… if your data do not look like a quadratic!
This is a post about global sea-level rise, but I put that message up front so that you’ve got it even if you don’t read any further.
The reputable climate-statistics blogger Tamino, who is a professional statistician in real life and has published a couple of posts on this topic, puts it bluntly:
Fitting a quadratic to test for change in the rate of sea-level rise is a fool’s errand.
I’d like to explain why, with the help of a simple example. Imagine your rate of sea-level rise changes over 100 years in the following way:
A new paper by Deser et al. (2012) (free access) is likely to have repercussions on discussions of local climate change adaptation. I think it caught some people by surprise, even if the results perhaps should not be so surprising. The range of possible local and regional climate outcomes may turn out to be larger than expected for regions such as North America and Europe.
Deser et al. imply that information about the future regional climate is more blurred than previously anticipated because of large-scale atmospheric flow responsible for variations in regional climates. They found that regional temperatures and precipitation for the next 50 years may be less predictable due to the chaotic nature of the large-scale atmospheric flow. This has implications for climate change downscaling and climate change adaptation, and suggests a need to anticipate a wider range of situations in climate risk analyses.
Although it has long been recognised that large-scale circulation regimes affect seasonal, inter-annual climate, and decadal variations, the expectations have been that anthropogenic climate changes will dominate on time scales longer than 50 years. For instance, an influential analysis by Hawking & Sutton (2009) (link to figures) has suggested that internal climate variability account for only about 20% of the variance over the British isles on a 50-year time scale.
C. Deser, R. Knutti, S. Solomon, and A.S. Phillips, "Communication of the role of natural variability in future North American climate", Nature Climate change, vol. 2, pp. 775-779, 2012. http://dx.doi.org/10.1038/nclimate1562
E. Hawkins, and R. Sutton, "The Potential to Narrow Uncertainty in Regional Climate Predictions", Bulletin of the American Meteorological Society, vol. 90, pp. 1095-1107, 2009. http://dx.doi.org/10.1175/2009BAMS2607.1
Does this sound familiar? A quantitative prediction is inconvenient for some heavily invested folks. Legitimate questions about methodology morph quickly into accusations that the researchers have put their thumb on the scale and that they are simply making their awkward predictions to feather their own nest. Others loudly proclaim that the methodology could never work and imply that anyone who knows anything knows that -it’s simply common sense! Audit sites spring up to re-process the raw data and produce predictions more to the liking of their audience. People who have actually championed the methods being used, and so really should know better, indulge in some obvious wish-casting (i.e. forecasting what you would like to be true, despite the absence of any evidence to support it).
Contrarian attacks on climate science, right?
Are the rising atmospheric CO2-levels a result of oceans warming up? And does that mean that CO2 has little role in the global warming? Moreover, are the rising levels of CO2 at all related to human activity?
These are claims made in a fresh publication by Humlum et al. (2012). However, when seeing them in the context of their analysis, they seem to be on par with the misguided notion that the rain from clouds cannot come from the oceans because the clouds are intermittent and highly variable whereas the oceans are just there all the time. I think that the analysis presented in Humlum et al. (2012) is weak on four important accounts: the analysis, the physics, reviewing past literature, and logic.
O. Humlum, K. Stordahl, and J. Solheim, "The phase relation between atmospheric carbon dioxide and global temperature", Global and Planetary Change, vol. 100, pp. 51-69, 2013. http://dx.doi.org/10.1016/j.gloplacha.2012.08.008
Switch to our mobile site