Emergent constraints on climate sensitivity in global climate models, Part 3

The two strongest potentially credible constraints, and conclusions

A guest post by Nic Lewis

In Part 1 of this article the nature and validity of emergent constraints[1] on equilibrium climate sensitivity (ECS) in GCMs were discussed, drawing mainly on the analysis and assessment of 19 such constraints in Caldwell et al. (2018),[2] who concluded that only four of them were credible. An extract of the rows of Table 1 of Part 1 detailing those four emergent constraints is given below.[3]


Name of constraint Year Correlation in CMIP5 Description
Sherwood D 2014 0.40 Strength of resolved-scale mixing between BL and lower troposphere in tropical E Pacific and Atlantic
Brient Shal 2015 0.38 Fraction of tropical clouds with tops below 850 mb whose tops are also below 950 mb
Zhai 2015 –0.73 Seasonal response of BL cloud amount to SST variations in oceanic subsidence regions between 20-40°latitude
Brient Alb 2016 –0.71 Sensitivity of cloud albedo in tropical oceanic low-cloud regions to present-day SST variations

Two of the those four constraints, Sherwood D and Brient Shal, were analysed in Part 2 and found wanting. In this final part of the article I discuss the remaining two potentially credible constraints, Brient Alb and Zhai – which have much higher correlation with ECS than do Sherwood D and Brient Shal – and formulate conclusions. Continue reading


Attribution of 2015-6 Phishing to APT28

In two influential articles in June 2016, immediately following the Crowdstrike announcement, SecureWorks (June 16 here and June 26 here) purported to connect the DNC hack to a 2015-6 phishing campaign which they attributed to APT28.  SecureWorks identified two malicious domains in their article. In today’s article, I’ll show that infrastructure from one domain are connected to domains identified as APT28 in early literature, while infrastructure from the other domain leads in an unexpected direction.

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Emergent constraints on climate sensitivity in global climate models, Part 2

The four constraints that Caldwell assessed as credible

A guest post by Nic Lewis

In Part 1 of this article the nature and validity of emergent constraints[i] on equilibrium climate sensitivity (ECS) in GCMs were discussed, drawing mainly on the analysis and assessment of 19 such constraints in Caldwell et al (2018; henceforth Caldwell),[ii] who concluded that only four of them were credible. All those four constraints favoured ECS in the upper half of the CMIP5 range (3.4–4.7°C). An extract of the rows of Table 1 of Part 1 detailing those four emergent constraints is given below.[iii]


Name of constraint Year Correlation in CMIP5 Description
Sherwood D 2014 0.40 Strength of resolved-scale mixing between BL and lower troposphere in tropical E Pacific and Atlantic
Brient Shal 2015 0.38 Fraction of tropical clouds with tops below 850 mb whose tops are also below 950 mb
Zhai 2015 –0.73 Seasonal response of BL cloud amount to SST variations in oceanic subsidence regions between 20-40°latitude
Brient Alb 2016 –0.71 Sensitivity of cloud albedo in tropical oceanic low-cloud regions to present-day SST variations

Caldwell regarded a proposed emergent constraint as not credible if it lacks an identifiable physical mechanism; is not robust to change of model ensemble; or if its correlation with ECS is not due to its proposed physical mechanism. The credible constraints identified in Caldwell are all related to tropical/subtropical low clouds and all except Brient Shal are significantly correlated with each other. Continue reading

DNC Hack due to Gmail Phishing??

In two influential articles in June 2016 (June 16 here and June 26 here), SecureWorks purported to link the then recently revealed DNC hack to Russia via a gmail phishing campaign which they had been monitoring since 2015 and which they attributed to APT28 (Fancy Bear). They had observed multiple phishing targets at hillaryclinton.com, dnc.org and personal gmail accounts of campaign officials and surmised that one of these targets at DNC must have been tricked by the phishing campaign, from which APT28 obtained access to the DNC server.

Their argument was quickly accepted by computer security analysts. In an influential article in October 2016, Thomas Rid, a prominent commentator on computer security, stated that this argument was the most important evidence in attribution of the DNC hack to Russia – it was what Rid called the “hackers’ gravest mistake”.

However, the connection of the DNC hack to the gmail phishing campaign, as set out in the SecureWorks article, was very speculative, even tenuous.  In addition, subsequent evidence in the DNC emails themselves conclusively refuted even this thin connection. To be clear, the issues pertaining to the DNC hack are distinct from the Podesta hack – which, though unknown at the time of the June 2016 SecureWorks’ article, can be convincingly attributed to gmail phishing accompanied by bitly link-shorteners.

