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.
That pretty much what I was asking in this post
If the 5 w/m2 imbalance is trendless then there is no justification for introducing a trend. Willis’ Fig 1 suggests an increase in CERES imbalance up to about 2003 but essentially flat after that. This pattern is not dissimilar to the surface temperature record.
IGNORE my previous post, i.e.
I completely mis-read Fig 1.
Willis Eschenbach: “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.”
Could you make the assumption behind this a little more explicit? It seems that you’re assuming that the freezing and thawing occurs only on the ice that appears and disappears, not on the ice that from above seems to remain permanently. So if dA is the area change and h is the thickness, you conclude that the volume change is h * dA.
But isn’t it possible that some freezing and thawing occurs beneath the “permanent” ice? Suppose, for example, (obviously, contrary to fact) all the ice took the form of a single cone, whose base is what we see from above. Then the volume change would not merely be proportional to the product of h and dA but instead proportional to the product of dA and the square root of the entire ice area, including that which is “permanent.” That would be a considerably different quantity.
“Adjusted” data, in all its sad ridiculous permutations in so-called “Climate Science,” is simply useless. This 5 W/m2 error means the satellite doesn’t work! Too bad…
As we are dealing with differences between large numbers, it is good to keep in mind what Prof. Walter Lewin of MIT says, “Any measurement that you make without knowledge of its uncertainty is meaningless”. Listen to this first lecture by Prof. Lewin at: http://ocw.mit.edu/courses/physics/8-01-physics-i-classical-mechanics-fall-1999/video-lectures/lecture-1/
His quote starts at about 4:40 min into the video.
Differences between measures values doubles the uncertainty, so that also has to be taken into consideration. As the measurement of ocean temperature is also uncertain, using this value to establish what the net radiative imbalance in the TOA is, is incredibly naïve.
Never forget, that even ARGO data were cooked to hide the decline. Willis (Josh) discarded all “too cold” readings fro ARGO sensors and still proud of it.
http://earthobservatory.nasa.gov/Features/OceanCooling/
Willis says:
I must admit, I don’t understand the logic behind setting the imbalance to +0.85 W/m2.
————————————————
The logic is Hansen’s model is within the margin of error of the measurements. It’s not ruled out, so they use it because his model predicts DOOOOOM!
Unfortunately, the margin of error is so large for those CERES TOA measurements, they don’t rule out many predictions. Even global cooling is supported.
mwhite: It wouldn’t be good for Willis’ health to do that course.
Be nice if Lord Monckton could do it, though.
No, no, I’m not suggesting that Lord M needs a heart attack …… .
Willis, many thanks for your two CERES TOA net flux imbalance/ OHC posts. Can I ask which particular CERES dataset you are using, and where the 0.85 W/m2 adjustment you refer to is documented?
The EBAF-TOA CMIP5 Data at http://ceres.larc.nasa.gov/cmip5_data.php has 5 variables, from 3 of which the net TOA imbalance can be derived. The related documentation, at http://ceres.larc.nasa.gov/documents/cmip5-data/Tech-Note_CERES-EBAF-TOA_L3B_Ed2-7.pdf , says that the SW and LW fluxes in EBAF Ed2.7 are adjusted to give a net TOA flux of 0.58 W/m2 over July 2005-June 2010, not 0.85 W/m2 (which was used in EBAF Ed1.0 and 2.5).
I think the adjustment to 0.58 W/m2 is a constant offset applied at all times, not a value that changes over time. And the last 13 years EBAF Ed2.7 global all-sky annual TOA imbalance data has a negligible trend. CERES data is, as you know, meant to be fairly stable over time even though its absolute accuracy is poor.
Whether 0.58 W/m2 is a reasonable imbalance estimate is debatable, but it looks rather more realistic than 0.85 W/m2 to me.
I agree with you about the OHC data fluctuations probably being largely noise.
There is no such thing as light black is there? Luv ya
@ur momisugly mwhite says:
January 5, 2014 at 2:10 am
“Climate Change: Challenges and Solutions. A FREE online course from the University of Exeter”
This looks like FREE propaganda, to me. Take the fungus teaser for example.
From the article (link below). This looks like just more hand-waving about an unproven threat from “climate change.” So, if you want to be brain-washed, take the course by all means.
“Fungal disease threat seen increasing
Fungal diseases are a major threat not just to wild plants and animals, but to us.
A new Nature paper shows we’re already heading for huge fungal damage to vital crops and ecosystems over the coming decades. If we don’t do more to stop these diseases’ spread, their impact could be devastating.
Fungi already destroy at least 125 million tonnes a year of rice, wheat, maize and potatoes and soybeans, worth $60 billion. Researchers estimate that in 2009-10, this lost food could have fed some 8.5 per cent of the world’s people. And this is just the result of persistent low-level infection; simultaneous epidemics in several major crops could mean billions starve.”
http://www.enn.com/climate/article/44265
Jim2: what we need is for someone who knows what he/she is talking about to take the course and expose it for what it really is.
Willis says:
“Now to me, starting by assuming that there is a major imbalance in the system seems odd.”
I submit it’s far worse than that–it’s criminal–because there are a lot of policy decisions that are based on that fallacy.
