Climate Sensitivity and What Lewis and Curry 2014 Has to Say About It 12

[rconnor]

Ah yes, it’s that time again folks. A paper is released, in this case Lewis and Curry 2014, that says climate sensitivity is on the low end of the spectrum and the “skeptic” community starts banging pots and pans claiming the ACC theory is dead. Well, like most things in the field of climate science, it's not nearly that simple. Let's look at the entire story.

Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR)
Equilibrium Climate Sensitivity (ECS) – the amount the planet will warm in response to a doubling of atmospheric CO2 concentration (the base is usually taken from preindustrial levels of 280 ppm). ECS includes both fast and slow feedbacks, so ECS is not fully realized for decades or centuries after CO2 is held constant.

Transient Climate Response (TCR) – similar to ECS but only includes fast responding feedbacks. In other words, TCR is the temperature rise at the time atmospheric concentrations hit 2x the baseline, not where it will settle out to. As slow responding feedbacks tend to be positive, TCR is smaller than ECS.

These two are not the same and should not be confused. Many “skeptic” arguments prey on this confusion, be careful.

The Body of Knowledge on Climate Sensitivity
First, here’s a good list of the spectrum of peer reviewed literature addressing climate sensitivity. If you actually want to understand the topic (instead of cherry picking things that fit your viewpoint), it’s import to look at the body of work, that’s kinda how science works. Here’s a graphical representation, from AR5 WG1 Fig Box 12.2-1:
[image

http://www.climatechange2013.org/images/figures/WGI_AR5_FigBox12.2-1.jpg

]

To claim that a single paper can definitely set climate sensitivity, is false. While on the low side, Lewis and Curry 2014 does sit within the spectrum of other estimates.

Lewis and Curry 2014
Now to the paper itself. Lewis and Curry 2014 (LC14) is very similar to Otto et al 2013 (they both take the energy balance model approach), just with different heat uptake rates and reference periods.

LC14 has a heat uptake rate (0.36 Wm^-2) that is almost half of Otto et al 2013 (0.65 Wm^-2). The uptake rate used in LC14 comes from a single model, not an ensemble mean, and is, surprise, surprise, a very low value (which leads to lower ECS).

The ending reference period (1995-2011) was selected to “avoid major volcanic activity”. Although this seems odd considering Vernier et al. 2011 found that volcanic activity greatly affected the 2000’s. Furthermore, it is well known that the last decade has been a La Nina dominated period which would further add a cooling bias to their ending reference period, and thus artificially lower their ECS and TCR estimates.

Now new evidence (Durack et al 2014) suggests that “observed estimates of 0-700 dbar global warming since 1970 are likely biased low. This underestimation is attributed to poor sampling of the Southern Hemisphere”. Using the results of Durack et al 2014, the ECS would rise (15% according to a tweet from Gavin Schmidt).

The paper makes no mention of Cowtan & Way 2013 which demonstrates and corrects the cooling bias in HadCRUT caused by a lack of coverage in the heavily warming Arctic. Therefore, much of the recent warming which is occurring in the Arctic is unaccounted for in this paper. This would cause an artificially lower value of ECS and TCR.

The paper also ignores Shindell 2014 and Kummer & Dessler 2014 (most likely because they are too recent). Both of these papers highlight the inhomogeneities in aerosol forcing which may cause energy balance models to underestimate ECS and TCR.

Finally, the rather simplistic technique used in LC14 (and Otto et al 2013 as well) ignores all non-linearities in feedbacks and inhomogeneities in forcings. The exclusion of these elements leads to a lowering bias in TCR and ECS. Due to the fact the sample period and technique used introduce lowering biases into the results, LC14 may be useful in establishing the lower bound of sensitivity but in no way offers a conclusive value for the median or best estimate.

