Peer Reviewed Study States The Obvious....

The present models are not adaquete. They totally missed the rapid melt of the Arctic Polar Cap. So, this article is stating the obvious. Now, are they going to build their model and see how it does in reality, or are they just going to bitch about the work that other people are doing?

The scientists are creating models to try to get a handle what the effects of the continued warming will be. Atmospheric physics coupled with oceanic currents and flows create a very complex situation, one fraught with many surprises. But to just sit and throw ones hands up, and say it is just too difficult is a typical Luddite reaction. And that is not what the people are stating in the article. They think they have a better approach. So, let's see their model, and test it.

Now if we really want to look at a worthless model, let's take a look at what the wingnut political approach to the warming has been. First, for the decades from the 60's to 2000, it was outright denial that anything at all was changing. Then, when it became so obvious that everyone could see that much was changing, it was, OK, so there is warming. It is A. Temporary, B. Not As Bad as Stated, and C. Has nothing to do with man's actions.

Of course, just as with the earlier denial of the existance of global warming, all three points are completely wrong. But, until major catastrophe, those that would be inconvenianced by facing the truth, will continue to lie.


A comparison of local and aggregated climate model outputs with observed data - Hydrological Sciences Journal

It is claimed that GCMs provide credible quantitative estimates of future climate change, particularly at continental scales and above. Examining the local performance of the models at 55 points, we found that local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale.

However, we think that the most important question is not whether GCMs can produce credible estimates of future climate, but whether climate is at all predictable in deterministic terms. Several publications, a typical example being Rial et al. (2004), point out the difficulties that the climate system complexity introduces when we attempt to make predictions. “Complexity” in this context usually refers to the fact that there are many parts comprising the system and many interactions among these parts. This observation is correct, but we take it a step further. We think that it is not merely a matter of high dimensionality, and that it can be misleading to assume that the uncertainty can be reduced if we analyse its “sources” as nonlinearities, feedbacks, thresholds, etc., and attempt to establish causality relationships. Koutsoyiannis (2010) created a toy model with simple, fully-known, deterministic dynamics, and with only two degrees of freedom (i.e. internal state variables or dimensions); but it exhibits extremely uncertain behaviour at all scales, including trends, fluctuations, and other features similar to those displayed by the climate. It does so with a constant external forcing, which means that there is no causality relationship between its state and the forcing. The fact that climate has many orders of magnitude more degrees of freedom certainly perplexes the situation further, but in the end it may be irrelevant; for, in the end, we do not have a predictable system hidden behind many layers of uncertainty which could be removed to some extent, but, rather, we have a system that is uncertain at its heart.

Do we have something better than GCMs when it comes to establishing policies for the future? Our answer is yes: we have stochastic approaches, and what is needed is a paradigm shift. We need to recognize the fact that the uncertainty is intrinsic, and shift our attention from reducing the uncertainty towards quantifying the uncertainty (see also Koutsoyiannis et al., 2009a). Obviously, in such a paradigm shift, stochastic descriptions of hydroclimatic processes should incorporate what is known about the driving physical mechanisms of the processes. Despite a common misconception of stochastics as black-box approaches whose blind use of data disregard the system dynamics, several celebrated examples, including statistical thermophysics and the modelling of turbulence, emphasize the opposite, i.e. the fact that stochastics is an indispensable, advanced and powerful part of physics. Other simpler examples (e.g. Koutsoyiannis, 2010) indicate how known deterministic dynamics can be fully incorporated in a stochastic framework and reconciled with the unavoidable emergence of uncertainty in predictions.
 
The present models are not adaquete. They totally missed the rapid melt of the Arctic Polar Cap. So, this article is stating the obvious. Now, are they going to build their model and see how it does in reality, or are they just going to bitch about the work that other people are doing?

The scientists are creating models to try to get a handle what the effects of the continued warming will be. Atmospheric physics coupled with oceanic currents and flows create a very complex situation, one fraught with many surprises. But to just sit and throw ones hands up, and say it is just too difficult is a typical Luddite reaction. And that is not what the people are stating in the article. They think they have a better approach. So, let's see their model, and test it.

Now if we really want to look at a worthless model, let's take a look at what the wingnut political approach to the warming has been. First, for the decades from the 60's to 2000, it was outright denial that anything at all was changing. Then, when it became so obvious that everyone could see that much was changing, it was, OK, so there is warming. It is A. Temporary, B. Not As Bad as Stated, and C. Has nothing to do with man's actions.

Of course, just as with the earlier denial of the existance of global warming, all three points are completely wrong. But, until major catastrophe, those that would be inconvenianced by facing the truth, will continue to lie.


A comparison of local and aggregated climate model outputs with observed data - Hydrological Sciences Journal

It is claimed that GCMs provide credible quantitative estimates of future climate change, particularly at continental scales and above. Examining the local performance of the models at 55 points, we found that local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale.

