Sentences with phrase «coupled model ensemble»

This parameter uncertainty has been studied before with a much smaller coupled model ensemble that could only be run on a supercomputer.

Not exact matches

Here, we review the likelihood of continued changes in terrestrial climate, including analyses of the Coupled Model Intercomparison Project global climate model enseModel Intercomparison Project global climate model ensemodel ensemble.

Seasonal atmospheric responses to reduced Arctic sea ice in an ensemble of coupled model simulations.

For the work of the Montana Climate Assessment, we employed an ensemble from the fifth iteration of the Coupled Model Intercomparison Project (CMIP5), which includes up to 42 GCMs depending on the experiment conducted (CMIP5 undated).

In an ensemble of fully coupled atmosphere - ocean general circulation model (AOGCM) simulations of the late Paleocene and early Eocene, we identify such a circulation - driven enhanced intermediate - water warming.

[Response: There are a couple of issues here — first, the number of ensemble members for each model is not the same and since each additional ensemble member is not independent (they have basically the same climatological mean), you have to be very careful with estimating true degrees of freedom.

Another example would be to explain why Arrhenius, who as a Swede was reportedly in favor of a little warming, obtained ECS results from his simple, laboriously hand - calculated model of CO2 - driven global warming that are only a factor of two higher than estimates by the current ensemble of coupled GCMs.

Ensemble simulations conducted with EMICs (Renssen et al., 2002; Bauer et al., 2004) and coupled ocean - atmosphere GCMs (Alley and Agustsdottir, 2005; LeGrande et al., 2006) with different boundary conditions and freshwater forcings show that climate models are capable of simulating the broad features of the observed 8.2 ka event (including shifts in the ITCZ).

Barthélemy et al., 5.0 (range from 4.1 to 5.5), Modeling Our estimate is based on results from ensemble runs with the global ocean - sea ice coupled model NEMO - LIM3.

The ensemble members are generated by coupled model breeding and by independent perturbations in the ocean and atmosphere.

Rowlands (2012) write, «Here we present results from a multi-thousand-member perturbed - physics ensemble of transient coupled atmosphere — ocean general circulation model simulations.

Natural variability from the ensemble of 587 21 - year - long segments of control simulations (with constant external forcings) from 24 Coupled Model Intercomparison Project phase 3 (CMIP3) climate models is shown in black and gray.

A new assimilation system (CERA) has been developed to simultaneously ingest atmospheric and ocean observations in the coupled Earth system model used for ECMWF's ensemble forecasts.

Since 2013 the ensemble forecasts have coupled the atmosphere - wave - ocean model from the start of the forecast.

Precipitation extremes and their potential future changes were predicted using six - member ensembles of general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5).

The impact of sea surface temperature bias was further investigated by using uncoupled atmospheric models with prescribed sea surface temperatures, and those 3 models each with differing complexity showed less severe double ITCZ bias than the ensemble of coupled models.

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.

Many numerical weather prediction centers now use coupled ocean - atmosphere models to produce ensemble forecasts on the subseasonal time scale.

The current operational ensemble forecast systems model sea ice dynamically using the LIM2 model within NEMO ocean model to represent the dynamic and thermodynamic evolution of sea ice within the coupled forecast system.

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

The fact that the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble mean accurately represents observed global OHC changes [Cheng et al., 2016] is critical for establishing the reliability of climate models for long - term climate change projections.

Using the Coupled Model Intercomparison Project (CMIP5) ensemble, Jascha Lehmann from Germany's Potsdam Institute for Climate Impact Research and colleagues rolled climate forward to 2100 and looked at the change in storm tracks under a high carbon - dioxide - emissions scenario.

His research activities revolve around tropical cyclone simulations and prediction models, 3D and 4D variational analysis schemes, ensemble forecasting techniques and coupling of mesoscale Numerical Weather Prediction (NWP) models to Atmospheric Transport and Dispersion (ADT) models.

At the pan-arctic level, the two coupled ice - ocean model ensemble simulations (Kauker, Zhang) show good agreement, in particular regarding ice conditions in the East Siberian Sea.

In his talk, «Statistical Emulation of Streamflow Projections: Application to CMIP3 and CMIP5 Climate Change Projections,» PCIC Lead of Hydrological Impacts, Markus Schnorbus, explored whether the streamflow projections based on a 23 - member hydrological ensemble are representative of the full range of uncertainty in streamflow projections from all of the models from the third phase of the Coupled Model Intercomparison Project.

The pan-arctic ensemble runs with a coupled ice - ocean model by Kauker et al. also indicate a distinct ice thickness anomaly in the East Siberian Sea, where thicknesses at the end of June 2010 are shown to be higher by a factor of roughly two as compared to the previous three years.

In contrast, the updated ensemble forecast from a coupled ice - ocean model submitted by Zhang (Figure 3) still shows the September ice edge further north than in 2009.

