Sentences with phrase «of statistical uncertainty»

One can dismiss Beenstock and Reingewertz because they are wading into an area of statistical uncertainty, but any of us who are making inferences about temperature trends should realize we are all wading in the same waters.

What is the justification for adjusting past values, and is there any way to convey the increasing level of statistical uncertainty in the USHCN values, like confidence intervals or error bars on charts?

As well, the statement «Whether we have the 1000 year trend right is far less certain» is in fact an admission of the lack of sufficient knowledge about the correctness of the application of the reconstruction procedure and really should not be interpreted as a scientific assessment of statistical uncertainty.

Hardly within the limits of statistical uncertainty.

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.

The usual health warnings were issued in the form of statistical uncertainty estimates, but these invitations to prudence were given less attention than they deserved by most consumers of the numbers.

Such issues of robustness need to be taken into account in estimates of statistical uncertainties.

The biggest problem, I believe, with the IPCC reports is the lack of discussion of statistical uncertainties.

Not exact matches

Until, and once the uncertainty is reduced, THEN we can get back to a cyclic economy with statistical smoothing that offers better predictions of our future.

there's really no room for the concept of an independent entity possessed of «will» in a worldview shaped by cause and effect; the only place for «will» to retreat to is the zone of true randomness, of complete uncertainty, which means that truly free will as such must be completely inscrutible [sic]... Statistical laws govern the decay of a block of uranium, but whether or not this atom of uranium chooses to fission in this instant is a completely unpredictable event — fundamentally unpredictable, something which simply can not be known — which is equally good evidence for the proposition that it's God's (or the atom's) will whether it splits or remains whole, as for the proposition that it's random chance.

The new approach contrasts with previous ways scientists analyzed and came to conclusions about sea level rise because it is «the only proper one that aims to fully account for uncertainty using statistical methods,» noted Parnell, principal investigator of the study conducted collaboratively with researchers at Tufts University, Rutgers University and Nanyang Technological University.

In her doctoral thesis, Henni Pulkkinen, Researcher at the Natural Resources Institute Finland (Luke), explored how the various sources of uncertainty can be taken into account in fisheries stock assessment by using Bayesian statistical models, which enable extensive combining of information.

This is intended to take account of some of the uncertainties inherent in data on whale populations, and requires only two kinds of data: current estimates and their statistical error; and historical details of catches.

The journal Basic and Applied Social Psychology recently banned the use of p - values and other statistical methods to quantify uncertainty from significance in research results

The U.S. Bureau of the Census is locked in a high - stakes political battle with Congress over a plan to reduce uncertainties in the year 2000 survey through statistical techniques rather than direct head counts.

The criteria should be applied on the basis of the available evidence on taxon numbers, trend and distribution, making due allowance for statistical and other uncertainties.

Carling Hay et al. provide a statistical reassessment of the tide gauge record which is subject to bias due to sparse and non-uniform geographic coverage and other uncertainties and conclude that sea - level rose by about 1.2 millimetres per year from 1901 to 1990.

However, one of the panel's reservations was that ``... a statistical method used in the 1999 study was not the best and that some uncertainties in the work «have been underestimated,»...» The panel concluded «Based on the analyses presented in the original papers by Mann et al. and this newer supporting evidence, the committee finds it plausible that the Northern Hemisphere was warmer during the last few decades of the 20th century than during any comparable period over the preceding millennium.

We will continue to learn more about nutrition as science progresses, but we should have a better foundation than a handful of unexplained statistical correlations on which to act in the face of uncertainty.

All of the relationships have statistical uncertainty.

Having said that, I do like the idea of combining statistical and structural uncertainty into one measure.

(in general, whether for future projections or historical reconstructions or estimates of climate sensitivity, I tend to be sympathetic to arguments of more rather than less uncertainty because I feel like in general, models and statistical approaches are not exhaustive and it is «plausible» that additional factors could lead to either higher or lower estimates than seen with a single approach.

To convert that into a useful time - varying global mean needs a statistical model, good understanding of the data problems and enough redundancy to characterise the uncertainties.

I believe that statisticians can contribute more to climate sciences in better description of the uncertainties, in addition to better calibration of statistical models.

When I read the media PR on this, it looked like BEST claimed better statistical methods, leading to lower estimates of the uncertainty in the temperature change - even at the decadal level.

Although some earlier work along similar lines had been done by other paleoclimate researchers (Ed Cook, Phil Jones, Keith Briffa, Ray Bradley, Malcolm Hughes, and Henry Diaz being just a few examples), before Mike, no one had seriously attempted to use all the available paleoclimate data together, to try to reconstruct the global patterns of climate back in time before the start of direct instrumental observations of climate, or to estimate the underlying statistical uncertainties in reconstructing past temperature changes.