However, we think that the most important question is not whether GCMs can produce credible estimates of future climate, but whether climate is at all predictable in deterministic terms. Several publications, a typical example being Rial et al. (2004), point out the difficulties that the climate system complexity introduces when we attempt to make predictions. “Complexity” in this context usually refers to the fact that there are many parts comprising the system and many interactions among these parts. This observation is correct, but we take it a step further. We think that it is not merely a matter of high dimensionality, and that it can be misleading to assume that the uncertainty can be reduced if we analyse its “sources” as nonlinearities, feedbacks, thresholds, etc., and attempt to establish causality relationships. Koutsoyiannis (2010) created a toy model with simple, fully-known, deterministic dynamics, and with only two degrees of freedom (i.e. internal state variables or dimensions); but it exhibits extremely uncertain behaviour at all scales, including trends, fluctuations, and other features similar to those displayed by the climate.
It does so with a constant external forcing, which means that there is no causality relationship between its state and the forcing. The fact that climate has many orders of magnitude more degrees of freedom certainly perplexes the situation further, but in the end it may be irrelevant; for, in the end, we do not have a predictable system hidden behind many layers of uncertainty which could be removed to some extent, but, rather, we have a system that is uncertain at its heart.

Do we have something better than GCMs when it comes to establishing policies for the future? Our answer is yes: we have stochastic approaches, and what is needed is a paradigm shift. We need to recognize the fact that the uncertainty is intrinsic, and shift our attention from reducing the uncertainty towards quantifying the uncertainty (see also Koutsoyiannis et al., 2009a). Obviously, in such a paradigm shift, stochastic descriptions of hydroclimatic processes should incorporate what is known about the driving physical mechanisms of the processes. Despite a common misconception of stochastics as black-box approaches whose blind use of data disregard the system dynamics, several celebrated examples, including statistical thermophysics and the modelling of turbulence, emphasize the opposite, i.e. the fact that stochastics is an indispensable, advanced and powerful part of physics. Other simpler examples (e.g. Koutsoyiannis, 2010) indicate how known deterministic dynamics can be fully incorporated in a stochastic framework and reconciled with the unavoidable emergence of uncertainty in predictions.




I highlighted the pertinent section for you olfraud.
 
Try reading with comprehension, Walleyes. As I stated, the models have been far too conservative. The melt of the ice caps, sea ice, and glaciers has been moving far faster than the models. Same for the rise in sea level. And then there is the little matter of the feedbacks we are seeing in the Arctic. Many of these were covered in the lectures in the AGU Conferance in 2009. Many more, I am sure, will be covered in this month's AGU Conferance.

I will be looking for your refutation of all the other scientists there. Care to point out which lecture it will be?
 
Try reading with comprehension, Walleyes. As I stated, the models have been far too conservative. The melt of the ice caps, sea ice, and glaciers has been moving far faster than the models. Same for the rise in sea level. And then there is the little matter of the feedbacks we are seeing in the Arctic. Many of these were covered in the lectures in the AGU Conferance in 2009. Many more, I am sure, will be covered in this month's AGU Conferance.

I will be looking for your refutation of all the other scientists there. Care to point out which lecture it will be?




The models do not even come close to replicating what has occured olfraud. If they can't do that they can't even begin to predict the future either. And you, who claim to have taken three years of geology classes, still can't get it through your skull that the things we are observing today began hundreds of years ago.

Feedbacks in the arctic? How about the fact that the ice has been rebounding for three years in a row from the record low levels observed in 2007. What feedback explains that olfraud?
 
Try reading with comprehension, Walleyes. As I stated, the models have been far too conservative. The melt of the ice caps, sea ice, and glaciers has been moving far faster than the models. Same for the rise in sea level. And then there is the little matter of the feedbacks we are seeing in the Arctic. Many of these were covered in the lectures in the AGU Conferance in 2009. Many more, I am sure, will be covered in this month's AGU Conferance.

I will be looking for your refutation of all the other scientists there. Care to point out which lecture it will be?




The models do not even come close to replicating what has occured olfraud. If they can't do that they can't even begin to predict the future either. And you, who claim to have taken three years of geology classes, still can't get it through your skull that the things we are observing today began hundreds of years ago.

Feedbacks in the arctic? How about the fact that the ice has been rebounding for three years in a row from the record low levels observed in 2007. What feedback explains that olfraud?

Unless you include coloring book pictures your going to have a hell of a debate on your hands when Jiggs reads this.
 
Try reading with comprehension, Walleyes. As I stated, the models have been far too conservative. The melt of the ice caps, sea ice, and glaciers has been moving far faster than the models. Same for the rise in sea level. And then there is the little matter of the feedbacks we are seeing in the Arctic. Many of these were covered in the lectures in the AGU Conferance in 2009. Many more, I am sure, will be covered in this month's AGU Conferance.

I will be looking for your refutation of all the other scientists there. Care to point out which lecture it will be?




The models do not even come close to replicating what has occured olfraud. If they can't do that they can't even begin to predict the future either. And you, who claim to have taken three years of geology classes, still can't get it through your skull that the things we are observing today began hundreds of years ago.

Feedbacks in the arctic? How about the fact that the ice has been rebounding for three years in a row from the record low levels observed in 2007. What feedback explains that olfraud?

Unless you include coloring book pictures your going to have a hell of a debate on your hands when Jiggs reads this.




:lol::lol::lol: My 4 and half year old does pretty good on that so I will live it to her as she is the professional on that front!
 

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