Disappearing the MWP at Icefields, Alberta

In today’s post, I’m going to critically examine another widely used tree ring chronology: the Icefields (Alberta) MXD RCS chronology of Luckman and Wilson (2005 pdf), used most recently in Wilson et al 2016.

I’ll show that the RCS technique used in the LW2005 MXD chronology eliminated high medieval values as a tautology of their method, not as a property of the data and that the Icefields data provides equal (or greater) justification for MXD RCS chronologies with elevated medieval values.  The measure of potential difference is previewed in Figure 1 below, which compares the reported LW2005 chronology (top panel) to an MXD chronology with elevated medieval values, calculated using equally (or more plausible) RCS methodology, with several other similar variations shown in the post.


Figure 1.  Top – MXD RCS chronology from Luckman and Wilson 2005 Figure 2; bottom – MXD RCS chronology calculated under alternative assumptions – see Figure 3 below. 

I will use random effects statistical techniques both to analyse the data and to place prior analysis in a more formal statistical context.  Because LW2005 coauthor Rob Wilson stands alone for civility in the paleoclimate world and because the present post is critical of past analysis, in some ways, I would have preferred to use another example. However, on the other hand, it is also possible that Rob will recognize that the techniques applied in the present post – techniques that are unfamiliar to dendros –  can yield fresh and important insight into the data and will look at the Icefields data a little differently.

Although the article in consideration was published more than a decade ago, the analysis in today’s article was impossible until relatively recently, because coauthor Luckman withheld the relevant data for over a decade.

Continue reading

New Light on Gulf of Alaska

Last week, I posted on the effect of ex post site selection on the Gulf of Alaska tree ring chronology used in Wilson et al 2016 (from Wiles et al 2014).  An earlier incarnation of this chronology (in D’Arrigo et al 2016) had had a severe divergence problem, a problem that Wiles et al had purported to mitigate. However, their claimed mitigation depended on ex post selection of modern sites that were 800 km away from the original selection.

Even with this ex post selection, I could not replicate the supposed mitigation of the divergence problem, because Wiles et al had not archived all of their data: it appeared that their ex post result depended on still unarchived data.  Subsequent to my post,  Greg Wiles commendably archived the previously missing Wright Mountain (but, unfortunately, neglected to archive the update to Eyak Mountain leaving the data less incomplete but still incomplete.)

The new data confirms my suspicion that the “missing” Wright Mountain data was inhomogeneous: it turns out that the average Wright Mountain RW was 21% higher than the average RW value from other modern sites.  Because Wiles et al used RCS with a single regional curve, the inclusion of this inhomogeneous data results in higher recent values.  By itself, the Wright Mountain data doesn’t actually go up in the last half of the 20th century, but inclusion of the inhomogeneous data translates the chronology upwards relative to subfossil data.   In the Calvinball world of RCS chronologies, the handling of inhomogeneous data is determined after the fact, with RCS chronologers seeming to be extremely alert to inhomogeneities that yield high medieval values (e.g. Polar Urals), but rather obtuse to inhomogeneities that yield high modern values, with decisions on site inhomogeneity always being made ex post. All too often, medieval-modern comparisons rest on Calvinball decisions, rather than the integrity of the data.

In today’s post, I’ll use the random effects techniques (consistently recommended at CA) to try to provide a structured consideration of this example.  Continue reading

Marvel et al. – Gavin Schmidt admits key error but disputes everything else

A guest article by Nicholas Lewis


Gavin Schmidt has finally provided, at the GISS website, the iRF and ERF forcing values for a doubling of CO2 (F2xCO2) in GISS-E2-R, and related to this has made wholesale corrections to the results of Marvel et al. 2015 (MEA15). He has coupled this with a criticism at RealClimate of my appraisal of MEA15, writing about it “As is usual when people try too hard to delegitimise an approach or a paper, the criticisms tend to be a rag-bag of conceptual points, trivialities and, often, confused mis-readings – so it proves in this case”. Personally, I think this fits better as a description of Gavin Schmidt’s article. It contains multiple mistakes and misconceptions, which I think it worth setting the record straight on. Continue reading

A Return to Polar Urals: Wilson et al 2016

Wilson et al 2016, like D’Arrigo et al 2006, includes a ‘Polar Urals’ chronology as one of its components.  Tree ring chronologies from Polar Urals and Yamal have long been a contentious issue at Climate Audit, dating back to the earliest days (see tags Yamal, Polar Urals).

