Sentences with phrase «largest uncertainties in climate models»

I mainly study responses of tropical low - clouds to perturbations since they induce the largest uncertainties in climate models and explain a significant part of the spread of climate sensitivity.

Not exact matches

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.

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?»

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).

Its behaviour looks like what happens in the real world, but Erickson stresses that «the uncertainties remain large», and that much more work needs to be done before such models can be used to predict future climate trends.

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.

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.

A new integrated climate model developed by Oak Ridge National Laboratory and other institutions is designed to reduce uncertainties in future climate predictions as it bridges Earth systems with energy and economic models and large - scale human impact data.

A new integrated computational climate model developed to reduce uncertainties in future climate predictions marks the first successful attempt to bridge Earth systems with energy and economic models and large - scale human impact data.

But while Lewis argues that the uncertainty in E is large and climate models do not give the value as accurately as we'd like, that does not justify ignoring that uncertainty entirely.

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

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

But it leaves a large uncertainty in the timing; more complex coupled ice - sheet and climate models are needed to model this more thoroughly in the future.

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.

The model results (which are based on driving various climate models with estimated solar, volcanic, and anthropogenic radiative forcing changes over this timeframe) are, by in large, remarkably consistent with the reconstructions, taking into account the statistical uncertainties.

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.

A new large uncertainty analysis that appeared this week in Nature shows that it is very difficult to get a climate sensitivity below 2 ºC in a climate model, no matter how one changes the parameters.

«This introduces a large uncertainty in the degree of warming predicted by climate change models.»

The largest source of uncertainty in today's climate models is clouds.

I am talking about a consensus of multiple lines of evidence (empirical evidence in addition to modeling, logic etc.) When there is a large degree of uncertainty, as there is in climate science, a consensus of evidence is most definitely very important.

And since CG2 I've just been more attuned to the large and deep uncertainties in all of climate science, especially the models.

Climate models project decreases of renewable water resources in some regions and increases in others, albeit with large uncertainty in many places.

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

This large spread in the predictions reflects the current diversity in the formulation of physics and initial conditions in the various models used, but also inherent uncertainty of the climate system.

However, even state - of - the - art climate models (GCMs) have systematic errors in simulation of different climate characteristics, which are often much larger than observations uncertainties (Covey et al. 2003).

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.

However, there remains uncertainty in the rate of sea ice loss, with the models that most accurately project historical sea ice trends currently suggesting nearly ice - free conditions sometime between 2021 and 2043 (median 2035).12 Uncertainty across all models stems from a combination of large differences in projections among different climate models, natural climate variability, and uncertainty about future rates of fossil fueluncertainty in the rate of sea ice loss, with the models that most accurately project historical sea ice trends currently suggesting nearly ice - free conditions sometime between 2021 and 2043 (median 2035).12 Uncertainty across all models stems from a combination of large differences in projections among different climate models, natural climate variability, and uncertainty about future rates of fossil fuelUncertainty across all models stems from a combination of large differences in projections among different climate models, natural climate variability, and uncertainty about future rates of fossil fueluncertainty about future rates of fossil fuel emissions.

But accepting the climate models as our currently best representation of the climate system, the observations unmistakably point to higher ECS being more likely, and a substantially higher ECS than previously thought as most likely — though the range of possible ECS obtained in this way is still wide, still indicating large uncertainties.

and «no data or computer code appears to be archived in relation to the paper» and «the sensitivity of Shindell's TCR estimate to the aerosol forcing bias adjustment is such that the true uncertainty of Shindell's TCR range must be huge — so large as to make his estimate worthless» and the seemingly arbitrary to cherry picked climate models used in Shindell's analysis.

The very high significance levels of model — observation discrepancies in LT and MT trends that were obtained in some studies (e.g., Douglass et al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from using the standard error of the model ensemble mean as a measure of uncertainty, instead of the ensemble standard deviation or some other appropriate measure for uncertainty arising from internal climate variability... Nevertheless, almost all model ensemble members show a warming trend in both LT and MT larger than observational estimates (McKitrick et al., 2010; Po - Chedley and Fu, 2012; Santer et al., 2013).

Decadal climate prediction is immature, and uncertainties in future forcings, model responses to forcings, or initialisation shocks could easily cause large errors in forecasts.

They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming2, 3.

It is well recognized that our inability to accurately simulate clouds in computer models is the largest uncertainty in climate change projections.

Although the most advanced theoretical climate models still leave uncertainty, particularly about the sign and magnitudes of the effects, on GHG feedbacks, of some low - and high - clouds, a consensus began to develop that threats of resulting increases in global temperature — and the very large risks associated with their possible consequences — deserved substantial increase in attention.

Other researchers uncovered large uncertainties in climate predictions made by the fifth phase of the Coupled Model Intercomparison Project (CMIP5), a widely used, multimodel tool for climate analysis.