Climate Audit

bender

Hansen was feeling good about himself in 1998

I had a related thought. It’s easy to “feel good about” one’s forecasts if one cherry picks time frames so that observed slopes are as steeply alarmist as one’s predicted slopes. As Steve M writes above, Hansen et al (1988) was published following a major El Nino. Meanwhile MBH98 was published following a monster El Nino. Based on the decadal pattern here (granted, n=2 cycles!) we can predict that the next major El Nino should result in another landmark publication where the hypothesis of an alarmist trend is resurrected. Shall we say … 2010? At that point GHG scenario C may start to look plausible again.

Thanks to Steve M and lucia and Willis for working up these graphics in the form of turnkey scripts.

bender

The nested blockquotes that I used in #4 did not work at all and now that comment is a disaster.
Is it possible to fix this? Nest blockquotes used to work.

Steve: IT doesn’t seem to anymore. I suggest italicizing the second nest of quotes. I tidied the above post for you.

henry

Hansen re-visited the predictions in Hansen et al (PNAS 2006) – this is his “warmest in a millllll-yun years” article

Yet he carried over the shaded area of the altithermal/eemian temps (listed as 6000 and 120,000 years ago, respectively). This flies in the face of the “warmest in a millllll-yun years” idea.

Using that shaded area seems to say that we are at least as warm (but not warmer than) as we were 6000 years ago.

And I’d still like to see which article he used as a reference to that idea.

James Bailey

Hansen’s testimony should be available in the Congressional Record of the 100th Congress. Unfortunately the online version at Thomas starts with the 101st Congress, and the online version at the GPO starts with the 104th Congress. The index though is searchable and has two listings, D429 23JN and D469 7JY. There is a document for a hearing on global warming before a House subcommittee on Energy and Power that took place July 7 and September 22 1988. Its SuDoc Number is Y 4.En 2/3:100-229 and its item number is 1019-A or 1019-B (microfiche). My GPO search showed there were three other hearings that year that mentioned global warming in the hearing title, none that were on the 23rd of June.
If anyone has easy access they could find these, some have been distributed to local libraries. Or obtain it through the GPO.
http://thomas.loc.gov/home/thomas.html
http://catalog.gpo.gov/F

Mhaze

I will send by email attachment the Hansen 062388 senate testimony transcript, written oral and attachments.

Steve: Received -thanks very much. These are now online here http://data.climateaudit.org/pdf/others/Hansen.0623-1988.oral.pdf and http://data.climateaudit.org/pdf/others/Hansen.0623-1988.written.pdf

Hu McCulloch

When say the Social Security Administration gives three projections of the system’s condition, ordinarily these are best case, worst case, and most likely case. So I think it’s reasonable to take Hansen at his word that case B was his most likely estimate, and not to read too much into his color choice.
Personally, I usually start with darkish blue since it looks nice in color and prints well in B&W. Then red, since it contrasts well with blue in color, and is a little lighter in B&W. Then green since it comes out a little paler than red in B&W. If I wanted to emphasize a line, I would try to make it the blue one, not the red one.

Steve: In mineral promotions, red is nearly always used to highlight the spot of interest.

BrianMcL

Re Glacierman in post 14

Perhaps what we see now is a climatological application of Goodhart’s Law?

Ian McLeod

Glacierman,

I think you are correct, bender said as much in #3. That said I doubt anyone has modelled the variability of the stock market as closely as Hansen has modelled the variability of Earth’s climate. Perhaps Ross could say a word or two about that.

I am not a fan of Hansen, he has many apologists, but strip away his bluster and political activism, you have a very smart individual and respected (in some circles) scientist. If he were not deviously clever, we would not be talking about him.

In many ways, Hansen (the so-called Shepherd of AGW) reminds me of Stephen Jay Gould (the controversial palaeontologist). Gould was to evolutionary psychology, as Hansen is to GW scepticism. He would infuriate his critics with his essays, books, and pet theories, but after he passed, his critics longed for his sharp mind.

Steve McIntyre

Another posting by NASA employee Gavin Schmidt defending Hansen, not mentioned in the above, was on December 2, 2004 here http://www.realclimate.org/index.php/archives/2004/12/michaels-misquotes-hansen-again/ , which appears to be Gavin’s very first post at realclimate (Dec 1, 2004 is the date of the realclimate introduction post.)

Gavin posted his defense of his boss in his “spare time”, which in this case occurred in the middle of a 9 to 5 Thursday. I wonder if Gavin booked Dec 2, 2004 off for “personal business”.

If you browse http://www.realclimate.org/index.php/archives/2004/12/ you’ll see how much effort they were then putting into preemptive strikes defending the HS ( MM2005 was then about to be published) and you’ll see why John A convinced me that I needed to try to defend myself. Thus, CA was born a couple of months later in Feb 2005 (carrying over a few website comments).

Bernie

Steve:
I believe these are the actual links
http://data.climateaudit.org/pdf/others/Hansen.0623-1988%20oral.pdf
http://data.climateaudit.org/pdf/others/Hansen.0623-1988%20written.pdf
I think there was a typo.

bender

#21 Interesting. He thought he could fool the politicians by cherry-picking his endpoint, 1987-88 – a choice, due to El Nino, that grossly exaggerates his trend. (Look what happened in 1989: La Nina.) He is smart enough to know what he was doing, in terms of creating a statistical distortion. He was sounding the alarm, promoting an activist agenda. And does anyone see any mention of a “precautionary principle”? All I see are distorted facts.

Glacierman

#15

Def. of Goodharts Law: `When a measure becomes a target, it ceases to be a good measure.’

I agree with Ian #17, he is a very smart guy. Smart enough to know how to make a trend seem as dire as possible by picking dates. If he is right, then placing activism and alarmism above scientific principals will make him a hero. But I believe that his actions are actually fueling the counter case to what he has been advocating. If he/they are so accurate and correct, their work should stand up to any scrutiny.

JaneHM

Steve

Did you hear Gavin Schmidt’s statements on the acceleration of global warming, tipping points etc on PBS Radio yesterday morning? PBS had him on for a whole hour
http://wamu.org/programs/dr/08/01/16.php#18143

Steve Geiger

sorry for the digression, but is not the difference in Hansen’s scenarios mearly the model boundary conditions (primarily, the change in ambient GHG concs over time?). Asside from the arguments over who is being dishonest about the testimony…didn’t scenario B turn out to be closer to what actually happened RE said boundary conditions and so should not that be the proper comparison result (over time) for the Hansen model at this time?

Thanks

Robert Wood

The separation between observations and Scenario C is quite intriguing: Scenario C supposes that CO2 are stabilized at 368 ppm in 2000 – a level already surpassed. So Scenario C should presumably be below observed temperatures, but this is obviously not the case.

This is such a telling observation

MarkW

I’m willing to bet that the growth in CO2 since 1988 has been much closer to the “business as usual” run, than it was to any of the other runs.

Roger Pielke. Jr.

As an aside, I note that in section 5.2.4 of the paper Hansen compares 7 years of observational data with the model runs. Gavin Schmidt went nuts when I compared 8 years of data with a model prediction (and I even concluded that little could be said on that time scale).

The more general conclusion is that forecast verification make climate modelers very nervous. It shouldn’t. Weather forecasters bust forecasts all the time and learn from it without going nuts. of course, weather forecasters have never been so bold as to claim that all of their forecasts are always right.

Raven

35 MarkW says:

I’m willing to bet that the growth in CO2 since 1988 has been much closer to the “business as usual” run, than it was to any of the other runs.

It depends on what he meant by “about 1.5% yr-1 emission growth”. If he was talking about the CO2 output by humans then Scenario A matches reality. If he is talking about the CO2 concentration in the atomosphere then he can argue that the acutual CO2 growth was less than what was assumed by Scenario A.

John Lang

Just noting that trace gases (CFCs and Methane) did meet the case of Scenario C. CFC concentrations have fallen now and Methane levels have stablized.

It is not really clear, however, what Hansen was assuming for CO2 in his Scenarios (CO2 and N2O have continued increasing at the trends of the mid-1980s.)

Concentrations of the GHGs from 1979 to 2006 are shown in the chart at this link.

SteveSadlov

I just received my monthly issue of one of the trade mags I get. It has a “lessons learned” article in it about Challenger. I will read it with great interest. There is what I consider to be an out of control management system at NASA. I will report out on the article later.

bender

The Hansen et al 2006 paper makes some interesting statements:

Our ranking of 2005 as the warmest year depends on the positive polar anomalies, especially the unusual Arctic warmth.

It’s unusual not just because of its magnitude, but because the GCMs don’t predict it. The GCMs are underestimating internal climate variability, which despite its “regionality” nevertheless contributes massively to globally averaged statistics.

Record, or near record, warmth in 2005 is notable, because global temperature did not receive a boost from an El Niño in 2005.

Proving that Hansen is fully aware how statistical distortions may arise from cherry-picking 1988 or 1998 as convenient dates to publish “landmark” papers.

“Say it three times every night before going to sleep*”: when the solar cycle peak coincides with El Nino it is time to sound the alarm.

* -raypierre

Peter Thompson

#44 Winnebago

Just so we’re clear, there is nothing civil about the term “denier”.

bender

Meanwhile, in la-la land, gavin Says:
11 January 2008 at 2:14 PM

http://www.realclimate.org/index.php/archives/2008/01/uncertainty-noise-and-the-art-of-model-data-comparison/langswitch_lang/sw

As promised, the distribution of 8 year trends in the different data sets:

Data ____ Mean (degC/dec) ___ standard deviation (degC/dec)

UAH MSU-LT: __ 0.13 ____ 0.25
RSS MSU-LT: __ 0.18 ____ 0.24
HADCRUT3v: ___ 0.18 ____ 0.16

In no case is the uncertainty low enough for the 8 year trend to be useful for testing models or projections.

This is special pleading, and it is fatal to Schmidt’s entire argument that the predicted and observed trends match. Maybe someone can pre-empt me and explain why? [Hint: I will simply invoke the only argument available to him when he was desperately trying to rebut Douglas et al.: the error on the GCMs is enormous.]

