Chaos, complexity, climate models and crap.
….At this point a special technique has been developed in mathematics. This technique, when applied to the real world, is sometimes useful, but can sometimes also lead to self-deception. This technique is called modelling. When constructing a model, the following idealisation is made: certain facts which are only known with a certain degree of probability or with a certain degree of accuracy, are considered to be "absolutely" correct and are accepted as "axioms". The sense of this "absoluteness" lies precisely in the fact that we allow ourselves to use these "facts" according to the rules of formal logic, in the process declaring as "theorems" all that we can derive from them.
It is obvious that in any real-life activity it is impossible to wholly rely on such deductions. The reason is at least that the parameters of the studied phenomena are never known absolutely exactly and a small change in parameters (for example, the initial conditions of a process) can totally change the result. Say, for this reason a reliable long-term weather forecast is impossible and will remain impossible, no matter how much we develop computers and devices which record initial conditions.
Vladimir Arnold
Here is the climate forecast for the next decade; although global warming will be held in check for a few years, it will come roaring back to send the mercury rising before 2014.
This is the prediction of the first computer model of the global climate designed to make forecasts over a timescale of around a decade, developed by scientists at the Met Office.
The new model developed at the Met's Hadley Centre in Exeter, and described in the journal Science, predicts that warming will slow during the next few years but then speed up again, and that at least half of the years after 2009 will be warmer than 1998, the warmest year on record.
The new model incorporates the effects of sea surface temperatures as well as other factors such as man-made emissions of greenhouse gases, projected changes in the Sun's output and the effects of previous volcanic eruptions - the first time internal and external variability have both been predicted.
What a load of crap.
Idiot alert warning comes on immediately with this statement
“projected changes in the Sun's output’
Solutions for the magentohydrodynamic equations and the Velikhov Thermoelectric instability of gases are of course NOBEL PRIZE winning papers.Not only that they will resolve the constraints of the tokomak fusion reactors producing unlimited electrical power 10 years ahead of estimates saving ITER 20 billion dollars.
From our daily life experience we know how fragile and complex biological, ecological and social systems behave. What do we mean by the term “complexity” in a scientific context? According to our view complex systems are comprised of multiple components which interact in a nonlinear manner thus the system behavior cannot be inferred from the behavior of the components. More specifically, these systems are characterized by
• structures with many components,
• dynamics with many modes,
• hierarchical level structures,
• couplings of many degrees of freedom,
• long-range spatial-temporal correlations.
We start our considerations with some general remarks on self-organization and non-linear dynamics in biology. In particular, we summarize some basic physical principles that lead to the emergence of complex structures in biological systems, such as openess, irreversibility, entropy export and feedback processes. It is well known from the thermodynamics of irreversible processes that systems may exhibit a rich variety of complex behavior if there is a supercritical influx of free energy. This energy may be provided in different forms, i.e. matter (chemical components, resources), high temperature radiation, or signals. What kind of complex behavior is observed in a system, will of course not only depend on the influx of energy but also on the interaction of the entities that comprise the system. Among the prominent examples that can be observed in biological systems are processes of pattern formation and morphogenesis and different types of collective motion, such as swarming.
Considering further non-linear interactions between the particles, such as attractive forces or interactions via chemical fields, we are able to derive a rather general framework for the dynamics
As we discussed previously Chaos and Complexity theory studies nonlinear processes: Chaos explores how complexly interwoven patterns of behaviour can emerge out of relatively simply-to-describe nonlinear dynamics, while Complexity tries to understand how relatively simply-to-describe patterns can emerge out of complexly interwoven dynamics.
As we have learned from nonlinear dynamics, complexity is not restricted to large hierarchical systems, also relatively simple dynamical models may show complicated behavior. Among the specific features of complex nonlinear processes, we mention:
• complicated trajectories and chaos,
• manifolds of spatial-temporal structures,
• the limited predictability of future behavior (positive Kolmogorov-Sinai entropy).
Further, we note that complexity may arise in dissipative as well as in conservative systems. In general complex systems in nature and society are of dissipative nature, i.e. they are based on energy “consumption” that allows self-organization processes. This, however, needs some physical requirements, such as:
• thermodynamic openess, i.e. the system exchanges energy, entropy and matter with the environment,
• that on average the system exports entropy, i.e. it imports energy of high value and exports energy of low value,
• that the system operates far from equilibrium, beyond a critical distance from the equilibrium state),
• that the causal relations in the system include (positive and negative) feedback and feed forward processes), i.e. the dynamics of the system is nonlinear.
Self-organization behaviour can be exhibited by far-from-equilibrium chemical systems as it was shown by the Nobel-prize winner (1977) in chemistry Ilya Prigogine. According to the results of his studies, the inorganic chemical systems can exist in highly non-equilibrium conditions impregnated with a potential for emergence of self-organizing chemical structures. The more complex the aggregation of these structures, the stronger the tendency for macro-molecules to organize themselves, this is also the response of the biosphere components.
The integration of the carbon cycle and the biosphere adds complexity to the meteorological components and complete failure of integration of the separate models into GCM PRECLUDES the models predictive capacity.as it is the major quantity of the amplification and modulation of atmospheric gas and climate its coupling is of course the most important.
The GCM do not use the complexities of the carbon cycle with the oscillations of the biosphere through the transformation of energy by biological process.
