Sentences with phrase «structural uncertainty in»

The basic picture is unchanged — model simulations were able to capture the historical variance in OHC (as best we know it now — there remains significant structural uncertainty in those estimates).

Choosing lower and upper limits that encompass the range of these results and deflating significance levels in order to account for structural uncertainty in the estimate leads to the conclusion that it is very unlikely that TCR is less than 1 °C and very unlikely that TCR is greater than 3.5 °C.

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).

As IPCC, in a search for objectivity in uncertainty assessment, has turned more to describing uncertainty in terms of the characteristics of ensembles of model outcomes, the deficiency in such an approach (its exclusion or limited treatment of systemic, structural uncertainty in models) has become increasingly apparent to the community (Winsberg 2010; Knutti et al. 2008; Goldstein and Rougier 2009).

The results is called structural uncertainty in the models (McWilliams, 2007; Parker, 2010).

This is of course the same Douglass et al paper that used completely incoherent statistics and deliberately failed to note the structural uncertainty in the observations.

There were two strands to our critique: i) that the statistical test they used was not appropriate and ii) that they did not acknowledge the true structural uncertainty in the observations.

There are basically two key points (explored in more depth here)-- comparisons should be «like with like», and different sources of uncertainty should be clear, whether uncertainties are related to «weather» and / or structural uncertainty in either the observations or the models.

What it shows is the effect of the structural uncertainty in individual GCMs (meaning that some of them are systematically high, others systematically low, due to flaws in the representation of the physics; most probably related to discretization / parametrization effects for clouds and / or aerosols).

We should be using alternate tunings to expand the representation of structural uncertainty in the ensemble, and I hope many of the groups will take this opportunity to do so.

The large structural uncertainties in observations hamper our ability to determine how well models simulate the tropospheric temperature changes that actually occurred over the satellite era.

However statistical calculations only provide an apparent rigor for the uninitiated and in relation to the IPCC climate models are entirely misleading because they make no allowance for the structural uncertainties in the model set up.

However statistical calculations only provide an apparent rigor for the uninitiated and in relation to the IPCC climate models are entirely misleading because they make no allowance for the structural uncertainties in the model set up -LRB-.

Performed research in non-linear equations, chaotic systems, and structural uncertainties in numerical models.

Not exact matches

«Persistent conflicts and their regional spillovers, security concerns, weaker - than - anticipated public investment (Afghanistan, Jordan), delays in implementation or completion of structural reforms (Jordan, Morocco, Pakistan, Tunisia), and political and policy uncertainty (Lebanon, Pakistan) continue to weigh on growth.

After years of economic expansion, the Turkish economy is set to continue struggling in 2017 on the back of growing terrorist acts, lack of structural reforms, falling tourism figures and increased political uncertainty.

Beyond the cyclical uncertainties affecting trading volumes, there may also be structural factors at play, particularly an official rethink of the benefits of open capital accounts, and fast - money cross-border capital flows in particular.

It is our task to address both their underlying and structural causes and their distressing consequences in order to reduce uncertainty and insecurity in the life of the people.

In light of the considerable uncertainty around the economic and fiscal outlook, including the risks posed to economic recovery by ongoing financial tensions in the eurozone and against the backdrop of a still large structural budget deficit and high and rising government debt, the Negative Outlook indicates a slightly greater than 50 % chance of a downgrade over a two - year horizon.&raquIn light of the considerable uncertainty around the economic and fiscal outlook, including the risks posed to economic recovery by ongoing financial tensions in the eurozone and against the backdrop of a still large structural budget deficit and high and rising government debt, the Negative Outlook indicates a slightly greater than 50 % chance of a downgrade over a two - year horizon.&raquin the eurozone and against the backdrop of a still large structural budget deficit and high and rising government debt, the Negative Outlook indicates a slightly greater than 50 % chance of a downgrade over a two - year horizon.»

In her doctoral thesis, Pulkkinen also discusses model uncertainty (structural uncertainty), which results from the fact that the phenomenon being researched can be explained with several — even contradicting — theories.

The materials community is interested in using the team's tool as part of an integrated computational materials engineering approach to design structural components — which could help optimize materials properties and reduce uncertainty for given applications.

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.

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.

In the middle of the article you wrote: «-LRB-...) deciding whether the differences are related to internal variability, forcing uncertainties (mostly in aerosols), or model structural uncertainty is going to be harder.&raquIn the middle of the article you wrote: «-LRB-...) deciding whether the differences are related to internal variability, forcing uncertainties (mostly in aerosols), or model structural uncertainty is going to be harder.&raquin aerosols), or model structural uncertainty is going to be harder.»

We can derive the underlying trend related to external forcings from the GCMs — for each model, the underlying trend can be derived from the ensemble mean (averaging over the different phases of ENSO in each simulation), and looking at the spread in the ensemble mean trend across models gives information about the uncertainties in the model response (the «structural» uncertainty) and also about the forcing uncertainty — since models will (in practice) have slightly different realisations of the (uncertain) net forcing (principally related to aerosols).

