There are 65 comments.

1. Inactive
   Mike H

   @MikeH

   Post author

   10:21 AM EDT ⋅ Apr 30, 2017

   drlorentz (View Comment):

   Z in MT (View Comment):

   Skyler (View Comment):
   There is no such thing as a global average temperature.

   There most certainly is such a thing, just how useful that thing is and how accurate it can be measured is a big question mark.

   I think Matt Ridley’s approach of “lukewarming” is much more constructive as it utilizes Ben Shapiro’s tip for arguing with liberal’s #3 – Concede points that doesn’t win them anything. Make CO2 caused global warming into a small plus, rather than a minus by using science.

   Please read Ridley’s essay in comment #8 above.

   @skyler is onto something. This publication makes a good case that a mean global temperature is meaningless. Briefly, the argument is that temperature is a thermodynamic intensive variable, so averaging it is meaningless.

   Edit: The article linked above was published in the Journal of Non-Equilibrium Thermodynamics 32, p.1 (2007).

   Thank you for posting this and for your input. I’m really glad someone explained why a global average temperature is dubious instead of just asserting it. I suspected it might be so, but as I said I’ve never really understood why. I haven’t made it through the whole paper yet, so maybe it discusses this, but can you think of a better way to measure such variations in heat on Earth? Is the idea that a doubling of CO2 “causes a rise of 1C” kind of meaningless as well? What would be a better way of discussing it?

2. Member
   The Reticulator

   @TheReticulator

   4:47 PM EDT ⋅ Apr 30, 2017

   drlorentz (View Comment):
   The credibility of the GCMs is the crux of the matter. And one way to evaluate their credibility is to compare their predictions of this thermodynamically meaningless metric (mean global temperature) to the measured values. The GCMs make other predictions, which also have not fared too well against measurement but temperature is the headline value that everyone wants to talk about.

   My disdain for CGMs is a separate issue that comes from what I’ve seen of large-system ecological and agricultural models of this kind.  (I’ve been trying to define in my mind what I mean by “this kind,” which is where I got hung up when preparing to reply to this earlier, but for now I’ll say that I know them when I see them.)

3. Inactive
   Mike H

   @MikeH

   Post author

   9:50 AM EDT ⋅ May 1, 2017

   @drlorentz In case you haven’t checked back yet. I’m interested in your response to my comment #61. Thanks!

4. Member
   drlorentz

   @drlorentz

   

   3:03 PM EDT ⋅ May 1, 2017

   Mike H (View Comment):
   can you think of a better way to measure such variations in heat on Earth?

   Internal energy is a better measure of what’s happening to the thermal balance of the Earth since it is an extensive variable. Temperature is taken as a proxy for internal energy but, as the JNET paper points out, it doesn’t really function that way. To see why this is problematic, consider that the first 2 or 3 meters of ocean have the same heat capacity of the entire atmosphere but the ocean mixed layer is typically 10 to 100 meters deep. Furthermore, there is some mixing of heat into the deeper ocean. Yet sea surface temperature (SST) is glibly averaged with air temperature as if this meant something:

   …we combine surface air temperatures over and with sea surface temperatures (SSTs) to form a global temperature index (Hansen, et al.)

   That said, temperature is a useful parameter in the following sense: GCMs make predictions about it (see #51). The models can be tested against the observables and one of those observables is temperature, not just averaged over the globe but also locally. Well, the models have not done very well. They have been tuned to “predict” the past but have fared poorly in predicting the future.

   It’s tough to make predictions, especially about the future. [attributed to Yogi Berra]

   I’ve been reading the works of Nassim Taleb lately. He’s scornful of both economic and climatological prognostications for much the same reasons: complexity and chaos. Like economic models, climate models do a great job with the past. Climatology is more like social science than physical science because it is an attempt to apply the methods that have been successful in physical science to a subject in which the interactions among the components are not understood. Modelers gloss over this problem by using statistical averages of parameters that conceal the complexity.

   As a fan of Karl Popper, I’m deeply suspicious of the non-falsifiable and of theories that do not properly consider uncertainties in forecasts. Henk Tennekes discusses this in detail.

   …those that advocate the idea that the response of the real climate to  radiative forcing is adequately represented in climate models have an obligation to prove that  they have not overlooked a single nonlinear, possibly chaotic feedback mechanism that Nature itself employs.

   As a sometime practitioner of Monte Carlo simulations, I’m well aware of the limitations of my much simpler models. I keep these on my desk:

   

5. Inactive
   Phil Turmel

   @PhilTurmel

   6:36 PM EDT ⋅ May 1, 2017

   John Hinderaker at PowerLine has a relevant post up today that reviews the latest work by Dr. James P. Wallace III, Dr. John R. Christy and Dr. Joseph S. D’Aleo.  John excerpts what I think is the money quote:

   The above analysis of Global Balloon & Satellite atmospheric temperature as well as Contiguous U.S. and Hadley Global Average Surface Temperature data turned up no statistical support for suggesting that CO2, even taken together with all other omitted variables, is the cause of the positive trend in the reported U.S. and Global temperature data.

   In fact, it seems very clear that the Global Warming that has occurred over the period 1959 to date can be quite easily explained by Natural Factor impacts alone. Given the number of independent entities and differing instrumentation used in gathering the temperature data analyzed herein, it seems highly unlikely that these findings are in error.

   Damning.