Sentences with phrase «modelling uncertainty also»

Not exact matches

They should also develop more sophisticated models that better incorporate statistical uncertainties.

Clouds also are the largest source of uncertainty in present climate models, according to the latest report of the Intergovernmental Panel on Climate Change.

Also, the model - based approach includes measures of uncertainty about our population estimates, which are not usually provided by more common approaches and are crucial for understanding the level of confidence we have about our estimates.»

In her doctoral thesis, Pulkkinen also discusses model uncertainty (structural uncertainty), which results from the fact that the phenomenon being researched can be explained with several — even contradicting — theories.

It also eliminates much of the uncertainty surrounding potentially ill effects; whereas various mathematical models may disagree about when and at what concentrations Arctic Ocean sea ice disappears, they all agree that at roughly 3 degrees C of warming, the far north will be ice - free.

Uncertainty about rain, little uncertainty about sea level rise Climate change could also affect precipitation in California, though the two models USGS used in its research produced differeUncertainty about rain, little uncertainty about sea level rise Climate change could also affect precipitation in California, though the two models USGS used in its research produced differeuncertainty about sea level rise Climate change could also affect precipitation in California, though the two models USGS used in its research produced different results.

She also explores the value of scientific thinking in our everyday lives, including the importance of scale, scientific modeling, uncertainty, and risk assessment.

«We have also found that there is significant uncertainty based on the spread among different atmospheric models.

Leung and Qian also participate in the North American Climate Change Assessment Program to use multiple global and regional climate models to better quantify uncertainties in projecting climate change.

These current uncertainties are also reflected in future climate projections by these models.

We are developing new analytical software tools that are founded in rock physics, but that also draw from predictive technology, machine learning, geological uncertainty analysis and geoscience modelling.

They will also evaluate the impact of model resolution and model physics, identified as the biggest sources of uncertainty in the existing modeling studies on irrigation effects.

Uncertainty quantification is also a focus for the U.S. Department of Energy (DOE) as eight national laboratories and six partner institutions collaborate to develop and apply the next generation of climate and Earth - system models to the challenges and demands of climate - change research.

The response to global warming of deep convective clouds is also a substantial source of uncertainty in projections since current models predict different responses of these clouds.

The choice of expected returns model itself is also a source of uncertainty.

The choice of an expected returns model is also a source of uncertainty.

Edmunds.com's price promise business model is designed to take the uncertainty out of pricing, speed up the buying process and also comes with the expectation that the customer will be given top - notch customer service.

Your reference to the Paleo is understood, however as with models there must be some inherent uncertainty in the different methodologies (particularly the transient constraints as recent data should also be accounted for in them).

The simpler models it seems to me are also subject to uncertainty, even they are at least understandable.

Gavin implicitly agrees about model uncertainty and states that climate change is also proven by other lines of eivdence — like paleoclimatology.

I am probably as aware of any reader here of modeling challenges in general, and can appreciate the work your groups have performed, but I can also appreciate the implications of the mismatch that prompted your post: there is fundamental uncertainty in the interaction of the complex mechanisms that drive climate change, including the human effect.

I also stated that the wide spread of model results further increases uncertainty.

f there is so much uncertainty in the observed data and the model outputs that one can not conclude that they are significantly different, then it also follows that one can not conclude that the models are accurately representing the real world.

The mid-Holocene (6000 years ago) and Last Glacial Maximum (~ 20,000 years ago) are also attractive targets of model validation, and while some successes have been noted (i.e. Joussaume et al, 1999, Rind and Peteet, 1985) there is still some uncertainty in the forcings and response.

It would also easily give a good intuitive feel for the uncertainties in the model.

If there is so much uncertainty in the observed data and the model outputs that one can not conclude that they are significantly different, then it also follows that one can not conclude that the models are accurately representing the real world.

They may also help researchers understand the effects of cloud cover, which also creates diffuse light and represents the biggest source of uncertainty in climate models, he says.

We can derive the underlying trend related to external forcings from the GCMs — for each model, the underlying trend can be derived from the ensemble mean (averaging over the different phases of ENSO in each simulation), and looking at the spread in the ensemble mean trend across models gives information about the uncertainties in the model response (the «structural» uncertainty) and also about the forcing uncertainty — since models will (in practice) have slightly different realisations of the (uncertain) net forcing (principally related to aerosols).

