Guest post by Mike Jonas,
Maybe, after all the attention being paid to the Wuhan virus, it’s time to do a bit of climate science again.
I have submitted a paper to a peer-reviewed journal, and, remarkably, the journal says it is happy for me to put it up on the web while it is in review, so here it is. But it hasn’t been easy getting to this point: I think that what I describe in the paper is of great significance to climate science, as you can tell from some of the wording in the paper, so I was keen to get it published in a peer-reviewed journal – the IPCC are supposed to work only from peer-reviewed papers. The first journal I submitted it to (in November 2019) took two months to tell me that the paper was outside the scope of the journal – a curious claim, I thought, since one of the relevant papers that I was citing had been published in that journal. The second journal took four months to tell me that it hadn’t yet been seen by a reviewer and it needed a different format for references, plus a few other minor format changes. So I withdrew the paper. I have made modest improvements to the paper over that period, but I doubt they will make much difference to reviews. Anyway, here’s hoping for third time lucky!
Here is the paper (and after the paper I’ve made some comments about how it relates to Richard Lindzen’s “Iris” theory):
– – – – – the paper: – – – – –
Cloud Feedback, if there is any, is Negative
Author: M Jonas
Affiliations: None
ABSTRACT
Virtually all the climate models referenced by the IPCC show a strong positive cloud feedback. Cloud feedback is the process by which a changing surface temperature affects cloud cover, which in turn affects surface temperature. In this paper, all monthly satellite data for sea surface temperatures and cloud cover over the oceans, for the whole available period of July 1986 to June 2017, is analysed, in order to test this feature of the climate models. As expected, the trends for the overall period are of rising sea surface temperatures and of falling cloud cover. But the analysis also shows an unexpected relationship between sea surface temperature and cloud cover: increases in sea surface temperature are associated with increases – not decreases – in cloud cover over the next few months. Moreover, the cloud cover increases tend to intercept a greater proportion of incoming solar radiation than they do of outgoing ocean radiation. The inevitable conclusion is that cloud feedback is negative. In any case, the observed reduction in cloud cover over the oceans between 1986 and 2017 could not have been a feedback from rising temperature. The implications for climate models are devastating.
KEYWORDS: Climate, Clouds, Cloud Feedback, SST, Sea Surface Temperature, Ocean
ABBREVIATIONS
dCloud – year-on-year change in cloud cover, dSST – year-on-year change in SST, GISS – (NASA) Goddard Institute for Space Studies, IPCC – Intergovernmental Panel on Climate Change, ISCCP – International Satellite Cloud Climatology Project, NASA – (US) National Aeronautics and Space Administration, NOAA – (US) National Oceanic and Atmospheric Administration, SST – Sea Surface Temperature.
1. INTRODUCTION
The ocean covers 70% of the global surface and stores more heat in the uppermost 3 metres than the entire atmosphere, so the key to understanding global climate change is inextricably linked to the ocean [7].
Spencer and Braswell [13], using satellite data and models, reported that atmospheric feedback diagnosis of the climate system remains an unsolved problem. They state: “The magnitude of the surface temperature response of the climate system to an imposed radiative energy imbalance remains just as uncertain today as it was decades ago“. By using satellite data only, over a longer period, this study resolves one of the major feedback issues, namely the sign of cloud feedback. The study uses empirical data only. In particular, it uses all of the period with data, it uses the whole global ocean area, and it does not use any models.
The IPCC [5] defines Climate Feedback as an interaction between processes when “the result of an initial process triggers changes in a second process that in turn influences the initial one“, and states that a way to quantify it is as “the response of the climate system to a global surface temperature change“. Cloud feedback is thus the process by which a changing surface temperature affects cloud cover, which in turn affects surface temperature. Note that cloud feedback does not depend on the original cause of the changing surface temperature.
Feedback response to temperature has been difficult to separate from other effects on temperature as the initial process is “nearly simultaneous with the temperature change“, but for the second process “there is a substantial time lag between forcing and the temperature response due to the heat capacity of the ocean” [13].
This study’s aim is to see if the effect of sea surface temperature (SST) on cloud cover can be detected. It does this by comparing changes in SST with later changes in cloud cover.
2. METHOD
Gridded monthly SST data (deg C) was downloaded from NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, in the USA National Oceanic and Atmospheric Administration (NOAA) [10]. Each grid cell is 1 degree latitude by 1 degree longitude.
Equal-area monthly cloud data (cloud cover % and IR optical depth) was downloaded from the International Satellite Cloud Climatology Project (ISCCP) in the NASA Goddard Institute for Space Studies (GISS), New York, NY [11]. Only data over the ocean was used. ie, land and coastal data was not used.
Data from the two datasets was used only for the months and areas for which there was both cloud data and complete SST data. SST data was missing in locations and months where there was some sea ice.
For each month, SST and cloud cover were averaged over the total ocean area. For SST, individual temperature readings were converted into Kelvin and raised to the 4th power before averaging, then converted back to degrees C. This ensured that the calculated average SSTs related correctly to outward radiation from the sea surface.
For each month after the first year, the global year-on-year change in SST was calculated (referred to here as “dSST”). These were then averaged over various numbers of months, and each average was recorded against the end month of the period being averaged. The global year-on-year change in cloud cover (“dCloud”) was averaged for the same number of months but for later periods that did not overlap the period used for dSST. For comparison purposes, dCloud was recorded for the same month as the related dSST, for each combination of number of months averaged and number of months later. The linear trend of dCloud against dSST was then calculated for each combination using the standard spreadsheet Trend function (NB. the trend was of dCloud against dSST, not against time).
3. RATIONALE
As stated by Spencer and Braswell [13], feedback response to temperature is “nearly simultaneous with the temperature change“. A change in SST should therefore result in a change in cloud cover (ie, cloud feedback) very soon after. There is therefore a possibility that cloud feedback can be detected in the monthly data. The relevant comparison to make is between SST change and cloud cover change in the following month or months.
This study uses year-on-year changes, and treats all calendar months equally, in order to avoid any possibility of seasonal effects. It avoids using overlapping periods for dSST and dCloud, in order to minimise any possibility of clouds’ influence on SST affecting results. And because a rapid effect is being looked for, it limits each overall period being looked at (from first dSST month to last dCloud month) to no more than 12 months.
The effect of cloud cover change is unlikely to show up in SST data within a month or within a few months [13], so comparing data the other way round – cloud cover change against SST change in the following month or months – is unlikely to be effective. However, clouds are known have a cooling effect on climate, see for example Pokrovsky [8] which calculates a (global) 0.07C warming effect for each 1% decrease in cloud cover.
4. RESULTS
4.1 Cloud Cover
In all cases with dCloud up to 9 months later than dSST, the linear trend of dCloud against dSST was positive, and this was a strong and consistent pattern. In other words, the months with higher dSST tended to be followed by months with higher dCloud, and lower dSST tended to be followed by lower dCloud.
The linear trends of dCloud against dSST are shown in Figure 1, and are shown graphically in Figure 2.
Linear trends of global average dCloud vs dSST – unweighted (ie, by equal area) +/- 2*Sigma
| Months later: | 1 mth | 2 mths | 3 mths | 4 mths | 5 mths | 6 mths | 7 mths | 8 mths | 9 mths | 10 mths | 11 mths |
| Months averaged | |||||||||||
| 1 mth | 1.21 +/- 0.68 | 1.46 +/- 0.67 | 1.74 +/- 0.67 | 1.34 +/- 0.68 | 1.23 +/- 0.68 | 1.25 +/- 0.68 | 0.91 +/- 0.69 | 0.95 +/- 0.69 | 0.91 +/- 0.70 | 0.63 +/- 0.70 | 0.07 +/- 0.71 |
| 2 mths | 1.62 +/- 0.58 | 1.72 +/- 0.58 | 1.56 +/- 0.59 | 1.41 +/- 0.59 | 1.31 +/- 0.59 | 1.14 +/- 0.60 | 1.05 +/- 0.60 | 0.96 +/- 0.61 | 0.63 +/- 0.61 | ||
| 3 mths | 1.74 +/- 0.55 | 1.67 +/- 0.55 | 1.55 +/- 0.56 | 1.41 +/- 0.56 | 1.27 +/- 0.56 | 1.15 +/- 0.57 | 0.93 +/- 0.58 | ||||
| 4 mths | 1.73 +/- 0.53 | 1.65 +/- 0.53 | 1.53 +/- 0.54 | 1.36 +/- 0.54 | 1.18 +/- 0.55 | ||||||
| 5 mths | 1.72 +/- 0.52 | 1.61 +/- 0.53 | 1.43 +/- 0.53 | ||||||||
| 6 mths | 1.65 +/- 0.52 |
Figure 1. Linear trends of global average dCloud against dSST, with 95% confidence levels. Each is averaged over the number of months given under “Months averaged”. The dCloud values are for the given number of months later.
Figure 2. As Figure 1, graphically. The outer range of the 95% confidence levels is shown.
Note that the trends can be expected to drop off as ‘Months Later’ increases, because of influence from the intervening months.
The trends are clearly visible in charts of dCloud vs dSST, and reflect the body of data not just some outliers. Some examples are shown in Figures 3, 4, 5.
Figure 3. dSST for one month, vs dCloud the following month.
Figure 4. dSST averaged over 3 months, vs dCloud averaged over the following 3 months.
Figure 5. dSST averaged over 6 months, vs dCloud averaged over the following 6 months.
The charts in Figures 3, 4, 5 are all to the same scale.
4.2 Cloud Opacity
Cloud with greater optical depth intercepts more radiation – both incoming solar radiation and outgoing ocean radiation. The above analysis was re-calculated with clouds weighted by opacity (opaqueness). Opacity, as used in this study, is derived from optical depth as follows:
Optical Depth d is given by
d = ln(Fr/Ft)
where
d is optical depth,
Fr is flux received,
Ft = flux transmitted.
The proportion q of radiation intercepted by cloud –
q = (Fr–Ft)/Fr
– is referred to here as “opacity”. q can be derived from the formula for d as
q = 1 – e^(-d)
Opacity q can legitimately be arithmetically averaged across sets of clouds.
When cloud data is weighted by opacity, the table of dCloud against dSST becomes:
Linear trends of global average dCloud vs dSST – weighted by cloud opacity +/- 2*Sigma
| Months later: | 1 mth | 2 mths | 3 mths | 4 mths | 5 mths | 6 mths | 7 mths | 8 mths | 9 mths | 10 mths | 11 mths |
| Months averaged | |||||||||||
| 1 mth | 1.44 +/- 0.64 | 1.51 +/- 0.63 | 1.85 +/- 0.63 | 1.45 +/- 0.64 | 1.26 +/- 0.64 | 1.29 +/- 0.64 | 0.86 +/- 0.65 | 0.90 +/- 0.65 | 0.88 +/- 0.66 | 0.63 +/- 0.66 | -0.02 +/- 0.67 |
| 2 mths | 1.75 +/- 0.53 | 1.84 +/- 0.53 | 1.66 +/- 0.54 | 1.47 +/- 0.54 | 1.32 +/- 0.55 | 1.11 +/- 0.55 | 1.00 +/- 0.56 | 0.93 +/- 0.56 | 0.60 +/- 0.57 | ||
| 3 mths | 1.87 +/- 0.49 | 1.77 +/- 0.50 | 1.61 +/- 0.50 | 1.43 +/- 0.51 | 1.25 +/- 0.51 | 1.11 +/- 0.52 | 0.89 +/- 0.52 | ||||
| 4 mths | 1.83 +/- 0.47 | 1.70 +/- 0.48 | 1.55 +/- 0.48 | 1.35 +/- 0.49 | 1.15 +/- 0.50 | ||||||
| 5 mths | 1.78 +/- 0.46 | 1.64 +/- 0.46 | 1.42 +/- 0.47 | ||||||||
| 6 mths | 1.68 +/- 0.46 |
Figure 6. Linear trends of global average dCloud against dSST, with 95% confidence levels, as in Figure 1 but with cloud cover weighted by opacity.