More later on the GS/RC double-standard on “weather vs climate” and “internal vs external variability”.

David Smith

The temperature trends for the US regions can be generated here . I see no alarming temperature trend in the southeast or midwest for the time period mentioned by Hansen. A regional precipitation plot is harder to generate but I think it will show nothing abnormal also.

On global temperature trends, one thing to note is that the 1997-2001 period was one of a strong La Nina (cool) followed by a strong El Nino (warm) followed by a strong La Nina (cool). This pattern masked (my conjecture) a “background” rise in global temperatures over that period, instead making it look like there was a rise circa 2000-2001 rather than 1995-2001.

My belief is that much of the 1995-2001 “global” rise was related to warming of the far north Atlantic (see here for the warm-season SST pattern). This warming released both sensible heat and humidity into the Arctic region, resulting in this Arctic/Subarctic air temperature increase .

If this far north Atlantic SST rise is related to the AMO and has peaked, and if the PDO is switching back to a cool phase which reinforces La Nina activity, then the global temperature pattern may be sideways for quite a few years. That would be a problem for gavin and Hansen.

lucia

On which scenario was primary:

I think you should take Hansen at his word that he always thought scenario B was the main one at least in some sense. I can’t find any spot in the paper where he calls this out, but when he showed detailed geographical predictions, he used scenario B.

Why was scenario A run longest? I have no idea. You’d have to ask Hansen. However, I can suggest a reason that is consistent with A not being the main computation. First, scenario A was run longest because it was run first. (See page 9342 just before section 2.)

So, why was scenario A run firsts? Well, NASA scientists do have bosses and program managers. I will also speculate that the forcing for scenario A was selected partly to foster discussions with programmatic types and to convince program managers that study was required. It actually fairly routine to do bounding calculations to show whether or not further review should be done, and “A” has the fingerprints of that sort of thing.

Had A shown nothing, B & C would never have been run. B&C were only run because A was not the main scenario.

On access to raw data:
I disagree with some of the choices Gavin made when comparing the Hansen’s model to data, and I disagree with his conclusions.

Still, I think this is unfair:

It seems likely that Schmidt, as a NASA employee, had access to the digital version that Hansen used in his 2006 paper and, unlike us at CA, did not have to digitize the graphics in order to get Hansen’s results for the three scenarios.

When I asked Gavin for the scenario temperature anomaly data in a comment at RC posted during Christmas break, he provided that data (I think about a week later). He also proactively provided files for the forcing data, which I hadn’t requested.

Gavin says the ABC anomaly data were digitized from the graph, but evidently not by him. My plots at my blog use those data. Gavin isn’t an author on the 1988 paper and I suspect he didn’t have access to the raw data.

I’d go furter and speculate that NASA’s programmatic requirements didn’t require Hansen to keep a file of the data points and the raw data no longer exist. ( I could grouse about gov’t agencies here. But, this is not, strictly speaking the fault of the individual scientists who may not remember to back up 1988 data and subsequently store it on easy to retrieve media over the course of two decades, which may involve moving to different offices, buildings etc. At Hanford, with waste tank work, we had specific requirements for keeping data imposed by program managers. The rules were a pain in the *&%, but they ensure that, eventually, key information can be obtained should things go wrong later. )

On resetting the baseline:
I don’t think there is an officially good way to reset the baselines given data now available and published. I wish modelers were in the habit of reporting the real honest to goodness thermometer temperature corresponding to the baseline. If they did, we’d at least have a preferred pin point.

I read a blogger somewhere suggest they just stick with Hansen’s first choice. That’s fair in some sense. But, on the other hand, he was able to eyeball agreement between 1958-1983 before publishing. Since the zero point for the baseline is rather arbitrary, it might be difficult for him to be entirely objective and avoid selecting one that showed the best agreement.

Right now, figuring out just how close the models and data are is a bit of a cherry pickers dream. Shifting the baseline within reasonable bounds alters one’s conclusion. Shifting the measurements around also do.

The one thing that is true: The models predict up. Temperatures went up. So, the values are qualitatively correct. Unfortunately, that still leaves a very wide uncertainty when using these to extrapolate forward.

@John Lang–
On the dividing point for projections/ predictions. It’s best not to pick the starting point for comparison based on the the temperature measurements themselves, or the results you want to get. This is already sort of a cherry pickers dream, but still, there are some rules making some cherries off limits!

I recommend looking at dates associated with when the work was initiated and first made public.

The manuscript was submitted in Jan 25, 1988. Given clearance processses at most labs, I think we can be confident the manuscript was, for all intents and purposes, complete no later than Thanksgiving 1987. Still, it’s unlikely the scientist finished computations in 1986 and left the data in a box for a year waiting to see if their prediction for 1987 was right. So, 1988 is the plausible date to beging testing forcast ability; 1987 is not.

The paper itself says computations for transients began in 1983, and results for scenario A were presented at a meeting in 1984. So, 1984 is the earliest possible data for testing forecast ability. At that point, the model was frozen, and other scenarios were run. Earlier results are, strictly speaking “post dictions”.

I picked 1984 to test the model. But, I think if you are trying to be fair, you need to pick 1984 or 1988. Picking 1987 is really sifting those cherries.

Aaron Wells

You nailed it exactly right David. This is exactly the theory that I have espoused, and you are the first person I have seen to echo it so succinctly. Thanks for putting in writing here what I never rose to the occasion to do.

Keith Herbert

In Hansen’s discussion with Michaels, he states the model scenario contained a large volcano. It turns out there was a volcano in the projected time period and Hansen claims models B and C followed actual measurements.
If a volcano is modeled, why isn’t an El Nino also modeled which would tend to increase the projected temperature changes?

bender

That you can approximate a 30-year ‘trend’ using a sequence of shorter trend lines (e.g. 8 years) does not imply the 30-year pattern is a GHG-caused trend. LTP/ocean upwelling can generate low-frequency anomalies that look like increasing trends over very short (relative to ocean circulation) time scales. Same for decreasing (or abating) “trends” – a point that agrees with DS’s #52.

bender

Where do the “wiggles” in the 1988 projections come from?

There are no wiggles in the ensemble error bars – which I have only ever seen plotted once – when GS was trying deperately to refute Douglas & Christy.

bender

why isn’t an El Nino also modeled which would tend to increase the projected temperature changes

ENSO is an ’emergent property’ of the ocean circulation. Therefore it *is* modeled, in the sense that all the thermodynamic and fluid dynamic processes that are thought to produce these kinds of effects are included in the GCMs. The only problem is that these features don’t emerge the way they are supposed to.

bender

lucia, would you have predicted the flattening of global CH4 in 1999?

Raven

lucia says:

That said: I doubt the warming trend has ended. I just doubt it.

Humans used to looking at various economic graphs which tend to always go up in the long run (population, stock market, housing prices, etc). I think this gives most humans a bias toward expecting continuous upward trends with periods of dips. Natural processes are different and do not follow the same rules. A phase reversal on the PDO or another Dalton minimum could send temps falling again no matter what the science of CO2 says.

Larry

I doubt the warming housing price trend has ended. I just doubt it.

– Conventional wisdom, 2006.

Raven

Bernie says:

In reality Scenarios A, B or C as proposed in 1988 hardly matters 20 years later – it is whether actual observations support the underlying assumptions of today’s models and at what point do the model builders acknowledge and explain how they have adjusted the models to fit with new observational data.

What you are describing is a catch 22 situation. We can’t evaluate the new models because there is not enough data. But we can’t evaluate the old models because they are out of date. We can’t do much about the lack of data but we can evaluate the old models.

SteveSadlov

Sorry I meant #66 … AKA “Raven says: January 17th, 2008 at 2:11 pm”

Mike B

Steve,

I took your method of zeroing the three projection scenarios:

In order to put the three Scenarios apples and apples to the GISS GLB temperature series (basis 1951-1980), I re-centered the three scenarios slightly so that they were also zero over 1958-1967.

and re-zeroed the GISS surface-ocean data to the same 1958-1967 period. Here is my scorecard for the 20 years (1988-2007) of out-of-sample projections:

Hansen A:

Average projected anomaly: 0.82 C
Average actual anomaly: 0.39 C (48% of projected)
20 of 20 years projected anomaly exceeded actual anomaly.

Hansen B:

Average projected anomaly: 0.58 C
Average actual anomaly: 0.39 C (68% of projected)
18 of 20 years projected anomaly exceeded actual anomaly (all but 1997 and 1998)
Last projected anomaly less than actual: 1998

Hansen C:

Average projected anomaly: 0.49 C
Average actual anomaly: 0.39 C (80% of projected)
17 of 20 years projected anomaly exceeded actual anomaly (all but 1988, 1996, and 1998)

I’d say scenario A is not only dead, it’s long dead.

As mentioned above, the forcing assumptions of scenario C have proven to be way off, so that one is pretty well dead, too.

Which leaves scenario B hanging on life support at best, with the last sign of brain-wave activity being the El Nino year of 1998.

lucia

@bender-

Would the amateur biogeochemist in you have predicted a flattening of CH4 in 1999? The amateur economist a housing market collapse in 2007? And so on. Things happen in dynamic systems.

There is no amateur bio-geochemist inside me. Or, if such a thing lurks within, it’s a very stupid ill-informed amateur biogeochemist. So….. if it’s a biological effect, I would have done no better than a coin flip and possibly worse.

Later on, if I get the simple model with CH4 as a forcing on heat ironed out, then maybe I’ll ask you to suggest an equation to predit CH4 as a function of temperature. That might be fun to do.

But first, I need to get these volcanos accounted for in detail, and see if I can get Temperatures right based on forcings in Watts/m^2.

I did expect this housing market collapse. That was kind of obvious; there were so many factors artificially boosting prices. My brother asked me if he should buy near the top of the bubble and I told him, “Don’t buy!” He told me he was going to go ahead anyway, but then life happened , he got busy and was spared a financial loss.

DRE

So bottom line:

GCMs that have been curve fit to past temperature data don’t have predictive value.(?)

Or in general:

Don’t bring extrapolation to a prediction fight.

