Observations About Global Circulation Models

In summary, the paper finds that there is a good agreement between model simulations of the 20th century despite the wide ranges in climate sensitivity. The discrepancy may be due to uncertainties in cloud feedback processes.
  • #1
John Creighto
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2
I want to post some interesting quotes about global circulation models. Well, I believe they have potential I believe that the application of the theory is often misapplied.

I am convinced they don’t know what they are doing when it comes to internal climate variability (”weather noise”) and statistical treatment of ensemble GCM runs. They start by assuming the GCMs are valid in terms of the noise structure they generate. Have they ever proven this? I think not.
http://www.climateaudit.org/?p=483#comment-208656



Yes and YES !
However why do you write “… IF the weather is an initial value problem …” ?
It is an initial value problem and I know of nothing that would suggest otherwise .
From that follows then that the clustering (or self similarity) forbids to operate some cut offs in the time scales by arbitrary considerations of “randomness below , signal above” .
L.Motl has challenged some post on RC blathering about “weather vs climate differences” by asking at what time scale climate emerges from the noise and what physical process governs that transition .
My take on it is that climate emerges after around 50 years because that is a typical time length of human awareness .
Shorter we don’t know enough and longer we forgot too much :)
http://www.climateaudit.org/?p=483#comment-209256

Thanks for the kind comments, bender and Tom, much appreciated.

Tom, I fully agree that there is considerable evidence to support the idea that weather is an initial value problem with exponential error growth. I start from this premise rather than justify it because I believe both AGW supporters and AGW sceptics agree on this point. This makes it a good place to start a debate, in order to find why we draw different conclusions from the same premise. My use of the word “if” is really due to habit, it is just my naturally cautious way of expressing the premise of a logical argument. (I blame the modal logic that I had to study as part of my university work!)

Bender made an excellent point on another thread which ties in with this nicely as well. Dr. Curry argued on the feedback thread that single model runs (what I would refer to as realisations) were not informative due to the fact that a single run could look very different due to natural variability. I was still in lurk mode and bit my tongue, but the good dr. bender made the point very well elsewhere. The real world is also only a single realisation that may also not be typical (whatever typical means in this context). Yet we are busily fitting our models to churn that out as the central result. This is a very important issue, and hopefully someone in the mainstream will wake up and smell the coffee on this point. The presence of fractional variability in climate makes this a fundamental problem.

PS. Watching closely for Dr. Browning’s review. Should be interesting.
http://www.climateaudit.org/?p=483#comment-209304
 
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  • #2
Here's some more stuff:

Of course Hitran is used .
Therefore collisionnaly induced emissions/absorptions (and no it is neither 0 nor negligible) are ignored because they are not in Hitran . I already noticed that people use Hitran like a magical word - if you say Hitran , you access to the Nirvana of infinite accuracy and the world where “the radiative transfer is a settled science” .
Well it is not in the famous details where the devil is .
Also CFCs are mentionned .
We know about everything about their radiative properties but we know very little about their distribution and have practically no past data .
http://www.climateaudit.org/?p=2696#comment-208196
 
  • #3
And some stuff on the tunning of GCM:

The question is: if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy. Kerr [2007] and S. E. Schwartz et al. (Quantifying climate change�too rosy a picture?, available at www.nature.com/reports/climatechange, 2007) recently pointed out the importance of understanding the answer to this question. Indeed, Kerr [2007] referred to the present work and the current paper provides the ��widely circulated analysis�� referred to by Kerr [2007]. This report investigates the most probable explanation for such an agreement. It uses published results from a wide variety of model simulations to understand this apparent paradox between model climate responses for the 20th century, but diverse climate model sensitivity.
...
It is believed that much of the range in model climate sensitivity is due to uncertainties in cloud feedback processes [Cess et al., 1996]. Although there are established data for the time evolution of well-mixed greenhouse gases, there are no established standard datasets for ozone, aerosols or natural forcing factors. Results from nine fully coupled climate models [Dai et al., 2001; Boer et al., 2000; Roeckner et al., 1999; Haywood et al., 1997; Mitchell et al., 1995; Tett et al., 2002; Meehl et al., 2004; Meehl et al., 2000] and two energy balance models [Crowley and Kim, 1999; Andronova and Schlesinger, 2000] have been used to consider the relationship between total anthropogenic climate forcing and climate sensitivity.
http://www.climateaudit.org/?p=2475

Of course aerosol tunning is not the only tunning factor. Tunning is also used for instance to compensate for sub grid scale turbulence latent and sensible heat transport.
http://www.cnrm.meteo.fr/icam2007/html/PROCEEDINGS/ICAM2007/extended/manuscript_98.pdf
 
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FAQ: Observations About Global Circulation Models

What are Global Circulation Models (GCMs)?

Global Circulation Models (GCMs) are computer simulations that use mathematical equations to represent the complex processes and interactions that occur in the Earth's atmosphere, oceans, land, and ice. They are used to predict future climate change and understand past climate patterns.

How do GCMs work?

GCMs use a three-dimensional grid system to divide the Earth's surface into small cells. Within each cell, the models use equations to simulate the physical processes that occur, such as air and ocean currents, temperature, and precipitation. The models also take into account external factors such as solar radiation, greenhouse gas emissions, and land use changes.

What are the limitations of GCMs?

One of the main limitations of GCMs is the complexity of the Earth's climate system, which makes it difficult to accurately represent all processes and interactions. Additionally, GCMs rely on assumptions and simplified representations of certain processes, which can lead to uncertainties in their predictions. Another limitation is the lack of data in some regions, which can affect the accuracy of the models.

How accurate are GCMs in predicting future climate change?

GCMs have been shown to accurately simulate past climate patterns and are generally considered reliable in predicting broad climate patterns, such as global temperature changes. However, they may not be as accurate in predicting regional or local climate changes, as those are influenced by more localized factors.

How are GCMs used in climate research and policy-making?

GCMs are an important tool in climate research and are used to study the potential impacts of climate change and inform policy decisions. They can help identify areas that are most vulnerable to climate change and aid in developing strategies to mitigate its effects. However, GCMs are just one tool among many used to understand climate change, and their predictions should be considered in combination with other sources of information.

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