An important new paper published today in Global and Planetary Change finds that changes in CO2 follow rather than lead global air surface temperature and that “CO2 released from use of fossil fuels have little influence on the observed changes in the amount of atmospheric CO2” The paper finds the “overall global temperature change sequence of events appears to be from 1) the ocean surface to 2) the land surface to 3) the lower troposphere,” in other words, the opposite of claims by global warming alarmists that CO2 in the atmosphere drives land and ocean temperatures. Instead, just as in the ice cores, CO2 levels are found to be a lagging effect ocean warming, not significantly related to man-made emissions, and not the driver of warming. Prior research has shown infrared radiation from greenhouse gases is incapable of warming the oceans, only shortwave radiation from the Sun is capable of penetrating and heating the oceans and thereby driving global surface temperatures.
The highlights of the paper are:
► The overall global temperature change sequence of events appears to be from 1) the ocean surface to 2) the land surface to 3) the lower troposphere.
► Changes in global atmospheric CO2 are lagging about 11–12 months behind changes in global sea surface temperature.
► Changes in global atmospheric CO2 are lagging 9.5-10 months behind changes in global air surface temperature.
► Changes in global atmospheric CO2 are lagging about 9 months behind changes in global lower troposphere temperature.
► Changes in ocean temperatures appear to explain a substantial part of the observed changes in atmospheric CO2 since January 1980.
► CO2 released from use of fossil fuels have little influence on the observed changes in the amount of atmospheric CO2, and changes in atmospheric CO2 are not tracking changes in human emissions.
The paper:
The phase relation between atmospheric carbon dioxide and global temperature
- a Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, N-0316 Oslo, Norway
- b Department of Geology, University Centre in Svalbard (UNIS), P.O. Box 156, N-9171 Longyearbyen, Svalbard, Norway
- c Telenor Norway, Finance, N-1331 Fornebu, Norway
- d Department of Physics and Technology, University of Tromsø, N-9037 Tromsø, Norway
Abstract
Using data series on atmospheric carbon dioxide and global temperatures we investigate the phase relation (leads/lags) between these for the period January 1980 to December 2011. Ice cores show atmospheric CO2 variations to lag behind atmospheric temperature changes on a century to millennium scale, but modern temperature is expected to lag changes in atmospheric CO2, as the atmospheric temperature increase since about 1975 generally is assumed to be caused by the modern increase in CO2. In our analysis we use eight well-known datasets; 1) globally averaged well-mixed marine boundary layer CO2 data, 2) HadCRUT3 surface air temperature data, 3) GISS surface air temperature data, 4) NCDC surface air temperature data, 5) HadSST2 sea surface data, 6) UAH lower troposphere temperature data series, 7) CDIAC data on release of anthropogene CO2, and 8) GWP data on volcanic eruptions. Annual cycles are present in all datasets except 7) and 8), and to remove the influence of these we analyze 12-month averaged data. We find a high degree of co-variation between all data series except 7) and 8), but with changes in CO2 always lagging changes in temperature. The maximum positive correlation between CO2 and temperature is found for CO2 lagging 11–12 months in relation to global sea surface temperature, 9.5-10 months to global surface air temperature, and about 9 months to global lower troposphere temperature. The correlation between changes in ocean temperatures and atmospheric CO2 is high, but do not explain all observed changes.
Tim Folkerts says:
September 11, 2012 at 7:17 pm
2) Here is the smoothed derivative of CO2 vs time.
http://www.woodfortrees.org/plot/esrl-co2/derivative/mean:12/from:1979/plot/uah/scale:0.25/plot/none
By averaging over 12 month, the intra-annual changes disappear, leaving only the interannual changes.
Perhaps the single most important feature is that the smoothed derivative is ALWAYS POSITIVE. That means that year-in and year-out, the CO2 is sloping up. There has never (in the data available) been a 12 month period where the CO2 has dropped.
You are, of course, correct. However, Bart would disagree with you. Bart, you see, has developed a ‘model’ (no sniggering, please). The dominant term for the prediction of CO2 growth in Bart’s model is as follows
k*(T-To)
Where (I assume) T is the temperature; To is some arbitrary baseline temperature (apparently ~0.6 deg below current temps) and k is a constant. Bart also includes a term representing a slow CO2 decay in his model, so in the case where T=To CO2 levels would actually fall ….very slowly.
