Sentences with phrase «modelling uncertainty based»

Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity.

Not exact matches

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.»

This uncertainty is changing because of improved supercomputer modelling of the movement of water through ecosystems, based on 20,000 locations around the world.

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

This environment, designated the Virtual Environment for Reactor Applications (VERA), incorporates science - based models, state - of - the - art numerical methods, modern computational science and engineering practices, and uncertainty quantification (UQ) and validation against data from operating pressurized water reactors (PWRs), single - effect experiments, and integral tests.

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?

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.

W.E. Walker, P. Harremoës, J. Rotmans, J. P. van der Sluijs, M.B.A. van Asselt, P. Janssen, and M.P. Krayer von Krauss (2003) Defining Uncertainty A Conceptual Basis for Uncertainty Management in Model - Based Decision Support, Integrated Assessment, Vol.4 No. 1 (2003), pp. 5 - 17.

Abstract: Based on the uncertainty of covariant matrix and value of expected return in risk assets, constraint tracking error for investment portfolio optimization model of VaR in additional transaction costs is constructed in this paper.

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.

There are uncertainties in parts of the general circulation models used to forecast future climate, but thousands of scientists have made meticulous efforts to make sure that the processes are based on observations of basic physics, laboratory measurements, and sound theoretical calculations.

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.

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.

Based on results from large ensemble simulations with the Community Earth System Model, we show that internal variability alone leads to a prediction uncertainty of about two decades, while scenario uncertainty between the strong (Representative Concentration Pathway (RCP) 8.5) and medium (RCP4.5) forcing scenarios [possible paths for greenhouse gas emissions] adds at least another 5 years.

In the rekognition of the uncertainties, the IPCC Good - Practice - Guidance - Paper on using climate model results offers some wise advice (first bullet point under section 3.5 on p. 10): the local climate change scenarios should be based on (i) historical change, (ii) process change (e.g. changes in the driving circulation), (iii) global climate change projected by GCMs, and (iv) downscaled projected change.

More complex metrics have also been developed based on multiple observables in present day climate, and have been shown to have the potential to narrow the uncertainty in climate sensitivity across a given model ensemble (Murphy et al., 2004; Piani et al., 2005).

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.

It is no greater than the uncertainty in the economic data and models on which equally far - reaching policy decisions must be based.

Although mainstream scientists do identify considerable uncertainties in their climate predictions, which are based on computer models, they are increasingly confident that global warming is a serious problem and often say that the uncertainties do not justify inaction.

She turns a blind eye to the lack of respect for uncertainty we see from folks like Ridley and Rose, as well as in the comments in these threads that talk of «economic suicide» — based on unvalidated and unverified economic modeling.

In a optimal comparison of observations with a model every empirical value should have a weight that varies only based on empirical uncertainties in the particular value, not on it's closeness to either end of the full period.

In fact, most uncertainties in the alarmist pseudo-science are internal contradictions and consequences of its shoddy practices: cherry picking data, making conclusions based on statistically insignificant observations, declaring trends based on variations that are within error margins, relying on computer models that contradict principles of the information theory, forging forecasts for unreasonably long time periods, etc..

That uncertainty can be broken down into 2 pieces: statements based on model weighting ignore uncertainty about how tight (and real) the constraint actually is, while statements based on an assumed functional relationship not only neglect uncertainty related to constraint validity, but also ignore uncertainty regarding what the correct functional relationship should actually be.

Based on current models, this is not the case everywhere, and continued model development and improvement is required to decrease the uncertainty and increase the utility of regional climate projections for adaptation decision making.

No acknowledgement of uncertainties, lack of measurements globally, lack of basis for increased confidence in man causing increased temperatures since 1976 or even the almost 20 years of level temperatures despite all predictions of the models.

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.

Those opposing policies on the basis of uncertainties about models often fail to acknowledge that the models could be wrong not only in overstating the impacts of climate change but also in greatly understating climate impacts.

Based on the rather vast uncertainties in aerosol forcing, and the substantial discrepancies between model projections of ocean heat uptake and measured heat uptake (ARGO), it strikes me as bizarre that the IPCC insists on excluding the possibility of quite low sensitivity, when there is a wealth of evidence for fairly low sensitivity.

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.

«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.

This range of uncertainty on the simulated NAO and its climate impacts cautions against over-interpreting results based on «only» 93 - years of data, be it from a model run or from nature.

We know the climate sensitivity to radiative forcing to be about 3 °C per 4 W / m2 of forcing to within something like a 10 % uncertainty, base on current climate modeling and the geological record (see Hansen et al., 2008) for details http://pubs.giss.nasa.gov/abs/ha00410c.html The natural (unforced) variability of the climate system is going to remain highly uncertain for the foreseeable future.

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.

Many physical modelers, and especially climate modelers, seems to think that as long as their models are «science - based» then there is no need to account for uncertainty in their outputs, notwithstanding that the model parameters are tuned with data, and that aspects of these models are likely to be ill posed (highly sensitive to small perturbations in the values assigned to parameters).

It is based on computer climate models fraught with uncertainties.

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.

Second, using measured atmospheric CO2 concentrations short circuits two layers of modeling which themselves are major sources of uncertainty, namely, estimating global emissions and, then, estimating the atmospheric CO2 concentrations (based on complex models of the global carbon cycle).

The uncertainty layer takes into account the errors from allometric equations, LiDAR based model, and randomForest model.

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.

This would not only improve the accuracy of the climate model forecasts but would provide a basis for computing the uncertainty which applies to each forecast.

Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly.

If we wish to reason about certain questions on the basis of uncertainty ranges we need cdfs and hence knowlege of pdfs and for these we need some function, in this case a prior, to add that important aspect of density that can not be derived from the likelihood function that results from the application of a statistical model to the experimental evidence.

The introduction to the first chapter, «Climate models and their limitations» cites the Rosenberg 2010 conclusion on uncertainties related to GCM outputs as bases for projecting climatic change impacts:

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.

Its almost funny to see folks model future temps based on such short incomplete & uncertain data sets... without noting error or uncertainty bars.

While climate contrarians like Richard Lindzen tend to treat the uncertainties associated with clouds and aerosols incorrectly, as we noted in that post, they are correct that these uncertainties preclude a precise estimate of climate sensitivity based solely on recent temperature changes and model simulations of those changes.

The best estimate and uncertainty range of the total direct aerosol RF are based on a combination of modelling studies and observations.

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).»

The resulting estimates are less dependent on global climate models and allow more realistically for forcing uncertainties than similar estimates based on forcings diagnosed from simulations by such models.