Guest essay by Charles Samuels
A recent article in the WUWT, The Daily Albedo Cycle, by Willis Eschenbach caught my eye when the author postulated that daily thunderstorm development was active in controlling temperatures in the tropics.
The article shows that during the day as certain temperatures are reached, thunderstorms and cumulus clouds develop which increases the albedo and reflects sunlight back into space, thereby cooling the atmosphere and ocean. Looking for a way to extend Eschenbach’s results it seemed reasonable that data on precipitable water, which is the total amount of water in a column of air, would be an indirect measurement of the amount of cumulus and cumulonimbus clouds in the tropics.
Precipitable Water (PW) data is available from the NOAA Earth System Research Laboratory from 1949 to 2014. The data is world-wide at 2.5 degree increments of latitude and longitude. A program was written to extract the yearly average PW for selected latitudes for the period of record. A graph of the results show that in the tropics from 5° N to 5° S the amount of PW decreased from about 1959 to 1997. Since the precipitable water in this area is primarily cumulus clouds and thunderstorms, it follows that there has been a decrease in such clouds during that period.
Fig 1 Precipitable Water and Sea Level Pressure for latitude 5.0 degrees north.
There was no such reduction in PW north of 5N or south of 5S. There are probably many reasons that could account for such a reduction but a look at Sea Level Pressure (SLP) for the same period shows slightly rising SLP while PW is falling. Higher pressure over the tropics would tend to provide more stable air that is not conducive to thunderstorm development.
Note that in Fig 1 the rising curve for Seal Level Pressure coincides with downward curve for PW until about 1997 when both curves flatten out. The correlation between the two is -0.759. It is interesting, but perhaps coincidental, that the period from 1997 is the same period as the current hiatus in global warming.
Those steps, 1972-73 and 1997-98, in air pressure seem to roughly correlate to El Nino’s. Comments?
Could be, I’ll look into that. The 1972-73 event is prior to the satellite era and the actual data points would have been widely scattered. In regard to the 1997-98 event, the data was checked for other tropical latitudes and the event was evident in all of them.
http://www.climate4you.com/images/HadCRUT3%20and%20TropicalCloudCoverISCCP.gif
Shows the cloud coverage decreasing as the temperature increases.
Seems to be the other way around: air temperature increasing as cloud coverage decreases.
Bingo, Katherine.
Katherine – Exactly- this shows the decline in cloud cover which results in decreasing albedo so that more solar SW radiation heats the tropical waters. For the peak in the quasi- millennial solar driven temperature cycle in about 2003 See
http://3.bp.blogspot.com/-IQygRNJHLdk/U9cLX35e1LI/AAAAAAAAAVs/evBAUhwPdSA/s1600/trendtempsto2914.png
For forecasts of the coming cooling based on the natural cycles see.
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
Here is a later look at the Millennial peak
http://www.woodfortrees.org/graph/rss/from:1980.1/plot/rss/from:1980.1/to:2003.6/trend/plot/rss/from:2003.6/trend
Norman, could you please stop drawing your trend across the 1997-2001 warming step.
Stop the pre-1998 warming trends at about 1996.5 for a more realistic representation of real trends.
AndyG55 June 14, 2015 at 11:19 pm
“Norman, could you please stop drawing your trend across the 1997-2001 warming step.
Stop the pre-1998 warming trends at about 1996.5 for a more realistic representation of real trends.
//////////////////////
The presentation of linear trend lines is one reason why Climate Science so woefully represents what is going on, and why teh ‘scientists’ fail to grasp the full impact of natural variation.
Of course, plotting a trend line requires a certain amount of subjective interpretation of the data, no matter how objective one is trying to be.
My take on Norman’s graph is that there should be a flat trend line at around the 0 deg mark between 1980 up to mid/late 1996, and another flat trend line around the 0.25deg mark between late 1999/beginning 2000 through to date.
