At the request of the authors, this was converted from a poster displayed at the AGU Science Policy Conference, Washington, June 24-26. – Anthony
By Paul C. Knappenberger and Patrick J. Michaels
Center for the Study of Science, Cato Institute, Washington DC
INTRODUCTION
Assessing the consistency between real-world observations and climate model projections
is a challenging problem but one that is essential prior to making policy decisions which
depend largely on such projections. National and international assessments often mischaracterize the level of consistency between observations and projections.
Unfortunately, policymakers are often unaware of this situation, which leaves them
vulnerable to developing policies that are ineffective at best and dangerous at worst.
Here, we find that at the global scale, climate models are on the verge of failing to
adequately capture observed changes in the average temperature over the past 10 to 30
years—the period of the greatest human influence on the atmosphere. At the regional
scale, specifically across the United States, climate models largely fail to replicate known
precipitation changes both in sign as well as magnitude.
On the first count, the near inability of climate model projections to contain the observed
global temperature trends, it is likely that the climate model overestimation of the earth’s
equilibrium climate sensitivity—an overestimation which averages about 40 percent—is
playing a large role in the models’ gross exaggeration of the current rate of temperature
rise (which, for example, has been virtually zero during the past 16 years).
On the second count, the general inability of general circulation models to even get the sign of the observed precipitation changes across the U.S. correct, much less the magnitude, likely stems from the complexities of the climate system on spatial and temporal scales that lie far beneath those of current generation GCMs.
GLOBAL TEMPERATURE
12-year Trends:
15-year Trends:
Global Average Surface Temperatures, 2001-2012:
Global Average Surface Temperature Projections, 2001-2020:
U. S. PRECIPITATION
Observed U.S. Precipitation Change:
precipitation differences by decade (relative to the 1901-1960 average) for each region. The far right bar is for 2001-2011. (Figure source: Draft National Assessment Report)
Projected U.S. Precipitation Change
precipitation increases, and brown, decreases. Hatched areas indicate
confidence that the projected changes are large and are consistently wetter or drier. White areas indicate confidence that the changes are small. (Figure source: Draft National Assessment Report)
Number of Years Before Predicted Changes Are Greater Than Natural Variability:
standard deviation (calculated using the 1896-2011 data) from the 1991-2011 average value (calculated using McRoberts and Nielsen-Gammon, 2011). Blue indicates projected increases, red indicates projected decreases. A “n/a” indicates that no consistent projection was made, “achieved” means that the projected change has already been exceeded (that is, the change from 1901-1960 to 1991-2011 was larger than the climate model projected change from 1901-1960 to 2070-2099). Highlighted values indicate two centuries or more.
Observations, 1951 – 2005:
Models, 1951 – 2005:
influence of internal climate variability. (Source: Polson et al., 2013)
CONCLUSIONS:
It is impossible to present reliable future projections from a collection of climate
models which generally cannot simulate observed change. As a consequence, we
recommend that unless/until the collection of climate models can be demonstrated to accurately capture observed characteristics of known climate changes, policymakers should avoid basing any decisions upon projections made from them. Further, those policies which have already be established using projections from these climate models should be revisited.
Assessments which suffer from the inclusion of unreliable climate model projections include those produced by the Intergovernmental Panel on Climate Change and the U.S. Global Climate Change Research Program (including the draft of their most recent National Climate Assessment). Policies which are based upon such assessments include those established by the U.S. Environmental Protection Agency pertaining to the regulation of greenhouse gas emissions under the Clean Air Act.
References:
Aldrin, M., et al., 2012. Bayesian estimation of climate sensitivity based on a
simple climate model fitted to observations of hemispheric temperature and global
ocean heat content. Environmetrics, doi: 10.1002/env.2140.
Annan, J.D., and J.C Hargreaves, 2011. On the generation and interpretation of
probabilistic estimates of climate sensitivity. Climatic Change, 104, 324-436.
Hargreaves, J.C., et al., 2012. Can the Last Glacial Maximum constrain climate
sensitivity? Geophysical Research Letters, 39, L24702, doi:
10.1029/2012GL053872
Intergovernmental Panel on Climate Change, 2007. Climate Change 2007: The
Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Solomon, S., et al.
(eds). Cambridge University Press, Cambridge, 996pp.
Lewis, N. 2013. An objective Bayesian, improved approach for applying optimal
fingerprint techniques to estimate climate sensitivity. Journal of Climate, doi:
10.1175/JCLI-D-12-00473.1.
