Sentences with phrase «climate system behavior»

Predicting climate change is one of the most complex problems facing scientists who have been striving to understand climate system behavior and improve Earth system models for years.

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.

So this change in upper atmospheric behavior can be considered part of the «fingerprint» of the expected global warming signal in the climate system.»

«The past behavior and dynamics of the Antarctic ice sheets are among the most important open questions in the scientific understanding of how the polar regions help to regulate global climate,» said Jennifer Burns, director of the NSF Antarctic Integrated Science System Program.

«We see processes that operate in the climate system that either don't operate in glacial times we've seen in the last 2 million years, or they operate very differently,» she said, citing the behavior of ice sheets as an example.

An overall objective, aside from the desire to assess alternative means to combine human social system models with climate models, is to provide a rational basis to determine whether human risk perception and associated changes in behaviors can significantly affect climate projections.

The possibility of things like El Nino is why I left myself an out with the rather cryptic phrase to the effect that SOMETIMES the reason for the climate change can be set off from the collective behavior of the system and considered as an external forcing.

The model accounts for the dynamic feedbacks that occur naturally in the Earth's climate system — temperature projections determine the likelihood of extreme weather events, which in turn influence human behavior.

We need effective and fair school discipline, with schools creating and nurturing a climate and culture that promote positive behavior, and we need targeted, consistent collaboration between the education, justice, and social service systems, as well as the communities they serve.

Posted in: School Climate & Culture, Success With Kickboard, Behavior RTI, Consistency, Customer Success, Early Warning System, MTSS, PBIS, Positive School Culture, Tier I

Schools are asked to conduct universal screening using the SRSS coming off of the MIBLSI Intervention Systems training or Promoting Positive School Climate (PPSC) Tier 2 Behavior Supports training.

An Event For: Schools are asked to conduct universal screening using the SRSS coming off of the MIBLSI Intervention Systems training or Promoting Positive School Climate (PPSC) Tier 2 Behavior Supports training.

See why admins love Classcraft's unique approach to Positive Behavior Intervention Systems, social & emotional learning, and school climate.

Posted in: School Climate & Culture, Behavior Progress Monitoring, Behavior RTI, Early Warning System, Using Culture Data

Randy Sprick presents a two - day session, «Create a Multi-Tiered System of Behavior Support: Improve School Climate and Discipline for All.»

Culture and Climate Coaches can help implement systems that promote positive behavior.

This central focus has helped staff build systems and sustainable practices while improving school climate, all of which has improved attendance, academic achievement and student behavior.

Q. Out of this appropriation, $ 598,000 the first year and $ 598,000 the second year from the general fund is provided to expand the number of schools implementing a system of positive behavioral interventions and supports with the goal of improving school climate and reducing disruptive behavior in the classroom.

Schools with a climate committee, PBIS (Positive Behavior Intervention Systems) team or other related committee are a good place to start.

AIR staff work with school leadership to implement positive behavior support systems to create a respectful climate and improve the relationships between students and teachers and students and their peers.

When using your Vehicle, UVO eServices automatically (or passively), including, through the use of telematics, collects and stores information about your Vehicle, such as: (i) information about your Vehicle's operation, performance and condition, including such things as diagnostic trouble codes, oil life remaining, tire pressure, fuel economy and odometer readings, battery use management information, battery charging history, battery deterioration information, electrical system functions; (ii) driver behavior information, which is information about how a person drives a Vehicle, such as the actual or approximate speed of your Vehicle, seat belt use, information about braking habits and information about collisions involving your Vehicle and which air bags have deployed; (iii) information about your use of the Vehicle and its features, such as whether you have paired a mobile Device with your Vehicle); (iv) the precise geographic location of your Vehicle; (v) data about remote services we make available such as remote lock / unlock, start / stop charge, parking location, climate control, charge schedules, and Vehicle status check; (vi) when there is a request for service made; and (vii) information about the Vehicle itself (such as the Vehicle identification number (VIN), make, model, model year, selling dealer, servicing dealer, date of purchase or lease and service history)(collectively, «Vehicle Information»).

Eco Pro adjusts the behavior of the climate control system and remaps the throttle response to squeeze a few more miles out of the battery.

Observations show chaotic behavior of the climate system on all time scales, including sudden regime transitions, as we documented in Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox, H. Held, N. de Noblet - Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas, 2004: Nonlinearities, feedbacks and critical thresholds within the Earth's climate system.

Again, as the temperature anomaly associated with this jump dissipates, we hypothesize that the climate system will return to its signal as defined by its pre-1998 behavior in roughly 2020 and resume warming.