It appears to me that Norman’s plot of the satellite data clearly shows that temperatures, as a trend, were flat between launch in 1979 through to the run up to the Super El Nino of 1998, and once again temperatures (as a trend) were flat post the 1998 Super El Nino through to date.
The plot of the data simply shows a one off and isolated warming event around the Super El Nino of 1998 that created a step change of about 0.25degC of warming that was released into the atmosphere and which has yet to dissipate. That event is, of course, of natural origin, not anthropogenic in origin.
Whether the heat released from the oceans by the 1998 Super El Nino will dissipate depends upon what happens next to the ‘pasue’/’hiatus’.
I agree with Katherine. Eschenbach’s albedo article is clear that clouds are developing in response to temperature on a daily basis.
Richard,
Are you saying the atmosphere has enough heat capacity to hold the heat added in 1998 all the way through the present day? How does this correlate to the atmosphere’s inability to hold the heat added on a typical day through the night to the next day?
Suggest comparing with:
A 39-Yr Survey of Cloud Changes from Land Stations Worldwide 1971–2009: Long Term Trends, Relation to Aerosols, and Expansion of the Tropical Belt Ryan Eastman & Stephen Warren, J. Climate
http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00280.1
Charles S.
Take a look at the AMO in that time period, negative Phase.
http://www.woodfortrees.org/plot/esrl-amo/from:1955/to:2000
modern Solar Maximum occurred from about 1950 to 2000.
Something else go look at.
Humid air is less dense, thus the lower pressure.
Less dense air is cooler too. It is called evaporative cooling, something the Climate Scientists don’t understand.
The arrogant idiocy of this comment by jinghis is astonishing. Jinghis knows little or nothing about climate science, but he has no trouble assuming that the experts know less than he does.
I suppose it is unfair of me to pick on jinghis, since this sort of thing is on display here from many commentators. But this one set me off.
Mike M
“I suppose it is unfair of me to pick on jinghis, since this sort of thing is on display here from many commentators.”
so says the Pot
Evaporate 1 litre of water and you can cool ~2200 Kg of air by 1˚C or ~4400 kG by 0.5C due to the Latent Heat of Vaporisation. It does not matter whether you do that at the surface of tropical seas or via the leaves of plants ( running flat out as they are off their faces on extra CO2).
And the “Settled Science” does not “model” this? Settled Science”? Sorry , “Settled Science” doesn’t do negative feedbacks. On the other hand, fat arsed science has settled into the leather with the Guardian/NYT etc and a glass of chardonnay checking the bank balance on the ipad to see if the regular funding came through OK.
@M Seward
+137
The decrease in precipitable water since the 1970s looks to me to be, in part, an aerosol effect. Decreased aerosols = less persistent clouds = increased precipitation.
And tropical precipitation has increased.
http://www.nasa.gov/centers/goddard/news/topstory/2007/rainfall_increase.html
I’d say decreased clouds doesn’t result directly decreased precipitable water. Rather, decreased clouds and decreased precipitable water both result from increased precipitation.
The global trend is essentially 0 change in 35 years. That plot is 0.1mm per day change up to 2005. Between 2005 and now there is a decrease in precipitation especially over oceans.
http://www.aqua.nasa.gov/uploads/gpcpsg_v2.jpg
Notice that the average is 2.5mm/day so 0.1mm per day is about a few percent and would be less than 1% for the tropics. Not exactly convincing.
I’m also not sure about “Decreased aerosols = less persistent clouds = increased precipitation.” Aerosols do not stop droplets growing. They stabilise micro droplets so that they exist for long enough to grow to a stable size. Plus, cloud cover and precipitable water both decreased. Blew away is the best suggestion.
‘Aerosols do not stop droplets growing’
No they don’t. Aerosols seed more droplets than would occur otherwise. Reducing WV, thus limiting droplet grow to precipitating size.
More persistent clouds (ie smaller droplets) from urban aerosols is reasonably well documented. A similar effect from forest aerosols is less well documented. and this is likely a deforestation effect.