Bernie

Raven: #70
I didn’t mean to suggest that we should not evaluate the models on their own terms. But it seems to me that we certainly cannot assume today’s models have exactly the same specifications and assumptions as those from 1988 – of course they could, but that would raise another set of issues! That is why I think we need to understand the key assumptions embedded in today’s models, such as the 2.5K for doubling CO2. The underlying assertion of all these models is a strong AGW – which can only be demonstrated if previous record high’s are exceeded on a fairly regular and frequent basis. In a sense this is a gross test of all the models that predict a strong AGW.

steven mosher

re 74. 20 year projections have no validity, unless of course they match observations.

Keith Herbert

#62 Thank you Bender.
Where I am stuck on this is Hansen’s seemingly prideful consideration of the volcanic activity in his models. These, it seems, are rare events that spike the system with CO2 (raised temp) or ash (lowered temp). If extra-ordinary volcanic events are significant to a climate model, why aren’t other extra-ordinary events such as the “effects” of rare but major El Ninos or La Ninas or changes in solar activity or any other non-standard state events.

And if these are considered, then there is the timeframe modeling of these events that can be concurrent or phased over years. And some of these models would vary the assumed contribution of CO2. The result is models A-ZZ, not just A, B or C. These models may be as (or more) accurate when compared later to temperature changes.
So how does one choose A, B or C when there are so many other possible models?

Andrew

Carrick,

Bender (comment #4), I brought up on RC one confounding effect that probably needs to be considered with respect to anthropogenic global warming.

If we take seriously the flattening out of the global mean temperature (i’m not suggesting that we should), this might be explained by a simultaneous improvement in CO2 emissions by industrialized nations coupled with increased aerosol production in third world nations. I know some people (e.g., Singer) are nervous about the link between CO2, aerosols and global temperature. Indeed they tend to regard the introduction of the aerosols as a “hack” to explain away the lack of warming in the 1945-75 period.

I write it off as a very real effect that is not well characterized by the models, probably because these models don’t model with enough accuracy the effect of the additional aerosol particles on cloud production to properly account for it’s full effect on temperature.

In any case, if I’m correct, then the apparent failure of Hansen’s prediction was not to foresee the industrialization of the 3rd world nations and its ramifications, and not some more basic problem with his climate model.

Certainly an interesting idea. But there are literally HUGE error bars on aerosols right now, so what your talking about is nothing but speculation. Without an empirically derived value for the effect, it actually is a hack to fit.

That of course could be said of just about anything in climate science at this stage, given the “low” or indeed “very low” level of scientific understanding on some of these subjects. If our level of scientific understanding of aerosols is low, then we can’t say we are justified in assuming that the account for the lack of warming between World Warm 2 and the the late 70’s. They might. Hardly a basis for sound policy. More research, and, you know, maybe some experiments are necessary.

But who’s suggesting anything like that? No one at realclimate, to my knowledge. I thought the “science is settled”. But maybe not?

sod

I thought the “science is settled”. But maybe not?

come on!
we are discussing a paper from 1988 and global warming wasn t that hot a topic back then.
the actual temperature is slightly below the most “plausible” scenario, especially if we accept Steve’s recentered graph. (though if you look at the graph, it might have been further away between 1970 and 80…)

now the skeptic movement seems to be too young to have made any testable predictions. (any predictions? some times i can t shake the feeling that you guys prefer to stay imprecise in that aspect..)

but there is a LOT of room at the bottom of that graph, and temperatures could have been falling for 20 years now…

Demesure

In fact, Hansen’s scenario A is even less than reality. He assumes an annual increase of 1.5%/year (a small exponential) whereas the lastest paper by Le Quéré (who predictably was a delight for the alarmist crowd and triggered sky-falling headlines in all languages) reported a more than 3%/year increase in CO2 emission.

So what is 100% wrong is Hansen’s CO2 absorption model. With this “business as usual” projections o 1,5%/year emissions increase for his scenario A, his models predicted a CO2 atmospheric content of 384 ppmV for 2006 (R. Pielke Jr’s graph in #44).
But in reality, even with >3%/year emissions increase since 2000 (damn Yankees who refuse to stop buying SUVs & Chinese to stop building power plants), atmospheric CO2 is still

David Anderson

One of the many things that puzzle me as non scientific citizen of average scientific knowledge in trying to follow the debate concerning AGW is understanding how, given the many “low” levels of uncertainty admitted by most experts, can anyone claim to “know” such a complex question?

When the IPCC makes a “non prediction” prediction of mean temperature response to manmade C0-2 is it defined to both latitude and altitude? Is it, and should it not be also be defined to T-max daytime and T min night time for both the surface and different altitudes and latitudes? Should the veracity of the GH theory not have to answer to these far more detailed predictions then to a simple estimation of increased surface temperature, and using whichever of the various means of arriving at a global average best matches that one parameter?

Do the tropical latitudes receive more incoming radiation then the sub tropic and poles?

Is the majority of the initial (before feedback) atmosphere heating resulting from increased CO-2 reduced by the fact that the majority (W-Sq-M at low latitude) of outgoing radiation is in the latitudes most saturated by water vapor?

Is not all over lapping absorption molecules the same, and therefore would it not take an exponentially larger increase in CO-2 to increase temperature in the tropics as opposed to the poles?

Would it not then be a mistake to assume a global average incoming watt per sq-m solar radiation and global average outgoing L-W radiation and global average Greenhouse effect for Co-2 and apply those global numbers to the tropics, when a higher percentage of both LW and SW radiation is in tropical latitudes where that increase in CO-2 has less effect?

Is it not also therefore true that the polar areas of least water vapor, where a greater temperature increase from doubling of Co-2 would have the most effect, has the least percentage of both incoming S-W and outgoing L-W radiation due to the incident angle of incoming Sun light, the high reflectivity of the snow and ice, and the greatly reduced outgoing L-W radiation due to this?

Would not then these very different percentages of both incoming and outgoing L-W and S-W radiation and different responses, to increased Co-2, produce very different results then using straight line global averages? (IE…lower response in high radiation areas and high response in low radiation areas) Would this not produce a lowered, “before feed back” estimate of the global response to increased C0-2?

Also, as I understand, any increase in heat would cause an increase in convection. Is this negative feedback quantified?
Is the further increase in cloud cover due to an increase in water vapor quantified?

Should not the IPCC “Prediction” be more like as follows…?

1. Incoming tropical W/sq-m. (A different calculation then for subtopic and polar latitudes)
2. Outgoing tropical L-W W/sq-m. (A different calculation then for subtopic and polar latitudes)
3. Doubling in tropics of Co-2 = X W/sq-m increase in temperature. (A different calculation then for subtopic and polar latitudes)
4. Subtract X W/sq-m due to increase in convection. (A different calculation then for subtopic and polar latitudes)
5. Subtract or add X W/sq-m due to increase in cloud cover and energy spent in precipitation.
6. The other cogent factors the experts believe, quantified to different latitudes etc.
7. Then a global statistical average of these factors.
8. Error bars assigned to all these estimates, and finale estimates.

Could not all this be done on a single spreadsheet linked to relevant papers and discussions, and also linked to a layman’s explanation? Sorry this is so long, but I think any response would help the average interested citizen get a handle on this issue. Any concise non technical feedback is appreciated.

David Anderson

One of the many things that puzzle me as a non-scientific citizen of average scientific knowledge in trying to follow the debate concerning AGW is understanding how, given the many “low” levels of uncertainty admitted by most experts, can anyone claim to “know” such a complex question?

When the IPCC makes a “non prediction” prediction of mean temperature response to manmade C0-2 such as discussed here, is it defined to both latitude and altitude? Is it, and should it not be also be defined to T-max daytime and T min night time for both the surface and different altitudes and latitudes? Should the veracity of the GH theory not have to answer to these far more detailed predictions then to a simple estimation of increased surface temperature, and using whichever of the various means of arriving at a global average best matches that one parameter?

Do the tropical latitudes receive more incoming radiation then the sub tropic and poles?

Is the majority of the initial (before feedback) atmosphere heating resulting from increased CO-2, reduced by the fact that the majority (W-Sq-M at low latitude) of outgoing radiation is in the latitudes most saturated by water vapor?

Is not all over lapping absorption molecules the same, and therefore would it not take an exponentially larger increase in CO-2 to increase temperature in the tropics as opposed to the poles?

Would it not then be a mistake to assume a global average incoming watt per sq-m solar radiation and global average outgoing L-W radiation and global average Greenhouse effect for Co-2 and apply those global numbers to the tropics, when a higher percentage of both LW and SW radiation is in tropical latitudes where that increase in CO-2 has less effect?

Is it not also therefore true that the polar areas of least water vapor, where a greater temperature increase from doubling of Co-2 would have the most effect, has the least W/sq-m percentage of both incoming S-W and outgoing L-W radiation due to the incident angle of incoming Sun light, the high reflectivity of the snow and ice, and the greatly reduced outgoing L-W radiation due to this?

Would not then these very different percentages of both incoming and outgoing L-W and S-W radiation and different responses to increased Co-2, produce very different results then using straight line global averages? (IE…lower response in high radiation areas and high response in low radiation areas) Would this not produce a lowered, “before feed back” estimate of the global response to increased C0-2?

Also, as I understand, any increase in heat would cause an increase in convection. Is this negative feedback quantified?
Is the further increase in cloud cover due to an increase in water vapor quantified?

Should not the IPCC “Prediction” be more like as follows…?

1. Incoming tropical W/sq-m. (A different calculation then for subtopic and polar latitudes)
2. Outgoing tropical L-W W/sq-m. (A different calculation then for subtopic and polar latitudes)
3. Doubling in tropics of Co-2 = X W/sq-m increase in temperature. (A different calculation then for subtopic and polar latitudes)
4. Subtract X W/sq-m due to increase in convection. (A different calculation then for subtopic and polar latitudes)
5. Subtract or add X W/sq-m due to increase in cloud cover and energy spent in precipitation.
6. The other cogent factors the experts believe, quantified to different latitudes etc.
7. Then a global statistical average of these factors.
8. Error bars assigned to all these estimates, and finale estimates.