Lindzen, R.S., and Y-S. Choi, 2011. On the observational determination of climate
sensitivity and its implications. Asia-Pacific Journal of Atmospheric Science, 47,
377-390.
Ring, M.J., et al., 2012. Causes of the global warming observed since the 19th
century. Atmospheric and Climate Sciences, 2, 401-415, doi:
10.4236/acs.2012.24035.
Schmittner, A., et al. 2011. Climate sensitivity estimated from temperature
reconstructions of the Last Glacial Maximum. Science, 334, 1385-1388, doi:
10.1126/science.1203513.
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No models will EVER accurately simulate actual climate behavior – the biggest supercomputer can’t do it, and all of the factors known and unknown can ever be correctly represented in them. Policymakers should avoid basing their decisions on models like the plague.
Assessments which suffer from the inclusion of unreliable climate model projections include …those established by the U.S. Environmental Protection Agency pertaining to the regulation of greenhouse gas emissions under the Clean Air Act.
We don’t suffer from “carbon pollution”
Our scourge is “model pollution.”
It’s the politicians who need to see this, but then, not many are willing to look just yet.
It’s happening, just slowly. As more wake up to the fact that basing decisions on extreme models makes them personally look bad no matter how they cut it – incompetent at best and outright criminal at worst – the whisper will spread and grow, and more will shy away. It’s frustrating waiting for that magical moment. They’re taking a dang long time for to get up to speed.
Well if you want your model to emulate the system you are measuring, you first have to know just what is the system you are measuring, and then you have to model that system; not some time averaged substitute for it.
I agree with the gist of the above poster-to-post. But, why in the WORLD did they write it in such weak, hesitating, style? The above post was not written in the robust, forthright, manner one uses when trying to convince someone. It has an odd, walk-on-eggshells, feel.
Which language would you use to argue your case?
1. A. Assessing the consistency between real-world observations and climate model projections is a challenging problem but one that is essential prior to making policy decisions which depend largely on such projections.
OR
B. Do climate model projections match reality? We must know the answer to this before we make major policy decisions based on models.
2. A. National and international assessments often mischaracterize the level of consistency between observations and projections.
OR
B. Many climate group reports say that models closely match reality when they do not.
3. A. Here, we find that at the global scale, climate models are on the verge of failing to
adequately capture… .
OR
B. Every single one of the global climate models has failed to project what temperatures actually did.
4. A. It is impossible to present reliable future projections from a collection of climate
models which generally cannot simulate observed change. As a consequence, we
recommend that unless/until the collection of climate models can be demonstrated to accurately capture observed characteristics of known climate changes, policymakers should avoid basing any decisions upon projections made from them. Further, those policies which have already be established using projections from these climate models should be revisited.
OR
B. Climate models that cannot hindcast, i.e., accurately project historical data, are useless at best, dangerously misleading at worst. No policy should be based on them. Any current policies that are based on them need to be fixed.
5. A. Assessments which suffer from the inclusion of unreliable climate model projections include … .
OR
B. Climate reports that use the failed models include… .
****************************
Also, the graphics used are generally quite poor at communicating the authors’ message. Most of the graphics do not communicate with clarity and do more to confuse than to illuminate.
***********************************************************
[Note: We had another Chip Knappenberger article on WUWT recently which (unless he was intentionally supporting CAGW) due to sloppy writing ended up supporting CAGW.]
Perhaps, the authors ARE trying to get the truth out, but can only do it in the most tentative manner due to the ever-present FUNDING issue… .
“Policymakers should avoid basing their decisions on models like the plague.”
[Chad Wozniak]
Now, THAT’s more like it! That’s the kind of robust language to use. #[:)]
“””””…..On the second count, the general inability of general circulation models to even get the sign of the observed precipitation changes across the U.S. correct, much less the magnitude, likely stems from the complexities of the climate system on spatial and temporal scales that lie far beneath those of current generation GCMs……”””””
Heck; they are not suggesting that there could be a Nyquist criterion failure here, are they ??
Nah; why would anyone care about a telephone system nicety !!.
Scientists traditionally use very conservative language, only stating what they know and can prove to be true and always taking painstaking care to point out the limits of their knowledge. In that respect the language used here is typically scientific. That it seems weak compared to what we are used to seeing from climate scientists simply demonstrates how far climate scientists have habitually departed from the scientific norm in their use of language.
These guys actually sound like scientists whereas the typical AGW supporting climate scientist these days sounds more like a political hack.
CONCLUSIONS:
OR
HAHAHAHAHAHAHA!
