Sentences with phrase «model ensemble using»

Bottom panels show the present - day, annually averaged sensible heat (c) and evaporation (d) fluxes poleward of 60N for a 16 - member CMIP5 climate model ensemble using the RCP8.5 scenario.

p.s. To compare to Vahrenholt's forecast, here's a comparison of earlier model projections of global temperature for the IPCC (prediction with the CMIP3 model ensemble used in the 4th IPCC assessment report, published in 2007) with the actual changes in temperature (the four colored curves).

Not exact matches

An ensemble of the 13 best performing models was used, both for CLIM and H - CLIM to predict the spatially - distributed mean and maximum area burnt for the period 1950 - 2015 to explore to what extent hydrology adds.

Using a hierarchical model, the authors combine information from these various sources to obtain an ensemble estimate of current and future climate along with an associated measure of uncertainty.

The idea that our universe — everything we can observe, including the laws of physics that shape it — is just one among a vast ensemble may seem the stuff of science fiction, but cosmologists build multiverse models using a theory called inflation.

Using an ensemble of five global hydrological models, the researchers examined the evolution of water availability, demand, and scarcity globally from 1971 to 2010.

In their study, the researchers used an ensemble of climate models to simulate the concentrations of ozone and PM2.5 in the years 2000 and 1850.

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 grUsing 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 grusing 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.

The research used historical data — mainly from North American, Europe and East Asia — and an ensemble of climate models to analyze the past and future risk of various extreme hot, wet and dry events, including the highest daytime and nighttime temperatures, mildest low temperatures, wettest days, and longest dry spells.

The scientists used the latest ensemble of climate models, prepared for the 5th assessement report of the International Panel on Climate Change.

They use «ensemble» modeling — which takes an average of many different weather models.

Because climate studies using multi-model ensembles are generally superior to single model approaches43, all nine fire weather season lengths for each location were averaged into an ensemble mean fire weather season length, hereafter referred to as «Fire Weather Season Length» (See Supplementary Methods).

Annan, J.D., J.C. Hargreaves, N.R. Edwards, and R. Marsh, 2005a: Parameter estimation in an intermediate complexity Earth System Model using an ensemble Kalman filter.

Hargreaves, J.C., J.D. Annan, N.R. Edwards, and R. Marsh, 2004: An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter.

The Schneider et al. ensemble constrained by their selection of LGM data gives a global - mean cooling range during the LGM of 5.8 + / - 1.4 ºC (Schnieder Von Deimling et al, 2006), while the best fit from the UVic model used in the new paper has 3.5 ºC cooling, well outside this range (weighted average calculated from the online data, a slightly different number is stated in Nathan Urban's interview — not sure why).

The analysis of processes contributing to climate feedbacks in models and recent studies based on large ensembles of models suggest that in the future it may be possible to use observations to narrow the current spread in model projections of climate change.

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.

«We use a massive ensemble of the Bern2.5 D climate model of intermediate complexity, driven by bottom - up estimates of historic radiative forcing F, and constrained by a set of observations of the surface warming T since 1850 and heat uptake Q since the 1950s... Between 1850 and 2010, the climate system accumulated a total net forcing energy of 140 x 1022 J with a 5 - 95 % uncertainty range of 95 - 197 x 1022 J, corresponding to an average net radiative forcing of roughly 0.54 (0.36 - 0.76) Wm - 2.»

Oct. 3, 2017 - A recent study by Lawrence Livermore National Laboratory (LLNL) scientists and collaborators is the first to use an ensemble of global chemistry climate models to estimate death rates from air pollution caused by the impact of climate change on pollutant concentrations.

No, that is not correct, both papers seek to determine whether the observational data are consistent with the models, however Douglass et al use a statistical test that actually answers a different question, namely «is there a statistically significant difference between the mean trend of the ensemble and the observed trend».

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).

We are in the process of downscaling individual global model projections, and preliminary results (as mentioned briefly in the paper) show substantial departures from the result using the ensemble mean.

One can temper that with studies of paleoclimate sensitivity, but the ensemble results still should be borne in mind, since doubling CO2 takes us into a climate that has no real precendent in the part of the climate record which has been used for exploring model sensitivity, and in many regards may not have any real precedent in the entire history of the planet (in terms of initial condition and rapidity of GHG increase).

Regardless of whether you use a linear or sigmoid extrapolation or a CCSM4 AR4 ensemble, you are using a model.

In addition to using ensembles to get the best prognoses, it is also important to continually work on improving the models themselves.

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!

An additional interesting question is whether the huge ensemble of models used in this study is actually more valuable than the 20 or so models generates by the best efforts of the world's modeling centers for the IPCC.

[Response: I wasn't trying to suggest that tacit knowledge was some kind of opinion that all scientists must agree with, but rather it is the shared background that, say, everyone using climate models has — i.e. why we use initial condition ensembles, how we decide that a change in the code is significant, what data comparisons are appropriate etc..

I have linearly extended the ensemble mean model values for the post 2003 period (using a regression from 1993 - 2002) to get a rough sense of where those runs might have gone.

The use of «ensemble forecasting» (# 15 and # 23) presupposes that the number of tweakable parameters significantly exceeds that required for fitting the model.

He claims that this can be corrected for, but he still isn't using the proper null — in M&N they show the results from the ensemble means (of the GISS model and the full AR4 model set), but seem to be completely ignorant of the fact that ensemble mean results remove the spatial variations associated with internal variability which should be the exact thing you would use!

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.).

You yourself in your 2008/9 reconstructions provided an ensemble of reconstruction possibilities, yet now you seem fixated on one model and one NH record (derived using mainly RW data which clearly only had skill at time - scale > 20 years).

Some of the models also involve ensemble calculations, and again it may be instructive for the climate modellers to describe something about the use of these, especially as the public has been involved in some ensemble calculations being run on their pc's at home.

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.

Instead modelers use Monte Carlo methods in which the same deterministic model is run many times with slightly different starting conditions (a model ensemble).

The claim to get around the two problems of an geometric increase in error, and using absolute values of temperature that are not Earth's, is the argument that the model ensemble got the heat transfer correctly in atmosphere and in the ocean.

The modelers and IPCC's use of these models as anomalies underscores a basic problem with the ensemble, the absolute temperature values which impact TOA.

Knutti et al. (2006), using a different, perturbed physics ensemble, showed that models with a strong seasonal cycle in surface temperature tended to have larger climate sensitivity.

Uncertainty in model response is investigated using a perturbed physics ensemble in which model parameters are set to alternative values considered plausible by experts in the relevant parameterization schemes.»

We used an ensemble of ice sheet model runs and plausible Earth models to place bounded constraints on our mass change estimate.

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.

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 NEMO model provides the dynamic ocean model used in the ensemble prediction system and the seasonal forecast system (S4).

The dark blue shading represents the envelope of the full set of 35 SRES scenarios using the simple model ensemble mean results.

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.

We determine its likely evolution for three intergovernmental panel on climate change (IPCC) special report on emission scenarios (SRES) for austral summer and winter, using a multi-model ensemble of IPCC fourth assessment report models which resolve stratospheric ozone recovery.

The use of very large atmospheric model ensemble to assess potential anthropogenic influence on the UK summer 2012 high rainfall totals, Bulletin of the American Meteorological Society, 94, No 9.

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.