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Dynamical means that the parts change and interact continuously.
Not exact matches
There may be reason to strongly suspect that in any sufficiently complicated
dynamical system model (such as climate) with stochastic parameters (e.g., exactly when and where a lightning strike starts a major wildfire or a major submarine earthquake perturbs ocean circulation in a region or a major volcanic eruption introduces stratospheric aerosols), it is almost certain that any given run of the model will have periods of significant deviation from the
mean of multiple runs.
So our point is, of course, that regardless of what mechanisms are responsible for a
mean change in the Walker circulation in the AR4 simulations you've analyzed, if the simulations are not getting the
dynamical mechanisms discussed in our post right, it is quite conceivable that at least the amplitude and perhaps the sign of the change in the Walker circulation is wrong, regardless of what mechanisms are responsible for the changes in the simulations analyzed.
Besides I strongly oppose (like R.Pielke and many others) the idea that the «global time average of the surface temperature» has any physical
meaning or is a valid metrics to measure the «climate» and I can't see the beginning of a valid reason why it should correlate to any relevant
dynamical parameter.
µ is a probability (yes a probability is (also) a measure) T is the
dynamical law that our system obeys T (x) are then trajectories (orbits) of the
dynamical states in X. Of course «iterating T»
means following the system's states in time.
July 2016 Sea Ice Outlook predictions of Sea Ice Probability (SIP) from 5
dynamical models, the
mean of the 5, and the standard deviation across the 5 SIP forecasts.
While probabilistic projections of the global
mean and regional temperatures exist, process - based probabilistic assessments of large - scale
dynamical systems such as the Atlantic overturning are still missing.
In general, the heuristic approaches forecast a
mean September extent around 4.1 million km2, whereas the statistical and
dynamical modeling approaches both suggest
mean September extent near 5.1 million km2, with the
dynamical modeling contributions showing a narrower range.
Indeed, it is likely that the regional patterns of past climate changes, rather than simple hemispheric or global
mean temperature trends, will best inform our understanding of the
dynamical mechanisms involved.
Depending on what is
meant by «robust», I would venture that modern schemes for integrating the
dynamical core equations in climate models are no more «robust» than in those in the NWP codes.