In today’s post, I’m going to look at the narrow issue of the connection between the gmail phishing campaign and the DNC hack and whether it contributes to Russian attribution of the DNC hack.

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Emergent constraints on climate sensitivity in global climate models, Part 1


Their nature and assessment of their validity

A guest post by Nic Lewis

There have been quite a number of papers published in recent years concerning “emergent constraints” on equilibrium climate sensitivity (ECS) in comprehensive global climate models (GCMs), of both the current (CMIP5) and previous (CMIP3) generations. The range of ECS values in GCMs has remained almost unchanged since the early days of climate modelling; in the IPCC 5th Assessment Report (AR5) it was given as 2.1-4.7°C for CMIP5 models.[i]

From the IPCC 1st Assessment Report (FAR) to AR5, the main cause of the large uncertainty as to ECS in GCMs has been the difficulty of simulating clouds and their behaviour.[ii] This has led to cloud feedback differing between GCMs even as to its sign – and to little confidence that the true level of cloud feedback lies within its range in GCMs. Progress in understanding cloud behaviour and related convective dynamics and feedbacks has been painfully slow. We shall see in this 3-part article that emergent constraint approaches have the potential to offer useful insights into cloud behaviour, however the main focus will be on to what extent they narrow the uncertainty range of ECS in GCMs. Continue reading

Arrest of the “Lurk” Banking Trojan Gang

On June 2, 2016, in a major police operation in Russia, 50 hackers from the Lurk banking trojan gang were arrested following 86 raids (Security Week here). Their malware was used for bank fraud (especially in Russia) and ransomware all over the world. The full extent of their activities became clear only after their arrest. In today’s post, I’m going to look back at U.S. computer security analysis (especially by Cisco Talos) prior to the arrests by Russia.  The post contains an Easter egg relating to attribution of the DNC hack, but that will be a story for a different day. Continue reading

Marvel et al.’s new paper on estimating climate sensitivity from observations

A guest post by Nic Lewis

Introduction and summary

Recently a new model-based paper on climate sensitivity was published by Kate Marvel, Gavin Schmidt (the head of NASA GISS) and others, titled ‘Internal variability and disequilibrium confound estimates of climate sensitivity from observations’.[1] It appears to me that the novel part of its analysis is faulty, and that the part which isn’t faulty isn’t novel.

As some readers may recall, I found six serious errors in a well-publicised 2016 paper by Kate Marvel and other GISS climate scientists on the topic of climate sensitivity.[2] Two of the six errors were subsequently corrected.

With regards to the new Marvel et al paper, I find that:

  • the low ECS estimates Marvel et al. obtain when using current (CMIP5) climate models’ historical simulation data arise from using a period with unbalanced volcanic forcing, with the low bias disappearing when that problem is addressed; and
    .
  • the low ECS estimates they obtain when using data from AMIP simulations (those where models are driven by observed evolving sea-surface temperature patterns as well evolving forcing) are not news. They more likely indicate problems with CMIP5 models’ ocean modules, than (as Marvel et al. suggest) that internal variability in recent decades was particularly unusual.

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Reply to Patrick Brown’s response to my article commenting on his Nature paper

Introduction

I thank Patrick Brown for his detailed response (also here) to statistical issues that I raised in my critique “Brown and Caldeira: A closer look shows global warming will not be greater than we thought” of his and Ken Caldeira’s recent paper (BC17).[1] The provision of more detailed information than was given in BC17, and in particular the results of testing using synthetic data, is welcome. I would reply as follows.

Brown comments that I suggested that rather than focusing on the simultaneous use of all predictor fields, BC17 should have focused on the results associated with the single predictor field that showed the most skill: The magnitude of the seasonal cycle in OLR. He goes on to say: “Thus, Lewis is arguing that we actually undersold the strength of the constraints that we reported, not that we oversold their strength.”