Whereas the D’Arrigo et al 2006 version had one of the biggest blades in the entire D’Arrigo et al 2006 portfolio (ending at nearly 4 sigma, with the smooth at 2.5 sigma), the new version, while slightly elevated in the 20th century, remains under 1 sigma.


Figure 1. ‘Polar Urals’ chronologies (scaled to SD Units): top- from D’Arrigo et al 2006 (RCS); bottom – Wilson et al 2014.  The top series is actually the Yamal chronology (Briffa, 2000), while the bottom series is a Polar Urals MXD version from Briffa et al (2013). 

In today’s post, I’ll first discuss the dramatic reduction in the blade of the ‘Polar Urals’ chronology from D’Arrigo et al 2006 to Wilson et al 2016 (which uses a variation of the Polar Urals MXD chronology from Briffa et al 2013).  This doesn’t take long to explain.

I’ll then use the rest of the post to discuss the bewildering thicket of adjustments to the Polar Urals MXD chronology in Briffa et al 2013.  I would be astonished if any practising paleoclimatologist (including the lead author of Wilson et al 2016) has ever made any attempt to figure out what Briffa (or more likely, Melvin actually did) in Briffa et al 2013.  I doubt that the editor and peer reviewers did either. Continue reading

Picking Cherries in the Gulf of Alaska

The bias arising from ex post selection of sites for regional tree ring chronologies has been a long standing issue at Climate Audit, especially in connection with Briffa’s chronologies for Yamal and Polar Urals (see tag.)  I discussed it most recently in connection with the Central Northwest Territories (CNWT) regional chronology of D’Arrigo et al 2006,  in which I showed a remarkable example of ex post selection.

In today’s post, I’ll show a third vivid example of the impact of ex post site selection on the divergence problem in Gulf of Alaska regional chronologies.  I did not pick this chronology as a particularly lurid example after examining multiple sites. This chronology is the first column in the Wilson et al 2016 N-TREND spreadsheet and was the first site in that collection that I examined closely.  It is also a site for which most (but not all) of the relevant data has been archived and which can therefore be examined. Unfortunately, data for many of the Wilson et al 2016 sites has not been been archived and, if past experience is any guide, it might take another decade to become available (by which time we will have all “moved on”). Continue reading

Cherry-Picking by D’Arrigo

One of the longest standing Climate Audit issues with paleoclimate reconstructions is ex post decisions on inclusion/exclusion of data, of which ex post decisions on inclusion/exclusion of sites/data in “regional [treering] chronologies” is one important family.  This was the issue in the original Yamal controversy, in response to which Briffa stated that they “would never select or manipulate data in order to arrive at some preconceived or unrepresentative result”. However, Briffa and associates have never set out ex ante criteria for site inclusion/exclusion, resulting in the methodology for Briffa regional reconstructions seeming more like Calvinball than science, as discussed in many CA posts.

Unlike Briffa, D’Arrigo has candidly admitted to the selection of data to arrive at a preconceived result. At the 2006 NAS panel workshop, Rosanne D’Arrigo famously told the surprised panelists that you had to pick cherries if you want to make cherry pie.   Again in 2009 (though not noticed at the time), D’Arrigo et al 2009 stated that they could “partially circumvent” the divergence problem by only using data that went up:

The divergence problem can be partially circumvented by utilizing tree-ring data for dendroclimatic reconstructions from sites where divergence is either absent or minimal. (Wilson et al., 2007; Buntgen et al., in press; Youngblut and Luckman, in press).

Portfolio managers would have like to have a similar option in constructing portfolios: if, after the fact, you pick stocks that went up, it would be trivially easy to “circumvent” market downturns.  That paleoclimatologists seem so obtuse to this simple observation is a major puzzlement.