This is consistent with both the June and July (Figure 3) ensemble predictions from a coupled ice - ocean model submitted by Zhang, which show considerably more ice in the East Siberian Sea compared to 2009, and it is consistent with the June statistical forecasts submitted by Tivy, which also predict a greater ice area than in 2009 and above - normal ice concentrations along the coasts.

The ensemble prediction from a coupled ice - ocean model submitted by Zhang shows considerably more ice in the East Siberian Sea compared to 2009.

This approach provides a hybrid assessment of the combined influence of anthropogenic climate change [determined from the ensemble - mean of the CESM - LE or from the multi-model Coupled Model Intercomparison Project phase 5 (CMIP5) archive (Taylor et al. 2012)-RSB- and observed NAO variability on climate over the coming decades.

This appears to be related to a poor representation of the spatial relationships between rainfall variability and zonal wind patterns across southeast Australia in the latest Coupled Model Intercomparison Project ensemble, particularly in the areas where weather systems embedded in the mid-latitude westerlies are the main source of cool - season rainfall.

In comparison, the Coupled Models Intercomparison Project Phase 5 (CMIP5) ensemble mean accounts for 87 % of the observed global mean temperature variance.

Here we use an ensemble of simulations with a coupled ocean — atmosphere model to show that the sea surface temperature anomalies associated with central Pacific El Niño force changes in the extra-tropical atmospheric circulation.

Barthélemy et al, 5.1 (4.5 - 5.6), Modeling Our estimate is based on results from ensemble runs with the global ocean - sea ice coupled model Nucleus for European Modeling of the Ocean Louvain - la - Neuve Sea Ice Model (NEMO - Lmodel Nucleus for European Modeling of the Ocean Louvain - la - Neuve Sea Ice Model (NEMO - LModel (NEMO - LIM3).

GFDL NOAA (Msadek et al.), 4.82 (4.33 - 5.23), Modeling Our prediction for the September - averaged Arctic sea ice extent is 4.82 million square kilometers, with an uncertainty range going between 4.33 and 5.23 million km2 Our estimate is based on the GFDL CM2.1 ensemble forecast system in which both the ocean and atmosphere are initialized on August 1 using a coupled data assimilation system.

The method is a sea ice - ocean model ensemble run (without and with assimilation of sea - ice / ocean observations); the coupled ice - ocean model NAOSIM has been forced with atmospheric surface data from January 1948 to 7 July 2015.

We used ensemble members 2 to 5, which consist of coupled ocean - atmosphere climate models; outputs represent a range of probable monthly precipitation values generated by CESM [43].

Our estimate is based on results from ensemble runs with the global ocean - sea ice coupled model NEMO - LIM3.

We used ensemble members 2 to 5, which consist of coupled ocean - atmosphere climate models; outputs represent a range of probable monthly precipitation values generated by CESM.

Alternatively, an automated procedure based on a cluster initialization algorithm is proposed and applied to changes in 27 climate extremes indices between 1986 — 2005 and 2081 — 2100 from a large ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations.

Examples include the forecasting of tropical cyclone tracks, heavy rainfall, strong winds, and flood prediction through coupling hydrological models to ensembles.

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

Barthélemy et al., 3.8 (2.6 - 4.6), Modeling (ice - ocean) Our estimate is based on results from ensemble runs with the global ocean - sea ice coupled model NEMO - LIM3.

Our prediction is based on the GFDL - FLOR ensemble forecast system, which is a fully - coupled atmosphere - land - ocean - sea ice model initialized using a coupled data assimilation system.

Here we present results from a multi-thousand-member perturbed - physics ensemble of transient coupled atmosphere — ocean general circulation model simulations.

Elevated sea temperatures drive impacts such as mass coral bleaching and mortality (very high confidence), with an analysis of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble projecting the loss of coral reefs from most sites globally by 2050 under mid to high rates of ocean warming (very likely).

Kauker et al., (AWI / OASys); 3.72 (3.30 - 4.14), Modeling Estimated from ensemble coupled sea ice - ocean model runs based on atmospheric reanalyses fields from 1994 - 2013.

Barthelemy et al, 4.70 (3.50 - 5.50), Modeling Our estimate is based on results from ensemble runs with the global ocean - sea ice coupled model NEMO - LIM3.

We examine the annular mode within each hemisphere (defined here as the leading empirical orthogonal function and principal component of hemispheric sea level pressure) as simulated by the Intergovernmental Panel on Climate Change Fourth Assessment Report ensembles of coupled ocean - atmosphere models.

A recent study by C10 analysed a number of different climate variables in a set of SMEs of HadCM3 (Gordon et al. 2000, atmosphere — ocean coupled version of HadSM3) from the point of view of global - scale model errors and climate change forcings and feedbacks, and compared them with variables derived from the CMIP3 MME. Knutti et al. (2006) examined another SME based on the HadSM3 model, and found a strong relationship between the magnitude of the seasonal cycle and climate sensitivity, which was not reproduced in the CMIP3 ensemble.