It should be noted that the results of Lewis and Curry 2104 implicitly accept and endorse the core of the Anthropogenic Climate Change theory; namely that increases in atmospheric CO2 will result in increases in global temperatures and that feedbacks will amplify the effect. For example, if you feel that the recent rise in global temperatures is due to land use changes and not CO2, then the TCR and ECS to a doubling of CO2 should be near zero. Or, if you feel that "it's the sun" and not CO2 then the TCR and ECS to a doubling of CO2 should be near zero. The recent change in climate is "just natural" and not CO2 you say? Well then TCR and ECS should, again, be near zero. So, if you've found yourself claiming any of the preceding and now find yourself trumpeting the results of LC14 as proof for your side, then you, unfortunately, are deeply confused. If you want to accept LC14's value for TCR of 1.33 K as THE value for TCR (which it isn't), then you also accept that majority of global warming is due to anthropogenic CO2 emissions.

What About Other Papers that Claim Lower Sensitivity?
As I stated from the outset, Lewis and Curry 2014 is hardly the only paper to address climate sensitivity. Beyond that, it’s hardly the only paper to suggest that climate sensitivity is on the lower end of the IPCC spectrum. I’ve addressed a few already but there are more (Lindzen 2001, Spencer & Braswell 2008, etc.). However, almost all of these papers have been found to have some significant flaws that cast doubt on their conclusions. Various peer reviewed rebuttals to these papers are listed below. I’d welcome readers to review the rebuttals and the original authors response to them.
[image

http://static.guim.co.uk/sys-images.../11/1397174224604/lowSensitivityRebuttals.jpg

]

...But What if Climate Sensitivity WAS Lower Than Expected
Let’s ignore all this for a second and pretend that, with Lewis and Curry, we can definitively say that climate sensitivity is lower than expected. Then what? Does this completely debunk the ACC theory? Does this mean rising CO2 levels really aren’t a concern? Well, many “skeptics” would say “YES!” but they do so without ever actually examining the issue.

According to Myles Allen, head of the Climate Dynamics group at Oxford:

A 25 per cent reduction in TCR would mean the changes we expect between now and 2050 might take until early 2060s instead…So, even if correct, it is hardly a game-changer…any revision in the lower bound on climate sensitivity does not affect the urgency of mitigation

.

The issue is that, with atmospheric CO2 levels rising as quickly as they are, a lower TCR does not mean anything significant. It just means that the effects will be delayed slightly. So even if “skeptics” were correct in saying that climate sensitivity is definitely at the lower end of the IPCC range (which they’re not), it would have no substantial impact on future global temperatures or the need to control CO2 emissions.

So, Lewis and Curry 2014 is:
1) Inconclusive to definitely say that climate sensitivity is on the low end of the IPCC spectrum
2) The results are suspect and appear to include numerous biases that would lead to lower TCR and ECS
3) Even if it were conclusive and accurate, it would still not suggest that reductions in CO2 emissions are unnecessary. In fact, it adds to the scientific body of knowledge that temperatures will continue to rise to unsafe levels if we continue with the status-quo, just maybe a decade later than other estimates.

(Note: I’ve started this new thread to discuss climate sensitivity specifically. It is an important topic that popped up in another thread and I felt it merited its own discussion. I would, as much as possible, like to keep the conversation on this subject…although this is likely wishful thinking)

 

[GregLocock]

No this is what you actually said abut the graph when you posted it

"one cannot conclude that the results are trending to the lower end as this is unsupported by the data (in fact the inverse is supported by the data)"

So you claimed your data which should never had a straight line drawn through it in the first place, with an R^2 of 0.05, 'supports' your argument.




Cheers

Greg Locock


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[rconnor]

I have since retracted the statement in parentheses in 3 subsequent posts, all of which I quoted again for you.

You continually pick the most minor, most irrelevant aspects of these posts to comment on and completely ignore the heart of the discussion. Now either address the criticism or quit wasting my time with this charade.

 

[GregLocock]

I think we've reached the end.


