These types of statements seem silly to me. They are essentially stating (again) that the model is the reality to which you compare the world!

These types of statements seem silly to me. They are essentially stating (again) that the model is the reality to which you compare the world!

Thanks, David – I looked at the reply to to Lockwood and FrÄohlich – very interesting.

“Shaviv’s paper is inadequately referenced in many ways as I noted, perhaps as an astrophysicist in another field he doesn’t know the literature, I don’t know. There are many things to look into.”

As well as Shaviv’s ocean calorimetry maybe we can do something with cloud frequency and density data versus CRF? I use Giovanni a lot to access the body of NASA and NATA-linked data so might have a go. Plenty of scope for anyone who is interested in getting into the remote sensing data.

http://disc.sci.gsfc.nasa.gov/giovanni/G3_manual_parameter_appendix.shtml

Thanks, David – I looked at the reply to to Lockwood and FrÄohlich – very interesting.

“Shaviv’s paper is inadequately referenced in many ways as I noted, perhaps as an astrophysicist in another field he doesn’t know the literature, I don’t know. There are many things to look into.”

As well as Shaviv’s ocean calorimetry maybe we can do something with cloud frequency and density data versus CRF? I use Giovanni a lot to access the body of NASA and NATA-linked data so might have a go. Plenty of scope for anyone who is interested in getting into the remote sensing data.

http://disc.sci.gsfc.nasa.gov/giovanni/G3_manual_parameter_appendix.shtml

Shaviv’s paper is inadequately referenced in many ways as I noted, perhaps as an astrophysicist in another field he doesn’t know the literature, I don’t know. There are many things to look into. One is that if 50% of variation is due to forcing is over the ocean, ocean temperatures should lead surface and overall temps. Cloudiness, and neutron counts should give a predictive jump on the the monthly guess the temperature competition.

Steve: Your notes on PDO are also suggestive of a resolution of PDO, and CRF forcings. I need to do some posts on the sense of ‘amplification’, ‘sensitivity’ and ‘feedbacks’ as used by Shaviv.

Shaviv’s paper is inadequately referenced in many ways as I noted, perhaps as an astrophysicist in another field he doesn’t know the literature, I don’t know. There are many things to look into. One is that if 50% of variation is due to forcing is over the ocean, ocean temperatures should lead surface and overall temps. Cloudiness, and neutron counts should give a predictive jump on the the monthly guess the temperature competition.

Steve: Your notes on PDO are also suggestive of a resolution of PDO, and CRF forcings. I need to do some posts on the sense of ‘amplification’, ‘sensitivity’ and ‘feedbacks’ as used by Shaviv.

“Another interesting point to note is that the solar cycle induced variations in low-altitude cloud cover [Marsh and Svensmark, 2000b], presumably from CRF modulation over the oceans (where CCNs are most likely to be a bottleneck), give rise to a radiative imbalance which can be estimated [Marsh and Svensmark, 2000a; Shaviv, 2005] to be of order 1.1 ± 0.3 W/m2 over the past two cycles.”

that CCNs over the ocean are most likely to be a ‘bottleneck’. However, Shaviv unfortunately doesn’t give any references to support such a very significant (albeit ‘off the cuff’) claim.

Speaking mathematically, a ‘bottleneck’ is essentially a rate limiting factor on a single mechanism within a chain of causation.

So, this raises the interesting question of whether, if this statement can be verified (and I’m double checking the specific literature on this), a reduction in this bottleneck has the ability to amplify the CRF forcing factor. I might add here that I probably agree with Shaviv that this probably applie at the present time as it is well known that the sulfate emitted by ocean-going cargo vessels (through the burning of fuel oil) form tracks of low level clouds across the oceans easily tracked by satellite and aerial photography.

But CCNs over the ocean are largely produced as sulfuric acid and ammonium sulfate etc from the UV-catalysed oxidation of dimethysulfide (DMS) and this in turn is a ‘byproduct’ of cyanobacterial productivity. In theory, cyanobacterial productivity is itself controlled by the availability of essential nutrients, particularly Fe, Si and P, temperature, and the availability of dissolved CO2 (!) as cyanobacteria absorb carbon from dissolved carbon dioxide and bicarbonate and respire O2. This means that, all other things being equal, cyanobacterial productivity should increase with increasing atmospheric CO2.

I also note that, on a global scale, and during an interglacial as now, cyanobacterial productivity is very likely closely linked to the long timescale changes in magnitudes and location of upwelling of more saline, nutrient-bearing waters which constitute the action of major cycles such as the PDO.

