Sentences with phrase «modelling uncertainty find»

Not exact matches

Despite receiving scrutiny from the Bitcoin community due to legal uncertainties and suspected framework challenges, the majority of the commission finds the model law acceptable.

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

The heightened risk of rainfall found in the meteorological modelling led to an increase in the peak 30 - day river flow of 21 % (uncertainty range: -17-133 %) and about 1,000 more properties at risk of flooding (uncertainty range: -4,000-8,000).

In fact, we find the model range is an excellent predictor of observed trends and their uncertainty due to random chaotic processes in the atmosphere and ocean.»

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.

Stakeholders of Montana agriculture may find the cumulative uncertainty of inexact crop models built on inexact climate models frustrating, but it is as important to understand the sources of uncertainty as it is to realize that temperatures are rising.

The heightened risk of rainfall found in the meteorological modelling led to an increase in the peak 30 - day river flow of 21 % (uncertainty range: -17 — 133 %) and about 1,000 more properties at risk of flooding (uncertainty range: -4,000 — 8,000).

The new model found that temperature uncertainty associated with the social component was of a similar magnitude to that of the physical processes, which implies that a better understanding of the human social component is important but often overlooked.

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).

We find that this effect is present in all model grids tested and that theoretical uncertainties in the models, correlated spectroscopic errors, and shifts in the asteroseismic mass scale are insufficient to explain this effect.

They use climate models to understand likely changes in the future and the uncertainty associated with those predictions, and explain their findings using such popular indicates as the Palmer drought index.

A unifying thread can be found in Parreno's dedication to alternative models of exhibition display, in which distinctions are blurred and uncertainties cultivated.

«This uncertainty is illustrated by Pollard et al. (2015), who found that addition of hydro - fracturing and cliff failure into their ice sheet model increased simulated sea level rise from 2 m to 17 m, in response to only 2 °C ocean warming and accelerated the time for substantial change from several centuries to several decades.»

Am I the only one that finds odd that the observations have to be within the uncertainty of the models?

I was wondering for some time now, how much the findings of the work of scientists, be it the IPCC, be it the PIK in Potsdam or what have you, can be taken for granted in order for policy makers to make valuable decisions (e.g. cutting carbon emissions by half by 2050) and if the uncertainties in the models might outweigh certain decisions to reduce carbon emissions so that in the end it might happen that these uncertainties make these decisions obsolete, because they do not suffice to avoid «dangerous climate change»?

When comparing with alternative models of plant physiological processes, we find that the largest uncertainties are associated with plant physiological responses, and then with future emissions scenarios.

So, of course there are uncertainties in the findings, as in any attribution and detection result, there is a remaining chance that the observed change is due to internal climate variability (5 - ish %) particularly if the models would underestimate that variability.

``... since uncertainty is a structural component of climate and hydrological systems, Anagnostopoulos et al. (2010) found that large uncertainties and poor skill were shown by GCM predictions without bias correction... it can not be addressed through increased model complexity....

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.

I find NO references in either thread or in Weitzman to the last twenty years of formal decision - theoretic work on models of decision under uncertainty (as opposed to risk).

We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30 % more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151 % for non-HYBRID4 models.

Results of climate policy analysis under deep uncertainty with imprecise probabilities (Kriegler, 2005; Kriegler et al. 2006) are consistent with the previous findings using classical models.

In particular, the studies described in Finding # 4 need to be repeated with improved models and with an experimental design that reflects the uncertainties in natural and human - induced forcings.

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.»

The Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties impose upon the lead authors to assign subjective levels of confidence to their findings: «The AR5 will rely on two metrics for communicating the degree of certainty in key findings: 1 Confidence in the validity of a finding, based on the type, amount, quality, and consistency of evidence (e.g., mechanistic understanding, theory, data, models, expert judgment) and the degree of agreement.

Thus I find the NIPCC review reports of further scientific literature to be stimulating to explore all the uncertainties involved, not just the «consensus» models.

Michael Tobis I find Curry's «Italian flag» models to be a very helpful mode of trying to convey to the lay public the major «uncertainty» issues involved.

These uncertainties may partly explain the typically weak correlations found between paleoclimate indices and climate projections, and the difficulty in narrowing the spread in models» climate sensitivity estimates from paleoclimate - based emergent constraints (Schmidt et.

Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models.

This scale factor was based on simulations with an early climate model [3,92]; comparable forcings are found in other models (e.g. see discussion in [93]-RRB-, but results depend on cloud representations, assumed ice albedo and other factors; so the uncertainty is difficult to quantify.

«Quantified measures of uncertainty in a finding expressed probabilistically (based on statistical analysis of observations or model results, or expert judgment).»

I find this idea of declaring ensembles of models with giant uncertainties to be «non-excludable» to be excruciatingly uninteresting.

Depending on your prior, and the particular properties of the model, you will probably end up with a substantial uncertainty in its sensitivity - just as most people find when they do this with the real world.

Gavin still finds qualitative value in a reasoned interpretation of model output, while I claim further that there's still value in quantifying uncertainty if the results aren't distributed for public consumption.

Using an AR1 noise model, we find that these differences imply a 1σ uncertainty in the acceleration of the instrument drift of 0.011 mm / y2.

What is more, they found that better computer models or observational data will not do much to reduce that uncertainty.