The structural uncertainty represents the uncertainty inherent in the DNDC model and is set using independent validation data (directly measured daily methane fluxes on benchmark sites) available at the time of methodology publication.

Additional data will become available in the future, allowing the structural uncertainty deduction factors to be updated.

In addition, as more fields are registered, structural uncertainty should decline, so the structural uncertainty deduction depends on the number of fields in all projects registered on ACIn addition, as more fields are registered, structural uncertainty should decline, so the structural uncertainty deduction depends on the number of fields in all projects registered on ACin all projects registered on ACR.

In other words, the analysis neglects structural uncertainty about the adequacy of the assumed linear model, and the parameter uncertainty the analysis does take into account is strongly reduced by models that are «bad» by this model - data mismatch metric.

Part II addressed uncertainties in external forcing data sets used in the attribution studies and the relevant climate model structural uncertainties.

His research focuses on the estimation of the «social cost of carbon,» including the proper discount rate to be used in cost - benefit analyses and the implications of structural uncertainty for policy solutions.

The structural uncertainties above are not expressed in trivial intermodel variability, but lie at the core of IPCC climate modeling, including its reliance on the radiative forcing paradigm.

If AR5 settles no other issue, it needs to resolve the highly significant structural uncertainties with IPCC statistics and in modeling cloud albedo.

Although 2014 was a turnaround year for renewables after two years of shrinkage, multiple challenges remain in the form of policy uncertainty, structural issues in the electricity system — even in the very nature of wind and solar generation, with their dependence on breeze and sunlight.

Because the differences between the various observational estimates are largely systematic and structural (Chapter 2; Mears et al., 2011), the uncertainty in the observed trends can not be reduced by averaging the observations as if the differences between the datasets were purely random.

This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty.

Apergis, N & Lau, MCK 2015, «Structural breaks and electricity prices: Further evidence on the role of climate policy uncertainties in the Australian electricity market», Energy Economics, vol.

Instead, this message is just another warning, in a growing series of cautionary tales, that the particular application of CBAs or IAMs to climate change seems more inherently prone to being dependent on subjective judgments about structural uncertainties than most other, more ordinary, applications of CBAs or IAMs.

My interest in this arose from the realization that in fact uncertainty was higher than generally realized in engineering fluid dynamics and structural mechanics calculations.

let's take this to an extreme... suppose that internal variability is zero... then the «within group» s.d. is zero... suppose that models agree pretty well with each other and observations fall within the tight band of model projections... then by steve's method you create the average of models and call it a model... with an s.d. of zero... show that the model falls outside the observational s.d.... proclaim that the model fails... claim that this is a test of modelling... hence extrapolate that all models fail... even though observations fall slap bang in the model range... this result is nonsensical... per tco it isn't how models are used... where's structural uncertainty?

Fat - tailed structural uncertainty, along with great unsureness about high - temperature damages, can outweigh discounting in climate - change economics.

Even just acknowledging more openly the incredible magnitude of the deep structural uncertainties that are involved in climate - change analysis — and explaining better to policymakers that the artificial crispness conveyed by conventional IAM - based CBAs [Integrated Assessment Model — Cost Benefit Analyses] here is especially and unusually misleading compared with more ordinary non-climate-change CBA situations — might go a long way toward elevating the level of public discourse concerning what to do about global warming.

The report also highlights that there are several challenges remainaing, including climate change, policy uncertainty, difficulties in creating policy that keeps pace with technological change and structural issues in the electricity system.

Uncertainties in the model and forcing are acknowledged by the AR4 (Chapter 9): «Ideally, the assessment of model uncertainty should include uncertainties in model parameters (e.g., as explored by multi-model ensembles), and in the representation of physical processes in models (structural Uncertainties in the model and forcing are acknowledged by the AR4 (Chapter 9): «Ideally, the assessment of model uncertainty should include uncertainties in model parameters (e.g., as explored by multi-model ensembles), and in the representation of physical processes in models (structural uncertainties in model parameters (e.g., as explored by multi-model ensembles), and in the representation of physical processes in models (structural uncertainty).

Structural uncertainties are generally described by giving the authors» collective judgment of their confidence in the correctness of a result.

However, far less well documented is the true magnitude of uncertainty arising from the formulation of feedback mechanisms, often termed «structural uncertainty» in the IPCC reports.

There are a number of statements in the IPCC report which are quite correctly qualified with respect to structural uncertainty, and yet the conclusions, very often, do not take these qualifications into account.

4 This flexibility allows us to analyze the effect of structural uncertainties present in existing AOGCMs.

The two obvious contributors to the uncertainty are the structural biases in the proxies and the sampling error from estimating GAST from 5 - 61 SST observations.

There is also regulatory uncertainty in key sectors like mining, a need for structural reforms and continuing (and well publicised) political issues which affect business confidence.