So, if Shaviv really does believe that ” …over the oceans (where CCNs are most likely to be a bottleneck)” is the case, an inescapable corollary of this is that the historic/paleoclimatic degree of CRF modulation of cloud production itself may also diminished or magnified by just where the planet is in the PDO cycle and what the contemporary atmospheric partial pressure of CO2 is.

This leads us right back to Bill Gray’s paper at Heartland-2 and Roy Spencer’s recent comments on his blog regarding the correct understanding of the role of the PDO.

“Another interesting point to note is that the solar cycle induced variations in low-altitude cloud cover [Marsh and Svensmark, 2000b], presumably from CRF modulation over the oceans (where CCNs are most likely to be a bottleneck), give rise to a radiative imbalance which can be estimated [Marsh and Svensmark, 2000a; Shaviv, 2005] to be of order 1.1 ± 0.3 W/m2 over the past two cycles.”

that CCNs over the ocean are most likely to be a ‘bottleneck’. However, Shaviv unfortunately doesn’t give any references to support such a very significant (albeit ‘off the cuff’) claim.

Speaking mathematically, a ‘bottleneck’ is essentially a rate limiting factor on a single mechanism within a chain of causation.

So, this raises the interesting question of whether, if this statement can be verified (and I’m double checking the specific literature on this), a reduction in this bottleneck has the ability to amplify the CRF forcing factor. I might add here that I probably agree with Shaviv that this probably applie at the present time as it is well known that the sulfate emitted by ocean-going cargo vessels (through the burning of fuel oil) form tracks of low level clouds across the oceans easily tracked by satellite and aerial photography.

But CCNs over the ocean are largely produced as sulfuric acid and ammonium sulfate etc from the UV-catalysed oxidation of dimethysulfide (DMS) and this in turn is a ‘byproduct’ of cyanobacterial productivity. In theory, cyanobacterial productivity is itself controlled by the availability of essential nutrients, particularly Fe, Si and P, temperature, and the availability of dissolved CO2 (!) as cyanobacteria absorb carbon from dissolved carbon dioxide and bicarbonate and respire O2. This means that, all other things being equal, cyanobacterial productivity should increase with increasing atmospheric CO2.

I also note that, on a global scale, and during an interglacial as now, cyanobacterial productivity is very likely closely linked to the long timescale changes in magnitudes and location of upwelling of more saline, nutrient-bearing waters which constitute the action of major cycles such as the PDO.

So, if Shaviv really does believe that ” …over the oceans (where CCNs are most likely to be a bottleneck)” is the case, an inescapable corollary of this is that the historic/paleoclimatic degree of CRF modulation of cloud production itself may also diminished or magnified by just where the planet is in the PDO cycle and what the contemporary atmospheric partial pressure of CO2 is.

This leads us right back to Bill Gray’s paper at Heartland-2 and Roy Spencer’s recent comments on his blog regarding the correct understanding of the role of the PDO.

“Together, with the TSI variations, we find that the ratio between the cloud + TSI variations compared with the change in the solar constant is: 1:3 ± 0:4 W/m2.”

The ratio is dimensionless. The paper actually shows:
“=1.3 ± 0.4″

This is compared to the ratio of ocean heat flux to the change in solar constant = 1.2 ± 0.3.

This shows that the theory of clouds modulated by CRF fully accounts for the ocean heat flux over the solar cycles. Wow! Svensmark should be pleased!

Please note also that the paper has two significant typos (I think!).

Equation (18) shows the heat flux derived from sea level rise (SLR) per change in TSI = 1.68 ± 0.6, but the equation is mislabeled with SST.

Then Equation (19) shows the heat flux derived from sea surface temperature (SST) per change in TSI = 1.15 ± 0.35, but the equation is mislabeled with SLR!

“Together, with the TSI variations, we find that the ratio between the cloud + TSI variations compared with the change in the solar constant is: 1:3 ± 0:4 W/m2.”

The ratio is dimensionless. The paper actually shows:
“=1.3 ± 0.4″

This is compared to the ratio of ocean heat flux to the change in solar constant = 1.2 ± 0.3.

This shows that the theory of clouds modulated by CRF fully accounts for the ocean heat flux over the solar cycles. Wow! Svensmark should be pleased!

Please note also that the paper has two significant typos (I think!).

Equation (18) shows the heat flux derived from sea level rise (SLR) per change in TSI = 1.68 ± 0.6, but the equation is mislabeled with SST.

Then Equation (19) shows the heat flux derived from sea surface temperature (SST) per change in TSI = 1.15 ± 0.35, but the equation is mislabeled with SLR!