Sentences with phrase «model ensemble simulations»

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.

Not exact matches

The first is the development of a comprehensive, closely coordinated ensemble of simulations from 18 modeling groups around the world for the historical and future evolution of the earth's climate.

Using results from simulations conducted using an ensemble of sophisticated models, Ricke, Caldeira, and their co-authors calculated ocean chemical conditions that would occur under different future scenarios and determined whether these chemical conditions could sustain coral reef growth.

In a unique study set - up, the scientists first compared simulation results from a large ensemble of wheat crop growth models with experimental data, including artificial heating experiments and multi-locational field trials.

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

To understand the role of human - induced climate change in these new records they compare simulations of the Earth's climate from nine different state - of - the - art climate models and the very large ensemble of climate simulations provided by CPDN volunteers for the weather@home ANZ experiments for the world with and without human - induced climate change.

New and unique model simulations have also been made available through the MaRIUS project under which CPDN created a very large ensemble of possible weather and extreme weather in Europe from the beginning of the 20th century up to the end of the 21st.

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.

The initial LASSO implementation on the Cumulus cluster will be for ARM's Southern Great Plains site in Oklahoma and will focus on high - resolution model simulations of shallow clouds driven by ensembles of forcing inputs.

Methods: In these experiments, the research team conducted large ensembles of simulations with two state - of - the - art atmospheric general circulation models by abruptly switching the sea - surface temperature warming on from January 1st to focus on the wintertime circulation adjustment.

Murphy, J.M., et al., 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations.

All forecasted SST series were pooled and for each calendar year the forecasted nest abundances is the model average for the ensemble of 200 simulations, essentially, deterministic models within a stochastic shell [59].

A large ensemble of Earth system model simulations, constrained by geological and historical observations of past climate change, demonstrates our self ‐ adjusting mitigation approach for a range of climate stabilization targets ranging from 1.5 to 4.5 °C, and generates AMP scenarios up to year 2300 for surface warming, carbon emissions, atmospheric CO2, global mean sea level, and surface ocean acidification.

M2009 use a simplified carbon cycle and climate model to make a large ensemble of simulations in which principal uncertainties in the carbon cycle, radiative forcings, and climate response are allowed to vary, thus yielding a probability distribution for global warming as a function of time throughout the 21st century.

The most successful attempts to do this have used either global or continental statistics (as above), or thousands of model simulations of a local event (which use an initial condition ensemble to provide statistical power).

The model weather is the part of the solution (usually high frequency and small scale) that is uncorrelated with another simulation in the same ensemble.

Multi-model Ensemble — a set of simulations from multiple models.

The «models used» (otherwise known as the CMIP5 ensemble) were * not * tuned for consistency for the period of interest (the 1950 - 2010 trend is what was highlighted in the IPCC reports, about 0.8 ºC warming) and the evidence is obvious from the fact that the trends in the individual model simulations over this period go from 0.35 to 1.29 ºC!

I did so, and in so doing pointed out a number of problems in the M&N paper (comparing the ensemble mean of the GCM simulations with a single realisation from the real world, and ignoring the fact that the single GCM realisations showed very similar levels of «contamination», misunderstandings of the relationships between model versions, continued use of a flawed experimental design etc.).

For Figure 1, global mean temperatures are plotted from the HadCRUT4 and GISTEMP products relative to a 1900 - 1940 baseline, together with global mean temperatures from 81 available simulations in the CMIP5 archive, also relative to the 1900 - 1940 baseline, where all available ensemble members are taken for each model.

To illustrate this point, the following graph shows one simulation from the CMIP3 model ensemble:

The A1B simulation is just the results from (I think) a 3 member ensemble of the ECHAM5 model run as you suggest.

In any specific model, the range of short term trends in the ensemble is quite closely related to their simulation of ENSO - like behaviour.

Recently I have been looking at the climate models collected in the CMIP3 archive which have been analysed and assessed in IPCC and it is very interesting to see how the forced changes — i.e. the changes driven the external factors such as greenhouse gases, tropospheric aerosols, solar forcing and stratospheric volcanic aerosols drive the forced response in the models (which you can see by averaging out several simulations of the same model with the same forcing)-- differ from the internal variability, such as associated with variations of the North Atlantic and the ENSO etc, which you can see by looking at individual realisations of a particular model and how it differs from the ensemble mean.

Based on results from large ensemble simulations with the Community Earth System Model, we show that internal variability alone leads to a prediction uncertainty of about two decades, while scenario uncertainty between the strong (Representative Concentration Pathway (RCP) 8.5) and medium (RCP4.5) forcing scenarios [possible paths for greenhouse gas emissions] adds at least another 5 years.