These authors have shown that the «alternative» reconstruction promoted by McIntyre and McKitrick (which disagrees not only with the Mann et al reconstruction, but nearly a dozen independent reconstructions that agreee with the Mann et al reconstruction within statistical uncertainties) is the result of censoring of key data from the original Mann et al (1998) dataset.

In fact, the nominal statistical significance levels of all statements are quite a bit stronger than we assess them couched in likelihood language, in order to account for remaining uncertainties.

The pitfalls of producing statistical judgements are endless, however I just want to note that it is only right at the very, very end (within just a few years of 1980) that the instrumental record or any of the proxies, first emerge above any of the possible projected lines that can be drawn through the uncertainty envelope.

In other words, it is possible that the the climate system does exhibit some kind of long - term chaos in some circumstances, but that the forcing is strong enough to wipe out any significant uncertainty due to initial conditions — at least if one is content to forecast statistical quantities such as, for example, decadal mean January temperatures in some suitably large region, or perhaps temperature variances or quartiles taken over a similar period.

Cox et al. provide a statistical uncertainty range for a single study, ignoring structural uncertainty and systematic biases resulting from their choice of model and method.

The IPCC range, on the other hand, encompasses the overall uncertainty across a very large number of studies, using different methods all with their own potential biases and problems (e.g., resulting from biases in proxy data used as constraints on past temperature changes, etc.) There is a number of single studies on climate sensitivity that have statistical uncertainties as small as Cox et al., yet different best estimates — some higher than the classic 3 °C, some lower.

So: The study finds a fingerprint of anthropogenic influences on large scale increase in precipitation extremes, with remaining uncertainties — namely that there is still a possibility that the widespread increase in heavy precipitation could be due to an unusual event of natural variability.The intensification of extreme rainfall is expected with warming, and there is a clear physical mechanism for it, but it is never possible to completely separate a signal of external forcing from climate variability — the separation will always be statistical in nature.

I will argue that the uncertainties make it necessary to look at many different methods for downscaling (regional climate models and statistical downscaling) as well as the largest possible range of (sensible) GCMs.

This work is complicated, involving lots of statistical methods in extrapolating from scattered sites to a global picture, which means that there's abundant uncertainty — and that there will be abundant interpretations.

I advise military evaluators to RIGOROUSLY assess the assumptions of statistical models (not to be confused with physical processes) upon which climate scientists, solar scientists, etc. base estimates of uncertainty.

Unfortunately it shows, in my judgment, a current tendency by this group of scientists and their supporters to react by down grading some serious and basic statistical errors and inabilities to place uncertainty measures on results to a level of «minor» flaws and to characterize the criticisms as personal attacks.

And I really wish people wouldn't talk about «statistical uncertainty» of models.

There is currently no consensus on the optimal way to divide computer resources among finer numerical grids, which allow for better simulations; greater numbers of ensemble members, which allow for better statistical estimates of uncertainty; and inclusion of a more complete set of processes (e.g., carbon feedbacks, atmospheric chemistry interactions).

Our experiments suggest that both statistical approaches should yield reliable reconstructions of the true climate history within estimated uncertainties, given estimates of the signal and noise attributes of actual proxy data networks.

Given the statistical uncertainty in determining pre-1800s temperatures (see graph below) that requires greater than 50 % of the warming be attributed to anthropogenic factors.

To me that is completely irrelevant, because it was already known that there are limits of modeling performance that are stricter that those set by the statistical uncertainties.

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.

If we have inadequate sampling, and short time intervals, the statistical uncertainties from random fluctuations and random measurement errors can be large, but would tend to cancel out as the number of observations and length of time increases.

As a «rule of thumb», bias (Type B) uncertainties may be similar to statistical (Type A) uncertainties.

It seems to me that the issue is not so much that the IPCC AR4 chapter 9 authors have made an error in determination of the sensitivity in Fig 9.20, but rather that there is unacknowledged structural uncertainty in their methods for determining climate sensitivity (both statistical and physical / conceptual).

Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea - ice thickness initialization using statistical predictors available in real time.

Data from the ERSSTv3b and COBE data sets were also used in place of HadISST1 and gave similar results suggesting that the uncertainties do not depend strongly on the statistical assumptions made in creating HadISST1.

Thus tolerance for Risk is meaningless in such questions; Uncertainty removes all sense of Risk entirely, no cost / benefit formulation, no statistical distribution containing a mean, no Risk preference can be expressed.

In his talk, «Statistical Emulation of Streamflow Projections: Application to CMIP3 and CMIP5 Climate Change Projections,» PCIC Lead of Hydrological Impacts, Markus Schnorbus, explored whether the streamflow projections based on a 23 - member hydrological ensemble are representative of the full range of uncertainty in streamflow projections from all of the models from the third phase of the Coupled Model Intercomparison Project.