The article I linked to found a 5% increase in tropical rainfall over 27 years. That’s a large effect. The largest increase was in the region of the Southeast Asian archipeligo. The area of the tropics which has seen the most deforestation over recent decades.
Otherwise, propose a physical mechanism that increases precipitation, decreases precipitable water and decreases clouds?
The title to the graph that you linked to “The trend of increasing rainfall over the tropics was clearer once NASA scientists removed the effects on rainfall of major volcanic eruptions and El Niño from the data record. This figure shows how much each year’s tropical rainfall differed from the long-term average rainfall over the entire 27-year period.”
Its clearly about 0.1mm/day over that 27 year period. If that’s a 5% increase, then the rainfall was only 2mm/day on average. Are you telling me that it rains less in the tropics than elsewhere? Something is seriously amiss with the analysis. And there is a comment below about the precipitable water plot might not be real. Nothing to explain in the end.
Raining when it cools a little means a decrease in precipitable water and cloud cover when it warms up again. Aerosols make it rain when the air is saturated. Its more complicated than that but we are getting above my pay bracket.
Chuck, good to see you sharing your wealth of knowledge on this site. Thanks.
That fits my hypothesis that high solar activity reduces global cloudiness and allows more solar energy into the oceans.
Tropopause height falls over the equator and rises over the poles so that the jets become more zonal and the climate zones shift poleward.
Falling tropopause height is associated with descending air as in high pressure cells so the subtropical high pressure cells either side of the equator expand when the sun is more active which gives higher surface pressure and less humidity and rainfall at the surface just as observed.
High solar magnetic field strength reduces the incoming cosmic ray flux – and reduces the clouds and decreases albedo .Fits well with Svensmarks ideas. Yes?
Yes it fits Svensmark but I am doubtful about cosmic rays affecting the latitudinal positions of the jets and climate zones.
For that reason I prefer my hypothesis that the sun changes the gradient of tropopause height between equator and poles by acting on ozone differently between levels and latitudes.
http://joannenova.com.au/2015/01/is-the-sun-driving-ozone-and-changing-the-climate/
As you probably know I base my forecasts in the main on the natural cycles so obvious in the temperature record. For making reasonable forecasts all we really have to decide is where we are with regard to the quasi- millennial cycle. The key illustration is Fig 5 ( taken from Humlum)
http://4.bp.blogspot.com/-JRFF7ZFvgKw/U81X0SC899I/AAAAAAAAAS8/PIcfIxO3QUQ/s1600/GISP2%2520TemperatureSince10700%2520BP%2520with%2520CO2%2520from%2520EPICA%2520DomeC.GIF
This Fig combined with Fig 9
http://2.bp.blogspot.com/-4nY2wr6L-WY/U81v9OzFkfI/AAAAAAAAATM/NA6lV86_Mx4/s1600/fig5.jpg
shows that we are just approaching, right at or just past a millennial peak.
Figs 14 and 13 at
http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html
indicates that the driver peak was at about 1991 – a 12 year lag brings the temperature peak to 2003 as illustrated in an earlier comment.
Your mechanism re ozone and EUV may well operate in conjunction with the cosmic ray effect Here’s what I say re processes in the blog.
“NOTE!! The connection between solar “activity” and climate is poorly understood and highly controversial. Solar “activity” encompasses changes in solar magnetic field strength, IMF, CRF, TSI, EUV, solar wind density and velocity, CMEs, proton events etc. The idea of using the neutron count and the 10Be record as the most useful proxy for changing solar activity and temperature forecasting is agnostic as to the physical mechanisms involved.
Having said that, however, it is reasonable to suggest that the three main solar activity related climate drivers are:
a) the changing GCR flux – via the changes in cloud cover and natural aerosols (optical depth)
b) the changing EUV radiation – top down effects via the Ozone layer
c) the changing TSI – especially on millennial and centennial scales.
