## Do clouds control climate?

Clouds have a net average cooling effect on the earth’s climate. Climate models assume that changes in cloud cover are a feedback response to CO2 warming. Is this assumption valid? Following a study with Euan Mearns showing a strong correlation in UK temperatures with clouds, we  looked at the global effects of clouds by developing a combined cloud and CO2 forcing model to sudy how variations in both cloud cover [8] and CO2 [14] data affect global temperature anomalies between 1983 and 2008. The model as described below gives a good fit to HADCRUT4 data with a Transient Climate Response (TCR )= 1.6±0.3°C. The 17-year hiatus in warming can then be explained as resulting from a stabilization in global cloud cover since 1998.  An excel spreadsheet implementing the model as described below can be downloaded from http://clivebest.com/GCC

Best fit(Acalc)  to data(H4)  using  TCR=1.4C

A basic uncertainty for climate science is in understanding the net effect of clouds on the radiative balance of the earth [3].  Clouds regulate solar heating by increasing the planet’s albedo while simultaneously absorbing infrared (IR) from the surface. This interplay between albedo and greenhouse effect of clouds is complex and varies with latitude and with season. The net radiative forcing from cloudy regions is So(1-α) –F, where F is the outgoing IR and α is cloud albedo. On a global scale the Earth Radiation Budget Experiment (ERBE) measurements have shown a net cooling of around -13 watts/m2, which is four times that expected from a doubling of CO2 alone [3].  However, more recent measurements from the Clouds and the Earth’s Radiant Energy System CERES [5] show that the net average cooling effect of clouds is larger (-21 W/m2) (Figure 1b). It is often assumed that changes to cloud cover are a feedback to CO2 forcing rather than an independent phenomenon. A change in climate can induce cloud changes which then feedback into the initial climate change. This effect is built into most Climate models, which then result in a mean cloud feedback of between 0-2 W/m2/°C [6]. Radiative forcing from increasing CO2 levels is rather well understood [7], but its direct impact on cloud cover is unclear. Feedbacks cannot be too large compared to the Planck response as otherwise they soon become unstable $\Delta{T} = \frac{\Delta{T_0}}{(1-f)}$ as f approaches 1.

Global cloud cover variations measured by a number of satellites under the guidance of the International Satellite Cloud Climatology Project (ISCCP) are subject to uncertainty linked to data acquisition methods [10], and viewing biases [11]. However, we have found previously [12] that using sunshine hours at surface as an inverse-proxy for cloud cover confirms the ISCCP results over the UK. In private correspondence, NASA have also provided assurance that data acquisition and corrections are now reliable and that the ISCCP data are therefore robust.

Figure 1a showing the ISCCP global averaged monthly cloud cover from July 1983 to Dec 2008 over-laid in blue with Hadcrut4 monthly anomaly data. The fall in cloud cover coincides with a rapid rise in temperatures from 1983-1999. Thereafter the temperature and cloud trends have both flattened. The CO2 forcing from 1998 to 2008 increases by a further ~0.3 W/m2 which is evidence that changes in clouds are not a direct feedback to CO2 forcing.

Fig 1b: CERES measured data on global cloud forcing. Reflected short wave radiation reduces surface heating by ~44 watts/m2 which is offset by cloud absorption of outgoing IR therefby increasing the greenhouse effect from clouds by ~26watts/m2. This results in a net cooling effect from cloudy skies globally of -22 watts/m2. This figure is used to define the NCF = 0.91. [5]

Figure 1a shows the ISCCP global averaged cloud cover [8] compared to Hadcrut 4 global temperature anomalies [4]. Until 1998 cloud cover decreased in line with increasing CO2 levels, which may support the existence of a CO2 feedback. However, since 1998 both cloud cover and temperatures have remained flat while CO2 forcing has continued to rise. This is evidence that cloud cover does not depend simply on CO2 forcing alone and may itself be a major natural driver for climate change. There is no direct evidence that cloud cover varies in response to CO2 and the ISCCP data discussed here is cyclic in nature which cannot be explained by unidirectional CO2 forcing. We have therefore developed a model that treats clouds and CO2 forcing independently and separately. Mearns and Best [10] have reported evidence that changes in cloud cover can explain approximately 40% of the UK surface temperature changes since 1956 especially during summer months (June, July, August)[10]. We now apply essentially the same model on a global scale using data from the ISCCP [9] that we downloaded from the US National Oceanic and Atmospheric Administration (NOAA) web site [ref] since the NASA web site has been disabled [ref] referenced to measured surface temperature data from CRU-Hadley (HADCRUT4)[8]. ISCCP cloud data is available beyond 2008 but is not yet in the public domain.

Cloud forcing model. We define the net cloud-forcing factor (NCF) as the resultant balance between albedo and Green House (GH) effects for clouds. In effect NCF is used to describe the ratio of the combined forcing (cloud transmissibility and GH effect) of clouds relative to that for clear skies. Effectively (1-NCF) is the net cooling factor of clouds with respect to clear skies. Radiative energy balance is then given by

$(1-CC)\times{S_0} + CC\times{NCF}\times{S_0} = \epsilon \sigma\ T^4$

where, S0 for clear skies is taken as a global average 240 W/m2 [13]. Therefore for each month, m, the incoming net insolation is

$S(m) = (1-CC(m))S_0 + CC(m) \times NCF \times S_0$

The calculated temperature change for Tcalc (m) is then given by

$\Delta{T(m)} = \frac{(S(m)-S(m-1))}{3.5}$

where 3.5 Wm-1°C-1 is the Planck response  DS/DT for 288K and is the increase in IR radiation for a 1oC rise in surface temperature. We initialize the model by normalising the first data point Tcalc(July 1983) = Thcrut(July 1983) and then calculate all subsequent  monthly temperatures based only on the measured changes in ISCCP Cloud Cover (CC).

$T_{calc}(m) = T_0(m-1) + \Delta{T(m)}$

We fix NCF = 0.91 as measured by the CERES for global net cloud forcing.

