Mann and the Oxburgh Panel

The Mann libel case has been attracting increasing commentary, including from people outside the climate community. Integral to Mann’s litigation are representations that he was “investigated” by 6-9 investigations, all of which supposedly gave him “exonerations” on wide-ranging counts, including “scientific misconduct”, “fraud”, “academic fraud”, “data falsification”, “statistical manipulation”, “manipulation of data” and even supposed findings that his work was “properly conducted an fairly presented”. Mann also represented that these investigations were widely covered in international and national media and thus known to Steyn and the other defendants.

In today’s post, I’ll look closely at the Oxburgh panel, one of the investigations cited in Mann’s pleadings. However, contrary to the claims in Mann’s litigation, not only did the Oxburgh panel not exonerate Mann, at their press conference, Oxburgh panelist David Hand, then President of the Royal Statistical Society, made very disparaging and critical comments about Mann’s work, describing it as based on “inappropriate” statistics that led to “exaggerated” results. These comments were widely reported in international media, later covered in a CEI article that, in turn, was reported by National Review. Moreover, information obtained from FOI in the UK a couple of years ago shows that Mann objected vehemently to criticism from Oxburgh panelist, which he characterized as a “rogue opinion” and unsuccessfully sought a public apology.

Mann’s claim that the Oxburgh panel “exonerated” Mann on counts ranging from scientific misconduct to statistical manipulation to proper conduct and fair presentation of results has no more validity than his claim to have been awarded a Nobel prize for his supposedly seminal work “document[ing] the steady rise in surface temperatures during the 20th Century and the steep increase in measured temperatures since the 1950s.” Continue reading


The Copyright of the The Copenhagen Diagnosis

Readers may recall The Copenhagen Diagnosis, a (so-to-speak) non-governmental international climate assessment published in November 2009 and targeted by activists at influencing deliberations at the Copenhagen conference. Because it coincided with Climategate, it received little-to-no critical attention at climate blogs. Thus, I suspect that few, if any readers, will (without peeking) be able to guess the answer to today’s trivia question about The Copenhagen Diagnosis: who holds the copyright to The Copenhagen Diagnosis itself? Continue reading

University of New South Wales on Sub-Charter

The Sydney Morning Herald reports that the University of New South Wales is a signatory to the sub-charter of the Akademik Shokalskiy:

To retrace Mawson’s voyage the AAE used the New Zealand tourism company Heritage Expeditions to sub-charter the Shokalskiy, an ice strengthened ship owned by the Russia government. Turney and Fogwill’s employer, UNSW, signed the sub-charter contract.

I don’t know how liability for rescue costs is allocated. However, the fact that the University of New South Wales is a party to the sub-charter places its potential liability in a new light. However, in most legal proceedings, plaintiffs look for the party with the deepest pockets, which, in this case, would be the University of New South Wales.

Statements by Greg Mortimer in the report to IAATO (not public yet but seen by the Sydney Morning Herald) place blame for evacuation delays on the conduct of Turney’s on-ice party and exonerate the Russian captain.

New Details on the Ship of Fools

The precise chronology of the Ship of Fools on December 23 has been a topic of interest on skeptic blogs, including my recent post demonstrating the falsity of Turney’s excuses. However, up to today, this chronology had received zero media coverage, despite several reporters from major media on the Ship of Fools.

Today, there are two stories (BBC and Sydney Morning Herald , both of which contain damning information (especially the latter.) Note embedded link in latter article h/t Bob Koss, with important details not reported in the main article.

Here are new details on the day’s chronology.

13:00 Ship Time (midnight GMT) – The first group left the ship (Luck-Baker). Luck-Baker reported that their instructions were as follows:

I was in the first group to leave the ship at about 13:00 ship time (midnight GMT on 23 December), and take a short zodiac journey across the water to the ice edge.

The excursion was already a couple of hours behind schedule. This was because one of the three all-terrain amphibious vehicles called argos that were to take us to the islands had earlier flooded with water while it was transported from the ship to the ice edge.

Once our group was on the ice, the 12 of us were divided among two quad bikes and two argos. Then we were driven to the Hodgeman Islands in a strong southerly wind which blew drift up into the air, creating a hazy visibility.

We were told that we would have a maximum of one hour at the islands, after the 20 minute drive to the site. Then we would have to be ready to return on the vehicles bringing the next party. This sounded like an efficient system of relaying teams back and forth between ship and the islands.

At 14:30 Ship Time (Luck-Baker) – only 90 minutes later – the captain of the vessel said that the ice was starting to close in and, according to Greg Mortimer, they “hit the evacuation button”.

However, it took over 4 hours to evacuate, a time that Mortimer agrees was an “excessively long time”.

According to the BBC report, the “initial message to evacuate the area was not heard by some key people”. They surmise that the radios might have been out of range and that “for whatever reason, people on the ice appear not to have responded to satellite phone calls made from the bridge.”

