Sentences with phrase «observational estimates for»

Remarkably, the Marvel et al. reworked observational estimates for TCR and ECS are, taking the averages for the three studies, substantially higher than the equivalent figures for the GISS - E2 - R model itself, despite the model exhibiting faster warming than the real climate system.

On the other hand, the observational estimates for charter schools that contribute to the lottery study are larger than the observational estimates for other charter schools (though the latter are still positive and significantly different from 0).

Not exact matches

Of note, our point estimate for premature death exceeds the annual number of U.S. deaths from cervical cancer (3,909), asthma (3,361), or influenza (3,055).45 If a randomized control trial were to demonstrate similar effects to those reported in the observational literature, the «number needed to treat» with optimal breastfeeding to prevent a case of maternal hypertension would be 35, to prevent a maternal MI would be 135, and to prevent a case of breast cancer would be 385.

The performance of different propensity - score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies.

The team compared the scattering coefficient obtained by their approach with the scattering coefficient measured on board the aircraft and found good agreement between the estimated and measured scattering coefficients for a wide range of observational conditions.

We estimate that ~ 35 % of KOIs are false positives due to contamination, when performing a first - order correction for observational bias.

[109] The observational thresholds for planet detection in the habitable zones via the radial velocity method are currently (2017) estimated to be about 50 M ⊕ for Alpha Centauri A, 8 M ⊕ for B, and 0.5 M ⊕ for Proxima.

[11] Asteroseismic analyses that incorporate the tight observational constraints on the stellar parameters for α Cen A and / or B have yielded age estimates of 7000484999999999999 ♠ 4.85 ± 0.5 Gyr, [7] 7000500000000000000 ♠ 5.0 ± 0.5 Gyr, [27] 5.2 — 7.1 Gyr, [28] 6.4 Gyr, [29] and 7000652000000000000 ♠ 6.52 ± 0.3 Gyr.

The visualization covers the period June 2005 to December 2007 and is based on a synthesis of a numerical model with observational data, created by a NASA project called Estimating the Circulation and Climate of the Ocean, or ECCO for short.

It's noteworthy, however, that the observational estimates of pilot high school treatment effects are larger for schools used in the lottery study than for other pilot schools.

In an effort to gauge the external validity of our lottery estimates, we computed observational estimates that rely solely on statistical controls, with separate effects for schools in and out of the lottery sample.

The match across designs is not as good for pilot high schools, where the observational analysis for lottery schools produces substantial and significant positive estimates, while the lottery results for ELA and math are small and not significantly different from 0 (though the match for writing is good).

The observational results for pilot ELA are more negative than the corresponding lottery estimates, while the opposite is true for math.

Observational models are estimated by OLS and include separate variables for years in lottery sample pilot schools, lottery sample charter schools, nonlottery sample pilot schools, and nonlottery sample charter schools.

For example, observational estimates of the effects of attending a charter middle school in the lottery study are 0.17 σ for ELA and 0.32 σ for maFor example, observational estimates of the effects of attending a charter middle school in the lottery study are 0.17 σ for ELA and 0.32 σ for mafor ELA and 0.32 σ for mafor math.

However, what we have seen since 2009, when states began to adopt what were then (and in many ways still are) viewed as America's «new and improved» or «strengthened» teacher evaluation systems, is that for 70 % of America's teachers, these teacher evaluation systems are still based only on the observational indicators being used prior, because for only 30 % of America's teachers are value - added estimates calculable.

She used R (i.e., a free software environment for statistical computing and graphics) to simulate correlation scatterplots (see Figures below) to illustrate three unique situations: (1) a simulation where there are two indicators (e.g., teacher value - added and observational estimates plotted on the x and y axes) that have a correlation of r = 0.28 (the highest correlation coefficient at issue in the aforementioned post); (2) a simulation exploring the impact of negative bias and a moderate correlation on a group of teachers; and (3) another simulation with two indicators that have a non-linear relationship possibly induced or caused by bias.