The effect on climate of the combination of these solar drivers will vary non-linearly depending on the particular phases of the eccentricity, obliquity and precession orbital cycles at any particular time.
Of particular interest is whether the perihelion of the precession falls in the northern or southern summer at times of higher or lower obliquity.”
To clarify the top Fig is Fig 5 at the linked blog and the second Fig is Fig 9 in the blog.
Charles, first, my thanks to you for this piece. I am impressed by your effort. However, when I looked at your Figure 1, I saw that the “data” for precipitable water went back to 1949 and I thought “Say what? Since when do we have global data on precipitable water back to 1949?”
Unfortunately, you have not provided a link to the actual data that you used. My search of the organization you named, the NOAA Earth System Research Laboratory, didn’t reveal any such dataset.
Nor do I believe that there is such a dataset. I say this because all I could find at the NOAA Earth System Research Laboratory site was the output of a computer reanalysis model, which it looks like you used. While these models are interesting, they are nothing but the codified best guesses of the people that programmed the computers. The outputs of these models are not data as we normally understand the word. Unfortunately, they have been named “reanalysis data” by the modeling community, in what appears to be an unconscious attempt to provide them with a false legitimacy. Despite the name, they are not any type of data, whether “reanalysis data” or any other kind.
They are outputs of the same computer models that have done so poorly in emulating the real world.
Such “reanalysis” models take as input our usual scarce, sparse, intermittent, spotty, localized, poorly distributed actual observational data. These observations are then input into a global climate model, and the model then gives its guess as to all of the observations that are missing … I’m sure you can see the problems with using the output of such a model, including decreasing accuracy as it looks further and further into the past.
In addition, with such reanalysis models we have very different amounts of observational data for the different variables involved. For sea level pressure (SLP), for example, we have been taking those measurements at thousands of sites around the world for centuries. As a result, the SLP estimates of the reanalysis model are tightly constrained by reality, and most of the output of the reanalysis model is conditioned on actual data.
For precipitable water, by contrast, we have very, very little data. This means that almost everything in your Figure 1 about precipitable water is just computer model guesses.
This leads to another problem with reanalysis models—there is no way to test or check how good their guesses for a particular year and a particular variable might be. There is no “reality check” on precipitable water in 1950.
Another major problem with the reanalysis models is over-linearity. Climate model global temperature output, for example, can be emulated to a high degree of accuracy as a lagged linear transformation of the inputs. The models are linear calculating automatons.
Nature, on the other hand, specializes in edges, in discontinuities, in patches. Climate models can’t do that kind of thing well. So a climate model’s best guess at what is happening at a point halfway between A and B will likely be some kind of average of the two.
And using such an average seems reasonable at first glance … but in the real world, the answer is generally not that average at all. Instead it is more likely to be either A or B.
For example, if point A is in a cloud, and point B is in clear air, then the point halfway is not likely to be “half cloudy”. Instead it is likely to be either in the cloud or in clear air … and nothing like the best guess of the computer.
The combination of the linearity of the models and their inability to deal with edges leads to a false degree of correlation between the various parts of the model.
For example, the output of reanalysis models has shown a correlation between small 11-year solar variations and the temperature … but only because a computer climate model will almost always show a correlation between what the programmer has identified as “inputs” or “forcings” and the output.
And if you think that means that sunspots affect the temperature, it means nothing of the sort. It just means that climate models are creatures wired linearly direct from input to output who don’t do edges well at all.
Similarly, depending on just how the climate model is programmed, I can easily see that it might find a strong correlation between say sea level pressure and precipitable water … but again, I fear that this says little about the real world. All it means is that such a correlation exists INSIDE THE MODEL. Nothing else.
So although I find your analysis interesting … I also fear that we can put very, very little weight on it.
I’m sorry to have been the bearer of bad news, but the existence of internal correlations between numerical variables in the guts of a climate model means very little about the real world.
Finally, please do not let this discourage your inquisitive nature. I applaud the effort you’ve put into this and the spirit behind it.
My best to you,
w.