Luck-Baker also reports that the logistics was unequal to the evacuation:

However there was a lack of organisation to supervise and enforce it. A number of us were at the islands for about two hours, having wandered off in small groups with the scientist whose work we were particularly interested in.

In the thrilling environment in which we now found ourselves, it was easy to lose track of time. We were surrounded by Adelie penguins and Weddell seals, and the white cliffs of the great East Antarctic Ice Sheet towered high with both beauty and menace, in the middle distance.

So for example, when the vehicles arrived with the second party of visitors, there were only three people at the pick-up area ready to return to the ship.

One of those returnees was a female tourist who had fallen into freezing seawater through a snow-concealed tidal crack in the fast ice. She was wet up to waist height and needed to be transported back to the ship as quickly as possible.

Timing issue
The difficulty was there were too many people and not enough seats on the argos and quad bikes to take everyone back in one convoy – even when these vehicles were carrying one more passenger than they were designed to.

The SYdney Morning Herald SMH says that there were 22 people on the ice, of which 15 were at Hodgman Islands. They provide the following new detail map:
map_HogemanIslands_420x485

They have an interesting video showing the unloading of the Argos. They say that both Turney and Fogwill had satellite phones and that each of the six drivers/staff members had a VHF radio.

The SYdney Morning Herald account adds the remarkable claim that Turney took more passengers into the field even after the evacuation notice had been issued:

A passenger standing near Professor Turney overheard the voyage leader, Greg Mortimer, telling him over the radio to bring passengers back to the ship so it can leave. But minutes later, Professor Turney drove six more passengers into the field. The overloaded vehicle had no space to collect returning passengers.

The longer linked account expands as follows:

A passenger, who was standing near Turney when Mortimer called the leader from the ship’s VHF radio, recalled their conversation: “Chris, [captain] Igor has just said we need to expedite people back from the islands so we can get out of here,” said Mortimer.

Turney, standing on the ice edge, repeated the message to confirm he had heard right.
“Affirmative,” said Mortimer.
“If I take this lot out, how long can we stay?” Turney said.
Mortimer repeated that everybody needed to get back to the ship.

The passenger was stunned by the conversation, even more so when, a few minutes later, Turney loaded an Argo with six passengers and drove off towards the Islands.

Update Jan 22- In an interview with CNN today (h/t Alex C), Turney denied that there had been any warning at 2:30 pm, while admitting that he had taken “science team members” by Argo to the islands at 3:00 pm (after the captain and Mortimer claim to have issued a recall alarm):

We had – the team got out by about 5:30. We were still taking science team members out at 3 o’clock, or so. There wasn’t any concern, at the time, that this was a significant issue.

Turney also argued in the interview that this delay didn’t “matter”, on the grounds that they would have been toast anyway – not the most convincing excuse IMO.
End Update.

The BBC report continued:

At 3.43pm a passenger onboard the Shokalskiy overheard Mortimer again speaking with someone on the VHF: “Everybody get into a small area and wait until they get a ride back. They [are] not to walk anywhere [and] are to [stay] together,” the passenger wrote in their diary at the time. Fifteen minutes later a quad bike and an Argo arrived with another load of people, who were transferred to the ship via Zodiac.

“The anger on Greg’s face when we arrived back was noticeable,” said one passenger.

An hour and a half later and the final four on the ice, which included Turney, pulled up in the second Argo. Footage on a passenger’s Go Pro digital camera read 5.35pm.

It was 6.15pm before the Shokalskiy finally departed the fast ice.

Several passengers took video footage of the view from their porthole as the ship departed in open water. By 7pm ice topped with about a metre of snow surrounded the ship. It crawled through dense pack ice most of the night.

The SMH stated the usually loquacious Turney (and other expedition leaders) refused to answer questions about December 23:

Professor Turney, Dr Fogwill and Mr Mortimer all declined to answer questions about the events of December 23.


Mobile Sea Ice

The new BBC article also contains statements that (implicitly) support Climate Audit’s rejection of Turney’s untrue attribution of his problems to a breakout of “fast ice” that could not have mitigated.

Murray Doyle, captain of the rescue vessel, Aurora Australis (and presumably far more experienced than Turney) stated that, rather than conditions being impossible to predict or mitigate, “conditions around the Mertz glacier were typical for the past few years.”

Luck-Baker also quoted another source, not named but “with considerable nautical knowledge of East Antarctica”, who said that “with the weather forecast” as it was that day, “this was not a good place to be”.

Tony Press, head of the Antarctic Climate and Ecosystem Coordinated Research Centre in Tasmania, also endorsed the obvious point that they needed to have a planned exit strategy in potentially hazardous conditions:

They need to plan accordingly and have an exit strategy which can be executed in timely fashion if the conditions become threatening.”

