Sentences with phrase «uncertainties in trend estimates»

... the uncertainties in trend estimates using just data since 2000 are much larger than the trend estimates themselves.

She even computes the uncertainty in that trend estimate (using fancy statistics), and uses that to compute what's called a «95 % confidence interval» for the trend — the range in which we expect the true warming rate is 95 % likely to be; it can be thought of as the «plausible range» for the warming rate.

For instance, he emphasizes that when computing uncertainty in a trend estimate you have to take into account something called autocorrelation, which means the noise isn't the simple type called «white noise.»

Because the noise level at a single location is so large, the uncertainty in the trend estimate at a single location will be large.

Well, if you start to take longer trends, then the uncertainty in the trend estimate approaches the uncertainty in the expected trend, at which point it becomes meaningful to compare them since the «weather» component has been averaged out.

Not exact matches

First, the quality of the data is important: whether it is the LGM temperature estimates, recent aerosol forcing trends, or mid-tropospheric humidity — underestimates in the uncertainty of these data will definitely bias the CS estimate.

Von Schuckmann & Le Traon (2011) also estimate the errors in global trends from the period analysed, and also future error uncertainty.

These measurements become sparser as we go further back in time, hence trend estimates become more uncertain; of course we fully accounted for this uncertainty in our analysis.

This is not simply the uncertainty in estimating the linear trend, but the more systematic uncertainty due to processing problems, drifts and other biases.

Have you computed the uncertainty level in your estimate of the «decadal trend»?

First, the quality of the data is important: whether it is the LGM temperature estimates, recent aerosol forcing trends, or mid-tropospheric humidity — underestimates in the uncertainty of these data will definitely bias the CS estimate.

One estimate of that error for the MSU 2 product (a weighted average of tropospheric + lower stratospheric trends) is that two different groups (UAH and RSS) come up with a range of tropical trends of 0.048 to 0.133 °C / decade — a much larger difference than the simple uncertainty in the trend.

Since you don't seem to know how meaningless «decadal trends» are, you use the only data set that gives you what you want and ignore the others, and you act as though there's no uncertainty in your «trend» estimate, your level of certainty amounts to nothing more than hubris.

Although there is still some disagreement in the preliminary results (eg the description of polar ice caps), a lot of things appear to be quite robust as the climate models for instance indicate consistent patterns of surface warming and rainfall trends: the models tend to agree on a stronger warming in the Arctic and stronger precipitation changes in the Topics (see crude examples for the SRES A1b scenarios given in Figures 1 & 2; Note, the degrees of freedom varies with latitude, so that the uncertainty of these estimates are greater near the poles).

Her work explores observed trends in droughts and heat waves and estimating the uncertainty associated with observations.

Uncertainties in the regression models and fits used to distinguish between periodic variations and trends in the different databases appear to be a significant source of uncertainty in the estimates of longterm trends.

To appreciate the issues involved in comparing estimates of surface and lower tropospheric temperature trends, it is necessary to have at least a rudimentary understanding of these three kinds of measurements and the uncertainties inherent in each of them.

They are simply a first estimate.Where multiple analyses of the biases in other climatological variables have been produced, for example tropospheric temperatures and ocean heat content, the resulting spread in the estimates of key parameters such as the long - term trend has typically been signicantly larger than initial estimates of the uncertainty suggested.

Uncertainties of estimated trends in global - and regional - average sea - surface temperature due to bias adjustments since the Second World War are found to be larger than uncertainties arising from the choice of analysis technique, indicating that this is an important source of uncertainty in analyses of historical sea - surface Uncertainties of estimated trends in global - and regional - average sea - surface temperature due to bias adjustments since the Second World War are found to be larger than uncertainties arising from the choice of analysis technique, indicating that this is an important source of uncertainty in analyses of historical sea - surface uncertainties arising from the choice of analysis technique, indicating that this is an important source of uncertainty in analyses of historical sea - surface temperatures.

The similarity of these observationally - based «NAO book - end» trend maps with those derived directly from the leading EOF of the set of 40 CESM - LE SLP trend maps (Fig. 2) attests to the robustness of the results, the utility of the method of Thompson et al. (2015) to estimate uncertainty in trends from the statistics of a Gaussian time series, and the fidelity of CESM's simulation of the NAO.

In paleoclimate, if you want to know the certainty of the average trend in the blade of the stick, you wouldn't take the extremes of all the inputs to calculate uncertainty, you take the variance in the output and use some method i.e. monte carlo or a DOF estimatIn paleoclimate, if you want to know the certainty of the average trend in the blade of the stick, you wouldn't take the extremes of all the inputs to calculate uncertainty, you take the variance in the output and use some method i.e. monte carlo or a DOF estimatin the blade of the stick, you wouldn't take the extremes of all the inputs to calculate uncertainty, you take the variance in the output and use some method i.e. monte carlo or a DOF estimatin the output and use some method i.e. monte carlo or a DOF estimate.

These NAO «book - ends» provide an estimate of the 5 — 95 % range of uncertainty in projected trends due to internal variability of the NAO based on observations superimposed upon model estimates of human - induced climate change.

Because the differences between the various observational estimates are largely systematic and structural (Chapter 2; Mears et al., 2011), the uncertainty in the observed trends can not be reduced by averaging the observations as if the differences between the datasets were purely random.

To estimate the uncertainty in an average or a trend, all that is required is to calculate the average or trend for each of the 100 realizations, and estimate the uncertainty from the distribution of the results.

The uncertainty in the global mean sea level trend is estimated to be of ± 0.5 mm / yr in a confidence interval of 90 % (1.65 sigma), whereas the uncertainty of the regional mean sea level trends is of the order of 2 - 3 mm / yr with values as low as 0.5 mm / yr or as high as 5.0 mm / yr depending on the region considered (Legeais et al., 2018, under review).

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).

That period 1995 - 2009 was just 15 years — and because of the uncertainty in estimating trends over short periods, an extra year has made that trend significant at the 95 % level which is the traditional threshold that statisticians have used for many years.»»

The models, note, only over estimate recent temperature trends by 18 %, half the expansion of the uncertainty range - and that overestimation has been eliminated from the attribution by scaling in any event.

The wedge labelled «Estimated ARC trend uncertainty» represents the spread of potential bias in the satellite data relative to the end of the time series.

By the statistical evaluation of the different climate developments simulated, the uncertainties in climate projections can be better estimated and reduced, for example, for rainfall trends.

Here's his argument: test each possible start year from 1990 through 2010, use just the data from then on to estimate the trend (the warming rate), and estimate the uncertainty in the trend.

Figure 3.2: b) Observation - based estimates of annual five - year running mean global mean mid-depth (700 — 2000 m) ocean heat content in ZJ (Levitus et al., 2012) and the deep (2000 — 6000 m) global ocean heat content trend from 1992 — 2005 (Purkey and Johnson, 2010), both with one standard error uncertainties shaded (see legend).

These errors, as well as the influence of decadal and multi-decadal variability in the climate, have been taken into account when estimating linear trends and their uncertainties (see Appendix 3.

They confused the uncertainty in how well we can estimate the forced signal (the mean of the all the models) with the distribution of trends + noise.