Yes Willis. It is difficult to write critiques that have a negative theme, in a positive tone. The English language does not lend itself to such entreprise. However, this was great attempt. Well done.
Willis,
You wrote “The outputs of these models are not data as we normally understand the word. Unfortunately, they have been named “reanalysis data” by the modeling community, in what appears to be an unconscious attempt to provide them with a false legitimacy. Despite the name, they are not any type of data, whether “reanalysis data” or any other kind.”
It is worse than this: the modellers typically refer to their computer output as “data”, even when the models are projections.
I don’t think there is any intent to deceive; “data” is just a convenient word to use. But I suspect it may lead to self-deception via a positive feed back loop. The more they call their computer output “data”, the more likely they are to think of the output as being real; the more they think of the output as being real, the more likely they are to think of it as data.
Willis, thanks for the critique, you are right about the data to a certain extent, but when the analysis is made it is using real data. Prior to 1978 that would have been upper air soundings for precipitable water and since then sounding data from NOAA satellites is also used in the analysis. In the case of precipitable water i assume the analysis merely interpolates existing data to create a data grid.
Charles Samuels June 15, 2015 at 8:39 am
Thanks for the response, Charles. Unfortunately, I simply don’t understand what you mean when you say that:
You have done your analysis using reanalysis climate model output. That is not data. It is the output of a climate model, plain and simple.
So which data are you referring to as “real data”? Where is the real dataset of the upper air soundings for precipitable water that was used? Where is the analysis of that “real data” to which you are referring?
Next, which analysis are you assuming “merely interpolates existing data”? Because it is assuredly NOT the reanalysis model. They don’t work that way. They use real observational data as the input to the model, but the output is not simply an interpolation of the observations—it is the usual climate model mashed potatoes.
Finally, even if you simply used the interpolation of an observational dataset, that is meaningless for the reasons I posted above—nature does edges, and interpolation has no edges at all.
All the best,
w.
Willis writes “You have done your analysis using reanalysis climate model output. That is not data. It is the output of a climate model, plain and simple.”
I couldn’t agree more Willis. Having a lack of understanding of the data happens on the pro warming side of the debate all the time. We all need to be especially careful to avoid that mistake.
I followed up on this and went to the source. Cathy Smith of NOAA said: “Can you remind me which reanalysis you used? If it’s the NCEP one (it was), all observations are assimilated into the model. This would include ships, satellites, station data, planes, etc. They are assimilated using a sophisticated algorithim that is able to use observations that are at different times, may contain errors, or may have subgrid variability. Then the model is run forward so that physically consistent secondary variables (such as precipitation, fluxes, etc) and primary variables (winds at all levels…) are output. This is repeated,
So, while the NCEP reanalysis is model generated, it is not the quite same thing as model data.”
She also gave links to papers that discuss the issue. The most interesting to me was a paper comparing the various Precipitable Water data sets:
http://journals.ametsoc.org/doi/pdf/10.1175/BAMS-86-2-245
Thank you Willis.
I appreciate this example of your scientific integrity. A good example to follow.
Thanks Willis, I thought it might be aircraft or sonde data, ah well.
I liked your attack on the ‘edges’ of models and don’t recall seeing much on that written before. The constraints applied at boundaries between cells in gridded models has always seemed a fudge to me, but as a data person I always lacked the time and mathematics to really understand why every modeller seemed to get away with it. It makes sense if you know the answer you need and have limited numbers of variables, but that is not true even for simple climate models ( if their purpose is understanding systems rather than justifying a policy) and the fact that you need to constrain something in a model surely indicates there is a feedback process there you have missed? In short, its not the fit to data that matters, it’s how constrained that fit was. Are you aware if any publications discussing the mechanics of climate models? Aerodynamicists use models extensively and still get bitten by the real world. It would be nice to see some professional fluid dynamics folks have a look in the climate modellers black boxes…presumably the code is published in the same way as raw data?