Turney’s defenders have attempted to transfer blame from the expedition to the Russian captain. However, Mortimer (though not Turney) squarely acknowledged that the delays were the “responsibility of the expedition team, not Captain Kiselev.”

Why Now?
The entrapment of the Ship of Fools has been the topic of many articles over the past month. Luck-Baker (and the Guardian reporters, Alok Jha and Laurence Topham) were embedded and timely reports would have been more relevant than puffs. Neither of the Guardian reporters have yet reported on events. And while Luck-Baker’s present article is welcome, it’s taken him a full month to write on the events of December 23. Why the silence until now?

Ship of Fools

Like many others, I’ve been intrigued by the misadventures of the Ship of Fools. Dozens of tourist vessels visit the Antarctic without becoming trapped by ice. So it’s entirely valid to inquire into why the one tourist vessel led by a “climate scientist” became trapped by ice.

The leader of the expedition, Chris Turney (also a secondary Climategate correspondent and co-signer of Lewandowsky’s multisignatory letter in the Conversation), claimed that the incident could not have been predicted. He said that they were trapped by a sudden “breakout” of multi-year ice (“fast ice”) that had previously been part of the ice shelf and that there was no way that they could have anticipated this. Turney’s claim has been uncritically accepted by the climate community e.g. Turner of the British Antarctica Survey here.

However, like other recent claims by Turney, this claim is bogus. In fact, Turney was trapped by sea ice that had been mobile throughout December 2013. This can be easily seen by examining readily available MODIS imagery (see MODIS here) leading up to the incident, as I’ll do in today’s post. Continue reading

Ice Storms and Climate Change

Thousands of homes in Toronto, including ours, are without power due to an ice storm. More precisely, freezing rain builds up as ice on tree branches, which then break, taking down power lines with them. When I was out this morning, I heard and saw a couple of large branches fall. They are warning that it may take 72 hours or more to restore service. This is a much bigger problem in winter than summer as the nights are well below freezing and freezing pipes becomes a problem. In our case, we also have hot water heating and freezing of this system is also a worry. Fortunately, there hasn’t been any wind so far – but there could be wind tonight, which would really exacerbate the problem.

Thus far, we haven’t heard any attempts to relate the ice storm to rolling loaded dice, but it’s early yet.

A New Alaskan d18O Series

PAGES2K Arctic introduced a lake sediment d18O series from Kepler Lake, Alaska that hadn’t been used in previous studies. Although O18 data is a workhorse of paleoclimate, O18 data from Alaska (or, for that matter, anywhere in the Arctic hemisphere between 90E and 90W – going east) is very scarce. Thus, the appearance of a new d18O series from Alaska is of considerable interest. I’ll show why by comparing the new data to other O18 information: Continue reading

Varved Inconsistency

Since AR4, there have been a series of new multiproxy studies, several of which were cited in AR5 (Mann et al 2008; Ljungqvist et al 2010; Christiansen and Ljungqvist 2012; Shi et al 2013). A distinctive feature of these and other recent multiproxy studies is the incorporation of varve thickness and near-equivalent mass accumulation rate (MAR) series, in which varve thickness (positively oriented) is interpreted as a direct proxy for temperature. The following table shows the usage of varve thickness and near-equivalent mass accumulation rate (MAR) series in post-AR4 multiproxy studies (“long” series shown below). It is evident that the varve thickness data in multiproxy studies is anything but “independent”.

varve thickness in multiproxy table
Table 1. Varve thickness and MAR (mass accumulation rate) series used in multiproxy studies which are both “long” (including the medieval period) and which have not been truncated in the modern period. Both logged and unlogged versions are used. In a couple of cases, the mass accumulation rate is limited to organics (“dark”). I’ve also included the Igaliku pollen accumulation rate series, because it appears to me to be closely related to MAR series. XRD (Xray density not included).

One of the most obvious features of the above table is the repeated use of a small number of varve thickness series used in Kaufman et al 2009: Big Round, Blue, C2, Donard, Iceberg and Lower Murray Lakes. Five of the six series were used in Shi et al 2013. In my recent discussion of Shi et al 2013, I observed that a composite of the five series (and the same is true for all six) had something of an HS-shape, though the series otherwise had negligible common “signal” (as demonstrated clearly by their eigenvalues). Further, several of the series (especially Iceberg which had been discussed in prior CA posts) had serious problems, compromising or potentially compromising any potential utility as a temperature proxy. This certainly suggested to me that the somewhat HS-ness of the varve thickness composite was more likely to be an artifact of selection from a noisy network rather than actual scientific knowledge. Skeptic blogs have long discussed this phenomenon, but it is one to which academic literature in the field has been wilfully obtuse.

