Guest Post by Willis Eschenbach
We have gotten three more years of data for the CERES dataset, which is good, more data is always welcome. However, one of the sad things about the CERES dataset is that we can’t use it for net top-of-atmosphere (TOA) radiation trends. Net TOA radiation is what comes in (downwelling solar) minus what goes out (upwelling longwave and reflected solar). The difference between the two is the energy that is being stored, primarily in the ocean.
The problem is that according to the raw, unadjusted CERES data, there’s an average net TOA radiation imbalance of ~ 5 W/m2 … and that amount of imbalance would have fried the planet long ago. That means that there is some kind of systematic error between the three datasets (solar, reflected solar, and longwave).
So, the CERES folks have gone for second best. They have adjusted the CERES imbalance to match the Levitus ocean heat content (OHC) data. And not just any interpretation of the Levitus data. They used the 0.85 W/m2 imbalance from James Hansen’s 2004 “smoking gun” paper. Now to me, starting by assuming that there is a major imbalance in the system seems odd. In any case, since the adjustment is arbitrary, the CERES trends in net TOA radiation are arbitrary as well. Having said that, here’s a comparison of what the Levitus ocean heat content (OHC) data says, with what the CERES data says.
Figure 1. CERES and Levitus ocean heat content data compared. The CERES data was arbitrarily set to an average imbalance of +0.85 W/m2 (warming).
I must admit, I don’t understand the logic behind setting the imbalance to +0.85 W/m2. If you were going to set it to something, why not set to the actual trend over the period of the CERES data? My guess is that it was decided early on, say in 2006, when the trend was much closer to +0.85 W/m2 and people still believed James Hansen. In any case, the way they’ve set it doesn’t tell us much. Let’s see what else we can learn from the two datasets. First lets take a look at the full Levitus dataset, and its associated error estimates.
Figure 2. The Levitus ocean heat content (OHC) dataset (upper panel), and its associated error.
I gotta say, I’m simply not buying those errors. Why would the error in 2005 be the same as the error in 1955?
In any case, we’re interested in the period during which the CERES and the Levitus datasets overlap, which is March 2000 to February 2013. To compare the two, we can adjust the CERES trend to match the Levitus data. Figure 3 shows that relationship. I’ve included the error data (light black lines.
Figure 3. Ocean heat content, with the trend of the CERES data re-adjusted to match the Levitus data. Light black lines show standard error of the Levitus data.
Now, I’m sure that you all can see the problems. In the CERES data, the change from quarter to quarter is always quite small. And this makes sense. The ocean has huge thermal mass. But according to the Levitus data, in a single quarter the ocean takes huge jumps. These lead to excursions that are much larger than the error bars.
To visualize this, we can plot up the quarter-to-quarter changes in ocean heat content. Figure 4 shows that relationship.
Figure 4. Quarterly changes in the ocean heat content. Note that this shows the quarterly change in OHC, so the units are different from those in Figures 1 and 3. Standard errors of the quarterly change are larger than those of the quarterly data, because two errors are involved in the distance between the two points.
As Figure 4 highlights, the disagreements between the Levitus and the CERES data are profound. For some 60% of the Levitus data, the error bars do not intersect the CERES data …
Conclusions? Well, my first conclusion is that I put much more stock in the CERES data than I do in the Levitus data. This is because of the very tight grouping of the CERES data in Figures 3 and 4. Here are the boxplots of the data shown in Figure 4:
Figure 5. Boxplots of the quarter-to-quarter differences of the Levitus and CERES datasets.
Remember that the tight grouping of the CERES data is the net of three different datasets—solar, reflected solar, and longwave. If you can get that tight a group from three datasets, it indicates that even though their accuracy is not all that hot, their precision is quite good. It is for that reason that I put much more weight on the CERES data than the Levitus data.
And as a result, all that this does is reinforce my previous statements about the error bars of the Levitus data. I’ve held that they are way too small … and both Figures 3 & 4 show that the error bars should be at least twice as large.
Next, the CERES data doesn’t vary a lot from a straight line. In particular, it doesn’t show the change in trend between the early and the later part of the Levitus record.
Finally, the CERES data provides a very precise measurement of the quarterly changes in OHC. Not only is their overall variation quite small, but they are highly autocorrelated. In no case are they greater than 0.5e+22 joules.
So for me, until the Levitus quarter-to-quarter changes get down to well under 1e+22 joules, I’m not going to put a whole lot of weight on the Levitus data.