AS MODELS REPRESENTING CURRENT CLIMATE THESE ARE WORTHLESS!
Climate models are based on the idea that rising CO2 levels cause rising global temperatures, and more importantly rising CO2 levels are the dominant driver of temperature changes.
If you pop this in your browser
http://www.woodfortrees.org/plot/hadcrut4gl/from:1960/plot/esrl-co2/scale:0.01/offset:-3.2
you will clearly see temperature does not slavishly follow CO2
http://www.woodfortrees.org/plot/esrl-co2/isolate:60/mean:12/scale:0.25/plot/hadcrut3vgl/isolate:60/mean:12/from:1958
this graph shows changes in temperature PRECEED changes in CO2
If rising CO2 levels are not the cause of rising temperature then climate sensitivity has no physical existence, and the climate models are based on a false assumption which somewhat reduces the likelihood of them producing anything related to the real world (ha).
In addition there is clearly a cyclic pattern in the data which has been noted many times, for example, http://rankexploits.com/musings/2013/the-occams-razor-oscillatory-model/
or if you prefer it in pictures http://jeremyshiers.com/blog/global-temperature-rise-do-cycles-or-straight-lines-fit-best-may-2013/2cosfit/
As far as I know the models make no allowance for naturally occurring cycles (actually there is a long list of things models don’t take account of).
So policy is being driven by make believe.
I wonder who is making money out of this.
Personally I feel 24 news media has a lot to blame, after all they have 24 hours every day to be filled with something. Yesterday Sky News in UK broadcast a projected ramble by some old bloke from Friends of the Earth who state emphatically (and without being challenged in anyway) UK should not use shale gas, renewables are the answer. In any case shale gas wouldn’t work and wouldn’t bring down energy prices as “IT’S A DIFFERENT TYPE OF SHALE GAS TO THAT IN THE US”
Why GCMs fail so badly…
In ‘Science’ of 31 May, 2013, you can read a very good article by two of the world’s climatologists on why the General Circulation Models (GCMs) used to predict future climate fail so spectacularly. Drs Bjorn Stevens of the Max Planck Institute for Meteorology, Hamburg, Germany, and Sandrine Bony, of the Laboratoire de Météorologie Dynamique – Institute Pierre Simon Laplace, CNRS, University of Pierre and Marie Curie, Paris, France, present a 2-page article titled: “What are climate models missing?”. Here they lay the answer out bare and blatantly:“…an adequate description of basic processes like cloud formation, moist convection, and mixing is what climate models miss most.”
To us at WUWT, this may not be new information, but to others who have buried their heads in sand for a couple of decades and relied on the climate specialists knowing their job, it may come as a shock?
Another excerpt from their article reads:
“The increase in complexity has greatly expanded the scope of questions to which GCMs can be applied (5). Yet, it has had relatively little impact on key uncertainties that emerged in early studies with less comprehensive models ( 6). These uncertainties include the equilibrium climate sensitivity (that is, the global warming associated with a doubling of atmospheric carbon dioxide), arctic amplification of temperature changes, and regional precipitation responses. Rather than reducing biases stemming from an inadequate representation of basic processes, additional complexity has multiplied the ways in which these biases introduce uncertainties in climate simulations ( 7, 8).
For instance, a poor understanding of what controls the distribution of tropical precipitation over land, and hence vegetation dynamics, limits attempts to understand the carbon cycle ( 9). Similarly, uncertainties in arctic amplification of warming hinder predictions of permafrost melting and resultant changes in soil biogeochemistry.»
5. G. M. Flato, WIREs Clim. Change 2, 783 (2011).
6. S. Bony et al., in Climate Science for Serving Society:
Research, Modelling and Prediction Priorities, G. R. Asrar,
J. W. Hurrell, Eds. (Springer, Berlin, 2013).
7. Q. Min, S. Wang, Geophys. Res. Lett. 35, L02406 (2008).
8. I. B. Ocko, V. Ramaswamy, P. Ginoux, Y. Ming, L. W.
Horowitz, J. Geophys. Res. 117, D20203 (2012).
9. P. Good et al., J. Clim. 26, 495 (2013).”
So, the truth is out – even in Science! – Will this bring any reaction or explanation as to why our governments still back such sloppy science, at all?
Intriguing article and interestingly I can throw further historic light on this subject by referencing my recent article here
http://judithcurry.com/2013/06/26/noticeable-climate-change/
in it I demonstrated that ‘noticeable’ climate change is a normal state if we look at a real world example such as the Central England record dating to 1659 and reconstructed by myself to 1538.
http://judithcurry.com/2013/06/26/noticeable-climate-change/
Virtually no two consecutive decades are the same which is the scale that humans are affected by.