Blog discussion has been mostly based on red noise examples. So I think that readers may be interested in seeing the phenomenon at work with actual data.

In the course of examining literature on varves, it quickly became evident that specialist literature prior to the relatively recent multiproxy articles had regarded thick varves as evidence of glacier advance (rather than “warmth”). Readers (and myself) wondered how the prior consensus (so to speak) that thick varves were related to glacier advance (and vice versa) had been replaced by a model in which thick varves were now interpreted as evidence of warmer temperatures. This proved to be an interesting backstory. I’ll also contrast the varve thickness series from Iceberg Lake, a canonical series in Kaufman et al 2009 and subsequent multiproxy studies, with “non-canonical” varve thickness series from Silvaplana, Switzerland and Hvitarvatn, Iceland, where thin varves are interpreted as evidence of warmth. Continue reading

Does the observational evidence in AR5 support its/the CMIP5 models’ TCR ranges?

A guest post by Nic Lewis

Steve McIntyre pointed out some time ago, here, that almost all the global climate models around which much of the IPCC’s AR5 WGI report was centred had been warming faster than the real climate system over the last 35-odd years, in terms of the key metric of global mean surface temperature. The relevant figure from Steve’s post is reproduced as Figure 1 below.

Fig.1 TCR post CMIP5 79-13 temp trends_CA24Sep13
Figure 1 Modelled versus observed decadal global surface temperature trend 1979–2013

Temperature trends in °C/decade. Models with multiple runs have separate boxplots; models with single runs are grouped together in the boxplot marked ‘singleton’. The orange boxplot at the right combines all model runs together. The default settings in the R boxplot function have been used. The red dotted line shows the actual increase in global surface temperature over the same period per the HadCRUT4 observational dataset.


Transient climate response

Virtually all the projections of future climate change in AR5 are based on the mean and range of outcomes simulated by this latest CMIP5 generation of climate models (AOGCMs). Changes in other variables largely scale with changes in global surface temperature. The key determinant of the range and mean level of projected increases in global temperature over the rest of this century is the transient climate response (TCR) exhibited by each CMIP5 model, and their mean TCR. Model equilibrium climate sensitivity (ECS) values, although important for other purposes, provide little information regarding surface warming to the last quarter of this century beyond that given by TCR values.

TCR represents the increase in 20-year mean global temperature over a 70 year timeframe during which CO2 concentrations, rising throughout at 1% p.a. compound, double. More generally, paraphrasing from Section 10.8.1 of AR5 WG1,TCR can be thought of as a generic property of the climate system that determines the global temperature response ΔT to any gradual increase in (effective) radiative forcing (ERF – see AR5 WGI glossary, here ) ΔF taking place over a ~70-year timescale, normalised by the ratio of the forcing change to the forcing due to doubling CO2, F2xCO2: TCR = F2xCO2 ΔT/ΔF. This equation permits warming resulting from a gradual change in ERF over a 60–80 year timescale, at least, to be estimated from the change in ERF and TCR. Equally, it permits TCR to be estimated from such changes in global temperature and in ERF.

The TCRs of the 30 AR5 CMIP5 models featured in WGI Table 9.5 vary from 1.1°C to 2.6°C, with a mean of slightly over 1.8°C. Many projections in AR5 are for changes up to 2081–2100. Applying the CMIP5 TCRs to the changes in CO2 concentration and other drivers of climate change from the first part of this century up to 2081–2100, expressed as the increase in total ERF, explains most of the projected rises in global temperature on the business-as-usual RCP8.5 scenario, although the relationship varies from model to model. Overall the models project about 10–20% faster warming than would be expected from their TCR values, allowing for warming ‘in-the-pipeline’. That discrepancy, which will not be investigated in this article, implies that the mean ‘effective’ TCR of the AR5 CMIP5 models for warming towards the end of this century under RCP8.5 is in the region of 2.0–2.2°C.


Observational evidence in AR5 about TCR

AR5 gives a ‘likely’ (17–83% probability) range for TCR of 1.0–2.5°C, pretty much in line with the 5–95% CMIP5 model TCR range (from fitting a Normal distribution) but with a downgraded certainty level. How does that compare with the observational evidence in AR5? Figure 10.20a thereof, reproduced as Figure 2 here, shows various observationally based TCR estimates.

 Fig.10.20a
Figure 2. Reproduction of Figure 10.20a from AR5
Bars show 5–95% uncertainty ranges for TCR.[i]

.
On the face of it, the observational study TCR estimates in Figure 2 offer reasonable support to the AR5 1.0–2.5°C range, leaving aside the Tung et al. (2008) study, which uses a method that AR5 WGI discounts as unreliable. However, I have undertaken a critical analysis of all these TCR studies, here. I find serious fault with all the studies other than Gillett et al. (2013), Otto et al. (2013) and Schwartz (2012). Examples of the faults that I find with other studies are:

Harris et al. (2013): This perturbed physics/parameter ensemble (PPE) study’s TCR range, like its ECS range, almost entirely reflects the characteristics of the UK Met Office HadCM3 model. Despite the HadCM3 PPE (as extended by emulation) sampling a wide range of values for 31 key model atmospheric parameters, the model’s structural rigidities are so strong that none of the cases results in the combination of low-to-moderate climate sensitivity and low-to-moderate aerosol forcing that the observational data best supports – nor could perturbing aerosol model parameters achieve this.

