Guest Post by Willis Eschenbach
There’s a much-cited paper (129 citations) from 1994 called “On the Observed Near Cancellation between Longwave and Shortwave Cloud Forcing in Tropical Regions” by J. T. Kiehl (hereinafter Kiehl1994), available here. The paper makes the following claim (emphasis mine):
ABSTRACT
Observations based on Earth Radiation Budget Experiment (ERBE) satellite data indicate that there is a near cancellation between tropical longwave and shortwave cloud forcing in regions of deep convective activity. Cloud forcing depends on both cloud macrophysical properties (e.g., cloud amount, cloud height, etc.) and on microphysical properties (e.g., cloud particle size, particle shape, etc.). Hence, the near cancellation in the tropics could be due to either the macrophysical or the microphysical properties of these clouds, or a combination of these effects.
Now, to me that’s a pretty curious and surprising claim. The paper says that both in the Indonesian Region, as well as over the entire tropical Pacific, deep convective tropical clouds have no net effect on radiation, ostensibly because the longwave (positive) and reflected shortwave radiation (negative) cancel each other out. Let me call this the “cancellation hypothesis”. Kiehl supports the cancellation hypothesis inter alia with his Figure 1:
Figure 1. The first figure in Kiehl1994. The vertical axis shows shortwave cloud forcing, which is the amount of sunlight reflected by clouds in watts per square metre (W m-2). The horizontal axis shows the longwave cloud forcing, which is the change in top-of-atmosphere longwave forcing from the clouds. By convention, the reflected solar radiation is shown as negative, presumably because it is cooling the earth. Data is from April 1985.
And that looks pretty convincing … but, despite the 129 citations of the paper, I’m a suspicious fellow who believes firmly in the famous fallibility of experts. So I thought I’d use the CERES data and see if the cancellation hypothesis held up. Figure 2 shows that result, for the same Indonesian Region used by Kiehl.
Figure 2. Replication of the Kiehl study, using the ten-year April averages for the specified region to remove annual variations. Each square symbol represents a 1°x1° gridcell (N = 1500).
Instead of one single month’s data, I’ve used the averages of the ten Aprils in the CERES dataset.
Now, as you can see from Figure 2, the Kiehl hypothesis of cancellation has a big problem. The CERES results do not bear out Kiehl’s claims in the slightest. Instead, they support my hypothesis that increased tropical clouds cool the surface. As you can see, on average the loss from the reflected sunlight is about 20% or so greater than the gain from increased IR. This means that there is no cancellation. Instead, the clouds have a net cooling effect.
The paper goes on to say:
One feature of the cloud radiative forcing obtained from the Earth Radiation Budget Experiment ( ERBE) is the near cancellation between the longwave cloud forcing and the shortwave cloud forcing in tropical deep convective regions. This result was clearly shown by Kiehl and Ramanathan ( 1990) for the Indonesian convective region (here reproduced in Fig. 1 ). Further analysis of tropical deep convective regions of net cloud radiative forcing indicates that this is a ubiquitous feature that occurs over either ocean or land regions.
This is a more expansive claim than the one in the Abstract. Here the paper says that the cancellation happens over both land and ocean. So I thought I’d divide the Indonesian Region shown above into land and ocean regions. In addition, rather than average the months as in Figure 2, Figures 3 & 4 show all available April data for the entire time period. First, Figure 3 shows the land. I have colored the points by surface temperature of the gridcell.
Figure 3. As in Figure 2, but showing only the land. N=1,320.
Well, this shows that whatever might be happening in the Indonesian Region in the way of a linear relationship between reflected shortwave and longwave, it is definitely NOT happening over the land. The land shows little in the way of any relationship between shortwave and longwave.
Figure 4 shows the ocean data for the same Indonesian region.