But it is the value of T which is key to the ‘success’ of his model since as long as T is greater than To the model predicts that CO2 levels will grow and since temperatures have, since 1958, been higher than To, Bart’s on to a winner. You might note that there is a certain circular logic in the selection of To. Basically, providing To is below any temperature since 1958, it’s pretty easy to fix it so his model works.
In fact the value To is essentially determined using the assumption that the model works.
So to summarise with an example if T=To+0.5, say, and that this temperature results in a 2ppm rise in CO2 levels then Bart’s model predicts a 2ppm for every year that T remains at that level. If it remains constant at To+0.5 for 10 years then CO2 will rise by 2ppm or 20ppm in total – regardless of human CO2 emissions (even if they were ZERO) .
As Ferdinand Engelbeen has expertly pointed out Bart’s model flies in the face of all the available evidence. In particular
(i) The CO2 response to temperature is fairly immediate, i.e. it is fully realised within a couple of years.
(ii) His model fails for virtually every other period in earth’s history, e.g. 1000-1850, ice ages, pre-glacials, post-glacials … you name it and the model turns out to be a crock.
Bart gets round these minor inconveniences in 2 ways, i.e. he denies the validity of contradictory evidence or he introduces ‘regime changes’. Tactics all mathematical modellers should learn to adopt.
Have fun!
Ferdinand Engelbeen says:
September 12, 2012 at 12:30 am
“Depends of the factors involved: if the response of CO2 levels to temperature is modest and the response of temperature to CO2 levels is modest, the mutual increase is modest too.”
Then you have “severely attenuated in some fashion” the positive feedback. Show your equations.
John Finn says:
September 12, 2012 at 3:51 am
I’m not “getting around” anything. I am adhering to what the data tell us. Reality is not open to negotiation. If you can’t face it or understand it, that is your problem.
John Finn says:
September 12, 2012 at 12:43 am
“We know that CO2 levels have remained in a narrow range during the 800 or so years before ~1900 despite temperature fluctuations over that time. But let me guess …… you don’t believe ice core data. How convenient!”
I do not know what conditions prevailed outside the interval of modern observation. You do not know what conditions prevailed outside the interval of modern observation. The data from the ice cores is tenuous at best – there are no means to verify them. But, if they are accurate, it just means a different relationship prevailed during those times. Regime changes in climate are not at all rare.
But, we do not need to resolve those issues to know what has been the prevailing dynamic since at least 1958, the era in which we have the best, most modern, and most direct measurements. And, inter alia, the era in which CO2 measurements have indicated the lion’s share of the rise in atmospheric CO2.
Your arguments are consistently woven from logical fallicies, in this case, the argument from incredulity. You seem to think that telling us how you cannot imagine something being the case reflects poorly on the argument, rather than on your own willfully enforced inability to think outside the box.
Bart says:
September 12, 2012 at 9:24 am
Then you have “severely attenuated in some fashion” the positive feedback. Show your equations.
Forgot where I placed the calculations, but as far as I can see, the CO2 “gain” was 0.8 and the temperature gain with feedback was 0.1. Further, the no-runaway overall gain is maximum 1, not 2…
See further: http://en.wikipedia.org/wiki/Positive_feedback#Basic_positive_feedback
A rebuttal appears at RealClimate:
http://www.realclimate.org/index.php?p=13053
Bart says:
September 12, 2012 at 9:51 am
I do not know what conditions prevailed outside the interval of modern observation.
There is not a shred of evidence that the current conditions of temperature or any other climatic factor are outside whatever the conditions were in the past 800,000 years. Temperatures were higher in many periods, including trees growing up to the Arctic Ocean in the previous interglacial, 100 kyr ago at average 2°C higher than today, up to 5-10°C in Alaska and Siberia, where only tundra grows today. Temperature was certainly higher in the Holocene Optimum, 6000-7000 years ago, during the warm Roman period and probably during the MWP.
Despite the 5000 years much warmer Eemian, the CO2 levels were around 300 ppmv, measured in ice cores with a resolution of 500-600 years (and shorter), with an incredible linear ratio of 8 ppmv/°C. The influence of (T-To) was unmeasurably small in that period. Neither was it any higher than near zero during the whole Holocene. But suddenly the current climatic conditions deliver a constant increasing increase in CO2 only based on a few tenths of a degree difference with a completely arbitrary baseline that fits the curve.
