Sentences with phrase «models and observations when»

How Thorne et al could credibly claim that there is no reasonable evidence of a fundamental disagreement between tropospheric temperature trends from models and observations when uncertainties in both are treated comprehensively is mind - boggling.

«Thorne et al. ended with the conclusion that «there is no reasonable evidence of a fundamental disagreement between tropospheric temperature trends from models and observations when uncertainties in both are treated comprehensively».»

Not exact matches

This dataset contains millions of genomic sequences from a diverse set of rice varieties that, when combined with phenotyping observations, gene expression, and other information, provides an important step in establishing gene - trait associations, building predictive models, and applying these models to breeding.

Medvigy and Jeong found that prediction modeling for the entire United States indeed improves dramatically when the analyses include data from macro-scale observations, meaning from multiple sites spread over a large area.

NOAA's Coral Reef Watch uses satellite observations of sea surface temperatures and modeling to monitor and forecast when water temperatures rise enough to cause bleaching.

Combining computer models constructed largely by Princeton University researchers with on - ship observations, the researchers determined the movement and energy of the waves from their origin on a double - ridge between Taiwan and the Philippines to when they fade off the coast of China.

«Its existence was predicted by the standard model of particle physics and the fact that there's — we got a glimpse of it, it looks like it may very well be there — is a real victory for that model of science where you test, you put forward conceptual models of the way the world or the universe works and test those models against the observations and see the extent to which they can predict new observations and when they do, it gives you increased confidence in the models.

Right now, all of the models that forecasters use, as well as observations of what's currently happening in the tropical Pacific, are indicating that this El Niño is going to be a strong one come late fall and winter, when it usually peaks.

When combined with General Circulation Models, such observations would be essential in understanding the hydrological cycle and seasonal variation on Titan.

This involves a combination of satellite observations (when different satellites captured temperatures in both morning and evening), the use of climate models to estimate how temperatures change in the atmosphere over the course of the day, and using reanalysis data that incorporates readings from surface observations, weather balloons and other instruments.

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.).

When combined with previous observations, new research with the Heterodyne Instrument for Planetary Wind And Composition (HIPWAC) joined to the large aperture of the Subaru telescope supports the model that Titan has currents or jet streams at high latitudes racing through its upper atmosphere (stratosphere) at speeds of approximately 756 km / hour (470 miles / hr.).

This dataset contains millions of genomic sequences from a diverse set of rice varieties that, when combined with phenotyping observations, gene expression, and other information, provides an important step in establishing gene - trait associations, building predictive models, and applying these models to breeding.

Consistent with this observation, the strength of the inverse association with risk of type 2 diabetes was similar for decaffeinated (multivariate RR 0.81 [95 % CI 0.73 — 0.90]-RRB- and caffeinated coffee consumption (0.87 [0.83 — 0.91]-RRB- when expressed for a one - cup increment in consumption per day and simultaneously included in the multivariate model.

This article is primarily about (1) the extent to which the data generated by «high - quality observation systems» can inform principals» human capital decisions (e.g., teacher hiring, contract renewal, assignment to classrooms, professional development), and (2) the extent to which principals are relying less on test scores derived via value - added models (VAMs), when making the same decisions, and why.

In the global mean, there isn't much of an issue for the mid-troposphere — the models and data track each other when you expect they would (the long term trends or after volcanoes, and don't where you expect them not to, such as during La Niña / El Niño events which occur at different times in models and observations).

When this is done for predicting elections, say, something called «stratification» is used, where observations are qualified by (in this case) spatial extent, time of day, and other auxiliary variables and the response state of atmosphere considered as conditioned on these, and the model evaluated comparably, where it can be.

Modelling is generally shunned in attribution in favor of observation, but I do agree that climate science must turn to modelling when necessary, and that the statements in the 2010 post about using a lab are quite accurate and inModelling is generally shunned in attribution in favor of observation, but I do agree that climate science must turn to modelling when necessary, and that the statements in the 2010 post about using a lab are quite accurate and inmodelling when necessary, and that the statements in the 2010 post about using a lab are quite accurate and insightful.

Wadhams and the AMEG argue that observations are more to be relied on than models, especially when the models have proved unreliable in the past.

There is one major caveat with these products though, and that is that while the model isn't changing over time, the input data is and there are large variations in the amount and quality of observations — particularly around 1979 when a lot of satellite observations came on line, but also later as the mix and quality of data has changed.

When we do so with CMIP5 and with the three main temperature records we fins an underlying trend for CMIP5 matching that for Fyfe et al, and for CMIP4, and a discrepancy between observations and models far closer to that obtained by Foster and Rahmstorf than that obtained by Fyfe et al..

As you clearly know, nothing could be more important than understanding and propagating error when evaluating the level of quantitative knowledge yielded by some experiment, observation, or sequential calculation — including use of a model to predict a result.