Yet the 10 to 90 percentile for the trends among the models is 0.036 — 0.35 °C / dec — a much larger range (+ / - 0.19 °C / dec)-- and one, needless to say, that encompasses all the observational estimates.

One reason why these estimates keep getting revised is that there is a continual updating of the observational analyses that are used — as new data are included, as non-climatic factors get corrected for, and models include more processes.

Averaged the two observational time series to create an estimated actual temperature for each year.

Whether you are gullible enough to accept the figures as accurate depends on how much credibility you put in the multitude of observational measurements taken by different methods over many decades by diverse groups of researchers that form a strong consilience of mutually supporting evidence for the validity of the estimates and the possible errors.

Even worse: to numbers with error estimates, it adds a number without proper error estimate (the observational uncertainty for 1993 - 2003 is included, but who would claim this is an error estimation for future ice flow changes?).

As these figures show, estimates from both models and observational data consistently find that the most likely climate sensitivity value is approximately 3 °C for a doubling of CO2.

Studies surveyed Millar, R. et al. (2017) Emission budgets and pathways consistent with limiting warming to 1.5 C, Nature Geophysics, doi: 10.1038 / ngeo3031 Matthews, H.D., et al. (2017) Estimating Carbon Budgets for Ambitious Climate Targets, Current Climate Change Reports, doi: 10.1007 / s40641 -017-0055-0 Goodwin, P., et al. (2018) Pathways to 1.5 C and 2C warming based on observational and geological constraints, Nature Geophysics, doi: 10.1038 / s41561 -017-0054-8 Schurer, A.P., et al. (2018) Interpretations of the Paris climate target, Nature Geophysics, doi: 10.1038 / s41561 -018-0086-8 Tokarska, K., and Gillett, N. (2018) Cumulative carbon emissions budgets consistent with 1.5 C global warming, Nature Climate Change, doi: 10.1038 / s41558 -018-0118-9 Millar, R., and Friedlingstein, P. (2018) The utility of the historical record for assessing the transient climate response to cumulative emissions, Philosophical Transactions of the Royal Society A, doi: 10.1098 / rsta.2016.0449 Lowe, J.A., and Bernie, D. (2018) The impact of Earth system feedbacks on carbon budgets and climate response, Philosophical Transactions of the Royal Society A, doi: 10.1098 / rsta.2017.0263 Rogelj, J., et al. (2018) Scenarios towards limiting global mean temperature increase below 1.5 C, Nature Climate Change, doi: 10.1038 / s41558 -018-0091-3 Kriegler, E., et al. (2018) Pathways limiting warming to 1.5 °C: A tale of turning around in no time, Philosophical Transactions of the Royal Society A, doi: 10.1098 / rsta.2016.0457

It also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability.

As I interpret the evidence, the observational data tend to confirm the modeling for these individual feedbacks at least semiquantitatively, and this suggests to me that the climate sensitivity estimates are probably not grossly in error, even if precise quantitation still eludes us.

Under «effective radiative forcing» 20th century observational studies match complex models and paleoclimatology's best estimates for CO2 climate sensitivity.

In the long term, the PAGES 2k community recommends the continued development of methods that incorporate network, observational, and chronological uncertainty into quantitative estimates of past climate variability, including approaches that allow for quantitative calibration and validation of low - frequency variability.

The fact that our pf ′ values (even for 30 - year TLT trends) are sensitive to the addition of a single year of observational data indicates the dangers of ignoring the effects of interannual variability on signal estimates, as was done, for example, in Douglass et al. [2007].

Carrick «Keep in mind the estimates for the half - life of CO2 emissions is on the order 800 years (based on correlational studies)» niclewis September 24, 2014 at 3:00 pm I'm not sure that is supported by good observational evidence.

None of the Annan / Hargreaves priors go below zero, and while this may be physically realistic it does not allow for the fact that the observational data generate negative sensitivities, mostly because of ocean cycle warming and cooling effects that the radiative forcing estimates do not take into account.

