Sentences with phrase «coverage bias»

We hope this will help to advance the discussion on coverage bias in the temperature record.

The impact of coverage bias on recent trends has been reduced, but only slightly.

The approach is very similar to the one I adopted using the GISS dTs data above, with one important difference - every month in the reanalysis data is used to determine a distribution of possible coverage biases for each individual month in the HadCRUT4 record.

Moreover, the ERA - interim version used by ECMWF to investigate possible coverage bias differs from the main dataset.

The 2016 increase over 2015 was much larger in the analyses that account for missing areas, especially the Arctic, providing additional impetus to address coverage bias among research groups that still have not done so.

HadCRUT4 still has significant coverage bias (confirming the result from Figure 6) which is somewhat masked by the increase in SST trend.

Third, tide gauge coverage 70 years ago was not as good as today's, and much of the variability is simply due to noise and factors like coverage bias, as discussed by Rahmstorf et al. (2012) and Tamino.

I turn now to the claims about incomplete, and changing, data coverage biasing down HadCRUT4 warming by 15 percentage points.

But 2014 was also notable scientifically for the emergence of a previously under - examined scientific issue: namely coverage bias in observed surface temperature series, especially the HadCrut4 record issued by the UK Met Office.

The possibility of coverage bias in HadCRUT4 has since been independently examined by ECMWF using their well - regarded ERA - Interim reanalysis dataset.

In the meantime I hope to offer my own rudimentary solutions to the problem of coverage bias in a forthcoming article.

This coverage bias issue was identified nearly 10 years ago and has been highlighted in the peer - reviewed literature since at least 2010.

However, the impact of coverage bias is pretty clear; it can be seen by simply looking at a coverage and anomaly map as we did here, or by assessment of coverage bias using GISTEMP, or by the less valid but independent assessment using UAH.

Much of it concerns the problem of coverage bias, so reviewing my previous articles «HadCRUT3, Cool or Uncool?»

This is despite the coverage bias being largely unaddressed.

It all comes down to coverage bias.

This can be answered by breaking down the coverage bias contributions into latitude bands, shown in Figure 5.

Both GISTEMP and UAH suggest that the differences we see in Figure 3 are due to coverage bias.

It looks as though most of the difference between the recent and long - term trends in HadCRUT3 can be explained by just the coverage bias and the impact of the El - Nino cycle.

A 12 - month (rather than 60 month) moving average has been used in this case, revealing the substantial year - on - year variation in the coverage bias.

We assumed that in addressing the coverage bias in HadCRUT4 we would bring it into agreement with the GISTEMP record from NASA.

Last November we published a paper in Quarterly Journal of the Royal Meteorological Society on the subject of coverage bias in the Met Office HadCRUT4 temperature record.

So does use of the infilled Cowtan and Way dataset increase the 1930 — 50 to 1995 — 2015 TCR estimate by anything like 15 %, the coverage bias for CMIP5 models reported in REA16 for the full historical period?

The initial paper was on the impacts of coverage bias in the hadcrutv4 dataset — this new update focuses on the differences between our result and GISS» with the evidence suggesting that GISS is underestimating Arctic warming because of using GHCNv3 as an input.

I can assure you, seeing the project grow and the results emerge in more or less real time, the coverage bias issue was not suspected to be quite as large nor quite as tied to the 1998 - present window as it was.