I think most folks here at WUWT realize that it is the nature of the earth’s vast, deep salty oceans and the precipitable water column are what leads to our very stable climate. Even during Ice Ages, temps vary not by more than -10ºC from present. Which of course leads to very substantial NH glaciation, but that still provides a very habitable tropical and lower mid-tropics zones for megafauna. Let’s realize what we can agree on, then look at the differences.
So to the post at hand, the data. The subject of negative feedbacks needs to be considered in the causal relationship. Temps increasing yet the cloud cover is falling, then a stabilization period beginning in/around 1999/2000. Exactly what one would expect if a complex system was responding to increased forcing… whether from CO2 induced elevated LWIR back-radiation, elevated solar activity, or internal ocean cycles, or any of that combination, etc. With our current data in hand, we cannot parse out these partial derivatives from the equation of climate. No matter how hard we might try, our current data falls short.
The GCMs do a minor part of that equation, and of course succeed in failing miserably, which the modellers currently are unwilling to admit for reasons of money, reputation, and government career.
The bottom line is that Earth’s demonstrated temperature regime for the last 10K yrs has been stable within 2ºC. The climate system has a much longer evolved response feedback regime (hundreds of millions of years) to counter the small increase in man’s CO2 forcing. With that, we are quibbling over peanuts while the CAGW elephants run amok and try to use it on a naive public for political effect. And science ultimately will be the loser when the real nature of Earth’s climate complexity becomes too obvious for the modellers and climastrolgists at NOAA;NASA to ignore.
I am skeptical that the region between 5 deg N and 5 deg S worldwide is capturing the maximum precipitable water. One of the most consistent features on satellite pictures are the lines of clouds at the intertropical convergence zone (ITCZ). The position of the ITCZ varies with the seasons and more. Satellite pictures of the NE Pacific ocean show the main ITCZ there is north of the equator, and .even 5 deg N. The author did not specify which longitudes were used. (The whole Earth?) The chart title said only 5 deg N.
NOAA-NWS – Ocean Prediction Center (OPC):
http://www.opc.ncep.noaa.gov/UA/entire_UA.gif
From http://www.opc.ncep.noaa.gov/UA/entire_UA.gif
This graphic is updated daily and shows the current position of the ITZC
Obviously clouds are the great unknown, and uncertain factor in Global Warming/Climate Change.
The matter is extremely complex since clouds are 3 dimensional objects and in a way are even more dimensional than that since their composition influences their reflective and absorption characteristics. If that was not complex enough, slight changes in the time of formation, or dissipation has a significant impact upon solar insolation reaching the oceans below, and of course, the ocean may itself have a variable albedo due to bio diversityissues and the albedo below a cloud is as important as the albedo of the cloud itself since it impacts what would happen but for the cloud.
It has alawys been the case that the most obvious explanation for yearly variations in temperaures and this can include a trend (whether this be warming or cooling) running over a lengthy period of time is due to slight changes in cloud behavoir and patterns.
A trend over a period of 20 years is nothing given that the oceans and atmosphere have been around for about 4 billion years. 20 years of say diminishing cloudiness is no more unusual than toss a coin 2 or 3 times and it coming up heads say twice in a row or three times in a row.
In the overall scheme of things 20 (or even 50 years) is a blink of the eye, and given that climate is constantly evolving minor changes over periods as short as 30 years are meaningless.
The null hypothesis has yet to be displaced, namely that all changes in temperature/climate that we may be observing over the curse of the last 100 or so years is natural variation and the most probable cause is subtle changes in cloud patterns/behavoir.
Changes in cloudiness, can potentially explain everything that we have observed. But like everything, the hard data is lacking and we are limited by this.
