Sentences with phrase «model process coupling»

Each model has many divergent solutions within the limits of feasible initial values and structural instability induced by the changes in depth of model process coupling.

Not exact matches

It turns out, however, that the coupling of models of ice dynamics with thermo - mechanical models of the solid earth allows a more accurate view of the processes that are melting the Greenland ice.

Peng says they chose CLM as the hosting framework to implement the new model because it is more process - based and can be coupled with climate models.

Under - ocean wave process modeling wasn't very good a couple of decades ago, he said, but understanding and supercomputer technology has greatly improved.

The new ACME land model includes a fully - coupled reactive transport scheme for these biogeochemical processes.

To date, Singer and Michaelides have used it to identify real climate change over a broad region, but they are in the process of coupling STORM to a runoff model to explore scenarios of climate change and how they might really affect the magnitude and the frequency of runoff.

Controlling the parameters influencing how such quantum heat engine models work could dramatically increase our power to manipulate the quantum states of the coupled atom - cavity, and accelerate our ability to process quantum information.

High - speed images of a common laser - based metal 3D printing process, coupled with newly updated computer models, have revealed the mechanisms behind material redistribution, a phenomenon that leads to defects in printed metal parts, Lawrence Livermore National Laboratory (LLNL) researchers reported...

development of two - way coupling between WRF and CCSM to represent the upscaled effects of climate hot spots such as the Maritime Continent, the subtropical eastern boundary regime, and the monsoon regions where global climate models fail to simulate the complex processes due to feedback and scale interactions.

Our expertise in ink optimization and printing process modeling coupled to microstructural optimization tools will further the development of high performance, lower cost fuel cells.

Abstract: Surface ocean wind datasets are required to be of high spatial and temporal resolution and high precision to accurately force or be assimilated into coupled atmosphere - ocean numerical models and understand ocean - atmospheric processes.

Aug. 1, 2017 - High - speed images of a common laser - based metal 3D printing process, coupled with newly updated computer models, have revealed the mechanisms behind material redistribution, a phenomenon that leads to defects in printed metal parts, Lawrence Livermore National Laboratory (LLNL) researchers reported...

Improving the Understanding and Model Representation of Processes That Couple Shallow Clouds, Aerosols, and Land - Ecosystems — Thursday, December 15, 10:20 to 10:35 a.m., Moscone West 3010

In particular, an experimental study on the permeability of prebiotic vesicle membranes composed of binary lipid mixtures allows us to construct a semi-empirical model where protocells are able to reproduce and undergo an evolutionary process based on their coupling with an internal chemistry that supports lipid synthesis.

The laboratory is also using chemical crosslinking - based structural mass spectrometry techniques coupled with molecular modeling to determine structures of large protein complexes and characterize protein conformational changes involved with physiological processes.

Our process of regular and frequent peer observations of instructional practice is coupled with ongoing coaching, modeling of proven techniques, and support in the classroom by AIR's turnaround coordinator.

Three different iterations of the sports car have been produced so far, with a couple of special - edition models built in the process.

Several major automakers are in the process of bringing all - new battery - electric models to market, but a couple of surprising segments that were barely blips on the radar are making a comeback that few had predicted a mere three years ago: two - door and four - door coupes.

The weakening of the Walker circulation arises in these models from processes that are fundamentally different from those of El Nià ± o — and is present in both mixed - layer and full - ocean coupled models, so is not dependent on the models» ability to represent Kelvin waves (by the way, most of the IPCC - AR4 models have sufficient oceanic resolution to represent Kelvin waves and the physics behind them is quite simple — so of all the model deficiencies to focus on this one seems a little odd).

Just because the current consensus of coupled GCM model studies and other process studies strongly suggest an anthropogenic cause for the current (1989 - centered) climate trend, can not and should not bully the objective science of defining observed climate trends, into hypothesizing and projecting the 1989 - centered climate forward in time even one year.

Syllabus: Lecture 1: Introduction to Global Atmospheric Modelling Lecture 2: Types of Atmospheric and Climate Models Lecture 3: Energy Balance Models Lecture 4: 1D Radiative - Convective Models Lecture 5: General Circulation Models (GCMs) Lecture 6: Atmospheric Radiation Budget Lecture 7: Dynamics of the Atmosphere Lecture 8: Parametrizations of Subgrid - Scale Physical Processes Lecture 9: Chemistry of the Atmosphere Lecture 10: Basic Methods of Solving Model Equations Lecture 11: Coupled Chemistry - Climate Models (CCMs) Lecture 12: Applications of CCMs: Recent developments of atmospheric dynamics and chemistry Lecture 13: Applications of CCMs: Future Polar Ozone Lecture 14: Applications of CCMs: Impact of Transport Emissions Lecture 15: Towards an Earth System Model

The ensemble and seasonal forecast systems use a coupled atmosphere - ocean model, which includes a simulation of the general circulation of the ocean and the associated coupled feedback processes that exist.

[Our study] reinforces the need for climate models to include fully coupled stratospheric dynamical - radiative - chemical processes if they are to more accurately simulate and predict future climate variations.»

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 deProcesses 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 deprocesses (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

Chami, M., B. Lafrance, B. Fougnie, J. Chowdhary, T. Harmel, and F. Waquet, 2015: OSOAA: A vector radiative transfer model of coupled atmosphere - ocean system for a rough sea surface application to the estimates of the directional variations of the water leaving reflectance to better process multi-angular satellite sensors data over the ocean.

As the researchers point out, the findings reinforce the need for climate models to include fully coupled stratospheric dynamical - radiative - chemical processes.