The models do a reasonable job back to 1500AD in noting the amount of variability over a long period such as 50 years or more, which is the margin of error on most proxies which tend to be very imprecise.
However they fail to note the decadal variability which is up to ten times greater.
in other words models use a very coarse sieve through which annual and decadal changes fall.
they therefore give a completely false impression of historic climate stability best seen in figure 4
of the link. it is irresponsible for govts to make policy decisions based on the misleading impression of climate stability in the past given by models
tonyb
Gee, if those MODELS are right, people in the Rockies better buy snow blowers as snow fall is going to be 10 to 20% above “normal” whatever that is … in contrast to the USGS that says ski resorts are going to be in trouble from lack of snow. Oh, and the snow pack in the Canadian Rockies was in fact about 20% above normal this year large and with a stationary low dumping on it we have big time floods, not droughts. Of course they include one of the driest periods in recent history in their baseline so areas showing more precip. should not be a surprise to anyone.
@ur momisugly Janice Moore – June 27, 2013 at 10:13 pm
I understand what you’re saying but the style they write in is typical for published manuscripts. The reason being if your conclusions are based upon a 95% confidence interval (alpha = 0.05 or P < 0.05) there is up to a 5% probability your conclusion could be wrong and the result of chance. As such, when writing the conclusions you will typically see language like "lending evidence to support" rather than "this proves."
It's not so much walking on egg shells but rather a form of respect paid to the reader. To emphatically assert your findings are poof of something to a reader who understands statistics can be seen as novice.
Many warmists are reluctantly admitting the temperature stall. But they say “We’ve had stalls and drops before within this general warming trend. The stall is natural, and the [catastrophic] warming will resume in the future.”
The problem with this view is that the warmists had led us to believe that CO2 driven warming was to supersede everything natural; indeed we got the impression that the warming by now would be already hellish, and the seas were to have already risen substantially (consider Hansen in 1988 said that Manhattan would be under water by 2008). Their models assumed that any natural trends were going to be easily overpowered by the extreme and dominant man-made warming trend.
I don’t think the models are on the “verge” of failure. They have already failed, no: they have FAILED big time with a capital F and a capital the rest. The models represent Hansen et al’s vision of a doomed future world, and that future has arrived, and all is fine. Not an iota of sea level rise that I can see, and the temperatures seem just like before. No change. If there is some technical way to say that the models have not quite yet actually failed, you’d have to agree that in spirit they have failed. In fact at a quick glance one could see that the models are obviously way off the mark.
Ocean temperatures are critical for long term climate variability. In the N. Atlantic ocean’s surface temperature has followed with a degree of accuracy, with some delay geologic and tectonic history of highly active area on the each side of the Arctic circle.
http://www.vukcevic.talktalk.net/NAP-SST.htm
This activity has experienced strong downturn in the last decade, the SST may or may not follow, but past history (providing my calculations are at a reasonable level of accuracy) suggests that the SST is on the verge of downturn.
Are these natural oscillation predictable?
Geologically speaking not, but because of certain inbuilt delay (most likely due to low velocity of the oceanic currents) there is a degree of predictability,
Further more the observation data of the solar magnetic cycle combined with the Earth magnetic oscillations produce more detailed correlation
http://www.vukcevic.talktalk.net/GSC1.htm
(subject to the calculations accuracy) again indicate a forthcoming downturn.
Climate models failing to perform is a bad omen for the climate science, not to mention billions invested in the unprofitable sources of energy based on the projections of the unsound ‘global temperature’ modeling.
It is time to for science to turn to investigation of natural causal relationships.
You think Obama, Gore, Cameron, Merkel, Rudd, Hollande, Barroso, Pauchari, and all the rest of them care two hoots about how good their precious ‘models’ are?
You think 97%(TM) of the “Climate Scientists” really believe in their hearts that their models are any good for anything (except for milking grant funding)?
They are SAVING THE PLANET!
(Well, obscenely feathering their nests, actually – and who cares about the economy or the world’s poor and vulnerable?)
DON’T confuse them with facts, they’ve made up their minds!
This isn’t a criticism of Knappenberger and Michaels posting. It’s excellent. One to keep. But don’t imagine that any of the crooks who are running this scam will spend a second to reconsider their position.
And one major policy implication is that you shouldn’t do what the UK did.