Precipitable water and thunderstorms
I presented a simple formula for the estimation of wet bulb temperature and precipitable water and was published in Indian J. Met. Hydrol. Geophysics [1976] 27: 163-166
Tw = T [0.45 + 0.006 x h (square root of {p/1060}) where Tw is the wet bulb temperature in oC, T is the dry bulb temperature in oC, h is the relative humidity in % — all these may be daily or hourly values; p/1060 is the pressure correction factor. And precipitable water vapour gm/sqr. cm is given as:
W = c’ x [square of {Tw}] wherein c’ = 1/Square of c, c is a regression coefficient varies with the season [with high correlation] – January to December are: 11.4, 11.8, 12.8, 13.0, 12.2, 11.5, 10.6, 10.6, 10.6, 11.4, 11.4 & 11.4
A simple method of estimating thunder storms, appeared in Indian J. Met. Hydrol. Geophys. [1977] 28:215-217
K’ = 850 mb due point temperature – 700 mb dew point temperature
If K’ is = – 8 the occurrence or non-occurrence of thunderstorm can be forecasted depending up on the 24 hourly change of 850 mb dew point temperature.
If it is negative, non-occurrence can be forecasted and if it is positive there is a possibility of occurrence of thunderstorm within 24 hours. The above analysis is made for 00 and 12 GMT values so that there will be an overlapping period of 12 hours. If they are dissimilar, no thunderstorm is forecasted.
Dr. S. Jeevananda Reddy
Thanks, Dr. Reddy. A link to your paper “A simple method of estimating thunder storms” would be greatly appreciated.
w.
“Since the precipitable water in this area is primarily cumulus clouds and thunderstorms, it follows that there has been a decrease in such clouds during that period.”
But how does the distribution vary during day and night. One could easily conceive of more during the day and significantly less during the night so it could still reflect more sunlight.
Many of us have experienced seeing river ‘fog’
http://www.dailymail.co.uk/news/article-2516098/Photos-breathtaking-river-fog-Grand-Canyon-ONCE-IN-A-DECADE-weather-phenomenon.html
Perhaps not on the scale of that shown in the Grand Canyon in the above link, but, clouds/fog form with very small changes in temperature/humidity/pressure.
What we really need is a large experiment, a large tower, perhaps, 30m (100ft) tall, 3m (12ft) in diameter, well insulated, with an energy source at the top and water at the bottom.
Instrument it at 1m intervals for temp and humidity.
Allow to stabilise with the equivalent of a few hundred watts input at the top.
The increase/decrease energy in and monitor cloud formation / temperature / humidity change.
Would we see a non linearity/limiting conditions occur as the cloud regulated the temperature of water at the bottom of the rig?
The problem with your experiment is that it is sealed. Moist air expands, the temperature and pressure drops. That doesn’t happen in a sealed container.
Then you have factor in the difference in radiation sources, long wave vs short etc, wind, etc. etc. As Judith Curry says it is a wickedly difficult problem.
I would not ‘seal’ the chamber, but allow controlled air flow to maintain a balanced situation. The radiation source would have to replicate in total, that would be received by the atmosphere at the height of the top of he chamber. Wind would only be that generated within the chamber caused by differential heating/cooling.
As an engineering concept I am sure that all aspects that we are aware of could be controlled to make the experiment worthwhile.
If you look at the satellite temperature record for the tropics, i.e. the area of the earth between the tropic of Cancer and Capricorn (+/- 23.5 degrees of latitude) you will find that the temperature of this part of the earth has not warmed since 1979 – the entire period over which satellite temperature measurements have been made. Seems like a fine example of negative feedback.
Data can be found here: http://www.nsstc.uah.edu/data/msu/t2lt/uahncdc_lt_5.6.txt
Correct me if I’m wrong but I believe increased water vapor in the atmosphere is one of the major feedbacks in the climate models. If the water vapor does not increase significantly or an increase in clouds caused by increased water vapor create a negative feedback then catastrophic global warming is not a possibility.
Add one more to the list who get it : ) And not only that, but increased water vapor by itself causes cooling. It is called a swamp cooler in the Southwest.
http://patriotpost.us/opinion/19138
It is all about ENSO versus precipitable water NOT CO2 positive water vapor positive feedbacks which do not exist.
