Sentences with phrase «of climate modeling uncertainty»

This is where the understanding of climate modeling uncertainty is lost in the scientific communications to the public by the politicians and vocal advocates that drive climate change discussions.

Tim Palmer's presentation was superb and very relevant to our discussions of climate model uncertainty.

More complete exploration of climate model uncertainty, including unknowns and model structural uncertainty

Not exact matches

Reducing uncertainties in the models could lead to better long - term assessments of climate, Esposito says.

Some of the largest uncertainties in current climate models stem from their wide - ranging estimates of the size and number of dust particles in the atmosphere.

Modeling future climate scenarios is a notoriously tricky science, involving wide margins of uncertainty, myriad variables and a profusion of data.

By improving the understanding of how much radiation CO2 absorbs, uncertainties in modelling climate change will be reduced and more accurate predictions can be made about how much Earth is likely to warm over the next few decades.

Therefore, Caldeira said, the more important question - and one of the largest sources of uncertainty in climate models - is «will the end of oil usher in a century of coal, or will it usher in a transition toward low - carbon - emitting technologies?»

Three approaches were used to evaluate the outstanding «carbon budget» (the total amount of CO2 emissions compatible with a given global average warming) for 1.5 °C: re-assessing the evidence provided by complex Earth System Models, new experiments with an intermediate - complexity model, and evaluating the implications of current ranges of uncertainty in climate system properties using a simple model.

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).

Any detailed, careful reading of the climate models includes a great deal of uncertainty.

The uncertainty associated with future climate projections linked to economic possibilities of what people will do is far larger than the uncertainty associated with physical climate models.

Using a hierarchical model, the authors combine information from these various sources to obtain an ensemble estimate of current and future climate along with an associated measure of uncertainty.

Clouds also are the largest source of uncertainty in present climate models, according to the latest report of the Intergovernmental Panel on Climate climate models, according to the latest report of the Intergovernmental Panel on Climate Climate Change.

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.

«A cloud system - resolved model can reduce one of the greatest uncertainties in climate models, by improving the way we treat clouds,» Wehner said.

For example, when examining hurricanes and typhoons, the lack of a high - quality, long - term historical record, uncertainty regarding the impact of climate change on storm frequency and inability to accurately simulate these storms in most global climate models raises significant challenges when attributing assessing the impact of climate change on any single storm.

But calculating the fraction of warming is a far more contentious task, points out climatologist Stephen H. Schneider of Stanford University, because of the inherent uncertainty and variability of climate models.

However, he says, «Aerosol effects on climate are one of the main uncertainties in climate models.

Gary Geernaert, director of DOE's Climate and Environmental Sciences Division, states that «it is critical that federal investments to advance climate science for use by both public and private stakeholders must place significant priority on incorporating uncertainty quantification methodologies into modeling framClimate and Environmental Sciences Division, states that «it is critical that federal investments to advance climate science for use by both public and private stakeholders must place significant priority on incorporating uncertainty quantification methodologies into modeling framclimate science for use by both public and private stakeholders must place significant priority on incorporating uncertainty quantification methodologies into modeling frameworks.

By 2100, the choice of driving climate model conditions dominates the uncertainty, but by 2200, the uncertainty in the ice sheet model and the elevation scheme are larger.

That uncertainty is represented in the latest crop of global climate models, which assume a climate sensitivity of anywhere from about 3 to 8 degrees F.

Those data, to be collected this year and next, could improve climate models, which account poorly for these atmospheric interactions and contain «horrific» uncertainties about the levels and behaviour of water vapour at stratospheric altitudes, Austin says.

«The model we developed and applied couples biospheric feedbacks from oceans, atmosphere, and land with human activities, such as fossil fuel emissions, agriculture, and land use, which eliminates important sources of uncertainty from projected climate outcomes,» said Thornton, leader of the Terrestrial Systems Modeling group in ORNL's Environmental Sciences Division and deputy director of ORNL's Climate Change Science Insclimate outcomes,» said Thornton, leader of the Terrestrial Systems Modeling group in ORNL's Environmental Sciences Division and deputy director of ORNL's Climate Change Science InsClimate Change Science Institute.

As can be seen your graph, our climate models make a wide range of predictions (perhaps 0.5 - 5 degC, a 10-fold uncertainty) about how much «committed warming» will occur in the future under any stabilization scenario, so we don't seem to have a decent understanding of these processes.