As can be seen from Figures 1 and 6, the increased cloud cover associated with increased SST tends also to have greater opacity for several months. There is therefore not just an increased area of cloud, there is also an increased amount of cloud (as judged by its ability to intercept radiation).
4.3 Effect on incoming and outgoing radiation
In order to determine the relative effect of the cloud changes on incoming and outgoing radiation, the analysis was re-calculated with clouds weighted (a) by opacity multiplied by incoming solar radiation (monthly solar radiation data from [2]), and (b) by opacity multiplied by outgoing ocean radiation (from SST). Note that a difference between (a) and (b) would come primarily from the different distributions of incoming solar radiation and outgoing ocean radiation by latitude, not from variations in solar output.
The table of dCloud against dSST then becomes as shown in Figures 7 and 8.
Linear trends of global average dCloud vs dSST – weighted by cloud opacity and solar radiation +/- 2*Sigma
| Months later: | 1 mth | 2 mths | 3 mths | 4 mths | 5 mths | 6 mths | 7 mths | 8 mths | 9 mths | 10 mths | 11 mths |
| Months averaged | |||||||||||
| 1 mth | 1.60 +/- 0.73 | 1.79 +/- 0.72 | 2.25 +/- 0.71 | 1.91 +/- 0.72 | 1.68 +/- 0.73 | 1.76 +/- 0.73 | 1.16 +/- 0.74 | 1.22 +/- 0.74 | 1.22 +/- 0.75 | 0.99 +/- 0.75 | 0.16 +/- 0.76 |
| 2 mths | 2.07 +/- 0.60 | 2.27 +/- 0.59 | 2.15 +/- 0.60 | 1.97 +/- 0.61 | 1.79 +/- 0.61 | 1.51 +/- 0.62 | 1.37 +/- 0.63 | 1.31 +/- 0.63 | 0.95 +/- 0.64 | ||
| 3 mths | 2.31 +/- 0.55 | 2.28 +/- 0.55 | 2.13 +/- 0.56 | 1.92 +/- 0.57 | 1.70 +/- 0.58 | 1.54 +/- 0.58 | 1.28 +/- 0.59 | ||||
| 4 mths | 2.34 +/- 0.53 | 2.23 +/- 0.53 | 2.07 +/- 0.54 | 1.84 +/- 0.55 | 1.60 +/- 0.56 | ||||||
| 5 mths | 2.31 +/- 0.51 | 2.17 +/- 0.51 | 1.93 +/- 0.53 | ||||||||
| 6 mths | 2.22 +/- 0.50 |
Figure 7. Linear trends of global average dCloud against dSST, with 95% confidence levels, as in Figure 1 but with cloud cover weighted by opacity multiplied by incoming solar radiation.
Linear trends of global average dCloud vs dSST – weighted by cloud opacity and ocean radiation +/- 2*Sigma
| Months later: | 1 mth | 2 mths | 3 mths | 4 mths | 5 mths | 6 mths | 7 mths | 8 mths | 9 mths | 10 mths | 11 mths |
| Months averaged | |||||||||||
| 1 mth | 1.44 +/- 0.68 | 1.56 +/- 0.67 | 1.91 +/- 0.67 | 1.52 +/- 0.68 | 1.33 +/- 0.68 | 1.38 +/- 0.68 | 0.93 +/- 0.69 | 0.99 +/- 0.69 | 0.96 +/- 0.70 | 0.71 +/- 0.70 | 0.02 +/- 0.71 |
| 2 mths | 1.80 +/- 0.56 | 1.91 +/- 0.56 | 1.74 +/- 0.57 | 1.56 +/- 0.57 | 1.42 +/- 0.58 | 1.21 +/- 0.58 | 1.10 +/- 0.59 | 1.03 +/- 0.59 | 0.68 +/- 0.60 | ||
| 3 mths | 1.94 +/- 0.52 | 1.86 +/- 0.53 | 1.71 +/- 0.53 | 1.53 +/- 0.54 | 1.36 +/- 0.54 | 1.22 +/- 0.55 | 0.99 +/- 0.55 | ||||
| 4 mths | 1.92 +/- 0.50 | 1.81 +/- 0.50 | 1.66 +/- 0.51 | 1.47 +/- 0.51 | 1.26 +/- 0.52 | ||||||
| 5 mths | 1.89 +/- 0.48 | 1.75 +/- 0.49 | 1.54 +/- 0.50 | ||||||||
| 6 mths | 1.80 +/- 0.48 |
Figure 8. Linear trends of global average dCloud against dSST, with 95% confidence levels, as in Figure 1 but with cloud cover weighted by opacity multiplied by outgoing ocean radiation.
For the first few months, all the trends weighted by incoming solar radiation are higher than those weighted by outgoing ocean radiation. This shows that the increases in cloud cover associated with increases in SST have a larger effect on incoming radiation than on outgoing radiation. In other words, they are net cooling.
5. CONCLUSION
The data shows that there is a positive correlation between changes in SST and later changes in cloud cover. As stated above, it is known that clouds have a global cooling effect, and the analysis has shown that as the changes in cloud cover associated with changes in SST increase, they do indeed have a larger cooling effect. The inescapable conclusion is that any cloud feedback is negative.
This conclusion has profound implications for climate models. All of the models referenced by the IPCC are parameterised with positive cloud feedback – “the GCMs all predict a positive cloud feedback” – and they attribute nearly half of all anthropogenic global warming to cloud feedback [9]. This is a possible reason for most climate models overestimating global warming [4] [12], although Anagnostopoulos [1] says that there are much more significant problems.
A further conclusion with profound implications for the climate models is that the observed reduction in cloud cover between 1986 and 2017 was not a feedback from rising temperatures and that it was powerful enough to eventually override any negative cloud feedback. The IPCC report [9] suggests that the models do not recognise the possibility that clouds can have any behaviour other than as a feedback:- in Key Uncertainties they only say of clouds: “Large uncertainties remain about how clouds might respond to global climate change“. This study shows that clouds must have important behaviour that is not included in the models.
The implication for the climate models is devastating. It must be questioned whether they are fit for purpose.
6. DISCUSSION
6.1 Interpretation
The most reasonable interpretations would appear to be:
- The changes in cloud cover as in Figure 1 are caused by the changes in SST. If this is the case, then cloud feedback is negative.
or - The changes in cloud cover and the changes in SST are both caused by some unknown factor (ie, correlation is not causation). If this is the case, then cloud feedback is zero.
or - A combination of 1 and 2 applies. If this is the case, then cloud feedback is not as negative as in 1 but it is still negative.
6.2 Other Interpretations
Any interpretation other than the above seems extraordinarily unlikely, and in any case would completely invalidate the climate models referenced by the IPCC. For example:
(a) If an increase in SST actually caused there to be less clouds, then some unknown factor or factors would have to be operating in the opposite direction with greater effect. ie, some unknown factor or factors would have to be creating more clouds after an increase in SST even though an increase in SST was causing there to be less clouds.
(b) If an increase in SST does cause more clouds, but if in fact clouds have a warming effect, not cooling, then some unknown factor or factors would have to be operating over the longer term to both reduce cloud cover and to increase SSTs.
As stated above, both these possibilities are extremely unlikely, and can surely be discounted. Alternative (b) in particular appears to be ruled out anyway by the above analysis using incoming and outgoing radiation weightings.
6.3 Quantification
It would be tempting to quantify the negative cloud feedback using the linear trends between dCloud and dSST as reported above and the linear trends of SST and cloud cover over the period that was analysed.
Such quantification would be unjustifiable at this stage, because until the mechanisms involved are reasonably well understood, it would be far too unreliable. There is an obvious possible mechanism for cloud feedback being negative:- as oceans warm they release more water vapour into the atmosphere, which then forms more clouds over the next few months. This concept is supported by the observation by Durack [3] of an increased hydrological cycle. However, the mechanism needs to be evaluated against the findings here before it can be assumed to be the relevant mechanism.
6.4 What Next?
Quantification of cloud feedback is clearly needed, as in 6.3 Quantification.
But further investigation into the behaviour of clouds is also needed:
This study shows that the observed reduction in cloud cover between 1986 and 2017 was not a feedback from rising temperatures. It follows that some of the increase in SST over this period could have been caused by independent cloud cover reduction. Finding out what caused the cloud cover reduction would be a major step forward for climate science. Some work has already been done, eg. [6] [14].
On a more general level:- It is abundantly clear that the climate models cannot represent the behaviour of clouds. It must therefore be seriously questioned whether the models, as currently structured, will ever be of any value at all for predicting future climate. It would be reasonable to consider the advice given by Anagnostopoulos [1] that a paradigm shift is needed.
6.5 Overall Trends
Charts of SST (Deg C) and Cloud cover (%) are given in Figures 9 and 10. These support the statement in the Abstract: “As expected, the trends for the overall period are of rising sea surface temperatures and of falling cloud cover.”.
Figure 9. SST data over the study period, with linear trend.
Figure 10. Cloud cover data over the study period, with linear trend.
The sources of the SST and cloud data are given in 2. METHOD.
Acknowledgements
Most of the data was accessed using Panoply software developed and provided free of charge by NASA Goddard Institute for Space Studies.
