And it says nothing about ensemble projections which are bracketed
by structural uncertainty.
Not exact matches
Taking a step back from the immediate
uncertainty over Greece (see here for the latest updates), I'm struck
by how similar the situation is to those created
by the old 1980s / 1990s IMF and World Bank
Structural Adjustment Programmes (SAPs).
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.»
Of course, this would need to be justified
by some analysis, which itself would be subject to some
structural uncertainty... and so on.
It's a matter of
structural uncertainty and one that is easily addressed
by the scientists.
``... since
uncertainty is a
structural component of climate and hydrological systems, Anagnostopoulos et al. (2010) found that large
uncertainties and poor skill were shown
by GCM predictions without bias correction... it can not be addressed through increased model complexity....
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.
Judith, is the distinction Nic makes above between evidence for climate sensitivity «from simulations
by AOCGMs... and from observational evidence that is either direct or intermediated through simple Energy Balance Models» relevant to this «
structural uncertainty»?
Structural uncertainty: is characterized
by «unknown unknowns».
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.
With this kind of fundamental non-robustness, the outcomes of CBAs or IAMs are held hostage
by core
structural uncertainties about how high temperature change and high productive capacity should be combined to yield utility.
We know the models differe model to model and don't use them to create supertight estimates
by averaging, because we know there is
structural uncertainty.
* James Annan on the inclusion of
structural uncertainty; «testing modelling as an approach» (paraphrasing)... truth being bracketed
by the model spread, seems intuitively reasonable.
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?
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.
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.