Sentences with phrase «of uncertainty in»

Pekka, «Use of state - of - the - art statistical methods could substantially improve the quantification of uncertainty in assessments of climate change.»

Even were we to achieve the impossible and have access to a comprehensive exploration of uncertainty in parameter space, the shape of various distributions extracted would reflect model constructs with no obvious relationship to the probability of real - world behaviour.

[2] Uncertainty ranges for the predictions are derived from cross-validation based estimates of uncertainty in the relationships between the predictors and the future warming.

The ensemble member approach is commonly used to approximate a measure of uncertainty in modeled results.

All are experts in their fields and have expertise relating to the role of uncertainty in climate change or how best to communicate it.

Weather represents the biggest area of uncertainty in this outlook.

Because Schwartz's model is simpler it is easier to account for and quantify the uncertainty in it (in fact much of the uncertainty in complex GCMs is hidden eg see Stainford et al referenced in the post), so if you take the view that you are interested not just in the mean but the variation in the estimate Schwartz's model, despite being simpler, gives you better information.

There are several major sources of uncertainty in making projections of climate change.

Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change.

Any change in a model can produce divergent solutions that are not predictable beforehand — it is the nature of the nonlinear Navier - Stokes equations — this extends to the range of uncertainty in climate data and to the number and breadth of couplings.

The findings have generated vigorous international debate about an issue that remains a key area of uncertainty in climate science.

However, multiple sources of uncertainty in the chain from climate forcing to impact model limit confidence in specific predictions.

How Plants Respond to Elevated Carbon Dioxide How Plants Respond to Elevated Carbon Dioxide University of California, San Diego May 2, 2006 An important source of uncertainty in predictions about...

With nonstationary statistics the standard error of the fit over past years is not a good measure of the uncertainty in the prediction.

While there is still some degree of uncertainty in all these components, the largest source of uncertainty in today's climate models are clouds.

When you have two data sets that disagree, often nobody digs in to figure out all the different sources of uncertainty in the different analysis.

Most investors understand that there is a great deal of uncertainty in markets and that the returns on investment in any given year are difficult to predict.

Moreover, to cover the full range of uncertainty in the historical volcanic eruption data, we even try the case with 3 times the best estimate of volcanic forcing.

Note that these regression maps are intended to convey the 5 — 95 % range of uncertainty in the simulated interannual NAO regression values arising from sampling fluctuations, and should not be interpreted as patterns occurring in any individual simulation.

Over decades, improvements in observations of the present climate, reconstructions of ancient climate, and computer models that simulate past, current, and future climate have reduced some of the uncertainty in forecasting how rising temperatures will ripple through the climate system.

This finding contradicts the often - held assumption that projected warmer conditions always favor mosquitoes and clarifies some of the uncertainty in complex feedbacks involving climate and climate change influences on vectors and virus transmission, enabling more targeted public health action by using location - specific knowledge of vector responses to climate.

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.

In summary, our results show that in the CESM - LE, the range of uncertainty in projected NAO trends and associated influences on SAT and P over the next 30 years can be obtained to a large degree from the Gaussian statistics of NAO variability during the historical period, with some regional exceptions possibly associated with AMOC variability.

Further, the large - scale imprint of the NAO on surface climate imparts spatial coherence to this leading source of uncertainty in future climate trends, with implications for agricultural and water resources.

Even seemingly straightforward questions, for example «is X safe» and «is X not safe» seem like (effectively) asking the same question but they may require entirely different analysis and give different results — because of uncertainty in the data, in the results, the logical conclusions / inference required etc. etc..

The ice core dO18 was used with neglect of any uncertainty in temperature.

That is because researchers have underestimated the degree of uncertainty in calculating rates of economic growth on both a global and regional per capita basis.

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.

Yeah, they're keeping that a huge secret: Section 8.6.3.2 of AR4 is called «Clouds,» and contains the statement «cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates.»

Uncertainties of estimated trends in global - and regional - average sea - surface temperature due to bias adjustments since the Second World War are found to be larger than uncertainties arising from the choice of analysis technique, indicating that this is an important source of uncertainty in analyses of historical sea - surface temperatures.

After the 1960s bias uncertainties dominate the total and are by far the largest component of the uncertainty in the most recent data.

By Dr. Tim Ball It is not surprising that Roe and Baker explained in a 2007 Science paper that, «The envelope of uncertainty in climate projections has not narrowed appreciably over the past 30 years, despite tremendous increases in computing power, in observations, and in the number of scientists studying the problem.»

Note, my weights were not determined using any fancy analysis, but integrate my sense of uncertainty in CO2 sensitivity, model uncertainties, and particularly the wild card that is natural variability.

Finally, the estimates of biases and other uncertainties presented here should not be interpreted as providing a comprehensive estimate of uncertainty in historical sea - surface temperature measurements.

By example, this Integrated Science Exploration Environment is proposed for exploring and managing sources of uncertainty in glacier modeling codes and methods, and for supporting scientific numerical exploration and verification.

[Hegerl et al, 2007]-RRB-, I obviously can see the large degree of uncertainty in the results from the different studies.

This assessment allows for a greater level of uncertainty in the 21st century than in the 20th century, retaining the 50 % mean score albeit with a greater level of overall certainty.

ISO / IEC Guide 98 - 3:2008 is a reissue of the 1995 version of the Guide to the Expression of Uncertainty in Measurement (GUM), with minor corrections.

For a more general discussion of uncertainty in historical SST measurements and data sets see: Kennedy, J.J. (2013) A review of uncertainty in in situ measurements and data sets of sea - surface temperature.

Dr Curry is interested in better estimates of the uncertainty in our estimates, and that fact might give a simple relevant bound.

PS By then, we might have resolved some of the uncertainty in attribution and know much more that we do today, right?

Also, as to this: «Note, my weights were not determined using any fancy analysis, but integrate my sense of uncertainty in CO2 sensitivity, model uncertainties, and particularly the wild card that is natural variability.»

E.g. even though there is a lot of uncertainty in precise numbers, scientists still proclaim that it's very likely (ie with a likelihood of more than 90 %) that human activity has caused most of the warming over the 20th century.

Even more ideally though you'd show the range of uncertainties that exist in the literature so that I, and everyone else, gets some idea of what the field in totality thinks of uncertainty in those temperatures.

Recall that you accused Al Gore of fraud for removing error bars from an IPCC graph while failing to include error bars or even any discussion of uncertainty in 20 graphs on the preceding five pages of your January 2011 presentation.

With a climate sensitivity of roughly 1 from «settled» CO2 science, some evidence for natural shifts in global climate of 0.5 - 1.0 degK, and a fair amount of uncertainty in feedbacks, my Italian flag (based on physics) will probably be mostly white if climate sensitivity is > 2.5.

We used methods that carefully consider potential sources of uncertainty in the data, including uncertainty in proxy calibration and in dating of the samples»

Given that, the overwhelming source of uncertainty in the attribution is linked to the model themselves.

Traceable accounts of final judgements of uncertainty in the findings and conclusions are, where possible, maintained.

It presents: (1) results of an IEA study quantifying the cost of uncertainty in the process of climate policy evolution, (2) results from interviews with investment departments of electric utilities, and (3) initial policy conclusions.