarian climate change studies. figshare. http://dx.doi.org/10.6084/m9.figshare.951971 and the discussion paper ‘Agnotology: learning from mistakes’: http://www.earth-syst-dynam-discuss.net/4/451/2013/esdd-4-451-2013.html.

    When scrutinised, we find that the so-called cycles in observed climate can have any cause. They may not even be robust, but may be part of a transient behaviour in a chaotic system. However, there are also some weak ‘solar signal’ (variations that seem synchronised with sunspots) in the climate data, and the most intriguing feature seems to be around the North Atlantic, as Lockwood observes (http://iopscience.iop.org/1748-9326/8/3/035049/article). Apart from that, there is little evidence suggesting a strong connection between the current global warming and solar activity – there has been little long-term change in the sun since the 1950s, except for the most recent weak solar cycle (http://goo.gl/8Dtkrt).

    There is a European project that looks into questions like these: COST-TOSCA. More information about this can be found on http://lpc2e.cnrs-orleans.fr/~ddwit/TOSCA/TOSCA/Home.html.

    Rasmus Benestad

  • Geoff Sharp

    Dr Usoskin states:

    The issue is that no one knows when the new Grand Minimum occurs and no one really knows what would happen then. I call such extended minima of suppressed solar activity Grand Minima, since the Maunder Minimum (lasting from 1645 till about 1700 or 1712) is only one of those. Later minima, such as the Dalton Minimum (ca. 1800 AD) and modern (ca. 1900 AD) ones were not really Grand Minima, in neither depth or duration.

    There is new science which makes it very easy to predict any solar cycle/grand maximum/grand minimum in the future that is now recognized by science. This knowledge has been around since 2008 and can be utilized to hindcast the Holocene with very good accuracy. The new method is published in a peer reviewed journal(Sharp 2013) and is also backed up by another paper by McCracken et al(2014).

    The new knowledge remedies the errors made by Landscheidt and Charvàtovà and can be downloaded for free at:


    and the McCracken paper is available at:


    Once the theory is understood grand minima can be recognized by the dynamo disruption occurring rather than the depth of the grand minima involved. Grand minima such as the Dalton observe this dynamo disruption and cannot be ignored.

  • Captdallas2

    Greg Goodman, it is unrealistic to expect surface temperature anomaly to respond quickly and it is also unrealistic to expect region temperature anomaly to accurately represent energy anomaly.

    RSS provides TLT temperatures in absolute values via Climate Explorer which allows estimation of energy anomaly using Stefan-Boltzmann. Not ideal by any stretch, but a better way to at least double check relationships.


    That compares the estimated energy anomaly for the regions using in the UAH example. That is more realistic considering the differences in land to water ratio plus absolute temperature. If you want a more rapid response, there are stratospheric data sets which are also available in absolute values. As a general rule, only the tropical surface band is useful for direct comparison to solar and even that has more than enough noise to challenge most folks thanks to delays of roughly ~27 and ~96 months associated with ocean surface and bulk layer mixing (QBO and ENSO mainly). Since solar and volcanic are likely interrelated, before long things can get a bit complicated.

  • gymnosperm

    Very interesting perspective.(Responding to Ilya Usokin). More solar variation but also lots of “randomness”. If this randomness can be taken as the result of a very complex system of imputs rather than an excuse to go play golf, we have a lot of work to do.

  • gymnosperm

    Jose Vaquero: If you believe the “little ice age” was caused by internal variability,do you believe a true ice age can be caused by internal variability? Not a stadial, the five to ten million year affairs with wide wings.

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Off-topic comments (click to expand)
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  • Raymond Arritt

    In this CD post we have two representatives of the consensus view (large CO2 influence on recent climate with little solar influence), two from the contrarian view (solar has been dominant), and one in the middle. This is not my field, so I am curious whether this is a representative sampling of expert views on this topic.