The CO2 radiative forcing model: The change in CO2 forcing for month (m) is calculated using the formula [7]

$S(m) = 5.3\ln(\frac{CO_2(m)}{CO_2(m-1)})$

where ΔS  is the monthly change in radiative forcing,CO2(y)/ is the concentration of CO2 in the atmosphere for month y, and CS is a factor representing climate sensitivity. CO2 values are the measured monthly Mauna-Loa data [14]. The model with CS=1.0 then corresponds to an equilibrium climate sensitivity (ECS) of 1.1°C. However, when the model is applied to contemporaneous temperature data, CS corresponds instead to the transient climate response (TCR). Climate models with net positive feedbacks yield larger values of ECS of between 2 to 5°C [15].  The model value of CS is to be determined empirically from the data.

We apply the model to calculate global temperature anomalies from variance in global cloud cover after normalising the start point (July 1983) to the measured global average temperature and then compare model output to measurements of actual temperature variance as recorded by HadCRUT4. Our criterion for goodness of fit between the model and HadCRUT4 is based on the use of χ2 per degree of freedom (χ2/df). For χ2 we take a measurement error of 0.1°C for the monthly anomalies.  The χ2 results found by varying CS values with NCF fixed at 0.91 are shown in Figure 2b. A minimum in χ2 is found for CS = 1.45 corresponding to TCR = 1.6 °C.  The error on CS is determined by how much variation is needed to shift  χ2/df by 1 σ.

Fig 2 a) Results of the model calculations (Tcalc) for the best fit value of CS =1.45 (TCR=1.6 °C) compared to monthly Hadcrut4 anomaly data.
b) Variation of χ2 per degree of freedom calculated between the predicted and the measured anomalies calculated for different CS values taking NCF=0.91 as measured by CERES.

Clouds and CO2 alone cannot explain all the variations in monthly global temperatures. It is known that explosive volcanic eruptions and ENSO[E1]  also have transient effects on global temperatures, and for this reason it is no surprise that the minimum χ2/df > 1.0. However the main trend is well reproduced by the model as shown in Figure 2a which compares the best-fit value of CS to the real measured data. The general warming trend until 1998 can mostly be explained by the fall in cloud cover during that period. The flattening off in temperature since 1998 coincides with a leveling off in global cloud cover. To explain observed warming over the full period by a CO2 dependent term alone with clear skies (NCF=1) would require TCR = 2.2 °C resulting in an approximate linear increase of 0.7 °C over this time period. Examining the yearly change in cloud forcing shows that it increased from 225.5 W/m2 in 1984 to 262.2 W/m2 in 1999, or an increase in forcing of ~0.7 watts/m2. CO2 forcing with TCR = 1.6 °C increased by 0.54 W/m2 over the same time period.  This result demonstrates that more than half of the rapid warming observed in the 1980s and 1990s can be explained by a decrease in cloud cover. Since 1999 net cloud forcing has remained approximately constant (-0.2 W/m2), while CO2 forcing has increased by a further 0.58 W/m2.

Results for the summer cloud analysis for a) Northern Hemisphere with model TCR=1.0C and NCF=0.9 and b) Southern Hemisphere with model TCR = 1.65C and NCF=0.91. The Hadcrut4 data and the model data for each year are the averaged results for June, July and August in case a) and for December, January and February for case b). In the latter case the year is assigned to that of December. There is a clear difference in dependence with CS for the Northern and Southern Hemispheres. Changes in cloud cover have a greater impact in the northern hemisphere than in the southern hemisphere. This affects the best fit values for CS for each hemisphere.

There are marked seasonal variations in cloud cover for each hemisphere – particularly in the southern hemisphere. In order to isolate differences between the long-term effects of clouds in each hemisphere we have made summer averages of temperature and cloud cover (June, July, August (JJA) for Northern Hemisphere (NH) and December, January, February (DJF) for Southern Hemisphere (SH)) and then compared the model with the hemispheric HadCRUT4 anomaly data. For the average summer hemispheric insolation we take a value of S0=312 W/m2 which is 240 W/ m2 corrected for the angle of the sun for summer months. The results of this analysis are shown in Figure 3.  There is a clear difference between the northern hemisphere and southern hemisphere. The response to cloud forcing in the northern hemisphere is stronger with fixed NCF=0.91, and leads to a lower c2 fitted value for CS (TCR = 1.0 ± 0.3°C). The southern hemisphere shows a smaller cloud forcing response with correspondingly larger values for CS (TCR = 1.65 ± 0.3°C). This difference is most likely due to dominance of oceans in the southern hemisphere. By studying each hemisphere separately and by eliminating as far as possible seasonal effects, the global result is confirmed. These results demonstrate that over half the warming observed between 1983 and 1999 is due to a reduction in cloud cover mainly effecting the northern hemisphere. The apparent slowdown in warming observed since 1999 coincides with a stabilization of global cloud cover. In an analysis of cloud and temperature variance in the UK, Mearns and Best [12] reach a similar conclusion which is that approximately 50% of net warming since 1956 is due to a net reduction in cloud cover. However, in that study NCF was estimated empirically to be 0.54, significantly lower than the CERES value of 0.91 used here. A lower NCF factor means that clouds are having a larger effect and the difference between the global and UK results may reflect latitude and the fact that UK data are land based only.

In conclusion, natural cyclic change in global cloud cover has a greater impact on global average temperatures than CO2. There is little evidence of a direct feedback relationship between clouds and CO2. Based on satellite measurements of cloud cover (ISCCP), net cloud forcing (CERES) and CO2 levels (KEELING) we developed a model for predicting global temperatures. This results in a best-fit value for TCR = 1.4 ± 0.3. Summer cloud forcing has a larger effect in the northern hemisphere resulting in a lower TCR = 1.0 ± 0.3. Natural phenomena must influence clouds although the details remain unclear, although the CLOUD experiment has given hints that increased fluxes of cosmic rays may increase cloud seeding [19].  In conclusion, the gradual reduction in net cloud cover explains over 50% of global warming observed during the 80s and 90s, and the hiatus in warming since 1998 coincides with a stabilization of cloud forcing.

