Sentences with phrase «mathematical models of the climate»

Over the next decade a few scientists devised simple mathematical models of the climate, and turned up feedbacks that could make the system surprisingly variable.

Using mathematical models of the climate shifts he calculated the probability of the periodicity.

Johnny Von Neumann ruled out all possibility of mathematical modeling of climate back in the fifties (See Turing's Cathedral).

On the other side, Professor Andr e Berger and colleagues developed a mathematical model of the climate system, rated today as a «model of intermediate complexity» [6, 7] to solve the dynamics of the atmosphere and ice sheets on a spatial grid of 19 × 5 elements, with a reasonably extensive treatment of the shortwave and longwave radiative transfers in the atmosphere.

Not exact matches

Murali Haran, a professor in the department of statistics at Penn State University; Won Chang, an assistant professor in the department of mathematical sciences at the University of Cincinnati; Klaus Keller, a professor in the department of geosciences and director of sustainable climate risk management at Penn State University; Rob Nicholas, a research associate at Earth and Environmental Systems Institute at Penn State University; and David Pollard, a senior scientist at Earth and Environmental Systems Institute at Penn State University detail how parameters and initial values drive an ice sheet model, whose output describes the behavior of the ice sheet through time.

The researchers built a complex series of mathematical models to recreate the dynamic interaction between the main potential drivers of extinction (dingoes, climate and humans), the long - term response of herbivore prey, and the viability of the thylacine and devil populations.

The researchers then used a mathematical model that combined the conflict data with temperature and rainfall projections through 2050 to come up with predictions about the likelihood of climate - related violence in the future.

«Factors affecting extinction and origination of species are surprisingly different, with past climate change having the highest impact on extinction but not on originations,» notes researcher Daniele Silvestro from the GGBC who developed the mathematical model used in the study.

Climate models are mathematical representations of the interactions between the atmosphere, oceans, land surface, ice — and the sun.

The researchers then employed a number of scientific theories and a set of sophisticated calculations to arrive at a mathematical framework to diagnose how climate model resolution affected the simulation of the location and dynamics of the jet stream.

A: Climate models are mathematical representations of the interactions between the various aspects of the climate system including the atmosphere, oceans, land surface, ice, and tClimate models are mathematical representations of the interactions between the various aspects of the climate system including the atmosphere, oceans, land surface, ice, and tclimate system including the atmosphere, oceans, land surface, ice, and the Sun.

This is how large complex systems function from a mathematical point of view, systems far more complex than ordinary global climate models.

The two scientists, with colleagues from the UK, the U.S., the Netherlands and Czechoslovakia, report in Nature Climate Change that they used mathematical models to simulate the effect of temperature rise as a response to ever - greater global emissions of greenhouse gases into the atmosphere, from the combustion of fossil fuels.

A: Climate models are mathematical representations of the interactions between the various aspects of the climate system including the atmosphere, oceans, land surface, ice, and tClimate models are mathematical representations of the interactions between the various aspects of the climate system including the atmosphere, oceans, land surface, ice, and tclimate system including the atmosphere, oceans, land surface, ice, and the Sun.

During the next decade a few scientists worked up simple mathematical models of the planet's climate system and turned up feedbacks that could make the system surprisingly sensitive.

If «[t] he inconvenient truth remains,» according to Philip Stott, that «climate is the most complex, coupled, nonlinear, chaotic system known,» then like flipping a coin, It will not matter if we devise a mathematical model to combine the data of the last 100 flips with a dataset reflecting the 100 flips before that — even if you consider want to consider how many tails you got on the previous 1,000 flips — the odds for the next flip still will be 50 - 50.

The functions form an orthonormal basis on the sphere, so the mathematical properties of the representation are well understood (indeed, it seems to be used in the climate models).

Without doubt mathematical models are acknowledged to have great limitations in predicting behaviors of complex systems and for this reason if model outputs are to be used to support climate change policies all the limitations of models should be acknowledged and understood.

It is clear that mathematical or computer models of such complex systems as human beings, environmental chemistry, or world climate normally have a short shelf life.

Bart R. I'm so happy that finally you have realized that the climate so physically complex, that all mathematical and computer models of it exist only in Pretendland.

After all models are mathematical representations of the climate.

Climate models are mathematical representations of the interactions between the atmosphere, oceans, land surface, ice — and the sun.

Another strategy is to use a climate model — not a climate simulation like most computer models are, but a simple mathematical model — which includes the affect of ENSO.

He has made fundamental contributions to the mathematical and physical foundations of computer models for the dynamics of fluid flows, for weather prediction, and for climate simulation.

There are mathematical fatal flaws in all the models that can not be overcome even if supercomputers improve by an order of magnitude, and if Rob Ellisons nonlinear dynamic chaos concerns can be overcome by enough ensemble runs to discern their main climate strange attractors.

However, it is these careful, meticulous, lengthy mathematical analyses — and not the half - baked modeling used by the IPCC — that are more likely to produce a reliable interval of climate sensitivity.

While mathematical models help researchers understand certain aspects of the climate, he says, models are useless for predicting what's going to happen more than five days from now.

As soon as a global climate model readjusts a vertical column to unphysically alter the large scale solution in order to maintain hydrostatic balance (overturning due to unrealistic heating parameterizations necessitate this adjustment), there is no mathematical theory that can justify the nature of the ensuing numerical solution.

The SAP 3.1 report describes complex mathematical models used to simulate the Earth's climate on some of the most powerful supercomputers, and assesses their ability to reproduce observed climate features, and their sensitivity to changes in conditions such as atmospheric concentrations of carbon dioxide.

The mathematical model for this is probably a space of local attractors with complex poincare cycles moving the climate around them, and with events or just plain time evolution causing comparatively sudden «jumps» between attractors.

Several models are created (in fact not a few of the dynamical El Nino models have GHG influences calculated in), each with its own set of «how climate works» mathematical scenarios, which are then compared to the statistical models.

Climate models are, at heart, giant bundles of equations — mathematical representations of everything we've learned about the climate Climate models are, at heart, giant bundles of equations — mathematical representations of everything we've learned about the climate climate system.

These models — which are comprised of mathematical equations based upon fundamental principles of physics and chemistry — can be used to conduct «controlled experiments» involving the Earth's climate system.

may give cause for some to question the wider role of climate change and not solely global warming, that are induced by anthropogenic emissions, changes in land use, water quality etc for which there is direct empirical data in the form of images, and not in mathematical treatments of theory and simulated models.

Scientists at GFDL develop and use mathematical models and computer simulations to improve our understanding and prediction of the behavior of the atmosphere, the oceans, and climate.

In the 1960s, atmospheric scientists developed the first mathematical models to understand the dynamics of the Earth's climate, starting with atmospheric models coupled to simple surface models (e.g., [171]-RRB-.

«The use of mathematical computer models of the atmosphere is indispensable in achieving a satisfactory understanding...» Matthews et al. (1971), p. 49; a followup study the next year, gathering together the world's leading climate experts, likewise endorsed research with GCMs.

Ome would expect that our mathematical models would by now be able to faithfully reproduce current average global temperatures, but this is not so — the IPCC models all exaggerate their predictions, also indicating a lack of understanding and validation of climate models.