In today’s post, I’ll show an absolutely breathtaking example of biased ex post picking by D’Arrigo et al in the D’Arrigo et al 2006 CNWT chronology.  It was impossible for anyone to identify the full measure of this bias at the time or for many years afterwards, as D’Arrigo and coauthors failed to archive data at the time and refused to provide it when requested. They were supported in their refusal by IPCC WG1 Co-Chair Susan Solomon, who, as CA readers are aware, threatened me with expulsion as an IPCC AR4 reviewer for seeking supporting data for D’Arrigo et al 2006 (then cited in preprint by AR4).   The data showing the cherry picking only became available in 2014 as part of a belated archiving program in the final year of Gordon Jacoby’s life.  

Continue reading

Marvel et al.: Implications of forcing efficacies for climate sensitivity estimates – update

A guest article by Nicholas Lewis


In a recent article I discussed the December 2015 Marvel et al.[1] paper, which contends that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) derived from recent observations of changes in global mean surface temperature (GMST) are biased low. Marvel et al. reached this conclusion from analysing the response of the GISS-E2-R climate model in simulations over the historical period (1850–2005) when driven by six individual forcings, and also by all forcings together, the latter referred to as the ‘Historical’ simulation. The six individual forcings analysed were well-mixed greenhouse gases (GHG), anthropogenic aerosols, ozone, land use change, solar variations and volcanoes. Ensembles of five simulation runs were carried out for each constituent individual forcing, and of six runs for all forcings together. Marvel et al.’s estimates were based on averaging over the relevant simulation runs; taking ensemble averages reduces the impact of random variability.

In this article I will give a update on the status of two points I tentatively raised in my original article. Continue reading

Bob Carter

I was very saddened to learn of the sudden death of Bob Carter ( here here).   He was one of the few people in this field that I regarded as a friend.  He was only a few years older than me and we got along well personally.

carterI will not attempt to comment on his work as that is covered elsewhere, but do wish to mention something personal.  In 2003, when I was unknown to anyone other than my friends and family, I had been posting comments on climate reconstructions at a chatline.  Bob emailed me out of the blue with encouragement, saying that I was looking at the data differently than anyone else and that I should definitely follow it through.  Without his specific encouragement, it is not for sure that I ever would have bothered trying to write up what became McIntyre and McKitrick (2003) or anything else.

We’ve met personally on a number of occasions over the years – at AGU in 2004 or 2005, and on several occasions at Erice, most recently last summer.  He was always full of good cheer, despite continuing provocations, and unfailingly encouraging.








Appraising Marvel et al.: Implications of forcing efficacies for climate sensitivity estimates

A guest article by Nicholas Lewis

Note: This is a long article: a summary is available here.


In a recent paper[1], NASA scientists led by Kate Marvel and Gavin Schmidt derive the global mean surface temperature (GMST) response of the GISS-E2-R climate model to different types of forcing. They do this by simulations over the historical period (1850–2005) driven by individual forcings, and by all forcings together, the latter referred to as the ‘Historical’ simulation.

They assert that their results imply that estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) derived from recent observations are biased low.

Marvel et al. use the GISS-E2-R historical period simulation responses to revise estimates of the transient climate response (TCR) and equilibrium climate sensitivity (ECS) from three observationally-based studies: Otto et al. 2013, Lewis and Curry 2014 and Shindell 2014. Their revisions give figures that are substantially higher than in the original studies. Remarkably, the Marvel et al. reworked observational estimates for TCR and ECS are, taking the averages for the three studies, substantially higher than the equivalent figures for the GISS-E2-R model itself, despite the model exhibiting faster warming than the real climate system. Not only is the GMST increase simulated by GISS-E2-R is higher than that observed, but the ocean heat uptake rate is well above the observed level.[2] No explanation is given for this surprising result. Continue reading

Update of Model-Observation Comparisons

The strong El Nino has obviously caused great excitement in the warmist community.  It should also cause any honest skeptic/lukewarmer to re-examine whether observations remain inconsistent with models. In today’s post, I’ll show two comparisons: 1) CMIP5 models (TAS) vs HadCRUT4; 2) CMIP5 models (TLT) vs RSS (UAH is only negligibly different).  For this post, I’ve used the same scripts as I used in earlier comparisons.  Continue reading