Could not all this be done on a single spreadsheet linked to relevant papers and discussions, and also linked to a layman’s explanation? Sorry this is so long, but I think any response would help the average interested citizen get a handle on this issue. Any concise non technical feedback is appreciated.

Steve McIntyre

I’ve added the following paragraph:

To clarify, I do not agree that it was appropriate for Michaels not to have illustrated Scenarios B or C, nor did I say that in this post. These scenarios should have been shown, as I’ve done in all my posts here. It was open to Michaels to take Scenario A as his base case provided that he justified this and analysed the differences to other scenarios as I’m doing. Contrary to Tim Lambert’s accusation, I do not “defend” the exclusion of Scenarios B and C from the Michaels’ graphic. This exclusion is yet another example of poor practice in climate science by someone who was then Michael Mann’s colleague at the University of Virginia. Unlike Mann’s withholding of adverse verification results and censored results, Michaels’ failure to show Scenarios B (and even the obviously unrealistic Scenario C) was widely criticized by climate scientists and others, with Klugman even calling it “fraud”. So sometimes climate scientists think that not showing relevant adverse results is a very bad thing. I wonder what the basis is for climate scientists taking exception to Michaels, while failing to criticize Mann, or, in the case of IPCC itself, withholding the deleted Briffa data.

I wrote this post late last night and about 9 am this morning, I noticed that Hansen et al 1988 had included a sentence that Scenario B was the “most plausible”; I inserted this in the post above and amended an incorrect statement. I’ve also inserted an update referring to the Hansen testimony which MHaze has made available.

Sam Urbinto

George M: The charts show each year’s mean anomaly compared to the base period. That’s why you have to know if somebody is talking about growth rates for any increment versus growth rates expressed in terms of the quantity, and not mix up absolute values with trends. If it’s at +.5 this year and +.3 last year, then it’s +.2 But next year it might be -.2 back to +.3

It’s like giving an agency a million dollars this year and eight-hundred thousand next year and describing it as cutting their budget.

Or even mixing things up more. If I say the CO2 trend over the last decade is 2, it’s a different discussion than saying CO2 levels went up 20 over the last decade, which is a different discussion than saying the levels are now about about 390 ppmv. (And equating that to some percentage higher than year X).

bender

Steve M has no secretarial help. Are you volunteering?

Roger Pielke. Jr.

It is completely irrelevant which scenario Jim Hansen advertised as his favorite in 1988. Completely. I do not believe that it is possible to accurately predict energy use, emissions, policy developments, or technological innovations, among other things relevant to future forcings. So there is not point in faulting Hansen for not accurately predicting any of these things.

What he did in 1988 was entirely appropriate — try to map out the range of future possibilities based on some manner of bounding the uncertainties. With hindsight we are able to go back to that 1988 forecast and identify which scenario most approximated reality as use that as the basis for evaluating the performance of the forecast. It is quite interesting that Hansen’s forecasts in 1988 did not bound the possibilities as the observed record lies outside his realization space.

The A/B CO2 part of the projection was pretty good, and the other forcing agents less so. But the good news is that many of these played much less of a role in the overall forcing. So either A or B are fair comparisons against the observed record. C is obviously unrealistic.

The differences between A and B are largely irrelevant out to 2007, and it seems fairly clear that the actual temperature record under performed the 1988 prediction. People who try to make this about Pat Michaels and Scenario A are desperately trying to change the subject. But so too are those trying to make this about Hansen and Scenario A. It should be about forecast verification.

Like the IPCC in 1990, the Hansen 1988 forecast overshot the mark. This is no crime, but a useful data point in trying to understand the predictive capabilities of climate models (not so good as yet, see Rahmstorf et. al 2007 for evidence that the 2001 IPCC has missed the mark). Gavin Schmidt’s claims that the 1988 Hansen prediction was right, and so too was the 1990 IPCC, so too was the 1995 IPCC, so too was the 2001 IPCC, so too was the 2007 IPCC are laughable on their face, as these predictions are not consistent with one another.

It is amazing how resistant some people (especially some modelers) are to forecast verification. Some of this is obviously for political reasons, some for pride, but the exercise that Steve has conducted here is fair and of great value.

bender

It is completely irrelevant which scenario Jim Hansen advertised as his favorite in 1988.

I was about to write the same thing. A scenario is not a forecast. It is an arbitrary input and was clearly labelled as such. Not at all the same thing as a parameter, hidden away somewhere, with derivation obscure or unknown, and subject to considerable, measurable, but undisclosed uncertainty. The scenarios are what they are. It’s the model that matters.

John Lang

I note that Hansen’s 1988 predictions are a true test of the most important question in the global warming question – that of what is the climate/temperature sensitivity to increases/doubling of CO2 and other GHGs.

The predictions are certainly too high. Whether they missed by 20%, 50% or 100% seems to be open to interpretation.

But the predictions are 100% directly related to the climate sensitivity question. Being off by 20% means that global warming will still be a significant problem and the sensitivity assumptions of 3.0C per doubling of CO2 is close to being correct.

Being off by 50% means that global warming is something we should be concerned about and the climate sensitivity assumptions are just a little high.

Being off by 100% means that global warming will not be a problem at all and the sensitivity measure is vastly overstated.

In my mind, the first big test is somewhere in between something to be concerned about and nothing to worry about.

Another few years of data should actually answer the question. Next month’s satellite temperature measurements might also the question (if you are a person who follows these closely because another month of La Nina-induced temperature declines will signal Hansen is more than 100% off the mark because there will be NO temperature increase over 29 years of increasing CO2.)

perplexed

If the issue is limited to the evaluating, post hoc, the accuracy of any respective computer model, then I would certainly agree that any of the prospective assumptions made long ago as to emissions scenarios are irrelevant. In fact, why can’t you go one step further and forget about even trying to decide which of the scenarios presented were the most realistic and just dig up the model, plug in the emissions numbers, volcanic eruptions, etc. from the last couple decades and see how well the model holds up. And in all honesty, it probably wasn’t very constructive to criticize testmony made and papers written decades ago.

There is, however, a point to be made about exercising caution when evaluating the forward-looking output of a computer model, particularly when those models are used to advocate policy changes on the assumption that the computer model accurately simulates the earth’s climate, and more particularly when there is no demonstrable track record of the predictive accuracy of the model. A computer, after all, does nothing more than what it’s told to do. As I see it, you can think of a computer model as being a “forecast” in one of two ways. To the extent that you are evaluating the efficacy of the computer model itself, the predictive nature of what inputs are used is really a moot issue. You’re going to have to wait some number of decades anyway to evaluate the model’s predictive ability (i.e. once the model has been constructed, leaving it be without tweaking the parameters etc.), and at that point you might as well just pump the actual recorded data into it. And to the extent that there is some effort now being made to evaluate the accuracy of computer models past, now that there is at least some means to do so, it’s a very worthy effort. (Although I personally don’t see how it could take less than a century or so to actually develop enough of a track record to justify relying on a model where the signal being verified takes something on the scale of decades to even distinguish, but that’s a separate issue.)

But to the extent that the model is being used to predict future climate events, such as droughts, which was what Hansen’s model was being used for at the time, the only forecasts being made are what to use as inputs – the model’s accuracy being assumed. In the case of the 1988 Hansen et al. model, there didn’t appear to be any usable estimate of the model’s reliability – in other words, how likely is it, that given the proper input, the model gives results within a tolerable range of error. Reviewing the Congressional testimony, Hansen merely states that the model’s output of past climate variables reasonably corresponds to the actual data, which isn’t really relevant absent a showing that other possible climate models with differing sensitivities to GHGs would not be able to reproduce the climate of the years past. And to be fair, the article in the Journal of Geophysical Research describing the results of the three scenarios of the model noted many of the uncertainties regarding the assumptions that went into the model.

When you cut to the chase, however, if a computer model is being used to tell people to do something – e.g., go to your basement because there’s a tornado coming, the only forecast that matters is the output, which includes what projections are made about what the inputs are going to be. Until there comes a day when climate modelers are able to demonstrate the ability of their models to simulate the Earth’s climate, in all it’s complexity, sufficiently well to generate consistently accurate predictions as to temperature, drought, hurricanes, etc., given the proper emissions inputs, then distinguishing between the ability to predict those emissions inputs and the ability of the model to accurately simulate the response to those emissions inputs isn’t really meaningful.

kim

Eric brightens with a flash of skepticism.
=========================

bender

#107 Yes, the scenarios matter insofar as you want to avoid GIGO. It’s “which is Hansen’s favorite?” that is irrelevant. (Except that it does serve to illustrate the point that the switch from pure science to alarmism happened in the 1980s, before the lobbyists and mass media had started advocating a “precautionary principle”. But that would be OT.)

Paul

This is a great debate that will run and run. I have been their myself in the world of macroeconometric models.

But there is really one key area that has primacy in assessing the efacacy of models and forecasts based on their use. That is turning points. The fact that Hansen was correct in a binary sense, in his forecast that temperatures would keep rising is of limited value given the non-stationary nature of the target variable.

These models seem specified in such a way that they will exihibit long run (intra decadal) properties of monotonically increaseing temeprature (on the assumption that CO2 concentrations continue to rise). If we see a turning point in the target variable over similar scales (not saying it will happen, but the evolution of the temp series fom 2000 to 2007 could easily form the inflexion and potential stationary max. if you look at it objectively), then the strongest proponents of these GCMs will have to take a long hard look at the direction of their work and mor importantly, how they present it to policy makers and public.

JamesG

Lucia says: “The one thing that is true: The models predict up. Temperatures went up. So, the values are qualitatively correct. Unfortunately, that still leaves a very wide uncertainty when using these to extrapolate forward.”