….At this point a special technique has been developed in mathematics. This technique, when applied to the real world, is sometimes useful, but can sometimes also lead to self-deception. This technique is called modelling. When constructing a model, the following idealisation is made: certain facts which are only known with a certain degree of probability or with a certain degree of accuracy, are considered to be "absolutely" correct and are accepted as "axioms". The sense of this "absoluteness" lies precisely in the fact that we allow ourselves to use these "facts" according to the rules of formal logic, in the process declaring as "theorems" all that we can derive from them.
It is obvious that in any real-life activity it is impossible to wholly rely on such deductions. The reason is at least that the parameters of the studied phenomena are never known absolutely exactly and a small change in parameters (for example, the initial conditions of a process) can totally change the result. Say, for this reason a reliable long-term weather forecast is impossible and will remain impossible, no matter how much we develop computers and devices which record initial conditions.
Vladimir Arnold
Here is the climate forecast for the next decade; although global warming will be held in check for a few years, it will come roaring back to send the mercury rising before 2014.
This is the prediction of the first computer model of the global climate designed to make forecasts over a timescale of around a decade, developed by scientists at the Met Office.
The new model developed at the Met's Hadley Centre in Exeter, and described in the journal Science, predicts that warming will slow during the next few years but then speed up again, and that at least half of the years after 2009 will be warmer than 1998, the warmest year on record.
The new model incorporates the effects of sea surface temperatures as well as other factors such as man-made emissions of greenhouse gases, projected changes in the Sun's output and the effects of previous volcanic eruptions - the first time internal and external variability have both been predicted.
What a load of crap.
Idiot alert warning comes on immediately with this statement
“projected changes in the Sun's output’
Solutions for the magentohydrodynamic equations and the Velikhov Thermoelectric instability of gases are of course NOBEL PRIZE winning papers.Not only that they will resolve the constraints of the tokomak fusion reactors producing unlimited electrical power 10 years ahead of estimates saving ITER 20 billion dollars.
From our daily life experience we know how fragile and complex biological, ecological and social systems behave. What do we mean by the term “complexity” in a scientific context? According to our view complex systems are comprised of multiple components which interact in a nonlinear manner thus the system behavior cannot be inferred from the behavior of the components. More specifically, these systems are characterized by
• structures with many components,
• dynamics with many modes,
• hierarchical level structures,
• couplings of many degrees of freedom,
• long-range spatial-temporal correlations.
We start our considerations with some general remarks on self-organization and non-linear dynamics in biology. In particular, we summarize some basic physical principles that lead to the emergence of complex structures in biological systems, such as openess, irreversibility, entropy export and feedback processes. It is well known from the thermodynamics of irreversible processes that systems may exhibit a rich variety of complex behavior if there is a supercritical influx of free energy. This energy may be provided in different forms, i.e. matter (chemical components, resources), high temperature radiation, or signals. What kind of complex behavior is observed in a system, will of course not only depend on the influx of energy but also on the interaction of the entities that comprise the system. Among the prominent examples that can be observed in biological systems are processes of pattern formation and morphogenesis and different types of collective motion, such as swarming.
Considering further non-linear interactions between the particles, such as attractive forces or interactions via chemical fields, we are able to derive a rather general framework for the dynamics
As we discussed previously Chaos and Complexity theory studies nonlinear processes: Chaos explores how complexly interwoven patterns of behaviour can emerge out of relatively simply-to-describe nonlinear dynamics, while Complexity tries to understand how relatively simply-to-describe patterns can emerge out of complexly interwoven dynamics.
As we have learned from nonlinear dynamics, complexity is not restricted to large hierarchical systems, also relatively simple dynamical models may show complicated behavior. Among the specific features of complex nonlinear processes, we mention:
• complicated trajectories and chaos,
• manifolds of spatial-temporal structures,
• the limited predictability of future behavior (positive Kolmogorov-Sinai entropy).
Further, we note that complexity may arise in dissipative as well as in conservative systems. In general complex systems in nature and society are of dissipative nature, i.e. they are based on energy “consumption” that allows self-organization processes. This, however, needs some physical requirements, such as:
• thermodynamic openess, i.e. the system exchanges energy, entropy and matter with the environment,
• that on average the system exports entropy, i.e. it imports energy of high value and exports energy of low value,
• that the system operates far from equilibrium, beyond a critical distance from the equilibrium state),
• that the causal relations in the system include (positive and negative) feedback and feed forward processes), i.e. the dynamics of the system is nonlinear.
Self-organization behaviour can be exhibited by far-from-equilibrium chemical systems as it was shown by the Nobel-prize winner (1977) in chemistry Ilya Prigogine. According to the results of his studies, the inorganic chemical systems can exist in highly non-equilibrium conditions impregnated with a potential for emergence of self-organizing chemical structures. The more complex the aggregation of these structures, the stronger the tendency for macro-molecules to organize themselves, this is also the response of the biosphere components.
The integration of the carbon cycle and the biosphere adds complexity to the meteorological components and complete failure of integration of the separate models into GCM PRECLUDES the models predictive capacity.as it is the major quantity of the amplification and modulation of atmospheric gas and climate its coupling is of course the most important.
The GCM do not use the complexities of the carbon cycle with the oscillations of the biosphere through the transformation of energy by biological process.
posted by maksimovich | 1:44 AM
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