Figure 4. As in Figure 2, but showing only the ocean. Note that the scale is slightly larger, to include all of the individual data. N=17,736
Well, this clarifies matters somewhat. First, about 40% of the land gridcells, but only about 10% of the ocean gridcells, have longwave cloud forcing greater than the shortwave forcing. Next, the land is pretty tightly clustered, with no apparent pattern. The ocean is different. Over the ocean the longwave is proportional to the shortwave … but it is a long ways from cancelling out. Instead, there’s a net cooling of -13.7 watts per square meter over the region. And the amount of cooling increases as the forcings increase. By the time the cloud reflections are up to 100 W m-2, the longwave is only up to 75 W m-2. That is a cooling from the clouds of about 25 W/m2, and not a cancellation under any meaning of the word.
In addition, there are a number of the warmest gridcells (red) which are on the left of the group (lots of reflection, little longwave).
Kiehl goes on to show his Figure 2, which compares the entire tropical Pacific region, from 10N 140E to 10S 90W. He shows a different kind of graph for this region, viz:
Figure 5. Kiehl Figure 2, showing the longwave and shortwave cloud forcing separately as functions of temperature.
Based on this graph, he makes the even more expansive claim that the cancellation of long-and shortwave radiation occurs across the Pacific, viz:
This figure illustrates that the cancellation between these two forcings occurs not only in the western tropical Pacific region of Fig. 1 but also across the entire tropical Pacific Ocean region. Even in regions of colder SSTs (298 K) the cancellation is apparent.
I found his style of graph in Figure 2 to be notably uninformative regarding the purported Pacific-wide cancellation. So I repeated his Figure 1 style of graph using the Pacific data, to see whether the forcings actually cancelled all across the Pacific. Figures 6 and 7 shows that result, for land and ocean, and reveals that there are large problems with this second claim as well.
Figure 6. As in Figure 2, but covering a larger area, the entire tropical Pacific Ocean as specified in the title. This figure shows land only. N=348.
Again, there is no clear pattern over the land, merely a cluster of data.
Figure 7. As in Figure 6, but covering ocean only. Note the slightly larger scale than in Figure 4. N=63,132.
This actually is pretty interesting. First off, again the ocean and land are different, with the land results clustered as in the Indonesian Region. Regarding the ocean, in only 5% of the gridcells does the longwave ever exceed the shortwave (area above the central diagonal line). In order for Kiehl’s cancellation hypothesis to be true, the average of a number of gridcells over an area would have to fall on the central diagonal line … which is clearly impossible with only 5% of them above the line.
Nor do things get better when we look at the entire dataset, and not just the April data. Figure 8 shows all of the data for the Pacific-wide area shown in Figure 7 (ocean only).
Figure 8. All months of data for the Pacific-wide area as in Figure 7
Next, rather than cancellation, there are a whole lot of gridcells where the longwave is smaller, and often much smaller, than the reflected shortwave. When there is solar reflection of a hundred watts per square metre, the longwave is only around sixty watts per square metre, for a full 40 W/m2 of cooling. And even on average the cooling is nearly 20W/m2 … not what we call cancellation on my planet.
Finally, his claim that “Even in regions of colder SSTs (298 K) the cancellation is apparent” is not upheld by the data. The temperature of 298 K [25°C] is shown in blue in Figure 7, and it is the farthest from cancellation of any of the data.
CONCLUSIONS
The main result is that the CERES data clearly and emphatically falsifies the cancellation hypothesis. In general, the longwave and shortwave are far from cancelling each other in the tropical deep convective areas.
A secondary result is that this clearly shows how the politicization of the field has affected the scientific process. Kiehl’s claims were very tempting to the theorists and modelers, because cancellation meant that they didn’t have to concern themselves with the deep convective processes, aka thunderstorms—the could simply repeat Kiehl’s claim that the shortwave and longwave cancelled each other out. And as a result, when more detailed data became available, the original claims of Kiehl1994 were never questioned.
Now, all we need is some automated method to notify the 129 people who cited the cancellation hypothesis in other scientific papers that the rumored cancellation has been cancelled for the duration …
All the best,
w.
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ed.
You miss the point here. The purpose of the post was to examine the hypothesis which was accepted in the 1994 paper. He has falsified it. And yes Lindzen has showed that the data suggested their was some self-regulation. I don’t think, however, that they had such a weight of data and yes there may be limitations to the approach (as you say cloud types, weather conditions etc.) – but just look at the weight of data. In MHO he’s falsified the arguments put forward in the Kiehl paper. Don’t be so mean spirited.