Lucia you have this completely back-to-front. In reality, the temperatures went up so they made the temperature in the models go up. You can make them go down just as easily by increasing that aerosol forcing within it’s uncertainty bounds and the earlier “ice-age” model projections did exactly that – using surface temperature as a target. We know the aerosol forcing is exclusively used by modelers to match the surface record while keeping the CO2 sensitivity high, because the aerosol experts have concluded this. We also know that the uncertainty bars in most of the inputs are huge, and some are “best guesses” based on prior bias. And now Gavin et al finally admit that the error bars on the outputs are consequently huge too (which was always blindingly obvious to more responsible computer modelers). So not only are the models useless for extrapolation, they are useless for anything. We can be as accurate just with pencil and paper. If a computer model of mine proves inaccurate then I don’t allow anyone to use it because lives depend on it being right. [snip]

lucia

JamesG 116–
My comments were specifically related to the Hansens ABC scenarios. After they ran the model and published in 1988, the temperatures went up. After the published, they could no longer go in and tweak the predictions.

It’s true they could have inserted more aerosols, less forcing or done any number of things before that. Had they done that their model might have predicted temperatures went down. But, the fact is, they at least picked a value for aerosols etc. that ended up projecting “up”. They published. Afterwards, the weather cooperated and the average temperature are “up”.

That much is simply true. No matter whether you are a alarmist, warmer, skeptic, denier or just a passerby, there is no point in denying that at least qualitatively, Hansen was right when he predicted “up” in 1988.

It is fair to have different opinions about what, exactly, that his having been qualitatively correct means about AGW or anything else.

Lance

Lucia,

That much is simply true. No matter whether you are a alarmist, warmer, skeptic, denier or just a passerby, there is no point in denying that at least qualitatively, Hansen was right when he predicted “up” in 1988.

Well since the upward trend was well established, and his whole “CO2 causes global warming” theory would be falsified by any other result (constant or decreasing temperatures) it is hardly surprising that Hansen’s models would produce predictions of increasing temperature.

Mike Davis

Hansen in Time magazine 1988:
Testifying before a congressional committee last week, James Hansen, an atmospheric scientist who heads NASA’s Goddard Institute, riveted Senators with the news that the greenhouse effect has already begun. During the first five months of 1988, he said, average worldwide temperatures were the highest in the 130 years that records have been kept. Moreover, Hansen continued, he – is 99% certain that the higher temperatures are not just a natural phenomenon but the result of a buildup of carbon dioxide (CO2) and other gases from man- made sources, mainly pollution from power plants and automobiles. Said Hansen: “It is time to stop waffling and say that the evidence is pretty strong that the greenhouse effect is here.”

pjaco

Steve Mc-

Out of curiosity, why do you constantly refer to Gavin Schmidt as “NASA employee”? You sound a bit hysterical, repeating it some 11 times in this thread alone.

Also, I am curious as to what appears to be your (willful?) misunderstanding of what BAU signifies. It is not, nor has it ever been, a stand in for predicted reality. It is, and always has been, a scenario of unchecked emissions growth not countered by regulation, significant volcanism, or economic restraint. Would you make the claim that BAU is the predicted reality still today- with so many governments and companies now cognizant of the issue and attempts to reduce emissions already taking place? If so, you will probably find yourself in a tiny minority, if not alone, in that interpretation.

Kyoto, Pinutabo, and the collapse of the Soviet Union with its resulting emissions decrease obviously most resemble B, and as Hugh and lucia say, it is perfectly reasonable to take Hansen at his word in choosing B rather than A as most likely.

JamesG

Lucia/Phil – Look at it this way. The temperatures can go up or down: You have a 50-50 chance of being right and if you follow the current trend it’s the much easier option. In the ice-age scare NASA predicted down when it was going down (from human pollution) and they were wrong. Hansen then predicted up (from human pollution) when it was already going up and he was right. Then in the 1998 el niño peak he got panicky and said it was getting worse. Then in 2000 he saw the temperature dip and said that maybe CO2 and aerosols are now canceling each other out – lets concentrate on soot (from human pollution). Then later, the temperature goes up and he says CO2 and soot combined will cause armageddon. Two things are clear; a) the temperature record governs his thinking, not the models, b) whatever happens to temperature it’s always our fault because he expects nature to flat-line. It’s not impressive at all.

Gunnar

>> Lucia/Phil – Look at it this way. The temperatures can go up or down: You have a 50-50 chance of being right and if you follow the current trend it’s the much easier option.

When you make all your predictions during solar minima, your chances are better than 50-50.

Carrick

I should also emphasize, and I think sometimes this point is missed by critics of the current GW scenarios, that the policy issue related to whether we should moderate CO2 production is very different than the problematic task of predicting changes in future industrial production.

If we know that increasing CO2 leads (long term) to a given climate sensitivity, then this is a problem that has to be addressed. And that has nothing to do with whether James Hansen’s Jedi Mind Tricks [tm] allow him to prognosticate future industrial trends.

Mike B

#127

As I said look at the GISS and satellite data included on the graph and justify that assertion from the point of view of the mid 80s. On the GISS curve 1986 had the ~same T as 1961, hindsight is 20/20, from the 87 perspective it was by no means clear that we were in a period of global warming.

Phil, Here are the GISS anomalies from 1948-1987:

Year Anomaly
1948 -4
1949 -6
1950 -15
1951 -4
1952 3
1953 11
1954 -10
1955 -10
1956 -17
1957 8
1958 8
1959 6
1960 -1
1961 8
1962 4
1963 8
1964 -21
1965 -11
1966 -3
1967 0
1968 -4
1969 8
1970 3
1971 -10
1972 0
1973 14
1974 -8
1975 -5
1976 -16
1977 13
1978 2
1979 9
1980 18
1981 27
1982 5
1983 26
1984 9
1985 6
1986 13
1987 27

I think it’s safe to say that an “early adopter” of AGW would look at these data and find what they wanted to see.

Jeff A.

Fair enough, pjaco. Are those all Kyoto signatories? And did they implement plans to reduce emissions, or was it incidental? Also, what’s the source for this information?

Every report I’d seen is that the major western countries’ emissions have all gone up, except for the US in 2006 (if the data is to be believed).

Also, because one set of countries has reduced emissions, they are still a small fraction of the whole. I still don’t see how the fall of the USSR is going to drastically curtail GLOBAL methane.

Andrew

I can start to get back to you on that. There’s data on emissions of CO2 here:
http://cdiac.ornl.gov/ftp/trends/co2_emis/
Unfortunately, there’s no nice simple global graph of others, and there doesn’t seem to be data on methane or other stuff. Sad.

This graph only goes up to 1991, and it claims to be for the USSR (which is weird, since it goes back to 1830)

But you can already see the decline. The other thing to remember, of course, is that CO2 emissions aren’t necessarily indicative of other emissions. So I’m not sure this helps at all, frankly.

Sam Urbinto

Eyeballing the top (first) graph, it sure looks to me like like C is off by .5C and A and B more so.

?

Roger:

“I do not believe that it is possible to accurately predict energy use, emissions, policy developments, or technological innovations, among other things relevant to future forcings.”

I totally agree.

steven mosher

I’m having trouble pulling up the comments on Eli’s blog.

A few days back He said ” I’m trained in physics, the earth is sphere”

I pointed out in a comment that “I was trained in poetry and the earth is an Oblate spheriod”

Since then I have not been able to access the rabbit’s hole. Weird. No complaints. It’s
a smelly mess.

Phil.

Re #139

Eli Rabett shows that Hansen’s 1988 model just slightly overestimates the actual temperature record.

Of course, he is using Hansen’s temperature data to validate Hansen’s model.

Of course, that is not a good practise.

What really needs validation is Hansen’s temperature data.

As long as people are going to use Hansen’s temperature data to assess whether global warming is occurring, there will always be an increasing global warming problem.

What do you feel about the arbitrary post hoc adjustment of tropospheric temperature (which includes some stratospheric contamination) to match “Hansen’s temperature data” and then using that as a comparator, is that good practise (sic.)?

lucia

@Phil–
Could you elaborate? (I’m getting the impression you are driving at something?)

Kenneth Fritsch

I have to admit that I get a little frustrated by the analyses of Hansen’s A, B and C scenarios done at CA and RC and particularly so when it is done in piecemeal fashion. I never am able to get my teeth sufficiently into the meat of the matter so that one could make a reasonable evaluation of the scenarios inputs and then evaluation of the temperature prediction based on the actual observed inputs.

What one would need would be a detailed item by item and side by side comparison of the actual annual inputs for the model versus the observed inputs and then an annual climate model temperature change prediction, on global and regional basis, based on the actual inputs versus the actual temperature changes. It is my view that if one seriously wanted to evaluate the basis of the scenarios this is what one would do.

I think that an analysis is not done in this manner indicates that the scenarios were never intended to be evaluated out-of-sample. Instead I can only conclude that they were put forward, in good faith no doubt, as a scientifically based best guess at that point in time for future temperatures. They must have been initially considered throw away scenarios with uncertainties admitted qualitatively, if not quantitatively, at the time to be used to get the attention of those who were potential movers in climate policy. The uncertainty problems and issues would be minimized in that effort and then climate science and modelers would move on to better estimates.

But lo and behold those scenarios (at least B and C even though it is not clear if they were close for the reasons based on predicted inputs) gave temperature trends reasonably close to the actual — providing one is allowed to select the temperature data set.

Even at that, Hansen makes no major claims for his modeled scenarios as recently as 2006. Here are some excerpts from a PNAS paper by Hansen, Sato, Ruedy, Lo, Lea and Medin-Elizade that was contributed by James Hansen, July 31, 2006.

http://www.pnas.org/cgi/reprint/0606291103v1

We can compare later in this post what Hansen et al said in 2006 with what was said in the initial scenario paper in 1988.

Scenario A was described as ‘‘on the high side of reality,’ because it assumed rapid exponential growth of GHGs and it included no large volcanic eruptions during the next half century. Scenario C was described as ‘‘a more drastic curtailment of emissions than has generally been imagined,’ specifically GHGs were assumed to stop increasing after 2000. Intermediate scenario B was described as ‘‘the most plausible.’

It becomes clear from the following excerpt that the climate model for the scenarios in 1988 was predicting/projecting land and sea temperatures. I assume that included the differential warming of land to sea, but then the best comparative temperature change is inexplicably chosen as one somewhere between land and land-sea temperature change.

