Sentences with phrase «bias uncertainty estimate»

Structural bias uncertainty estimate comes from three independent proxy types, each with a 2 - sigma of 3C, so the 2 - sigma on the mean is about + -1.7 C.

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.

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

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.

The IPCC range, on the other hand, encompasses the overall uncertainty across a very large number of studies, using different methods all with their own potential biases and problems (e.g., resulting from biases in proxy data used as constraints on past temperature changes, etc.) There is a number of single studies on climate sensitivity that have statistical uncertainties as small as Cox et al., yet different best estimates — some higher than the classic 3 °C, some lower.

A better approach would be to use a window of months about the current month to obtain a time - dependent estimate of both the bias and the uncertainty due lack of coverage.

We can apply my simpler bias analysis (which we can now see is limited in that it does not provide an uncertainty estimate for the estimated bias) to HadCRUT3 / 4.

Using the whole period of data leads to an uncertainty estimate which is a compound of the desired uncertainty, and a bias estimate based on an average over the total span of the reanalysis dataset.

Lyman and colleagues combined different ocean monitoring groups» data sets, taking into account different sources of bias and uncertainty — due to researchers using different instruments, the lack of instrument coverage in the ocean, and different ways of analyzing data used among research groups — and put forth a warming rate estimate for the upper ocean that it is more useful in climate models.

A time series of global - average, bias - adjusted SSTs with all uncertainty estimates combined is shown in Figure 11.

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.

At that point, we were linearly incorporating the estimated biases rather than their uncertainties.

Finally, the estimates of biases and other uncertainties presented here should not be interpreted as providing a comprehensive estimate of uncertainty in historical sea - surface temperature measurements.

«Introduce» suggests that previous estimates of the «uncertainties» have strongly under estimated the «Type B» or bias uncertainties.

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.

Until multiple, independent estimates of SST biases exist, a signicant contribution to the total uncertainty will remain unexplored.

The major uncertainties in satellite measurements of upper air temperature are due to sensor and spacecraft biases and instabilities, the characteristics of which need to be estimated by performing satellite intercalibrations during overlapping intervals.

To suggest that this may be a taken as a validation of F&P requires rigorous validation of these two assumptions and a formal error estimate for the uncertainty of the hindcast to 1850 showing it to be substantially smaller than F&P bias that is being evaluated.

The background to the hypotheses and the initial checks of the HadSST3 data set are given in the HadSST3 paper as is the uncertainty analysis associated with difficulties in estimating the biases.

This bias may be explained by a misrepresentation of mixed - phase extratropical clouds, often pinpointed as playing a key role in driving global - cloud feedback and uncertainties in climate sensitivity estimates (e.g., Tan et.

and «no data or computer code appears to be archived in relation to the paper» and «the sensitivity of Shindell's TCR estimate to the aerosol forcing bias adjustment is such that the true uncertainty of Shindell's TCR range must be huge — so large as to make his estimate worthless» and the seemingly arbitrary to cherry picked climate models used in Shindell's analysis.

In terms of remediation of the friction of scepticism in science, in recent years I have been stressing the proper, classic use of estimates of uncertainty in data, particularly one - sided bias mechanisms.

Sure, the case can be made that documented TOB changes implies an adjustment to records to remove a bias, plus added uncertainty because the adjustment is an estimate.

The uncertainty in method bias for any of these adjustment algorithms has to be estimated differently and is possible, I think, with proper benchmark testing, as I noted previously, where at least we can determine the limitations of these approaches..

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.

The uncertainty, and potential for politically motivated bias, in all such SCC estimates is astronomical, no matter who does them.

«Despite the wealth of metadata that is now available, it is not possible to estimate the biases in an exact manner so an attempt has been made to assess the potential uncertainties in the biases that arise from assumptions made in the process of aggregating the information.

a Uncertainties (2 sigma) du to: data gaps and random errors estimated by RSOA (heavy solid); SST bias - corrections (heavy dashes); urbanisation (light dashes); changes in thermometer exposures on LAT (light solid).

The two obvious contributors to the uncertainty are the structural biases in the proxies and the sampling error from estimating GAST from 5 - 61 SST observations.

FWIW: Your handwaving argument estimating what the bias in instrumental measurements based on uncertainty in IPCC projections of warming is fundamentally unsound.

However unlike the Jones et al. estimates of uncertainty, the optimum average also includes uncertainties in bias corrections to SST up to 1941 (Folland and Parker, 1995) and the uncertainties (as included in Figure 2.1) in the land data component that are due to urbanisation.

We are most confident in the methodological strengths of the longitudinal design and future longitudinal analyses.7 More caution is needed in interpreting our prevalence estimates, but in spite of the methodological uncertainties of using a non-probabilistic sample, we believe this, like many other quota samples, is likely to give estimates similar to a probabilistic sample (which may be subject to different biases, as we have shown with the NATSISS).23