Knutti and Tomassini (2008): This study used initial estimates of aerosol forcing totalling −1.3 W/m² in 2000, in line with AR4 but far higher than the best estimate in AR5. Although it attempted to observationally-constrain these initial estimates, the study’s use of only global temperature data makes it impossible to separate properly greenhouse gas and aerosol forcing, the evolution of which are very highly (negatively) correlated at a global scale. The resulting final estimates of aerosol forcing are still significantly stronger than the AR5 estimates, biasing up TCR estimation. The use of inappropriate uniform and expert priors for ECS in the Bayesian statistical analysis further biases TCR estimation.

Rogelj et al. (2012): This study does not actually provide an observationally-based estimate for TCR. It explicitly sets out to generate a PDF for ECS that simply reflects the AR4 ‘likely’ range and best estimate; in fact it reflects a slightly higher range. Moreover, the paper and its Supplementary Information do not even mention estimation of TCR or provide any estimated PDF for TCR.

Stott and Forest (2007): This TCR estimate is based on the analysis in Stott et al. (2006), an AR4 study from which all four of the unlabelled grey dashed-line PDFs in Figure 10.20a are sourced. It used a detection-and-attribution regression method applied to 20th century temperature observations to scale TCR values, and 20th century warming attributable to greenhouse gases, for three AOGCMs. Gillett et al. (2012) found that just using 20th century data for this purpose biased TCR estimation up by almost 40% compared with when 1851–2010 data was used. Moreover, the 20th century greenhouse gas forcing increase used in Stott and Forest (2007) to derive TCR (from the Stott et al. (2006) attributable warming estimate) is 11% below that per AR5, biasing up its TCR estimation by a further 12%.

In relation to the three studies that I do not find any serious fault with, some relevant details from my analysis are:

Gillett et al. (2013): This study uses temperature observations over 1851–2010 and a detection-and-attribution regression method to scale AOGCM TCR values. The individual CMIP5 model regression-based observationally-constrained TCRs shown in a figure in the Gillett et al. (2013) study imply a best (median[ii]) estimate for TCR of 1.4°C, with a 5–95% range of 0.8–2.0°C.[iii] That compares with a range of 0.9–2.3°C given in the study based on a single regression incorporating all models at once, which it is unclear is as suitable a method.

Otto et al. (2013): There are two TCR estimates from this energy budget study included in Figure 10.20a. One estimate uses 2000–2009 data and has a median of 1.3°C, with a 5–95% range of 0.9–2.0°C. The other estimate uses 1970–2009 data and has a median of slightly over 1.35°C, with a 5–95% range of 0.7–2.5°C. Since mean forcing was substantially higher over 2000–2009 than over 1970–2009, and was also less affected by volcanic activity, the TCR estimate based on 2000–2009 data is less uncertain, and arguably more reliable, than that based on 1970–2009 data.

Schwartz (2012): This study derived TCR by zero-intercept regressions of changes, from the 1896–1901 mean, in observed global surface temperature on corresponding changes in forcing, up to 2009, based on forcing histories used in historical model simulations. The mean change in forcing up to 1990 (pre the Mount Pinatubu eruption) per the five datasets used to derive the TCR range is close to the best estimate of the forcing change per AR5. The study’s TCR range is 0.85–1.9°C, with a median estimate of 1.3°C.

So the three unimpeached studies in Figure 10.20a support a median TCR estimate of about 1.35°C, and a top of the ‘likely’ range for TCR of about 2.0°C based on downgrading 5–95% ranges, following AR5.


The implication for TCR of the substantial revision in AR5 to aerosol forcing estimates

There has been a 43% increase in the best estimate of total anthropogenic radiative forcing between that for 2005 per AR4, and that for 2011 per AR5. Yet global surface temperatures remain almost unchanged: 2012 was marginally cooler than 2007, whilst the trailing decadal mean temperature was marginally higher. The same 0.8°C warming now has to be spread over a 43% greater change in total forcing, natural forcing being small in 2005 and little different in 2012. The warming per unit of forcing is a measure of climate sensitivity, in this case a measure close to TCR, since most of the increase in forcing has occurred over the last 60–70 years. It follows that TCR estimates that reflect the best estimates of forcing in AR5 should be of the order of 30% lower than those that reflected AR4 forcing estimates.

