Promotional flyers now available in German and Spanish

These have sat on my to-do-list for far too long but I have now finally found time to place the German and Spanish translations of the flyers that were taken to COP in Lima on the website. These can be found at http://www.surfacetemperatures.org/promotional_materials. My thanks to Enric Aguilar,  Stefan Bronnimann, Renate Auchmann and Victor Venema for the significant efforts to undertake these translations. Also a big thanks is due to NCEI graphics team for their efforts to re-render the original flyers in multiple languages.

The promotional materials are freely available and encouraged for re-use in any forum that may help raise interest in and knowledge of ISTI and its aims. Please feel free to take copies anywhere and everywhere that is relevant.

The Karl et al. Science paper and ISTI

Note: this post is partly personal opinion.

I suspect when this is being posted at unembargo time there will be a whole slew of stories running on the news media and blogs about the Karl et al. paper in Science (I shall add a link to the actual paper if I remember later). But given the use of the ISTI databank in the analysis - its first high profile use in anger and a testament to all those years of hard work by very many colleagues (principally Jared Rennie) - some may come towards this little quiet corner of the internet. So here are some quick thoughts.

Karl et al. find greater recent period warming using a new set of land and sea surface temperature records than their operational versions used in NCEI's monitoring products to date. They conclude that there is no statistical evidence for a slowdown in the rate of warming in the new estimate calling into apparent question the much discussed 'hiatus'.

Firstly, to be clear, most of the change in trend documented in Karl et al. arises not from the land (the focus of ISTI) but rather from the sea surface temperature dataset changes. These changes relate to their now calculating ship bias adjustments throughout the record, and accounting for the transition from predominantly ships to predominantly buoys since the 1980s. There is no doubt that buoys read colder than ships (attested to in multiple published analyses) - so in not previously accounting for this the prior NCDC analysis had a marked propensity to underestimate sea surface temperature changes in the most recent period. There are other changes in the sea surface temperature dataset documented in Huang et al and Liu et al. These are secondary in terms of recent trends but still important for certain applications. For example, ERSSTv4 likely captures far better ENSO variations prior to 1920 or so. This, however, is a land surface air temperatures blog so I shall wax lyrical no further on the matter of SSTs. I can try to answer questions on ERSSTv4 in the comments (I was a co-author on the ERSST analyses) if you have any burning questions.

So, onto land temperatures. Karl et al. apply the pre-existing pairwise homogenization algorithm used in GHCNv3 to the databank version 1.0.1 release. Effectively this is going from considering these:

to considering these:

The effect of going from the 7,280 stations in GHCNMv3 to applying the same algorithm to the databank (although not all 32,128 stations as many were too short or isolated or incomplete - Karl et al. mentions 'double' so somewhere around 15,000 were likely used) is very much smaller than the effect of the sea surface temperature changes despite the step change in station count and coverage. The most recent period trends in Karl et al. over land exhibit a little more warming (c.10%) than GHCNv3 does, but its not remotely statistically siginificant. It'll be interesting to look, down the line, at what proportion of that change arises from improved coverage and what proportion to changes in areas of common sampling and to consider the effects on common stations and a slew of other analyses. Presumably this will be part of a broader analysis under GHCNv4 which will be built off the databank release, again using PHA. There may be additional innovations, in part arising from the SAMSI/IMAGe/ISTI workshop held in Boulder last summer. 

There are two additional questions that arise:

1. Does this analysis obviate the need for ISTI?

Absolutely not.

Without ISTI the land side of Karl et al would not have been possible for starters. But more generally this is but one estimate and we most definitely need multiple estimates. We are also yet to run the PHA algorithm and others through the benchmarks through which additional insights and improvements are expected to accrue. We also know there remain lots and lots of data out there to rescue and incorporate into the holdings and use to get still better estimates of the global, regional and local changes. So, much work to be done and we have only just started to scratch the surface of what is possible.

2. Does it call into question the slew of papers of the recent hiatus / pause / slowdown?

Not really.

The NCDC estimate (and GISS which uses the same marine and land basis estimates) was already at the low-end of the family of available estimates of global mean behaviour and this simply puts them back within or just above these estimates for their trends over 1998 to 2012 / 2014. The slowdown is also less marked in all of the datasets now in part because of the additional two and a bit years since the AR5 reported periods in which we appear to be flipping to a positive IPO (this will become clearer with time) which will cause enhanced short-term surface warming.

But, in part this is a question of which hypothesis to test. Karl et al are testing whether there has been a detectable change in the observed trend behaviour. The answer is no, and pretty much was anyway according to a number of prior analyses. The modern period adjustments and innovations in Karl et al. simply strengthen that conclusion.

Arguably the more interesting hypothesis to test is whether the observations are consistent with the family of climate model projections. Here the Karl et al adjustments take the NCDC dataset from inconsistent (3 sigma) to suspicious (2 sigma) (here I am adopting metrology Guide to Uncertainties in Measurements language for clarity - in that context Karl et al. analysis takes us from k=3 to k=2, k=1 (within 1 sigma) would be deemed consistent).

Furthermore, the questions of mechanistic understanding of decadal variability that all these studies have focussed upon are societally relevant and will improve our understanding of the climate system. Not only that but the insights will be used to improve climate models and therefore future predictions and projections. So, the existing literature on the topic is undoubtedly highly valuable. Doubtless there will be those saying they aren't / weren't.

Concluding remarks

To conclude, worryingly not for the first time (think tropospheric temperaures in late 1990s / early 2000s) we find that potentially some substantial portion of a model-observation discrepancy that has caused a degree of controversy is down to unresolved observational issues. There is still an undue propensity for scientists and public alike to take the observations as a 'given'. As Karl et al. attests, even in the modern era we have imperfect measurements.

Which leads me to a final proposition for a more scientifically sane future ...

This whole train of events does rather speak to the fact that we can and should observe in a more sane, sensible and rational way in the future. There is no need to bequeath onto researchers in 50 years time a similar mess. If we instigate and maintain refernce quality networks that are stable SI traceable measures with comprehensive uncertainty chains such as USCRN, GRUAN etc. but for all domains for decades to come we can have the next generation of scientists focus on analyzing what happened and not, depressingly, trying instead to inevitably somewhat ambiguously ascertain what happened.