Sentences with phrase «do uncertainty analysis»

*** 1979 - 2014 has a trend of 0.0122... I think it would be interesting to do an uncertainty analysis and see if the uncertainty around 0.0122 encompasses 0.0168 (and I'm guessing it does).

Nic, I am fairly sure that F&G did not do an uncertainty analysis of the mathematical issues with the surface statistical models, such as HadCru.

Don't individual studies do uncertainty analysis?

Not exact matches

The fact is that while your executives and managers may express interest in more data (more metrics, more dimensions, raw data access, more chart choices, etc.) this is an indicator of uncertainty, not of an interest to do more robust analysis.

The authors note that cost - benefit analyses of sustainable land management scenarios «can be done even with limited data availability, «and underscore that, despite an inevitable degree of uncertainty, «it is imperative to take action now, as every day sees the loss of more productive land that will have to be gained back.»

Humans don't deal well with either uncertainty or ambiguity, notes James Hammitt, director of the Harvard Center for Risk Analysis.

David Fahey, an atmospheric scientist at the National Oceanic and Atmospheric Administration in Boulder, Colorado, said that the researchers will need to do additional analyses to reduce the «significant uncertainties associated with the role of black carbon in the climate.»

How does the lack of uncertainty analyses affect the calculation of risk?

While the Strengthening Forensic Science panel included two statisticians, the National Academies» America's Climate Choices panels did not include a single statistician, despite the many data, data analysis, uncertainty, and decisionmaking issues.

In addition, model intercomparison studies do not quantify the range of uncertainty associated with a specific aerosol process, nor does this type of uncertainty analysis provide much information on which aerosol process needs improving the most.

There are limitations in using a Monte Carlo simulation, including the analysis is only as good as the assumptions, and despite modeling for a range of uncertainties in the future, it does not eliminate uncertainty.

If you want to do a more precise analysis, fine — you'd need to properly include the uncertainty ranges and you would come to the same conclusion as me — as far as one can tell within uncertainty, the non-CO2 anthropogenic forcings approximately balance.

But one can see in this graph in an instant that while the ice MIGHT do different things, currently its trajectory is towards rapid collapse, and one senses immediately that Schweiger might still be in the middle of his unendingly bland sentence — «this analysis will change the predicted timing of the «ice free summer» but large uncertainties will likely remain.....

In your blog commentaries you claim absolute attribution and do not mention these uncertainties in your analysis.

I strongly suspect (and I admit to not having done this analysis — this is just my opinion «by eye») that the records are indistuinguishable within their one - standard - deviation uncertainties in the 19th century.

The whole POINT of doing what if analysis is our uncertainty about what certain facts will be.

If she accepts that attribution is amenable to quantitative analysis using some kind of model (doesn't have to be a GCM), I don't get why she doesn't accept that the numbers are going to be different for different time periods and have varying degrees of uncertainty depending on how good the forcing data is and what other factors can be brought in.

Uncertainty is not an obstacle the way I see it, and we can include that in planning — that is what is done in all risk analysis.

It has several major advantages over PCA including that it doesn't produce negative (non-real) results and you can incorporate uncertainty into the analysis so you can limit significance of low - level or missing data.

Other than anecdotal analyses, little has been done to quantitatively assess the uncertainties.

[The main conclusion of this analysis is that sea level uncertainty is not smaller now than it was at the time of the TAR, and that quoting the 18 - 59 cm range of sea level rise, as many media articles have done, is not telling the full story.

Do you feed in this type of information into your uncertainty analysis?

Those who like the the idea of having their skepticism subjected to a «more nuanced analysis» and granted «valorisation of the scientific norm of scepticism» (do we get a percentage of «uncertainty»?)

Results do not address all sources of uncertainty, but their scale and scope highlight one component of the potential health risks of unmitigated climate change impacts on extreme temperatures and draw attention to the need to continue to refine analytical tools and methods for this type of analysis

Older analyses (e.g., Tett et al., 2002) did not take account of uncertainty due to sampling signal estimates from finite - member ensembles.

We can apply my simpler bias analysis (which we can now see is limited in that it does not provide an uncertainty estimate for the estimated bias) to HadCRUT3 / 4.

What is needed instead is for economists to step up and do the analyses of the costs and benefits of GHG emissions and of proposed policies — including stating the uncertainties on their results.

The other thing is that SST and SAT have different variances and different uncertainties and they respond with different lags, so I UNLES Vaugh does some work with synthetic data FIRST to prove that the methods he applies to this data actually work, I'd say the signal analysis is flawed from the start since the «signal», the temperature curves are not really physical metrics.

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.

Unwillingness to combine the evidence in this way might be justified by the difficulties of estimating the full range of uncertainties of each analysis, but if the likelihood curves are taken seriously, combining all independent evidence is a natural procedure that should be done.

As I will discuss in Parts II and III of the Decision Making Under Climate Uncertainty series (I will get back to that soon I hope), there are a lot of other types of studies and analyses that climate scientists might be doing to support decision making, that the current focus of the IPCC is arguably distracting from.

How does your analysis allow you to distinguish between a climate sensitivity to changes in CO2 - effected radiative forcing of 0 K / (W.M ^ -2) and say 0.3 K / (W.M ^ -2), if there are these large uncertainties in the values of the forcings?

Are you saying that the scientific community, through the IPCC, is asking the world to restructure its entire mode of producing and consuming energy and yet hasn't done a scientific uncertainty analysis?

Too bad Annan didn't understand my talk, since it was targeted particularly at people like him who are pushing the idea that CO2 sensitivity is 3C http://www.jamstec.go.jp/frsgc/research/d5/jdannan/probrevised.pdf and think that Bayesian analysis can actually provide such an answer in the face of such large uncertainty.

Smith et al (I think) did this in their SST uncertainty analysis.

Note too that when the resulting cost benefit analyses are done, to support Federal actions, the grand uncertainties become irrelevant.

I do not need a «robust analysis of uncertainty» to conclude that the accepted trends are calculated from garbage data, and can have no possible result other than to produce a much higher trend than an analysis that properly accounted for these factors.

On his blog, tamino does the statistical analysis of the BEST data and finds that because the timeframe in question is so short, the uncertainty is too large to say for certain that the short - term trend in question is any different than the long - term trend.

But I do know the difference between a simple linear interpolation and principal component analysis, and I can calculate the two standard deviations range of uncertainty on a white noise linear trend.

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.

Since we can not reduce the uncertainty in these four key inputs, it seems we can not do much to reduce the uncertainty in the cost benefit analyses.

It is arguably time to tackle the tropospheric humidity issue, but this should be done from the perspective of comparing multiple data sources and assessing the uncertainty, before publishing trend analyses in the context of saying something about climate change.

However, at this point, no one has done a rigorous error or uncertainty analysis on the data, so Landsea's statements about the trends are not supported by any rigourous analysis at this point.

Structural uncertainties arise from an incomplete understanding of the processes that control particular values or results, for example, when the conceptual framework or model used for analysis does not include all the relevant processes or relationships.

Personally I agree with Smith's interpretation, we have not done the full analysis of model uncertainty.

Given the damage that the wrong intervention can do, the proper response to uncertainty isn't * inaction *, but * further analysis and data gathering *.