Terms such as “gas skeptics” and “climate skeptics” aren’t really very descriptive, but they refer to sentiments that have something in common: unpredictable behaviour...
Statistics is remarkably predictable
The individual gas molecules are highly unpredictable, but the bulk properties of the gases are nevertheless very predictable thanks to physics. More specifically the laws of thermodynamics and the ideal gas law.
The bulk aspects of the gases are a result of the statistical properties of a vast number of particles. Statistics is surprisingly predictable even if the individual cases are not.
Just look at Las Vegas and the insurance industry which make a living on the fact that probabilities (statistics) are predictable. Even economists pin their hope on statistics, and the medical sciences would never be where they are now without the predictive power of statistics.
A “gas skeptic” would say that you cannot predict the state of the gas because the molecules are unpredictable. This is analogous to saying that climatic states cannot be predicted because the weather is unpredictable (a “climate skeptic”).
Climate is weather statistics
Climate can be viewed as weather statistics. Early climatological work was dedicated to survey of how the weather statistics varied from place to place and over the seasons.
There are clear effects of physical factors (latitude, mountains, distance to the coast) on the statistical character of the weather and the weather statistics (climate).
In other words, the statistical properties are a result of the physical processes and conditions present and are readily predicted from e.g. geographical factors, seasonal variations in the solar inclination, the atmospheric composition and the planet’s distance to the Sun.
The weather statistics (eg probabilities) are predictable in spite of the chaotic and nonlinear character of weather itself.
There are some examples where the question about predicting the exact state is mixed up with the question of predicting the statistical properties of the system, even by people with some experience in climate research.
Some of them are useful for further learning, and there is a number of them in a ‘report’ (“Climate models for the layman”) that Judith Curry has written for a British interest group that calls itself “GWPF”.
Curry’s report has also been used to back Norwegian contrarians who support the effort of a populist politician to get a seat in the parliament.
The analogy to a “gas skeptic” above illustrates why Curry’s claim is misconceived because it is false that the climate models are unfit to make predictions about the future climate just because the atmosphere behaves in a nonlinear fashion due to the Navier-Stokes equations.
The Navier-Stokes equations describe the atmospheric flow (winds), but the key equations for climate change involve the laws of thermodynamics and the way the different gases absorb different frequencies in the electromagnetic spectrum.
The most important nonlinear component in this respect include scattering processes, phase transitions, and cloud formation.
A potential feedback paradox
Curry also introduces a potential paradox in her report when she emphasises natural variations. The magnitude of natural temperature variation are regulated by feedback processes and have physical causes. The climate sensitivity also involve such feedback processes.
Any feedback process based on temperature will act on both natural and forced changes in the temperature. If such feedbacks result in pronounced natural temperature variations, they also imply that the climate sensitivity is high.
Examples of such feedbacks include increased atmospheric humidity and reduced snow/ice cover. Processes involving clouds are more uncertain, but they too are likely to be affected by temperature (convection) and act to modify the climatic response.
There are also feedbacks relevant to forced variation as well as internal variability which don’t always mean that higher amplitude natural variability necessarily indicates greater climate sensitivity.
For example, the fact that there is enhanced variability in the 3-7 year ENSO band is a result of climate dynamics (Bjerkenes feedbacks) resonating with wave propagation timescales.
Other examples include distinct oscillatory models of variability with decadal and longer timescales, related also to oceanic Rossby wave propagation and gyre spinup processes, or timescales associated with the AMOC.
It is possible to get enhanced variability on those timescales as a result of dynamical mechanisms without needing to appeal to higher climate sensitivity.
Nevertheless, the bottom line is that Curry must prove that the feedbacks involved in the natural variations are different to those affecting the climate sensitivity before she can conclude that natural variability dominates over a warming due to increasing greenhouse gases.
It’s not the sun
When Curry believes that the changes in earth’s temperature are due changes in the sun, it is important to keep in mind that the variations in the sun only affect as a small fraction of earth’s energy input. Amplifying feedback processes are needed to explain the magnitude of the observed changes.
Curry makes a point of the temperature increase before the 1940s, and that the CO2 concentrations were low then. But she seems to have forgotten that the forcing is proportional to the logarithm of the concentration: the effect of an increase is initially higher with lower concentrations.
The changes in the climate before 1940 were a result a combination of factors when there was an increase in the number of sunspots that coincided with increasing CO2-concentrations.
It is well-known that the sunspot record suggests an increase up to the 1950, but various solar indicators indicate no long-term trend in the sun since the 1950.
Only the increase in the greenhouse gases can explain a forced warming since the 1950s because no other physical forcings exhibit long-term trends since then.
Another issue is that early temperature record does not give as complete global data coverage as more recent measurements. The global temperature analysis is based on smaller sample in the early part, for which we expect to see stronger random sampling fluctuations.
This is consistent with what Figure 4 in Curry’s report shows. However, she misinterpreted this as being strong natural variability in the early part of the record.
Curry also makes the same mistake as John Christy by using the ensemble mean as a yardstick for the models (here): model evaluations must be based on the individual simulations taking into account the spread of the ensemble run.
It’s not just the temperature
The climate sensitivity is one indicator for the consequences of a global warming which only accounts for the change in temperature, but it is important not to ignore that changes in the global hydrological cycle may also have a severe impact on society.
It is possible that a weaker temperature increase is associated with a larger shift in the convective activity and more pronounced changes in the rainfall patterns (Benestad, 2016).
The comprehensive picture and consistency
I often find it useful to look at the comprehensive picture in science and look for consistencies, both when it comes to physics and the logic.
A curious twist in Curry’s report is (a) her claim that climate models have exaggerated climate sensitivity because they did not reproduce the observed warming over the 2000-2015 period and then (b) her emphasis on natural variations having scales of “weeks, years, decades, centuries and millennia”.
If the claims hypothetically were correct, then how would she know that the temperature variations over brief intervals are not just a result of the natural variations that she emphasised?
We should expect some brief periods with both rapid as well as slow warming (Easterling and Wehner, 2009), and some of the model simulations have indicated a weak warming over the same period. This is explained in the IPCC AR5 (Box 9.2).
Another question is whether the warming rate reported by the AR5 was correct, and more recent studies suggest artificially weak warming connected to changing observational networks (Karl et al, 2015). This has been discussed here. Hence, Curry’s claim about slower warming rates has lost substance.
There is a curious remark in Curry’s report about the climate models’ inability to match the phase and timing of the natural variations. Yes, it is true, but it is also a well-known fact.
The way it is stated in the report makes me think that Curry has not understood what the climate modelling community is trying to do, however. My suspicion is strengthened when she makes a point about the model simulations not including future changes in the sun and volcanic eruptions.
The elementary misconceptions revealed by Curry’s “Climate Models for the layman” surprise me. Does she really not understand the flaws presented here or is she trying to sow confusion?
by: rasmus @ 13 March 2017
- R.E. Benestad, "A mental picture of the greenhouse effect", Theoretical and Applied Climatology, 2016. http://dx.doi.org/10.1007/s00704-016-1732-y
- D.R. Easterling, and M.F. Wehner, "Is the climate warming or cooling?", Geophysical Research Letters, vol. 36, 2009. http://dx.doi.org/10.1029/2009GL037810
- T.R. Karl, A. Arguez, B. Huang, J.H. Lawrimore, J.R. McMahon, M.J. Menne, T.C. Peterson, R.S. Vose, and H. Zhang, "Possible artifacts of data biases in the recent global surface warming hiatus", Science, vol. 348, pp. 1469-1472, 2015. http://dx.doi.org/10.1126/science.aaa5632
original story HERE
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