From my Real Science comment: Having adopted a climate program similar to what the US House passed in 2009, the UK is at just the very beginning and super easy part of a 40 year journey into energy and economic oblivion. Maybe it’s poetic justice and self-loathing for their centuries of imperialism, and now they will become an international pygmy.
Anyway, the news:
(Reuters) – Britain’s risk of electricity blackouts by 2015 is more serious than previously thought, regulator Ofgem warned on Thursday.
The country’s spare electricity supply margin could fall as low as 2 percent in 2015/16, down from around 14 percent currently. Last year Ofgem gave an estimate of 4 percent.
“Electricity supplies are set to tighten faster than previously expected in the middle of this decade,” Ofgem said in a report, adding that the chance of supply disruptions would rise to one in 12 years in 2015/16 from one in 47 years now.
Britain has seen a vast number of power plants close and being mothballed due to emissions-reduction policies and the loss-making economics of gas-fired power plants.
The policy process is political. Obama has to wait for his re-election to unveil his climate change policy although he could be aware that most of the countries committed to Kyoto are now having second thoughts. The models and “scientific” works are just icing to policy decisions already made. The policy makers might have relied more on Delphi methods using selected experts and externally supported by the consensus science. The Delphi method could be gleamed from the use of averages from various models after all Delphi method is just the consensus of experts.
Lies, damned lies, and climate science are poor grounds for any policies, much less life-and-death decisions affecting the poorest and most sensitive parts of the global economy. Dante would have been challenged coming up with suitable rewards for those ramming through said policies.
Sorry for the OT, but this is sad/funny can’t decide which:
http://www.telegraph.co.uk/earth/environment/10146081/Twitchers-flocking-to-see-rare-bird-saw-it-killed-by-wind-turbine.html
Jeremy Shiers says:
June 27, 2013 at 10:59 pm
////////////////////////
Spot on.
The first and paramount step would be to test the validity of the fundamental assumptions against empirical observational data. One needs to know whether the assumptions are robust, since if they are not robust, and if there is still some measure of concern as to whether climate change would be serious or not, adaption (and not mitigation) is the best policy.
Adaption works every time. If in the real world (not the cyberspace of computer models) the problem is not serious, we will not have to adapt, or not to a significant degree. Further, a warming climate in reality may turn out to be beneficial and we will enjoy that benefit rather than depriving ourselves of the benefit should steps be taken to mitigate. Further climate change may be real but not driven by GHCs adaption (not mitigation) is the only course that workls in this scenario.
Mitigation on the other hand is a failed venture if either, the problem is not serious, or GHCs do not drive the climate and therefore reducing emissions has no effect and the warming continues unabated because it is a naturally driven process, alternatively if in the real world a warming climate is beneficial.
The more uncertain that you are of the fundamentals, the stronger the argument for adaption becomes. Has anyone stopped to consider, but for policy towards climate change adopted by western countries, whether the western world would be in the economic mess that it is in today. I suggest that there is a strong argument to the effect that had the western world adopted a policy of adaption, and not wasted trillions of dollars on the climate issue, the west would not be in a deep recession and/or would quickly have recovered.
In the UK (and this applies to much of Europe) the cost of our energy is twice high as it need be simply because of the approach to climate change. This has had a serious impact on manufacturing and its competitiveness on the world stage and hence on corporate taxes, employment (adverse effect on income tax, and welfare benefit payments) and social unrest. Yet further, much industry has been driven overseas where there is less regulation and cheaper energy which has had an adverse effect on the balance of payments (the country ends up importing far more than it exports which long term bankrupts a country) and has enriched our competitors 9why give them this step up). If we had kept our manufacturing industry, there would not be levels of youth unemployment ranging between about 18% to just over 60% (depending upon which EU country you look at). This may have been an unforeseen consequence of the policy choice of mitigation, but it goes to emphasise why adaption is always the superior policy.
A strange world when just as the models are failing the most, those who have always been saying so are finally relegated at the highest level to the ‘flat earth society’.
As Galileo was forced to recant and say the earth stood still, as he walked out he was heard to say “and yet it moves”. Now they are saying the temperature ‘moves’ when its standing still. Nobody ever bothers to check the data.
But there is one computer that can do it …
likely stems from the complexities of the climate system on spatial and temporal scales that lie far beneath those of current generation GCMs.
An article of faith in the climate modelling community, but almost certainly wrong in my opinion. There is no evidence that decreasing spatial and temporal scales (with faster computers) has in any way improved climate models.
I could go on, but would likely offend those with religous faiths.