Sentences with phrase «model uncertainty estimates»

Not exact matches

Some of the largest uncertainties in current climate models stem from their wide - ranging estimates of the size and number of dust particles in the atmosphere.

Using a hierarchical model, the authors combine information from these various sources to obtain an ensemble estimate of current and future climate along with an associated measure of uncertainty.

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.»

«Our results show that the uncertainty estimates of greenhouse gas inventories depend on the calculation method and on how the input data for the model, such as weather and litterfall data, have been averaged,» says Aleksi Lehtonen, researcher at the Natural Resources Institute Finland (Luke).

That would seem to be a good test of whether the method produces a good estimate of TCR independent of the uncertainty in E. I tried such a thing, and my main objection to the Shindell (2014) paper is that when I test the «simple» Otto method vs. the Shindell method on the same model set in the paper, the Otto et al (2013) method still seems to perform better.

The estimated size of and uncertainty in current observed warming rates attributable to human influence thus provides a relatively model - independent estimate of uncertainty in multi-decade projections under most scenarios.

Quoting the IPCC 1.4 to 5.8 Â °C estimate (for doubling CO2) outside current agreements among models that the uncertainty is most likely in the 2.5 to 4Â °C range or failing to point out that discrepancies (used by skeptics) between surface and troposphere warming have been resolved, is misleading in my view.

We encourage contributions on current and prospective observation technologies for GHGs, modeling studies to quantify budgets and / or uncertainties in GHG flux estimates, and evaluation and benchmarking of GHG estimates from Earth System Models using contemporary observations.

For each star, we present estimates and uncertainties of mass, age, radius, luminosity, core hydrogen abundance, surface helium abundance, surface gravity, initial helium abundance, and initial metallicity as well as estimates of their evolutionary model parameters of mixing length, overshooting coefficient, and diffusion multiplication factor.

Ideally, one would want to do a study across all these constraints with models that were capable of running all the important experiments — the LGM, historical period, 1 % increasing CO2 (to get the TCR), and 2xCO2 (for the model ECS)-- and build a multiply constrained estimate taking into account internal variability, forcing uncertainties, and model scope.

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.

«We use a massive ensemble of the Bern2.5 D climate model of intermediate complexity, driven by bottom - up estimates of historic radiative forcing F, and constrained by a set of observations of the surface warming T since 1850 and heat uptake Q since the 1950s... Between 1850 and 2010, the climate system accumulated a total net forcing energy of 140 x 1022 J with a 5 - 95 % uncertainty range of 95 - 197 x 1022 J, corresponding to an average net radiative forcing of roughly 0.54 (0.36 - 0.76) Wm - 2.»

Emphasize that estimates are being made because of uncertainties in the model and the input data.

The cooling in the graph shown is indeed 0.1 °C only as you observed, the 0.3 °C arises when we, conservatively, estimate all uncertainties in the modeling and the forcings.

The model results (which are based on driving various climate models with estimated solar, volcanic, and anthropogenic radiative forcing changes over this timeframe) are, by in large, remarkably consistent with the reconstructions, taking into account the statistical uncertainties.

(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.

The estimated size of and uncertainty in current observed warming rates attributable to human influence thus provides a relatively model - independent estimate of uncertainty in multi-decade projections under most scenarios.

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

But since there are reasonable estimates of the real world GMT, it is a fair enough question to ask why the models have more spread than the observational uncertainty.

Ideally, one would want to do a study across all these constraints with models that were capable of running all the important experiments — the LGM, historical period, 1 % increasing CO2 (to get the TCR), and 2xCO2 (for the model ECS)-- and build a multiply constrained estimate taking into account internal variability, forcing uncertainties, and model scope.

Although there is still some disagreement in the preliminary results (eg the description of polar ice caps), a lot of things appear to be quite robust as the climate models for instance indicate consistent patterns of surface warming and rainfall trends: the models tend to agree on a stronger warming in the Arctic and stronger precipitation changes in the Topics (see crude examples for the SRES A1b scenarios given in Figures 1 & 2; Note, the degrees of freedom varies with latitude, so that the uncertainty of these estimates are greater near the poles).

Many different models have now demonstrated that our understanding of current forcings, long - term observations of the land surface and ocean temperature changes and the canonical estimates of climate forcing are all consistent within the uncertainties.

One could even argue that since most of the uncertainty resides on the high sides of the estimates, that the models are a conservative treatment — certainly from a risk perspective.

Using a whole suite of climate models (the CMIP5 models), we have tested how well our temperature - based estimate can reflect the actual trend of the AMOC, and have arrived at an uncertainty of plus or minus one million cubic metres per second.

Uncertainties in the regression models and fits used to distinguish between periodic variations and trends in the different databases appear to be a significant source of uncertainty in the estimates of longterm trends.

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.

The population is well defined when the model is defined and the uncertainties of the parameter estimates are specified.

