Sentences with phrase «comparing observations and models»

«We compare observations and models to figure out how well our models are performing, as well as how we should interpret our space - based observations.»

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.

Changes in fall - winter rainfall from observations (top panel) as compared to model simulation of the past century (middle panel), and a model projection of the middle of the 21st century.

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.

By comparing the models to recent observations of clusters in the Milky Way galaxy and beyond, the results show that Advanced LIGO (Laser Interferometer Gravitational - Wave Observatory) could eventually see more than 100 binary black hole mergers per year.

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.

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)

Various global temperature projections by mainstream climate scientists and models, and by climate contrarians, compared to observations by NASA GISS.

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.

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

Students learn about and revisit ways to represent observations and information and compare the use and effectiveness of maps, compare / contrast, models, and graphic organizers in presenting and evaluating data.

Recording Observations: Have students record their observations about which flooding scenario caused more damage to the model houses and the floodplain, and compare these to the observations of Observations: Have students record their observations about which flooding scenario caused more damage to the model houses and the floodplain, and compare these to the observations of observations about which flooding scenario caused more damage to the model houses and the floodplain, and compare these to the observations of observations of their peers.

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.

All predictions (whether in a laboratory or natural setting) are based on such models and it is only from comparing predictions to observations that one progresses.

How should one make graphics that appropriately compare models and observations?

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.

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.

As has been noted by others, this is comparing model temperatures after 2020 to an observation - based temperature in 2015, and of course the latter is lower — partly because it is based on HadCRUT4 data as discussed above, but equally so because of comparing different points in time.

All in all the science of hurricanes does appear to be much more fun and interesting than the average climate change issue, as there is a debate, a «fight» between different hypothesis, predictions compared to near - future observations, and all that does not always get pre-eminence in the exchanges about models.

See Stowasser & Hamilton, Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models, Journal of Climate 2006; Lauer et al., The Impact of Global Warming on Marine Boundary Layer Clouds over the Eastern Pacific — A Regional Model Study, Journal of Climate 2010.

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.

Scientists use data gathered from ARM's fixed, mobile, and aerial facilities worldwide to address these issues and compare the observations to their models.

The researchers focused on comparing model projections and observations of the spatial and seasonal patterns of how energy flows from Earth to space.

One could take the outcomes of different starting conditions, or use of different model parameters, and compare them against observations.

Researchers looking to compare climate model - simulated clouds and cloud observations from the ARM Climate Research Facility can access a helpful new tool.

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.

The radiative flux predicted by the HIRLAM weather model was compared to observations made in Jokioinen and Sodankylä.

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.

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..

We compare the output of various climate models to temperature and precipitation observations at 55 points around the globe.

Santer (2003) compares the rise of the tropopause from 1979 - 1999 from observations (reanalysis) to the rise calculated by a model driven by anthropogenic and non-anthropogenic forcings.

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.

This paper covers the historical experiments — comparing model results from 850-2005 to observations and proxy reconstructions — as well as some idealized experiments designed to measure metrics such as climate sensitivity, transient climate response, and carbon cycle feedbacks.

I haven't seen a study that compared the number of studies based upon actual observations as compared to model studies and sensitivity beliefs either come to think of it.

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 compare aircraft observations to modeled CH4 distributions by accounting for a) transport using the Stochastic Time - Inverted Lagrangian Transport (STILT) model driven by Weather Research and Forecasting (WRF) meteorology, b) emissions from inventories such as EDGAR and ones constructed from California - specific state and county databases, each gridded to 0.1 ° x 0.1 ° resolution, and c) spatially and temporally evolving boundary conditions such as GEOS - Chem and a NOAA aircraft profile measurement derived curtain imposed at the edge of the WRF domain.

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).

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.»

This month's report includes a compilation of ship - based observations from the Geographic Information Network of Alaska IceWatch program, a discussion of the August modeling contributions and how they compare to June and July, a look at the predictions in specific regions, and a discussion of current ice and weather conditions.