Cheers

Greg Locock


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[IRstuff]

The whole assertion that this is all merely "curve fitted," is belied by the very thing that some complain about, namely, the rather large spread in the predictions. If it were merely curve fitting, then this left-wing, pinko, commie conspiracy would simply agree on the value of temperature rise, and everyone would curve fit their way into the same predicted values. The fact that they don't, and can't, come up with the same answer points to the fact that it's not curve fitting, and that it's probably too complicated to do that anyway, even if they could.

TTFN
faq731-376

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Of course I can. I can do anything. I can do absolutely anything. I'm an expert!

 

[rconnor]

I think we’ve reached the end

Ok.

Disappointingly, you’ve offered nothing but attempts to obfuscate the argument and left us here:
[ol 1]
[li]GregLocock's own analysis leads to TCR values higher (2.1-2.3 K) than the IPCC best estimate of 1.8 K and much higher than the LC14 value of 1.3 K. So what are you arguing? That TCR is higher or lower than the IPCC estimate? (Should I assume "lower" and that you'll just forget about the other analysis?) GregLocock has stated that his curve fit analysis is likely flawed. I would agree with him.[/li]
[li]There is no strong evidence to support a statement that "sensitivity estimates are dropping over time" (a R^2 of 0.08 is evidence of nothing...especially when the start date was adjusted to give a negative correlation)[/li]
[li]Even if a strong correlation existed, it does not logically follow that sensitivity estimates are trending towards a more accurate and lower value. Evidence is required to demonstrate how these lower estimates are "more valid" than other estimates and techniques. Nothing has been brought forward. Just because you like lower values more because they fit your ideological preferences doesn't make them more valid.[/li]
[li]Building on #3, evidence has been brought forward that demonstrates that LC14 (and other energy budget model techniques) are actually less valid. They are over simplistic models that contain numerous biases that lead to artificially low sensitivity values. While these methods may be applicable to establishing a lower bound, they are not valid as a "best estimate". Especially when incorporating the most up-to-date data and research (Durack 2014, Cowtan and Way 2013, etc.), the sensitivity estimates for LC14 (and other energy budget models) would be higher.[/li]
[li]Even if TCR was definitively 1.3 K (which it's not), it would mean that IPCC estimates for temperature rise would still be correct but would just be ~10 years early (i.e. impacts expected to happen in 2050 would, instead, happen in 2060). This is not a deal breaker and would still require mitigation to avoid future temperature rise.[/li][/ol]

As I stated, GregLocock is not the only one that’s argued “sensitivity is lower than the IPCC thinks!” Now that this argument has been shown to have serious and fundamental flaws, it’s not solely on GregLocock to defend it. Of course, no one HAS to defend it but it would be disingenuous to not do so but then parrot this argument again in a future thread (i.e. use a “zombie argument”). Furthermore, given that low sensitivity is crucial to the skeptic position, not having any proof supporting low sensitivity would be quite damaging to this position.

 

[GregLocock]

OH, you can't stop

My summary is that since the IPCC was formed in 1988 the 10 year average peer review reported CO2 sensitivity has dropped consecutively since then. rconnor wishes to ignore the later findings, which of course makes any discussion of trends moot.

Irstuff- the whole point about the curve fitting is that the climate models are trained using some of the historical data.

The end.



Cheers

Greg Locock


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[rconnor]

GregLocock,

I was simply restating what you had been ignoring in case someone else wanted to take up a defense of the assertion that sensitivity is lower than expected. If you would like to leave the conversation, please feel free to do so. I would like to continue to discuss the topic as I feel it is important and central to the climate change debate.

Unsurprisingly, GregLocock’s latest response continues on the trend of ignoring the criticism put forth against your viewpoint:
“My summary is that since the IPCC was formed in 1988 the 10 year average peer review reported CO2 sensitivity has dropped consecutively since then” – See issue #2. This statement hinges on a R^2 of 0.08, which is evidence of nothing. Furthermore, GregLocock himself has stated “a straight line fit was a stupid idea”. I would agree that it is was a stupid idea by me in the first place (and have said so repeated after making the initial mistake). A linear trend without understanding the data points and what they mean is useless (see issue #3). But he then took that “stupid idea”, changed the start date and now uses it as his only support for his assertion.

