Sentences with phrase «given model ensemble»

More complex metrics have also been developed based on multiple observables in present day climate, and have been shown to have the potential to narrow the uncertainty in climate sensitivity across a given model ensemble (Murphy et al., 2004; Piani et al., 2005).

Not exact matches

I am inclined to stay on the southwestern side of the model guidance, given the rather consistent forecasts of the ECMWF and its ensemble.

The Schneider et al. ensemble constrained by their selection of LGM data gives a global - mean cooling range during the LGM of 5.8 + / - 1.4 ºC (Schnieder Von Deimling et al, 2006), while the best fit from the UVic model used in the new paper has 3.5 ºC cooling, well outside this range (weighted average calculated from the online data, a slightly different number is stated in Nathan Urban's interview — not sure why).

It's a cool - looking ensemble dress though given the petite stature of the model I was worried about the length on me.

Neither is there reason to shift the range — most models (including large ensembles with systematic variation of uncertain paramaters) give values smack in the middle of the traditional range, near 3 ºC.

[Response: The model ensemble is supposed to be a collection of possible realisations given the appropriate forcing.

1) Regarding the 1970s shift, Ray mentions that: «It's not evident why the smooth trend in 20th century climate forcing should give rise to such an abrupt shift, and indeed the individual members of the model ensemble do not show a clearly analogous shift.»

It's not evident why the smooth trend in 20th century climate forcing should give rise to such an abrupt shift, and indeed the individual members of the model ensemble do not show a clearly analogous shift.

We can derive the underlying trend related to external forcings from the GCMs — for each model, the underlying trend can be derived from the ensemble mean (averaging over the different phases of ENSO in each simulation), and looking at the spread in the ensemble mean trend across models gives information about the uncertainties in the model response (the «structural» uncertainty) and also about the forcing uncertainty — since models will (in practice) have slightly different realisations of the (uncertain) net forcing (principally related to aerosols).

Unlike the ENSO and IOD SST forecasts, the seasonal outlooks are based on the last three weeks of forecasts, i.e. five separate model runs combining to make a 165 - member ensemble, as this was shown to give higher skill.

Careful calibration and judicious combination of ensembles of forecasts from different models into a larger ensemble can give higher skill than that from any single model.

These budgets give the lowest estimates of allowed emissions and are the simplest to convert into policy advice, but they suffer from the same problem of probabilistic interpretation as TEBs since they are dependent on simple climate models with uncertainty ranges calibrated to the CMIP5 ensemble.

Given that it seems that averages of ensembles of model results is the only accepted method of presenting results of chaotic response for practical use, what is gained by attempting to tune a single «realization».

In this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed NAO record in combination with the model's radiatively - forced response (given by the ensemble - mean of the 40 simulations).

For example, seven of the twenty ensemble members from the Alfred Wegener Institute modeling group gave Outlook estimates above 5.0 million square kilometers.

Don't they give away the game when they use «ensembles» of model runs to get the best fit for hindcasting?

The weighted ensemble average for CMIP3 (blue thick line) and CMIP5 (red thick line) are estimated by given equal weight to each model's ensemble mean.

The weighted ensemble average for CMIP5 (red thick line) is estimated by given equal weight to each model's ensemble mean.

A much more sensible thing to do is invert the order of importance — assume that nature did what nature is most likely to do, so that the systematic deviation from this in even a small ensemble of samples is rather unlikely give an correct model.

It is true that the model replications of past conditions are not perfect, which is to be expected given the chaotic variations of the climate about its now - changing baseline; however, the ensemble of model simulations has been tested against previously observed perturbations to climate (such as the response to volcanic eruptions) and overall they correspond well with what is observed to occur.

Due to internal climate variability, in any given 15 - year period the observed GMST trend sometimes lies near one end of a model ensemble an effect that is pronounced in Box 9.2, Figure 1a, b since GMST was influenced by a very strong El Niño event in 1998

Given an ensemble of models from which an observable variable takes the mean value m 1 = 0 (without loss of generality) and standard deviation s 1, and an observation of this variable which takes the value m 2 with associated uncertainty s 2, the observation is initially at a normalised distance m 2 / s 1 from the ensemble mean.

Nature provides only one single realization of many possible realizations of temperature variability over time from a whole distribution of possible realizations of a chaotic system for the given climate conditions, whereas the ensemble mean of models is an average over many of the possible realizations (117 model simulations in this case).

Re «model in a given run» They have used initial condition ensembles where available.

What should happen is that people should stop trying to think that counting finite samples of model ensembles can give a probability.

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).