Here we count the rank of observation
among model ensemble members and create histogram, so the number of rank in horizontal axis is from one to the number of ensemble plus one.
The rank of observed PRCP in the present climate
among model ensembles is shown in Fig. 4.
However, the effective degree of freedom may be different
among model ensembles, and the statistical test for the uniformity also depends on n obs.
Not exact matches
The idea that our universe — everything we can observe, including the laws of physics that shape it — is just one
among a vast
ensemble may seem the stuff of science fiction, but cosmologists build multiverse
models using a theory called inflation.
Among our two biggest concerns are (a) the realism of the control - run variability in our
model and the AR4
ensemble, and (b) the quality and spatial representativeness of the pre-1900 obs.
Decreases in precipitation over many subtropical areas are evident in the multi-model
ensemble mean, and consistency in the sign of change
among the
models is often high (Wang, 2005), particularly in some regions like the tropical Central American - Caribbean (Neelin et al., 2006).
Same as Fig. 3 but for rank of observation for SAT trend (1986 — 1990)
among the climate
model ensembles.
Same as Fig. 3 but for rank of observation for clear - sky SW radiation at the top of the atmosphere (1986 — 1990)
among the climate
model ensembles
Same as Fig. 3 but for rank of observation for LW CRF at the top of the atmosphere (1986 — 1990)
among the climate
model ensembles
Whereas each
model demonstrates some sort of multidecadal variability (which may or may not be of a reasonable amplitude or associated with the appropriate mechanisms), the
ensemble averaging process filters out the simulated natural internal variability since there is no temporal synchronization in the simulated chaotic internal oscillations
among the different
ensemble members.
Rank of minimum spanning tree (MST) without observation
among MSTs of observation plus
model ensemble members removing each
ensemble members
These results suggest that the structural diversity is important in order to include the observation
among the spread of
model ensembles.
Among the various techniques, the AR4 AOGCM
ensemble provides the most sophisticated set of
models in terms of the range of processes included and consequent realism of the simulations compared to observations (see Chapters 8 and 9).
The
model outputs are generally presented as an average of an
ensemble of individual runs (and even
ensembles of individual runs from multiple
models), in order to remove this variability from the overall picture, because
among grownups it is understood that 1) the long term trends are what we're interested and 2) the coarseness of our measurements of initial conditions combined with a finite
modeled grid size means that
models can not predict precisely when and how temps will vary around a trend in the real world (they can, however, by being run many times, give us a good idea of the * magnitude * of that variance, including how many years of flat or declining temperatures we might expect to see pop up from time to time).