Sentences with phrase «between model ensembles»

Especially for the climate variables such as SAT, SLP, SW and LW clear - sky radiation as shown in Fig 6 (1), (3), (6), and (9), the average of errors in MMEs and SMEs are similar, but the distances between model ensembles in MMEs are larger than SMEs.

As found in Y12, the histograms of SMEs tend to have the peaks at the highest and lowest rank, but the details of this varies between the model ensembles and variables.

In order to identify the differences between model ensembles more clearly, the range of horizontal axis is chosen from 3 to 18

Number of models used for analysis is different between model ensembles and climate variables

Right now I don't see a good fit between the model ensemble and the observations in the period 1910 to 1945.

Not exact matches

«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.»

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».

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.).

However, despite the same range of ECS in the CMIP5 models as in the CMIP3 models, there is no significant relationship across the CMIP5 ensemble between ECS and the 20th - century ERF applied to each individual model (Forster et al., 2013).

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.

However, relationships between observable metrics and the predicted quantity of interest (e.g., climate sensitivity) can be explored across model ensembles.

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

On the relationship between the meridional overturning circulation, alongshore wind stress, and United States East Coast sea level in the Community Earth System Model Large Ensemble (Journal of Geophysical Research - Oceans)

«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.»

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.

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.

Mean modeled winter precipitation from CESM LME ensemble members 2 to 5 show unsystematic differences in Southwest winter precipitation variability between each other and with our NADA PDSI time series (Table 1, S1 Fig).

I believe that there may be a larger issue with estimation of ECS from models, even before one considers how an ensemble of within - model or between - model results might sensibly be amalgamated into a pdf.

Webb et al (2013)[ix], who examined the origin of differences in climate sensitivity, forcing and feedback in the previous generation of climate models, reported that they «do not find any clear relationships between present day biases and forcings or feedbacks across the AR4 ensemble».

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.

(To be precise, the model ensemble average surface temperature rise for 2011 - 2030 varies slightly between 0.62 and 0.67, depending on the emissions scenario, relative to the 1980 - 99 baseline.

In CMIP5 there is no correlation between aerosol forcing and sensitivity across the ensemble, so the implication that aerosol forcing affects the climate sensitivity in such «forward» calculations is false... The spread of model climate sensitivities is completely independent of historical simulations.»

The model ensemble also could not accommodate the «pause» or «slowdown» in warming between the two large El Niños of 1997 - 8 and 2015 - 6.

«Epistemology is here applied to problems of statistical inference during testing, the relationship between the underlying physics and the models, the epistemic meaning of ensemble statistics, problems of spatial and temporal scale, the existence or not of an unforced null for climate fluctuations, the meaning of existing uncertainty estimates, and other issues.

Between 801 and 1800 ce, the surface cooling trend is qualitatively consistent with an independent synthesis of terrestrial temperature reconstructions, and with a sea surface temperature composite derived from an ensemble of climate model simulations using best estimates of past external radiative forcings.

Arguing against the model vs real world comparison «Here Judith is (I think) referring to the mismatch between the ensemble mean (red) and the observations (black) in that period... However, the observations are well within the spread of the models and so could easily be within the range of the forced trend + simulated internal variability.»

Originally the number of ensembles was 129, but one ensemble member is excluded because of the unrealistic cooling drift, caused by the interaction between negative SST anomalies and low cloud cover which is known to sometimes occur in models of this type (e.g. Stainforth et al. 2005, supplementary information).

For example in the case of Knutti et al. (2006), a strong relationship between current behaviour and equilibrium climate sensitivity, that is found to hold across a single model ensemble, has no skill in predicting the climate sensitivity of the members of the CMIP3 ensemble.

On the other hand, in the SMEs, the reliability varies between climate variables and model ensembles.

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.

Knutti et al. (2010a) investigated the behaviour of the state - of - the - art climate model ensemble created by the World Climate Research Programme's Coupled Model Intercomparison Project Phase 3 (CMIP3, Meehl et al. 2007), and found that the truth centred paradigm is incompatible with the CMIP3 ensemble: the ensemble mean does not converge to observations as the number of ensemble members increases, and the pairwise correlation of model errors (the differences between model and observation) between two ensemble members does not average to zero (Knutti et al. 2010a; Annan and Hargreaves 2010; hereafter Amodel ensemble created by the World Climate Research Programme's Coupled Model Intercomparison Project Phase 3 (CMIP3, Meehl et al. 2007), and found that the truth centred paradigm is incompatible with the CMIP3 ensemble: the ensemble mean does not converge to observations as the number of ensemble members increases, and the pairwise correlation of model errors (the differences between model and observation) between two ensemble members does not average to zero (Knutti et al. 2010a; Annan and Hargreaves 2010; hereafter AModel Intercomparison Project Phase 3 (CMIP3, Meehl et al. 2007), and found that the truth centred paradigm is incompatible with the CMIP3 ensemble: the ensemble mean does not converge to observations as the number of ensemble members increases, and the pairwise correlation of model errors (the differences between model and observation) between two ensemble members does not average to zero (Knutti et al. 2010a; Annan and Hargreaves 2010; hereafter Amodel errors (the differences between model and observation) between two ensemble members does not average to zero (Knutti et al. 2010a; Annan and Hargreaves 2010; hereafter Amodel and observation) between two ensemble members does not average to zero (Knutti et al. 2010a; Annan and Hargreaves 2010; hereafter AH10).

The rank histogram analysis discussed in Sect. 2.2 only considers the rank ordering of models and observations, and thus information on the distances between observation and ensemble members is missing.

On the other hand, in SMEs, the reliability varies between climate variables and model ensembles.

MSTs (minimum sum of distances between ensemble members, Table 5) and the averages of the distances between the observations and model ensemble members (Fig. 6) are calculated.

The analysis methods include the explanation of the calculation of rank histogram and the statistical test for the reliability (2 — 2), the formulation of EDoF (2 — 3), and the distances between observation and model ensemble members (2 — 4).

In addition to the rank histogram we explore other ways of evaluating the ensemble, analysing the distances between models and observational data by calculating the minimum spanning trees (e.g., Wilks 2004) and the average of the distances between the observation and the models for all the ensembles.

For this reason, in the next section we investigate the relationship between the observations and model ensembles based on their distances.

Once the pair-wise distances between observation and model ensemble members defined in Eq.

In terms of model error, Y12 investigated only the relationship between the errors of ensemble mean and standard deviation of model ensemble members.

Owing to computer resource limitations, each modeling group has to make tradeoffs between horiz / vertical resolution, fidelity of physical parameterizations, and the number of ensemble members.