References

1. Randall, D. A. Cloud Feedbacks. Frontiers in the Science of Climate Modeling (2006).

2. Randall, D. A. & Wood, R. A. Climate Models and Their Evaluation. (Cambridge Univ. Press: Cambridge [u.a.], 2007).

3. V. Ramanathan, R.DCess, E.F. Harrison, P.Minnis, B.R. Barkstrom, E. Ahmad, D. Hartmann, Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment, Science, Vol 243, P 57, 1989

4. Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice (2012), Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010, J. Geophys. Res., 117, D05127,

5. Richard P. Allan, Combining satellite data and models to estimate cloud radiative effects at the surface and in the atmosphere, RMetS Meteorol. Appl. 18: 324–333, 2011

6. Bony, S. et al. How Well Do We Understand and Evaluate Climate Change Feedback Processes? Journal of Climate 19, 3445–3482 (2006).

7. Myhre, G., E.J. Highwood, K.P.Shine,, F.Stordal, New Estimate of Radiative ForcingDur to Well Mixed Greenhouse Gases, Geophys. Ress. Lett. 25, 2715-2718, 1998

8. Monthly averaged ISCCP cloud data Data derived from file MnCldAmt.nc, Catalog. (2009) http://www.ncdc.noaa.gov/thredds/catalog/isccp/catalog.html

9. Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones (2012), Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset, J. Geophys. Res., 117,

10. Rossow, W. B. & Schiffer, R. A. Advances in Understanding Clouds from ISCCP. Bulletin of the American Meteorological Society 80, 2261–2287 (1999).

11. Evan, A. T.; A. K. Heidinger, and D. J. Vimont (2007). Arguments against a physical long-term trend in global ISCCP cloud amounts. Geophy. Ress. Lett 34 (L04701)

13. K.E. Trenberth, J.T. Fasullo & J. Kiehl, Earth’s Global Energy Budget, Bulletin of the American Meteorological Society (2009)

14. Keeling, C. D. et al. Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus 28, 538–551 (1976).

15. Solomon, S. et al. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge Univ. Press: Cambridge [u.a.], 2007).

16. J. Kirby et al. Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature 476, 429–433 (2011).

Note: I have posting this only now because after a long review process the paper was finally rejected. I am beginning to despair of any outsider ever getting anything published in a climate science journal!

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## Tidal effects in polar regions

The tidal force acting on the oceans generates tidal currents due to the tractional component parallel to the surface. This is because horizontally there is no net gravitational force acting to counteract motion. This tractional force reaches a maximum at around 45 degrees to the central tidal bulge. Chiefio has a nice article describing how the declination of the moon during spring tides can effect induced currents in the polar regions.

Fig 1: Diagram showing how the tractional tidal component at the poles depends strongly on the lunar declination angle.

I have been using the JPL ephemeris and the French INPOP10 ephemeris to investigate how this has varied over the last 2000 years. I calculate the maximum net annual tide based on the positions of the sun and moon, the maximum tractional component at 65N (arctic circle) and what I call the ‘melting index’. The melting index is meant to represent an extra ice melt influence of tidal motion working with summer insolation. It is zero during the arctic winter and proportional to average daily insolation times tidal acceleration in summer.

Fig 2: Recent daily tidal components. The lunar tide is shown in blue, the solar tide in red. Green shows the tractional acceleration at 65N.

The tractional component at 65N typically varies between zero and half the net tidal force. The daily variations in tides show a rich structure of spring and neap tides enhanced by perihelion of the earth and lunar orbits. However are there long term changes in these cycles affecting climate?

Next I looked at the long term dependence by calculating for each year the maximum for each tidal component. Figure 3 shows the annual maximum tides and  their tractional component at 65N. There is a regular oscillation of ~8.8 years in the tractional force with the precession of the lunar orbit.

Fig 3: Maximum annual tides and their tractional force exerted at 65N

However if we instead find the maxima of the tractional component at 65N independently to the maximum annual tide then there is a far more stable dependency. The following plot shows  the maximum tide and the maximum tractional acceleration calculated independently for each year. Superimposed in red is what I call the ‘melt index’ which is defined as the tractional acceleration weighted by its offset (in days) from midsummer maximum insolation (June 21).

MI = 1.0 – ABS(date-June21)/(April1-June21)

If date < April  or date > August  MI=0

Fig 4: The black curve shows the annual maximum tide. The blue curve shows independently the maximum tractional tide at 65N. Both are essentially constant with time. However the melt index shown in red shows a regular ~40 year cycle showing coincidence of maximum tide and high summer insolation.

The maximum traction at the poles is now also nearly constant but there is a definite cyclical effect on the timing with the seasons in they occur in as picked up by the melt index. If we assume that large tides combined with high insolation enhances the arctic ice summer melting then this follows a roughly 40 year cycle.

Fig 5: Zoom in of last 500 years

This graph is a zoom in on the last of the period 1600 – 2100.  If the tides influence the summer melt then we would expect to see a roughly 40 year regular variation  in ice extent.

Conclusions. I find no evidence for  variations in inter-annual variations in the maximum  tides on earth  over  a 2000 year period using either JPL ephemeris or INPOP10. The maximum tides however show an 8.8 year variation in their tractional forces at the arctic circle (65N). However if instead we calculate the maximum tractional force independently then I also find no long term variation.  The only variation which may effect polar climates is  seen in the timing of the tractional force with the seasons. The occurrence of maximum tides  in the arctic coincident with maximum solar insolation does  have a ~40 years cycle. The tidal currents thereby induced may effect the summer melting of sea ice.

Next I will look at far longer timescales into the influence of Milankovitch  cycles on the earth’s orbital eccentricity which directly affects tides.

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## Understanding Tides

There has been a long animated discussion about tides at Wattsupwiththat which highlighted a confusion about both the causes of tides and their strength. Several people are adamant that tides are a universal phenomena experienced by any object near a large gravitational source. They argue that tides are caused by the gradient of the 1/r^2 field.

While an extended object falling into a star  or black hole will experience a transient  tidal force, it is only bodies in orbit around each other that experience long term tidal forces acting on the surface. The earth is in orbit about the earth-moon barycenter. It is not in an inertial frame. It is in an accelerating frame of reference, similar to the way that the rotation of the earth about its axis causes a reduction in g at the equator.

So what is the real cause of tides on earth and why is the lunar tide larger than the solar tide ? Here is my derivation for  the formulae for tides.

