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and Its Predictability |
Some define the climate of a region in terms of its average weather over a large number of years; i.e., the mean values of temperature, cloudiness, rainfall and so forth. This notion of climate is not adequate because the variability of weather is another essential aspect of the climate of a region. For example, Texas is noted for the variability of its weather.
The definition above could be extended to include not only mean values but also measures of variation such as ranges, extreme values and variances. However, what is being touched upon is that it is the probability distributions that define the climate of a region. These probability distributions of course depend upon the season and could be subject to other cycles and trends. They could also be subject to change due to exogenous circumstances such as volcanic eruptions.
Another name for probability distributions is statistical distributions. This name better suits weather phenomena. The interesting question is whether these statistical distributions of weather are completely general or whether they necessarily belong to a family of probability distributions and hence are describable by a limited set of parameters. Many but not all variables in nature have a so-called normal or bell-shaped distribution. Such distributions are completely specified by two parameters, the mean and variance. There is a central limit theorem which says that variables which are the sum of a large number of independent random variables will tend to have a normal distribution.
As an example of this matter consider the distribution of the changes in average global temperatures.
This is a reasonable approximation of a normal distribution. For more on the question of whether the distribution of changes in global average temperature is normal see Normality
Although the central limit theorem is quite general there are limits to its applicability. The independent random variables referred to in the previous sentence have to have finite variance.
There is a generalized central limit theorem which would suggest that the weather characteristics being the result of a large number of independent random variables would have a Levy-stable distribution. The normal distribution is one of those Levy-stable distributions. For an application of Levy-stable distributions to rainfall data see Rainfall in San Jose.
The probability distribution of weather changes cyclically with the season. This would not be construed as a change in climate. There are other cyclic changes in the probability distributions of weather phenomena that are part of climate and not a change in climate, Besides the seasonal cycle the other most obvious cycle is the diurnal (time of day) cycle. Another one is the ENSO (El Niño Southern Oscillation). More controversial is the Pacific Multidecadal Oscillation. It is an approximately sixty-year cycle that leads to a thirty-year upswing in average global temperature and a thirty-year downswing. The ignoring of this natural cycle has led to a great deal of confusion concerning climate change.
The Pacific Multidecadal Oscillation is evident in the average global temperature statistics from the early 19th century.
The average global temperature declined from about 1940 to 1975. From 1975 it started increasing. Global warming alarmists wanted to construe all of the increase in average global temperature as being due to the increase in CO2 in the atmosphere. The problem was that about the year 2000 the average global stopped increasing and remained steady for more than a decade, despite the fact that the CO2 content of the atmosphere continued to increase. The recent increases in AGT are due to ENSO rather than the increase in CO2. Some alarmists looked for ways to revise the procedure for estimate the average temperature of the Earth's surface. With 70 percent of the Earth's surface covered with oceans the global average is sensitive to the limited temperature readings of ships, there is a lot of room for revisions that would show an increase in global temperature since 2000. But the readings of atmospheric temperature from satellites do not show such an increase.
Ironically if the global warming alarmists accept the Pacific Multidecadal Oscillation then they would have an explanation for the absence of global temperature increases since 2000 and furthermore a stronger justification for the case they are trying to make. That is that the effect of increased CO2 has increased in recent years and if it had not the average global temperature would have started declining after about 2005. Thus by being dishonest about the Pacific Multidecadal Oscillation they ended up weakening their case.
The Climate Data Center for the National Oceanic and Atmospheric Administration in Asheville, North Carolina has the best store of climate data. In the 1990's this organization, under the direction of Thomas R. Karl, began considering questions concerning possible shifts in the distributions of weather variables in connection with the debate on global warming. The results published suggested an increase in extreme weather events. It would be desirable if this organization separated itself from the polemics concerning global warming and climate change and focused on fundamental matters such as the nature of the distributions and how to rigorously test for changes.
In the early 1960's the meteorologist and mathematician Edward Lorenz was investigating nonlinear mathematical systems as analogues of weather forecasting models. By fortuitous chance he discovered the mathematical phenomenon of chaos.
A chaotic system is one that exhibits an extreme sensitivity to initial conditions. Thus if forecasters are working with initial conditions that deviate ever so slightly from the true values that initial error will grow exponentially so that it will be so large as to make the forecast no better than a random guess and therefore useless.
The question of the predictability of climate was investigated by Edward Lorenz before his death in 2008 at age 90. His work on chaos theory established why weather could not be accurately predicted beyond a week to ten days. Given this limit there is a serious question of whether future climate can be predicted. Lorenz' answer on the predictability of climate was a tentative affirmative. His paper is included in the published proceedings of a conference on the question of predictability, The Predictability of the Weather and Climate (2006) edited by Tim Palmer and Renate Hagedorn.
Lorenz in the article cited above notes that the time pattern for the forecast error would look something like the following.
The scale for the error E is on the right. The scale for the logarithm of error is on the left. The period of the exponential growth in error transitions into a phase in which the error cannot get any larger. Lorenz refers to this as saturation.
[…] when the predicted and actual states become as far apart as randomly chosen states -- when the error reaches saturation.
Lorenz thinks entirely in terms of a step-by-step prediction of atmospheric conditions from one point in time to the next. What may be needed is an entirely different approach in terms of statistical distributions. The Liouville Equation involves such an approach. A step in this direction is the practice of ensemble forecasting. Since the initial conditions are not none exactly it is sensible to do forecasts for a set of initial conditions near the presumed initial conditions to get some notion of range to conditions that might develop.
There are, however, some further topics in the step-by-step prediction of atmospheric conditions that need to be considered.
For systems characterized by a deterministic transformation of the type
one thing of interest is equilibria; i.e., values of x such that x=f(x). There are some remarkable mathematical theorems, called fixed point theorems, having to do with the existence of such solutions. Repeating cycles can be included in such analysis by looking fixed points of iterations of the transformation. For example, two period cycles are fixed points of f(f(x)). (Equilibria are just special cases of a two period cycles.)
One can also look at how the distribution for x_{t} get transformed into the distribution for x_{t+1}. It is this perspective that is relevant in the matter of climate. A stable climate would be one that the distributions of weather variables at one point in time leads to the same distributions at later points in time. What is need is a fixed point theorem for distributions. Such a fixed point would constitute a stochastic equilibrium.
In statistical mechanics a system will, starting from a specific point, go through all of the possible configurations. Furthermore the average of characteristic of the system as it follows the time path from any initial starting point is the same and is equal to the average for probability distribution, usually called the ensemble, for the system.
This is what is assumed without any theoretical justification in climate model projections. That is to say, the projecters run their models and presume that some average of the projections will be the same as the average for the future distribution of the weather variables. This might be so, and then again it might not be so.
The notion of climate as being long term average weather must be extended to involve the statistical distributions of weather. And not only statistical distributions but the relationships of these to cycles such as season, time of day, the ENSO phenomenon and the Pacific Multidecadal Oscillation.
Along with the perception of climate as involving statistical distributions there has to be developed a new mode of analysis for predicting changes in statistical distributions.
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