Sentences with phrase «comparing models with observations»

HYDRA will investigate the sensitivity to, and uncertainties in, rainfall, evaporation and river runoff to changes in land use and the carbon cycle by comparing models with observations from the last 50 years.

The new study said that almost all of 19 global climate models underestimated rainfall in the world's biggest tropical forest after the scientists compared the models with observations of 20th century climate.

rgb says this of Monckton's and Spencer's attempts to compare models with observations (and others like Rose's seen in the press).

Not exact matches

These dust particles have surprisingly diverse mineral content and structure as compared with models of interstellar dust based on previous astronomical observations.

Nesvorný and his colleagues followed particles released in their model from various types of comets or from asteroids and compared the particles» fates with observations of the zodiacal dust cloud.

The models were run without the influence of greenhouse gases, so that the scientists could compare those results with the observations from 33 years of satellite precipitation data.

The study compared Simmons» and other scientists» models with field observations and laboratory experiments.

To check their model forecast, as the dry season has gotten underway, the researchers have compared their initial forecast with observations coming in from NASA's precipitation satellite missions» multisatellite datasets, as well as groundwater data from the joint NASA / German Aerospace Center Gravity Recovery and Climate Experiment (GRACE) mission.

In this technique, scientists initiate a computer model with data collected before a past event, and then test the model's accuracy by comparing its output with observations recorded as the event unfolded.

In February, Australian and American researchers who compared ocean and climate modeling results with weather observations published findings in Nature Climate Change advancing earlier studies that explored the oscillation's global influence.

Comparing the experimental observations with a theoretical model, they determined that the switching timescale is set by interactions between the atoms themselves, rather by external control parameters.

Then they compared the model results with the observations from the CO2 sensor network.

Where climate sensitivity is estimated in studies involving comparing observations with values simulated by a forced climate model at varying parameter settings (see Appendix 9.

Figure 1: Annual average TOA shortwave cloud forcing for present - day conditions from 16 IPCC AR4 models and iRAM (bottom center) compared with CERES satellite observations (bottom right)

When the researchers compared the X-ray absorption data gathered by the spacecraft with computer models of distribution of normal matter across the Milky Way, they discovered that their observations couldn't be explained by a smooth, uniform distribution of gas.

In fact, the calculation has been done very carefully by Hansen and co-workers, taking all factors into consideration, and when compared with observations of ocean heat storage over a period long enough for the observed changes to be reliably assessed, models and observations agree extremely well (see this article and this article.).

These include using the same model used to detect the planet instead to fit synthetic, planet - free data (with realistic covariance properties, and time sampling identical to the real data), and checking whether the «planet» is still detected; comparing the strength of the planetary signal with similar Keplerian signals injected into the original observations; performing Bayesian model comparisons between planet and no - planet models; and checking how robust the planetary signal is to datapoints being removed from the observations.

Modelling of Tau Ceti's dust disk observations by the astronomers indicate, however, that the mass of the colliding bodies up to 10 kilometers (6.2 miles) in size may total around 1.2 Earth - masses, compared with 0.1 Earth - masses estimated to be in the Solar System's Edgeworth - Kuiper Belt (Greaves et al, 2004).

This approach allowed them to compare the rate and distribution of warming predicted by models with those shown in observations.

Comparing the observations with computer models, the astronomers concluded that filaments are probably formed when slow shockwaves dissipate in the interstellar clouds.

Working with Tom Chase, a colleague at the institute, the researchers were comparing climate simulations from the Community Land Model — part of a select group of global models used in the Intergovernmental Panel on Climate Change's 2007 climate change report — against observations.

How does the Literacy Classroom Visit Model compare with other walkthroughs, observations, or classroom visits used in your school or district?

We don't compare observations with the same time period in the models (i.e. the same start and stop dates), but to all model projections of the same time length (i.e. 60 - month to 180 - month) trends from the projected data from 2001 - 2020 (from the A1B run)(the trends of a particular length are calculated successively, in one month steps from 2001 to 2020).

Global warming deniers * pull similar dirty tricks with the comparison of global temperature with model projections — for example, by plotting only the tropical mid-troposphere, and by comparing observations with the projections of scenarios which are furthest from reality.

Comparing models to observations is perfectly fine, but the comparison has to be apples - with - apples and the analysis has to be a little more sophisticated than saying «look at the lines» (or «linear striations»).

Even with a near - perfect model and accurate observations, model - observation comparisons can show big discrepancies because the diagnostics being compared while similar in both cases, actually end up be subtly (and perhaps importantly) biased.

I think once you fully understand our methodology that you will find it to be a reasonable approach to comparing observations with model expectations on large spatial (global), but intermediate (5 - 15 years) temporal scales.