For 2 bodies in orbit the “centrifugal force” must balance the gravitational force. The centrifugal force is constant within a solid body in the same way that it is for a plate spinning on a stick.

Fig 1

$\frac{GMm}{r^2} = M\omega^2d$

Therefore the centrifugal acceleration on body M is $\omega^2d = \frac{Gm}{r^2}$

Now consider the net force per unit mass acting on point a). Assuming that $\phi$ = 0 we get

$\frac{Gm}{(r-R \cos \theta)^2}$ $- \frac{Gm}{r^2}$

$= \frac{Gm}{r^2}( (1 - \frac{R}{r \cos \theta})^{-2} - 1)$

assuming that r>>R we can do a binomial expansion to get

$= \frac{Gm}{r^2}(1 +\frac{2R}{r}\cos \theta - 1)$

$= \frac{2GmR \cos \theta}{r^3}$

This derives the approximate formula for the tidal force. There are two bulges centered on $\theta = 0 (and) \theta =\pi$

Now we can do the full calculation where the angle $\phi$ is no longer zero and thereby identify how the tidal force acquires a vertical component.

Calculation of the angle phi. Distance a-m is the hypotomuse

The distance a-m is by Pythagorus

$\sqrt{R^2 \sin^2 \theta + (r - R \cos \theta)^2}$

$= \sqrt{R^2 +r^2 -2rR \cos \theta}$

Gravity acting on point a) is therefore =  $\frac{Gm}{R^2 + r^2 -2rR \cos \theta}$

Net tidal force now has 2 components

$Fx = Gm( \frac{ \cos \phi}{R^2 + r^2 - 2rR \cos \theta} - \frac{1}{r^2})$

$Fy = \frac{ -Gm \sin \phi}{R^2 + r^2 - 2rR \cos \theta}$

where

$\cos \phi = \frac{r-R \cos \theta}{\sqrt{R^2 + r^2 - 2rR \cos \theta}}$

and

$\sin \phi = \frac{R \sin \theta}{\sqrt{R^2 + r^2 - 2rR \cos \theta}}$

Now let’s compare the two solutions by calculating the effective tidal acceleration. Figure 3 shows the approximate formula in red and the two components of the exact result in blue.

Fig 3: Comparison of the exact solution for tides with the approximation. There is now a significant vertical (y) component to the tidal force.

On earth the gravitational acceleration ‘g’ = 9.8 m/s^2  This can be compared with the above “tidal” accelerations of ~ 10^-6 m/s^2. So the moon’s tidal force is 10 million times less than the earth’s gravitational force at the surface.  This is not going to do any direct heavy lifting of the oceans! Instead it is the tractional component of the tidal force parallel to the surface which moves vast quantities of ocean.  Now with the addition of the vertical component Fy the vector diagrams of tidal forces looks more like this:

Fig 4: Vector diagram showing resultant tidal force (fx,Fy)

Note that the largest tractional forces are at larger theta angles. This drag of water currents throughout the depth of the ocean results in both tidal bulges. Figure 5 shows the tractional north-south force parallel to the earth’s surface which is unaffected by the earth’s gravity and therefore moves water from outside the bulge towards the centre.

Fig 5: The tractional force moving vast quantities of water in the oceans to cause the tides.

The position and strength of these tractional forces is constantly changing during the lunar month, during the 18.6 year precession cycle and during longer time scale astronomical cycles. So the way I like to understand tides is that both the moon and the sun exert a horizontal drag force on the oceans and atmosphere. Twice a month we get spring tides when the sun and moon line up at  new moon and the full moon, whose strength depends on the coincidence with both orbital perigees.  Long term astronomical variations must have an effect on  climate as orbital parameters slowly change increasing or decreasing ocean mixing and atmospheric dynamics.

Postscript

There is a new argument about whether centrifugal forces play any role at all in tides. In some sense both sides in this argument are correct. You don’t need to use the centrifugal force to derive the formula for tides. This is because there is a perfect balance between the centrifugal force and the gravitational force at the center of the earth when in orbit around the earth-moon barycenter. This balance also determines the strength of tides on earth. When in doubt see what Feynman says.

What do we mean by “balanced”? What balances? If the moon pulls the whole earth toward it, why doesn’t the earth fall right “up” to the moon? Because the earth does the same trick as the moon, it goes in a circle around a point which is inside the earth but not at its center. The moon does not just go around the earth, the earth and the moon both go around a central position, each falling toward this common position. This motion around the common center is what balances the fall of each. So the earth is not going in a straight line either; it travels in a circle. The water on the far side is “unbalanced” because the moon’s attraction there is weaker than it is at the center of the earth, where it just balances the “centrifugal force.” The result of this imbalance is that the water rises up, away from the center of the earth. On the near side, the attraction from the moon is stronger, and the imbalance is in the opposite direction in space, but again away from the center of the earth. The net result is that we get two tidal bulges.

A body in free fall into the sun experiences ever increasing tidal forces until it is torn apart. A body in orbit eperiences varying tidal forces depending on the eccentricity of the orbit. So on a purely logical basis Willis and Greg are correct because centrifugal forces don’t enter into the formula for tides. However in order to calculate the variations of tides on earth you need to include orbital dynamics because they change the earth-moon distance.

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## The AR5 Attribution Statement

The following conclusion of AR5 undoubtably had the most political impact.

“It is extremely likely that human activities caused more than half of the observed increase in GMST from 1951 to 2010.”

Humans cause  global warming with 95% confidence thereby confirming that tough action is needed to enforce carbon targets in Europe and elsewhere. Skeptics have been proved wrong etc.

Fig 10.5 from AR5. ANT is the net anthropogenic forcing. Natural forcings (AMO/PDO) are essentially considered to be zero , as are natural (random) variations.  However, I still do not understand how the ANT errors get to be  smaller after adding GHG and OA together ! I am assured this is because modeling  errors rather than observational errors are relevant. ( see:  realclimate post )

Pretty convincing eh!

Well now just 4 months later we have a new claim that the 17 year hiatus in warming has instead been caused by an increase in trade winds over the western pacific – part of the Pacific Decadel Oscillation (PDO). A Met Office report on the storms and flooding now affecting parts of the UK says that perturbations to the  Jet stream has been driven in part by “persistent rainfall over Indonesia and the tropical West Pacific”.