Secondly, computer - models are told what to think from the off - set — giving the impression that the knowledge programmed into them is COMPLETELY CORRECT and the means with which that knowledge is applied and interpreted is also adequate to counter any uncertainty that may or may not exist!

«We also need to investigate the altitude and seasonal dependence of the changes, and to analyse different climate models and warming scenarios to quantify the uncertainties.»

More complex metrics have also been developed based on multiple observables in present day climate, and have been shown to have the potential to narrow the uncertainty in climate sensitivity across a given model ensemble (Murphy et al., 2004; Piani et al., 2005).

DOE and the scientific community at large were also alarmed at apparent uncertainties within global climate models.

Indeed with iterative models where the previous «prediction» is the source of data for the next model iteration not only errors but also uncertainty will propagate.

If the uncertainties in the models are large enough to make the A and B scenarios still unfalsified then the C scenario is also unfalsified.

The global Aerosol Model Intercomparison project, AeroCom, has also been initiated in order to improve understanding of uncertainties of model estimates, and to reduce them (Kinne et al., 2Model Intercomparison project, AeroCom, has also been initiated in order to improve understanding of uncertainties of model estimates, and to reduce them (Kinne et al., 2model estimates, and to reduce them (Kinne et al., 2003).

We must also communicate the growth in model uncertainty as model predictions of the future advance farther and farther from the present climate state.

That uncertainty can be broken down into 2 pieces: statements based on model weighting ignore uncertainty about how tight (and real) the constraint actually is, while statements based on an assumed functional relationship not only neglect uncertainty related to constraint validity, but also ignore uncertainty regarding what the correct functional relationship should actually be.

Since the uncertainties in Q and N are much larger than in ΔTs (a factor influencing our choice of regression model; see appendix), uncertainty in Q — N is linearly related to uncertainty in Y, so our assumption is also approximately equivalent to assuming a uniform prior in Y.»

Also, as to this: «Note, my weights were not determined using any fancy analysis, but integrate my sense of uncertainty in CO2 sensitivity, model uncertainties, and particularly the wild card that is natural variability.»

Those opposing policies on the basis of uncertainties about models often fail to acknowledge that the models could be wrong not only in overstating the impacts of climate change but also in greatly understating climate impacts.

Ensembles made with the same model but different initial conditions only characterize the uncertainty associated with internal climate variability, whereas multi-model ensembles including simulations by several models also include the impact of model differences.

Our methodology also accounts for internal climate variability and other external drivers such as volcanic eruptions, as well as uncertainties in the proxy reconstructions and model output.

Analyses of tide gauge and altimetry data by Vinogradov and Ponte (2011), which indicated the presence of considerably small spatial scale variability in annual mean sea level over many coastal regions, are an important factor for understanding the uncertainties in regional sea - level simulations and projections at sub-decadal time scales in coarse - resolution climate models that are also discussed in Chapter 13.

This large spread in the predictions reflects the current diversity in the formulation of physics and initial conditions in the various models used, but also inherent uncertainty of the climate system.

Structural uncertainty is attenuated when convergent results are obtained from a variety of different models using different methods, and also when results rely more on direct observations (data) rather than on calculations.

Computer models (hansen's particularly for IPCC) are also used to reduce the uncertainty in the energy budget items.

Current limitations of ice - sheet modelling also increase uncertainty in the projections of 21st - century sea - level rise (Meehl et al., 2007 Section 10.6.4.2) used to assess coastal impacts in this report.

Since the uncertainties in Q and N are much larger than in [delata] Ts (a factor influencing our choice of regression model; see appendix), uncertainty in Q - N is linearly related to uncertainty in Y, so our assumption is also approximately equivalent to assuming a uniform prior in Y.

It also presents a new set of estimates of the uncertainties about future climate change and compares the results will those of other integrated assessment models.

... Possibly, deterministic climate models could also assist in establishing such a relationship, which, if proven to be significant, could be incorporated in a nonstationary stochastic framework of climatic uncertainty.»