References
1. Anagnostopoulos GG et al 2010: A comparison of local and aggregated climate model outputs with observed data. Hydrological Sciences Journal 55(7), 1094–1110.
https://doi.org/10.1080/02626667.2010.513518
2. Coddington et al 2015: NOAA Climate Data Record (CDR) of Total Solar Irradiance (TSI), NRLTSI Version 2 [monthly TSI]. NOAA National Centers for Environmental Information doi:10.7289/V55B00C1. Data downloaded Jan 2020 from https://www.ncei.noaa.gov/data/total-solar-irradiance/access/monthly/
3. Durack et al 2012: Ocean Salinities Reveal Strong Global Water Cycle Intensification During 1950 to 2000. Science 27 Apr 2012 Vol. 336, Issue 6080, pp. 455-458 DOI: 10.1126/science.1212222
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6. Kamide Y 2007: Effects of the Solar Cycle on the Earth’s Atmosphere (in Handbook of the Solar-Terrestrial Environment). Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-540-46315-3_18
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8. Pokrovsky OM 2019: Cloud Changes in the Period of Global Warming: the Results of the International Satellite Project. Russian Academy of Sciences;
https://doi.org/10.31857/S0205-9614201913-13
9. Randall DA et al 2007: [Climate] Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon S et al (editors.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
10. Reynolds RW et al 2002: An improved in situ and satellite SST analysis for climate. Journal of Climate, 15, 1609-1625. Data accessed 26-30 Aug 2019 at https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html
11. Rossow WB and Schiffer RA 1999: Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society, 80, 2261-2288, doi:10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2. Data accessed 26 Aug 2019 at https://www.ncei.noaa.gov/data/international-satellite-cloud-climate-project-isccp-h-series-data/access/isccp/hgm/
12. Spencer RW 2013: STILL Epic Fail: 73 Climate Models vs. Measurements, Running 5-Year Means.
13. Spencer RW and Braswell WD 2011: On the Misdiagnosis of Surface Temperature Feedbacks from Variations in Earth’s Radiant Energy Balance. Remote Sensing 2011, 3(8), 1603-1613; https://doi.org/10.3390/rs3081603
14. Svensmark J et al 2016: The response of clouds and aerosols to cosmic ray decreases, Journal of Geophysical Research – Space Physics, 2016, DOI: 10.1002/2016JA022689.
– – – – – end of paper – – – – –
The paper may have to be changed to satisfy the review process. But in the meantime, the paper presented above is the exact paper that has been presented to the journal.
“Iris” Theory
Richard Lindzen et al hypothesised in September 2000 that Earth might have an “Adaptive Infrared Iris” which provides a significant negative feedback to surface temperature changes – the “Iris” theory. If I have understood that paper correctly, then my paper above does not support the “Iris” theory, because the “Iris” theory is based on surface warming resulting in less clouds of a particular type at the tropics, whereas I found that the data associated cloud increase with surface temperature increase.
NB, my findings don’t disprove the “Iris” theory either, because the “Iris” theory is based on specific cloud reduction in a limited area, whereas my analysis uses only global SST and cloud data.
In January 2002, Bing Lin et al argued against the “Iris” theory, saying that the cloud changes were actually warming not cooling, and therefore that the feedback referred to was positive not negative.
My paper provides no more support for Bing Lin’s argument than it does for Richard Lindzen’s, for the same reason: Bing Lin refers to the same cloud reduction.
Incidentally, arguments based on different types of cloud won’t work if anyone tries to use them to disprove my findings. That’s because surface temperature increases are followed by cloud increases. If the cloud type involved somehow manages to make that a positive feedback – in spite of my finding re cloud opacity – then the positive feedback will create even more cloud which will deliver even more positive feedback, etc, etc. But … in the longer term, as surface temperature increases, there’s less cloud, not more, so my argument in 6.2 (b) applies.
And one final comment: All I have done is to analyse the data. I don’t provide any theories, I don’t use any models, and I don’t use any sophisticated statistical tricks or cherry-picking to manipulate the data into a dubious finding. All the available data is used, and the only process used is simple weighted (and unweighted) averaging. The chart of cloud change against temperature change goes the “wrong” way. Period.
IPCC don’ need no stinkin’ cloud science!
Just “parameterization”, ie clouds can be whatever IPCC wants them to be.
Most of the last 200 Ma have been warmer than today’s Holocene. So before the Ice Age started 3.5 Ma ago was it mostly cloudy? And why this Ice Age when it is mostly sunny?
Aren’t you basically arguing that clouds are not the primary driver of climate change? Clouds are an emergent phenomenon dependent on temperature, which as far as I can reason, could not be an independent factor causing climate change, unless there are conditions for cloud formation that are independent of temperature (such as Svensmark’s poorly-supported hypothesis about galactic cosmic rays seeding cloud formation, modulated by solar wind).
So before the Ice Age started 3.5 Ma ago was it mostly cloudy?
Where’s your evidence for that claim?
Clouds have a net cooling effect because they reflect sunlight out to space to prevent it warming the surface.
Don’t we see a Solar/ Cosmic Rays signal in the cloud cover ?
The Svensmark hypothesis would predict the opposite trend to what is actually occurring.
Less solar activity -> more GCRs -> more ionization -> more nucleation sites for condensation -> more cloud cover
There has been less solar activity, and there have been more GCRs, but there is steadily decreasing cloud cover. Coupled with Willis Eschenbach’s many analyses here on WUWT, I find Svensmark’s hypothesis to be invalidated.
Rich: nucleation sites, ash in smoke was a good source of such once upon a time. Then the smoke was improved by such devices as filters and electrostatic-precipitators.
We have just had a test of the nucleation idea.
Transport and industry shut down for lockdown across the industrialised world.
We should have seen a drop in cloud cover if nucleation from particulates is a limiting input.
Only if there is a linear non-thresholding relationship between the number of nucleating particles and the amount of clouds.
Yes, I understand your point, but if GCRs make so little difference that putting scrubbers on some power plants in Beijing will completely counteract the effect, what difference does it make whether the effect is real?
If I walk through a room in my house during winter, the air undeniably receives heat from my body. Assuming the outside temperature and other weather conditions are constant, room temperature must rise imperceptibly. But obviously it is my furnace and thermostat actually responsible for “climate conditions” in my house. The fact that the hypothesis is true that my body heat contributes to warming the room must not be confused with the idea that my body heat is a significant factor in heating my house.
If GCR-induced clouds are difficult to detect under optimal conditions and the actual trend of cloud cover runs counter to the hypothesis, then the effect is at best trivial, like my body heat warming the room.
Another way to say this is that your argument (meiggsmtn, Mark W) is that particulate pollution and aerosols are much more important for cloud cover than the variation in nucleation sites that can result from the Svensmark effect. At the lowest levels of solar activity in living memory (thus the highest level of GCRs per the hypothesis), we are not seeing an effect that can overcome the effect of reduced particulate pollution.
The hypothesis would be that clouds have a net cooling effect. If there are fewer nucleation sites for condensation, there will be fewer clouds, if there are more nucleation sites, there will be more clouds. Increasing pollution in the 1950s and 1960s correlated with a cooling scare by the mid-1970s. Then increasing improvements in air quality correlated with a warming trend, leading to the global warming scare of the 1990s. Then a resurgence of pollution from industries moving to China accelerating in the early 2000s, and China having very poor controls on pollution at the time, led to a hiatus in the warming trend. Recently, with the fracking revolution, there is a trend to burning more natural gas and less coal while burning coal with better pollution controls even in China. And lo and behold, the warming trend resumed.
In principle, CO2 could be irrelevant here. Although the recent temperature trend correlates with the recent CO2 trend, that was not the case during the global cooling scare or the Hiatus. At some level of pollution control, you would expect that aerosols and particulates will fall to a natural lower limit. If cloud cover is falling due to lower particulate/aerosols, it will necessarily plateau. If it is cloud cover mediating the temperature, then temperatures should also stabilize at the higher level. Then if CO2 continues to rise, as it almost certainly will, we should once again see the temperature-CO2 correlation break down. If continued growth in fossil fuel use outstrips pollution control efforts, we may once again see rising CO2 and falling temperatures.
That won’t cause any of the Climate Change warriors to scratch their heads, though.
It wasn’t just scrubbers in China, it was scrubbers all over the world.
It was also catalytic converters on cars and a whole host of other changes as well.
PS: The fact that industrial pollution can swamp the affect is not evidence that prior to industrial pollution, there was no affect.
You think a single variable is responsible for cloud cover?
Actual science must be easy in Rich’s world.
Why would you think that, PI? I have many times ranted about how complex the actual climate system is. But when it comes to my once-beloved Svensmark hypothesis, the problem is that if a factor is thought to be a decisive cause of something, it necessarily requires that the purported effect will correlate with the factor.
The flip side of “correlation is not causation” is “causation requires correlation”.
I merely noted that the Svensmark hypothesis is at odds with any such correlation. Willis has shown this, much more rigorously than my hand-waving, enough times to disabuse me of my earlier enthusiasm. That’s called acknowledging that the science is complex and if the data doesn’t match the hypothesis, get a new hypothesis. The Svensmark hypothesis is probably true, but a very minor effect.
When you read ” it uses the whole global ocean area, and it does not use any models.” your spirits lift and your attention is attracted. What a surprise (not) to find yet another negative feed-back control in such a stable system!
Of course. Stable system have negative feed-backs to stabilize them.
Intuitively this should be correct. Great work.
If there was a positive feedback the surface temperature would have spiraled out of control many millions of years ago, before anyone accused mankind of upsetting the obviously very delicate balance by releasing CO2 to the atmosphere.
You rather miss the point. The Planck f/b is THE dominant f/b and it’s negative. The rest of the game is about trying to play with just how negative it is. That is where cloud f/b comes in.
In fact the Planck f/b is so dominant that it is not even recognised as a f/b but as the base to which all parameter tweaking is compared. This is where Monckton went wrong in adopting this logic in all his “Baud” nonsense. When IPCC talk about total net f/b being +ve or -ve they are EXCLUDING Planck and discussing whether all the rest can make Planck more negative or less negative.
It’s all word games masquerading as science.
Good analysis. Serious drought time coming up. I suspect you will find less uv coming through due to increase in ozone.
The scatter plots – calculate “r” correlation coefficient, r^2 coefficient of determination, and assess strength of correlation.
https://researchbasics.education.uconn.edu/r_critical_value_table/
Also, plot 90% confidence interval (95% exceedance lower line and 5% exceedance upper line).
I wouldnt publish it either. The abstract starts by attacking the IPCC, uses words like ‘virtually’ and ‘in any case’ and clearly sets out to destroy climate models.
This is not science, this a hatchet job, with the kind of lose language of an emotive argument.
(as for the science itself, there is no suggestion of a mechanism whereby high SSTs lead to high cloud cover, months afterwards, and how this fits in with the long term trend of reducing cloud cover )
Observations of the real world, instead of a computer monitor… HOW DARE HE!
Your religion is strong in you.
Oh climate change is BS, dont get me wrong, but this paper isnt science.
My original reaction was rather similar. Firstly, the title itself is like an opening salvo. Something more objective like ” On the determination of the effect of surface temperature on cloud cover”.
Then the abstract sounds like a rebuttal more than a study. When hoping to get something past the usual partisan gatekeeping, I don’t think adopting a challenging attitude is likely to increase likelihood of acceptance.
Now the actual contents look interesting but it will take longer to go through it to form an opinion.
I consider Mike Jonas to be very sharp technically, so expect it has merit. There are a few technical points I will point out later. The opposite sign of long term and short term correlation looks like it could be very important.
I have long been suspicious of the claimed +ve cloud feedback, I did not think it would be that easy to disprove. Hopefully this can be improved and get published.
Yep, the contents merit a better title and abstract, and I would like to see the mechanism explored.
“as for the science itself, there is no suggestion of a mechanism whereby high SSTs lead to high cloud cover, months afterwards“.
There is no suggestion of a mechanism, but the data shows that it happens. So whatever mechanism is in play, it isn’t in the climate models. That’s why I say, in the paper:
“This study shows that clouds must have important behaviour that is not included in the models.
