Guest Post by Willis Eschenbach
I’ve been thinking about the Argo floats and the data they’ve collected. There are about 4,000 Argo floats in the ocean. Most of the time they are asleep, a thousand metres below the surface. Every 10 days they wake up and slowly rise to the surface, taking temperature measurements as they go. When they reach the surface, they radio their data back to headquarters, slip beneath the waves, sink down to a thousand metres and go back to sleep …
At this point, we have decent Argo data since about 2005. I’m using the Argo dataset 2005-2012, which has been gridded. Here, to open the bidding, are the ocean surface temperatures for the period.
Figure 1. Oceanic surface temperatures, 2005-2012. Argo data.
Dang, I like that … so what else can the Argo data show us?
Well, it can show us the changes in the average temperature down to 2000 metres. Figure 2 shows that result:
Figure 2. Average temperature, surface down to 2,000 metres depth. Temperatures are volume-weighted.
The average temperature of the top 2000 metres is six degrees C (43°F). Chilly.
We can also take a look at how much the ocean has warmed and cooled, and where. Here are the trends in the surface temperature:
Figure 3. Decadal change in ocean surface temperatures.
Once again we see the surprising stability of the system. Some areas of the ocean have warmed at 2° per decade, some have cooled at -1.5° per decade. But overall? The warming is trivially small, 0.03°C per decade.
Next, here is the corresponding map for the average temperatures down to 2,000 metres:
Figure 4. Decadal change in average temperatures 0—2000 metres. Temperatures are volume-averaged.
Note that although the amounts of the changes are smaller, the trends at the surface are geographically similar to the trends down to 2000 metres.
Figure 5 shows the global average trends in the top 2,000 metres of the ocean. I have expressed the changes in another unit, 10^22 joules, rather than in °C, to show it as variations in ocean heat content.
Figure 5. Global ocean heat content anomaly (10^22 joules). Same data as in Figure 4, expressed in different units.
The trend in this data (6.9 ± 0.6 e+22 joules per decade) agrees quite well with the trend in the Levitus OHC data, which is about 7.4 ± 0.8 e+22 joules per decade.
Anyhow, that’s the state of play so far. The top two kilometers of the ocean are warming at 0.02°C per decade … can’t say I’m worried by that. More to come, unless I get distracted by … oooh, shiny!
Regards,
w.
SAME OLD: If you disagree with something I or anyone said, please quote it exactly, so we can all be clear on exactly what you object to.
Ulric Lyons said:
“Because stronger trade winds means La Nina, and that is during positive AO, which is when the jet is more poleward.”
I can’t go along with your implication that El Nina or El Nino conditions determine the state of AO.
It seems clear that AO responds to the top down solar effect as well as to the bottom up oceanic effect.
mickyhcorbett75 says:
March 2, 2014 at 11:01 am
—————————————
Very nice explanation.
george e smith says:
March 2, 2014 at 9:56 pm
“Couldn’t possibly be that northern winters occur around perihelion, while southern winters occur at aphelion, and are therefore colder and longer ??
Nah ! Can’t be that easy; must be some other reason.”
Sure, makes sense and is a convenient explanation – but is it necessarily true? What if, on larger time scales that behavior alternates? What could be learned by posing and seeking an answer to the question?
It would seem to me that if your explanation were correct, over time the NH would experience an accumulating ‘heat surplus’ (for lack of a better term), and that it would likely lead to some sort of “flow”, perhaps Pole-To-Pole.
I’m just being inquisitive.
A suggestion that these time lapse graphs make it hard to pick up on January each year (for example) so the eye can lock in with seasons.
Could a sound be added, like a voice saying 09 10 11 …. each time a new year arrives?
Ecen a dot at the side that comes on in January would be a great help.
BTW, I can’t see how the top animated gifs are consistent with the trend maps for the sea off West Australia. My eye cannot see a change on the blink graphs with colours at 6 deg separation, yet there is supposed to be a trend there of about 1.4 – 2 deg in the surface data. Can’t see it. Must be my eyeballing getting bad.
