Sentences with phrase «climate modeling exercise»

Most importantly, I feel as if I've contributed to something that is fundamental science (e.g. physics and physical chemistry), that I expect will stand the test of time (unlike the climate modeling exercise du jour) and hope will define theoretical cloud physics at the beginning of the 21st century.

It was only possible through the participation of thousands of members of the public in the work's biggest ever climate modelling exercise: they offered up spare processing capacity on their home computers to run the calculations via the Climate Prediction citizen science climate modelling programme.

In the recently completed «distributed computing» global climate modelling exercise, for example, they tested thousands of different scenarios.

Not exact matches

The climate projections show on this map are based on Representative Concentration Pathway 2.6, 4.5, and 8.5 (van Vuuren et al., 2012) experiments run by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) exercise (Taylor et al., 2012).

The CMIP5 modelling exercise involved many more experiments and many more model - years of simulation than previous CMIP projects, and has been referred to as «the moon - shot of climate modelling» by Gerry Meehl, a senior member of the international steering committee, WGCM.....»

Yes if models were simply a curve fitting exercise, then any difference between predicted and actual climate would falsify a model.

The CMIP5 modelling exercise involved many more experiments and many more model - years of simulation than previous CMIP projects, and has been referred to as «the moon - shot of climate modelling» by Gerry Meehl, a senior member of the international steering committee, WGCM.....»

There is, however, a point to be made about exercising caution when evaluating the forward - looking output of a computer model, particularly when those models are used to advocate policy changes on the assumption that the computer model accurately simulates the earth's climate, and more particularly when there is no demonstrable track record of the predictive accuracy of the model.

The fact that we can't control China, India, and other countries makes all this discussion, the expensive satellites, expensive super-computer models, and money dumped on climate science an exercise in futility as far as mitigation of global warming is concerned.

The whole point of this exercise is attribute virtually ALL of the late 20th c. rise to CO2 fit a high climate sensitivity model to it and project / extrapolate the defective model out to 2100.

VPmK was a stunningly unconvincing exercise in circular logic — a remarkably unscientific attempt to (presumably) provide support for the IPCC model [s] of climate — and should be retracted.

The upshot is that rejecting climate models as too sensitive to carbon because of model - data misfit is an erroneous exercise — as argued by Marotzke & Foster this year — absent at least the considerations discussed here.

These data sets can then be incorporated into any modelling exercise, providing consistent parameters for each emissions trajectory, and a consistent foundation for all climate modelling teams anywhere in the world.

That's because comparing the average global temperature created by climate models and the global average temperature from observationally - based datasets — the heart of the Michaels and Knappenberger exercise — is to compare

Multi-decadal predictions of climate probabilities, as well as all climate statistics based on the global and regional and global climate models are deterministic model exercises.

(2) You again quote T. Howard et al suggesting this modeling exercise, with limited validation of the models, demonstrates something about the performance of future ocean / climate dynamics.

As an exercise, try a super-simple model that predicts that weather and climate for the next few years will match that of the early»50s.

As others have already noted, climate models have absolutely no predictiive ability at all, so the whole exercise is a complete and utter waste of time and effort.

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As we have extensively documented in, Roy Spencer has a propensity for performing curve fitting exercises with a simple climate model by allowing its parameters to vary without physical constraints, and then making grandiose claims about his results.

Forget the dodge that the models are just academic exercises useful for learning about climate.

IRTORISKI The study investigated how the use of cost benefit analysis in climate change adaptation planning can be streamlined such as to be useful both for initial prioritisation of natural hazards according to their risk and for initial comparison of measures regarding a particular hazard, while avoiding complex model exercises.

Of course this «modelling» exercise is intended to support the Committee on Climate Change's carbon budgets.

Climate modelling is a «what if» exercise.

Even if climate models were perfect — and we know they are not, which is why they are called «models» — one would have to question the validity of any such exercise.

This all started when Ray Ladbury started telling me that climate models were not «statistical models but physical models» and that «Climate science is NOT and [sic -RCB- exercise in curve fitting&climate models were not «statistical models but physical models» and that «Climate science is NOT and [sic -RCB- exercise in curve fitting&Climate science is NOT and [sic -RCB- exercise in curve fitting».

Zorita and von Storch (2005) and von Storch et al. (2004) carried out such an exercise using temperature output from the ECHO - G climate model.

The level of scientific understanding is not at a level where computers can be usefully used to model climate, so why are we wasting this massive resource on a futile exercise.

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This is probably going to be typical using other profiles, and such an exercise will quickly give a sense of the effect and variability without having to resort to a full climate model.

Now a paper in Nature Climate Change presents the results from a new modelling exercise using six different «integrated assessment models» (IAMs) to limit global temperatures in 2100 to below 1.5 C.