What they don’t point out though is that this explanation is totally at odds with the explanation given in the AR5 chapter 10 which defined  the above attribution statement. Peter Stott of the Met Office was the lead author of this chapter. If explanations for the pause in warming  can change so fast so that now PDO is the dominant effect then surely also the attribution statement needs itself to be revised.

AR5 writes:

The observed recent warming hiatus, defined as the reduction in GMST trend during 1998–2012 as compared to the trend during 1951–2012, is attributable in roughly equal measure to a cooling contribution from internal variability and a reduced trend in external forcing (expert judgement, medium confidence).The forcing trend reduction is primarily due to a negative forcing trend from both volcanic eruptions and the downward phase of the solar cycle. However, there is low confidence in quantifying the role of forcing trend in causing the hiatus because of uncertainty in the magnitude of the volcanic forcing trends and low confidence in the aerosol forcing trend. Many factors, in addition to GHGs, including changes in tropospheric and stratospheric aerosols, stratospheric water vapour, and solar output, as well as internal modes of variability, contribute to the year-to-year and decade- to-decade variability of GMST.

The Atlantic Multi-decadal Oscillation (AMO) could be a confounding influence but studies that find a significant role for the AMO show that this does not project strongly onto 1951–2010 temperature trends.

They go on to write

Zhou and Tung (2013a) show that GMST are consistent with a linear anthropogenic trend, enhanced variability due to an approximately 70-year Atlantic Meridional Oscillation (AMO) and shorter-term variability. If, however, there are physical grounds to expect a nonlinear anthropogenic trend (see Box 10.1 Figure 1a), the assumption of a linear trend can itself enhance the variance assigned to a low-frequency oscillation. The fact that the AMO index is estimated from detrended historical temperature observations further increases the risk that its variance may be overestimated, because regressors and regressands are not independent. Folland et al. (2013), using a physically based estimate of the anthropogenic trend, find a smaller role for the AMO in recent warming. To summarize, recent studies using spatial features of observed temperature variations to separate AMO variability from externally forced changes find that detection of external influence on global temperatures is not compromised by accounting for AMO-congruent variability (high confidence). There remains some uncertainty about how much decadal variability of GMST that is attributed to AMO in some studies is actually related to forcing, notably from aerosols. There is agreement among studies that the contribution of the AMO to global warming since 1951 is very small (considerably less than 0.1°C; see also Figure 10.6) and given that observed warming since 1951 is very large compared to climate model estimates of internal variability (Section 10.3.1.1.2), which are assessed to be adequate at global scale (Section 9.5.3.1), we conclude that it is virtually certain that internal variability alone cannot account for the observed global warming since 1951.</blockquote>

There is no mention at all of the PDO in this chapter. But  suddenly just a few months later it is now the PDO that is responsible for the global warming hiatus and seemingly the Met Office is confirming this. If that is the case then the whole basis behind Fig 10.5 where  both natural forcing and natural variation were reduced to  just  0.0± 0.1C disappears.

In addition the logic behind the SPM attribution statement must also need revisiting.

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I had  hoped to calculate from the JPL ephemeris how the regular 100,000 and 400,000 cycle  in the eccentricity of the earth-moon orbit around the sun  would also effect the moon-earth orbital eccentricity. Such variations could dramatically change the tidal forces acting on earth and play a leading role in triggering interglacials.

The seminal work on solar system dynamics has been done over decades by the French group at Observatoire de Paris. The latest paper La2010: A new orbital solution for the long term motion of the Earth gives the most accurate orbital parameters for the earth over the last 20 million years. However it also shows just how difficult it is to disentangle the moon’s orbit around the earth from their barycentre orbit around the sun, making it almost impossible to predict any long term changes in tides. Their quoted  error in calculating the moon’s eccentricity the last 1 Mya is ±0.03 compared to ±0.00000005 for the earth eccentricity around the sun!

So it is indeed quite possible that major changes in the moon’s orbital parameters will occur as the eccentricity of the Earth-Moon barycentre increases every 100,000 years. However it is almost impossible to calculate how large is the effect.

LA2010 also published some very accurate eccentricity values for the earth over the last 20 million years and into the future. I plotted these out and could not help but notice that the earth’s next 400 y cycle looks almost exactly the same as that which occurred roughly 3 million years ago. That was during an when  glaciations followed the 41,000 year obliquity cycle. Exactly the same eccentricity cycle is observed nearly 5 million years ago. There is therefore a larger 2.8 million year super-cycle in the earth’s eccentricity .

LA2010 calcluations of the earth’s eccentricity over a 4 million year period spanning the present day.

The graph below shows  the L04 stack sediment data from 3 million years  ago (colder is larger dO18). The larger the obliquity the larger are the extremes between the seasons. For large eccentricity the precession term then introduces a significant north-south asymmetry which depends on which particular northern/southern hemispheric season aligns with perihelion.

Combined fits compared to data 3 million to 1 million years ago

I am proposing that it was only when the obliquity cycle became insufficient to trigger an interglacial 800,000 years ago that the moon’s resonant tidal effect was needed to complete the job. This was the cause of the switch in phase to a 100,000 year glacial cycle.

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## Tidal variations

Finally I have a working program which calculates the strength of tides based on the relative positions of the moon and the sun. This is based on the JPL ephemeris which is the most accurate available and is also used for space probes. The calculation is based on the relative distances of the moon and sun from the earth at any day over the last 60 years. The net tidal force is the vector sum of both components during their respective orbits. To sum these I take the dot product of the position vectors and apply a factor Mass/R^3 to both terms. Twice a month the earth experiences spring tides corresponding to the new moon and the full moon.  This is because the sun’s tidal force aligns more or less in the same direction to the moon’s tide at new moon and again slightly less at full moon. This is then amplified when the new moon coincides with perihelion of the earth’s orbit around the sun. These are perihelion spring tides and up to 4 times the strength of neap tides.

Fig 1. The net effect of the solar and lunar tides are reinforced when they align in the same plane. Maxima occur when the relative distances are minimised. Every 18000 y they coincide in a perfect plane with all distances minimised.