A true «prediction» can't be made because the result will depend on the future volcanic eruptions and other influences on albedo, but you can run the model for each of a couple dozen stochastic processes for the future volcanic activity.

Experience with solution algorithms, data assimilation methods and tools, coupling of components and processes, nonlinear and linear solvers, limiters, and / or other numerical issues common with complex codes within earth system models of varying complexity

Better characterization of the physical processes (including feedbacks) in the present coupled - global land surface climate models will certainly prove beneficial in stipulating future - projection scenarios and outcome.

Models incorporating stratospheric layers — despite differing greatly in their formulation of fundamental processes such as atmosphere - ocean coupling, clouds or gravity wave drag — show consistent responses in the troposphere.

The economics of nuclear power in a mass - production model like what HPG has proposed are radically better — France and Japan have relatively inexpensive and reliable nuclear reactors precisely becuase they developed a couple of standardized designs and then deployed them widely, working out the bugs in the process.

Due to computational constraints, the equilibrium climate sensitivity in a climate model is usually estimated by running an atmospheric general circulation model coupled to a mixed - layer ocean model, because equilibrium climate sensitivity is largely determined by atmospheric processes.

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.

To describe and understand the physical processes responsible for climate variability and predictability on seasonal, interannual, decadal, and centennial time - scales, through the collection and analysis of observations and the development and application of models of the coupled climate system, in cooperation with other relevant climate - research and observing programmes.

Nine global vegetation models (GVMs)(meaning vegetation processes are simulated, but not necessarily vegetation dynamics), four of which were DGVMs, were used in the Coupled Climate — Carbon Cycle Model Intercomparison Project (3).

We emphasize that our study uses sensitivity experiments, which do not include other important processes found in coupled climate models (e.g., ocean dynamics).

Few computers can meet the demands that high - resolution, coupled models place on processing speed and working memory.

CAS = Commission for Atmospheric Sciences CMDP = Climate Metrics and Diagnostic Panel CMIP = Coupled Model Intercomparison Project DAOS = Working Group on Data Assimilation and Observing Systems GASS = Global Atmospheric System Studies panel GEWEX = Global Energy and Water Cycle Experiment GLASS = Global Land - Atmosphere System Studies panel GOV = Global Ocean Data Assimilation Experiment (GODAE) Ocean View JWGFVR = Joint Working Group on Forecast Verification Research MJO - TF = Madden - Julian Oscillation Task Force PDEF = Working Group on Predictability, Dynamics and Ensemble Forecasting PPP = Polar Prediction Project QPF = Quantitative precipitation forecast S2S = Subseasonal to Seasonal Prediction Project SPARC = Stratospheric Processes and their Role in Climate TC = Tropical cyclone WCRP = World Climate Research Programme WCRP Grand Science Challenges • Climate Extremes • Clouds, Circulation and Climate Sensitivity • Melting Ice and Global Consequences • Regional Sea - Ice Change and Coastal Impacts • Water Availability WCRP JSC = Joint Scientific Committee WGCM = Working Group on Coupled Modelling WGSIP = Working Group on Subseasonal to Interdecadal Prediction WWRP = World Weather Research Programme YOPP = Year of Polar Prediction

Besides adding to the overall complexity of AOS models, coupling increases the number of processes with a nonfundamental representation (i.e., similar to a parameterization), because, for the most part, the governing equations are not well determined for the model components other than fluid dynamics.

In a scientific problem as potentially complicated as climate, there is another modeling practice that is increasingly important: AOS models are open - ended in their scope for including and dynamically coupling different physical, chemical, biological, and even societal processes.

Furthermore, a model that could realistically simulate the impact of increasing atmospheric particle concentration on climate must eventually include the simultaneous coupled effects of all the important atmospheric processes, such as fluid motions and cloud microphysics, in addition to the radiative transfer effects.»

Such biases would also affect simulations for present and future climate scenarios, highlighting the importance to carefully consider the representation of high - latitude climate and sea ice processes in coupled climate models.

Much process - based research coupling field work, remote sensing, and modeling is required to advance assessment of the likelihood of a threshold - crossing leading to abrupt sea - level rise from the ice sheets, as well as to improve projections of moregradual sea - level rise that could lead to threshold - crossing events in other systems.

Identify new sources of predictive skill and improve predictions of weather, water, and climate through observations, understanding, and modeling of physical processes and phenomena of the coupled Earth system.

A study by Mitrovica et al. (2015) has demonstrated that the combination of lower estimates of the GMSL rise between 1900 and 1990 (~ 1.2 mm yr - 1), improved modeling of the GIA process and the signal due to core - mantle coupling in ancient eclipse observations resolves «Munk's enigma.»

Predictions of sea ice changes will have large uncertainties without sustained observations; improved understanding of ice, ocean, land, and atmospheric processes; and advances in coupled and system models.

New structural forms are needed for climate models that are capable of simulating the natural internal variability of the coupled ocean - atmosphere system on timescales from days to millennia and that can accurately account for the fast thermodynamic feedback processes associated with clouds and water vapor.»

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.

The first order objective is to acquire a practical capability (coupled atmosphere - ocean general circulations climate modes) to model the seasonal and geographic variability of the climate system in terms of physics / mathematics - based processes.

The early scientific reviews suggest a couple of reasons: firstly, that modelling the climate as an AR (1) process with a single timescale is an over-simplification; secondly, that a similar analysis in a GCM with a known sensitivity would likely give incorrect results, and finally, that his estimate of the error bars on his calculation are very optimistic.