There is indeed one condition where the current period importantly differs with any other period in the 800 kyear past: the ever increasing release of fossil fuel CO2.
John Finn says:
September 12, 2012 at 3:51 am
Bart gets round these minor inconveniences in 2 ways, i.e. he denies the validity of contradictory evidence or he introduces ‘regime changes’. Tactics all mathematical modellers should learn to adopt.
Indeed, someone interested in science would acknowledge that even only one contrary observation would invalidate his model and look for a better explanation. Others stay with their model at all costs. Not the privilege of the Mannians in this world I am afraid.
Ferdinand Engelbeen says:
September 12, 2012 at 12:55 pm
“Indeed, someone interested in science would acknowledge that even only one contrary observation would invalidate his model and look for a better explanation.”
And yet, you deny the contrary evidence of the temperature-CO2 rate of change relationship.
Your “contrary evidence” is all open to alternative explanations. This evidence is not.
v25721 says:
September 12, 2012 at 12:30 pm
Fails to address the argument.
Ferdinand Engelbeen says:
September 12, 2012 at 12:20 pm
“Further, the no-runaway overall gain is maximum 1, not 2…”
In a discrete time system description, the dividing line between positive and negative feedback is unity gain. The terms “positive” and “negative” in terms of feedback are referenced to analog systems theory. Feedbacks in a discrete time equivalent system are mapped through the exponential function, fd = exp(f*Ts), where f is the feedback for the analog system, fd is that for the discrete equivalent, and Ts is the sample period.
So, you are merely repeating the truism that positive feedback is destabilizing, while negative feedback is stabilizing.
One important detail to recognize is that the comparison in the graphs like http://www.woodfortrees.org/plot/esrl-co2/derivative/mean:24/scale:5/offset:-0.4/plot/gistemp/from:1959 is between temperature and the AVERAGE derivative of CO2 concentration.
The average is taken for 12 months BEFORE and12 months AFTER. In other words, if you want to use the CO2 to predict the temperature in January 2010, you need to include CO2 information from Jan 2009 – Jan 2011. You are including FUTURE CO2 levels for the prediction of PAST temperature. That calls into question the cause/effect argument on its head.
Of course, a more detailed analysis might show the actual cause/effect relationship, but a quick glance at the two graphs is insufficient.
For those following the discussion this far, I recommend this an analysis:
http://troyca.wordpress.com/2012/08/31/comment-on-the-phase-relation-between-atmospheric-carbon-dioxide-and-global-temperature/
A couple excerpts:
Tim Folkerts says:
September 12, 2012 at 5:08 pm
This is just an ordinary filtering operation, with the phase delay taken out. And, since it is a plot of the derivative of CO2 versus temperature, you can be assured of the direction of causality, because there is a 90 deg phase lag introduced when you integrate. If it makes you feel better, use a 12 month average instead of 24.
tjfolkerts says:
September 12, 2012 at 5:18 pm
Meh. He made up a simple 1-box model so that some of the CO2 output would be proportional to temperature with a delay, and proved it behaved as he set it up to behave. It’s nothing like the real world, where the CO2 derivative is proportional to the re-baselined temperature anomaly, including all the variation as well as the slope.
Again, this is why the human attribution is falsified. These conditions are not replicated in his model.
Bart says: “Meh. He made up a simple 1-box model so that some of the CO2 output would be proportional to temperature with a delay, and proved it behaved as he set it up to behave. It’s nothing like the real world, where the CO2 derivative is proportional to the re-baselined temperature anomaly, including all the variation as well as the slope.”
Bart, I think you missed two important facts.
” … where the CO2 derivative is proportional to the re-baselined temperature anomaly ..”
“Re-baselining” means you are throwing out a constant term. There is a positive term in the CO2, which means that most of the CO2 change is due to humans. Only a small change is due to response to the temperature changes (and this fact has been know since long before this analysis).
“He made up a simple 1-box model so that some of the CO2 output would be proportional to temperature with a delay, and proved it behaved as he set it up to behave. “
This concern was specifically addressed in the comments.