”rconnor wishes to ignore the later findings” – See issue #4. There is a massive difference between ignoring findings (as GregLocock has done) and demonstrating that some findings are not as valid as others (as I have done, see the first post). LC14 and other energy budget model techniques are not valid for establishing a “best estimate”. Even without correcting for their inherent cooling biases (Shindell 2014, Kummer & Dessler 2014), if you incorporate up-to-date data (Durack et al 2014, Cowtan & Way 2013), these estimates are higher than first calculated.

”which of course makes any discussion of trends moot.” – See issue #3. In general, a blind trend is be evidence of nothing, unless you understand the data points and the larger context behind the data/trend. Specific to this case, an absurdly weak trend, skewed by flawed papers at the end of the trend, says NOTHING about the best estimate of sensitivity. So discussing (incredibly weak) trends in isolation of anything else (as GregLocock wants to) is moot because it’s unscientific and illogical. You need to introduce a broader understanding of topic in order to understand the trend. The second you do that (see issue #3, #4 and #5) it becomes clear that there is NO logical link between “Since 1988, sensitivity estimates have fallen with an R^2 of 0.08” to “sensitivity is lower than the IPCC estimates, so climate change will have no negative impacts in the future”.

 

[rconnor]

And yet more evidence to suggest that energy budget models are incapable of developing a best estimate for climate change – Andrews et al. (2014) (AMS Journal of Climate).

Overly simplistic energy budget models can only assume linear feedbacks. This assumption is inconsistent with the best understanding of the science at the moment. Andrews et al. (2014) demonstrates that feedbacks in GCMs are nonlinear and increase over time. From the abstract:

The nonlinearity is shown to arise from a change in strength of climate feedbacks driven by an evolving patter of surface warming. In 23 out of the 27 AOGCMs examined the climate feedback parameter becomes significantly (95% confidence) less negative – i.e. the effective climate sensitivity increases – as time passes.

Therefore, any method that assumes linear feedbacks, which all energy budget models do, would appear to lead to an artificially lower sensitivity value. This paper can be added to issue #3.

Now, Andrews et al. (2014) alone cannot definitively quantify the impact that (incorrectly) assuming linear feedbacks has on energy budget models. Optimistically, the impact of (incorrectly) assuming linearity could be negligible on the actual sensitivity value. I would expect that the assumption would have less impact on TCR (shorter time scales) than ECS. Also, if the word “model” or “GCM” causes your eyes to go blood shot with rage, you can completely ignore this paper. In any case, we are still left with issues #2 to #5 and nothing to support the assertion that sensitivity is lower than the IPCC range.

 

[btrueblood]

"Overly simplistic energy budget models can only assume linear feedbacks. "

That itself seems an overly simplistic statement, but perhaps you mean to define the term "overly simplistic energy budget models" thereby?

I have built and run a number of models, using energy methods, which included a variety of non-linear dissipation variables. It's not rocket science, except when it is.

"Andrews et al. (2014) demonstrates that feedbacks in GCMs are nonlinear and increase over time."

Without some more in-depth reading into what drove that statement, it sounds like "my model is nonlinear, so it must be better". Wouldn't the real goal be to base those feedbacks on real physical phenomena? I.e. something you could model in laboratory scale.

 

[rconnor]

btrueblood, your points are well taken. I will expand upon some of those statements you question to hopefully address your comments. You can find LC14 here (full paper).

LC14 use the following formulas to calculate ECS and TCR:
[image

http://s30.postimg.org/6wt1o0f9t/LC14_Equations.png

]

These equations are, essentially, what all energy budget methods work off of. The paper states (on page 3, right below the TCR equation): “Both equations (1) [ECS] and (2) [TCR] assume constant linear feedbacks”.

Regarding your comment “my model is nonlinear, so it must be better”, that is not quite accurate. Paleoclimatology supports nonlinear feedbacks as well as GCMs. You cannot explain past climate changes if you assume linear feedbacks. So, linear feedbacks are not supported by our current understanding of the science, they are used as an assumption to simplify the problem.