Two thirds of the 43% increase in estimated total anthropogenic forcing between AR4 and AR5 is accounted for by revisions to the 2005 estimate, reflecting improved understanding, with the increase in greenhouse gas concentrations between 2005 and 2011 accounting for almost all of the remainder. Almost all of the revision to the 2005 estimate relates to aerosol forcing. The AR5 best (median) estimate of recent total aerosol forcing is −0.9 W/m2, a large reduction from −1.3 W/m2 (for a more limited measure of aerosol forcing) in AR4. This reduction has major implications for TCR and ECS estimates.

Moreover, the best estimate the IPCC gives in AR5 for total aerosol forcing is not fully based on observations. It is an expert judgement based on a composite of estimates derived from simulations by global climate models and from satellite observations. The nine satellite-observation-derived aerosol forcing estimates featured in Figure 7.19 of AR5 WGI range from −0.09 W/m2 to −0.95 W/m2, with a mean of −0.65 W/m2. Of these, six satellite studies with a mean best estimate of −0.78 W/m2 were taken into account in deciding on the −0.9 W/m2 AR5 composite best estimate of total aerosol forcing.


TCR calculation based on AR5 forcing estimates

Arguably the most important question is: what do the new ERF estimates in AR5 imply about TCR? Over the last century or more we have had a period of gradually increasing ERF, with some 80% of the decadal mean increase occurring fairly smoothly, volcanic eruptions apart, over the last ~70 years. We can therefore use the TCR = F2xCO2 ΔT/ΔF equation to estimate TCR from ΔT and ΔF, taking the change in each between the means for two periods, each long enough for internal variability to be small.

That is exactly the method used, with a base period of 1860–1879, by the ‘energy budget’ study Otto et al. (2013), of which I was a co-author. That study used estimates of radiative forcing that are approximately consistent with estimates from Chapters 7 and 8 of AR5, but since AR5 had not at that time been published the forcings were actually diagnosed from CMIP5 models, with an adjustment being made to reflect satellite-observation-derived estimates of aerosol forcing. However, in a blog-published study, here, I did use the same method but with forcing estimates (satellite-based for aerosols) taken from the second draft of AR5. That study estimated only ECS, based on changes between 1871–1880 and 2002–2011, but a TCR estimate of 1.30°C is readily derived from information in it.

We can now use the robust method of the Otto et al. (2013) paper in conjunction with the published AR5 forcing best (median) estimates up to 2011, the most recent year given. The best periods to compare appear to be 1859–1882 and 1995–2011. These two periods are the longest ones in respectively the earliest and latest parts of the instrumental period that were largely unaffected by major volcanic eruptions. Volcanic forcing appears to have substantially less effect on global temperature than other forcings, and so can distort TCR estimation. Using a final period that ends as recently as possible is important for obtaining a well-constrained TCR estimate, since total forcing (and the signal-to-noise ratio) declines as one goes back in time. Measuring the change from early in the instrumental period maximises the ratio of temperature change to internal variability, and since non-volcanic forcings were small then it matters little that they are known less accurately than in recent decades. Moreover, these two periods are both near the peak of the quasi-periodic ~65 year AMO cycle. Using a base period extending before 1880 limits one to using the HadCRUT surface temperature dataset. However, that is of little consequence since the HadCRUT4 v2 change in global temperature from 1880–1900 to 1995–2011 is identical to that per NCDC MLOST and only marginally below that per GISS.

In order to obtain a TCR estimate that is as independent of global climate models as possible, one should scale the aerosol component of the AR5 total forcing estimates to match the AR5 recent satellite-observation-derived mean of −0.78 W/m2. Putting this all together gives ΔF = 2.03 W/m2 and ΔT = 0.71, which, since AR5 uses F2xCO2 = 3.71 W/m, gives a best estimate of 1.30°C for TCR. The best estimate for TCR would be 1.36°C without scaling aerosol forcing to match the satellite-observation derived mean.

So, based on the most up to date numbers from the IPCC AR5 report itself and using the most robust methodology on the data with the best signal-to-noise ratio, one arrives at an observationally based best estimate for TCR of 1.30°C, or 1.36°C based on the unadjusted AR5 aerosol forcing estimate.

I selected 1859–1882 and 1995–2011 as they seem to me to be the best periods for estimating TCR. But it is worth looking at longer periods as well, even though the signal-to-noise ratio is lower. Using 1850–1900 and 1985–2011, two periods with mean volcanic forcing levels that, although significant, are well matched, gives a TCR best estimate of 1.24°C, or 1.30°C based on the unadjusted AR5 aerosol forcing estimate. The TCR estimates are even lower using 1850–1900 to 1972–2011, periods that are also well-matched volcanically.