The implication for the climate models is devastating. It must be questioned whether they are fit for purpose.“
But the long term trend is reduced cloud cover, while according to you giving increased cloud cover in a narrow window a few months after an SST high.
There is a long term negative correlation, and temporal short term positive correlations.
I dont see how you can make the two fit without some very complicated mechanism.
“ I dont see how you can make the two fit without some very complicated mechanism.” isn’t my problem, it’s the modellers’ problem. I showed the data and I pointed out that there must be unknown mechanisms at work, and I referred to a couple of papers that may help to understand them, but perhaps the most important aspect of all this is that it shows that the models are useless.
Certainly a relevant question is why there is a long-term trend toward less cloud cover. Matt implies causality when referring to the current correlation. It seems to me that the long term trend is an independent effect that is being resisted (negative feedback) by the short-term effect you describe in your paper.
The data presented clearly show that over the past 30 years the temperatures have gone up, and the cloud cover has gone down. One must simply assume that the temperature increase was caused by some factor other than cloud cover, and that cloud cover feedback has simply ameliorated the temperature increase.
Over the last 30 years air has gotten cleaner as smokestacks have been cleaned up.
That’s sufficient to explain the drop in clouds.
Tom Johnson – There are other possibilities. It seems more likely to me as I say in the paper that “some of the increase in SST over this period could have been caused by independent cloud cover reduction”. We have increasing SST and reducing cloud cover, and we know that clouds are net cooling, so it seems inevitable that some of the SST increase is caused by the cloud reduction. The paper shows that the cloud reduction is not an SST feedback, which is why I say independent cloud cover reduction. That’s something that appears to have no known mechanism recognised by the IPCC.
“The implication for the climate models is devastating.”
That’s why they won’t review it, Mike. You should have left that sentence out. They see that statement and they run for the hills. 🙂
As usual Tom, I find myself nodding in agreement at your comments.
Exactly what I was saying. The paper is an attack, it is not contributing to the sum of human knowledge, but merely an attack.
OK, the models are junk, we all know that. As an engineer I know full well the laminar to turbulent boundary layer transition can not be modelled (fluid dynamics), and that an atmosphere is vastly more complex, and less understood. But attacking them so directly discredits the contents of the paper.
I’d take his comment about language and tone seriously – I had the same reaction.
Your submission may be rejected on that alone.
The models are GIGO pseudoscientific crap, sure – but using words like “devastating “ works only in an opinion piece, and even then.
If you want attention from the scientifically minded, let the data/analysis do the taking.
Matt. –> Real nice, attack the author not the data or how the data is used.
As to “no suggestion of a mechanism”, did Einstein tell how and why the speed of light is a constant? Did Newton tell how and why gravity works? This is an empirical study and it is what physical science is supposed to be. It studies and proves physical causes and effects. Mechanisms can be hypothesized and tested at a later time.
Look, if you want science to become a series of attacks then go ahead, ruin it completely.
It’ll be a damn shame though.
Showing that a theory is wrong is a critical part of the scientific process. In fact, our knowledge of the physical world can’t advance without this part. We simple can’t know what is right if we are not allowed to discover and prune away what is wrong. I know it seems like an “attack” to people that have invested their egos in the theory, but it is not. It’s not wise to make your work part of your personal identification.
I know but dont do it with pejorative and vindictive language.
Science must be a place of considered and serious discussion, not emotive sensationalism.,
It’s really not an attack to name the models from which your results differ.
As for the science, there is no requirement that an author provide a mechanism to explain his results. If it unknown, then so be it, that is just the state of things.
Clouds reflect twice as much short wave insolation as they reduce outgoing long wave radiation.
Each percent increase in clouds reflects 3W/sq.m:
https://1drv.ms/b/s!Aq1iAj8Yo7jNg0bl2B46ioIcDZ1C
Each percent increase in clouds reduces OLR by 1.5W/sq.m:
https://1drv.ms/b/s!Aq1iAj8Yo7jNg0IRyDIi8vnTC1Gt
So each percent increase in cloud results in a net reduction in energy at the surface by 1.5W/sq.m.
The charts are produced from satellite data available from NASA Earth Observations in 2018.
Maybe less UV due to a less energetic and quiet Sun emitting less UV also contributes to a cooling global ocean. Which will further cool the ocean creating less cloud cover, which will cool but eventually allow more warming of the SST from increased solar insolation creating more cloud while global temps are slowly drifting down. And around and round we go.
Must be part of the short term climate oscillation that looks like part of a sine wave over 30-35 years which some think is evidence of man made climate change since the 1980’s when we warmed slightly. When the Sun becomes more energetic with sunspot activity, then the global temps go up a little within these short term cycles as clouds wax and wane over the oceans where the heat content creates the majority of clouds, more if warmer SST and less clouds if cooler SST. Sort of makes common sense.
Hence the Pause continues, or we have some slight cooling the next 30-35 years now. This is just part of the natural variation going on, but maybe a signifiant one. We need to understand and predict natural variation before anyone can claim that CO2 is now the principal driver of climate. And it is probably a lot more complicated, especially if Svensmark and cosmic rays are also a player, but would fit this narrative if Sun activity is actually a bigger deal than some think. Besides the Sun, clouds are probably the next largest contribution to short term natural variation. Science is a process, not an outcome. Publish the paper.
Do not get it ! You show two figures (9 and 10) with a negative correlation, so called overall trend. Before that your analysis shows a positiv correlation between dSST and dCloud.
If the incremental changes have a positive correlation, then the overall correlation should also be positive ?
I asume that I miss something in your analysis. Would appreciate your clarification.
Increasing temp results in more clouds, more clouds put a slight damper on the temperature increase, also called negative cloud feedback.
That’s what I was referring to when I said, at the end of the article, “The chart of cloud change against temperature change goes the “wrong” way.“.
Putting it simply, if cloud feedback was positive then cloud change should go the opposite way to SST change, just as cloud% goes the opposite way to SST. But cloud change goes the same way as SST change, so cloud feedback must be negative not positive.
Maybee I am slow learner, but do you mean that figure 10 is erroneous ?
Do you mean that there is a short term positive correlation between SST and Cloud, but the long term goes in the oppsite direiction. So, if SST was the only thing influencing cloudiness the long term trend would be the oppsite ?
The fact that it is not so, means that there is something else influencing the cloud% ?
Did I get it ?
Anyway, great work ! To me it is a mystery that so little attention has been paid to clouds. It is obvious from all radiation measures that they have a significant impact. Both on reflection and “reradiation”. Instead people (?) focus on a change from 0.03% CO2 to 0.04% CO2.
Jonas – Yes, you get it. There isn’t anything complicated about this paper, it just finds that the short term and long term correlations go in opposite directions, so the long term effect must occur in spite of the short term effect, not helped by it. Figures 9 and 10 aren’t erroneous, they are the long term part of the picture.
I need some help understanding this.
More clouds equal more cooling, correct?
Figure 9. shows a steady 30 year increase in Sea Surface Temperature, correct?
Figure 10. shows a steady 30 year decline in Cloud Cover Percentage, correct?
Why is that not exactly what we would expect to see?
Perhaps you need to clarify what positive and negative feedback mean?
Sorry – I just do not get this.
Steve Z. – Yes, clouds have a cooling effect. That’s why I say “as expected” in “As expected, the trends for the overall period are of rising sea surface temperatures and of falling cloud cover.“. The data shows something unexpected: that in the shorter term an SST increase is followed by a cloud increase, which has a cooling effect. IOW, if it is a feedback then it is negative. [If warming causes something to happen which delivers more warming, that’s a positive feedback. If it delivers some cooling, that’s a negative feedback.] The models need a lot of positive feedback in order to match the late 20th-century warming. And that’s why I say in the paper “The implication for the climate models is devastating.“.
Sorry, sir, but that was not simply put to me. Sorry for being dense.
Your data shows a positive correlation SST –> short-term-later greater cooling cloud cover.
If this were the only cause of cloud cover the long-term integral sum would be going up.
The real-world data shows an opposite trend.
You conclude that since your data is correct something other than SST must be lowering cloud cover.
Did I get it?
Yes. (But data is just data).
I sincerely hope that the author won’t have to wait for years (like Svensmark or Frank) to be able to publish in a peer reviewed journal. Especially since these results give a fundamental insight into cloud feedback.
Add in this from a NASA scientist…
https://notrickszone.com/2019/08/29/nasa-we-cant-model-clouds-so-climate-models-are-100-times-less-accurate-than-needed-for-projections/
“NASA has conceded that climate models lack the precision required to make climate projections due to the inability to accurately model clouds. ”
and there should be fun ahead 🙂
More supporting data.
https://notrickszone.com/2020/06/04/in-6-new-studies-scientists-agree-clouds-play-a-central-role-in-regulating-the-earths-climate/
“the GCMs all predict a positive cloud feedback”
I think you should quote the whole sentence from AR4 8.6.2.3:
” The mean and standard deviation of climate sensitivity estimates derived from current GCMs are larger (3.2°C ± 0.7°C) essentially because the GCMs all predict a positive cloud feedback (Figure 8.14) but strongly disagree on its magnitude.”
A further sense of proportion would be gained if you pointed out that this statement is based on papers of 2006 or earlier.
The referencing here is thin. In general, when you have a claim that “This conclusion has profound implications for climate models” you need a more thorough account of what models actually say.
Hmmm. How about the full quote:
“Using feedback parameters from Figure 8.14, it can be estimated that in the presence of water vapour, lapse rate and surface albedo feedbacks, but in the absence of cloud feedbacks, current GCMs would predict a climate sensitivity (±1 standard deviation) of roughly 1.9°C ± 0.15°C (ignoring spread from radiative forcing differences). The mean and standard deviation of climate sensitivity estimates derived from current GCMs are larger (3.2°C ± 0.7°C) essentially because the GCMs all predict a positive cloud feedback (Figure 8.14) but strongly disagree on its magnitude.“.
1. I think I am being reasonable when I present “the GCMs all predict a positive cloud feedback” as “the GCMs all predict a positive cloud feedback“.
2. The full quote shows that cloud feedback increases their estimate of climate sensitivity from 1.9 to 3.2. That’s a very big increase.
3. If the models “strongly disagree on its magnitude.“, that weakens their argument, not strengthens it. I don’t think I am doing them a disservice by not including this part of the sentence, especially as the IPCC tends to hide major discrepancies between models by using ensemble means.
4. It’s not my fault if the IPCC, in its later publications, couldn’t even put a figure on climate sensitivity. I have to get information from where it’s available.
5. I’ll leave judgement of thin referencing to the reviewers.
“How about the full quote”
Well, yes. By all means. The point is that you are claiming to refute what the IPCC says, and if you only quote a very unrepresentative part, that won’t succeed.
“If the models “strongly disagree on its magnitude.“, that weakens their argument”
No, your argument is that the IPCCs account based on GCMs fails because of its reliance on a proposition about cloud feedback. If they aren’t relying on that (because they acknowledge a wide range in results), then it is your argument that fails.
1.9 to 3.2 is their proposition on cloud feedback, not mine. How can reporting it the way they did in the IPCC report not be “relying”??? The models may have a wide range, but the average is a very long way from small. I think my representation is scrupulously fair, but in the end it’s up to the reviewers.