Temperature change from climate models, including that reported in 1988 (12), usually refers to temperature of surface air over both land and ocean. Surface air temperature change in a warming climate is slightly larger than the SST change (4), especially in regions of sea ice. Therefore, the best temperature observation for comparison with climate models probably falls between the meteorological station surface air analysis and the land–ocean temperature index.

The excerpt below references what I suspected in my musings above. That the closeness of the Scenario B predicted temperature change and the actual change is accidental because of the unforced variations is made clearer by looking at the figure below the excerpt. The model control run depicted in that figure shows some very trending like excursions that came out of the model without inputting any forcings. First one sees a 0.3 to 0.4 degree C upward trend for the first 50 years then a trend downward of about 0.4 degrees C over the next 25 years and finally an approximate upward trend of 0.2 degrees C over the final 25 year period.

Close agreement of observed temperature change with simulations for the most realistic climate forcing (scenario B) is accidental, given the large unforced variability in both model and real world.

Finally, the 2006 paper states plainly that any assessment of the scenarios must wait for another decade of results – without presenting a statistical basis for how this would be determined.

Forcings in scenarios B and C are nearly the same up to 2000, so the different responses provide one measure of unforced variability in the model. Because of this chaotic variability, a 17-year period is too brief for precise assessment of model predictions, but distinction among scenarios and comparison with the real world will become clearer within a decade.

In the original 1988 scenario paper Hansen et al make the following excerpted statements about the scenarios:

Click to access 1988_Hansen_etal.pdf

Scenario A assumes that growth rates of trace gas emissions typical of the 1970s and 1980s will continue indefinitely..

…Scenario B has decreasing trace gas growth rates, such that the annual increase of the greenhouse climate forcing remains at the current level..

..Scenario C drastically reduces trace gas growth between 1990 and 2000 such that the greenhouse climate forcing ceases to in crease after 2000..

Finally the scenarios are summarized in the following excerpt:

These scenarios are designed to yield sensitivity experiments for a broad range of future greenhous forcings. Scenario A, since it is exponential, must eventually be on the high side of reality in view of finite resource constraints and environmental concerns, even though the growth of emissions in Scenario A (~1`.5% per year) is less than the rate typical of the past century (~4% per year). Scenario C is a more drastic curtailment of emissions of chlorfluorocarbon (CFC) emissions by 2000 and reduction of CO2 and other trace gas emissions to a level such that the annual growth rates are zero (ie., the sources balance the sinks) by year 2000. Scenario B is perhaps the most plausible of the three cases.

Notice that Scenario A is in effect the trend of business as usual for the 1970s and 1980s without volcanos and some other, I assume, minor effects added that were not used in the other scenarios and actually was less than the century trends to that point. No explanation is then given why Scenario B was presented as the “perhaps” the most plausible of the three cases. Note that conditioner “perhaps” is in the original and not in the 2006 paper.

The difficulty in selecting inputs for scenarios becomes easier to understand when one looks at the included graph in (presented below the excerpts) and reads the following excerpt from the paper “Trends of Measured Climate Forcing Agents”, by James Hansen — February 2002:

http://www.giss.nasa.gov/research/briefs/hansen_09

A closer look at actual data is given in Figure 1. The picture of accelerating growth was valid from end of World War II until 1980, by which time the greenhouse gas forcing was increasing at a rate of 5 W/m2 per century. It is no wonder that climate models in the 1980s began to predict the possibility of dramatic climate effects in the 21st century. A continuation on that path would have led to the equivalent of doubled CO2, a forcing of 4 W/m2, by about 2025.
In reality, although greenhouse gases continue to increase, the growth rate has slowed to about 3 W/m2 per century. A big factor has been the phase-out of chlorofluorocarbons (CFCs), which was accomplished by cooperative international actions. The growth rate of methane (CH4) has fallen by two-thirds, at least in part due to slowing of the growth of sources. The growth rate of CO2 flattened out in the past 25 years, as the rate of growth of fossil fuel use declined from 4% per year to about 1% per year.

An Inquirer

Lucia,
Thanks for your comments on aerosols and the link. I have perused a few of these articles, and they prompt the following comments. At first, the information led me to have an increased respect for Global Climate Models, but after further reflection, the size of the respect increase is somewhat limited. A pro-AGW website provided this basic information (in general — with a strong emphasis on approximate). If we include solar variations and volcanoes (natural sources), then we can explain about 10 to 15% of past variations in temperature. If we include only human sources of CO2 and aerosols, then we can explain 60 to 70% of past variations. And we include all four, then we can explain 85 to 90% of variations. My longstanding concern has centered on the sources of aerosol data. CO2 as an exogenous variable could be replaced by any variable that has an overhaul upward trend — world population or # of presidential libraries or # of World Series, or sightings of Elvis. Of the four variables, aerosols really make the difference in explaining temperature variations, so it would be important that aerosol values come from legitimate and verifiable data series. Your link revealed that some aeorosol data has been collected since the late 1990s. That’s comforting, but as you essentially point out, there seems to be a large black box on how modelers put in aerosol data for data going farther back. By cherry picking data inputs for aerosols, you can make CO2 — and other GHGs — appear to be the driving force. As the IPCC points out, there are a number of variables for which we have low understanding. If aerosol numbers were not so conveniently picked, it could be that the rise in recorded temperatures over the past few decades has been due to those variable so little understood. This latter consideration must be given its due before conclusions are reached — it appears that temperatures have been on the increase for at least 200 years, long before CO2 emerged as a likely culprit. (And a note to readers of Watts, I understand that the GISS-stated increases could be overstated, but still the temperatures have increased.)

lucia

@An Inquirer:
First, I’m not a big fan of GCMs, but I also not quite as big a detractor as some here. I think GCM’s give at least qualitatively correct results. I disagree with what strike me as some overstatements about their quatitative accuracy I read some places. I’m not entirely sure they give more accurate quantittative predictions of the global mean temperature than simpler o-d, 1-d, 2-d models. (That said: there is great promise in GCM’s; only they have the potential to help predict local effects. I don’t know how well they do at this, but my impression is: They give at least good qualitative results.)

My longstanding concern has centered on the sources of aerosol data. CO2 as an exogenous variable could be replaced by any variable that has an overhaul upward trend — world population or # of presidential libraries or # of World Series, or sightings of Elvis.

Yes. If you do a true curve fit and fit the data to any any exogenous variable with ah upward trend, you can fit the data.

With regard to GCM’s though, you need to provide some justification why the particular exogenous variable causes “forcing”. What they call forcing is related to something called “optical depth”, which can actually be measured in labs (for some things.) So say you wanted to include “Elvis Sightings” in your GCM. You would have to ask: there a physical mechanism that would indicate the optical depth of the atmosphere increases with Elvis sightings? No. There is not. Are there any laboratory measurements relating optical depth of any gas to Elvis sigthings? No.

Because the answer to both is no, you don’t get to add Elvis sightings. That leaves parameters that are known to affect optical depth.

So, the question with regard to the forcings per se are:
1) Are the ones chosen physically justified? (How well?)
2) Do we know the magnitude of the forcing well?
3) Have we missed any? (This is generally the hardest question. We never really know the anwer to this. )

You can ask that of each forcing. With regard to Aerosols, the answer to (1) is, yes. It’s physically justified. And to (2), I don’t really know how well we know the magnitude. (I mean this in the blandest way. This is simply something I don’t happen to know. Others who actually deal with the effects of aerosols on radation may know the answer very well.)

Of the four variables, aerosols really make the difference in explaining temperature variations, so it would be important that aerosol values come from legitimate and verifiable data series. Your link revealed that some aeorosol data has been collected since the late 1990s. That’s comforting, but as you essentially point out, there seems to be a large black box on how modelers put in aerosol data for data going farther back. By cherry picking data inputs for aerosols, you can make CO2 — and other GHGs — appear to be the driving force.

Well… sort of. There is room for some cherry picking, but likely not so much as you think.

Be a bit careful in concluding that I “essentially point out” there is a large black box. It’s certainly a large black box if one must rely on my knowlege of the existing data about the role of aerosols in forcing. There may be much more data. I’m aware of ARM only because my husband worked on that program (dealing with water vapor measurments.) The fastest way to find a link to show that there are many experimental programs to study aerosols was to google his name and the topic. ARM funds lots of “in field” studies; the experimentalists tend to collect at the same meetings. So, I knew I’d find papers discussing experimental studies of the effect of aerosols at meetings that Jim attended!

So, that link doens’t represent the universe of results. It just shows that there is experimental support for the role of aerosols– that’s not just picked out of nowhere.

But is there some uncertaintly in the forcing due to aerosols? I suspect there is quite a bit.

As the IPCC points out, there are a number of variables for which we have low understanding. If aerosol numbers were not so conveniently picked, it could be that the rise in recorded temperatures over the past few decades has been due to those variable so little understood.

Yes. And the only real response to that is: As far as I can tell, there relatively good physical basis for aerosols. That said: I’m not an expert on aerosols.

Still, I have a wordsmitthing quibble. I wouldn’t say they were just “conveniently” picked, but do acknowledge that there role was only recognized after people began to suggest CO2 was increaseing the temperature. So, from the point of view of a full blow skeptic, this can look like it was “conveniently picked”, because of the timing.

Nevertheless, there is some verificaiton after the role of aerosols was identified. The role of aerosols was recognized before pinatubo blew, and the temperature did drop after pinatubo blew. GCM runs that include aerosols catch that temperature drop; those that don’t, can’t.

Moreover, it is true the models predicting the post-Pinatubo temperature drop were run before the temperature dropped. (I don’t have that paper here, so I don’t know precisely how good the match was. )

That means: in fairness, at a minimum, you need to give modelers credit for trying to provide true forcasts based on the forcing estimates for aerosols, and those forecasts were tested against data.

This latter consideration must be given its due before conclusions are reached — it appears that temperatures have been on the increase for at least 200 years, long before CO2 emerged as a likely culprit. (And a note to readers of Watts, I understand that the GISS-stated increases could be overstated, but still the temperatures have increased.)

Temperatures have been mostly increasing for a long time.

