Are you a user of global temperature data? If so, have you ever thought about the meaning of the word “mean” in “global mean surface temperature”?
I’m guessing that for most people the answer to the second question is “no”. “Mean” is such a ubiquitous concept that we don’t think about it much. But let me try and persuade you that it might need a little more thought. Continue reading What does ‘mean’ actually mean?
There had been speculation that record low temperatures would be coming to the United States in early December, and this had been framed as either evidence against global warming in general or that cold air outbreaks are increasing due to climate change.
World Weather Attribution (WWA) presents a quantitative study of this cold air outbreak. WWA researchers compute how rare the outbreak was and how it is affected by human-caused greenhouse gases. The analysis uses the same methods as WWA used in the peer-reviewed analysis of the cold extremes in the Midwest in the winter of 2013 – 2014 (van Oldenborgh et al, 2015). Continue reading U.S. Deep Freeze, December 2016
Much evidence has accumulated that temperature extremes and variability are changing. Accurately diagnosing such changes is of vital societal interest, not least because human induced climate change is often expected to materialise primarily through changes in the extreme tails.
Quantifying these features of climate time series statistically in climate models and observations is not straightforward. To a large extent, that is because extreme events are rare by definition, a fact that seems hardly surprising. This fact implies, however, that conventional methods quickly break down when it comes to the tails. This blog post serves s a cautionary note, in which we discuss how apparently very simple methods can result in severely biased estimates, and how this can be avoided1,2.
Guest post by Sebastian Sippel, MPIB, based on Sippel et al. (2015) Continue reading How to quantify changes in climate extremes without inducing artefacts?