The AR5 has a lot to say about this, and I don’t think your paper will fare well if you ignore it. They have a chapter and a FAQ on the issue (7.1). Some FAQ quotes:
“Many possible types of cloud–climate feedbacks have been suggested, involving changes in cloud amount, cloud-top height and/or cloud reflectivity (see FAQ7.1, Figure 1). The literature shows consistently that high clouds amplify global warming as they interact with infrared light emitted by the atmosphere and surface. There is more uncertainty, however, about the feedbacks associated with low-altitude clouds, and about cloud feedbacks associated with amount and reflectivity in general.”
(Your paper deals mainly with low clouds)
“There is as yet no broadly accepted way to infer global cloud feedbacks from observations of long-term cloud trends or shorter-time scale variability. Nevertheless, all the models used for the current assessment (and the preceding two IPCC assessments) produce net cloud feedbacks that either enhance anthropogenic greenhouse warming or have little overall effect. Feedbacks are not ‘put into’ the models, but emerge from the functioning of the clouds in the simulated atmosphere and their effects on the flows and transformations of energy in the climate system. The differences in the strengths of the cloud feedbacks produced by the various models largely account for the different sensitivities of the models to changes in greenhouse gas concentrations.”
There is a lot more to it.
“(Your paper deals mainly with low clouds)“. I use cloud area and optical depth, for all clouds over the ocean.
“ There is as yet no broadly accepted way to infer global cloud feedbacks from observations of long-term cloud trends or shorter-time scale variability.“. Thanks for posting that, it might come in handy, now that there is a way to infer global cloud feedback from shorter-timescale variability: that’s what my paper does. All that is needed now is to be able to add “accepted“.
From the paper:
“the GCMs all predict a positive cloud feedback”
From Nick’s quote of the IPCC:
“all predict a positive cloud feedback (Figure 8.14) but strongly disagree on its magnitude.”
Tell me again Nick how your qoute disproves the original quote.
It is always suspicious when someone picks out a few words from a sentence to quote, and the next word was “but”.
1) The “but” was from your quote Nick, it wasn’t something I said.
2) Once again Nick doesn’t even try to answer the question.
The “but” is in IPCC report AR4. Nick thinks I should have included it. I don’t think it was that relevant. The reviewers will have the deciding vote.
“As stated by Spencer and Braswell [13], feedback response to temperature is “nearly simultaneous with the temperature change“. A change in SST should therefore result in a change in cloud cover (ie, cloud feedback) very soon after. There is therefore a possibility that cloud feedback can be detected in the monthly data. The relevant comparison to make is between SST change and cloud cover change in the following month or months.”
This is pretty basic to your thesis. You are basing it on what many will regard as one fringe paper, which won’t make publication easier. But worse, you aren’t accounting for the feedback loop. You don’t have a time scale for the cloud cover change influencing temperature. Since cloud cover acts on heat flux, which takes time to create a temperature response, this matters.
I covered that in the paper.
Incidentally, the time taken for cloud changes to affect SST is irrelevant, because I’m dealing only with the sign of cloud feedback.
“the time taken for cloud changes to affect SST is irrelevant, because I’m dealing only with the sign of cloud feedback”
No, it is relevant, because your reasoning is based only on the immediate response of cloud to temperature. If the response of temperature to cloud takes much longer, then you need evidence of what association there is on that time scale. If warm sea produces clouds, then a lot of rain, then a lot of clear sky, then the net result may still be that warm seas make warmer seas.
I still think you’ll have trouble relying on Spencer and Braswell. Your task is to convince readers, starting with the reviewers. If they don’t believe S&B, they won’t believe you. Worse, if they know of papers which counter S&B, to which you don’t refer, then they will feel cheated.
We’ll see, but I have to say that I find your comments curious. My paper deals only with cloud feedback. The fact that the first part of cloud feedback is quick (SST -> cloud) and the second part is slow (cloud -> SST) is slow is not exactly controversial. I thought it needed a citation, and I found S&B, but there are probably a few dozen others I could have used.
I really don’t need to know what happens on the longer timescale – not for this paper which is purely about the sign of cloud feedback. As I explain in the paper, cloud feedback is necessarily a short-term feature, (a) because clouds react quickly to SST, and (b) because the trends can be expected to drop off as ‘Months Later’ increases, because of influence from the intervening months. It’s relevant too that the cloud feedback is shown to be in the opposite direction to the long term trends, because that shows that cloud feedback is not a (positive) contributor to the long term trend.
What I suggest happens is that cloud feedback has a brief and small effect, via a brief change to radiation reaching the ocean. The effect is small for the reason you state (“cloud cover acts on heat flux, which takes time to create a temperature response”). But I don’t care about how small it is. The essential point is that it is not positive.
You make a good case here. It would strengthen the paper to anticipate the sort of criticism that will be leveled against it and include this sort of explanation in the paper.
As we have seen on WUWT, the dishonest critics (here I am certainly not referring to Nick), will do their drive-by dismissals citing some part of the canon of the Green religion that you failed to address. In this I think that Nick’s critique could be helpful to you.
It is quite clear that the monthly anomaly troposphere temperatures from RSS and UAH show a high variability on the monthly time scale. The only viable explanation is interaction of cloud cover and SST. An increase from 15 C to 16 C of SST increases the equilibrium water content of the air immediately above the water by 7%, some of which has to produce clouds a couple of days later and a couple of hundred miles away. The real question is “Why isn’t the cloud cover feedback more obvious ?” It certainly is obvious to your senses when a cloud obscures the sun for a few minutes. And the “Solar radiation” graph from home weather stations commonly show drops from 800 W/sqM to 200 W/ sqM when a cloud goes over. One possibility is that the cells in the models are too large and parameterize these small scale effects incorrectly.
“ One possibility is that the cells in the models are too large and parameterize these small scale effects incorrectly.”
And part of that parameterization is failure to recognize and differentiate between first order and second order feedbacks.
I like the “car cruise control” analogy for this. Say a car is going down the highway, and the only “instruments” the statisticians have are a ruler to measure how far the gas pedal is pressed, and the reading on the speedometer.
In the basic “flat highway” case, the statisticians will find that the higher the reading on their ruler, the higher the reading on the speedometer. They will agree it is a strong feedback. We can see it makes sense….press the gas and you go faster.
Then, unknown to the statisticians, turn on the “cruise control”, set it at a highway speed, and put the car on a slightly hilly highway. The cruise control is a strong feedback. Now the situation will be that the car will slow down a little going uphill and the cruise control will force down the gas pedal to maintain speed. The statisticians numbers will show that speed decreases in conjunction with increased gas pedal depression. If they can take readings quickly enough, they will find the results of their readings are not in phase. If they can’t read the results fast enough, or simply don’t understand the source of the phenomenon they are measuring, they can easily come to the conclusion that “more gas pedal depression equals slower car speed”….Oops…which shows that in the presence of a strong feedback you have not considered, you can easily reach the wrong conclusion.
I suggest Clouds are the strong feedback in the climate system.
So Nick, do you know off papers that counter S&B that you would care to cite?
Well, here is just one.
“In addition, observations presented by LC11 and SB11 are not in fundamental disagreement with mainstream climate models, nor do they provide evidence that clouds are causing climate change. Suggestions that significant revisions to mainstream climate science are required are therefore not supported.”
You don’t have to agree that SB11 has been refuted beyond all doubt to see that it can’t be used as the sole reference.
You may also recall that the editor in chief of the journal that published S&B resigned, saying that it should not have been published, and the review system was faulty. Not a good look for the sole paper being relied on.
Nick Stokes – I’ll wait to see what the reviewers say, but you really do not have a valid point. You are playing the man (S&B) not the ball. Your criticism is that I cite S&B, not that the feature I use the citation for is incorrect. The fact that clouds respond quickly to SST changes is not in doubt. The fact that SST responds slowly to cloud changes is not in doubt. I don’t use S&B for anything else.
In fact, if you read the paper carefully, I don’t use S&B for anything other than part of the rationale for looking at the data. S&B has no bearing whatsoever on the paper’s findings – the data shows what it shows regardless of anything that S&B might say.
Nick –> You are picking at things that the paper never dealt with and in most cases properly said so.
If you have a problem with the data used or the associations shown then deal with them, not with sundry other issues.
As to the S&B paper, when I checked it has not yet been directly refuted and withdrawn so is still a legitimate reference. Reviewers are not supposed to judge the worthiness of conclusions of already reviewed and published papers as a prerequisite to approving the paper they are reviewing. Reviewers are to insure that conclusions from other papers are quoted correctly and used properly.
“so is still a legitimate reference”
But it is the only one, so the question is, is it a convincing reference? And why is it the only one?
“Reviewers are to insure that conclusions from other papers are quoted correctly and used properly.”
And that they are representative. If you just quote S&B, there is a problem.
Reviewers need to be convinced that the argument is correct.
If I might be so bold as to address you directly, Nick, are you seriously asking why there is only one published paper that is cited as refuting the ststus quo of the warmist position, when you know full well that the warmists have done their best to ensure that papers critical of their position will never be published based not on science but merely position?
I actually think many of your comments present a good opportunity to strengthen the paper and pre-refute arguments against its being published, but between that and playing the man, not the ball, this is one of the weakest if the data itself is strong. Actual data is what the warmist position holders seem to do their beat to deny. Would you have any argument with just publishing the data with absolutely no commentary? Would that make it more or less valuable?
I like Mike Jonas’ plain analyses of the data and his honesty.
From my own ignorant logical viewpoint since 2007, I have had difficulties accepting the positive cloud feedback, which would likely have resulted in thermal runaway at some stage in the past geologic time.
His “attack” on the models is called for, as the models in their current form does not remotely reflect observations. Thus, it is nice to see an approach that can help resolve the failure of the models.
I agree. The edifice of AGW is built on the shakiest of foundations which does not require anything other than clear thinking to demolish.
1. There is no evidence that, on any historical timescale, changes in CO2 concentrations have preceded changes in temperature. Ice core evidence is that CO2 increases lag temperature increases by several hundreds of years.
2a. What evidence there is (based on Arrhenius’ laboratory experimentation and not, so far as I know, replicated in the real world) implies an increase of ~1.2°C per doubling of atmospheric CO2 concentrations.
2b. Such a figure is unlikely to be anything other than beneficial (assuming it is even reliably measurable — Hansen himself is of the opinion that “global average temperature is not a useful metric”). The only method by which the scarier projections of the climate science community and the political ambitions of the UNIPCC can be justified is by the creation of feedbacks and forcings for which nobody has yet provided defensible scientific justification.
2c. Mike Jonas is not the first person to make the — eminently logical — claim that if the positive feedback postulated by the promoters of the AGW idea actually existed then it has already had a multiplicity of opportunities to create the “runaway global warming” that they claim is inevitable and has “passed” on every one so far. No-one has explained why this time is any different to any of the others.
