Sentences with phrase «model ensemble members»

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

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

Rank of minimum spanning tree (MST) without observation among MSTs of observation plus model ensemble members removing each ensemble members

If the ensemble members are collectively far away from the observation (compared to their distances from each other), then the MST omitting the observation is smaller than the MSTs removing model ensemble members.

Here we count the rank of observation among model ensemble members and create histogram, so the number of rank in horizontal axis is from one to the number of ensemble plus one.

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).

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 very high significance levels of model — observation discrepancies in LT and MT trends that were obtained in some studies (e.g., Douglass et al., 2008; McKitrick et al., 2010) thus arose to a substantial degree from using the standard error of the model ensemble mean as a measure of uncertainty, instead of the ensemble standard deviation or some other appropriate measure for uncertainty arising from internal climate variability... Nevertheless, almost all model ensemble members show a warming trend in both LT and MT larger than observational estimates (McKitrick et al., 2010; Po - Chedley and Fu, 2012; Santer et al., 2013).

Nevertheless, almost all model ensemble members show a warming trend in both LT and MT larger than observational estimates (McKitrick et al., 2010; Po - Chedley and Fu, 2012; Santer et al., 2012).

Not exact matches

From at least Lorius et al (1991)-- when we first had reasonable estimates of the greenhouse gases from the ice cores, to an upcoming paper by Schneider von Deimling et al, where they test a multi-model ensemble (1000 members) against LGM data to conclude that models with sensitivities greater than about 4.3 ºC can't match the data.

What I notice is that consistent corrections to the model, and attention to the behavior of the individual ensemble members brings model projections and the long extrapolation into agreement (# 44) while short extrapolations probably should not be attacked based on possible low frequency variability owing to a scale mismatch.

You can ameliorate this a little by only selecting a single ensemble member from each model (e.g. only the red dots, or only blue dots, or randomly selecting from each ensemble etc.) before doing any analysis.

[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.

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.

There is one model that has two out of three showing significant weakening, and two that show one out of two members with weakening, but I'm not sure how much credence to put on such small ensembles.

Fig. 4 there should be at total of eight models (including GFDL CM2.1) that have at least one ensemble member outside the p = 0.05 confidence interval for weakening.

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

Also, about 2/3 of the individual ensemble - members (46 out of 68) from all the model runs have linear trends that indicate at least a nominal weakening — this is significantly different from what one would be expected from a Binomial distribution with a 50 % probability.

If a model is going to purport to actually represent the real world, reality must be a reasonable member of the ensemble.

1) Regarding the 1970s shift, Ray mentions that: «It's not evident why the smooth trend in 20th century climate forcing should give rise to such an abrupt shift, and indeed the individual members of the model ensemble do not show a clearly analogous shift.»

It's not evident why the smooth trend in 20th century climate forcing should give rise to such an abrupt shift, and indeed the individual members of the model ensemble do not show a clearly analogous shift.

Wang et al., 4.9 + / -0.4, Modeling The outlook is based on a real time CFSv2 ensemble of 40 members initialized from May 21 - 30, 2012.

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

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).

Unlike the ENSO and IOD SST forecasts, the seasonal outlooks are based on the last three weeks of forecasts, i.e. five separate model runs combining to make a 165 - member ensemble, as this was shown to give higher skill.

Trends from most ensemble members and models nevertheless remain smaller than the observed value.»

doi: 10.1007 / s00382 -012-1313-4 who report quite limited predictive skill in two regions of the oceans on the decadal time period, but no regional skill elsewhere, when they conclude that «A 4 - model 12 - member ensemble of 10 - yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation.

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.

I recall a scenario (January 2011, IIRC) where all of the models and most of their ensemble members projected what would have been a catastrophic snowstorm for the east coast of the U.S..

Wang et al. (NOAA / NWS / NCEP); 5.0 ± 0.5; Model The outlook is based on a CFSv2 ensemble of 40 members initialized from 17 - 26 May 2011.

The model's ensemble - mean EOF accounts for 43 % of the variance on average across the 40 ensemble members, and is largely similar to observations although the centers - of - action extend slightly farther east and the southern lobe is weaker (maximum amplitude of approximately 2 hPa compared to 3 hPa in observations; Fig. 3c).

Wang et al, 3.9 + / -0.3, Model The outlook is based on a CFSv2 ensemble of 40 members initialized from Jul 27 - Aug 5, 2012.

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).

The mean minimum ice extent in September, averaged across all ensemble members and corrected for forward model bias, is our projected ice extent.

In forcing the model with these winds, only the case of using winds from 2007 to project 2008 sea ice extent produce less sea ice than the observed 2007 ice extent (Ensemble member 7).

Wu and Grumbine, 5.06 (± 0.58), Modeling The projected Arctic minimum sea ice extent from the NCEP CFSv2 model with revised CFSv2 May - June - July ICs using 92 - member ensemble forecast is 5.06 million km2 with a SD of 0.58 million km2.

For example, seven of the twenty ensemble members from the Alfred Wegener Institute modeling group gave Outlook estimates above 5.0 million square kilometers.

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].

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).

The mean ice extent in September, averaged across all ensemble members, corrected for forward model bias is our projected ice extent.

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.

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).

Our projected Arctic sea ice extent from the NCEP CFSv2 model with June 2013 revised - initial condition using 30 - member ensemble forecast is (surprisingly increased to) 4.7 million square kilometers with a standard deviation of 0.4 million square kilometers.

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

Thus, we extend the VIC ensemble using a computationally efficient statistical emulation model, which approximates the combined output of the two - step process of statistical downscaling and hydrologic modeling, trained with the 23 member VIC ensemble.

For all the ensemble members, we used one regression model using 27 years of past model data and NSIDC Merged SMMR and SSM / I sea ice concentration data to estimate and correct for systematic model bias.

Used CFSv2pp dynamical model; Twenty ensemble members are used.

Finally, we present results from two 5 - member ensembles of atmospheric model simulations.

Right panels show the predictability horizon for annual mean precipitation (above the dashed line), soil water averaged from the surface, and total water storage (below the dashed line), estimated from the 39 individual 10 member hindcast experiments (red) and the 1st order Markov model with 10,000 ensemble members (black circle) for the b the northern, d southern, and f these difference indices.

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.