The value of the variance for the process noise in the above was arbitrarily chosen to be the same as the empirically estimated observational variance of the observations in the separate cases of the HadCRUT4 series and GISTEMP.

As mentioned above, climate scenarios that are developed for impacts applications usually require that some estimate of climate change be combined with baseline observational climate data, and the demand for more complete and sophisticated observational data sets of climate has grown in recent years.

Nic writes «Given Forster & Gregory's regression method and observational error assumptions, the error (and hence probability) distribution for the resulting slope coefficient estimate can be derived from frequentist statistical theory, as used in science for many years.»

Given Forster & Gregory's regression method and observational error assumptions, the error (and hence probability) distribution for the resulting slope coefficient estimate can be derived from frequentist statistical theory, as used in science for many years.

Estimates of natural variability from an AOGCM provide a critical input in deriving, by comparing temperature estimates from the simple model with observations, a likelihood function for the parameters jointly at each possible combination of parameter settings (and in one or two cases AOGCMs provide surrogates for some of the observationEstimates of natural variability from an AOGCM provide a critical input in deriving, by comparing temperature estimates from the simple model with observations, a likelihood function for the parameters jointly at each possible combination of parameter settings (and in one or two cases AOGCMs provide surrogates for some of the observationestimates from the simple model with observations, a likelihood function for the parameters jointly at each possible combination of parameter settings (and in one or two cases AOGCMs provide surrogates for some of the observational data).

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).

When accounting for the studies which don't include a direct forcing estimate the average from satellite - based observational studies is -1.0 W / m ^ 2.

Given the short period of the records are the observational estimates of the Hurst exponents stable enough to be used as a test for the models?

Various mechanisms have been proposed for this hiatus in global warming3, 4,5,6, but their relative importance has not been quantified, hampering observational estimates of climate sensitivity.

It is difficult to digitise the Figure 8.18 values for years affected by volcanic eruptions, so I have also adjusted the widely - used RCP4.5 forcings dataset to reflect the Section 7.5.3 observational estimate of current aerosol forcing, using Figure 8.18 and Table 8.7 data to update the projected RCP4.5 forcings for 2007 — 2011 where appropriate.

In the light of the current observational evidence, in my view 1.75 °C would be a more reasonable central estimate for ECS than 3 °C, perhaps with a «likely» range of around 1.25 — 2.75 °C.

It can not be right, when providing an observationally - based estimate of ECS, to let it be influenced by including GCM - derived estimates for aerosol forcing — a key variable for which there is now substantial observational evidence.

[10] Weighting models by the likelihood of the observed TLC reflection — SST relationship at the model's best estimate (mean) of it, widening the observational uncertainty to allow for the average uncertainty of the model estimate means, is a more reasonable approach.

However, it appears that the constrained best - estimate for ECS that Zhai et al. derive is simply the unweighted mean and standard deviation of ECS values for the seven models having seasonal variability derived relationships of low cloud extent with SST that are consistent with their observational estimate.

Table 8.7 only gives estimated AFs for 2011, but Figure 8.18 gives their evolution from 1750 to 2010, so it is possible to derive historical figures using the recent observational AFari + aci estimate as follows.

Marvel don't give any results for the relative contributions of diferent forcings to their increases in observational TCR / ECS estimates.

[15] A crude revised central estimate is 3.4 °C, being the median ECS of the 7 models (CGCM3.1, HadCM3, CanESM2, IPSL - CM5A, MRI - CGCM3, NCAR - CAM5, NorESM1 - M) whose seasonal variability lies within the uncertainty range for the observational estimate, after substituting the Brient & Schneider consistency assessment for the 4 models where if differs radically.

To find the IPCC's best observational (satellite - based) estimate for AFari + aci, one turns to Section 7.5.3 of the SOD, where it is given as − 0.73 W / m ² with a standard deviation of 0.30 W / m ².

This is the most relevant case for comparison with observational estimates, as the effect of individual forcings can not be observed in the latter.