Sentences with phrase «other model uncertainties»

With the experiment we want to investigate how important such changes are relative to other model uncertainties.

Not exact matches

Their goal is to create premium priced moorings for a few elite boaters, following a «sky box» model, while subjecting others to the uncertainty of an annual lottery.

At nearly a dozen other sites, the authors report, the chronological results are neither reliable nor valid as a result of significant statistical flaws in the analysis, the omission of ages from the models, and the disregard of statistical uncertainty that accompanies all radiometric dates.

An environmental modeler presents how the science works, but a decision scientist uses decision theory to take into account both the scientific information in a model and its uncertainty, and to help policymakers weigh that information against other factors, Crawford - Brown says.

Among others, this uncertainty is subject to the uptake of Tesla's Model 3 car, for which production started this month.

A new integrated climate model developed by Oak Ridge National Laboratory and other institutions is designed to reduce uncertainties in future climate predictions as it bridges Earth systems with energy and economic models and large - scale human impact data.

Our model is ultimately translatable to other disease systems and shows how it can be done even under uncertainty of key parameters.»

«The primary uncertainty in sea level rise is what are the ice sheets going to do over the coming century,» said Mathieu Morlighem, an expert in ice sheet modeling at the University of California, Irvine, who led the paper along with dozens of other contributors from institutions around the world.

Bansal said that, in the future, the model could be implemented to help improve guidance from experts in other industries, including the biofuel industry and semiconductor industry, that typically operate under heavy supply uncertainty.

I would agree that unforeseen changes in ocean circulation could throw off model predictions, there are surely other wildcards too, but uncertainty like that is not your friend if you want to argue against avoiding climate change.

By linking imaging methods to rock physics, and by discovering, understanding and modelling relations to other geological data, we will reduce the uncertainty associated with exploration through cover.

As research leaders in developing and using models to provide scientific insights into weather and climate change, Qian and others are striving to understand uncertainty in systems and modeling to improve projections and help prepare vulnerable regions for potential climate change impact.

Scientific knowledge input into process based models has much improved, reducing uncertainty of known science for some components of sea - level rise (e.g. steric changes), but when considering other components (e.g. ice melt from ice sheets, terrestrial water contribution) science is still emerging, and uncertainties remain high.

This method tries to maximize using pure observations to find the temperature change and the forcing (you might need a model to constrain some of the forcings, but there's a lot of uncertainty about how the surface and atmospheric albedo changed during glacial times... a lot of studies only look at dust and not other aerosols, there is a lot of uncertainty about vegetation change, etc).

The leaded gas adventurers have profitably polluted the world on a grand scale and, in the process, have provided a model for the asbestos, tobacco, pesticide and nuclear power industries, and other twentieth - century corporate bad actors, for evading clear evidence that their products are harmful by hiding behind the mantle of scientific uncertainty.

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

For tunings and other estimates, the model parameters should show the uncertainty initially.

Modelling uncertainty currently is such that in some climate models, this amount of freshwater (without any other forcing) would shut down deep water formation, in some it wouldn't.

There are two classes of uncertainty in models — one is the systematic bias in any particular metric due to a misrepresentation of the physics etc, the other is uncertainty related to weather (the noise).

If she accepts that attribution is amenable to quantitative analysis using some kind of model (doesn't have to be a GCM), I don't get why she doesn't accept that the numbers are going to be different for different time periods and have varying degrees of uncertainty depending on how good the forcing data is and what other factors can be brought in.

The models don't by any means capture the uncertainty in their forecasts, and their are a large number of other sources of uncertainty in the models used to forecast emissions from concentrations).

I would agree that unforeseen changes in ocean circulation could throw off model predictions, there are surely other wildcards too, but uncertainty like that is not your friend if you want to argue against avoiding climate change.

Simultaneously, many of the models use Monte Carlo methods to deal with some other types of uncertainty than the ones we are talking about here (if there's a volcano, how many hurricanes there are, and whatnot).

Here, I think the basic disagreement between Pat Frank and the reviewers is that the reviewers want to treat the value of 4W / m ^ 2 as though it is really accurately known from considerations outside the modeling effort itself, and Pat Frank wants to show how much uncertainty in the modeling results is added when it is treated as any other poorly known parameter..

Even when the representation of all included processes is decided the models include many parameters that are only weakly constrained by other data, meaning there is parametric uncertainty (2)(McWilliams, 2007; Betz, 2009a; Parker, 2010; Katzav, 2013).

