Sentences with phrase «uncertainty in climate models at»

And of course there's still substantial uncertainty in climate models at the regional scale in war - prone places (again, a prime example is the set of countries along the southern fringe of the Sahara Desert, where models still clash on which areas will grow drier or wetter; see my Somalia posts.)

Not exact matches

Mission leaders were relieved and eager to begin their studies of cloud and haze effects, which «constitute the largest uncertainties in our models of future climate — that's no exaggeration,» says Jens Redemann, an atmospheric scientist at NASA's Ames Research Center in Mountain View, California, and the principal investigator for ObseRvations of Aerosols above CLouds and their IntEractionS (ORACLES).

A recent study in the Journal of Environmental Management carried out by researchers at the European Forest Institute and their partners in the FP7 funded MOTIVE project (Models for Adaptive Forest Management) discusses how forest managers and decision makers can cope with climate uncertainties.

Dr. Yun Qian, atmospheric and climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste,climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Triestemodeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste,Climate Modeling and Projection» in TriesteModeling and Projection» in Trieste, Italy.

Dufresne, 2005: Marine boundary - layer clouds at the heart of tropical cloud feedback uncertainties in climate models.

Wigley et al. (1997) pointed out that uncertainties in forcing and response made it impossible to use observed global temperature changes to constrain ECS more tightly than the range explored by climate models at the time (1.5 °C to 4.5 °C), and particularly the upper end of the range, a conclusion confirmed by subsequent studies.

Part of the uncertainty in the attribution is of course how realistic the «noise» in the models is — and that can be assessed by looking at hindcasts, paleo - climate etc..

At the tail end of the full paper, capping a paragraph about a weak spot in the analysis — that the observed trend in extreme precipitation events exceeds what is produced by various climate models — comes a sentence about uncertainties:

When we talk about future climate change, our discussion often stalls at the uncertainties inherent in scientists» statistical models and forecasts.

A new study by Prof Jason Lowe and Dr Dan Bernie at the UK's Met Office Hadley Centre takes these CMIP5 models and tries to account for additional uncertainties in the carbon budget associated with feedbacks, such as carbon released by thawing of permafrost or methane production from wetlands, as a result of climate change.

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.

I won't repeat what I said on an earlier forum, but a quick look at Paul Williams» presentation on numerical errors in climate modeling shows a host of issues that would lead me to assign a rather high uncertainty to the model results, and then we have the uncertainties in the physical models themselves.

Analyses of tide gauge and altimetry data by Vinogradov and Ponte (2011), which indicated the presence of considerably small spatial scale variability in annual mean sea level over many coastal regions, are an important factor for understanding the uncertainties in regional sea - level simulations and projections at sub-decadal time scales in coarse - resolution climate models that are also discussed in Chapter 13.

At the moment, the uncertainties in modeling and complexities of the ocean system even prevent any quantification of how much of the present changes in the oceans is being caused by anthropogenic climate change or natural climate variability, and how much by other human activities such as fishing, pollution, etc..

Why isn't a TCR type of simulation, but instead using actual history and 200 year projected GHG levels in the atmosphere, that would produce results similar to a TCR simulation (at least for the AGW temp increase that would occur when the CO2 level is doubled) and would result in much less uncertainty than ECS (as assessed by climate model dispersions), a more appropriate metric for a 300 year forecast, since it takes the climate more than 1000 years to equilibrate to the hypothesized ECS value, and we have only uncertain methods to check the computed ECS value with actual physical data?

In practice, this sequential and conditional approach to representing uncertainty in climate scenarios has at least one severe limitation: at each stage of the cascade, only a limited number of the conditional outcomes have been explicitly modelleIn practice, this sequential and conditional approach to representing uncertainty in climate scenarios has at least one severe limitation: at each stage of the cascade, only a limited number of the conditional outcomes have been explicitly modellein climate scenarios has at least one severe limitation: at each stage of the cascade, only a limited number of the conditional outcomes have been explicitly modelled.

«At each step (of the CO2 calculations) uncertainty in the time signals of climate change is introduced by errors in the representation of earth's system processes in modelling.»

«The uncertainties of the climate models have not been studied sufficiently at all, and the established science tries to keep quiet about this situation in public.»

Back at # 46 Gavin said in relationship to Hargreaves «Skill and Uncertainty in climate models» (and the Wiley site is now available):

In UKCIP08, for example, we are handling this problem by combining results from two different types of ensemble data: One is a systematic sampling of the uncertainties in a single model, obtained by changing uncertain parameters that control the climate system; the other is a multi-model ensemble obtained by pooling results from alternative models developed at different international centerIn UKCIP08, for example, we are handling this problem by combining results from two different types of ensemble data: One is a systematic sampling of the uncertainties in a single model, obtained by changing uncertain parameters that control the climate system; the other is a multi-model ensemble obtained by pooling results from alternative models developed at different international centerin a single model, obtained by changing uncertain parameters that control the climate system; the other is a multi-model ensemble obtained by pooling results from alternative models developed at different international centers.

As such, we may consider the MME as sampling at least some of our uncertainties in how a climate model should be constructed.