What about estimating TCR over a shorter timescale? If one took ~65 rather than ~130 years between the middles of the base and end periods, and compared 1923–1946 with 1995–2011, the TCR estimates would be almost unchanged. But there is some sensitivity to the exact periods used. An alternative approach is to use information in the AR5 Summary for Policymakers (SPM) about anthropogenic-only changes over 1951–2010, a well-observed period. The mid-range estimated contributions to global mean surface temperature change over 1951–2010 per Section D.3 of the SPM are 0.9°C for greenhouse gases and ‑0.25°C for other anthropogenic forcings, total 0.65°C. The estimated change in total anthropogenic radiative forcing between 1950 and 2011 of 1.72 Wm-2 per Figure SPM.5, reduced by 0.04 Wm-2 to adjust to 1951–2010, implies a TCR of 1.4°C after multiplying by an F2xCO2 of 3.71 Wm-2. When instead basing the estimate on the linear trend increase in observed total warming of 0.64°C over 1951–2010 per Jones et al. (2013) – the study cited in the section to which the SPM refers – (the estimated contribution from internal variability being zero) and the linear trend increase in total forcing per AR5 of 1.73 Wm-2, the implied TCR is also 1.4°C. Scaling the AR5 aerosol forcing estimates to match the mean satellite observation derived aerosol forcing estimate would reduce the mean of these two TCR estimates to 1.3°C.


So does the observational evidence in AR5 support its/the CMIP5 models’ TCR ranges?

The evidence from AR5 best estimates of forcing, combined with that in solid observational studies cited in AR5, points to a best (median) estimate for TCR of 1.3°C if the AR5 aerosol forcing best estimate is scaled to match the satellite-observation-derived best estimate thereof, or 1.4°C if not (giving a somewhat less observationally-based TCR estimate). We can compare this with model TCRs. The distribution of CMIP5 model TCRs is shown in Figure 3 below, with a maximally observationally-based TCR estimate of 1.3°C for comparison.
.

Fig.3 TCR post CMIP5 TCRs Ross

Figure 3. Transient climate response distribution for CMIP5 models in AR5 Table 9.5
The bar heights show how many models in Table 9.5 exhibit each level of TCR

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Figure 3 shows an evident mismatch between the observational best estimate and the model range. Nevertheless, AR5 states (Box 12.2) that:

“the ranges of TCR estimated from the observed warming and from AOGCMs agree well, increasing our confidence in the assessment of uncertainties in projections over the 21st century.”

How can this be right, when the median model TCR is 40% higher than an observationally-based best estimate of 1.3°C, and almost half the models have TCRs 50% or more above that? Moreover, the fact that effective model TCRs for warming to 2081–2100 are the 10%–20% higher than their nominal TCRs means that over half the models project future warming on the RCP8.5 scenario that is over 50% higher than what an observational TCR estimate of 1.3°C implies.

Interestingly, the final draft of AR5 WG1 dropped the statement in the second draft that TCR had a most likely value near 1.8°C, in line with CMIP5 models, and marginally reduced the ‘likely’ range from 1.2–2.6°C to 1.0–2.5°C, at the same time as making the above claim.

So, in their capacity as authors of Otto et al. (2013), we have fourteen lead or coordinating lead authors of the WG1 chapters relevant to climate sensitivity stating that the most reliable data and methodology give ‘likely’ and 5–95% ranges for TCR of 1.1–1.7°C and 0.9–2.0°C, respectively. They go on to suggest that some CMIP5 models have TCRs that are too high to be consistent with recent observations. On the other hand, we have Chapter 12, Box 12.2, stating that the ranges of TCR estimated from the observed warming and from AOGCMs agree well. Were the Chapter 10 and 12 authors misled by the flawed TCR estimates included in Figure 10.20a? Or, given the key role of the CMIP5 models in AR5, did the IPCC process offer the authors little choice but to endorse the CMIP5 models’ range of TCR values?
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[i] Note that the PDFs and ranges given for Otto et al. (2013) are slightly too high in the current version of Figure 10.20a. It is understood that those in the final version of AR5 will agree to the ranges in the published study.

[ii] All best estimates given are medians (50% probability points for continuous distributions), unless otherwise stated.

[iii] This range for Gillett et al. (2013) excludes an outlier at either end; doing so does not affect the median.

The “Canonical” Varve Thickness Series

Shi et al 2013 use the following five varve thickness series, all of which have become widely used in multiproxy series since their introduction in Kaufman et al 2009: Big Round Lake and Donard Lake, Baffin Island; Lower Murray Lake, Ellesmere Island; and Blue Lake and Iceberg Lake, Alaska. Some of these proxies have been discussed from time to time, with an especially detailed discussion of Iceberg Lake (see tag here.)

