Historical feed - in data is also used to fine -
tune prediction models.
Going forward, the team is fine -
tuning the prediction model in two ways.
Not exact matches
The strength of this technique is that the
model is continuously fine -
tuned — it compares its
predictions against the real - world data and self - corrects in near - real time.
The team can also use that satellite data to fine -
tune their
model's
predictions for magma overpressure in the future.
These real - world observations will help USGS scientists fine -
tune their
models and improve their damage
predictions before the next big storm.
«There are lot of knobs in those
models that have to be
tuned to describe reality,» Wehr says, «and if you have the wrong idea about what forests have been doing, then you turn the knobs wrong and your
predictions will be off.»
Knowing how and why climate
models are
tuned and which targets are used is essential to avoiding possible misattributions of skillful
predictions to data accommodation and vice versa.
This is quite subtle though — weather forecast
models obviously do better if they have initial conditions that are closer to the observations, and one might argue that for particular climate
model predictions that are strongly dependent on the base climatology (such as for Arctic sea ice)
tuning to the climatology will be worthwhile.
Regarding TimTheToolMan's
prediction as to how the IPCC and the climate science community will deal with the issue, the more one thinks about the IPCC's dilemma, the more one should believe that this is just what they will do, they will
tune AR5's
modeling in ways which get a better recent fit, sacrificing some historical fit to do so, and then they will produce a series of new papers which rewrite the history of ocean heat content to match.