  • Kenneth Richard

    Here’s what the IPCC says about the total amount of radiative forcing in W/m2 contributed by human activity (mostly burning fossil fuels) from the years 1750-2005:
    “The understanding of anthropogenic warming and cooling influences on climate has improved since the TAR, leading to very high confidence that the effect of human activities since 1750 has been a net positive forcing of +1.6 [+0.6 to +2.4] W m–2.”
    So, according to the IPCC, the total amount of radiative forcing contributed by humans to the energy flux balance has been just 1.6 W/m2 during the 256 years between 1750 and 2005. The IPCC authors also wrote this about how climate change occurs:
    “Global climate is determined by the radiation balance of the planet (see FAQ 1.1). There are three fundamental ways the Earth’s radiation balance can change, thereby causing a climate change: (1) changing the incoming solar radiation (e.g., by changes in the Earth’s orbit or in the Sun itself), (2) changing the fraction of solar radiation that is reflected (this fraction is called the albedo – it can be changed, for example, by changes in cloud cover, small particles called aerosols or land cover), and (3) altering the longwave energy radiated back to space (e.g., by changes in greenhouse gas concentrations). In addition, local climate also depends on how heat is distributed by winds and ocean currents. All of these factors have played a role in past climate changes.”
    Of course, (3) is where the 1.6 W/m2 of anthropogenic RF comes into play. The changes in surface incident solar radiation (2) are elicited primarily by changes in clouds and aerosols (albedo). Simply put, more cloudiness and aerosol depth means less sunlight penetrates to the Earth’s surface, or dimming. Less cloudiness and aerosol depth means more sunlight reaches the Earth’s surface, called brightening.
    There is a well documented record of global brightening and global dimming trends during the 20th century. And this record clearly shows a correlation with temperature, and a much, much stronger radiative forcing (W/m2) influence relative to the anthropogenic influence. During the 1980s to 2000s alone, albedo changes (brightening) contributed between 2.5 and 10 W/m2 of radiative forcing, which directly corresponds to the warming that took place during this period. This easily dwarfs the radiative forcing strength of the anthropogenic influence alleged by the IPCC (0.3 W/m2 per decade between 1951 and 2011).
    Below are some of the many papers that document the global brightening and dimming trends of the 20th century, including the dimming that led to flat to cooler temperatures during the 1950s-1980s, and the brightening that led to the warmer temperatures in the 1980s, 1990s, and 2000s.
    Traditionally the Earth’s reflectance has been assumed to be roughly constant, but large decadal variability, not reproduced by current climate models, has been reported lately from a variety of sources. There is a consistent picture among all data sets by which the Earth’s albedo has decreased over the 1985-2000 interval. The amplitude of this decrease ranges from 2-3 W/m2 to 6-7 W/m2 but any value inside these ranges is highly climatologically significant and implies major changes in the Earth’s radiation budget.
    Radiative forcing in both the short and long-wave lengths reaching the Earth’s surface accounted for more than 80% of the inter-annual variations in the mean yearly temperatures measured at Potsdam, Germany during the last 120 years [1893-2012]. Three-quarters of the increase in the long-wave flux was due to changes in the water content of the lower atmosphere; the remainder [25%] was attributed to increases in CO2 and other anthropogenic, radiatively active gases. Over the period radiative forcing in the short-wave flux [solar forcing] slightly exceeded [0.76 W/m2 per decade] that in the long wave [0.64 W/m2 per decade].

    [The total long term radiative forcing for the Sun was 9.12 W/m2 for the 120-year period. The total long term radiative forcing from CO2 was 1.92 W/m2 for the 120-year period.]
    Data from this summation method suggest that surface incident solar radiation increased at a rate of 6.6 W m−2/decade−1 (3.6%/ decade) from 1992 to 2002 (brightening) at selected sites.
    Total global solar shortwave (G) irradiation and sunshine duration were recorded at nine Spanish stations located in the Iberian Peninsula. Averaged series (using the nine locations) showed a statistically significant decrease in annual G [global dimming] from 1950 to the mid 1980s (−1.7%dc−1) [-8.5 W/m2] together with a significant increase [global brightening] from the mid 1980s to 2011 (1.6%dc−1) [+8 W/m2].
    http://onlinelibrary.wiley.com/doi/10.1029/2008JD011290/abstractThe decadal trend shown in the 5-year running mean indicates a period of rapid increase [solar radiation reaching the surface/brightening] starting in late 1930s and continuing to early 1950s with a change of 10 W m2. The dimming trend from the early 1950s to the late 1980s shows a decrease of 13 W m2. The subsequent increase starting in late 1980s is about 10 W m 2 by 2005. These changes are not confined to a small number of stations in western Europe, but shared by more than 400 other sites where global irradiance has been continuously observed for more than 40 years.
    We find distinct patterns of dimming and brightening in the aerosol optical depth and thus clear-sky downward surface shortwave radiation (SSR) in all analyzed subregions. The strongest brightening between 1973 and 1998 under clear-sky conditions is found in mid-Europe (+3.4 W m−2 per decade [8.5 W m−2 total], in line with observations).
    Long-term variations in solar radiation at Earth’s surface (S) can affect our climate, the hydrological cycle, plant photosynthesis, and solar power. We observed an overall increase in S [solar radiation] from 1983 to 2001 at a rate of 0.16 watts per square meter (0.10%) per year [3.04 W/m-2 total].
    [T]here has been a global net decrease [of 3.6%] in 340 nm cloud plus aerosol reflectivity [which has led to] an increase of 2.7 W m−2 of solar energy reaching the Earth’s surface and an increase of 1.4% or 2.3 W m−2 absorbed by the surface [between 1979 and 2011].
    Literature estimates for the overall SSR decline during dimming (1950s to 1980] range from 3 to 9 W m−2, and from 1 to 4 W m−2 for the partial recovery during subsequent brightening [1980s to 2000] (Stanhill and Moreshet 1992; Liepert et al. 1994; Abakumova et al. 1996; Gilgen et al. 1998; Stanhill and Cohen 2001; Alpert et al. 2005; Kvalevag and Myhre 2007; Kim and Ramanathan 2008; Wild 2009).
    The annual sunshine duration mean time series shows a decrease from the early 1960s to the late 1970s [in Iran], in line with the widespread dimming of surface solar radiation observed during this period. By the early 1980s, there is an increase in sunshine through the end of the 20th century, aligning with a well-known and well-documented brightening period.

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  • N4EDT

    What effect does the precession of the earth have on the earth’s reception of solar energy? I believe the earth is currently in a more vertical position than it was during the 1700s.

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