Such ensembles could provide a misleading estimate of forecast uncertainty because they do not systematically explore modelling uncertainty (Allen et al., 2002; Allen and Stainforth, 2002).

Sensitivity of the climate to carbon dioxide, and the level of uncertainty in its value, is a key input into the economic models that drive cost - benefit analyses, including estimates of the social cost of carbon.

«The author is simply asserting that uncertainties in published estimates [i.e., model precision — P] are not «physically valid» [i.e., not accuracy — P]- an opinion that is not widely shared.»

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).

Let's compute the warming rate using each 30 - year segment of the Berkeley data, together with the estimated uncertainty in that rate, using an ARMA (1,1) model for the noise just to feed the «uncertainty monster.»

Lyman and colleagues combined different ocean monitoring groups» data sets, taking into account different sources of bias and uncertainty — due to researchers using different instruments, the lack of instrument coverage in the ocean, and different ways of analyzing data used among research groups — and put forth a warming rate estimate for the upper ocean that it is more useful in climate models.

These budgets give the lowest estimates of allowed emissions and are the simplest to convert into policy advice, but they suffer from the same problem of probabilistic interpretation as TEBs since they are dependent on simple climate models with uncertainty ranges calibrated to the CMIP5 ensemble.

Further estimates of internal variability can be produced from long control simulations with climate models... Expert judgments or multi-model techniques may be used to incorporate as far as possible the range of variability in climate models and to assign uncertainty levels, confidence in which will need to be assessed.»

Do you want an Italian flag estimate for the uncertainties in the GISS model?

What many previous emergent - constraint studies have done is to take such a band of observations and project it onto the vertical ECS axis using the estimated regression line between ECS and the natural fluctuations, taking into account uncertainties in the estimated regression model.

The SASBE could, for example, be used to constrain a radiative transfer model to provide top - of - the - atmosphere radiances with traceable uncertainty estimates.

> Advances in climate change modelling now enable best estimates and likely assessed uncertainty ranges to be given for projected warming for different emission scenarios.

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.

However, the physician has not derived his estimate of uncertainty from a computer model with no predictive validity.

«uncertainty» (in the IPCC attribution of natural versus human - induced climate changes, IPCC's model - based climate sensitivity estimates and the resulting IPCC projections of future climate) is arguably the defining issue in climate science today.

«We use a massive ensemble of the Bern2.5 D climate model of intermediate complexity, driven by bottom - up estimates of historic radiative forcing F, and constrained by a set of observations of the surface warming T since 1850 and heat uptake Q since the 1950s... Between 1850 and 2010, the climate system accumulated a total net forcing energy of 140 x 1022 J with a 5 - 95 % uncertainty range of 95 - 197 x 1022 J, corresponding to an average net radiative forcing of roughly 0.54 (0.36 - 0.76) Wm - 2.»

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.

These NAO «book - ends» provide an estimate of the 5 — 95 % range of uncertainty in projected trends due to internal variability of the NAO based on observations superimposed upon model estimates of human - induced climate change.

In general, these studies have shown that different ways of creating scenarios from the same source (a global - scale climate model) can lead to substantial differences in the estimated effect of climate change, but that hydrological model uncertainty may be smaller than errors in the modelling procedure or differences in climate scenarios (Jha et al., 2004; Arnell, 2005; Wilby, 2005; Kay et al., 2006a, b).

GFDL NOAA (Msadek et al.), 4.82 (4.33 - 5.23), Modeling Our prediction for the September - averaged Arctic sea ice extent is 4.82 million square kilometers, with an uncertainty range going between 4.33 and 5.23 million km2 Our estimate is based on the GFDL CM2.1 ensemble forecast system in which both the ocean and atmosphere are initialized on August 1 using a coupled data assimilation system.

Methodological advances since the TAR have focused on exploring the effects of different ways of downscaling from the climate model scale to the catchment scale (e.g., Wood et al., 2004), the use of regional climate models to create scenarios or drive hydrological models (e.g., Arnell et al., 2003; Shabalova et al., 2003; Andreasson et al., 2004; Meleshko et al., 2004; Payne et al., 2004; Kay et al., 2006b; Fowler et al., 2007; Graham et al., 2007a, b; Prudhomme and Davies, 2007), ways of applying scenarios to observed climate data (Drogue et al., 2004), and the effect of hydrological model uncertainty on estimated impacts of climate change (Arnell, 2005).

To estimate the uncertainty range (2σ) for mean tropical SST cooling, we consider the error contributions from (a) large - scale patterns in the ocean data temperature field, which hamper a direct comparison with a coarse - resolution model, and (b) the statistical error for each reconstructed paleo - temperature value.

Because Schwartz's model is simpler it is easier to account for and quantify the uncertainty in it (in fact much of the uncertainty in complex GCMs is hidden eg see Stainford et al referenced in the post), so if you take the view that you are interested not just in the mean but the variation in the estimate Schwartz's model, despite being simpler, gives you better information.