Sentences with phrase «mean of the model simulations»

In this study, we primarily investigate the reliability of the climatology (long - term mean of model simulation) of large - scale features of climate model ensembles, but we also consider the trend for surface air temperature where transient simulations are available (that is, for the coupled ocean — atmosphere models).

Not exact matches

This can be shown by means of a simulation model of reproduction, combining the monthly probability of conceiving, the risk of miscarriage and the probability of becoming permanently sterile, all depending on age (Leridon, 2004).

By means of building simulations and advanced calculation models, the researchers came to the conclusion that using plenty of energy is both an economic and an environmental advantage, while it is also inexpensive and green.

Maps of median TAE averaged across 23 model simulations for (a) and (b) mean surface air temperature, (c) and (d) highest daily maximum temperature, (e) and (f) lowest daily minimum temperature, (g) and (h) total precipitation, and (i), (j) maximum 1 - d precipitation for (a), (c), (e), (g) and (i) June - August and (b), (d), (f), (h) and (j) December - February.

That would mean a new emphasis on controlling unruly plasma, understanding plasma's interactions with the solid surfaces of the reactor, and improved modeling and simulation.

If, for example, the crucial effects of neutrinos were included in some detailed treatment, the computer simulations could only be performed in two dimensions, which means that the star in the models was assumed to have an artificial rotational symmetry around an axis.

An international group of atmospheric chemists and physicist could now have solved another piece in the climate puzzle by means of laboratory experiments and global model simulations.

Consequently, in the past 20 years his research has evolved from an early focus on prioritizing the effects that humans have on coral reefs and the role that marine protected areas play in conserving biological diversity and ecological processes, to developing theoretical and simulation models of coral reefs that will help predict and suggest alternatives to reduce detrimental effects, to developing practical means to restore degraded reefs through manipulation of the food web and management.

(Top left) Global annual mean radiative influences (W m — 2) of LGM climate change agents, generally feedbacks in glacial - interglacial cycles, but also specified in most Atmosphere - Ocean General Circulation Model (AOGCM) simulations for the LGM.

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 means that Illustris might be modeling real galaxies better than it seems, and coupling of a dust reddening model to the simulation output might improve the correspondence between the mismatched vote fraction distributions at lower stellar masses.

Points illustrate the mean probability that a tiger population of given starting size will decline to extinction over 1,000 model simulations both with canine distemper virus (CDV) infection (black dots) and a control scenario without CDV (open diamonds).

This is hypothesized to result from fresh water input into the Northern Hemisphere (although it is worth noting that the transient simulations of this sort fix the magnitude of the freshwater perturbation, so this doesn't necessarily mean that the model has the correct sensitivity to freshwater input).

When looking for SYSTEMATIC deviations between data and model simulations, one calculates the mean and the standard deviation of the mean for each and compares.

Figure 1.4 http://cybele.bu.edu/courses/gg312fall02/chap01/figures/figure1.4.gif shows the natural variability of the annual mean surface temperature on several different spatial scales from a climate model simulation for 200 years.

Does that mean the model simulations have not accounted for the unusually deep solar minimum or would that not make much of a difference?

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.).

Second, the absolute value of the global mean temperature in a free - running coupled climate model is an emergent property of the simulation.

Recently I have been looking at the climate models collected in the CMIP3 archive which have been analysed and assessed in IPCC and it is very interesting to see how the forced changes — i.e. the changes driven the external factors such as greenhouse gases, tropospheric aerosols, solar forcing and stratospheric volcanic aerosols drive the forced response in the models (which you can see by averaging out several simulations of the same model with the same forcing)-- differ from the internal variability, such as associated with variations of the North Atlantic and the ENSO etc, which you can see by looking at individual realisations of a particular model and how it differs from the ensemble mean.

We can derive the underlying trend related to external forcings from the GCMs — for each model, the underlying trend can be derived from the ensemble mean (averaging over the different phases of ENSO in each simulation), and looking at the spread in the ensemble mean trend across models gives information about the uncertainties in the model response (the «structural» uncertainty) and also about the forcing uncertainty — since models will (in practice) have slightly different realisations of the (uncertain) net forcing (principally related to aerosols).

Direct comparison of the radiances predicted by the model to those observed by AIRS in the thermal spectral regions dominated by water vapor absorption provides a means of assessing the simulation of water vapor in the climate model at the high level of detail provided by spectral measurements.

«The 10 model simulations (a total of 700 years of simulation) possess 17 non-overlapping decades with trends in ENSO - adjusted global mean temperature within the uncertainty range of the observed 1999 - 2008 trend -LRB--0.05 to +0.05 C per decade).»

The modelers could consider varying the change i n forcing in a long series of simulations / samples from the model population (4, 3.5, 3, 2.5 etc) until they got some means consistently below the mean temperature.

Results show that higher - resolution models significantly improve the simulation of mean precipitation, the distribution of precipitation, and spatial patterns, intensity and seasonality of precipitation extremes.

Chris Jones and Chris Bretherton at the University of Washington invented an algorithm to speed up cloud resolving model simulations by cleverly exploiting a known timescale separation between fast eddies and the rate at which they evolve the horizontal mean state of a limited domain LES.

«When initialized with states close to the observations, models «drift» towards their imperfect climatology (an estimate of the mean climate), leading to biases in the simulations that depend on the forecast time.