Willis Eschenbach says:
March 2, 2014 at 3:37 pm
“Actually, a definite integral and an average are very, very closely related measures….
…In other words, the average is the integral divided by n … it’s not crucial, just sayin’ …”
Thank you Willis. That was the point I was making. In fact, depending on the sampling rate used, an average is a very close approximation to the true of a curve Integral over the same time period (divided by ‘n’ as you correctly observed – but that is just a scaling issue).
EDIT: …true Integral of a curve…
Damn fingers….. Behind my brain as usual.
george e. smith says:
March 2, 2014 at 12:36 pm
“Why all this sudden interest in the Nyquist sampling theorem ?”
Because to go from a point sampling instrument (in either time or space) to a field that in-fills between those instrument readings requires that you honour Nyquist?
As almost all our instruments are not continuously recording, full area coverage, ones – then Nyquist is being assumed to have been met all the time (pun) without that necessarily being so.
You can observe an instrument has changed its readings over time (when compared to itself) and be without challenge. To suggest that a field that the instrument is sampling has changed with the same/similar parameters over the same time span is just a bit more difficult (to get accurate anyway).
Is the heat in the atmosphere even a significant portion of the heat of earth’s surface? If I recall my eighth grade science correctly, the weight of the atmosphere amounts to the weight of thirty-three feet of fresh water. These Argo floats are measuring two thousand meters of ocean. The weight of the atmosphere represents just half a percent of what Argo is measuring.
Stephen Wilde says:
“I can’t go along with your implication that El Nina or El Nino conditions determine the state of AO”
It is well established that the trade winds tend to be stronger during more positive AO conditions, that means La Nina with a more northerly jet stream, and not El Nino as you propose. I did not imply which determines which.
So cool reading buoys were excluded because they affected the result to such an extent that they could not account for the sea level changes. Hmmm, we have been able to measure temperature accurately for many years now. On the other hand the sea level data is very suspect. The tide gauges have all sorts of problems but collectively give a sea level rise only on half of that shown by the satellites. But then NASA has admitted that there are serious problems with the altitude measurements from the present satellite as it has no Earth reference point by which it can calibrate to account for changes in the atmospheric layers the radiation passes through.
I would have thought that temperature was the accurate measure and question the accuracy of the sea level data, rather than the other way around.
Were any of these “cool reading” buoys ever retrieved and tested ?
I’ve been nagged since previously reading this. So, the oceans have warmed without pause while the lower atmosphere has paused? What does that mean? The science is only the first of my requirements to trust anyone who wants to “do something” about “catastrophic global warming,” but it seems there is rather less of a pause and more of a shift?
Willis:
Any chance of a look at the code for this? I assume it is in R. If so, did you use ‘maptools’ or something else for the plot?
per Ed,Mr Jones
“”””””…..Sure, makes sense and is a convenient explanation – but is it necessarily true? What if, on larger time scales that behavior alternates? What could be learned by posing and seeking an answer to the question?…..”””””
Ed, I don’t have the foggiest idea, if it is true, or not, although I presume it is one or the other.
When I went to school, it was called “problem solving.” You took the information you had, been given, and you applied logical thinking, bounded by the laws of nature, to try and rationally explain, a possible mechanism for the results. It was then a task, either for oneself or others, to devise and perform experiments to test the thesis for validity.
You asked the question, and cited your observation of the apparent behavior. I didn’t even check to see if I agreed with your observations (still haven’t). I merely took what I know of the system (earth’s elliptical orbit), to concoct a plausible mechanism. It is well known that earth has a very large north/south asymmetry, in many ways. The orbit, is a potential cause for many of these.
But I don’t know.
Well it is said; “you can lead a horse to water, but you can’t make him drink.”
So I opined, that “Climate” is the integral of “weather” ; integral, being a simple summation of discrete values or events, or a continuous summation of infinitesimals.
Willis E. assisted by Richard LH disagreed with that, and asserted that an integral (climate) is just n times the “average” of the weather. Well they actually said you divide the integral of the “weather” by n to get the average., which is the “climate”.
Well I’m getting a little rusty these days, so does anybody have a reference for a reputable graph of “weather” plotted versus time, that is for a long enough time, to qualify as “climate”.
