Sentences with phrase «n't model uncertainty»

First, doesn't the model uncertainty include both model noise (i.e., weather fluctuations) and systematic differences among the models?

Fustey's takeaway message is that standard deviation can't model uncertainty.

Not exact matches

While the uncertainty in the results from Jacobson's model and his own experiments is large, Ramanathan said he «wouldn't rule out that black carbon is the second - largest global warmer.»

She adds, however, that although there is good evidence that countershading acts as a defense mechanism, there will always be some uncertainty about interpreting countershading in dinosaurs, because we can't present a model Psittacosaurus to their natural predators to see which type of pattern provides the best protection.

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.»

Kendall says uncertainty about the new regimen's role prompted her group's interest in creating a computer model to assist health care groups and governments in deciding whether or not to switch to the new regimen, which uses a combination of seven drugs.

As can be seen your graph, our climate models make a wide range of predictions (perhaps 0.5 - 5 degC, a 10-fold uncertainty) about how much «committed warming» will occur in the future under any stabilization scenario, so we don't seem to have a decent understanding of these processes.

Giving statistically accurate and informative assessments of a model's uncertainty is a daunting task, and an expert's scientific training for such an estimation may not always be adequate.

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.

But while Lewis argues that the uncertainty in E is large and climate models do not give the value as accurately as we'd like, that does not justify ignoring that uncertainty entirely.

Observations of gravitational lensing at that time already hinted the presence of dark energy, but both due to the small sample size and large uncertainty in the theoretical modeling of lensing rates the result was not widely accepted.

For example, models don't currently include permafrost methane emissions — as there's too much uncertainty about them.

Therefore, I wouldn't attach much credence, if any, to a modelling study that didn't explore the range of possibilities arising from such uncertainty in parameter values, and particularly in the value of something as crucial as the climate sensitivity parameter, as in this example.

This paper suggests that models with sensitivity around 4ºC did the best, though they didn't give a formal estimation of the range of uncertainty.

In addition, the authors do not account for uncertainties in the simple model whose sensitivity is fitted.

Given that clouds are known to be the primary source of uncertainty in climate sensitivity, how much confidence can you place in a study based on a model that doesn't even attempt to simulate clouds?

However, in view of the fact that cloud feedbacks are the dominant contribution to uncertainty in climate sensitivity, the fact that the energy balance model used by Schmittner et al can not compute changes in cloud radiative forcing is particularly serious.

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).

This could be because of the structural deficiency of the model, or because of errors in the data, but the (hard to characterise) uncertainty in the former is not being carried into final uncertainty estimate.

It is not all that earthshaking that the numbers in Schmittner et al come in a little low: the 2.3 ºC is well within previously accepted uncertainty, and three of the IPCC AR4 models used for future projections have a climate sensitivity of 2.3 ºC or lower, so that the range of IPCC projections already encompasses this possibility.

What is more surprising is the small uncertainty interval given by this paper, and this is probably simply due to the fact that not all relevant uncertainties in the forcing, the proxy temperatures and the model have been included here.

In addition, model intercomparison studies do not quantify the range of uncertainty associated with a specific aerosol process, nor does this type of uncertainty analysis provide much information on which aerosol process needs improving the most.

By scaling spatio - temporal patterns of response up or down, this technique takes account of gross model errors in climate sensitivity and net aerosol forcing but does not fully account for modelling uncertainty in the patterns of temperature response to uncertain forcings.

The authors propose a conceptual model integrating privacy concerns, self - efficacy, and Internet experience with uncertainty reduction strategies and amount of self - disclosure and then test this model on a nation - wide sample of online dating participants (N = 562).

You won't have to worry uncertainty around what kind of vehicle you're getting when you choose a CPO Jaguar, because each of these models has undergone a thorough 165 - point inspection by a factory - trained and certified Jaguar technician.

Adding the Uncertainty Index to the model does not improve its efficacy.

