Sentences with phrase «uncertainties in climate projections»

My understanding is that the major uncertainties in climate projections on time scales of more than a few decades are unlikely to be resolved in the near future.

By the statistical evaluation of the different climate developments simulated, the uncertainties in climate projections can be better estimated and reduced, for example, for rainfall trends.

The uncertainties in climate projections originate in the representation of processes such as clouds and turbulence that are not resolvable on the computational grid of global models.

This diversity of emergent constraints highlights the commitment of the climate community to narrowing uncertainties in climate projections.

The natural climate variability induced by the low - frequency variability of the ocean circulation is but one of the causes of uncertainties in climate projections.

Seminars addressed scientific issues affecting uncertainties in climate projections.

The overall uncertainty in climate projections, however, remains relatively unchanged.

Given the uncertainty in climate projections, there can be surprises that may cause more dramatic disruptions than anticipated from the most probable model projections.

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.»

Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear obser - vational constraint that favors one version of the authors» model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections.»

Also, a very large source of uncertainty in climate projections is the unknown future development of emissions, land use and solar activity.

A key problem for reducing the uncertainty in climate projections is historical records of change are often too short to test the skill of climate models, raising concerns over our ability to successfully plan for the future.

«We used the MIT Integrated Global System Model (IGSM), which quantifies various sources of uncertainty in climate projections,» Gao told environmentalresearchweb.

-LRB-... besides to point out that «the uncertainty in climate projections associated with the physical climate model is smaller than the uncertainty associated with the models of emission scenarios that are used to project carbon dioxide emissions»)

They include those items ignored, glossed over, or deliberately misrepresented; projections are consistently wrong; the science has not advanced, a 2007 paper in Science by Roe and Baker concludes; «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»; and claims of impending disasters that simply do not make scientific sense.

Not exact matches

We've narrowed the uncertainty in surface warming projections by generating thousands of climate simulations that each closely match observational records for nine key climate metrics, including warming and ocean heat content.»

Let me amplify on # 37: here's the other RealClimate link (that James» blog point to) I should have put in my comment about climate sensitivity and how uncertainty in aerosols relates to future climate projection: http://www.realclimate.org/index.php?p=115.

Throughout its climate modeling phase, Exxon researchers, like outside scientists, grappled with the uncertainties inherent in climate model projections.

An important goal of climate research is to reduce and characterize uncertainty in the climate change projections so that they can be more useful for assessing climate change impacts and developing adaptation and mitigation strategies.

Their survey uncovered significant uncertainties in current climate projections of the intensity and vertical structure of the low - level convergence of moisture to and upper - level divergence of heat away from the tropics.

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.

In the end the improvement of climate projections depends upon reducing both sources of uncertainty and arriving at joint probability profiles.

These current uncertainties are also reflected in future climate projections by these models.

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.

As research leaders in developing and using models to provide scientific insights into weather and climate change, Qian and others are striving to understand uncertainty in systems and modeling to improve projections and help prepare vulnerable regions for potential climate change impact.

Dr. Yun Qian, atmospheric and climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste,climate modeling scientist at Pacific Northwest National Laboratory, was invited to organize and direct an international workshop on «Uncertainty Quantification in Climate Modeling and Projection» in Trieste,Climate Modeling and Projection» in Trieste, Italy.

The big takeaway from this study: While there is uncertainty in projections for changes in the climate indices reviewed here (especially El Niño and La Niña), this study serves to alert us to the fact that the climate impacts that our local coastal communities face are based in large part on changes that occur on both a large, global scale and over the long, decadal term.

Scientists are using airborne observations of atmospheric trace gases, aerosols, and cloud properties from the North Slopes of Alaska to improve their understanding of global climate, with the goal of reducing the uncertainty in global and regional climate simulations and projections.

Prior to joining ECI, she completed her Ph.D. at Oregon State University, where she worked on the weather@home project over western US region, looking at drivers of extreme drought events in the US, future regional climate change projections over the western US, as well as investigating uncertainties due to internal variability and physical parameter perturbations.

If we can get climate models to more credibly simulate current cloud patterns and observed cloud changes, this might reduce the uncertainty in future projections

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.

