Sentences with phrase «models against observations»

Strategy # 2 bypasses the need to calibrate the climate models against observations.

To this end, they compare the output of the models against observations for present climate.

• Lack of formal model verification & validation, which is the norm for engineering and regulatory science • Circularity in arguments validating climate models against observations, owing to tuning & prescribed boundary conditions • Concerns about fundamental lack of predictability in a complex nonlinear system characterized by spatio - temporal chaos with changing boundary conditions • Concerns about the epistemology of models of open, complex systems

«Its existence was predicted by the standard model of particle physics and the fact that there's — we got a glimpse of it, it looks like it may very well be there — is a real victory for that model of science where you test, you put forward conceptual models of the way the world or the universe works and test those models against the observations and see the extent to which they can predict new observations and when they do, it gives you increased confidence in the models.

Not exact matches

Scientific models lead to theories which can be tested against observations.

By validating model results against geological observations, the study indicates that changes in runoff, sea level and wave energy have profoundly affected the past evolution of the Great Barrier Reef not only in regard to reefs evolution but also sediment fate from source - to - sink.

They tested high - performance computational models against known geological and geophysical observations of the Central Anatolian Plateau, and demonstrated that a drip of lithospheric material below the surface can account for the measured elevation changes across the region.

But, says Mezzacappa, «At the end of the day, we're going to need some observations against which we can check our models.»

They go on to suggest that «lowering levels of TNF may be an effective strategy in improving host defense against S. pneumoniae in older adults,» and that, «although it may be counterintuitive to limit inflammatory responses during a bacterial infection, [some existing] clinical observations and our animal model indicate that anti-bacterial strategies need to be tailored to the age of the host.»

In fact, the way science progresses is by conceptual models being put forward and then testing them against observations.

Likewise, while models can not represent the climate system perfectly (thus the uncertainly in how much the Earth will warm for a given amount of emissions), climate simulations are checked and re-checked against real - world observations and are an established tool in understanding the atmosphere.

They tested the model against regional forest mortality observations from scientific forest plots, aerial surveys done by the U.S. Forest Service, and satellite measurements.

«Climate models need to be validated against observations,» she said.

The models need to be tested against observations, to make way for new and improved models.

An adjustment is necessary because as climate models are continually evaluated against observations evidence has become emerged that the strength of their aerosol - cloud interactions are too strong (i.e. the models» «aerosol indirect effect» is larger than inferred from observations).

The universe is home to countless galaxies more massive than the Milky Way, which should, in theory, be bursting with star formation, but they aren't — an observation that goes against most current models of the universe and star formation.

Lin, W.Y., and M.H. Zhang, 2004: Evaluation of clouds and their radiative effects simulated by the NCAR Community Atmospheric Model against satellite observations.

Individual components continue to be improved via systematic evaluation against observations and against more comprehensive models.

Working with Tom Chase, a colleague at the institute, the researchers were comparing climate simulations from the Community Land Model — part of a select group of global models used in the Intergovernmental Panel on Climate Change's 2007 climate change report — against observations.

Regarding all these hypotheticals of Earth - ssytem timescale feedbacks, etc - before results are brought forward with high confidence and reach a level of minimal academic disagreement, they should be understood physically, be exhibited in a range of models from simple to complex, begin to emerge in observations against natural variability, are shown to be robust to methodological choices and interpretation, and are borne out paleoclimatically.

Once calibrated, the model can be run and evaluated against observations not included in the calibration process.

To wit: Those who work with computer models of climate trust them more than is warranted by the validation that can be done against the (limited) available observations.

And are those predictions in different cases then tested against observations again and again to either validate those models or generate ideas for potential improvements?

It is argued that uncertainty, differences and errors in sea ice model forcing sets complicate the use of models to determine the exact causes of the recently reported decline in Arctic sea ice thickness, but help in the determination of robust features if the models are tuned appropriately against observations.

The model variables that are evaluated against all sorts of observations and measurements range from solar radiation and precipitation rates, air and sea surface temperatures, cloud properties and distributions, winds, river runoff, ocean currents, ice cover, albedos, even the maximum soil depth reached by plant roots (seriously!).

Are ocean models so robustly based on first principles that they can be trusted without validation against sound observations over the time scales of interest?