Regards,
w.
NOTE: see my previous post for the data and code.
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What’s the source of the Levitus inaccuracy and imprecision? Make a wild guess. Please. 😉
A simple point, made clearly. Nice one Willis.
The big lie in all this is uncertainty, as Curry and others have been saying for a long time.
Here they are wilfully missing out the sampling error and just using the measurement error. The early error estimations that you provide here are farcile. How anyone can be stupid enought to publish something like amazes me.
This is the old micro-kelvin accuracy BS all over again. You can measure one stop sample very accurately but that does not tell you how accurately that sample reflects the world ocean.
But basically neither is of any use for climate analysis. Right ?
What strikes me about fig 3 is just how stable the satellite derived heat content is despite the comilation of three different datasets which could add all sorts of noise and confounding variables the heat content shows very little variation.
In contrast the Levitus data clearly is strongly infulenced by SST. We see the post El Nino drop and the 2003 heat surge. In contrast the rad data shows these fluctuations are completely irradicated, indeed there is a slight opposite effect.
You know it’s almost as if the climate system had a strong negative feedback or even an industrial “PID” controller ensuring stable heat content.
The other notable feature, accepting the hypothesis of a steady systematic error in CERES is that there is basically not the slightest correlation between the two datasets at any scale (apart from the artificial matching of the overall slope).
What is the correlation coeff of these two series?
As Stephen Richards says, if the CERES and the Levitus data sets are both demonstrably wrong or inaccurate using them together will only make any conclusions even more suspect, not improve understanding. Willis’ point about CERES adjustment demonstrates the fundamental flaw in almost every ‘climate’ measurement, as so succinctly labelled in the Climategate readme file, FUDGE factors.
If there is to be some expectation of correlation there is at least one missing factor from the equation. Latent heat. OHC is _the_ major reservoir but its not the only one.
The heat energy required to evaporate a mass of water is enough to raise it’s temp by about 50 deg C. Then there’s the ice/water phase change.
The TOA values reflect the net sum, OHC does not.
” Exeter has just launched a ‘massive open online course’ on climate change which the public are all invited to sign up – all for free.”
http://blogs.spectator.co.uk/ross-clark/2014/01/who-is-behind-the-ship-of-fools/
Perhaps Willis might want to check it out? and anyone else wit the time.
“Climate Change: Challenges and Solutions. A FREE online course from the University of Exeter”
http://www.exeter.ac.uk/climatechangecourse/
Watch the trailer
https://www.futurelearn.com/courses/climate-change-challenges-and-solutions
always the same in climate science : here come the magic trick..and then…
let’s assume the error is systematic…
errors, uncertainties ans circular thinking.
Greg says:
January 5, 2014 at 2:03 am
Thanks, Greg. While everything you say is true, it doesn’t affect the data very much for two reasons. First, the size of the energy stored as latent heat doesn’t change much over the year. This is because at the same time that ice is forming in the Arctic, it is thawing in the Antarctic, and vice versa. As a result, the net storage as ice is only the difference between these two terms.
Global sea ice area, for example, only varies by about 6 million km^2 over the year. The amount being thawed and frozen is the thin stuff, and averages only about a metre thick. Lets be generous, call it 2 metres thick.
That gives us 1.2e+13 cubic metres of ice melted/frozen annually, which is about 1.1e+13 tonnes. To melt this takes ~ 330 megajoules per tonne. This gives us a total of 0.4e+22 joules, or a swing of 0.2e+22 joules per quarter. This is small in the current context.
The second reason this doesn’t affect the data is that this annual swing of 0.4e+22 joules is the regular swing due to ice. As a result, the regular recurring part of it would be removed with the rest of the seasonal variations, and all that would remain would be the residual annual variations in ice volume.
Net result? Latent heat is a small effect.
Best regards,
w.
Greg says:
January 5, 2014 at 1:48 am
Good question, Greg. The correlation Levitus—Ceres is 0.68 … which sounds impressive until you know that the correlation of the Levitus data with a straight line is 0.75.
w.
So, the CERES folks have gone for second best. They have adjusted the CERES imbalance
That rather says it all.
Their models don’t stack up, so they adjust them to say what they think they should say.
In any stable system at thermal equilibrium there CANNOT be any difference between heat in and heat out. Whilst the earth cannot in any way be at thermal equilibrium it has to be compared to space where all the long wave ends up.
