Sentences with phrase «historical model runs»

Then compare the result for each case with the PDF derived from the 5 (or whatever) historical model runs, and use the result to generate a weighting for the model's simulations.

Fig. 1 Violinplots of monthly surface temperature results for 9 GISS ER historical model runs (1880 - 2003), their average (orange), and the HadCRUT3 global surface temperature dataset (red).

[CAPTION: Fig. 1 Violinplots of monthly surface temperature results for 9 GISS ER historical model runs (1880 - 2003), their average (orange), and the HadCRUT3 global surface temperature dataset (red).

Not exact matches

The problem is when investors adopt theories and models that embed the most optimistic assumptions possible, run contrary to historical evidence, or embed subtle peculiarities that actually drive the results (see, for example, the «novel valuation measures» section of The Diva is Already Singing).

For assessing the global ocean - carbon sink, McKinley and her co-authors from the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory, NCAR and the University of Colorado Boulder used the model to establish a long - running climate scenario from historical data.

Nicole Sharp, an aerospace engineer who runs a Tumblr blog on fluid dynamics, and Jordan Kennedy at Harvard University gathered data from historical records and ran experiments on how molasses flows under various conditions, then fed it all into computer models.

The first issue is that calculating TCR for each model from historical «All forcing» runs (histAll) requires using the same simulations and time periods as used in calculating the forcings.

This model looks at the historical data of a climate variable (e.g., temperature) and has a best - fit line running through these data.

Ideally, one would want to do a study across all these constraints with models that were capable of running all the important experiments — the LGM, historical period, 1 % increasing CO2 (to get the TCR), and 2xCO2 (for the model ECS)-- and build a multiply constrained estimate taking into account internal variability, forcing uncertainties, and model scope.

We got the official run - down of the new Widowmaker at this year's Goodwood Festival of Speed, and to celebrate the launch of the new range - topping neunelfer, Porsche rolled out some historical GT2 and GT2-esque models.

The ad, which names this as a «fried laforza» from the «peanut Farina» factory, does not list the model year or running condition, but given the historical penchant for the LaForza to remain temperamental at best, we're guessing a trailer is probably needed.

The tools also allow you to run Monte Carlo simulations, find historical efficient frontiers, and test quantitative and factor based investing models.

When I quizzed Thorsten about this a couple of years ago he told me that they hadn't done a historical run (let alone an LGM) with the model and that he didn't think it would be any good.

Ideally, one would want to do a study across all these constraints with models that were capable of running all the important experiments — the LGM, historical period, 1 % increasing CO2 (to get the TCR), and 2xCO2 (for the model ECS)-- and build a multiply constrained estimate taking into account internal variability, forcing uncertainties, and model scope.

As a check of this, one could comparing the climate model simulations of temperature change using the historical forcing runs with the temperature change produced by the same models under CO2 - only forcing runs * at times of equivalent total forcing change *.

Then, for the next year, pick another random historical year's weather, run the model forwards another year, and so on.

The GCM model performance in warming in minimum temperatures through the long historical runs is not good at all.

Nowadays, a common check is to see how the models check with historical records: ice core samples have given us enough data about the ice ages to be able to run the models in «ice age mode» — and they seem to agree very well with the data.

Training consisted of running the model repeatedly over several days using as input eight years of Albuquerque's historical weather data taken from NREL's SOLMET database.

Thanks for the 3 papers, the 1st was a real eye openner but was essentially confirming what I said which is that most model attribution expts based on comparison of historical forcing and runs held at pre - industrial condition suggest long term change is essentially forced.The point is the Karnauskas paper bucks the trend in understanding as expressed by the Ipcc (the consensus or whatever).

«Lewis in subsequent comments has claimed without evidence that land use was not properly included [viii] in our historical runs, and that there must be an error [ix] in the model radiative transfer.

It might be very useful to run a computer model simulation in which the ENSO is constrained to follow its known historical behavior, so we can see how it might have affected actual history rather than a gereric «earth system.»

Tropical pacific surface waters easily warm just as much in model - runs that apply historical external forcing values and let the simulated ENSO cycle do its random stuff.

The «HIST» model runs use historical data for climate forcing, to estimate the average temperature change (and other variables) simply due to climate forcing.

The models were run with historical forcing up to 2000 and projected forcing after that.

I chose that because a) there are nine runs available and b) the GISS model is one of the more complex models and c) it's one of the longest historical runs (n = 1488 months).

This question is of interest to me because I just took a look at the GISS EH model's historical runs.

This section deals with the realistic case where models have run ensembles of historical simulations of various sizes.

Both DECK and the CMIP6 Historical Simulation should be run for each model configuration used in the subsequent CMIP6 - Endorsed MIPs.

I would predict that the Aldrin method would not be able to accurately determine 2xCO2 ECS for CMIP5 models if tested against historical runs rather than 1 % CO2 per year, and that it would tend towards significant underestimates.

This project ran two climate model experiments: one, «Historical» included both human - caused greenhouse gas emissions and natural emissions, such as volcanoes; the second, «HistorialNat» included only the natural emissions, and deliberately left out human - caused emissions, to see how the climate might have changed without them.

These models (I have more than 35 years experience running these) ideally require a historical record of long duration that is used for calibrating (fitting the data) for part of the record and then validating (checking the sim results with measured data) for the remaining part of the record.

I can appreciate that curve fitting doesn't really make sense when talking about running climate models initialised on historical data and comparing the output to observations.

-LSB-...] in subsequent comments has claimed without evidence that land use was not properly included [viii] in our historical runs, and that there must be an error [ix] in the model radiative -LSB-...]

Remember that the scientists believe their models and their assumptions about a strong CO2 effect, so they have modeled the non-anthropogenic effect by running their models, tuning them to historical actuals, and then backing out the anthropogenic forcings to see what is left.

To help address these challenges, scientists run hurricane models calibrated with observations over the historical period to project future trends and understand the major factors driving these trends.

Models which don't make big excursions (can) win (given any overfit to the historical record) because they don't bounce around so unpredictably on every damned run, so they can fit the historical record more closely.

Recommendations for verification are: 1) comparison to other models 2) degenerate tests 3) event validity 4) extreme event validity 5) extreme condition tests 6) «face» validity tests 7) fixed value tests 8) historical data validation 9) internal validity (stochastic runs) 10) multistage validation 11) parameter variability - sensitivity analysis 12) predictive validation 13) traces 14) turing tests (i didn't know what this is so googled ECWMF turing test, and i got 150 hits)