Sentences with phrase «regional modelling predictions»

If anyone can point me toward on - line data that documents near term regional modelling predictions and documents actual results after the fact I would appreciate it.

Not exact matches

Better predictions would require improved climate - measurement tools, more sophisticated climate models that work on regional scales, and a better organized system to integrate all the data, the report concludes.

«This model is a major step forward in our effort to improve the prediction of regional climate change, particularly involving water resources.»

Scientists are involved in the evaluation of global - scale climate models, regional studies of the coupled atmosphere / ocean / ice systems, regional severe weather detection and prediction, measuring the local and global impact of the aerosols and pollutants, detecting lightning from space and the general development of remotely - sensed data bases.

By working on the still - not - fully - cracked nut of estimating changes in hurricane frequency and intensity in a warming climate, Gabe and his colleagues ended up with a modeling system with seasonal skill in regional hurricane prediction.

In collaboration with the National Center for Atmospheric Research, Leung has been developing and applying advanced regional climate models that will help improve the predictions of climate change and its impacts.

Prior to this, he did his Ph.D. thesis at the Barcelona Supercomputing Center, developing a new global and regional model for the prediction of the mineral dust.

However, to comminicate to policymakers that the models provide skillful multi-decadal regional and global predictions grossly oversells their capability.

The aspect of climate modeling that I've always had the most trouble believing are regional climate predictions.

I'm a skeptic when it comes to all the dire predictions coming out of climate models which have been shown to be useless for regional predictions.

These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry bias in climate models downstream of the Sierras de Córdoba in a key agricultural region.

The June, July, and August SIO reports received a total of 106 contributions for pan-Arctic extent predictions (based on multiple methods: statistical, dynamical models, estimates based on trends, and subjective information) along with contributions for Alaska regional extent predictions, descriptive regional contributions, and pan-Antarctic extent predictions — a new SIO feature for 2017.

Can the models provide skillful predictions of changes in regional climate statistics on multi-decadal time scales?»

A top - down climate effect that shows long - term drift (and may also be out of phase with the bottom - up solar forcing) would change the spatial response patterns and would mean that climate - chemistry models that have sufficient resolution in the stratosphere would become very important for making accurate regional / seasonal climate predictions.

A common theme in the debate is that since models can not make accurate predictions on regional and decadal scales, then they can not make accurate predictions on global or century scales.

The meeting will mainly cover the following themes, but can include other topics related to understanding and modelling the atmosphere: ● Surface drag and momentum transport: orographic drag, convective momentum transport ● Processes relevant for polar prediction: stable boundary layers, mixed - phase clouds ● Shallow and deep convection: stochasticity, scale - awareness, organization, grey zone issues ● Clouds and circulation feedbacks: boundary - layer clouds, CFMIP, cirrus ● Microphysics and aerosol - cloud interactions: microphysical observations, parameterization, process studies on aerosol - cloud interactions ● Radiation: circulation coupling; interaction between radiation and clouds ● Land - atmosphere interactions: Role of land processes (snow, soil moisture, soil temperature, and vegetation) in sub-seasonal to seasonal (S2S) prediction ● Physics - dynamics coupling: numerical methods, scale - separation and grey - zone, thermodynamic consistency ● Next generation model development: the challenge of exascale, dynamical core developments, regional refinement, super-parametrization ● High Impact and Extreme Weather: role of convective scale models; ensembles; relevant challenges for model development

Type 2 dynamic downscaling refers to regional weather (or climate) simulations in which the regional model's initial atmospheric conditions are forgotten (i.e., the predictions do not depend on the specific initial conditions), but results still depend on the lateral boundary conditions from a global numerical weather prediction where initial observed atmospheric conditions are not yet forgotten, or are from a global reanalysis.

The attempt to distinguish between the terms «projection» and «prediction», whether by the IPCC or others, has introduced an unnecessary confusion to the impacts and policy communities regarding the skill of regional and local multi-decadal climate model runs.

This skill must be assessed by predicting global, regional and local average climate, and any climate change that was observed over the last several decades (i.e. «hindcast model predictions»).

Multi-decadal predictions of climate probabilities, as well as all climate statistics based on the global and regional and global climate models are deterministic model exercises.

Roger states that one can not consider climate model predictions (his type 4) at the regional scale when their predictive skill in hindcast mode is not demonstrated.

The model must also provide accurate predictions of changes in climatic conditions (i.e. the climatic statistics) on the regional and local scale?

As they have matured, climate models are being increasingly used to provide decision - relevant information to end users and policy makers, whose needs are helping define the focus of model development in terms of increasing prediction skill on regional and decadal time scales.