Just a simple graph; time on the x-axis, and “weather” on the y-axis, so I can try to approximately integrate it, using Simpson’s rule or something to get a reasonably good value for the “climate”.
I thought it would be simple to understand; weather events happen, a tornado over there tears down a shopping center, a heavy rainstorm over there, washes a hill into a river and changes its course, a hurricane over there, rips up some shallow bottom, and seals off a current path, to re-route a major tidal current; and so on. It seems trivial to add up all these disparate events, and see an overall reshaping of everything; but I’m having a hard time plotting it on a graph to figure out the area. How do you average a tornado, and a heat wave ??
Willis,
Is the 4000 Argo float data homogenous?
Ferd….
Your conspiracy doesn’t make sense. The ARGO devices were each calibrated in the lab for accuracy before they were deployed. They were first deployed in 2003. They achieved the target of 3000 operational units in 2007. Each device had, at the original time of the launch in 03, a projected 4 year life span. Some of the devices were replaced in 2007 because of a glitch, which produced a strong cooling trend in the data….
But! Even after the data from the suspect units was removed, there was STILL a slight cooling trend as of July 2008. Here is what he said at the time:
“There has been a very slight cooling, but not anything really significant”.
That’s from March 2008, after the data had been scrubbed.
So it doesn’t seem like Dr. Willis was trying to scrub the cooling, or else it would have been completely eliminated.
Now, keep in mind that the ARGO units were at the time coming to the end of their projected lifespan. It is quite possible that the early units, as they aged, did indeed deviate from accuracy. It’s quite possible that the error would indeed be dominantly in one direction, especially if the thermometers had the same defect. I work in the spa repair industry, and have spent many years replacing electronic temp sensors that have fallen out of calibration… Things break eventually. The typical failure of the temp sensors that I’ve seen in almost 20 years in this career is for the faulty device to measure water temps on the cool side, sending data to the control circuit saying the spa is say, 101, instead of the correct accurate temp of 104, thus causing the spa to get too hot and tripping the high temp safety circuit and shutting down the spa. That is much much more common than the temp sensor reading too high.
Getting back to ARGO. Now, those units that were found to be inaccurate were replaced. But they would have been replaced eventually anyway because of the inherent lifespan of the first generation devices. So it’s not as if those specific devices would still be in operation now. Further, if the devices were not faulty, and they only scrubbed the data, and the oceans were indeed still cooling, the newer ARGO devices would STILL be detecting a cooling trend, and since the data has not been scrubbed since the 2008 series, that trend would STILL show up.
Since you made an assumption here, I’m going to make one of my own. I haven’t read the details of exactly how many devices were discarded or reprogrammed in the 2008 purging. But I’d bet top dollar that one of the determining factors was to subject each device to a standard temperature check in a lab to see if the device indeed was still reading the accurate temperature. That is a very easy test to do, and if it fails calibration, then the data used from that device must be thrown out.
Lastly, I do find it interesting that many skeptics didn’t have much of a problem with the adjustments when they were fresh in 2008, but only now wish to discredit them when the results since the adjustments have made the “slight cooling” disappear.
http://www.abc.net.au/unleashed/28636.html
PS. I’m also a skeptic, but I don’t appreciate others using the term skeptic to throw around baseless accusations. It only give the rabid alarmists more ammo to use against us.
george e. smith says:
March 3, 2014 at 2:39 pm
“So I opined, that “Climate” is the integral of “weather” ; integral, being a simple summation of discrete values or events, or a continuous summation of infinitesimals.
Willis E. assisted by Richard LH disagreed with that, and asserted that an integral (climate) is just n times the “average” of the weather. ”
I think you have misunderstood what it was I said. I observed that an ‘average’ is the equivalent of a ‘step wise integral’ (i.e. counting the squares on a graph paper of the area under a curve over a given time period).
I agree that over longer periods of time Weather will blur into climate., indeed I consider 15 years to be long enough to split the available data into two bins, Climate (i.e. greater than 15 years), and Weather, Seasons, Annual, etc. (i.e. less than 15 years).