In a recent paper published in Nature Communications, using both observations and a coupled Earth system model (GFDL - ESM2G) with a more realistic simulation of the Atlantic Meridional Overturning Circulation (AMOC) structure, and thus reduced mean state biases in the North Atlantic, the authors show that the decline of the Atlantic major hurricane frequency during 2005 — 2015 is associated with a weakening of the AMOC directly observed from the RAPID program.

Figure 1: Estimation of the observed signature of internal variability in the observed 20th century global mean temperature in climate model simulations

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.

We estimate the low - frequency internal variability of Northern Hemisphere (NH) mean temperature using observed temperature variations, which include both forced and internal variability components, and several alternative model simulations of the (natural + anthropogenic) forced component alone.

• Calibrate the retrospective simulations of ice thickness from our numerical model against the aggregate of all the observation systems by removing the mean difference between the model and the observations to create a Calibrated Model Ice Thickness Remodel against the aggregate of all the observation systems by removing the mean difference between the model and the observations to create a Calibrated Model Ice Thickness Remodel and the observations to create a Calibrated Model Ice Thickness ReModel Ice Thickness Record.

In this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed NAO record in combination with the model's radiatively - forced response (given by the ensemble - mean of the 40 simulations).

In a broader sense, when models are used to test hypotheses about the climate variables used in their simulations, they conform to the concept of an experiment as a means of performing tests to gain knowledge about the world around us.

Global mean temperatures from climate model simulations are typically calculated using surface air temperatures, while the corresponding observations are based on a blend of air and sea surface temperatures.

Another point: — it's clear that an individual models separate runs can generate many different potential futures, so that you wouldn't necessarily expect the mean to match the actual mean of the individual future of the observations if the variance of the simulations was wide.

Analyses of tide gauge and altimetry data by Vinogradov and Ponte (2011), which indicated the presence of considerably small spatial scale variability in annual mean sea level over many coastal regions, are an important factor for understanding the uncertainties in regional sea - level simulations and projections at sub-decadal time scales in coarse - resolution climate models that are also discussed in Chapter 13.

The fact that the CMIP simulations ensemble mean can reproduce the 1970 — 2010 US SW temperature increase without inclusion of the AMO (the AMO is treated as an intrinsic natural climate vari - ability that is averaged out by taking an ensemble mean of individual simulations) suggests that the CMIP5 models» predicted US SW temperature sensitivity to the GHG has been significantly (by about a factor of two) overestimated.

Baseline (i.e., mean 1971 — 1999) global varies between 461 Pg C and 998 Pg C, and increases with ΔMLT for all vegetation models under all 110 climate and CO2 increase scenarios (Fig. 1)(see Materials and Methods and SI Text for details of simulations).

«The fact that the CMIP simulations ensemble mean can reproduce the 1970 — 2010 US SW temperature increase without inclusion of the AMO (the AMO is treated as an intrinsic natural climate variability that is averaged out by taking an ensemble mean of individual simulations) suggests that the CMIP5 models» predicted US SW temperature sensitivity to the GHG has been significantly (by about a factor of two) overestimated.»

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).

An estimate of the forced response in global mean surface temperature, from simulations of the 20th century with a global climate model, GFDL's CM2.1, (red) and the fit to this evolution with the simplest one - box model (black), for two different relaxation times.

Larger interannual variations are seen in the observations than in the ensemble mean model simulation of the 20th century because the ensemble averaging process filters out much of the natural internal interannual variability that is simulated by the models.

Simulations where the magnitude of solar irradiance changes is increased yield a mismatch between model results and CO2 data, providing evidence for modest changes in solar irradiance and global mean temperatures over the past millennium and arguing against a significant amplification of the response of global or hemispheric annual mean temperature to solar forcing.

Where not available, and in the case of the «NAT» simulations, the mean for the 1996 to 2005 decade was estimated using model output from 1996 to the end of the available runs.

Climate simulations are consistent in showing that the global mean warming observed since 1970 can only be reproduced when models are forced with combinations of external forcings that include anthropogenic forcings (Figure 9.5).

Climate change by 2060 was computed as the difference (air temperature) or ratio (precipitation and solar radiation) of monthly mean climate between the GCM (unforced) control and 2xCO2 simulations at GCM grid boxes coinciding with the crop modelling sites (Figure 13.1 b).

Lindsay; 4.0 million square kilometers; Model The predicted mean ice extent in September is 3.99 + / - 0.30 million square kilometers, a record low, and it is based on the fractional area of ice and open water less than 0.4 m thick (G0.4) obtained from model retrospective simulatModel The predicted mean ice extent in September is 3.99 + / - 0.30 million square kilometers, a record low, and it is based on the fractional area of ice and open water less than 0.4 m thick (G0.4) obtained from model retrospective simulatmodel retrospective simulations.

The 10 model simulations (a total of 700 years of simulation) possess 17 nonoverlapping decades with trends in ENSO - adjusted global mean temperature within the uncertainty range of the observed 1999 — 2008 trend (− 0.05 ° to 0.05 °C decade — 1).

«A strong warming and severe drought predicted on the basis of the ensemble mean of the CMIP climate models simulations is supported by our regression analysis only in a very unlikely case of the continually increasing AMO at a rate similar to its 1970 — 2010 increase» 7

Assessments of our relative confidence in climate projections from different models should ideally be based on a comprehensive set of observational tests that would allow us to quantify model errors in simulating a wide variety of climate statistics, including simulations of the mean climate and variability and of particular climate processes.