Now look carefully at just how much the earth’s eccentricity modulates the net tidal forces. This is because the sun’s mass is 27 million times larger than the moon and small changes in earth-sun distance can have large effects. Currently the eccentricity is 0.0167 and can reach as high as 0.057 during Milankovitch cycles leading to a rough doubling of the direct solar tide. This then must also amplify  the net vector lunar-solar tide as indicated  above significantly. Small changes in sun-earth distance are amplified with respect to those of the moon by a mass ratio of 2.7*10^7 caused by the  1/R^3 dependence of tidal forces.

Now consider the coastal flooding this winter which has mainly effected  western coastal regions of the  UK. The main reason for this are the unusually strong spring tides rather than global warming. These storms have tended to coincide with unusually extreme tides. Next winter such flooding is unlikely to re-occur.

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## The Moon’s eccentric orbit

As far as I can tell no long term study of lunar eccentricity has been done, so  I decided to begin investigating this myself, even though I am a complete novice in astronomical calculations. The key resource for all planetary motions in the solar system is the JPL ephemeris, and the easiest way to access it is through their Horizon web interface. Initially I ran a simulation of the lunar orbit relative to the earth-moon barycentre over a 10 year period beginning in december 2000.

I was amazed  to discover that the moon’s orbit around the earth is anything but simple and the quoted parameters are just approximations. The eccentricity of the orbit is changing almost on a daily basis due to complex variations in gravitational  effects depending on the relative positions of the sun and the earth, and also Jupiter and Venus. Figure 1 plots the eccentricity from Dec 12 2000 until October 12 2010 on a daily basis. The eccentricity varies in total between the extreme values of  0.026 and 0.077. This is a huge range which can alone  change the strength of lunar tides up to a maximum of 20%.

Figure 1: Variations in eccentricity of the moon’s orbit around the Earth-Moon barycentre. The blue curve is a fit to two oscilations with time periods 31.8 days and 205.9 days. Click to expand.

There are at least 2 regular resonances which at first sight seems odd because neither coincide with the orbital period of the moon (27.32days) nor that of the earth (365.25 days). There are also beats in the amplitude. Following this german article, I made a least squares fit shown as the blue curve which reproduces almost perfectly the signal .

eccentricity(d) = 0.55 + 0.014cos(0.198*d + 2.148) + 0.0085cos(0.0305*d +10.565)

This variation in eccentricity changes the perihelion distance from the earth significantly causing  large variations in the strength of spring tides on a yearly basis. The eccentricity becomes a maximum when the semi-major axis of the orbit lines up with the sun. This happens every 205.9 days – more than half a year due to the precession of the orbit every 18.6 years.  The 31.8 day variation is I think  the regular orbital change in distance from the sun.

Horizons only goes back as far as 8000 BC, so in order to investigate paleoclimate effects of eccentricity of lunar orbit around the time of the last interglacial we now need to find another tool.

Posted in Astronomy, Paleoclimatology, Science | Tagged | 24 Comments

## Does the Moon trigger interglacials?

Why did the last 8 glacial periods only end when the earth’s orbit around the sun reached maximum eccentricity ? This is the real unsolved mystery of the Ice Ages as discussed in previous posts and recently on scienceofdoom.

With the last of these posts I finally thought there could be  a solution to this mystery based on  resonant interplanetary dust, but alas I could find no evidence whatsoever in TSI data and dismissed the idea. However I now realise that perhaps there is another solution which may have been looking us in the face all the time. The Earth already has a large resonant interplanetary body – the moon. There is no need to invent some hypothetical resonant dust. The trigger for interglacials is not changes in insolation but instead changes in  tidal forces acting on the oceans and cryosphere. Every 100,000 years super-tides must  occur whenever the decreased perihelion distance of the earth from the sun and the moon coincide and are synchronised with maximum eccentricity.  These tidal forces combined with already known increases in insolation could well be the trigger that initiates  the rapid break up and melting of the northern glaciers.

The  100,000 and 400,000 year cycles  in the ellipticity of the Earth’s orbit are caused by regular gravitational effects of the other planets as they orbit the sun,  particularly those of Jupiter and Saturn. Every 100,000 years the orbits of Jupiter and Saturn align themselves so that their net gravity perturbs the Earth’s orbit causing it to elongate and become more elliptical. This cycle reaches a maximum every 400,000 years in  regular fashion.

The moon also is  effected by the same regular (Milankovitz) induced variation in its orbit around the sun. This also causes an increased elliptical orbit of the moon around the Earth. Tidal forces vary as 1/r^3 so small changes in distance can have large effects on tides.

The gravitational force of the sun on the moon is more than twice that of the Earth. For an observer  in outer space the moon appears to orbit the sun just like any other planet. It’s orbit is perturbed by the Earth’s gravity making it slightly concave.  It is only from Earth that it appears to us to be in an elliptical orbit around the Earth.

Fig 1: “…to an observer in space the Moon must appear as a normal planet, traveling in an elliptical orbit with the Sun in one of the foci.” (Moore 2001)

Therefore the moon like any other planet is just as effected by the gravitational effects of all the other planets . It’s orbit becomes more elliptical both with respect to the sun AND respect to to Earth.

Spring tides occur when the sun and moon are aligned – new moon and full moon. When this alignment occurs at perihelion ( currently around Jan 2nd) the tides are far stronger. This is the main reason why UK storms this last week caused so much damage  because they also coincided with a super-tide at perihelion and new moon. The precession of the lunar orbit plane takes 18.6 years and changes the declination angle by ± 5 deg. Once every 2000 years the projection to the ecliptic of the major axis of the lunar orbit coincides with the Earth-Sun line at the perihelion increasing tidal forces by a further 12%.

Fig 2: orbital-plane of the moon with respect to the ecliptic plane.

Now we  consider the 100,000 year effect of a peak in the eccentricity and its effect on tides.