As I stated, this might not have a great impact on TCR but it would seem to have an impact on ECS. However, I’m not 100% sure of how much impact it has. So, at best, assuming linearity is probably not correct but probably not that impactful on TCR values. At worse, it leads to artificially lower sensitivity values.

And remember, in order for the skeptic assertion to be comprehensive, the asserters need to demonstrate why energy budget estimates, which lead to lower sensitivity estimates, are more valid than other methods, almost all of which lead to higher sensitivity estimates. If nothing else, Andrews et al. (2014) (and Vernier et al. 2011, Durack et al. 2014, Cowtan & Way 2013, Shindell 2014, Kummer & Dessler 2014, etc…) put that into question, if not support the exact opposite.

(I do have a contention with your last point regarding a laboratory scale model. As it is off topic, and I’ve addressed it before (7 Mar 14 16:52, 11 Mar 14 14:25 and 12 Mar 14 12:58, look for comments directed to GregLocock ), I’ll just leave you with this question – how would you create a laboratory scale model to study the interdependent effects of ocean/atmosphere dynamics, prevailing wind patterns, changes in prevailing wind patterns, ocean currents, changes in ocean currents, cloud formation, changes in albedo, etc, such that the can adequately capture the dynamics of the earth’s climate system AND find a way to speed up those interactions such that you can see the impact in 100 years? I don’t believe it can be done. Instead, you’d attempt to break those systems up into subsystems, study them by analyzing observed behavior (past and present) and then take that knowledge into a model. That’s exactly what climate scientists are doing.)

 

[rb1957]

"how would you create a laboratory scale model to study the interdependent effects of ocean/atmosphere dynamics, prevailing wind patterns, changes in prevailing wind patterns, ocean currents, changes in ocean currents, cloud formation, changes in albedo, etc, such that the can adequately capture the dynamics of the earth’s climate system" ... it's been done, or rather conceived of, in a rather amusing book (HHGTTG).

as for energy budget predictions vs GCM predictions, as an engineer I immediately like energy budget and if GCMs are incompatable with an energy budget i'd ask "where's the energy coming from ?". and as btb posted, those are very simple models, quite possibly too simple, and there's nothing (much) stopping someone from using a more complex model.

another day in paradise, or is paradise one day closer ?

 

[btrueblood]

Well, to argue the opposite, notice that the uncertainty band for the GCM's is ridiculously large, back up there in your first post. That the LC14 estimates (including the error band) of the ECR overlap the worst case error bands of the GCM's means you can't throw it out just because it gives a different (lower) number.

"You cannot explain past climate changes if you assume linear feedbacks." Based on, presumably, GCM's? And what database do you use to evaluate the models against these past climate changes, etc. etc. and on and on, off topic, ok I know, I'm raining on your parade and you'll p%55 all over me for it. But please just answer this: when the only argument that it must be nonlinear is because the competing computational method says it must be...is circular reasoning to some extent, is it not? Would not the best thing for the GCM advocates to do, would be to build an energy balance model that includes these nonlinear terms, to show us poor plebes how its done? There are ways to break an energy balance solution into smaller chunks, and evaluate the changes over smaller temperature intervals to see if the nonlinear terms show up again. But no, it's pointless, since the experts agree with you.

In the meantime, energy prices in places like the UP are skyrocketing, because utilities are not allowed (by Federal law, in order to maintain grid reliability) to shut down older, inefficient (and presumably high CO2 output) power plants, even though Federal law mandates, or will mandate, these shutdowns per the new EPA rules. I listened to a smarmy environmentalist saying it's the utilities fault, that they should have spent money to upgrade the power plants instead of fighting the new mandates...and thought "spoken like somebody who hasn't a clue how capitalism works". Yeah, I'm really ready to see how the new carbon taxes and/or cap & trade schemes work for me. Meanwhile, I still see an awful lot of BC tags down here in WA state, usually in line at the Costco station, filling up on gas.