Our (so-called) Masters are forcing their agenda on us through lies, lies, and more lies.
Paul Vuaghan: “Also some worthwhile questions about water vaporization/condensation (not to be confused with freeze/thaw)”.
That was my major point to Willis, though he seems to have read it as just refering to ice I did specifically mention latent heat of evap.
Willis, two things. First there has been a long term change of ice volume though we don’t really have any decent estimation of it’s size.
Second my main point was about evaporation. There is a net change in atmospheric water content on inter-annual time scales and this can be seen reflected in LOD. (LOD is a also a variable energy reservoir).
If El Nino dominated periods have more tropical (or extra tropical) cloud then there is an energy transfer to atmosphere . Conversely when it cools and precipitation gives up the latent heat (which will show up in outgoing IR) and a reduction in angular momentum.
This may go some way to explaining why CERES shows little variability and may be evidence of strong negative feedbacks being present.
charles the moderator says:
January 5, 2014 at 5:50 am
But consistently and inexorably in one direction?
Not likely.
re. correlation, since trend is fictitious you need to look at corr coeff of the rate of change as in fig 4. Then work out what is significant for that number of points.
RE. Exeter “course” . The two min intro telling me we are in a geological period called the anthropocene is enough to see through this faux science “course”. More propaganda disguised as science.
Looking at the longer “official” OHC record, there is some relation to the global temperature record as one should expect. Surely there is a significant lag in heating and cooling of the ocean 0-700. I note despite no warming for 17 years, the inflection to a lower slope in OHC is around 2003, suggesting a ~ 5 year lag. Perhaps a truer correction could be estimated by reducing the OHC record slope overall so that it comes out flat after 2003. We also know there is a positive component to the slope attributable to warming proponent analysis – we always have a range of choices from data variation to select from in plotting these things – the correction chosen based on Levitus is a good example.
http://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/heat_content55-07.png
Also the perhaps silly question: can there be any other forms the outgoing energy can take, or is there some sort of leakage in the coverage? I’m sure physicists have already dismissed such possibilities. And, why would there be no obvious variation resulting from the annual perihelion/aphelion in the orbit.
Look to the volume of Ice for your proxy of the net energy balance. The overall energy content of the oceans can not change as it is set by the surface pressure on it. The energy loses of the Earth are set by deep space and do not change. Only energy input from the Sun can change. We are getting a lesson on that even now. pg
Exeter:
About the course
The course is aimed at the level of students entering university, …
….
Requirements
No previous experience or qualifications required.
====
Entry requirements for “climate change” studies at Exeter do not seem to be very high. If that is typical it may go a long way to explaining a lot of the work we see getting published in this field.
( Small green ‘Gaia” prayer mat will be supplied to all applicants to the course ).
Nic Lewis says:
January 5, 2014 at 7:50 am
“http://ceres.larc.nasa.gov/documents/cmip5-data/Tech-Note_CERES-EBAF-TOA_L3B_Ed2-7.pdf , says that the SW and LW fluxes in EBAF Ed2.7 are adjusted to give a net TOA flux of 0.58 W/m2…not 0.85..|”
Nic, this would go someway toward flattening the slope after the inflection downwards at 2003 as I suggested in my comments above:
Gary Pearse says:
January 5, 2014 at 9:27 am
The most important info from the Hockey Schtick is missing: Launch of the RAVAN
precicion satellite, which will give the answer….
until now: Assumptions…
“””””””Satellite will launch in 2015 to measure Earth’s radiation budget for the first time
A satellite scheduled to launch in 2015 will “measure the absolute imbalance in the Earth’s radiation budget for the first time, giving scienti……..”””””
Joe Chang says: January 5, 2014 at 5:01 am
“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?”
Joe, you might want to check out this paper: http://academic.research.microsoft.com/Publication/6170526/global-mapping-of-terrestrial-primary-productivity-and-light-use-efficiency-with-a-process-based model. In the abstract, it says ” Gross photosynthetic production (GPP), net primary production (NPP), carbon storage, absorption of photosynthetically active radiation (APAR), and light-use efficiency (LUE) were addressed. Assuming an equilibrium state under the present environmental conditions, Sim-CYCLE estimated the annual global GPP and NPP as 124.7 and 60.4 Pg C yr-1, respectively. Based on the estimated APAR of 191.3 × 1021 J, the annual average biospheric LUEs for GPP and NPP were calculated as 0.652 and 0.315 g C MJ-1, respectively.”
I have seen higher gross numbers, GPP, up to 160 Pg C yr-1 in a study that looked at oxygen isotoypes and compared them. With these numbers you are looking at .315 x 191.3 x 10^21 joules, which is 6 x 10^22 joules per year.
5 W/m^2? Isn’t that about the difference between the older estimates of the absolute value of TSI at 1 AU and the newer ones? 1366 versus 1361. What’s CERES’s estimate for incoming solar?
John Finn says:
January 5, 2014 at 4:10 am
As long as there is an imbalance between the measurements of incoming and outgoing energy, this creates a trend in the net energy storage. As a result a 5 W/m2 imbalance gives a huge, unimaginably large trend.
w.