In key ways, this is akin to Steve McIntyre’s critique of the “hockey stick”. He showed that – even with “fake” data, the TECHNIQUE itself seemed to create the hockey stick shape, showing that the technique itself was flawed. Here we have “fake “data that similarly shows that the technique itself creates a “signal” even when it is known that the signal is completely fake.
tjfolkerts says:
September 12, 2012 at 8:31 pm
““Re-baselining” means you are throwing out a constant term. There is a positive term in the CO2, which means that most of the CO2 change is due to humans. “
Honestly, have you read what I have written? It’s not doing you or me any good if you just ignore my responses and repeat the same cant.
The baseline is arbitrary in any case, and has to be determined. Human inputs cannot provide the baseline because they do not represent a constant term, and that means there is no room for them.
<i<"This concern was specifically addressed in the comments."
It isn’t addressed. He did not test a real-world scenario, as I explained. Read.
““Re-baselining” means you are throwing out a constant term.”
At the very least, as a first step, recognize that it is not throwing out anything. There is nothing there to throw out. The temperature anomaly is an anomaly because it has a baseline already chosen for it which, for the purposes of modeling the physical process, is entirely arbitrary.
Here is a plot of emissions data. Do they look like they have a constant rate (top plot) to you? That’s all there is room for. There is only one viable conclusion: human inputs are being rapidly sequestered, and have insignificant overall effect.
Here is a plot of emissions data. Do they look like they have a constant rate (top plot) to you?
No they look like they’ve been increasing at a rate which is a constant multiple of some other variable …. can’t quite remember what it is now Anyway it’s shown in this plot here
http://www.drroyspencer.com/wp-content/uploads/engelbeen-3.jpg
Oh yeah – that’s it – got it now. It’s atmospheric CO2 levels. Quite clearly increasing CO2 concentrations are causing us humans to step up our emissions. It must be that because Bart reckons it can’t possibly be the other way round. Mind that R2 figure looks a bit unconvincing.
Bart says:
September 12, 2012 at 10:02 pm
Here is a plot of emissions data. Do they look like they have a constant rate (top plot) to you? That’s all there is room for. There is only one viable conclusion: human inputs are being rapidly sequestered, and have insignificant overall effect.
Bart, here is a plot of the increase in the atmosphere compared to the accumulated emissions and the temperature trend:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/temp_co2_acc_1900_2004.jpg
Thus if you compare the accumulated emissions with the integrated rate of change, the accumulation in the atmosphere, it is a near perfect fit:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/acc_co2_1900_2004.jpg
If you don’t trust the ice core CO2 measurements: the fit over 1960-2004 is also near perfect:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/acc_co2_1960_2006.jpg
even if you look at different CO2 measurement stations.
The correlation between temperature and accumulation in the atmosphere is of another order:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/temp_co2_1900_2004.jpg
It looks like that different processes are responsible for the huge variations in temperature with little effect on CO2 levels and a much slower process that is responsible for the longer term increase.
That is the whole essence of the discussion: your “model” assumes that the same process is responsible for both the short term variability and the decadal trend.
Even for the CO2 response on temperature, that is not the case. There is world of difference between the fast response of the oceans surface and vegetation to any temperature (and precipitation) change and the much slower responses by deep ocean exchanges and more permanent storage of carbon on land or even very much slower by precipitation and rock weathering. It is not one process at work, but a multitude of processes, each with its own response time.
You own reaction to my remark of where the 70/100 ppmv extra natural CO2 was coming from was: probably from the deep oceans, originating from the CO2/temperature levels of 800 years ago. But that is a different process, completely independent from the one that causes the fast changes in rate of change.
Thus there is no reason at all that the same factor is involved in both the fast responses as in the increase over time. Thus the observed increase over time can be as well as from deep ocean upwelling, volcanic releases or any other extra release from a natural process as from the human emissions.
But the human emissions fit all available observations, while every extra natural release does violate one or more observations. Add that to the near perfect fit of the increase in the atmosphere with the increasing release by humans…
Bart says:
September 12, 2012 at 2:08 pm
In a discrete time system description, the dividing line between positive and negative feedback is unity gain. The terms “positive” and “negative” in terms of feedback are referenced to analog systems theory.