3. If the climate models predict a positive cloud feedback that is because they have been programmed to predict a positive cloud feedback. And that is not a cynical criticism of modellers but it is a criticism of those in the scientific community whose minds are closed to the relevance of observations which are — at least according to this paper — indicating that cloud feedbacks are negative. Which is good news and considerably more logical (see 2b and 2c above) than the output of the models — unless, of course, you have some vested interest or enviro-political agenda in sophisticated guesswork being more reliable than facts!
This is a very interesting data analysis, and it would be important to see this published.
I am afraid, however, that this will not happen. The reasons in my opinion are linked to the attack to the models and to the limited references. In the academia a certain way of writing is needed, it’s questionable but it’s a matter of fact. Please don’t lose the possibility of publishing for “formal” reasons.
I would modify the wording in such a way that you result completely super partes, so that nobody can attack you on the language.
For anybody honest enough to look at the data, the data speaks very clearly. I think it’s more important to publish the data analysis than to comment the failure of models.
Regarding the references, I think it should appear evident that you read all the relevant literature. Your analysis is not linked to a specific interpretation so that citing more papers coming from different sources would add credibility without changing anything in the analysis or in the meaning of the paper.
I really hope you can publish this! As we say in Iyaly, in bocca al lupo! (a sort of “good luck”…)
Excellent paper.
About an “unknown” mechanism that could be responsible for the clouding effect, you should look at the cosmic rays effect and therefore about the magnetic field of the sun.
It has been shown that there is a strong correlation between cosmic rays intensity and clouds coverage. More cosmic rays, more clouds for a given concentration of H2O in the atmosphere.
And the amount of cosmic rays reaching our planet depends on the strength of the magnetic field of the sun as displayed by sunspots.
The magnetic field of the sun protects the earth from cosmic rays. But the magnetic field of the sun is the one parameter that changes a lot versus time. First it follows an 11 years cycle. Then these cycles can vary in intensity. We are in the low magnetic field period in between cycle 24 and 25.
Cycles 21, 22 and 23 were strong, Cycle 24 much weaker. Big question is how much strong will be cycle 25.
Historically, there has been a period of very low magnetic activity of the sun between 1660 and 1710 (Practically no sunspots for 40 years!) corresponding to a mini glaciar period. Very well documented.
The 11 years periodicity of the sun magnetic activity does not translate into a corresponding 11 years earth temperature cycle because of the strong amortization effect of the oceans. But for periods of 15-20 years and more there is a strong correlation.
I would be very interested in an investigation of the cloud coverage relationship with the sun magnetic activity!
@Mike Jonas
Typo
Missing “to” ( …are known to have…)
Also perhaps I’m tilting at windmills to insist on the plural nature of “data”, but
should be “The data show that…“
Pity about the typo – it’s in the paper that has already been submitted.
As for data being plural, well maybe you are correct but latin got left behind many generations ago. I’ve always used it as singular, and I think many others do. Changing it is not on my agenda.
Yeah I know I’m a dinosaur 🙂
Unfortunately, you have quite a few such irregular confusions of definition.
I agree with your basic premise.
Except, your writing makes a mess of the science. Far too many words that imply strong confirmation bias and sentences that begin on one topic and switch subjects within.
“But”s, “thus”, etc., are all terms used colloquially, not when defining science impacts. i.e. they are good for storytelling, but horrible for explaining science.
Other quibbles:
You begin the paragraph and the first sentence describing data, then suddenly switch to models within the first sentence.
Cambridge dictionary for sentence:
“a group of words, usually containing a verb, that expresses a thought in the form of a statement, question, instruction, or exclamation and starts with a capital letter when written”
Merriam-Webster dictionary for paragraph:
“a : a subdivision of a written composition that consists of one or more sentences, deals with one point or gives the words of one speaker, and begins on a new usually indented line”
Switching subjects within the first sentence and within a paragraph confuse the reader as they no longer know what the subject is.
A) Get rid of useless word! “but”, the, “also”, “virtually”, “in order”, “next few months”, “the inevitable”, “in any case”, etc etc…
• most of these may work for telling a story, but fail when expressing math or science.
• e.g. “next few months”. Which is it? two months? Three months? Five months?
What may be clear to you comes across to us as clear as mud.
B) Stick to proper grammar!
• Paragraphs describe one complete thought process. One topic!
Comparing one topic to another topic is another topic, e.g. data to models, is kept to their own paragraphs.
• Sentences introduce individual thoughts or define topics within the topic.
e.g. 2: “as expected”.
Who expects!?
Exactly what did they expect?
Keep in mind that “as expected” means the researcher is seeking to prove their personal opinions, not openly and freely to analyze all data!
e.g. 3: “in order to test this feature of the climate models”.
You tested climate models? Which ones? How many runs?
How did you test “this feature”?
What parameters did you use?
ATheoK:
Suppose I say:
In this paper the data is analysed.
That’s an OK sentence, but why was the data analysed? More information is needed:
In this paper the data is analysed. The analysis is done to test the models.
Well, that’s OK too, but it’s a bit clumsy. It works much better in a single sentence:
In this paper the data is analysed, in order to test the models.
– – –
“You tested climate models? Which ones? How many runs?“.
I tested a feature of the climate models.
“How did you test “this feature”?
What parameters did you use?“.
How I tested it is all explained in the paper. I didn’t need any parameters, just data.
– – –
Look, I do understand that this paper is a bit unusual. Science tends to drill down into things in ever increasing levels of detail, whereas this paper stands back and just looks at a very simple global view of the data. I looked at the forest not the trees. And that can be surprisingly difficult to put into simple language, because minutiae keep impinging. Maybe I could have put some things better, but what matters next is what the reviewers say, and I’ll wait for that.
A) You fail to address the words that imply opinion and conformation bias intentions.
B) You fail to address the colloquial usage words/phrases that reduce your study’s definition.
Clumsy?!
You claim short specific sentences that clearly convey information are clumsy?
All you have done with this explanation is rationalize your improper use of grammar to muddle up what you are alleging to specify.
Clarity is far more important than compound sentences that leave more questions than they answer.
“In this paper the data is analysed, in order to test the models”
Except you are not testing the models.
Nor are you testing “features” of the models.
You are assessing data and forming conclusions about the models and their alleged features.
…
It’s too hard to simply state that you are “stands back and just looks at a very simple global view of the data”?!
Or to specify why you are reviewing model output?
Or to explicitly define the multiple possibilities contained in that output and what each possibility indicates?
“what matters next is what the reviewers say”; really!?
I’m sorry, I thought you trying to convey science and findings.
I did not try to read your entire paper.
It took me over half an hour to chew through your abstract trying understand what you were introducing.
You apparently believe clarity is too hard to achieve and way overstated in value.
Do as you will. Good luck.
ATheoK – I’m always happy to accept corrections, and although I try to express ideas clearly I will fail at times. I’m a bit bemused by your taking half an hour to understand what I was introducing. The introductory part of the Abstract is
First I introduce the core issue – positive feedback in the models. For the benefit of those who are not fully familiar with feedback, I then describe what it is. The third sentence gives the purpose of the study – to test “this feature” of the models. Now maybe this isn’t perfect, because the word “feature” hadn’t been used, and you have to remember what feature was described in the first sentence. There were only three not-very-long sentences, so half an hour seems a bit slow to me. But I do accept that improvements are possible. Perhaps you would like to try a re-write of those three sentences? If the reviewers have the same problems as you, then that could be very helpful.
It is a reasonably clear paper for an ignoramus like me.
Sometimes, if something is intuitively right, it probably is right; we’ve all seen how a hot spell is resolved by overcasting and sometimes rain or thunderstorms. One reason pilots wear sunglasses is because of the ‘snow blindness’ from the clouds below. It just fits.
The climate could not possibly have cycled for aeons if there were a positive-feedback runaway, I fully agree.
Furthermore, I’ve seen a lot of dihydrogen monoxide clouds up there, but only ever seen carbon dioxide clouds at rock venues. So I am reasonably certain which is the significant compound.
So the long time frame shows decreasing cloud cover and increasing SSTs. But the short time frames show the opposite.
The SSTs drive the cloud cover is the assumption is seems. But they drive each other. While the SSTs need the Sun, the clouds need the water vapor. It’s like being married.
It’s possible that the short term feedback is of one sign and the long term one the other sign. If this is the case, that may be the definition of stability. Two opposing things. Where the short term is to cool and never dies while the long term one is to warm.
Earth has oceans and has had them for over a billion years. Not frozen and not lost to space. Stability.
With a water cooled engine, the short term response is to warm water. After 10 minutes, you can’t find an increase in temperatures. You can find short term responses with the cycling of the thermostat though. We have water and clouds. We can ask which is the heat source and which is the thermostat?
They frame clouds as the result. Which is not how you frame a thermostat. Some claim the Arctic as a result, when it’s a thermostat.
Mike:
You write on a subject close to my heart and I concur with your conclusion. Water/clouds provide a NEGATIVE feedback to the Greenhouse Effect. My route to this conclusion is very different and requires a change in mindset for it to be understood from that of radiation and statistical observation to that of basic science. You might say here – ‘From top down to bottom up’.
I write from the perspective of an engineer trained in the Royal Navy in the days of steam propulsion; so have good knowledge on the thermodynamics of water and its behaviour.
You say that there is an obvious possible? mechanism for cloud feedback being negative; but imply doubt. Well there is and it is well known and used on a daily basis particularly in our steam generating plants using the Rankine Cycle. This cycle is in fact the cycle which drives the Hydro Cycle in our atmosphere and a great deal of knowledge and experience is there to be harvested to explain the workings of clouds and their behaviour. For instance: From the steam tables it may be concluded that for every kilogram of water evaporated at the surface and later returning to earth as rain etc. some 694 Watthrs. of energy is pumped up into the clouds and beyond and dissipated. This done through the Rankine Cycle irrespective of CO2 or any purported increase in energy input by radiation.
This very large movement of energy/enthalpy up through the atmosphere for dissipation provides a strong NEGATIVE feedback. However quantifying its net effect is complex but not beyond the wit of Man to achieve. Sadly at my age I have neither the competence nor resources to do that. Perhaps you may care to look at this? I could elucidate further if you wish.
My regards – Alasdair. ( alasdairfairbairn220@gmail.com)
There are a lot of possible studies that could flow from this one. I have some ideas about where I might go next, but I think I will have to leave quantifying cloud feedback to others.
I do think the “possible mechanism” is very likely correct, but my main concern was not to get bogged down defending a stronger statement when it wasn’t central to the paper.
Once again the Good Model INM-CM4-8 is bucking the model builders’ consensus. The new revised INM model has a reduced ECS and it flipped its cloud feedback from positive to negative. The description of improvements made to the INM modules includes how clouds are handled.
As for the other models, they have added more positive cloud feedback, increasing their divergence from observations.
https://rclutz.wordpress.com/2020/01/26/climate-models-good-bad-and-ugly/
Yes.