This seems to be a workable and useful distinction.
It produces outcomes such as
http://climatedatablog.wordpress.com/combined/
where some order can be observed in what is happening.
ChrisQ says:
March 2, 2014 at 7:32 am
“From what I remember reading, argo floats have a resolution of 0.005C, which is by far the most accurate system for ocean temp measurement ever.”
Resolution is not the same as accuracy. Let’s say the temp is 10C but I measure it as 11C. It then changes to 10.005C and I measure 11.005C. I have a resolution of 0.005C but an accuracy of 1C.
I agree also with rgbatduke’s comments (March 2, 2014 at 7:02 am) re error bars. As always, no measurement uncertainty quoted. I also wonder whether the effects of long term stability (aging) of the electronic equipment have been considered. I don’t expect someone calibrates the floats every year 🙂
SteveP says:
March 4, 2014 at 1:51 am
“Resolution is not the same as accuracy.”
Indeed it is not.
If I have a water bath with a heater, a cooler and a stirrer in it then with just one high precision instrument I will not get close to a true picture of the contents of that bath. Depending on the distribution of the various factors I may require a sample for every 10’s cubic km before I get close to that and Argo is a LONG way from there right now.
It looks like this stakeout for the missing heat is turning up nothing.
Where do you think the Captain wants us to look next?
According to Argo the floats are accurate to +/-0.005C, which is pretty impressive IMO. See http://www.argo.ucsd.edu/FAQ.html. A company called Seabird manufacture some of the probes, namely the SBE41 (see http://www.seabird.com/products/spec_sheets/41data.htm). They claim long term stability of -0.002C over float life and repeatability of +/-0.001C (see http://www.seabird.com/technical_references/LongtermTSstabilityAGUDec08Handout2Pages.pdf).
“””””…..RichardLH says:
March 4, 2014 at 1:50 am
george e. smith says:
March 3, 2014 at 2:39 pm
“So I opined, that “Climate” is the integral of “weather” ; integral, being a simple summation of discrete values or events, or a continuous summation of infinitesimals.
Willis E. assisted by Richard LH disagreed with that, and asserted that an integral (climate) is just n times the “average” of the weather. ”
I think you have misunderstood what it was I said. I observed that an ‘average’ is the equivalent of a ‘step wise integral’ (i.e. counting the squares on a graph paper of the area under a curve over a given time period)……”””””
I haven’t misunderstood anything Richard; but both you and Willis have.
I asserted: “Climate is the integral of weather.” That is it.
You and Willis, want to count squares under a graph, or the equivalent of that.
So why don’t you show us a picture of a typical weather graph you would count the squares under.
I said not a word about Temperature, or rain fall, or wind speed, or cloud height, or hurricane names, or anything else. I said “weather.”, so show me a curve of weather, that you averaged to get climate.
Village Idiot says: @March 2, 2014 at 5:11 am
….I think the amount of heat stored could be described as “quite a lot”, especially as we’re being lead to believe that the Earth is undergoing a statistically significant and rapid cooling.
>>>>>>>>>>>>>>>>>
The question becomes what is driving the heat in the oceans? It certainly is not CO2.
GRAPH 1 and GRAPH 2
george e. smith says:
March 4, 2014 at 4:17 am
“so show me a curve of weather, that you averaged to get climate.”
The lowest resolution graphs I have time wise are for Annual as otherwise that signal totally dominates the picture, Obviously Seasonal and Weather are finer than that.
Last 34 years
http://climatedatablog.files.wordpress.com/2014/02/hadcrut-giss-rss-and-uah-global-annual-anomalies-aligned-1979-2013-with-gaussian-low-pass-and-savitzky-golay-15-year-filters-1979-on.png
Since 1800
http://climatedatablog.files.wordpress.com/2014/02/andersonmannammannloehlegisshadcrutrss-and-uah-global-annual-anomalies-aligned-1979-2013-with-gaussian-low-pass-and-savitzky-golay-15-year-filters1.png
Methodology
http://climatedatablog.wordpress.com/2014/02/19/first-post/