*sigh* I wish I knew how to read scatterplots. 🙁
Ah Science! It’s beautiful when it’s in action.
Willis Eschenbach says:
December 31, 2013 at 11:27 am
…. Say what? I have no idea what might be gained from that. A scientist is under no obligation to discover and identify the errors of other researchers….
Willis,
Thank you for taking the time to respond. Of course, your data stands on its own and you don’t “have” any obligation to prove them wrong or find fault with their methods…. but it helps a lot.
It demonstrates that you, at least, understand the argument (science) and that you can point at the flaws, because you understand it just a bit better than they did. This is new data you presented nearly 30 years in the future. They did not have this available at the time. “Here is the new proof and this is why”.
I don’t think it’s a question of “why do I need to” .. but rather “will it help my case?”
You are defending your position on a topic. Like a lawyer in a court room , not only be sure to make your own case, but refute the other side at the same time.
Science and data stand on its own. Yes, but you have to tell them all why we should change and the update the science.
In the end you’re trying to sway a jury (of your peers) and simply saying “I’m right. Here are my crime photos – draw your own conclusion”. You must also say “they were mistaken because they lacked “……. ” and here is new evidence and our best guess at the truth”!
It represents at least half your battle. You don’t “have to”… but if you want to win you “have to”.
Science is about proving your data is more correct and all others are misplaced or lacking and why. It’s also about trying to prove your own data is wrong and being unable to.
I wish you a very Happy New Year.
Keep up the good work.
Thank you Willis. Another piece of the puzzle in a very, very unsettled (and unsettling too) thing called “climate science”.
Whole lotta cancellation happening and overdue and in the pipeline. A repository of falsified or challenged papers would be a good start on bringing them to M&S (Movers and Shakers) attention. “Oh, that paper is in the Cancelled Conclusions database”. The Kiss of Death!
The paper Ed cited above indicates that Kiehl was arguing in his 1994 payer that the near cancelation seen in his data was a coincidence, rather than something fundamentally reliable, no?
ed. says:
December 31, 2013 at 12:02 pm
My hypothesis (involving cumulus and cumulonimbus) and this analysis have nothing to do with Lindzen’s Iris hypothesis, so it’s not clear what you mean.
w.
Kirk c says:
December 31, 2013 at 5:48 pm
Thanks, Kirk. I agree with your claim that it in general it “helps a lot”, but you have not replied to the issue I raised. I suspect that the problem is cherry-picked data. How would I prove that? And what good would it do even if I could prove it? How would it help a lot to show that?
w.
Willis, your lucid and relevant work here is a good illustration of my comment on the earlier ‘Peer review/Troll’s last hiding place’ thread, that critical review is perhaps decamping from the established journals to (parts of) the blogosphere. Some people are voting with their feet.
You fall firmly in the ‘could not replicate’ stream of review that should be part of the scientific mainstream. What is missing is a means of referring to your work plus the assembled comment stream as a citation.
It is is a real problem with this medium, and what is needed possibly is a neutral digest which can be lodged in a standardised location. Once anyone sees your work being cited it may give them courage to cite it in turn. At that point, Bingo, you’re in the Canon as you should be rather than an unacknowledged gadfly…
Stuart B
Willis Eschenbach: “Joe, I strive to make my work accessible to everyone from the interested layman to the scientific specialist. How much and exactly what information to include are tricky questions. Too much, and the reader bails out. Not enough, and they want a better explanation of e.g. what the data are, or what the logic is, or …”
Amen.
I may not know much, but I think I do know quite a bit about technical exposition, and in many if not most cases the overwhelming majority of the time I spent on it was dedicated to deciding what to include (usually very little) and what to exclude (usually most of it). The “right” answer, of course, depends on who the audience is. In my case, I usually had a much better idea of who the audience was than you can here, and I still got it wrong lamentably often. So I can’t tell you where to draw the line.