Fig 3: Top: The stack of ocean sediment (LR04) DO18 data which are a proxy for global temperatures (ice volume). The Blue curve is a parametric fit to Milankovitch terms and described here. The bottom curve is the NASA calculated  values of the earth’s eccentricity for the last 800,000 years

We see form Figure 3 that the earth’s eccentricity varies from about 0.06 to 0.005 and the interglacials coincide with peaks in eccentricity for the last 8 ice ages. The collapse of the Ice sheets occured very rapidly over a couple of thousand years. Tides are a tractional force whose greatest  effect is felt near the poles. During both the Arctic and Antarctic winters with zero insolation there are clear signals of tidal effects on temperature (1). Furthermore tides have also a direct effect on sea ice. Postlethwaite et al.[2] write

Tidal mixing within the water column and at the base of the sea ice cover can increase the heat flow from deeper water masses towards the surface causing decreased freezing and increased melting of sea ice and possibly the formation of sensible heat polynyas (Morales-Maqueda et al., 2004; Willmott et al., 2007; Lenn et al., 2010). The tidal currents can additionally increase the stress and strain on the sea ice and cause leads to open periodically within the sea ice cover (Kowalik and Proshutinsky, 1994).

Tidal forces therefore  act to break up ice sheets and change ocean heat flows. Spring tides occur when the moon and sun align together at new moon and full moon. The largest spring tides are when the Earth is at  closest distance to both the sun and the moon. The Milankovitch precession term also depends strongly on the eccentricity, so when every 26,000 years perihelion coincides with the northern summer so the arctic also receives maximum insolation. In addition once a month in summer the arctic experience super tidal forces leading to the breaking up of ice and enhanced heat mixing. The combination of the two effects starts the retreat of the ice sheets and the  positive feedback of lower albedo accelerates melting. Is this the real driver that forces the onset of an interglacial ?

How large can the tides get during 100,000y cycles of maximum eccentricity? Figures 4 and 5 show calculations of the change in tidal forces due to the sun and the moon for various values of orbital eccentricity. These calculations are based on the distance to the Earth for different times in the year for the sun, and in the sidereal month for the moon. Tides are tractional forces which depend on 1/R^3 which explains why the moon has a larger tidal pull on the oceans than does the much more massive sun. At spring tides the two tidal forces are superimposed

Fig 4: Relative strength of the solar lunar tidal force – proportional to 1/R^3

The largest solar tides are up to 20% higher than those we experience today. Now lets look at the same thing for the more important lunar tide.

Fig 5: Variation in strength of lunar tides with orbital eccentricity relative to today.

Now we see that spring lunar tides can be up to 60% higher than they are today. Lunar tides are currently about twice the strength of solar tides so we can estimate how much stronger spring tides could have been 15000 years ago. It seems very likely that spring tides were at least 50% stronger  than they are today. Is this why interglacial melting can only start when the earth is at maximum eccentricity because only then are  the tidal forces strong enough to break up the ice sheets ?

Summary

For 800,000 years glacial periods have ended abruptly once the orbit of the earth reaches a maximum orbital eccentricity. Milankovitch insolation theory cannot explain this 100,000 year cycle. The lunar and solar tides on earth are also affected by the same Milankovitch cycles. Their effect on ocean heat transfer and ice formation has been long documented. Maximum spring tides existing 15,000 years ago were likely at least 50% stronger than those today. This must have had the largest effect in polar regions and is directly linked to the 100,000 year eccentricity cycle. It therefore seems feasible that the moon really is responsible for the regular glacial cycle.

Note: A recent discussion on Ice Ages can be found  on Euan Mearns blog. There is a also a series of posts on scienceofdoom which review how climate models  struggle to explain the dynamics of ice ages. The most recent of these posts is very relevant.

References

1. The influence of the lunar nodal cycle on Arctic climate, Harald Yndestad,
2. The effect of tides on dense water formation in Arctic shelf seas, C. F. Postlethwaite, M. A. Morales Maqueda, V. le Fouest,*, G. R. Tattersall1,**, J. Holt, and A. J. Willmott, Ocean Sci., 7, 203–217, 2011

Posted in Climate Change, Ice Ages, Oceans, Paleoclimatology, Science | Tagged , , , | 46 Comments

## UK ECC committee – IPCC 5th Assessment Review

My hastily written submission to the committee  has just been published !  It was actually submitted a couple of days after the deadline so I am rather pleased it got accepted. My aim was to keep it short, avoid polemics, while trying to defuse the CAGW meme driving energy policy.

Submission by Clive Best

I am an independent scientist. I have a PhD in High Energy Physics and previously worked at CERN, Rutherford Lab, JET Fusion Experiment and the Joint Research Centre of the European Commission.  All opinions expressed here are entirely my own.

The fifth assessment report is a comprehensive and impressive review of the current status of climate science. Although the message remains one of warning, the results are actually rather encouraging. The relevant new conclusions of AR5 are:

1. The acknowledgement that up to half the observed warming since 1950 is due to natural effects.
2.  That climate sensitivity estimates have now been reduced.  Manmade warming is likely to be less than 2C for a doubling of CO2 (560 ppm).
3. The current pause in warming may well continue until 2030 due to a natural 60-year cycle in ocean dynamics (AMO/PDO) – see figure 1.  Thereafter we can expect another period of rapid warming.
4. All AR5 climate models overestimated warming till 2012 because they excluded this natural oscillation – see figure 2.
5. The risk of climate disruption and extreme weather impacts is very small.
6. Sea level rise is a modest 60cm by 2100. Sea defenses can easily cope with this.
7. There will always remain uncertainties in climate models due to the complexity of climate interactions.  As an example scientists still cannot model the dynamics of past glaciations nor predict when the next one will occur. Another ice age would be far more catastrophic than global warming.
8. The largest model uncertainties are clouds. Clouds play the same role as the white daisies do in Lovelock’s daisy world.

In general AR5 is good news for the world and good news for the UK.  The world has more time to adapt to a low carbon future than previously assumed.  Professor Dieter Helm makes a strong argument in  “The Carbon Crunch” for gas as a low carbon transition path. Other implications for UK energy policy are:

• Carbon targets can be relaxed. Investment in new wind generation too quickly is counter-productive as costs begin to outweigh benefits above 20% intrusion on the grid [1].
• More resources could then be invested in research and development on future technologies. For example:
• Energy storage
• Next generation solar, artificial photosynthesis
• Compact nuclear, thorium reactors
• Nuclear fusion, hybrid fusion/fission

Fig 1: An overall fit to 162 years of global temperature data

Fig 2: Comparison of CMIP5 models and observed temperature trends.