There is a «model» which has a certain sensitivity to 2xCO2 (that is either explicitly set in the formulation or emergent), and observations to which it can be compared (in various experimental setups) and, if the data are relevant, models with different sensitivities can be judged more or less realistic (or explicitly fit to the data).

More interestingly, in response to the second referee's objection that older SRES scenarios were used instead of the new RCP scenarios, Hansen replied: «Our paper compares observations (thus the past) and models, thus only deals with the past.

The evaluation of the model leaves much to be desired: no differences are shown compared with observations, and some errors are large.

A detailed reanalysis is presented of a «Bayesian» climate parameter study (Forest et al., 2006) that estimates climate sensitivity (ECS) jointly with effective ocean diffusivity and aerosol forcing, using optimal fingerprints to compare multi-decadal observations with simulations by the MIT 2D climate model at varying settings of the three climate parameters.

We know for instance that the temporal / spatial variability in these in - filled regions is different to where there are observations, which need to be thought about when comparing with model variability.

Once we have used real observations to understand the probability in the historical record, then we can use climate models to compare the probability in the current climate (in which global warming has occurred) with a climate in which there was no human - caused global warming.

Forest et al. 2006 compares observations of multiple surface, upper air and deep - ocean temperature changes with simulations thereof by the MIT 2D climate model run at many climate parameter settings.

The papers is directed to more detailed study of what has happened during the last few years comparing the observations with phenomena that are seen in the model.

[Update] Steve McIntyre has some interesting observations about the reviewer's report as well noting that the main shortcoming / error of the paper seems to be the fact that it compared observations with models where «no consistency was to be expected in the first place».

Consequently, short of waiting until after climate change has occurred, the best guide we have for judging model reliability is to compare model results with observations of present and past climates.Our lack of knowledge about the real climate makes it difficult to verify models.

The scientists, using computer models, compared their results with observations and concluded that global average annual temperatures have been lower than they would otherwise have been because of the oscillation.

And Ed compares CMIP5 models with updated observations; but instead of evidencing the «pause» some researches keep watching that figure as an evidence of global warming.

Ultimately, we will be able to compare multiple long term forecasts with observations, rather than relying simply on Hansen's model.

Revisionist and / or «still consistent with observations»: in terms of changing the assumptions, changing the amount of time necessary for a pause to be significant, changing tack to OHC, comparing real earth to the spread of all models, etc..

The motivation for this paper is twofold: first, we validate the model's performance in the Gulf of Mexico by comparing the model fields to past and recent observations, and second, given the good agreement with the observed Gulf of Mexico surface circulation and Loop Current variability, we expand the discussion and analysis of the model circulation to areas that have not been extensively observed / analyzed, such as the vertical structure of the Loop Current and associated eddies, especially the deep circulation below 1500 m.

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.

Climate projections have been remarkably difficult to constrain by comparing the simulated climatological state from different models with observations, in particular for small ensembles with structurally different models.

As I said, when comparing with observations over the short period being considered here, it makes more sense to compare with models that include natural internal variability (i.e.: GCMs — as in the final version) than against models that do not include this and only include externally - forced changes (ie: Simple Climate Models, SCMs, — as in the SOD vermodels that include natural internal variability (i.e.: GCMs — as in the final version) than against models that do not include this and only include externally - forced changes (ie: Simple Climate Models, SCMs, — as in the SOD vermodels that do not include this and only include externally - forced changes (ie: Simple Climate Models, SCMs, — as in the SOD verModels, SCMs, — as in the SOD version).

«In the case of the Arctic we have high confidence in observations since 1979, from models (see Section 9.4.3 and from simulations comparing with and without anthropogenic forcing), and from physical understanding of the dominant processes; taking these three factors together it is very likely that anthropogenic forcing has contributed to the observed decreases in Arctic sea ice since 1979.»

The point I want to make (and I made this point point in the Uncertainty Monster paper) is globally, the modeled spectral density of the variability, when compared with observations, is too high for periods of ~ 8 - 17 years, and too low for periods of 40 - 70 years.

«Causes of differences in model and satellite tropospheric warming rates» «Comparing tropospheric warming in climate models and satellite data» «Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures» «Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends» «Reconciling warming trends» «Natural variability, radiative forcing and climate response in the recent hiatus reconciled» «Reconciling controversies about the «global warming hiatus»»

In an earlier study (Labe et al., 2018a), we show that the CESM - LENS sea ice thickness compares well with satellite observations and output from an ice - ocean model.

Lindzen (2011) reports that Wentz et al. (2007) used space - based observations to measure how evaporation changed with temperature compared with results from models and found that in GCMs, evaporation rose 1 - 3 % for each 1 K warming, while observed evaporation rose approximately three times faster, at 5.7 %.