And there is another way to obtain that same result, that cloud feedback must be neutral or slightly negative. The energy budget methods give an ECS ~1.65. This is an implicit Bode f of ~0.25, while IPCC 3.2 implies f ~ 0.65. Using zero feedback ECS 1.2 => f =0, then WVF takes ECS to ~2.4 so f ~ 0.5 (AR4), and positive cloud feedback an additional f 0.15 to 0.65. (Remember Bode f components sum.) CMIP5 produced just over of half of ARGO (salinity) inferred ocean precipitation—INM CM4-8 and CM5-1 get it about right. Implies the CMIP5 WVF is too strong by about 2x except for INM. If cloud feedback is zero or slightly negative you arrive at a Bode f of ~ 0.25, squaring with energy budget observational estimates of ECS.
And, the physical reasons WVF is modeled too high is Eschenbach’s Tstorm regulatory hypothesis, combined with Lindzen’s related Tstorm adaptive infrared iris Cirrus observations. The models don’t compute either phenomenon. They are just parameterized because of the intractable Computational constraints preventing sufficiently small grid cells to Actually compute these things.
Thanks for you comment. Sounds like you’re making an argument. Have you written about the implied cloud feedback based on ECS of about 1.65?
Is there an oppornutity to push on this point?
For SST, individual temperature readings were converted into Kelvin and raised to the 4th power before averaging, then converted back to degrees C.
=============
Yes. A critical step overlooked in climate science anomalies. Do you average first then convert to radiation, or do you convert to radiation then average?
Mathematically it makes a huge difference in the result.
(A^4 +B^4)/2 NotEqual ((A+B)/2)^4
This is a fundamental error in climate science. They are averaging temperature, not energy. As a result the are violating the conservation of energy. Each time temperatures are adjusted or averaged there is a mathematical 4th power error created. End this error violates the conservation of energy.
So the modellers don’t actually understand the difference between temperature (an intensive variable) and energy (an extensive variable), meaning their models violate the Law of Conservation of Energy?
Blimey, is that true?!
If this is the case the entire edifice collapses.
“ Do you average first then convert to radiation, or do you convert to radiation then average?“.
It’s not really about what comes first. It’s just about averaging in a way that’s legitimate for the intended use.
ferdberple – Apologies. I’ve re-read your question and realised it was aimed at others. But the principle that averaging must be done in a way that’s legitimate for intended use does apply.
IPCC AR5 said that back in 2014.
I think if an electrical circuit analogy is going to be used in modeling, that cloud cover should be considered a resistance to radiation rather than a feedback. I have used this idea in analyzing met data and have come to the conclusion that the surface temperature of water is being controlled by the evaporation/condensation cycle. There is not much difference between the dew point temperature at the water/air interface at the surface and the dew point temperature at similar interfaces at the bottom of a thundercloud. Thus, not much radiative driving force.
…. the analysis has shown that as the changes in cloud cover associated with changes in SST increase, they do indeed have a larger cooling effect. The inescapable conclusion is that any cloud feedback is negative.
Sounds good to me, I would say that the precipitations may be the key to the negative feedback forcing warming/cooling cycles sequence
http://www.vukcevic.co.uk/PNFb.htm
or may be not / 🙂 .
Very good paper. A few comments.
1. The results should not be surprising. They reflect elementary physics. Water vapor pressure increases exponentially with temperature (at 20C roughly doubling for each 10C). Increasing SSTs means more water in the atmosphere, thus more cloud formation.
2. The time lag between SSTs and peak cloudiness, 3 months, corroborates the time lag between El Nino and peak atmospheric temperatures, 4 months. Both phenomena are the result of increased water evaporation from the oceans and its transport throughout the atmosphere by convection. These two phenomena show that the time required to fully disperse water vapor in the atmosphere from increased SSTs is of the order of 3 – 4 months.
3. There are two factors that control the rate of cloud formation, at any given atmospheric temperature, the content of water vapor in the atmosphere and its propensity to nucleate. Given that decreasing water vapor content can be discounted as the cause for decreasing cloudiness from 1986, Figure 10, (this paper and elementary physics say that the content of water vapor would have been increasing) then we should be looking at changes in the propensity for nucleation as the cause for this decreasing cloudiness.
4. Indeed, in comparing Figures 9 and 10, we see that almost all of the decrease in cloudiness happened before 1999. And from 1999 until 2015 was basically stable. SSTs on the other hand increased more or less consistently over the period. These two graphs show that, if anything, the decrease in cloudiness preceded the increase in SSTs and, since SSTs drive atmospheric temperatures, preceded Global Warming.
5. As I understand it, changes to the propensity to nucleate can be affected by the amount of such things as cosmic rays, space dust and pollutants (aerosols and particulate).
This paper adds another piece of evidence to the case that CO2 cannot be the cause of Global Warming. If the decreased cloudiness which, as the author’s state, leads to increased atmospheric temperatures, is not the result if increased SSTs, then it must be the result of an increased propensity to nucleation. This increased propensity for nucleation can be caused by solar/space effects and pollution.
Isn’t it ironic if one of the causes of Global Warming is indeed man-made, not from increasing pollution, but rather from the efforts to reduce pollution since the 1960s. Fortunately this effect is self-limiting.
You had me at “Hello.”
“The ocean covers 70% of the global surface and stores more heat in the uppermost 3 metres than the entire atmosphere, so the key to understanding global climate change is inextricably linked to the ocean [7].”
My forehead is sore from the pounding. Captain Obvious strikes again. We are on a water planet. Of course the land temperatures cannot be ignored, they just don’t count for much.
Indeed. It is the water cycle which determines climate, not the carbon cycle.
We do not have “basic physics” for evaporation, advection, condensation, cloud formation and precipitation. The claim that climate models are all founded in well established “basic physics” is a lie.
Hi Mike J.
I have done extensive research on clouds, but started a few levels lower. Before we start to research cloud feedback, we should have a close look at the CRE (cloud radiative effect) and there is a lot of subject matter I can tell you. Let me just summarize a few important points.
1. If you take a closer look at the modelling of the CRE (ERBE, CERES..) you will find huge inconsistencies. Over time the regional estimates on net CRE have gone from strongly negative to positive and vice verse. Really it looks like they have no idea what they are doing. The only thing that sticks is a global net CRE of some -20W/m2. They are just not certain where they should put it on the map, which is ironic since it has to go the opposite way.
2. I did not use satellite data, but rather weather records (also for quality concerns) to determine local CREs. I think this is very fundamental work everyone should start with in climate science, but no one ever did it seems. These data show strict positive CREs everywhere I looked, especially in the northern Pacific, where all CRE models so far show strong negative CREs. In other words, real life data totally falsify these models and discard them as junk science.
3. With the theoretic foundations and the empiric data fully supporting them, it is safe to say the global net CRE is indeed positive. At this point virtually ALL common climate science turns obsolete, since it is not so much GHGs but rather clouds which keep Earth warm. The implications are mighty!
4. Like all GHGs also CO2 plays only a minor role in Earths climate, providing a “GHE” of only about 1K. Accordingly the increase of atmospheric CO2 can not cause any significant warming, nor will the supposed primary feedback vapor contribute a lot to it.
5. Yet we need to consider AGW for another reason, which are high altitude artificial clouds (aka contrails) which should be a potent driver of global climate. Indeed we see an autonomous global warming starting only in the 1970s in perfect accordance with the introduction of the jet airliner and the increase in global air travel since. Unsurprisingly we see no warming at all in the Antarctic, where there is no air travel.
6. Although contrails cause warming, which will increase evaporation, they also obscure the sun for the natural weather systems below it. In a process I do not fully understand (but is well documented), direct solar radiation is a key factor in evaporation, as the photons “tickle” H2O molecules out of the water into the air. The constant increase of contrails over time thus can easily lead to higher temperatures AND less evaporation. That is while higher temperatures per se could only increase evaporation. I think this might answer some of the questions you have put up in your paper.
Leitwolf – interesting points. Worth noting perhaps that contrails should be part of the cloud data, both area and optical depth.
I might mention rain in passing. Droplets of water or ice form in clouds at high, cold altitudes and drop, bodily transporting their coldness to the surface. The more clouds the more rain.
Mr. Jonas, in your fourth paragraph under “2. Method”, you state: “For SST, individual temperature readings were converted into Kelvin and raised to the 4th power before averaging, then converted back to degrees C. This ensured that the calculated average SSTs related correctly to outward radiation from the sea surface.”
I do believe there are many problems with this proposed methodology. Among these are:
(1) the implicit assumption of constant emissivity for all ocean conditions, when emissivity is known to be highly variable with surface wave activity and the degree of ocean foam present,
(2) the apparent disregard of the issue of NET OUTWARD RADIATION from the sea surface, which must account for the inbound radiation coming from the sky, including whatever clouds are within the sea-surface’s hemispherical field-of-view . . . for example, one can image a warm front with clouds moving over a high latitude ocean wherein the cloud temperature is actually greater than the sea-surface temperature (aka, a temperature inversion), in which case there would be NEGATIVE NET radiation from the sea surface to the clouds. In essence, one cannot just focus on an outbound radiation = f (Tss^4) dependence, when in reality it is a net outbound radiation = f (Tss^4 – Tsky^4) dependence.
(3) the apparent absence of considering that the presence of (visible) clouds necessarily has an associated increase in humidity in the atmosphere adjacent to the clouds; the extent to which this increase in water vapor—apart from the clouds themselves—affects the correlation between SST and cloud coverage is certain to be significant due to LWIR absorption by water vapor in the path length between sea surface and cloud base.
Gordon A. Dressler –
(1) The data is of global monthly averages, so it is likely that surface conditions won’t have much impact, particularly as I’m looking at a global pattern not minuitae.
(2) I’m not working with net radiation. I use only total outward radiation and total inward radiation.
(3) Comment (1) applies to this too (substitute adjacent humidity for surface conditions).
Mike
Very interesting and significant findings, and we thank you for that.
I’m a bit concerned about your use of the term “intercepted” for the amount of incoming or outgoing radiation that is not reflected by clouds.
Radiation and radiative energy transfer are not my speciality (by a long margin) but I have the impression that radiation that encounters any non-transparent body is either “reflected” or “absorbed”. In which case, your term “intercepted” would have the identical meaning to “absorbed”.
If you are in fact using a non-standard word to describe a radiative process, this could be an excuse to dismiss you as “not a serious scientist”.
I stand to be corrected, and if I’m wrong, I sincerely apologise.
Also, you need to tone it down a bit. Wording like “The implication for the climate models is devastating. It must be questioned whether they are fit for purpose” is not the sort of dispassionate language normally used in science. It paints you as a skeptic (i.e. “denialist”) and a trouble-maker. Which of course you are, and good for you. But you should emulate the Trojan Horse and be non-threatening until you’re in the door.
If you use wimpy, toned-down language like this: “this study points to the possibility that the current generation of GCMs may not adequately represent the behaviour of clouds in a warming environment” you just might get it in the publishing stream. And not naming the IPCC in the text might help too. Cite IPCC publications as references instead.
We all know that GCM’s are useless because they give the wrong answer. But you mustn’t actually say so, because in Climate World*, GCMs are holy writ and supersede any and all empirical observations. Believers are allowed to insult and attack skeptics at will, but the converse is not true because they are right and we are funded by the oil industry.