I think some further explanation of the statement, «Observations show chaotic behavior of the climate system on all time scales, including sudden regime transitions» is warranted.

However, this apparent impulsive behavior explicitly highlights the fact that humanity is poking a complex, nonlinear system with GHG forcing — and that there are no guarantees to how the climate may respond.

The class of hypothetical climate shifts to which I allude involve fundamental changes in the frequency and amplitude of known oscillatory behavio (u) r of the atmosphere - ocean - cryosphere system and the potential emergence of new oscillatory behaviors.

If they are taken as reliable (i.e. persistent) features of the climate system then I can see how there might exist «a certain probability distribution of behaviors» (as conjectured by # 160).

Again, there is no solid evidence for any weirdness, special sensitivity of climate to the sun, or large solar variations, but instead a generally good match to expected behavior of the climate system.

Often, the proximate cause of the climate change is some parameter of the climate system that can be set off from the general collective behavior of the system and considered as a «given,» even if it is not external to the system strictly speaking.

As applied to the climate system, consider it a plausibility argument: the more rapidly and extensively the system is disturbed, the more we would expect that unexpected behaviors will emerge, and the further from expectations they will be.

Since you elected not to address the issue of models capability to represent critically - important glaciation - deglaciation episodes, now I have developed an impression that certain climate scentists have to learn a lot more about possibilities that are hidden in behavior of a large and complex dynamical system.

The fuss is mainly about normal behavior of the climate system.

Mathematical models allow scientists to simulate the behavior of complex systems (like climate) and explore how these systems respond to natural and human factors.

Additionally, they are discovering these various solar effects on climate here on earth, as well as on other planets in our solar system, and how they effect behavior, «regionally» and planet wide, in similar ways — for example there has been a long - term trend (+30 years) of increasing surface phenomena on Mars, including surface temps and albedo and the humongous sand storms, etc that occur.

Objection: Climate is an inherently chaotic system, and as such its behavior can not be predicted.

At UCI I work with Mike and Hossein in using a cutting - edge global climate modeling framework (UPCAM) to understand the behavior and role of low - level clouds in a perturbed climate system.

On the basis of the past behavior of the climate system — the current regime will persist for 20 to 30 years.

While I applaud the very clear explanation of some of the complexities of the climate system, it doesn't demonstrate that there are not simplifying equations for the aggregate behavior of the system.

These academic exercises give clues what kind of instrumentation needs to be deployed in order to correctly and meaningfully characterize and understand global behavior of the climate system.

Predicting climate behavior by fabricating correlations with little understanding of the system as a whole does require certain parts of the brain to go dead.

The actual chaotic behavior of Earth's climate system is much closer to the behavior of a Rayleigh - Beynard cell, which can be seen in the rather highly structured Hadley, Ferrel, and Polar cells that usually govern the large - scale circulation in the troposphere.

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.

So let me state my opinion that Roger is absolutely correct when he writes «Observations of the real world behavior of the climate system provide the best tool for understanding how the climate system works.»

Observations of the real world behavior of the climate system provide the best tool for understanding how the climate system works.

Somewhat belatedly, I offer thoughts on issues raised here from the perspective of someone with a relatively superficial knowledge of climate model construction but a reasonable understanding of how the climate system behaves and the physics underlying that behavior.

The system is deterministically chaotic — climate is a product of emergent behavior in the system.

Of course the Earth climate system is still accumulating energy, and more importantly, we seem to be at an important nexus point where that accumulation is showing nonlinear accelerating behavior.

In this paper, af - ter a brief tutorial on the basics of climate nonlinearity, we provide a number of illustrative examples and highlight key mechanisms that give rise to nonlinear behavior, address scale and methodological issues, suggest a robust alternative to prediction that is based on using integrated assessments within the framework of vulnerability studies and, lastly, recommend a number of research priorities and the establishment of education programs in Earth Systems Science.

In a system such as the climate, we can never include enough variables to describe the actual system on all relevant length scales (e.g. the butterfly effect — MICROSCOPIC perturbations grow exponentially in time to drive the system to completely different states over macroscopic time) so the best that we can often do is model it as a complex nonlinear set of ordinary differential equations with stochastic noise terms — a generalized Langevin equation or generalized Master equation, as it were — and average behaviors over what one hopes is a spanning set of butterfly - wing perturbations to assess whether or not the resulting system trajectories fill the available phase space uniformly or perhaps are restricted or constrained in some way.