I’m merely giving you one data point: one member of your audience–who, incidentally, is usually too lazy to read the whole referenced paper and gather the relevant data–can often benefit from more data description. This doesn’t necessarily mean that including such a description would be a net benefit. But maybe that data point will help you decide.
Willis, please consider responding to my cogent questions and comments up thread…
David A says:
December 31, 2013 at 5:59 am
and two comments below that .
Thanks in advance
================================================
Thank you Greg, yes, not all watts are equal.
Willis
Great insight. For publicity to get to the 126, try Drudge Report.
Recommend submitting to Retraction Watch under a new category of “Highly Cited but Falsified”.
As always, Willis, your work is a succinct and merciless standard with which to gauge status quo theories. I especially appreciate the scatter plot, which I had never seen used to such persuasive effect until your color-coded plots appeared on the scene. No linear, poly or other interpretive fit needed (the choice of which tends to bend data to one’s own ends). I like the way, over your many articles, how science gets revealed and consolidated – a work in progress approach.
Interestingly, the “near cancellation” theory gets greater support with increasing SST. Perhaps, at the sharp maximum limit of 31C (?) that you earlier revealed, the Kiele94 is “nearer”.
I’m with Bill Illis here. I think you have enough to put the final touches on a solid body of work on the earth’s response to heating.
Bill Illis says:
December 31, 2013 at 4:09 am
“With all the data and data-gathering expertise you have now, it should be possible to answer the cloud feedback question.”
Hi Willis,
Would you be willing to publish the code? And also the data as you have sliced and diced it – although that is probably taken care of in the code. Some people are claiming they don’t have a good enough understanding of what you did to affirm it.
Thanks.
jim2 says:
January 1, 2014 at 8:26 pm
The code is here … but it is a snarled tangle that is not only not user-friendly, it is actively user-aggressive, has the table manners of a wolverine, and needs to be beaten severely about the head and shoulders.
It has been used to produce maybe five posts at this point. Yeah, I know, I should do proper revision control … but I’m only one guy. I need minions. Or at least grad students. I do all of the computer programming, and all of the research, and all of the wandering around the property gazing at the sky and thinking about the theoretical underpinnings of the work. Plus I work at a day job remodeling houses, and I like to spend time with the gorgeous ex-fiancee …
In any case, there it is. As to the data, it’s here in R format, 168 megabytes of the variables as arrays …
Regarding how I analyzed it, well, it’s just a scatterplot. Take the CERES dataset called “cre_sw” (shortwave cloud radiative effect), it’s in the data listed above, and do a scatterplot with the CERES dataset called “cre_lw” (longwave cloud radiative effect) … you’ll need to mask out the areas you’re not interested in.
Regards,
w.
David, you’ve asked above that I comment on your ideas, viz:
David A says:
December 31, 2013 at 5:59 am
I fear that I didn’t answer because you seem to have a bee in your bonnet about something you call “energy residence time”, and I didn’t want to get into another endless discussion.
Short version. Energy residence time doesn’t change the temperature at all. Here’s an example. Suppose we have a brick in a very, very, very well insulated container. We measure the brick’s temperature with the built-in thermistor. 30°C.
Then we come back in an hour, and measure the temperature again. Still 30°C
Then we come back in another hour. All this time the brick is in radiative balance. Still 30°C.
Now, a few questions about the energy that has been residing in the brick for the two hours of the experiment:
• What is the residence time of the energy in the brick in this example?
• How is it calculated?
• How can you tell one bit of energy from another, to decide how long it has been residing in the brick?
• Is the residence time different after an hour elapses?
• Did the residence time change over the period of the experiment?
• What would be a sign that the residence time had changed?
• Suppose we open the door of the container, and the brick starts to cool. Has the energy residence time gone up, down, or stayed the same?
Serious questions …
w.
Willis you’re showing how it can be done, and I appreciate your efforts. I’m further informed, thank you. There is more to the story of atmospheric circulation than meets the eye, isn’t there? Now, how about that magnificent cooling engine, eh?
@ur momisugly Willis Eschenbach says:
January 1, 2014 at 11:58 pm
I’ve seen worse code and I’m glad you have a life!
Thanks.