Sentences with phrase «in hindcasts»

This is surely what is happening in hindcasts where aerosol forcing appears to me to be fine tuned to fit the data.

However, capturing the phenomena in hindcasts and previous forecasts does not in any way guarantee the ability of the model to capture the phenomena in the future, but it is a necessary condition.

The weather model used can and has been tested very frequently when applied in its weather forecasting application mode (type 1) and statistics of these in hindcasts (type 2), but are now extended to an application that is to my opinion a blend of type 2 and type 4 forecasts.

Roger seems to assume that skill in hindcasts is necessary for usefulness.

[Response: First off, he is confusing models that include the carbon cycle with those that have been used in hindcasts of the 20th Century and are the basis of the detection and attribution of current climate change.

Why is it not possible simply to turn up that «gain» on ocean currents in hindcasts until they match the temperature record more closely?

For graph 1, I used all the models with no picking to see which ones did better in the hindcast.

The resulting model is pretty much used «as is» in hindcast experiments for the 20th Century.

That is, they added in an a trend of 4 degrees / 41 years = 0.0975 deg / year, comparable to the 0.093 deg / year seen in the hindcast.

The performance of models using a climate sensitivity range of from 1.0 to 5.0 is essentially equal in hindcasting.

[Response: I don't recall having made any such statement, but regardless, the proof of the pudding is in the hindcasting.

Whilst the differentials and ranges in hindcast can be expected, the observations vs Giss was over estimated 115 times.No wonder you can not see the inverse solar signal.

They aim at the more predictable long - term trends (even in hindcasting), where internal variability tends to wash out.

Is this a case where some of the natural oscillations are (were) not emergent in the modeling results... even in a hindcast situation?

Let's say that we are given the results of calculations using the original Lorenz 1963 system and that these represent the data that we're going to use to tune up our model of the data in a hindcast exercise.

They are basically saying the latest updates to the models are coincidentally exactly what is needed to make the models match the most recent data in hindcast.

Do you understand that models which are sensitive to initial conditions can be made to match in hindcast by modifying the initial conditions?

Brunt's «Discussion» to Callendar's paper appears as pertinent today in terms of evaluating GCM's «skill» in hindcasting / forecasting (or lack thereof as Steve quantifies):

Or, in hindcast mode, I require many more degrees of freedom in our test vector; but from Nyquist, this requires skill at a much finer resolution.

Failing that, if you have no choice but to work in hindcast, you need more degrees of freedom in the output than you have degrees of freedom in the input.

The issue is that there is no reason why a GCM should perform worse than Callendar in hindcasting observed GLB temperature.

As for tone, I stand by my assertion that the general claim that models are validated by matching a test vector of 2 - 3 degrees of freedom in hindcast is scientifically an absolute joke.

The academic work has brought up very serious problems in drawing conclusions based on success in hindcasting.

This application of the models is made despite their inability to show multi-decadal regional and mesoscale skill in forecasting changes in climate statistics when run in a hindcast mode (e.g., see Pielke 2013, and also Section 13.5).

These models can be (and must be) tested in hindcast runs to assess their level of skill at predicting what actually occurred.

If one insists, they could be included, but there should be a disclaimer given to the policymakers that these regional forecasts have not shown skill when tested in a hindcast mode.

The authors» of the papers that I listed do indeed discuss skill at predicting (in hindcast runs) the ability of multi-decadal climate model runs to simulate the real world observed climate.

I have presented peer reviewed papers that do, in fact, falsify the models even with respect to their ability to predict (in hindcast) the current climate.

They clearly have not «proved» skill at predicting in a hindcast mode, changes in climate statistics on the regional scale, and even in terms of the global average surface temperature trend, in recent years they have overstated the positive trend.

I summarize a number of papers in my post on the lack of skill of the CMIP5 runs when performed in hindcast.

Finally, I reiterate my request for you and Jason to present papers that document a skill of the multi-decadal (Type 4) regional climate models to predict (in hindcast) the observed CHANGES in climate statistics over this time period.

As I show in my guest post, the CMIP models not only have not shown skill at predicting (in hindcast) regional changes in climate statistics, but often not even the current average climate!

Roger states that one can not consider climate model predictions (his type 4) at the regional scale when their predictive skill in hindcast mode is not demonstrated.

Even more importantly, unless they can actually be shown to be «plausible», it is not appropriate to present to the impacts community without the disclaimer that they have not shown skill at predicting the climate metrics of interest when the models are run in hindcast.

I have proposed that, in order to provide value to the impacts communities, the projections must have skill in predicting changes in regional climate statistics in hindcast runs.

Accuracy must be tested by comparing in hindcast runs of the models their ability to:

Please summarize papers that report on the added skill from Type 4 downscaling when run in hindcast.

One should realize that there is ALWAYS a chance that predictions do not come true, even if the model has shown skill in hindcast studies; 2.

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.

Thus these probability forecasts must also be validated in hindcast runs as a necessary condition to at all claim they should be used for input to impact studies for the coming decades.

In your blog post you mentioned a lot of recently published papers that show model simulations don't do well compared to observations even in hindcast.

This test, of course, needs to be performed in a hindcast mode?

You still have not presented examples of skillful predictions (in hindcast) of changes in climate statistics.

There are a number of tests (but more than just the skill in a hindcast) that feed our belief in the usefulness of a model, indeed in a sort of Bayesian manner.

This is what you would demand if you say the models in hindcast simulations should show skill; you primarily demand that we can predict the phase of natural variations.

The only way we know they have skill is running them in hindcast where we know what the forcings are (within some level of uncertainty).

I think Roger's main point is that a first condition for making plausible projections is that the models have skill in hindcast.

These studies are not Type 4 applications when run in a hindcast mode (which is the only scientific way (i.e. the comparison with actual real world observations) to evaluate their skill.

This can only be done in hindcast runs.

Recognizing when models fail in both hindcasting and forecasting does not take a PhD.