To clarify, I argued that BC17 undersold the statistical strength of the relationships involved, in the RCP8.5 2090 case focussed on in their Abstract, for which the signal-to-noise ratio is highest. But I went on to say that I did not think the stronger relationships would really provide a guide to how much global warming there would actually be late this century on the RCP8.5 scenario, or any other scenario. That is because, as I stated, I disagree with BC17’s fundamental assumption that the relationship of future warming to certain aspects of the recent climate that holds in climate models necessarily also applies in the real climate system. I will return to that point later. But first I will discuss the statistical issues. Continue reading

Polar Bears, Inadequate data and Statistical Lipstick

LipStbear

A recent paper Internet Blogs, Polar Bears, and Climate-Change Denial by Proxy by JEFFREY A. HARVEY and 13 others has been creating somewhat of a stir in the blogosphere. The paper’s abstract purports to achieve the following:

Increasing surface temperatures, Arctic sea-ice loss, and other evidence of anthropogenic global warming (AGW) are acknowledged by every major scientific organization in the world. However, there is a wide gap between this broad scientific consensus and public opinion. Internet blogs have strongly contributed to this consensus gap by fomenting misunderstandings of AGW causes and consequences. Polar bears (Ursus maritimus) have become a “poster species” for AGW, making them a target of those denying AGW evidence. *Here, focusing on Arctic sea ice and polar bears, we show that blogs that deny or downplay AGW disregard the overwhelming scientific evidence of Arctic sea-ice loss and polar bear vulnerability.* By denying the impacts of AGW on polar bears, bloggers aim to cast doubt on other established ecological consequences of AGW, aggravating the consensus gap. To counter misinformation and reduce this gap, scientists should directly engage the public in the media and blogosphere.

Reading further into the paper we find that this seems to be yet another piece of  propaganda to push a Climate Change agenda. In line with the high standards of climate science “communication”, there are over 50 occurences of various forms of the derogatory labels “denier” or “deny” in a mere five pages of text and two pages of references. Such derogatory language has become commonplace in the climate change academic world and reflects badly on the authors who use it.

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Brown and Caldeira: A closer look shows global warming will not be greater than we thought

A guest post by Nic Lewis

Introduction

Last week a paper predicting greater than expected global warming, by scientists Patrick Brown and Ken Caldeira, was published by Nature.[1]  The paper (henceforth referred to as BC17) says in its abstract:

“Across-model relationships between currently observable attributes of the climate system and the simulated magnitude of future warming have the potential to inform projections. Here we show that robust across-model relationships exist between the global spatial patterns of several fundamental attributes of Earth’s top-of-atmosphere energy budget and the magnitude of projected global warming. When we constrain the model projections with observations, we obtain greater means and narrower ranges of future global warming across the major radiative forcing scenarios, in general. In particular, we find that the observationally informed warming projection for the end of the twenty-first century for the steepest radiative forcing scenario is about 15 per cent warmer (+0.5 degrees Celsius) with a reduction of about a third in the two-standard-deviation spread (−1.2 degrees Celsius) relative to the raw model projections reported by the Intergovernmental Panel on Climate Change.”

Patrick Brown’s very informative blog post about the paper gives a good idea of how they reached these conclusions. As he writes, the central premise underlying the study is that climate models that are going to be the most skilful in their projections of future warming should also be the most skilful in other contexts like simulating the recent past. It thus falls within the “emergent constraint” paradigm. Personally, I’m doubtful that emergent constraint approaches generally tell one much about the relationship to the real world of aspects of model behaviour other than those which are closely related to the comparison with observations. However, they are quite widely used.

In BC17’s case, the simulated aspects of the recent past (the “predictor variables”) involve spatial fields of top-of-the-atmosphere (TOA) radiative fluxes. As the authors state, these fluxes reflect fundamental characteristics of the climate system and have been well measured by satellite instrumentation in the recent past – although (multi) decadal internal variability in them could be a confounding factor. BC17 derive a relationship in current generation (CMIP5) global climate models between predictors consisting of three basic aspects of each of these simulated fluxes in the recent past, and simulated increases in global mean surface temperature (GMST) under IPCC scenarios (ΔT). Those relationships are then applied to the observed values of the predictor variables to derive an observationally-constrained prediction of future warming.[2]

The paper is well written, the method used is clearly explained in some detail and the authors have archived both pre-processed data and their code.[3] On the face of it, this is an exemplary study, and given its potential relevance to the extent of future global warming I can see why Nature decided to publish it. I am writing an article commenting on it for two reasons. First, because I think BC17’s conclusions are wrong. And secondly, to help bring to the attention of more people the statistical methodology that BC17 employed, which is not widely used in climate science. Continue reading