Out of curiosity… do you have good thermometer based global data back to 1807? If you do, I’d love to see this! I’m fiddling with a little hobby lumped parameter model, and I’d like to be aware of any older thermometer data.

Francois Ouellette

#151 Kenneth,

There is an effect called the “certainty trough” (do a google search for more info), that states that the scientists closer to the source of knowledge are more uncertain than those who stand at some distance from the source. At the opposite end, those who are “alienated” by the knowledge (e.g. those who support an alternative theory) also have more uncertainty towards that knowledge. Those in the middle, e.g. journalists, users of the knowledge (those who “believe what’s in the manual”), are the ones with the lowest uncertainty. That effect was first proposed by David McKenzie in his book “Inventing accuracy” about nuclear missile guidance. In climate science, Myanna Lahsen in her text “Seductive simulations” has used it to explain different group’s attitudes towards models. She found that the “trough” model did not quite apply to modelers, because none of them is really at the source of the models: models are highly cooperative endeavors, that extend over many years, and each modeler is only responsible for a small portion of the code. Therefore modelers tend to show a higher degree of confidence towards the model than expected by the the “certainty trough” model.

In a piece of work I did for a course I followed last year, I tried to apply the model to the development of wind turbine technology. What I found is that the “certainty trough” model does not describe the situation where a technology (or, say, a new theory) is not yet well established. In that case, those closer to the source of knowledge do not show any uncertainty, quite the contrary, they are strong advocates of their theory or technology. In that case those at a distance show the greatest skepticism, while those alienated by the established theory or technology see greater promise in the new one. The “trough” is inverted! As the new technology or theory gains acceptance, the trough gradually inverts itself, but only if the new technology fulfills its promises.

I think you would find that in climate science, at least outside of the modeling community, most scientists would readily admit that there is still a lot of uncertainty. As you mention, this is implicitly or explicitly stated in the source literature itself. Somehow it disappears with distance, as for example in the IPCC reports, and it gets worse in popular media. However, it may well happen that if, for example, temperatures stop rising for another couple of years, the “trough” will start to invert again…

David Smith

Hansen cautioned us about the likelihood of “heat wave droughts” in the American Southeast and Midwest in the late 80s and 1990s. So, I’ve been looking for evidence of those, at least the heat wave portion.

Today’s chart concerns Saint Louis, Missouri, part of the American Midwest. I took a look at the daily high temperature records (like in your newspaper, where it reports that “the record high for January 22’nd is 12 degrees C, set in 1947”) and the decades in which those records were set. If the 1990s and 2000s have been seeing higher highs then these decadal records should hint at an uptrend.

The raw data is here . I excluded the 19’th century as I’m not clear as to when coverage began. I adjusted the 2000s data upwards by 1.25x to account for that period having but eight years so far.

The graphed data is here . I see nothing extraordinary about the 1990s. The 1930s and the 1950s were the times of heat extremes in Saint Louis. By contrast, the 2000s (so far) look ho-hum.

Mike B

bender writes:

The opening post by GS is convincing to me that it is unlikely that a positive, err, “trend” in temperature has abated since 1998. My question concerns the quantititative estimate of the slope of that trend. The gatekeepers’ job is to steer discussion toward yes/no debate on AGW, and away from a scientific discussion of the magnitude of slopes on those 8-year trend lines and the contribution attributable to CO2. The uncertainty on the slopes is high, and the proportion that can be attributed to CO2 even more uncertain. They do not want you to talk about that. They want to push the skeptic back to the less defensible, categorical position that GHG/AGW=0. Much easier to win that argument.

Furthermore, when you start splitting those trends into latitude bands 1) the uncertainties on the slopes get larger and 2) the empirical case for global warming is seriously questioned. The higher latitudes are certainly warming, but the trends aren’t everywhere.

I’ll post more later this week.

Kenneth Fritsch

Re: #55

I have a question. Where do the “wiggles” in the 1988 projections come from?

Roman M, Figure 1 in Hansen et al 1988, which I copied to my Post #146 in this thread, shows the climate model control run (without any forcings) with all the trends and wiggles you might see in a graph of actual temperatures over recent times. I am quite surprised that that graph has not been discussed in more detail here at CA on the Hansen scenario threads.

An Inquirer

Lucia,
I am impressed and grateful that you would take your time to respond and to help clarify your insights so that we have more light rather than heat on the issues. Most likely, we have agreement on most points, and sometimes in efforts to be concise and efficient in postings, a point can be misunderstood. Yes, I agree that aerosols and CO2 are legitimate inputs to GCMs and that Elvis sightings are not. And again, you have pointed out references that suggest aerosol values are not arbitrarly picked out of thin air to get good model fits. However, I do use the phrase “conveniently picked” which is a little softer than arbitrarly picked; skeptics (more skilled than I) who have examined the models maintain that aerosol input values have dubious legitimacy, and nothing that I have read in pro-AGW blogs have convinced me otherwise. So in my personal scorecard, skeptics on this point do not score a 10, but they are quite above 0.
As far as my 200-year old thermometer 🙂 — thanks for the opportunity to smile! I say “it appears that temperatures have been on the increase for at least 200 years” because NASA (as well as others) tells us that most glaciers in the Northern Hemisphere (at least the ones for which we some type of recording) have been retreating since the 1700s. Of course, other reasons could exist for glacier retreat (e.g. Kilimanjaro’s glacier retreat is apparently due not to increased temperatures but rather to decreased participation — and perhaps human clearing of trees has a role in that!), it still seems okay to assume that most glacier retreat is due to higher temperatures.

Steve McIntyre

#161. Lucia, this issue was discussed a while ago in connection with Kiehl’s paper (and presentation at AGU). Kiehl observed that there was an inverse correlation between sensitivity in climate models and the adopted aerosol history so that there was noticeably smaller variation in the projections than would be generated by the variation in the inputs. So there’s definitely some tuning going on. Running the GCMs across the spectrum of histories would spread out variability a lot.

On the other hand, I’m satisfied (and Ramanathan’s presentation was very convincing) that aerosols are not just a rabbit out of a hat, but a real effect.

MarkW

I have never met anyone who claimed that aerosols have no affect.
The debate has always been on whether models have a handle on aerosols.

In my opinion, Kiehl’s paper can also be read as the modelers putting in just enough aerosol’s to get the answers they were looking for.

henry

David Smith said (January 22nd, 2008 at 1:37 pm)

Re #159 Bill, I’m afraid that the extreme-temperature data could indeed suffer from some of the same problems (station relocations, physical changes near the instruments, changes in instruments, etc). But, being National Weather Service records, I hope that they are of higher quality than the backyard MMTS devices often shown on Anthony Watts’ site.

Unfortunately, most of the “backyard” MMTS devices shown on Anthony’s site ARE the official NWS devices, used to create the NWS records. The term “higher quality” is the question being answered at surfacestations.org.

henry

David Smith (#167)

Not sure if re-wording actually helped.

Again, the majority of the “National Weather Service staffed sites” are considered CO-OP stations, with volunteers taking readings, transcribing them onto forms, and sending them into the NWS.

As far as I know, very few of them have formal meteo training.

bender

Is it worth fixing the RSS sat data in these graphs?

lucia

Willis–
Hhmm.. yes. Kinks like that don’t look real! I’d ask whether the modelers cite where the data come from, but I know the way peer reviewing actually works. If a group accepts something as standard, it gets a pass.

Do you have any idea what the experimental values are?

bender

A comment for Phil and Boris addressing #22, #28, #104 that is best filed in this thread:
This exchange between GS and CK proves that the A scenario exerted undue influence on policy.

When I wrote #104 I did not realize that the issue of “Hansen’s favorite” had broader implications in terms of which scenarios were being used in IPCC composite projections. In this sense, his “favorite” does matter, quite a bit. If B is far more likely than A, then why does alarmist A weigh in as heavily in a composite projection?

pea in thimble

lucia

Willis,
I think we agree on at least three things:

* Aerosols have a real effect. It’s not like parameterizing using “Elvis sightings” –which was sort of what I was actually asked.

* The magnitude of the effect is not known particularly well. (In an email, Gavin suggests the uncertainty in the trends for Aerosol forcings could be a factor of 2. You suggest 3. FWIW, my background is analytical/ experimental. When a modeler says the uncertainty in his parameters is a factor of 2, I don’t tell them they are wrong. However, as a person trying to make some quick assessment, my working assumption is the uncertainty factor is 4 and remains so until I can do more checking. When an experimentalist says the uncertainty in a range of known values is 2, I assume the uncertainty is 2– or 1! )

* It’s not my area, so I don’t know all the specifics. If asked, I can only answer, based on the constraints I know about modeling in general and stereotypes I have about the sorts of answers I’m likely to get out of modelers or experimentalists! (Still, if someone asks me directly, I’ll say what I think, and also point out that it’s not my area.)

What I think: In the general idea that one must account for aerosols is correct. But within that range, modelers have a lot of leeway. They can pick and chose what effect they want to stuff in.

So, if — as in his question– Enquirer wants to liken adding the effect of aerosols to picking anything out of the blue– like “Elivs sightings”– then my answer is going to sound like I’m supporting aerosols general plausibility.

In contrast, if a modeler starts asking me to believe their long term projections are within a gnats ass of the correct numerical values….well… I’m not going to buy that unless they’ve been really and trully verified against measured data. And I’d prefer that verification not be done by the modeler or modeling team!

===========

On some specifics– since I have Hansen’s paper right here, here are some corrections on details:

Near as I can tell, Hansen et al. 1988 did not consider aerosols. It also had low climate sensitivity. When you add the aerosols, conventional wisdom is that there is an overall cooling effect (although this has not been demonstrated). Of course, when you add a cooling effect, you have to jack up the heating effect to re-balance the model. Thus, the climate sensitivity has to increase.

On the first two sentences:

Hansen 1988 et al. does include aerosols to at least some degree. On page 9362, if Hansen 1988, you’ll read how he dealt with stratospheric aerosols and relates that to volcanos. In Figure B1, he discusses tropospheric aerosols, desert aerosols and soot aerosols.

He also states the sensitivity of the GISSII model to doubling of CO2 from 315ppm to 630 ppm is 4.2 C. (page 9342) That’s on the high side, right?