* “Welcome to Climate World™, the theme park where conclusions come first! Enjoy Unprecedented Weather Events™ every time it rains! Experience Permanent Drought™ when it doesn’t rain! Take a dip in our our Rising Seas™ as they get ready to inundate your neighbourhood (not recommended for those with allergies to acidification)! Wallow in feelings of guilt, futility and self-worthlessness when you pass the Tipping Point Saloon™! Stop at the Wind-Power Café™ and enjoy a termite burger while you see eagles being chopped to pieces! Step in to the Mass Extinction Theatre™ and watch as millions of species are annihilated for your amusement! Have your fortune told by the beautiful, mysterious but untouchable Climate Models™! Hang out at the Homogenization Hut™ where the 1930s heat wave will vanish before your astonished eyes! And no day at Climate World™ is complete without a visit to our Inquisition Dungeon™ where you can enjoy stand-up comedy as skeptical scientists are insulted, fired from their jobs at prestigious universities and sent to beg in the street! Please be polite, don’t litter and try to not notice the big diesel generator as you leave.”
Smart Rock – “Intercepted” is the difference between reception and transmission as described by the providers of the data and as shown in the formulae I use for opacity. If that’s not clear to the reviewers I will hopefully have an opportunity to clarify.
About use of words: I used the words that I thought best described what I had found. If the reviewers don’t like them maybe they will say so. I suspect that a reviewer determined to protect the models wouldn’t let the paper through regardless of the words I use. So I used the most accurate words I could, and the rest is as they say in the lap of the gods.
Ref [10]. Providing links to blog posts in a manuscript is not good practice. When the title, clearly shown in the reference includes “epic fail” do NOT expect to get taken seriously.
This may do for an internet rant but don’t be surprised if it does not get a warm reception in peer review.
Technical point: doing OLS on scatter plots will under estimate the slope. So fig 4, for example will but understating the slope. Reverse the axes and do another OLS , the true relationship is likely between these two limits. Artful selection of axes when estimating CS is an institutionalized alarmist trick.
The best way to determine the lag is to plot the cross correlation function which clearly shows max correlation ( it seems clear this will be 3mo lag ). Spencer & Braswell 2011 papersshow such plots and could provide a legimate ref.
Very well done Mike.
Should the paper be rejected, there are a few comments above that can be used to improved your paper for resubmission…
Thanks, and yes there have been some very helpful comments, with useful links and ideas. And just so there’s no misunderstanding, Nick Stokes’ and other critical comments are among the very helpful ones.
1. Tropical and extra-tropic climate are very different, I don’t think that global averaging is the best method. I would suggest separating the two. They will certainly be different, possibly even in sign.
2. There is more than one type of cloud. Traditionally tropospheric and stratospheric cloud may have differing effects. Orthodox climatology has opposite contributions. This is likely to get picked up by any serious and fair-minded peer review.
I looked the effect of volcanoes on SST in a series of crosslinked graphs
https://climategrog.wordpress.com/hadsst3_volcano_lag_tropics/
It showed that tropics were very stable and extra-tropics changed notably. This implies much stronger f/b in tropical climate.
Presumably it would very simple to split the above analysis into tropical and extra tropical zones.
Greg – As I said in reply to another comment, I’m looking at the forest not the trees. It was enough to use the global data, ie. there was no need to break it down into regions (which would have introduced ever-increasing complications). And please note that I cross-checked using opacity and the patterns of inward and outward radiation. If the globe’s ocean area as a whole shows negative feedback, then there’s negative feedback. Period.
Figure 10. shows that cloud cover had its highest values when volcanic activity was at a peak in the 80 ties and beginning of the 90 ties with the most recent Level 6 eruption at Mt. Pinatubo in 91.
https://sciencing.com/volcanoes-erupted-last-100-years-7793285.html
Clouds form as the result of the electrical resistance between these 2 processes.
Swarm probes weakening of Earth’s magnetic field.
“Earth’s magnetic field is vital to life on our planet. It is a complex and dynamic force that protects us from cosmic radiation and charged particles from the Sun. The magnetic field is largely generated by an ocean of superheated, swirling liquid iron that makes up the outer core around 3000 km beneath our feet. Acting as a spinning conductor in a bicycle dynamo, it creates electrical currents, which in turn, generate our continuously changing electromagnetic field.”
https://phys.org/news/2020-05-swarm-probes-weakening-earth-magnetic.html
“The global atmospheric electric circuit and its effects on cloud microphysics
This review is an overview of progress in understanding the theory and observation of the global atmospheric electric circuit, with the focus on its dc aspects, and its short and long term variability. The effects of the downward ionosphere-earth current density, Jz, on cloud microphysics, with its variability as an explanation for small observed changes in weather and climate,”
https://iopscience.iop.org/article/10.1088/0034-4885/71/6/066801/pdf
Mike
Previous reports show increase in precipitation near the equator leading to droughts on the higher lats.
Click on my name for my report.
Mike Jonas, thank you for an exceptionally good paper.
Furthermore, this paper by Kauppinen and Malmi (2019) from the University of Turku, Dept. of Physics and Astronomy supports your hypothesis.
https://arxiv.org/abs/1907.00165v1
They state that, “The IPCC climate sensitivity is about one order of magnitude too high, because a strong negative feedback of the clouds is missing in climate models.”
They conclude that, “The low clouds control mainly the global temperature.”
Interesting but rather lightweight. Claims about out-gassing totally unsubstantiated. 4 of 6 refs to their own work.
”Increased cloud cover”
How does this reconcile with these findings?
Increase high level clouds Russia..
https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/1097-0088%28200008%2920%3A10%3C1097%3A%3AAID-JOC541%3E3.0.CO%3B2-5
overall long-term increase in total cloud amount over Australia,….
https://journals.ametsoc.org/doi/abs/10.1175/1520-0442(1992)005%3C0260:HROCAS%3E2.0.CO%3B2
Retrievals from APP and TPP show a strong increase (∼5 %/decade) in cloudiness during spring over the past two decades. A similar increase is found in meteorological surface observations from the NP drifting stations….
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2004GL020067
Increasing clouds also nicely explains the divergence between day/night temps too doesn’t it?
That should be ”decreased cloud cover” obviously!
I found this paper so interesting that I have read 4 times now. Maybee it says something about my learning ability ?
In fact I have tried to find some kind of errors, but I have not been able to do so.
I do have two questionmarks:
– you use the same opacity for incoming solar radiation as for outgoing longwave radtion. Is that assumption supported ?
– when you analyse the radiation balance,; how do you deal with day and night ? Do you assume that the cloudiness is the same during day and night ? I guess you see the point. If the clouds are there during nighttime but not daytime they will not reflect sunlight.
A reflection I made is that the I think you have found a correlation in the “rapid” variations in SST and Cloud. What if you take away the long term trends (just deviation from the trend line) and calculate the correlation between Cloud (x months later) and SST(month). A direct correlation instead of using dSST and dCloud ? In my mind the correlation should be there (since it exist in dSST and dCloud).
jonas – good questions.
1. There is only one optical depth given in the satellite data. Yes there may well be differences between inward and outward, but the correlation between the two can reasonably be expected to be strong. In any case, I’m looking at the overall pattern rather than needing super-accurate values for detailed calculations.
2. The satellites operate at night too, so I would expect there not to be a major day-night difference that would invalidate the data (which is in monthly averages).
3. To my mind, the best analysis is one that doesn’t fiddle with the data first. So if I can work with actual temperatures, rather than with de-trended temperatures, or with temperature anomalies, etc, I do.
Thanks for your reply. It did answer my questions.
I think your conclusions are very important. It puts a big big questionmark for the GCM.
As a matter of fact I think your conclusions fits with our intuitiv understanding. It make sense that cloudiness increas when water temperature increase. It make sense that it gets cooler when it is cloudy.
Looking forward to coming papers from you.
Don’t know if this applies.
If a satellite views clouds from a slanting angle it sees more low clouds than when it’s looking straight down. Changes in the population and orbits of satellites contributing to ISCCP data have tended to narrow the viewing angle to nearer the vertical. That will have reduced the reported cloudiness even if, in the real world, the cloudiness were unchanging or even increasing.
https://calderup.wordpress.com/2011/10/05/further-attempt-to-falsify-the-svensmark-hypothesis/
mike – I don’t know either, but (a) I expect that the satelliters do everything they can to deal with problems like that, and (b) because I’m only looking at 12 months max at a time, I don’t think it will be an issue.
As an exceptional global lab experiment with Covid we do know the contrails stopped and the air cleared quite dramatically (you can see the Himalayas)-
http://joannenova.com.au/2020/06/humans-do-ultimate-paris-lockdown-co2-hits-record-high-anyway/
This paper does not seem to have enough references. Where is support for IPCC assumes positive feedback, or that clouds have a cooling effect(I think some types have a warming effect).
Also, if you take temperatures to the 4th power, how are you then converting to C? Do you first take the 4th root of the average? Perhaps you should just not bother with degrees Celsius.
Thanks for checking.
Reference 9 for positive feedback (look for”[9]”).
Reference 8 for cloud cooling effect.
The temperature data is in deg C, and I and most others are more familiar working with deg C than K. It’s no big deal to convert between deg C and K each time. Yes you take the 4th root of the weighted average K^4 then take off the K-degC constant. The weight is kept too, so that it can be re-used in later averaging if needed.
Has anyone ever tried to find a correlation between sunspots numbers and cloud coverage?
Is there a place where the relevant data over extended periods of time would be available?
On a daily, weekly, monthly or yearly basis. It would be fascinating to find such a correlation!
Mike Jonas I would welcome the opportunity of talking to you.I am a joint author of a paper with Monckton et al which shows there is a fundamental error in the way climatologists have applied feedback analysis whose effect is to triple the alleged warming from CO2.The climate establishment is blocking publication.Pls send me yr t no to alexhenney@aol.com or phone me on +44 77685 71313 or +44 207 284 4217
The Feynman method is to guess.
Guess 1. Clearance of forest and scrub for agriculture leads to an increase of dissolved silica runoff into the oceans. The spring bloom of diatoms lasts longer as the limit on diatom growth is dissolved silica.
Consequence 1.1 Bloom of phytoplankton is delayed, reducing emission of dimethyl
sulphide. Less DMS, fewer clouds, particularly at the crucial 1500 -2500ft
level.
C1.2 Diatoms have a C4-like carbon fixation mechanism which discriminates less
against heavier isotopes of carbon. Diatom export of carbon to the deep
ocean will include relatively more C13. The atmosphere has relatively more
C12.
C1.3 Diatoms lack calcareous shells. When they die and rain out to the sea bed
they drag down less carbon. The atmosphere has more carbon dioxide.
G2. Alteration of the oceanic ecosystem favours phytoplankton which use surfactant release to smooth the surface, increasing light transmission to the deeper water. This smooths the surface and delays wave breaking. The ecosystem disturbance can be by pollution, silica runoff or nitrate fertilisation.
C2.1 Fewer waves lead to fewer salt aerosols and less cloud. It gets warmer.
C2.2 Smoother water has a lower albedo. It warms more readily.
G3. Anthropogenic oil pollution — runoff, spills, etc see Seawifs site — smooths an appreciable percentage of the ocean surface.
C3. As C2.
JF