The models are gauged against the following observation - based datasets: Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) for precipitation (1980 — 1999), European Centre for Medium Range Weather Forecasts 40 - year reanalysis (ERA40; Uppala et al., 2005) for sea level pressure (1980 — 1999) and Climatic Research Unit (CRU; Jones et al., 1999) for surface temperature (1961 — 1990).

It is the average long - wave cloud forcing error derived from comparing against observations, 20 years of hindcasts made by 26 CMIP5 models.

One could take the outcomes of different starting conditions, or use of different model parameters, and compare them against observations.

In general, the more complex a model, the less it assumes, and the more easily its individual assumptions can be tested against observations and other models.

Improving the representation of feedbacks in climate models, and checking them against observations, is probably the most important area of climate modelling research at present.

He concluded: «Model conditioning need not be restricted to calibration of parameters against observations, but could also include more nebulous adjustment of parameters, for example, to fit expectations, maintain accepted conventions, or increase accord with other model resModel conditioning need not be restricted to calibration of parameters against observations, but could also include more nebulous adjustment of parameters, for example, to fit expectations, maintain accepted conventions, or increase accord with other model resmodel results.

Lee, Y.H., P.J. Adams, and D.T. Shindell, 2015: Evaluation of the global aerosol microphysical ModelE2 - TOMAS model against satellite and ground - based observations.

Looks like a model is used as proof against observations.

It also seems unfair to say that the model - weighting approach is better because it doesn't rely on the existence of a linear relationship when you * chose * the variable to compare against observations on the basis of that variable providing a good linear fit to your predictand.

Each climate modeling group evaluates its own model against certain observations.

Errors associated with using this statistical model to determine the global average time series is estimated by subsampling the observations (primarily ship tracks) in the earlier period against reanalysis data for the modern period.

• Calibrate the retrospective simulations of ice thickness from our numerical model against the aggregate of all the observation systems by removing the mean difference between the model and the observations to create a Calibrated Model Ice Thickness Remodel against the aggregate of all the observation systems by removing the mean difference between the model and the observations to create a Calibrated Model Ice Thickness Remodel and the observations to create a Calibrated Model Ice Thickness ReModel Ice Thickness Record.

As I said, when comparing with observations over the short period being considered here, it makes more sense to compare with models that include natural internal variability (i.e.: GCMs — as in the final version) than against models that do not include this and only include externally - forced changes (ie: Simple Climate Models, SCMs, — as in the SOD vermodels that include natural internal variability (i.e.: GCMs — as in the final version) than against models that do not include this and only include externally - forced changes (ie: Simple Climate Models, SCMs, — as in the SOD vermodels that do not include this and only include externally - forced changes (ie: Simple Climate Models, SCMs, — as in the SOD verModels, SCMs, — as in the SOD version).

How hard would it be to just collect source code for the various models, and test them against different input parameters, as well as newer observations, and see which physics is likely to be more realistic?

Gray's crusade against global warming «hysteria» began in the early 1990s, when he saw enormous sums of federal research money going toward computer modeling rather than his kind of science, the old - fashioned stuff based on direct observation.

None of current models have a sufficient number of runs to overcome chaotic uncertainty and therefore can not be validated against observations.

This was established on the systematic comparison between models» predictions with actual observations obtained over almost one solar cycle (1998 — 2007) at four European ionospheric locations (Athens, Chilton, Juliusruh, and Rome) and on the comparison of the models» performance against two standard prediction strategies, the median - and the persistence - based predictions.

Note that the Russians validated their model tuning against observations — I think this is a major breakthrough.

Not all that is performed in models is for the purpose of direct comparison against observations.

Likewise, to properly represent internal variability, the full model ensemble spread must be used in a comparison against the observations, as is well known from ensemble weather forecasting (e.g., Raftery et al., 2005).

Personally I don't expect the IPCC to find science that compares observations against every single model projection ever made, whereas you seem to think they ought to.

And if they have the millions of dollars to make a model why wouldn't we expect them to test THAT model (and all of its cohort) against the observations and publish those results?

Models, like all scientific theory, have to be tested against real - world observations.

Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and / or model parameterizatModel forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and / or model parameterizatmodel control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and / or model parameterizatmodel experiments), thus overcoming to some extent issues of uncertainties in the observations and / or model parameterizatmodel parameterizations.