Oppenheimer and his co-authors use a technique known as «structured expert judgment» to put an actual value on the uncertainty that scientists studying climate change have about a particular model's prediction of future events such as sea - level rise.

By providing new data on how CO2 cycles through land and ocean plants, HIPPO will allow researchers to improve the accuracy of their climate models and reduce that uncertainty, Stephens said.

PNNL researchers play a key role in reducing uncertainty through improved process understanding and modeling of climate processes such as clouds and aerosols.

Click here for Part II, an accounting of Exxon's early climate research; Part III, a review of Exxon's climate modeling efforts; Part IV, a dive into Exxon's Natuna gas field project; Part V, a look at Exxon's push for synfuels; Part VI, an accounting of Exxon's emphasis on climate science uncertainty.

After the field campaign, Fast will perform computer simulations to help evaluate all of the field campaign data and quantify the uncertainties associated with using coarse grid global climate models to study megacity emissions and to determine the radiative impact of the Mexico City particulates on the local and regional climate.

Understanding how well climate models represent these processes will help reduce uncertainties in the model projections of the effects of global warming on the world's water cycle.

One of the largest uncertainties in global climate models (GCMs) is the response of clouds in a warming world.

«Current global climate models have failed to predict the rapid Arctic warming, and clouds are one of the largest uncertainties.

Stakeholders of Montana agriculture may find the cumulative uncertainty of inexact crop models built on inexact climate models frustrating, but it is as important to understand the sources of uncertainty as it is to realize that temperatures are rising.

Uncertainty quantification is also a focus for the U.S. Department of Energy (DOE) as eight national laboratories and six partner institutions collaborate to develop and apply the next generation of climate and Earth - system models to the challenges and demands of climate - change research.

Much of the uncertainty in projections of global climate change is due to the complexity of clouds, aerosols, and cloud - aerosol interactions, and the difficulty of incorporating this information into climate models.

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

Therefore, I wouldn't attach much credence, if any, to a modelling study that didn't explore the range of possibilities arising from such uncertainty in parameter values, and particularly in the value of something as crucial as the climate sensitivity parameter, as in this example.

There is still uncertainty about many aspects of the dynamics of climate change, and this will only be addressed by investment in climate models and the top - of - the - range supercomputers needed to run them.

Murphy, J.M., et al., 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations.

They got 10 pages in Science, which is a lot, but in it they cover radiation balance, 1D and 3D modelling, climate sensitivity, the main feedbacks (water vapour, lapse rate, clouds, ice - and vegetation albedo); solar and volcanic forcing; the uncertainties of aerosol forcings; and ocean heat uptake.

Due to the complexity of physical processes, climate models have uncertainties in global temperature prediction.

Given that clouds are known to be the primary source of uncertainty in climate sensitivity, how much confidence can you place in a study based on a model that doesn't even attempt to simulate clouds?

However, in view of the fact that cloud feedbacks are the dominant contribution to uncertainty in climate sensitivity, the fact that the energy balance model used by Schmittner et al can not compute changes in cloud radiative forcing is particularly serious.

«The inertia in the climate system makes it possible to predict, within model uncertainty, changes in flood hazards up to the year 2040, independent of the specific carbon emission pathway that is chosen by society within the next 25 years.»

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.

Reduction of these uncertainties will be crucial for evaluating and better constraining climate models.

It is not all that earthshaking that the numbers in Schmittner et al come in a little low: the 2.3 ºC is well within previously accepted uncertainty, and three of the IPCC AR4 models used for future projections have a climate sensitivity of 2.3 ºC or lower, so that the range of IPCC projections already encompasses this possibility.

M2009 use a simplified carbon cycle and climate model to make a large ensemble of simulations in which principal uncertainties in the carbon cycle, radiative forcings, and climate response are allowed to vary, thus yielding a probability distribution for global warming as a function of time throughout the 21st century.

«We use a massive ensemble of the Bern2.5 D climate model of intermediate complexity, driven by bottom - up estimates of historic radiative forcing F, and constrained by a set of observations of the surface warming T since 1850 and heat uptake Q since the 1950s... Between 1850 and 2010, the climate system accumulated a total net forcing energy of 140 x 1022 J with a 5 - 95 % uncertainty range of 95 - 197 x 1022 J, corresponding to an average net radiative forcing of roughly 0.54 (0.36 - 0.76) Wm - 2.»