I haven't got to the bottom of this yet, but there are several plausible explanations: (i) some of the simulations in the downloaded models from the CMIP3 ensemble stop early, affecting the whole envelope of results, (ii) the use of common EOFs fail to capture large - scale temperature patters that are too different from the past.

We can derive the underlying trend related to external forcings from the GCMs — for each model, the underlying trend can be derived from the ensemble mean (averaging over the different phases of ENSO in each simulation), and looking at the spread in the ensemble mean trend across models gives information about the uncertainties in the model response (the «structural» uncertainty) and also about the forcing uncertainty — since models will (in practice) have slightly different realisations of the (uncertain) net forcing (principally related to aerosols).

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).

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.

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.

To ensure their models are accurate, Ault said researchers distinguished and separated normal climatic variability from long - term atmospheric alterations, by using a new ensemble of climate change simulations.

The forcings and model simulations of the future are together called the CMIP5 ensemble and are what is shown in Figure 1a and b.

IPCC relied on climate models (CMIP5), the hypotheses under test if you will, to exclude natural variability: «Observed Global Mean Surface Temperature anomalies... lie well outside the range of Global Mean Surface Temperature anomalies in CMIP5 simulations with natural forcing only, but are consistent with the ensemble of CMIP5 simulations including both anthropogenic and natural forcing...» (Ref.: Working Group I contribution to fifth assessment report by IPCC.

The need for more simulations to characterise uncertainty is being further addressed through international initiatives to have many modelling groups contribute simulations to the same ensembles (e.g. CORDEX - COordinated Regional climate Downscaling EXperiment http://wcrp-cordex.ipsl.jussieu.fr/).

Ensemble - A group of parallel model simulations used for climate projections.

The need for more simulations to characterise uncertainty is being further addressed through international initiatives to have many modelling groups contribute simulations to the same ensembles (e.g. CORDEX — COordinated Regional climate Downscaling EXperiment http://wcrp-cordex.ipsl.jussieu.fr/).

Because the models are not deterministic, multiple simulations are needed to compare with observations, and the number of simulations conducted by modeling centers are insufficient to create a pdf with a robust mean; hence bounding box approaches (assessing whether the range of the ensembles bounds the observations) are arguably a better way to establish empirical adequacy.

Van Haren et al (2012) also nicely illustrate the dependence of regional skill on lateral boundary conditions: simulations of (historic) precipitation trends for Europe failed to match the observed trends when lateral boundary conditions were provided from an ensemble of CMIP3 global climate model simulations, while a much better correspondence with observations was obtained when reanalyses were used as boundary condition.

Ensembles made with the same model but different initial conditions only characterize the uncertainty associated with internal climate variability, whereas multi-model ensembles including simulations by several models also include the impact of model difEnsembles made with the same model but different initial conditions only characterize the uncertainty associated with internal climate variability, whereas multi-model ensembles including simulations by several models also include the impact of model difensembles including simulations by several models also include the impact of model differences.

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.

In this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed NAO record in combination with the model's radiatively - forced response (given by the ensemble - mean of the 40 simulations).

We make use of a 40 - member ensemble of climate change simulations under historical and RCP8.5 radiative forcing scenarios for the period 1920 — 2100 conducted with the Community Earth System Model Version 1 (CESM1; Hurrell et al. 2013).

I understand that they run an ensemble of simulations, but as the ice gets thinner with time, there are going to be events that the model does not take into account for the near - term.

They ran an ensemble of simulations with a climate model of intermediate complexity to evaluate the causes of past climate changes.

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.

The fact that the CMIP simulations ensemble mean can reproduce the 1970 — 2010 US SW temperature increase without inclusion of the AMO (the AMO is treated as an intrinsic natural climate vari - ability that is averaged out by taking an ensemble mean of individual simulations) suggests that the CMIP5 models» predicted US SW temperature sensitivity to the GHG has been significantly (by about a factor of two) overestimated.

«The fact that the CMIP simulations ensemble mean can reproduce the 1970 — 2010 US SW temperature increase without inclusion of the AMO (the AMO is treated as an intrinsic natural climate variability that is averaged out by taking an ensemble mean of individual simulations) suggests that the CMIP5 models» predicted US SW temperature sensitivity to the GHG has been significantly (by about a factor of two) overestimated.»

Internal and forced climate variability during the last millennium: a model - data comparison using ensemble simulations.

This external control is demonstrated by ensembles of model simulations with identical forcings (whether anthropogenic or natural) whose members exhibit very similar simulations of global mean temperature on multi-decadal time scales (e.g., Stott et al., 2000; Broccoli et al., 2003; Meehl et al., 2004).