A recently published paper by Ludecke et al. in Climate of the Past claims, as its main result, that “the climate dynamics is governed at present by periodic oscillations.”
In the past I’ve explored simple energy balance models for the evolution of global average temperature. One of the important things to note is that a “1-box” energy balance model — in which the entire climate system is considered to have a single time constant — isn’t really sufficient. It can give a pretty good fit, but for a more realistic estimate you need at least two boxes. One represents rapid response to climate forcing — think of it as the “atmosphere” if you wish. The other is for slower response — think of it as “ocean” if you wish, or as “upper ocean,” or as “everything else.” One could be even more realistic with more than two boxes, after all the deep ocean certainly effects climate but with a longer time scale still, and there’s the cryosphere on top of it all, but rather than go the way of the full-blown computer simulation model, let’s see what happens if we just use a 2-box model with two time constants. We’ll think of box 1 as the atmosphere, so that it should correspond to the surface temperature we’re all familiar with.
As ClimateProgress and others report, initial results are in from Cryosat-2. Since 2010, this European Space Agency satellite has surveyed polar ice to estimate its thickness, and by extension, its volume. It was a replacement for Cryosat-1, which was unfortunately destroyed in a launch failure. But the European Space Agency (ESA) considered this mission important enough to construct and deploy a replacement promptly, approving Cryosat-2 less than five months after the failure of Cryosat-1.
Zeke Hausfather has contributed a guest post at RealClimate about his latest publication studying the impact of the “urban heat island” (UHI) effect on temperature trends, and how effective present correction methods are. The two-sentence summary:
The simple take-away is that while UHI and other urban-correlated biases are real (and can have a big effect), current methods of detecting and correcting localized breakpoints are generally effective in removing that bias. Blog claims that UHI explains any substantial fraction of the recent warming in the US are just not supported by the data.
Do take a look at the RC post, it has much more detail, and at the paper.
If you keep an eye on global warming denier blogs, you expect to see some pretty stupid stuff. But every now and then they exceed expectation. Sometimes they even take it to a new level. This particular bit was featured by Anthony Watts, but it originates with Steve Goddard.
RealClimate has published their latest 2012 Updates to model-observation comparisons. I’ll take a different twist. Instead of comparing observations to computer model projections, I’ll compare them to very simple statistical projections.
In a year gone by I posted about what kind of betting terms I might consider appropriate for the reality of global warming (incidentally, that post contained an error which was corrected in an update, but the archived copy doesn’t include the update). The idea is to take annual average data (for global temperature) from 1975 through the end of 1999, then fit a trend line by linear regression. If the trend continues, then future data should probably be within two standard deviations of the extrapolated trend line. This is the “projected range” according to the existing trend. The “projected range” according to the not-still-warming theory is that future values should be within two standard deviations of the existing average (in that case, from 2001 through 2007).
I also mentioned that since it would be unlikely but far from shocking if a single future value were outside either range, I would require two (not necessarily consecutive) future years outside the range to decided against either claim — if I were a betting man.
Lots of time series, especially in geophysics, exhibit the phenomenon of autocorrelation. This means that not just the signal (if nontrivial signal is present), even the noise is more complicated than the simple kind in which each noise value is independent of the others. Specifically, nearby (in time) noise values tend to be correlated, hence the term “autocorrelation.”