Sentences with phrase «ensemble modeling approach»

Through an ensemble modeling approach, we were able to show that without anthropogenic effects, the droughts in the southwestern United States would have been less severe,» says co-author Axel Timmermann, Director of the newly founded IBS Center for Climate Physics, within the Institute for Basics Science (IBS), and Distinguished Professor at Pusan National University in South Korea.

Through an ensemble modelling approach, we were able to show that without anthropogenic effects, the droughts in the southwestern United States would have been less severe,» said Axel Timmermann, who directs a centre for climate physics at Pusan National University in South Korea.

Not exact matches

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.

This new approach did not specify any of the observed outcomes and left the existing model projections from the CMIP5 ensemble untouched.

As IPCC, in a search for objectivity in uncertainty assessment, has turned more to describing uncertainty in terms of the characteristics of ensembles of model outcomes, the deficiency in such an approach (its exclusion or limited treatment of systemic, structural uncertainty in models) has become increasingly apparent to the community (Winsberg 2010; Knutti et al. 2008; Goldstein and Rougier 2009).

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.

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.

I could understand the ensemble approach if the models are simulating relatively orthogonal (and uncoupled) facets of the climate.

The results of this observationally - based estimate are similar to those obtained directly from the CESM ensemble, attesting to the fidelity of the model's representation of the NAO and the utility of this approach.

crandles - In your example the things the plane models are trying to simulate are somewhat orthogonal, so the ensemble approach makes some sense.

The ensemble member approach is commonly used to approximate a measure of uncertainty in modeled results.

ly weren't able to re-run ensembles of these models with different parameter values, so instead, we just used a simple pattern - scaling approach to fit them to the data.

In Sect. 2, we describe the model ensembles and the application of the rank histogram approach, including a description of the statistical method used to define the reliability of model ensembles from the rank histogram, and a method for handling uncertainties in the observations.

One approach is to use an MME, which consists of simulations contributed by different models of climate research institutes from around the world, often referred to as an «ensemble of opportunity».

In the present study, simulations of the present - day climate by two kinds of climate model ensembles, multi-model ensembles (MMEs) of CMIP3 and single model ensembles (SMEs) of structurally different climate models, HadSM3 / CM3, MIROC3.2, and NCAR CAM3.1, are investigated through the rank histogram approach.

This idea of a «statistically indistinguishable» ensemble is common in the field of weather forecasting and other ensemble prediction fields, and under this paradigm the reliability of model ensembles can be evaluated through the rank histogram approach (Anderson 1996) whereby the distribution of the observed occurrence of an event in the prediction ensembles is evaluated.

We also check the validity of the rank histogram approach by comparing the model - data difference with the ensemble spread through calculating the root mean square model - data difference (RMSE), and the standard deviation of the ensemble (SD).