Sorry, I am an old guy from the analog times, thus I still use positive and negative feedbacks as in the good old times and so do most people not directly involved in process control…
The discussion was if there could be a positive feedback of CO2 on temperature without a runaway effect, if there is a positive feedback of temperature on CO2 levels. That is what was questioned. The answer is yes in the analog definition, as long as the overall gain factor is less than 1…
Ferdinand:
As you know, you and I strongly disagree about most things in this subject, so I take this opportunity to agree and support a comment you have made.
As you say, at September 13, 2012 at 5:22 am, a positive feedback with gain factor less than 1 does not provide a runaway effect. This is why a singer using a microphone can be in the same room as the amplified sound of his voice so long as he does not get too close to the speaker emitting the amplified sound.
Richard
Ferdinand Engelbeen says:
September 13, 2012 at 5:11 am
“Thus if you compare the accumulated emissions with the integrated rate of change, the accumulation in the atmosphere, it is a near perfect fit:”
It isn’t a perfect fit. It requires a bias and a scale factor. And, when you have integrated signals with all of the higher frequency information removed leaving only low order polynomials, matching them in such a way is a coin toss.
However, matching the integrated temperatue anomaly to CO2 is also a “perfect” fit, but it also matches in all the fine detail, which is far more difficult and unlikely to prove to be a false correlation.
“The correlation between temperature and accumulation in the atmosphere is of another order:”
The correlation is between temperature and CO2 rate of change. And, it is “near perfect”.
“Even for the CO2 response on temperature, that is not the case.”
You do not know any such thing about the dominant effect of temperature on CO2, you are merely asserting it. And, your assertion disagrees with the evidence.
Ferdinand Engelbeen says:
September 13, 2012 at 5:22 am
An overall net positive feedback results in instability, no matter the size, which only determines how long it takes to build. It is a basic equivalence relationship. So, either you are interpreting things incorrectly, or you have the positive feedback embedded within a dominant overall negative feedback loop, i.e., you have “severely attenuated in some fashion” the positive feedback.
richardscourtney says:
September 13, 2012 at 5:52 am
“This is why a singer using a microphone can be in the same room as the amplified sound of his voice so long as he does not get too close to the speaker emitting the amplified sound.”
That is because it is a positive feedback embedded within a greater negative feedback loop. Until he gets too close to the microphone, the positive feedback is attenuated enough that it does not result in a liimit cycle.
Go back to the originating comment
What I stated is true. There are processes embedded which assure that the CO2 to temperature effect is severely attenuated. If there were not, we would have a runaway effect. Thus, the data we have been examining not only falsify the driving effect of humans on CO2, but they also falsify catastrophic anthropogenic global warming. The entire contretemps is an unmitigated fiasco from start to finish. The only questions are, how long will it take humankind to recognize it, and how much damage will it do to the reputation of science when it becomes apparent?
Bart says:
September 12, 2012 at 9:51 pm
tjfolkerts says:
September 12, 2012 at 8:31 pm
““Re-baselining” means you are throwing out a constant term. There is a positive term in the CO2, which means that most of the CO2 change is due to humans. “
Honestly, have you read what I have written? It’s not doing you or me any good if you just ignore my responses and repeat the same cant.
Advice you should take! I see no indication that you paid any attention to Henry’s law and the van’t Hoff equation which govern the process we’re talking about.
Phil. says:
September 13, 2012 at 10:12 am
I don’t bend the data to suit the hypothesis, I bend the hypothesis to suit the data. The data are paramount. They trump any back-of-the-envelope expectations of what might happen under a constrained set of assumptions. The interface between the sea surface and the atmosphere is a lot more complicated than these first order, laboratory formulas, and you are missing the forest for the trees.
Bart says:
September 13, 2012 at 9:36 am
However, matching the integrated temperatue anomaly to CO2 is also a “perfect” fit, but it also matches in all the fine detail, which is far more difficult and unlikely to prove to be a false correlation.
The fine detail matches, no matter if temperature is responsible for the details only or for details and trend alike. If different processes are responsible for the fast variability than for the trend, then different factors are involved, but still the same correlation will be found.
