Climate ensemble
Encyclopedia
In physics
, a statistical ensemble is a large set of (mental) copies of a system, considered all at once; each copy of the system representing a different possible detailed realisation of the system, consistent with the system's observed macroscopic properties.
A climate ensemble involves slightly different models of the climate system. There are at least four different types, to be described below.
of different outcomes. This is proving to be very difficult due to a number of problems. These include:
. Modern climate models do a good job of simulating many large-scale features of present-day climate. However, these models contain large numbers of adjustable Parameters which are known, individually, to have a significant impact on simulated climate. While many of these are well constrained by observations, there are many which are subject to considerable uncertainty. We do not know the extent to which different choices of parameter-settings or schemes may provide equally realistic simulations of 20th century climate but different forecast for the 21st century. The most thorough way to investigate this uncertainty is to run a massive ensemble experiment in which each relevant parameter combination is investigated.
Having an initial condition ensemble can help to identify natural variability in the system and deal with it.
.
Physics
Physics is a natural science that involves the study of matter and its motion through spacetime, along with related concepts such as energy and force. More broadly, it is the general analysis of nature, conducted in order to understand how the universe behaves.Physics is one of the oldest academic...
, a statistical ensemble is a large set of (mental) copies of a system, considered all at once; each copy of the system representing a different possible detailed realisation of the system, consistent with the system's observed macroscopic properties.
A climate ensemble involves slightly different models of the climate system. There are at least four different types, to be described below.
Aims
The aim of running an ensemble is usually in order to be able to deal with uncertainties in the system. An ultimate aim may be to produce policy relevant information such as a probability distribution functionProbability distribution function
Depending upon which text is consulted, a probability distribution function is any of:* a probability distribution function,* a cumulative distribution function,* a probability mass function, or* a probability density function....
of different outcomes. This is proving to be very difficult due to a number of problems. These include:
- The ensemble has to be wide ranging to ensure it covers the whole range where the climate models may be good.
- Measuring what is a good model is difficult. This may need to consider not only errors in the observation but also in the model.
- Any prior assumptions about distribution can influence the probability distribution function produced.
Perturbed physics ensemble
Perturbed physics ensembles form the main scientific focus of the ClimatePrediction projectClimateprediction.net
Climateprediction.net, or CPDN, is a distributed computing project to investigate and reduce uncertainties in climate modelling. It aims to do this by running hundreds of thousands of different models using the donated idle time of ordinary personal computers, thereby leading to a better...
. Modern climate models do a good job of simulating many large-scale features of present-day climate. However, these models contain large numbers of adjustable Parameters which are known, individually, to have a significant impact on simulated climate. While many of these are well constrained by observations, there are many which are subject to considerable uncertainty. We do not know the extent to which different choices of parameter-settings or schemes may provide equally realistic simulations of 20th century climate but different forecast for the 21st century. The most thorough way to investigate this uncertainty is to run a massive ensemble experiment in which each relevant parameter combination is investigated.
Initial condition ensemble
Initial condition ensembles involve the same model in terms of the same atmospheric physics parameters and forcings, but run from variety of different starting states. Because the climate system is chaotic, tiny changes in things such as temperatures, winds, and humidity in one place can lead to very different paths for the system as a whole. We can work around this by setting off several runs started with slightly different starting conditions, and then look at the evolution of the group as a whole. This is similar to what they do in weather forecasting.Having an initial condition ensemble can help to identify natural variability in the system and deal with it.
Forcing ensemble
A model can be subjected to different forcings. These may correspond with different scenarios such as those described in the Special Report on Emissions ScenariosSpecial Report on Emissions Scenarios
The Special Report on Emissions Scenarios was prepared by the Intergovernmental Panel on Climate Change in 2000, based on data developed at the Earth Institute at Columbia University. The emissions scenarios described in the Report have been used to make projections of possible future climate...
.
Grand ensemble
A grand ensemble is an ensemble of ensembles. There has to be at least two nested ensembles. This is best illustrated in the diagram opposite.See also
- Ensemble (fluid mechanics)Ensemble (fluid mechanics)In fluid mechanics, an ensemble is an imaginary collection of notionally identical experiments.Each member of the ensemble will have nominally identical boundary conditions and fluid properties...
- Sensitivity analysisSensitivity analysisSensitivity analysis is the study of how the variation in the output of a statistical model can be attributed to different variations in the inputs of the model. Put another way, it is a technique for systematically changing variables in a model to determine the effects of such changes.In any...
- Uncertainty analysisUncertainty analysisCalibrated parameter does not necessarily represents reality, as reality is much more complex. Any any prediction has its own complexities of reality that cannot be represented uniquely in the calibrated model; tehrefore, there is a potential error. Such error must be accounted for when making...