Sentences with phrase «ensemble mean values»

The largest total discharge into the Arctic Ocean (ensemble mean values of 1.0 — 2.2 dSv) occurs during the onset of the Younger Dryas.

Annual means or totals for each pixel were averaged across all three reanalysis data sets to produce an ensemble mean value.

The Ensemble I mean value is 5.61 million km2 (bias included).

Not exact matches

The mapping of the downscaled results can be done for multi-model ensemble mean temperature trends as well as probabilities of exceeding certain threshold values.

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.

First I calculated the land - only, ocean - only and global mean temperatures and MSU - LT values for 5 ensemble members, then I looked at the trends in each of these timeseries and calculated the ratios.

Both of those approaches seem to me to be more scientifically justifiable than just taking the ensemble mean at face value.

In b, T g is shown relative to the value at the end of the calibration phase and where initial condition ensemble members exist, their mean has been taken for each time point.»

I know there are monthly mean values of sub-daily ensemble spread available, but these are not the same as the spread of the monthly averages calculated using each ensemble.

As I wrote originally, the differences between the sum of (ensemble mean) values for the individual forcing simulations and the Historical (All forcings) simulations are ~ 10 % for ΔT and iRF ΔF values.

The mean September value of the ensemble mean is 4.46 million kmÇ (bias corrected).

a Regressions of winter SLP and SAT trends upon the normalized leading PC of winter SLP trends in the CESM1 Large Ensemble, multiplied by two to correspond to a two standard deviation anomaly of the PC; b CESM1 ensemble - mean winter SLP and SAT trends; c b − a; d b + a. SAT in color shading (°C per 30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values Ensemble, multiplied by two to correspond to a two standard deviation anomaly of the PC; b CESM1 ensemble - mean winter SLP and SAT trends; c b − a; d b + a. SAT in color shading (°C per 30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values ensemble - mean winter SLP and SAT trends; c b − a; d b + a. SAT in color shading (°C per 30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values dashed).

SLP (contours; interval = 1 hPa with negative values dashed) and SAT (color shading; °C) from: a observations; b CESM1 simulation 14; c CESM1 ensemble mean; d CESM1 simulation 25.

The model's ensemble - mean P anomalies exhibit a realistic dipole pattern, with the largest positive values (in excess of 0.75 mm day − 1) over northern Europe, especially the west coast of Great Britain and Scandinavia, and largest negative values of comparable amplitude over southern Europe, particularly Portugal, Spain, and other countries bordering the Mediterranean Sea (compare Fig. 3e, g).

This range is constructed by computing the standard deviation (σ) of the 40 regression values at each grid box for each variable (SLP, SAT and P) based on detrended data during 1920 — 2012, and subtracting / adding these values (multiplied by two) from / to the ensemble mean regression value.

However, the ensemble mean September 2008 sea ice conditions, using the range of historical summer weather, gives a value of sea ice extent that is slightly more than 2007.

The modification of all feedback parameters results in changes of the sum of all feedbacks (water vapour, cloud, lapse rate and albedo), spanning a minimum — maximum range of 71 % (63 %) of the mean value for the correlated (uncorrelated) ensemble.

They actually regressed individual run values and then took the ensemble mean of the regression slopes.

The larger values of both signs are stippled, indicating that the ensemble mean is larger in magnitude than the inter-model standard deviation.

Honestly, the p - values should be generated by constructing a Monte Carlo ensemble of model results, per model, and looking at the actual distribution of (and variance of, autocorrelation of, etc) the ensemble of outcomes where the outcomes ARE iid samples drawn from a distribution of model results, and then use a correctly generated mean / sd to determinea p - value on the null hypothesis.

Given an ensemble of models from which an observable variable takes the mean value m 1 = 0 (without loss of generality) and standard deviation s 1, and an observation of this variable which takes the value m 2 with associated uncertainty s 2, the observation is initially at a normalised distance m 2 / s 1 from the ensemble mean.

The mean ΔT and ΔQ values from an ensemble of five runs were used.

Annual mean values are given for each ensemble member (r15) and the ensemble average.

The p values using the nine climate variables (denoted as «Overall» in Table 4) of MMEs are larger than the threshold (0.05, significant level = 5 %), which means that, according to this analysis, these ensembles have not been shown to be unreliable.