The mechanics of the models produce regional scale results, but, until the multi-decadal regional predictions of changes in climate statistics can be shown to be skilful, the added spatial resolution provides an erroneous illusion of skill.

They provide very significant added regional and local prediction skill beyond what is available from the parent global model (e.g. see http://weather.rap.ucar.edu/model/).

Within the confines of our work with RASM and CESM, we will: (i) quantify the added value of using regional models for downscaling arctic simulations from global models, (ii) address the impacts of high resolution, improved process representations and coupling between model components on predictions at seasonal to decadal time scales, (iii) identify the most important processes essential for inclusion in future high resolution GC / ESMs, e.g. ACME, using CESM as a test bed, and (iv) better quantify the relationship between skill and uncertainty in the Arctic Region for high fidelity models.

The report includes comments on modeling outlooks and on regional predictions, a summary of current conditions, key statements from each Outlook, and links to view or download the full outlook contributions.

=== > Fifth, we know that the $ billion $ super computer climate models used by these same scientists are fatally flawed, thus absolutely worthless regarding future global and regional climate predictions...

Other uses for regional modeling might be considered mundane: more accurate prediction of sea breezes, cloud (ceiling), high and low temperatures, heat indices, and others.

An important function of regional modeling is to assess the impact of new data sets on weather predictions.

When we talk about regional modeling or regional numerical weather prediction we are really doing the same thing except that we are focusing on more and more detail for the region where you are located.

That we tend to see much more discussion about global warming is I think because of the limitations of the climate models when they go to more regional and seasonal predictions and refinements of max versus min temperature trends.

I guess I would be more interested in assessing the model's predictions / simulations versus the global average climatic conditions or on significant regional scales like continents, tropics, northern hemisphere, tropopause etc..

The regional model's predictions of surface temperature changes over the eastern United States were compared to parallel forecasts made by the same GCM.

This paper shows the models are not accurate enough for specific locations to be relied on for their global and regional predictions in my opinion.

How do we know that the models representing global or regional climate are sufficiently reliable for predictions of future conditions?

Prior to this, he did his Ph.D. thesis at the Barcelona Supercomputing Center, developing a new global and regional model for the prediction of the mineral dust.

Climate models, unfortunately, are still unable to provide skillful predictions of changes in regional climate statistics on multi-decadal time scales at the detail desired by the impacts communities.

Not only have its models been conclusively wrong about CO2 - caused global warming over the last 15 years, but the climate models» regional predictions are often diametrically opposite of reality.

The models do nt attempt to predict weather, are poor at regional prediction and have no skill at even decadal level prediction.

At timescales beyond a season, available ensembles of climate models do not provide the basis for probabilistic predictions of regional climate change.

His current research includes global ocean modeling and data assimilation efforts as part of Estimating the Circulation & Climate of the Ocean (ECCO) consortium, as well as using ensemble methods for regional ocean analysis and prediction.

The two - day FAMOS workshop will include sessions on 2017 sea ice highlights and sea ice / ocean predictions, reports of working groups conducting collaborative projects, large - scale arctic climate modeling (ice - ocean, regional coupled, global coupled), small (eddies) and very small (mixing) processes and their representation and / or parameterization in models, and new hypotheses, data sets, intriguing findings, proposals for new experiments and plans for 2018 FAMOS special volume of publications.

Perhaps I'm missing something, but the main point of the paper seems to be that due to the inaccuracy of the models demonstrated for any particular station and time period, they will also have questionable validity for regional or global prediction at longer time scales.

In a series of Atlantic basin - specific dynamical downscaling studies (Bender et al. 2010; Knutson et al. 2013), we attempted to address both of these limitations by letting the Atlantic basin regional model of Knutson et al. (2008) provide the overall storm frequency information, and then downscaling each individual storm from the regional model study into the GFDL hurricane prediction system.

Our research group studies the meteorology and climate of both polar regions using regional climate models and numerical weather prediction models.

GFDL scientists focus on model - building relevant for society, such as hurricane research, weather and ocean prediction, seasonal forecasting, and understanding global and regional climate change.

The models have become accurate enough that they've been used for regional predictions of atmospheric temperatures, and those predictions have all been shown to need ACO2 in order for the models to match reality (See Figure 9.12, IPCC AR4 WG1 Chapter 9, page 695).

My favorite quote from that paper is: «Because ENSO is the dominant mode of climate variability at interannual time scales, the lack of consistency in the model predictions of the response of ENSO to global warming currently limits our confidence in using these predictions to address adaptive societal concerns, such as regional impacts or extremes (Joseph and Nigam 2006; Power et al. 2006).»