The science outcomes are part of an ongoing adaptive management process that is essential to sound decision making in the NPR - A and can serve as a model for other Arctic regions facing climate and land - use uncertainty.

In other words, the analysis neglects structural uncertainty about the adequacy of the assumed linear model, and the parameter uncertainty the analysis does take into account is strongly reduced by models that are «bad» by this model - data mismatch metric.

In a climate case, more so than any other policy - related case, courts need to inform themselves of the range of scientific opinions, the specific points of agreement and disagreements, the assumptions made by scientists, their theories and reasoning, the validity and accuracy of the models used, the unknowns, uncertainties, and gradations, etc..

Instead, we find that «uncertainty» is actually being used to express the statistical PRECISION of the computer modeling output sets with respect to each other, not with respect to the real world.

They may not have assumed the normal Gaussian as the PDF to explain their uncertainty in the underlying model and parameters, but we must because we have no other choice with the numbers that they have given us.

Estimates from proxy data1 (for example, based on sediment records) are shown in red (1800 - 1890, pink band shows uncertainty), tide gauge data in blue for 1880 - 2009,2 and satellite observations are shown in green from 1993 to 2012.3 The future scenarios range from 0.66 feet to 6.6 feet in 2100.4 These scenarios are not based on climate model simulations, but rather reflect the range of possible scenarios based on other kinds of scientific studies.

If AR5 settles no other issue, it needs to resolve the highly significant structural uncertainties with IPCC statistics and in modeling cloud albedo.

Epistemic uncertainties, on the other hand, are due to the insufficiency our models, ignorance in our choice of parameters, inaccuracy of our measurements, or other remediable offenses.

However, the separation of greenhouse gas response from the responses to other external forcing in a multi-fingerprint analysis introduces a small uncertainty, illustrated by small differences in results between three models (Figure 9.21).

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.

I ask that because I haven't seen evidence one way or the other that I think convincing — because I think that the uncertainties are too large in a number of ways (w / r / t to the range of sensitivity, w / r / t the massive unknowns about positive and negative externalities, w / r / t modeling future economies, etc..)

So we're beginning to understand the range better and the role of cloud processes, on the one hand, in the deep modelling systems, the role of observation uncertainties of some of the other methods for estimating it.

Climate models project decreases of renewable water resources in some regions and increases in others, albeit with large uncertainty in many places.

At the moment, the uncertainties in modeling and complexities of the ocean system even prevent any quantification of how much of the present changes in the oceans is being caused by anthropogenic climate change or natural climate variability, and how much by other human activities such as fishing, pollution, etc..

This general approach has been used before, for other aspects of climate system models, and has often significantly reduced overall uncertainty.

Ultimately there are uncertainties in the radiosondes, but the satellites don't find the scaling ratios either, and the models fail on most other measures.

Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear obser - vational constraint that favors one version of the authors» model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections.»

In most cases, these range from about 2 to 4.5 C per doubled CO2 within the context of our current climate — with a most likely value between 2 and 3 C. On the other hand, chapter 9 describes attempts ranging far back into paleoclimatology to relate forcings to temperature change, sometimes directly (with all the attendant uncertainties), and more often by adjusting model parameters to determine the climate sensitivity ranges that allow the models to best simulate data from the past — e.g., the Last Glacial Maximum (LGM).

The very high significance levels of model - observation discrepancies in LT and MT trends that were obtained in some studies (e.g., Douglass et al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from using the standard error of the model ensemble mean as a measure of uncertainty, instead of the standard deviation or some other appropriate measure of ensemble spread.

Our scientists use laboratory studies, field campaigns, multi-scale models, and other tools to address key questions and uncertainties related to clouds, aerosol particles, and precipitation.

My research group (Risk and Sustainable Management Group) presented a paper on this topic, and a number of others on modelling risk and uncertainty and the complex trade - offs between environmental flows, trees for carbon capture and irrigated agriculture.

A more detailed model of the dynamics of the changes might have a small effect on the outcome, but not at a level that would make any difference in comparison with the other uncertainties of the calculation.

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.

In addition, despite our effort to characterize and possibly minimize the climatic uncertainty, one should be aware of other sources of uncertainty (e.g., in the hydrological and hydraulic modeling, in the space - time discretization, in the impact model, among others) which affect complex modeling framework such as the one presented in this work.

«Epistemology is here applied to problems of statistical inference during testing, the relationship between the underlying physics and the models, the epistemic meaning of ensemble statistics, problems of spatial and temporal scale, the existence or not of an unforced null for climate fluctuations, the meaning of existing uncertainty estimates, and other issues.