When Copernicus, Kepler and Galileo found that science and observations did not support Ptolemy's clever and complex model of the solar system, the totalitarian establishment of their day advised such heretics to recant — or be battered, banished or even burned at the stake.

And when weighing between observations and models, the former trumps every tiAnd when weighing between observations and models, the former trumps every tiand models, the former trumps every time.

In - depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under - produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over-produce low - level clouds throughout the day.

More disconcerting, when models added the effects of CO2 and aerosols to natural factors (the red line labeled ALL), discrepancies between models and 1940s observations worsened.

When Armour factored rising sensitivity into that 2013 observation - based Nature Geoscience report and recalculated climate sensitivity, he got a best estimate of 2.9 º C — a value well within the IPCC's consensus range and the range predicted by 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).

It becomes a model when you try and describe the observation mathematically, so as to be able to extrapolate what happens when a variable changes (ie the amount of water, thickness of the pot, the duration of the heat source, etc.).

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.

And experiments aside, we have scores of empirical observations of rising temperatures, tropospheric lapse rates, energy flux measurements at the top of the atmosphere, modelling experiments, observations of other planetary bodies (Venus is an excellent example of what happens when CO2 concentrations rise well beyond the point of spectral saturation) that all support the greenhouse theory of atmospheres.

Structural uncertainty is attenuated when convergent results are obtained from a variety of different models using different methods, and also when results rely more on direct observations (data) rather than on calculations.

There is much greater similarity between the general evolution of the warming in observations and that simulated by models when anthropogenic and natural forcings are included than when only natural forcing is included (Figure 9.6, third row).

When we constrain the model projections with observations, we obtain greater means and narrower ranges of future global warming across the major radiative forcing scenarios, in general.

Modelling studies are also in moderately good agreement with observations during the first half of the 20th century when both anthropogenic and natural forcings are considered, although assessments of which forcings are important differ, with some studies finding that solar forcing is more important (Meehl et al., 2004) while other studies find that volcanic forcing (Broccoli et al., 2003) or internal variability (Delworth and Knutson, 2000) could be more important.

Your comment: «In fact you seemed to have gone to quite a lot of trouble to use an image of Model expectations from another source (with different timeframe and colour scale), when four images representing Model expectations were readily available right beside the Observations image.»

One very interesting bit of extra information the UW's Polar Science Center has shared this month, is how both PIOMAS (model) and CryoSat (satellite observations) are in agreement with each other when it comes to sea ice volume distribution.

Radiation Budget of the West African Sahel and its Controls: A Perspective from Observations and Global Climate Models, Miller et al, 8/2012, read more here; ``... GCMs underestimated the surface LW and SW CRF and predicted near zero SW CRE when the measured values were substantially larger...»

Differences between the regression slope and the true feedback parameter are significantly reduced when 1) a more realistic value for the ocean mixed layer depth is used, 2) a corrected standard deviation of outgoing radiation is used, and 3) the model temperature variability is computed over the same time interval as the observations.

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.

As I showed in my Hub, when you consider that metric, the agreement of models and observations is quite reasonable.

But almost universally, when they try to explain it, they all use the purely radiative approach, which is incorrect, misleading, contrary to observation, and results in a variety of inconsistencies when people try to plug real atmospheric physics into a bad model

The Sedlacek and Knutti paper is only about oceanic temperatures, not the land record, it shows that the models do a poor job matching observed oceanic changes over the 20th century when relying only on natural forcing, and that if the natural - only runs are scaled to have an overall trend that matches the observations, the models predict a more heterogeneous distribution of trends than was observed.

the purely radiative approach, which is incorrect, misleading, contrary to observation, and results in a variety of inconsistencies when people try to plug real atmospheric physics into a bad model

Let us also expect that the model is designed from the git - go to be verified by physical measurements and observations, and that your conceptual and detail designs will descibe what kinds of testing methods will be applied, how, and when.

When we look at the distributions (e.g. data, 1st difference, 2nd difference) of the observations of global temperature, and of modeled temperature, they are very different.

When the fanatics have to choose between models and observation they choose their religious faith every time.

Models are often tuned by running them backwards against several decades of observation, this is much too short a period to correlate outputs with observation when the controlling natural quasi-periodicities of most interest are in the centennial and especially in the key millennial range.

For a complete discussion of this see Essex: https://www.youtube.com/watch?v=hvhipLNeda4 Models are often tuned by running them backwards against several decades of observation, this is much too short a period to correlate outputs with observation when the controlling natural quasi-periodicities of most interest are in the centennial and especially in the key millennial range.

As for your comment, the fundamental point here is that there can be no «planet climate model» and «planet observations» when talking about attribution.