Further, there is no match of the trend in any other period of time than the current one, not even for the period 1900-1960, for which we have reasonable accurate, but (8 years) smoothed data:
http://www.ferdinand-engelbeen.be/klimaat/klim_img/co2_T_dT_em_1900_2005.jpg
Of course, your details match better than mine, but that can be solved: if the oceans surface + fast processes in vegetation are the main cause of the year by year variability, the total response to temperature changes is limited to 1-3 years, thus matching the details but without much effect on the trend. The trend then is caused by another variable (no matter if that is deep ocean exchanges, volcanoes or human emissions…
The correlation is between temperature and CO2 rate of change. And, it is “near perfect”.
As you know, by looking at the rate of change, you are attenuating the variability. The whole discussion is about the effect of the increase in the atmosphere, not about the variability of the year by year increase rate. And for the increase in the atmosphere, the “match” is not so perfect, compared to the match with the emissions (which doesn’t need a bias, only a factor; to match temperature you need both and it is the bias which makes the match in trend). A huge variability in temperature has only a small effect on the CO2 increase, but the overall temperature increase, only 2-3 times the short time variability, would be responsible for the total increase in CO2. That looks like two separate processes at work…
An overall net positive feedback results in instability
Not in the “old” definition.
From Wiki:
If the functions A and B are linear and AB is smaller than unity, then the overall system gain from the input to output is finite, but can be very large as AB approaches unity.[9] In that case, it can be shown that the overall or “closed loop” gain from input to output is:
Gc = A/(1-AB)
When AB > 1, the system is unstable, so does not have a well-defined gain; the gain may be called infinite.
You can have a gain of 0.8 for CO2 from temperature, and temperature can have a gain of 0.2 from the increase in CO2 that leads to an overall gain of:
Gc = 0.8/(1-0.8*0.2) = 0.95
thus an overall net positive feedback without a runaway reaction…
Bart
What are the values of k and Tau by the way?
Ferdinand Engelbeen says:
September 13, 2012 at 11:48 am
“The fine detail matches, no matter if temperature is responsible for the details only or for details and trend alike.”
You can’t break it up like that. The response is smooth across all frequencies. You take all, or nothing, and nothing is not a viable alternative.
“Further, there is no match of the trend in any other period of time than the current one, not even for the period 1900-1960, for which we have reasonable accurate, but (8 years) smoothed data:”
Already showed you were wrong.
“Of course, your details match better than mine, but that can be solved: if the oceans surface + fast processes in vegetation are the main cause of the year by year variability, the total response to temperature changes is limited to 1-3 years, thus matching the details but without much effect on the trend.”
Not allowed by the data. The response to temperature is smooth all the way down to the lowest frequencies observable.
“And for the increase in the atmosphere, the “match” is not so perfect, compared to the match with the emissions…”
The data are not perfect. This is an excellent match. And, it would be a better match with the more accurate satellite record. Note the regime change after Pinatubo.
When you are dealing with uncertain data, it is actually suspicious when you get too “perfect” a match. It suggests the record may have been “adjusted” to give a better fit.
“…which doesn’t need a bias, only a factor…”
If you choose not to choose, you still have made a choice. To match up the data prior to 1958, you have to use the proxy data, which have been calibrated and time shifted in order to give you a nice, pat little narrative, i.e., the deck has been stacked.
“…to match temperature you need both and it is the bias which makes the match in trend…”
But, not the curvature, and that is the key element which obviates significant human influence.
“Not in the “old” definition.”
When AB has magnitude less than one, you have net overall negative feedback. I was precise in what I stated.
Consider the following simple example. Suppose we have the relationship
dCO2/dt = k*(T – To)
and
dT/dt = -a*T + b*CO2 + f
where a and b are constants, and f is solar forcing. If b is any value greater than zero, this system is unstable. We can stabilize it by adding in an auxilliary variable, say x, such that
dx/dt = g*T
and subtracting it out
dT/dt = -a*T + b*CO2 – c*x + f
Then, the system is stable if b*k is less than c*g (and, of course, a is greater than zero). The action of x counteracts the effect of CO2, and it therefore has a small overall effect on temperature. x could be, for example, the effect of clouds. Of course, it might have an additional term to limit it, e.g.,
dx/dt = -x/tau + g*T
in which case, the system would be stable if other conditions hold. But, it cannot be stable without an additional reaction against the CO2 increase of temperature.
All, will be travelling for some time – without much Internet underway, so that will be it for me this round. Up to the next round…