Now on to the interpretive part of that paragraphs: yes, to match real earth climate data, if
a) you have a given model (say GISS II) with coded physics, with parameterization for physcial processes.
b) you run it with certain forcings, and match the trends in surface temperature from say 1950-1984) and then
c) you add a cooling effect, the model will afterwards almost certainly under post-dict the data.

So, to fix the mismatch after step ‘c’, the modeler can add a heating effect or fiddle with the internal workings of the model. (I guess I consider “the model” to be the parts that include the turbulent boundary layer, the cloud physics, etc. And the forcings to be “input”. The predictions are then “output”. So, what I mean is, to get the output to match the data, either you fiddle with internal parameterizations or you fiddle with the ‘input’– aka forcing anomalies.)

Fiddling with the internal workings to match a zero order trend like annual average mean surface temperature is tricky. The modler will be reluctant to do this. But, if your model was previously pretty close, and you get to run the code enough, you can fiddle with various choices in forcing anomalies and match the trends. (In the longer run you find better internal parameterizations. But as long as they are parameterized, there is occasionally little objective reason to prefer the parameterization to the older one.)

This process may simultaneously be respectable science and/ or ad hoc fitting. (Empiricism is the core of the scientific method. So, ultimately, fitting to data is done. Of course, we also find theories give structure to all this but theories only survive if their predictions ultimately fit the data.)

Whether the model fitting process is “science” or just “ad hoc fitting” ultimately depends on precisely on how it’s done, how much verification is done afterwards, what claims one makes about the predictions of the model one develops (or concocts) this way and what is going on in the modeler’s head. (I’ll dispense with the 10 paragraphs I could now write. . . )

(As for the Khiel data: Yep. That’s the sort of relationship I’d expect. Model results won’t be published unless they more-or-less match zero-order climate trends in the recent past. Basically, everyone is going to compare their model to the surface temperature trend, if it doesn’t match, it won’t be accepted by peer reviewers. Different models have different sensitivities. Since, if all published results are constrained to match the surface temperature trends, then those models with high senstitivities must pick low aerosol forcings and vice versa. )

This means that, in a very real sense, the main support we have for Climate Change is the empirical data! (That’s why, in my opinion, ultimately all the important arguments in the climate change debate end up being about the measured data!)

Willis said:

This is the reason that most people, myself included, think that they’re just making up the aerosol numbers. If they weren’t, if they were actually based on real data, they wouldn’t vary by a factor of three.

Oh… well, … tangent time! 🙂

You must be used to the luxury of precise and accurate data on which to base recommendations. I’ve been on projects where numbers for viscosity varied by a much more than a factor of three but the values weren’t made up. The values were measured from single core samples of aging radioactive waste in a tank. And, on projects where the estimate for the diffusivity of hydrogen through concrete varied by two orders of magnitude. (The concrete was special concrete for which diffusivity data did not exist; I had to estimate from available data for different types of concrete.)

Of course, we all admitted the range of uncertainty, considered what might happens for every possible range of physical parameters, and recommended decision makers to consider on the worst scenarios when trying to figure out which mitigation options for safety problems could be implemented in a manner that was both safe and effective.

So, the way I see it: Differing by a factor of 3 only means the values are imprecise. It doesn’t mean they are made up. (It certainly doesn’t mean that introducing the effects of cooing by aerosols is like introducing Elvis sightings into the model. ) Being off by 3 is a problem if you want the models to give more than results that are both precise and accurate. It may not be so bad if all you want is an estimate, knowing it could be off by quite a bit.

lucia

Willis– I thought 4.2C was on the high side? (Isn’t the range 1.5C to 4.5C for CO2 doubling? Or do I totally misunderstand something?

I haven’t read the aerosol thread yet. (I also have a job– but part time which is what I like.)

Sam Urbinto

Then there’s the hidden assumptions. One, that CO2 could even get up to 800. The other is that the anomaly would do anything if it did get to 800. Even if the anomaly is affected by CO2 in the first place, there’s nothing that says something else wouldn’t moderate it to 0

It seems like it’s all conjecture. If your model has to come out like everyone elses to be accepted, and the models can be tweaked to give you the answer you want, what good is it?

Reporter: “What’s the climate sensitivity for CFC gasses?”
Modeler: “What do you want it to be?”

😀

Hans Erren

here is a cartoon by Hansen showing a GHG sensitivity of 0.75 K/Wm-2 in the ice age, which is 2.7 K/2xCO2.

Can we defuse The Global Warming Time Bomb?

lucia

Oh… I think your fits are post feedback.

I am now in the process of creating the most bring climate blog on earth. However, I have posted the long boring answer here: at my blog Naturally, I have figures like this:

The short answer is: if the “planet GISSII” has a big time constant, and you increase the “forcing” linearly (or just in any monotonically increasing function of time), you’ll tend to under-estimate sensitivity by fitting lines near “time=0”, or using much model data near time=0. (I explain linear ramps.)

I don’t know what effective time constant “Planet GISS II” has, but if it’s large you’ll underestimate the model time constant that way. (If the time constant is very small, you’ll be just fine! )

Still, if you eyeball Hansen’s curves…. notice how flat the temperature profiles are near 1958-1970? That’s likely due to mostly to the fact that they started the runs from “perpetual 1958”, and so were at pseudo-equilibrium and then started to ramp up the forcing. On the one hand, starting from perpetual 1958 is probably better than starting from some unknown non-equilibrium value. But, still, that shape is what the way you’d expect that model data to look like if you start at equilibrium and then ramp the forcing up with time.

lucia

@Ken– Heh. There are many things no one knows about fluid dynamics. There are even more I don’t know. Lucky for the world, sometimes you can do a lot even when you don’t know many things!

Out of curiosity, do you drink your martinis under high pressure? 🙂

(I get the Trib Wed-Sunday, so I missed that article. )

lucia

Willis– No. Literally speaking there is no single time constant for a whole planet.

I haven’t written up precisely how the idea of treating the planet as a single average lump with one temperature relates to reality. I will if the curve fit doesn’t die due to its own internal inconsistencies,. (Otherwise, I’ll just say “Well, it didn’t work. So, no one needs to know what it would have meant had it worked! 🙂 )

The one thing I will say: with model this simple, you can’t worry about the heat on opposite sides of the planet (day/ night). That’s averaged out because the climate is “one lump” with one average temperature. Yeah, it’s hotter on the day side, but colder on the night side.

I don’t know your particular technical background. Some people here are stats, some physics, some econometrics etc. And I don’t know which are which. (I’m guessing your more physics, and less stats.)

But, from the physical side, not worry about some short time scale stuff is similar to not worrying about velocity fluctuations about a mean in a turbulent flow (for the purpose of curve fitting!) This time scale is more like an “integral time scale” rather than the time scale for individual eddies. (Note, in a turbulent flow, the integral time scale can vary in space. 🙂 )

I will however, be checking that the values I get aren’t pathologically incorrect when used to describe the month-to-month variations in the surface temperature. (Steve Moscher showed me where to find the correct Cru absolute data. Thanks Steve. As it happens, for any value of time constant/ sensitivity value for the “climate lump” there is a specific phase lag that I will expect between the time of maximum average solar radiance for the planet to the time of minimum. If the time constant for the anomalies is totally different from the one for absolute values, the model fails.)

Also, remember that I’m primarily using this idea to do curve fitting, not replace a GCM. So, from that point of view, the idea of a parametric relationship between Temperature(t) and Forcing(t) related by the dynamics of a simple system, ought to give a better fit saying

Ti =a + k1 F1i + K2 F2i + ……

Where ‘i’ is the index for time in a time series.

Of course, if this works, and I proceed to a two lump climate system, (like atmosphere and ocean). Two lumps would probably make the more physically oriented readers happier because the physics make more sense the more “lumps” you create for the planet. However, it will disappoint the statisticians. More lumps” means more physical parameters to fit to data. But, from a point of view of curve fitting, each parameter is a tuning knob!

lucia

Hmmm… Would a box of Fannie May Pixies be sufficient payment?

steven mosher

Re 190. Ni. Ni. Ni. Ni.

I want a pair of socks. Knitted in the finest harlequin pattern.

And a shrubbery, one that looks nice and not too expensive.

green man

This blog: http://www.climate-skeptic.com/2008/06/gret-moments-in.html

has updated the graph, 2008 has not been kind to the predictions.

pete best

Is anyone here an actualy peer reviewed climate scientist or is this thread full of scientists or informed laymen who seemingly would like to berate Gavin Schmidt and James Hansen without any real ability to do so ?

How many of you have posted at real climate in regard to the article as you reckon one thing and real climate another. Has climate science through the IPCC or other bodies refuted this aergument either way?

UC

I know many CA readers who have relevant PhD and have been censored at RC 🙂

Mikkel

Let me being by apologizing if this has already been brought up. I tried to skim the 199 previous posts but could have missed it.
Firstly I think it is fair to say that Hansen had Scenario B as his most likely in his article at least where it is stated pretty squarely. This could be different from his appearance in congress – I wouldnt know.
On a different side my main point is to draw attention to how we have actually been following Scenario A for the past 20 years. In terms of GHG emissions that is. According to Hansen 1988 he for Scenario A expects an increase in emissions of 1,5% yearly. According to IPCC 2007 we have gone from 39,4 to 49 GtCO2-eq from 1990-2004 which is a 1,6% yearly increase in emissions. Similarly Hansen 1988 cites 345 pmvv CO2 in the atmosphere with 1,5 increase annually in a business-as-usal world. According to CDIAC it is presently at 383,9 which would require an average annual increase of 1,945 pmvv.
If we have actually been following Scenario A in terms of emissions wouldn’t it then be fair to expect a better fit in terms of temperature development? I cant say if it follows B or C better but it is certainly not A by any standards.
Sorry again if I got it wrong or repeated the issue.

Steve: the difference between A and B is mainly in the CFCs, which are a lot less than A. So B is the most reasonable one to judge against.

oneuniverse

The link to Hansen’s 1988 oral testimony to Congress is broken. A copy is currently available here.