 

[rconnor]

LC14 uncertainty range overlaps with some GCMs - Yes, I've said from the beginning, while LC14 is on the low end, it's not entirely outside the IPCC range. Also, as I've said before, LC14 isn't inherently wrong IF your goal is establishing an ultimate lower bound. However, it is not applicable to establishing a best estimate due to the numerous issues raised in the first post. It’s important to note that these issues don’t simply introduce uncertainty, they introduce known and notable cooling biases.

Linear vs Nonlinear Feedbacks – It would be incorrect to say that nonlinear feedbacks are purely the outcome of GCMs. Nonlinearity of feedbacks can be easily observed in albedo. Recent observations of loss of sea ice are compared against paleoclimate reconstructions and, in both cases, the feedback of albedo change starts weak (little warming, little sea ice loss) and then strengthens (more rapid sea ice loss) before stabilizing at a higher point. Another example would be destabilizing of ocean currents that is noted as a key player in past climate changes and there are many more. While you need models to calculate the exact value of the forcing, you do not need models to know that it will be nonlinear.
Again, I stated that Andrews et al. (2014) highlights this but does not enable us to quantify the impact. So, the simplifying assumption that feedbacks are linear could have no notable impact or a notable cooling bias on LC14’s TCR value. I’m fairly confident, though not positive, that it does have a notable cooling bias on the ECS value as the nonlinearity of feedbacks is more noticeable further in the future (as per Andrews et al. (2014) and other studies).
Remember, the linear feedback assumption is only one of the many issues with LC14.
Also remember that if you have an issue with climate models, then you have an issue with LC14 in the first place as it too is dependent on models.
”There are ways to break an energy balance solution into smaller chunks, and evaluate the changes over smaller temperature intervals to see if the nonlinear terms show up again.” – This is difficult because one of the problems with an energy budget model is that it is greatly impacted by the reference period. Energy budget models are, more or less, about finding the slope of a straight line between two end points. The shorter the period, the more impactful the short term variability is and the greater the uncertainty. However, the longer the period, the less accurate the “straight line” assumption is. In either case, your choice of start “point” (it’s actually the average of a period) and end “point” greatly impact your results. As discussed, LC14 seems to pick points that lead to a lower sensitivity (anomalously low end point affected by aerosols and ENSO). One could just as easily pick points that lead to higher sensitivity (anomalously low start and anomalously high end) which would be equally invalid.
I completely agree with you that having a simple energy balance model that worked would be a great double check. However, they have problems. These problems may be minimized in time but LC14 is just too error-ridden with cooling biases to be valid as a best estimate. If LC14 fixed some glaring issues (incorporate Cowtan & Way and Durack, as well as fix the end point) then I wouldn’t be as dismissive of it, even though it would still have other issues (linear feedback assumption being one).
(Future Statement)”But GCMs have great amounts of uncertainty as well!” – Absolutely. But there are other methods for determining sensitivity than the output of GCMs and energy budget models. So wouldn’t the best way forward to take all the different valid techniques, across numerous reference periods, using different data sets to establish the most likely range for sensitivity? …kind of exactly like what the IPCC is doing?
And, as you have all ignored (except for rb1957), even if we take LC14 or the low end of the IPCC range at the “true” value for sensitivity, we are still left with temperatures approaching +3 deg C above pre-industrial periods without mitigation measures.

BC Tags in WA - Ya, been discussed a while back (21 Nov 13 16:57 and 27 Nov 13 15:37). TGS4 and I dug through quite a bit of data. Cross boarder trips were way up in BC but fuel consumption in WA actually went down over that same period. This pretty much disproves the anecdotal hypothesis that reductions in BC can be accounted for by increases in WA (or at least provides much more solid evidence than the “I see lots of BC plates” statement does).

 

[rconnor]

(Oops, sorry about formatting. Wrote on my phone and I suppose it didn't like the spacing.)