There are limitations in using a Monte Carlo simulation, including the analysis is only as good as the assumptions, and despite modeling for a range of uncertainties in the future, it does not eliminate uncertainty.

I can determine the standard uncertainty for all the measured variables from statistics It is falsifiable — i can move a body at a certain velocity for a certain time and measure the traveled distance If the traveled distance does not fit with calculated distance within the uncertainty calculated by using the international standard Guide to the expression of Uncertainty in Measurement the model mighuncertainty for all the measured variables from statistics It is falsifiable — i can move a body at a certain velocity for a certain time and measure the traveled distance If the traveled distance does not fit with calculated distance within the uncertainty calculated by using the international standard Guide to the expression of Uncertainty in Measurement the model mighuncertainty calculated by using the international standard Guide to the expression of Uncertainty in Measurement the model mighUncertainty in Measurement the model might be wrong.

But, as far as I can see, the «attacks» by vested interests are not even able to make legitimate points (e.g. uncertainty about the effects of clouds or aerosols in climate models).

[Response: Uncertainty in the observations is very different from the uncertainty due to possible weather variations that might have happened but didn't (the dominant term in the near - future modUncertainty in the observations is very different from the uncertainty due to possible weather variations that might have happened but didn't (the dominant term in the near - future moduncertainty due to possible weather variations that might have happened but didn't (the dominant term in the near - future model spread).

(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.

It seems clear that the new data (including HadSST3) will be closer to the models than previously, if not quite perfectly in line (but given the uncertainties in the magnitude of the Krakatoa forcing, a perfect match is unlikely).

But I would really have preferred if they had written in Helvetica, 30, Bold that the uncertainty band is not on the actual, as measured in the field, global average temperature, but on their matematical model of it, and because of the steps that model contain, probably an order of magnitude too optimistic with respect to the actual temperature.

We are currently exploring the impacts that updates in the forcings have on the CMIP5 model runs and exploring the range of uncertainty where we don't have solid information.

Cloud responses are more uncertain and that feeds in to the uncertainty in overall climate sensitivity — but the range in the AR4 models (2.1 to 4.5 deg C for 2xCO2) can't yet be constrained by paleo - climate results which have their own uncertainties.

I talked only about the topic of this post, which is: the mismatch betweem model results and observations, and it's implication for model uncertainty (since the mismatch can not be attributed to observation errors).

Neither of these cases imply that the forcings or models are therefore perfect (they are not), but deciding whether the differences are related to internal variability, forcing uncertainties (mostly in aerosols), or model structural uncertainty is going to be harder.

When you think about the uncertainties of economic models and how much money is invested using those models as a basis, the idea that we don't know enough about climate change is laughable.

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.

* Indeed, possible errors in the amplitudes of the external forcing and a models response are accounted for by scaling the signal patterns to best match observations, and thus the robustness of the IPCC conclusion is not slaved to uncertainties in aerosol forcing or sensitivity being off.

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 solar irradiance forcing is given as 0.4 + / - 0.2 W / m2 since 1850 (Fig 18, panel 1, Hansen et al, 2002 — note the the zero was not an uncertainty in panel 2, it was just what was put in that version of the model — i.e. they did not change solar then).

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.

When faced with durable uncertainty on many fronts — in the modeling of the atmosphere, in data delineating past climate changes, and more — pushing ever harder to boost clarity may be scientifically important but is not likely to be very relevant outside a small circle of theorists.

In their rejoinder MW claim they didn't agree with reducing the data set to 59 as follows: «the application of ad hoc methods to screen and exclude data increases model uncertainty in ways that are unmeasurable and uncorrectable.»

The model uncertainties are so huge they are not inconsistent with observations.

This paper suggests that models with sensitivity around 4ºC did the best, though they didn't give a formal estimation of the range of uncertainty.

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.

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).

And that gray uncertainty range is for the not - forcing - adjusted models, if I understand correctly.