If we were at thermal equilibrium there would be no weather but there would be climate.
Short fast comment regarding data and archival thereof:
Webarchive.org records from http://www.giss.nasa.gov/Data/GISTEMP seem to have been sent down the memory hole. For example http://web.archive.org/web/19970301012952/http://www.giss.nasa.gov/Data/GISTEMP/GLB.Ts.txt was a working link 4 days ago.
http://stevengoddard.wordpress.com/2014/01/05/history-is-a-thing-of-the-past-in-the-us/ details this, and Steve has also shown that google news search has been disabled as well as of a week or 2 ago. It seems as if someone doesn’t really like historic records or data lying around for just anyone to make use of.
Willis
Sorry if I appear a bit slow on this, but can you tell me: Does the raw CERES data imbalance (~5 w/m2) have a trend or is it simply a noisy constant average.
If it’s the latter is it, then, the case that the trendless data has been replaced by the “Levitus trend”. Figure 1 does suggest this but I wanted to be sure.
Thanks
Ocean heat is irrelevant. If the atmosphere is not warming, then there is no AGW relevance to any ocean heat data. The oceans warm or cool by 3 means : 1) geological (volcanoes, earth heat etc), 2) radiation from the sun and 3) convection from the atmosphere.
The only one that is relevant to the AGW debate is convection from the atmosphere. BUT if the atmosphere isn’t warming (for 17 years now), then we know that AGW can not be influencing ocean heat. Air gains and loses heat much faster than water, so the air MUST heat first before it can convect any heat into the ocean. If the atmosphere was warming and the ocean was warming, then you could link the two, but if the atmosphere is not warming then this is nothing more than another AGW lie.
ok for the calculation for ice but what about the same kind of calculation of water vapour in atmosphere?
and regarding the latent heat we should look at the amount of vater ice and vpaour in the climate system as a whole, inclouding clouds ,, rain rivers, makes ,( biomass??), aquifers..and so on…
i guess it doesn’t change much the whole thing ..
It is really disappointing to see just how climate monitoring satellites need to be adjusted to produce meaningful results.
I mean, there are weather satellites that seem to be able do what they were designed for, GPS satellites are amazing, etc. Instruments in space fail regularly of course and there is always some processing required for the basic data.
But the two dozen or so earth observing satellites never seem to produce a raw product that works despite the $billions spent on launching them and operating them. They always end up adjusting the data to match whatever the climate models/other theories expect the data to be. At the end of the day, that means all of the data cannot be relied on. Does one rely the adjustments made to the sea level satellite data for example?
The individual components of CERES, however, can still be tracked over time I assume. SW in, SW out etc. If they are out by 5 W/m2 on balance, how does that 5 W/m2 change over time.
Willis, some time ago I did a test for autocorrelation of yearly running trends in the OHC- Data. It works with Durbin- Watson http://en.wikipedia.org/wiki/Durbin–Watson_statistic . In my eyes the “d” is a term for the thermal inertia of the system you look at. It should not differ too much over time. You can calculate the “d” of running trends of the landtemps or the sst and you’ll find different values of course… the trends of landtemps have a greater “d” because the residuals to the trend are more independend than those of the sst. The OHC- data behave very strange: The “d” is changing very much over time. This seems to not very likely, that’s why the data could be suspicious.
5W/m2 is lot of missing heat. Wikipedia has total global photosynthesis at 130TW (I wonder how accurate this estimate is?). Radius of Earth is 6378km for a surface area of 511 ^12 m2. So photosynthesis would only account for 0.25W/m2?
Sampling bias (spatiotemporal pattern nonrandom) & error estimates based on false assumptions.
Bill Illis (January 5, 2014 at 4:39 am) asks a good question:
“If they are out by 5 W/m2 on balance, how does that 5 W/m2 change over time.”
Also some worthwhile questions about water vaporization/condensation (not to be confused with freeze/thaw).
‘I must admit, I don’t understand the logic …’ the first mistake is think this change was made on logical grounds . The next mistake is forget ‘the use ‘ of this data , its main value is political rather than scientific therefore you can understand why the changed it in the way they did .
Willis, as a couple commenters have already noticed. Greg may have a bigger point than ice caps. The Heat of Vaporization is much greater than the Heat of Fusion for water. Could part of the imbalance mismatch be simply changes to global humidity?