Sentences with phrase «observation and model ensemble»

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

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.

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

Not exact matches

Knutti, R., T.F. Stocker, F. Joos, and G.K. Plattner, 2002: Constraints on radiative forcing and future climate change from observations and climate model ensembles.

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.

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.

«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.»

Although a useful process to see which models should have more weight, and which ones should be discarded all together, the average that the ensemble produces will automatically have a higher correlation with observation data simply because of how far a set of numbers are spread out from each other.

Global climate model projections (in CMIP3 at least) appear to underestimate sea ice extent losses with respect to observations, though this is not universally true for all models and some of them actually have ensemble spreads that are compatible with PIOMAS ice volume estimates and satellite observations of sea ice extent.

As I pointed out on another thread (http://tinyurl.com/yntbat), Douglass et al. did something silly with the ensemble of climate models — apply computations that assume that they're raw observations, and treat them as «independent» models (*)-- see Section 2.3.

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.

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

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.

Sylvain, one of the main challenges of verifying climate models on a time scale of 1 - 2 decades is that natural forcing (solar and volcanic) is unknown plus the decadal ocean cycles are not deterministic and will not be simulated in a way that matches observations unless a very large ensemble is used.

It costs little to field the observations — the satellites and the radars, the surface in situinstruments, etc. to monitor conditions and their changes; to assimilate the data into variety of numerical models, to run these and form ensemble averages; to disseminate the findings.

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).

«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.»

Wang, 5.0 (± 0.27), Modeling A projected September Arctic sea ice extent of 5.0 million km2 is based on a NCEP ensemble mean CFSv2 forecast initialized from the NCEP Climate Forecast System Reanalysis (CFSR) that assimilates observed sea ice concentrations and other atmospheric and oceanic observations.

The method is a sea ice - ocean model ensemble run (without and with assimilation of sea - ice / ocean observations); the coupled ice - ocean model NAOSIM has been forced with atmospheric surface data from January 1948 to 7 July 2015.

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 standard deviation or some other appropriate measure of ensemble spread.

Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

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

Likewise, to properly represent internal climate variability, the full model ensemble spread must be used in a comparison against the observations (e.g., Box 9.2; Section 11.2.3.2; Raftery et al. (2005); Wilks (2006); Jolliffe and Stephenson (2011)-RRB-.

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).

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.»

I would bet that while the mean of the observations shifts upward to near the ensemble mean of the models, the uncertainty of the mean of observations would be hardly reduced at all: these time series are very strongly correlated as they all contain the same forced and unforced natural variations, see their Figure.

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.

C / decade and the simulated ensemble mean over the models, calculated from the grid boxes of the models where observations exist (which is flawed in my opinion, since excluding of mostly the high latitudes from the model data may emphasize a warm bias in lower latitudes in the models making them appear warmer than they are, but a possible cold bias of the global observations data set is not excluded in this way) had a trend of 0.3 deg.

Then, at each grid point, we compared the value of the observation with the ensemble of model values at each grid point, evaluating the rank of the observation in the ordered set of ensemble values and observed value.

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.

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).

He points out that the range of values for 2xCO2 ECS goes from 0.6 ºC (low end of Lindzen & Choi, 2011, observation - based on CERES satellite data) and 9.2 ºC (upper end of range of Knutti, 2002, based on a large ensemble of model simulations).

I don't know about Antarctica, and CMIP5 is definitely an improvement over CMIP3, but almost two thirds of the ensemble models do not agree «reasonably well» with observations.

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.

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.

If a model ensemble was perfect such that the true observed climatic variable can be regarded as indistinguishable from a sample of the model ensemble, then the rank of each observation lies with equal probability anywhere in the model ensemble, and thus the rank histogram should have a uniform distribution (subject to sampling noise).

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.

Among the various techniques, the AR4 AOGCM ensemble provides the most sophisticated set of models in terms of the range of processes included and consequent realism of the simulations compared to observations (see Chapters 8 and 9).