The figure below compares a simple scaled average of these five series to the Hvitarvatn varve thickness series (inverted so that the Little Ice Age is shown as “cold” rather than warm. See accompanying discussion of Hvitarvatn here. Whereas Miller et al reported that the 19th century at Hvitarvatn was the period of greatest glacier advance in the entire Holocene, the “Kaufman five” show 19th century levels similar to the 11th century medieval period, with an anomalously ‘warm” 20th century:

shi-2013_compare-to-hvitarvatn
Figure 1. Top – average of five Shi et al varve thickness series; bottom – Hvitarvatn varve thickness (inverted). All in SD Units.

There is no “common signal” in the Kaufman Five according to common methods. The median inter-series correlation is 0.00605, with negative interseries correlation as common as positive interseries correlation. If one examines the eigenvalues of the correlation matrix – a useful precaution in assessing whether the data contains a “common signal” – there are no eigenvalues that are separable from red noise as evident in the barplot below.

shi-2013_varve-eigenvalues
Figure 2. Eigenvalues of (Kaufman Five) Varve Thickness Series

Despite the overwhelming lack of common signal according to these criteria, the average of the Kaufman Five has a distinctly elevated 20th century. Here is a plot of the Kaufman Five. The lack of correlation and lack of significant eigenvalues is evident in the plot, where there is little in common among the series except for one feature: the 20th century in each series is somewhat elevated relative to the 19th century. (As noted above, the average of the five series has a somewhat elevated 20th century, but is relatively featureless in centuries prior to the 20th century, especially in comparison to the well-dated Hvitarvatn series.)

shi-2013_five-sediments
Figure 3. Five Varve thickness series used in Shi et al 2013 (SD Units.)

When parsed in detail, each of the Kaufman Five has troubling defects, some of which I’ll briefly discuss today and which I’ll try to follow up on.

The Iceberg Lake, Alaska series has profound inhomogeneities, especially in its 20th century portion. A major inhomogeneity is that varve thickness is related to distance to the inlet, an observation first made in comments at Climate Audit in comments on Loso 2006. Loso 2009 conceded this point (without mentioning Climate AUdit though it did acknowledge WIllis Eschenbach who corresponded with Loso on a different point) but its remedy (taking logarithms) was hopelessly inadequate to the problem. Dietrich and Loso 2012 acknowledges that inhomogeneities impact their reconstruction, but did not amend or withdraw the earlier series. Interestingly, Dietrich and Loso report glacier advance in Alaska commencing around 1250AD, almost exactly contemporaneous with the well-dated Hvitarvatn advance. The Iceberg Lake series, as used, has a late 20th century uptick coinciding with a major inhomogeneity, the effect of which cannot be separated under any plausible technique known to me.

Major features of the Big Round Lake series (as I’ve observed previously) correspond to major features of the Hvitarvatn series and there is a much higher inter-series correlation between these two series than to other series in the Kaufman Five. The only problem is that this correlation requires inversion of the Big Round series so that thicker varves are generated in the Little Ice Age. There are important geological parallels between the two sites: like Hvitarvatn, Big Round is a proglacial lake, the sediment volume of which is related to proximity of a nearby glacier, which advanced in the Little Ice Age to its Holocene maximum and receded in the 20th century. In order to use the Big Round series in its present orientation, specialists have to explain why its behavior is opposite to Hvitarvatn. And why one should interpret Big Round varve data as showing a Little Warm Period in Baffin Island during Iceland’s Little Ice Age (especially when glacier lines moved 500 m lower in Baffin Island during this period.) The reason why Big Round varves are oriented thick-up by the original authors (Thomas et al 2009) is that there is a positive correlation in the late 20th century between varve thickness and local temperatures. Together with the exclusion of the inhomogeneous Iceberg Lake series, inverting this series (as seems required) would obviously impact the average of the canonical series.

Like Hvitarvatn and Big Round Lake, Donard Lake (Baffin Island) is a proglacial lake whose sediment volume is controlled by proximity of a nearby glacier (Caribou Glacier). Once again, this glacier reached its Holocene maximum in the Little Ice Age, prior to its 20th century retreat. However, the Donard Lake varve thickness series has a slightly negative correlation to the Big Round Lake series. Rather than simply averaging these two incompatible series, specialists need to closely re-examine the data to explain the inconsistency. Donard Lake dating is one thing that needs close examination.

Thomas and associates have recently reported a third proglacial varve thickness from Baffin Island (Ayr Lake), for which they unable to report a significant correlation to instrumental temperature. Thus, they did not report a temperature reconstruction for this site. However, the absence of such correlation surely bleeds back to the other series, inviting a reconsideration of whether their supposed correlations to temperature were spurious – particularly in the context of their inconsistency with the well-dated Hvitarvatn series.

Because varve thickness in these proglacial lakes is profoundly affected by glacier proximity, there is no homogeneous relationship between varve thickness and temperature

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