In Frontiers of Climate Modeling, Jeffrey Kiehl says:
The study of the Earth’s climate system is motivated by the desire to understand the processes that determine the state of the climate and the possible ways in which this state may have changed in the past or may change in the future..
Earth’s climate system is composed of a number of components (e.g., atmosphere, hydrosphere, cryosphere and biosphere). These components are non-linear systems in themselves, with various processes, which are spatially non-local.
Each component has a characteristic time scale associated with it. The entire Earth system is composed of the coupled interaction of these non-local, non-linear components.
Given this level of complexity, it is no wonder that the system displays a rich spectrum of climate variability on time scales ranging from the diurnal to millions of years.. This level of complexity also implies the system is chaotic (Lorenz, 1996, Hansen et al., 1997), which means the representation of the Earth system is not deterministic.
However, this does not imply that the system is not predictable. If it were not predictable at some level, climate modeling would not be possible. Why is it predictable? First, the climate system is forced externally through solar radiation from the Sun. This forcing is quasi-regular on a wide range of time scales. The seasonal cycle is the largest forcing Earth experiences, and is very regular. Second, certain modes of variability, e.g., the El Nino southern oscillation (ENSO), North Atlantic oscillation, etc., are quasi-periodic unforced internal modes of variability. Because they are quasi-periodic, they are predictable to some degree of accuracy.
The representation of the Earth system requires a statistical approach, rather than a deterministic one.
Modeling the climate system is not concerned with predicting the exact time and location of a specific small-scale event. Rather, modeling the climate system is concerned with understanding and predicting the statistical behavior of the system; in simplest terms, the mean and variance of the climate system.
He goes on to comment on climate history – warm periods such as the Cretaceous & Eocene, and very cold states such as the ice ages (e.g., 18,000 years ago), as well as climate fluctuations on very fast time scales.
The complexity of the mathematical relations and their solutions requires the use of large supercomputers. The chaotic nature of the climate system implies that ensembles are required to best understand the properties of the system. This requires numerous simulations of the state of the climate. The length of the climate simulations depends on the problem of interest..
And later comments:
There is some degree of skepticism concerning the predictive capabilities of climate models. These concerns center on the ability to represent all of the diverse processes of nature realistically. Since many of these processes (e.g., clouds, sea ice, water vapor) strongly affect the sensitivity of climate models, there is concern that model response to increased greenhouse-gas concentrations may be in error.
For this reason alone, it is imperative that climate models be compared to a diverse set of observations in terms of the time mean, the spatio-temporal variability and the response to external forcing. To the extent that models can reproduce observed features for all of these features, belief in the model’s ability to predict future climate change is better justified.
Probably the biggest question to myself and the readers on this blog is the measure of predictability of the climate.
I’m a beginner with non-linear dynamics but have been playing around with some basics. I would have preferred to know a lot more before writing this article, but I thought many people would find Kiehl’s comments interesting.
In various blogs I have read that climate is predictable because summer will be warmer than winter and the equator warmer than the poles. This is clearly true. However, there is a big gap between knowing that and knowing the state of the climate 50 years from now.
Or, to put it another way – if it is true that summer will be warmer than winter, and it is true that climate models forecast that summer will be warmer than winter, does it follow that climate models are reliable about the mean climate state 50 years from now? Of course, it doesn’t – and I don’t think many people would make this claim in such simplistic terms. How about – if it is true that a climate model can reproduce the mean annual climatology over the next few years (whatever precisely that entails) does it follow that climate models are reliable about the mean climate state 50 years from now?
I haven’t found many papers that really address this subject (which doesn’t mean there aren’t any). From my very limited understanding of chaotic systems I believe that the question is not easily resolvable. With a precise knowledge of the equations governing the system, and a detailed study of the behavior of the system described by these equations, it is possible to determine the boundary conditions which lead to various types of results. And without a precise knowledge it appears impossible. Is this correct?
However, with a little knowledge of the stochastic behavior of non-linear systems, I did find Jeffrey Kiehl’s comments very illuminating as to why ensembles of climate models are used.
Climatology is more about statistics than one day in one place. Which helps explain why, just as an example, the measure of a climate model is not measuring the average temperature in Moscow in January 2012 vs what a climate model “predicts” about the average temperature in Moscow in January 2012. You can easily create systems that have unpredictable time-varying behavior, yet very predictable statistical behavior. (The predictable statistical behavior can be seen in frequency based plots, for example).
So the fact that climate is a non-linear system does not mean as a necessary consequence that it is statistically unpredictable.
But it might in practical terms – that is, in terms of the certainty we would like to ascribe to future climatology.
I would be interested to know how the subject could be resolved.
Frontiers of Climate Modeling, edited by J.T. Kiehl & V. Ramanathan, Cambridge University Press (2006)