Charles Samuels,
You wrote: “Since the precipitable water in this area is primarily cumulus clouds and thunderstorms”.
Actually, precipitable water is entirely water vapor: http://glossary.ametsoc.org/wiki/Precipitable_water
Charles Samuels,
I’m wondering what causes the trend in Figure 1. I would think there are two possibilities: a reduction in the source of water vapor (net evaporation) and/or an increase in the sink (precipitation).
The former would likely have to be due to a decrease in sea surface temperature. Other posters have alluded to this via AMO and ENSO. If that is the case, it would confirm what models predict for the behavior of the atmosphere even if the trend is the opposite of what the models predict. The latter would imply that the models are getting the oceans wrong, as Bob Tisdale has often pointed out.
An increase in precipitation in the tropics causing drying of air is, I think, the essence of Linszen’s Iris Hypothesis.
I think that the real value of your post, and Willis’s recent posts, is not that they will provide answers in themselves but in that they point towards key climate indicators that models should be tested against.
But that’s only fractions of a degree and few % cloud cover.
at least together with willis’s posts a serious attempt to explain and link the most unknown driver of our climate: clouds.
I’ve been intrigued by column water vapor numbers – this graph from climate4you.com (much great data there) shows column water vapor, both in 680-1000mb (~10,000 feet to surface) and 310mb to 680mb (~30,000 to 10,000 feet) – and a total.
One I find it interesting that total combined column water vapor largely mirrors the 680-1000mb levels.
Two – and more interesting to me – is the roughly level trend 1983-1989 in both lower and upper altitudes, followed by a downward step change at lower altitudes from 1989 to appx 1992 (with no appreciable change in upper altitude water vapor). Then we see water vapor increase pretty steadily thru 1998.
The most interesting part to me is the fairly strong downward step change 1998 thru 2000 at BOTH upper and lower altitudes …. with BOTH altitudes seeing a strong stable trend since, largely matching the pause in temps.
It seems the column water vapor at lower altitudes increased with temps thru the 90’s – until the peak event in 1998. Whereupon the upper altitides shed water vapor as well, which could be construed as leading to lower column water vapor levels overall – and stable temps and water vapor levels since.
To me that looks like a thermostat type effect as Willis describes. Upper level water vapor acts as a thermostat – when water vapor levels/temps reach a certain point, the upper levels release water vapor bringing more stability.
Then again I know just enough to be dangerous … 😉
http://climate4you.com/images/TotalColumnWaterVapourDifferentAltitudesObservationsSince1983.gif
Too busy to comment at length..but check out drypan evaporation data…they have dropped worldwide…
Charles Samuels June 16, 2015 at 11:33 am
My friend, thank you for your perseverance. However, I fear that what you have done is the equivalent of asking your barber if you need a haircut …
For starters, she says what you have used is “model generated output”, but not “model data” … say whaaaat? That’s just modeler NewSpeak. What is “model data”, and how is it not “model generated output”? On my planet “model data” is an oxymoron.
And yes, she agreed with what I said. The computer takes the observations and gives its best guess at all the thousands of times and places where there are no observations.
Now, such computer model output is interesting. But one big problem is, depending on the amount of infilling on your particular chosen variable, you could be looking at anything from 100% observations to 100% computer guesses, and there’s no way to tell the difference. So you simply can’t just grab some variable and assume it means something.
Another problem is the aforementioned linearity of the computer models, which don’t do edges well at all.
So you can’t just grab a reanalysis dataset and expect all of its dozens of datasets to correspond to the real world equally well … or well at all.
Now, that is very interesting. It compares the NCEP-NCAR reanalysis “data’ to a dataset I’d not heard of, the NVAR dataset. This appears to be a merging of various actual measurements of precipitable water. In my opinion, such a merging of actual data is likely to be closer to reality than the output of a climate model. I’ll need to take a look at that … so many datasets, so little time …
All the best, thanks for the response,
w.