References
1. A cost Benefit Analysis of Wind Power, Eleanor Denny, PhD. Thesis , 2007 University College Dublin.

Posted in AGW, Climate Change, Energy, nuclear, renewables, Science, wind farms | | 5 Comments

## Sherwood Forest

The paper: “Spread in model climate sensitivity traced to atmospheric convective mixing by Sherwood, Bony & Dufresne, Nature 2/1/14″  has generated a lot of media hype that global warming by 2100 will be > 4C. It  has also  recently been highlighted on realclimate.

Professor Sherwood, of the University of New South Wales in Australia, told The Guardian: “This study breaks new ground twice: first by identifying what is controlling the cloud changes and second by strongly discounting the lowest estimates of future global warming in favour of the higher and more damaging estimates.

“4C would likely be catastrophic rather than simply dangerous,” Sherwood said. “For example, it would make life difficult, if not impossible, in much of the tropics, and would guarantee the eventual melting of the Greenland ice sheet and some of the Antarctic ice sheet” – causing sea levels to rise by many metres as a result.

The basis of this paper is an interpretation by the authors that lower troposphere circulation  described by  high sensitivity models is  more favored by observation than that described by low sensitivity models. But just how robust is this claim and what does it really mean for future global warming? I have been struggling  my way through the details of this paper for the last two days. My conclusion is that  all the media hype and claims made by Prof. Sherwood simply  don’t stand up to serious scrutiny. There are far too many inconsistencies in the analysis to justify any such bold statements as the one above.  The evidence provided in the paper from direct observation is simultaneously poor, selective and says little about climate sensitivity, its evolution, nor even that much about clouds.

The authors appear to me  to have assumed at the outset what it was they consequently set out to prove. They assumed that increasing low tropospheric mixing as the earth warms will lead to less low clouds despite higher evaporation rates from the ocean. Less low level clouds means net positive cloud feedback in climate  models despite an implied increase in medium and convective clouds. The paper then analyses 42 climate models with differing sensitivities (ECS) to demonstrate that indeed those with high sensitivity have more low tropospheric mixing in the tropics than those with low sensitivity. In order to show this they define 2 indexes to “measure” this mixing effect.

Indices from models of different sensitivity and “observations (ERAI and MERRA)

1. S = (DR(700-850)/100.0 – DT(700-850)/9)/2 which they interpret as a drying term? S is high if there a large fall off in relative humidity than expected by the lapse rate falls. The pressure height conversion is 700mb = 3000m 850mb = 1500m (top of boundary layer)
2. D = <H(w2-w1))H(-w1)>/<-w2H(-w2)> where w1 is the vertical pressure velocity below the boundary layer and w2 is that above the boundary layer , H is a step function at the boundary.<> means average.

This is described  by the authors to be the ratio of ascending air mixing below the boundary level to  mixing above the boundary or as they call it large scale lower-troposphere mixing. The larger D is the more mixing below the boundary layer occurs.

They then demonstrate that high sensitivity models have different values of S and D than low sensitivity models in these parameters and assume that this is due differences in low cloud feedback, although they don’t actually prove that. They also don’t describe how they derived these model indices.  They  finally combine both indices to give one LTMI (lower tropospheric mixing index) by simply adding the two together. To compare these indices to  data they need to use precisely the same criteria and this is where the real problems start. The data they use are ERAI + MERRA re-analysis of satellite data, themselves partly dependent on models. These are the inconsistencies I have so far identified:

• They only compare one month (September) for comparison. Why choose September when there are clear seasonal variations of water content in the atmosphere? Are the model indices also restricted  just to September ? This is not explained and essentially rules out anyone reproducing their result.
•  When averaging D why did they chose to only use observational values from the eastern Pacific and Atlantic. Why exclude the Indian Ocean and western Pacific (or Indonesia for that matter used to evaluate S)? Is this an indication that  they are hiding problems say with monsoons that  change their result ?
•  They also don’t specify which year the data is taken from ? Are model results for the same year and the same geographic area as observations? Surely high sensitivity model values of their indices must evolve quickly with time by definition. So what fixed time are we talking about?
•  The S indices are derived from a completely different geographic area to the D indices! The  S indices are selected just from a region around Indonesia and some coastal areas. Why these particular areas? Is it perhaps because they know that the circulation is very high there? Should not the models then also be restricted to identical times and these narrow regions ? Otherwise comparison makes no sense. MERRA data is global and available monthly. How can you then combine S and D to give a single LTMI if they apply to completely separate geographic regions?
• Why is the error on S shown to be essentially zero for MERRA/ERAI ? Is that perhaps  just a simple mistake ?
• How do we know that the differences between model sensitivity are only due to low clouds rather than say differences in their treatment of water vapor feedback or lapse rate feedback ?
• As the paper is written it is impossible to reproduce their results from the MERRA data given the information provided. Which year was used ? What are the geographic selections? How was the averaging done ?.. etc.
• Why were none of the above points picked up under NATURE peer review?

The conclusion of the paper is based on the following assumptions.

High sensitivity models predict fewer low clouds in the tropics with increasing CO2 resulting in  positive feedback.  The underlying reasons for this  can be correlated to some arbitrary indices of moist circulation calculated  at specific locations for one instant in time. Observations made in these zones for these specific  indices are more similar to high sensitivity models than to low sensitivity models (at least for September).

Do other zones and times show similar correlations ? Even if the observed profile were shown to be  similar , does it logically follow  that the time evolution  would anyway follow  the high sensitivity models?  Where is the evidence for that ? The paper does not really demonstrate that high climate sensitivity is high. To do that you would need to demonstrate a time evolution over the tropics consistent with high sensitivity models. We should not accept the conclusions of the paper because as it stands, the evidence is far too  flimsy.

The test of a good paper is whether it provides sufficient information to allow someone to independently validate the results. This paper doesn’t and seems to me on balance to be more propaganda  than hard science.

Posted in AGW, Climate Change, climate science, GCM, Science | Tagged , , , , | 10 Comments