The study of past warm climates may not narrow uncertainty in future climate projections in coming centuries because fast climate sensitivity may itself be state - dependent» http://www.pnas.org/content/110/35/14162.full

In projecting climate variables such as temperature, precipitation, and humidity, there is generally a tradeoff between (a) the ability to produce high - resolution projections needed to inform local decisions and model local responses, and (b) the ability to sample uncertainty.

(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.

In the end the improvement of climate projections depends upon reducing both sources of uncertainty and arriving at joint probability profiles.

Well, it is a very ambitions and painstaking project which has managed to bring together all the aforementioned modeling groups which run specified model experiments with very similar forcings and then performed coordinated diagnostic analyses to evaluate these model simulations and determine the uncertainty in the future climate projections in their models.

The study of past warm climates may not narrow uncertainty in future climate projections in coming centuries because fast climate sensitivity may itself be state - dependent» http://www.pnas.org/content/110/35/14162.full

In fact it is the opposite — Hansen is actually claiming that the uncertainty in models (for instance, in the climate sensitivity) is now less than the uncertainty in the emissions scenarios (i.e. it is the uncertainty in the forcings, that drives the uncertainty in the projectionsIn fact it is the opposite — Hansen is actually claiming that the uncertainty in models (for instance, in the climate sensitivity) is now less than the uncertainty in the emissions scenarios (i.e. it is the uncertainty in the forcings, that drives the uncertainty in the projectionsin models (for instance, in the climate sensitivity) is now less than the uncertainty in the emissions scenarios (i.e. it is the uncertainty in the forcings, that drives the uncertainty in the projectionsin the climate sensitivity) is now less than the uncertainty in the emissions scenarios (i.e. it is the uncertainty in the forcings, that drives the uncertainty in the projectionsin the emissions scenarios (i.e. it is the uncertainty in the forcings, that drives the uncertainty in the projectionsin the forcings, that drives the uncertainty in the projectionsin the projections).

Uncertainties from differences in the climate projections are significantly smaller.

By putting the local climate into the context of the larger picture, analyzing the uncertainties, and evaluating the methods in terms of past changes, I think that local climate projections can provide useful information.

When I invoked 1944 and 1975 as being potentially (at least demonstrated to be possible) climate turning points which could be repeated today, I was trying to address the degree of uncertainty that could exist in our projections.

PLUMES (Pathways for linking uncertainties in climate model projections and effects)(2014 - 2018).

Projections of future climate changes in different emissions - scenarios are accompanied by error - bars representing the range of uncertainty.

Friedlingstein, P., Meinshausen, M., Arora, V.K., Jones, C.D., Anav, A., Liddicoat, S.K., and Knutti, R., 2014: Uncertainties in cmip5 climate projections due to carbon cycle feedbacks.

I note that any reasonable climate change class should highlight appropriate sources of uncertainties (e.g., in future projections of hurricane changes, cloud feedbacks, etc) and I prefer when they open up discussing to students, but I find that Judith carries this further into making up uncertainties based on her gut feeling, interpreting them in ways that make little sense, and most importantly, failing to recognize the errors in flawed sets of reasoning on fundamental topics.

In fact, uncertainties in how clouds change with warming are the primary source of the large spread in climate projections for the next century (Bony and Dufresne 2005; Vial et al. 2013; Brient and Schneider 2016In fact, uncertainties in how clouds change with warming are the primary source of the large spread in climate projections for the next century (Bony and Dufresne 2005; Vial et al. 2013; Brient and Schneider 2016in how clouds change with warming are the primary source of the large spread in climate projections for the next century (Bony and Dufresne 2005; Vial et al. 2013; Brient and Schneider 2016in climate projections for the next century (Bony and Dufresne 2005; Vial et al. 2013; Brient and Schneider 2016).

In a previous post, I described the concept of emergent constraints, which allow us to narrow uncertainties in climate change projections through empirical relationships that relate a model's climate response to observable metricIn a previous post, I described the concept of emergent constraints, which allow us to narrow uncertainties in climate change projections through empirical relationships that relate a model's climate response to observable metricin climate change projections through empirical relationships that relate a model's climate response to observable metrics.

To reduce uncertainties in climate - change projections, it is essential to prioritize the improvement of the most important uncertain physical processes in climate models.

Likewise the evaluation procedure does not incorporate uncertainty in future climate projections or species dispersal.

The sensitivity of the climate system to an imposed radiative imbalance remains the largest source of uncertainty in projections of future anthropogenic climate change.