Sentences with phrase «of uncertainty in climate models»

From this experiment we hoped to achieve a better understanding of the range of uncertainty in climate models due to the parameters in the sulphur cycle.

But the level of uncertainty in climate models should not be exaggerated.

They may also help researchers understand the effects of cloud cover, which also creates diffuse light and represents the biggest source of uncertainty in climate models, he says.

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?»

Yet, model projections of future global warming vary, because of differing estimates of population growth, economic activity, greenhouse gas emission rates, changes in atmospheric particulate concentrations and their effects, and also because of uncertainties in climate models.

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.

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.

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).

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.

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

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.

«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.

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

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.

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.

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.

By scaling spatio - temporal patterns of response up or down, this technique takes account of gross model errors in climate sensitivity and net aerosol forcing but does not fully account for modelling uncertainty in the patterns of temperature response to uncertain forcings.

But, as far as I can see, the «attacks» by vested interests are not even able to make legitimate points (e.g. uncertainty about the effects of clouds or aerosols in climate models).

(in general, whether for future projections or historical reconstructions or estimates of climate sensitivity, I tend to be sympathetic to arguments of more rather than less uncertainty because I feel like in general, models and statistical approaches are not exhaustive and it is «plausible» that additional factors could lead to either higher or lower estimates than seen with a single approach.

I believe that statisticians can contribute more to climate sciences in better description of the uncertainties, in addition to better calibration of statistical models.

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..

The main reason for this is the degree of uncertainties involved in regional climate modelling, as discussed in a previous post.

I am probably as aware of any reader here of modeling challenges in general, and can appreciate the work your groups have performed, but I can also appreciate the implications of the mismatch that prompted your post: there is fundamental uncertainty in the interaction of the complex mechanisms that drive climate change, including the human effect.

There are uncertainties in parts of the general circulation models used to forecast future climate, but thousands of scientists have made meticulous efforts to make sure that the processes are based on observations of basic physics, laboratory measurements, and sound theoretical calculations.

Modelling uncertainty currently is such that in some climate models, this amount of freshwater (without any other forcing) would shut down deep water formation, in some it wouldn't.

When faced with durable uncertainty on many fronts — in the modeling of the atmosphere, in data delineating past climate changes, and more — pushing ever harder to boost clarity may be scientifically important but is not likely to be very relevant outside a small circle of theorists.

The work of Schmittner et al. demonstrates that climates of the past can provide potentially powerful information to reduce uncertainty in future climate predictions and evaluate the likelihood of climate change that is larger than captured in present models.

Although there is still some disagreement in the preliminary results (eg the description of polar ice caps), a lot of things appear to be quite robust as the climate models for instance indicate consistent patterns of surface warming and rainfall trends: the models tend to agree on a stronger warming in the Arctic and stronger precipitation changes in the Topics (see crude examples for the SRES A1b scenarios given in Figures 1 & 2; Note, the degrees of freedom varies with latitude, so that the uncertainty of these estimates are greater near the poles).

I suspect that there will be considerably more uncertainty attached to this activity than there was to the attribution of climate change to anthropogenic activity — in part because the only guides we really have are the models and paleoclimate studies, both of which are subject to significant uncertainties.

Some attribution assessments that link events to dynamically driven changes in circulation have been criticized on the grounds that small signal - to - noise ratios, modeling deficiencies, and uncertainties in the effects of climate forcings on circulation render conclusions unreliable and prone to downplaying the role of anthropogenic change.

The agreement of this model with observations is particularly good and perhaps partly fortuitous, given that there is still uncertainty both in the climate sensitivity and in the amplitudes of the aerosol and solar forcings.