Sentences with phrase «on model hindcast»

Not exact matches

I understand the argument that past projections are based on estimated future forcings which can change, but this amounts to the same things as tuning hindcasts and declaring matching a hindcast to observations as a validation of your model.

This is of course one big reason why climate science has focussed on this particular metric — because the models can do a reliable and credible (validated through hindcasting recent and paleo climates) job at it!

Come on, you KNOW the models can hindcast if the right parameters are put in but they are pretty atrocius at forecasting.

Arnost's link to the Model E hindcast also illustrates how GCMs rely on volcanic aerosols to create inter-annual variability.

Climate model simulations confirm that an Ice Age can indeed be started in this way, while simple conceptual models have been used to successfully «hindcast» the onset of past glaciations based on the orbital changes.

Multiple models that all hindcast well when turned loose on the future diverge quickly, which doesn't give one a warm and fuzzy feeling.

If the models show a lack of skill and need tuning with respect to predicting (in hindcast) even the current climate statistics on multi-decadal time scales (much less than CHANGES in climate statistics), they are not ready to be used as robust projection tools for the coming decades.

Model estimates of temperatures prior to 2005 are a «hindcast» using known past climate influences, while temperatures projected after 2005 are a «forecast» based on a estimate of how things might change.

doi: 10.1007 / s00382 -012-1313-4 who report quite limited predictive skill in two regions of the oceans on the decadal time period, but no regional skill elsewhere, when they conclude that «A 4 - model 12 - member ensemble of 10 - yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation.

Models fail to hindcast past climate on local, regional and global level: http://joannenova.com.au/2012/05/we-cant-predict-the-climate-on-a-local-regional-or-continental-scale/

Particularly valuable is perhaps research related to difficulties in avoiding confirmatory bias in testing models through hindcasting, when the model builders have some, perhaps only qualitative, knowledge on the data to be used for testing already when they develop the model.

Leif would be right if he could propose an alternative model based on internal solar dynamics alone that performs better than mine in hindcasting the solar patterns.

With respect to confidence in the future based on hindcasts of the past, I would only say that even with The Perfect Model (tm) the hindcasts can only be as good as the data they are given.

And of course, the predictions are based on computer models which can not forecast or hindcast with any accuracy.

We don't even have the data needed to intelligently initialize the models we have got, and those models almost certainly have a completely inadequate spatiotemporal resolution on an insanely stupid, non-rescalable gridding of a sphere... the ongoing failure of the GCMs to actually predict or hindcast anything at all particularly accurately outside of the reference interval.»

You know, all those model «hindcasts» that were validated based on HadCrut and all the proxy studies that were calibrated on HadCrut.

The sensitivity of the models is, as I think you are saying, constrained by it's parametrizations, which are bounded by observational data on TOA radiation data etc. (although not all very tightly constrained) but this is not what is being questioned about the models, rather the issue is whether the model hindcasts matching historical temperatures to some degree is evidence that they have correct physics, or is merely a result of modelers making the choices for inputs which will produce a reasonable result.

Obviously, climate models whose hindcasts differ in sign from what is observed (Zhang et al., 2007), or which indicate that human influences are indistinguishable from natural changes (Sarojini et al., 2012) possess no skill in identifying a human - induced climate signal on observed precipitation across the U.S. and therefore should not be used to make future projections.

That said, there does need to be more critical evaluation of models based on the physics they include and the accuracy of their hindcast / forecast.

We find a close agreement between the CESM - based hindcasts and the Markov model, indicating that the largest contribution to the predictive skill of soil water on interannual to decadal timescales in CESM can be attributed to the damped persistence, which is partly governed by the evapotranspiration (Delworth and Manabe 1988), the total runoff, and the diffusion of soil moisture into the deeper soil levels as shown in the Eq.

The potential to make skillful forecasts on these timescales, and the ability to do so, is investigated by means of predictability studies and retrospective forecasts (termed hindcasts) using climate models and statistical approaches.

When compared to the Scharf / HAS / 7th grader model (i.e. a simple straight line with a slope defined by the 1979 - 1988 trend), it beats it in predictive skill, and wallops it in hindcasting, wherein the naive model with a slope of 0.5 C / 30 years predicts that in 1700 the temperature of the planet was 5C cooler than 2000, and in 1400 the temperature of the planet was 10C cooler, and yes, keep on going.

On the contrary, global warming is a problem for which the world is rather well equipped to make informed policy, thanks to the IPCC reviewing the best available scientific knowledge, and thanks to ensembles of hindcasting - capable models constrained by (real - world!)

Nic, considering the first part of your comment, let's write the response of a model over the hindcast and forecast periods as something like (A + e, B + d) where A and B are the forced response over the two intervals (which depends on the parameter choices) and e and d are gaussian deviates due to internal variability (which depends on random initial conditions).

If the counter argument is that these changes are a response to Global Warming - it would be really good to see a graph showing what the models predicted / hindcast on average for the global cloud cover.

e.g., take HALF the date to tune the model, then see how well it forecasts / hindcasts on the other half of the data.

Restore Academic «skin in the game» by funding on prediction accuracy curryja Proposal: Make grant funds were contingent on model forecast / hindcast accuracy.