Testimony Judith Curry on Arctic temperature seems to be a misquotation

Looks like the IPCC is not even wrong.

There has been a heated debate between Judith Curry (Climate Etc.) and Tamino (Open Mind) about the temperature in the Arctic. This debate was initiated by Curry's testimony before congress two weeks ago.

In her testimony Judith Curry quotes:

“Arctic temperature anomalies in the 1930s were apparently as large as those in the 1990s and 2000s. There is still considerable discussion of the ultimate causes of the warm temperature anomalies that occurred in the Arctic in the 1920s and 1930s.” (AR5 Chapter 10)

Tamino at Open Mind investigated this claim and found that recent temperatures were clearly higher as in the beginning of the 20th century. In his post (One of) the Problem(s) with Judith Curry Tamino concludes that the last IPCC report and Curry's testimony are wrong about the Arctic temperature increase:

"I think the IPCC goofed on this one — big-time — and if so, then Curry’s essential argument about Arctic sea ice is out the window. I’ve studied the data. Not only does it fail to support the claim about 1930s Arctic temperatures, it actually contradicts that claim. By a wide margin. It ain’t even close."

That sounded convincing, but I am not so sure about the IPCC any more.

Tamino furthermore wonders where Curry got her information from. I guess he found it funny that Judith Curry would quote the IPCC as a reliable source without checking the information. Replying to another question of mine, Judith Curry replied on twitter that she indeed got her information from the last (draft) IPCC report:

@VariabilityBlog I will deal with this nonsense on friday on my blog. I QUOTED the IPCC AR5
— Judith Curry (@curryja) January 21, 2014

Later she also wrote a reply on her blog, Climate Ect., starting with the above quote from the IPCC report.

Then the story takes a surprising turn, when Steve Bloom hidden in a large number of comments at AndThenTheresPhysics notes that the quote is missing important context. The full paragraph in the IPCC namely reads (my emphasis and the quote in Curry's testimony in red):

A question as recently as six years ago was whether the recent Arctic warming and sea ice loss was unique in the instrumental record and whether the observed trend would continue (Serreze et al., 2007). Arctic temperature anomalies in the 1930s were apparently as large as those in the 1990s and 2000s. There is still considerable discussion of the ultimate causes of the warm temperature anomalies that occurred in the Arctic in the 1920s and 1930s (Ahlmann, 1948; Veryard, 1963; Hegerl et al., 2007a; Hegerl et al., 2007b). The early 20th century warm period, while reflected in the hemispheric average air temperature record (Brohan et al., 2006), did not appear consistently in the mid-latitudes nor on the Pacific side of the Arctic (Johannessen et al., 2004; Wood and Overland, 2010). Polyakov et al. (2003) argued that the Arctic air temperature records reflected a natural cycle of about 50–80 years. However, many authors (Bengtsson et al., 2004; Grant et al., 2009; Wood and Overland, 2010; Brönnimann et al., 2012) instead link the 1930s temperatures to internal variability in the North Atlantic atmospheric and ocean circulation as a single episode that was sustained by ocean and sea ice processes in the Arctic and north Atlantic. The Arctic wide temperature increases in the last decade contrast with the episodic regional increases in the early 20th century, suggesting that it is unlikely that recent increases are due to the same primary climate process as the early 20th century. IPCC(2014, draft, page 10-43 to 10-44).

Steve Bloom dryly comments: "So it was a question in 2007." In other words, the IPCC was right, but Judith Curry selectively quoted from the report. That first sentence is very important, also the age of the references could have revealed that this paragraph was not discussing the current state-of-the-art. The data of the last six years makes a large difference between "with some goodwill in the same range of temperatures" to "clearly higher Arctic temperatures".

This is illustrated by one of the figures from Tamino's post, presenting the data:

This is the annual average temperature in the Arctic from 60 to 90 degrees North as computed by the Berkeley Earth Surface Temperature group. The smooth red line is computed using LOESS smoothing.

And the misquotation is not for lack of space in the testimony. In her blog post, Curry quotes many sections of the IPCC report at length and also the entire paragraph like it is displayed here, just somehow without the first sentence printed here in bold, the one that provides the important context.

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Related reading

The congressional Testimony by Curry: STATEMENT TO THE COMMITTEE ON ENVIRONMENT AND PUBLIC WORKS OF THE UNITED STATES SENATE Hearing on “Review of the President’s Climate Action Plan" 16 January 2014, Judith A. Curry.

(One of) the Problem(s) with Judith Curry by Tamino at Open Mind.

The reply by Curry about Tamino's post on her blog, Climate ect.

The answer to that by Tamino suggests that Curry's reply is not that convincing.

Also Robert Way contributed to the discussion at Skeptical Science: "A Historical Perspective on Arctic Warming: Part One". Robert Way made the round in the blog-o-sphere with the paper Cowtan and Way (2013), where they studied the recent strong warming in the Arctic and suggested that that may explain a part of the recent slowdown in the warming of surface temperature.

A previous post of mine of Curry's testimony, focussing on her suggestive, but non-committal language: "Interesting what the interesting Judith Curry finds interesting".

Peer review helps fringe ideas gain credibility

Stoat has a nice new post explaining how peer review works in practice. The climate ostriches sometimes suggest that peer review is just there to keep their ideas out of the scientific literature and derogatively call it pal review. Well, peer review is nowadays practiced in all sciences, so that seems rather far fetched. Thinking about it, I would argue that peer review could actually help the climate ostriches. There is one caveat: they would have a valid critique. That is the bigger problem for them.

So what is peer review, what is its function in science and how could it help the climate ostriches?

How does it work

The short description of peer review at Stoat is:

For a working scientist, peer review is just part of the job. You write up your work, you show it to your colleagues ..., you send it to the best journal you think you can get away with, and eventually you get the reviews back. These will be a mixture of “please cite my paper” (usually disguised as “you need to consider X”), typos, and the occasional well-considered thoughtful comment that genuinely improves things. You sigh, you happily incorporate the thoughtful stuff, you work out how much of the not-very-helpful stuff you can get away with blowing off, and you resubmit ... And sometimes you get a reviewer who really really doesn’t like your paper for what you regard as invalid reasons, and you have to decide whether to fight to the death or go elsewhere.

Function in science

In my previous post, The value of peer review for science and the press, I wrote about its function in science:

Peer review gives an article credibility. As such peer review is "just" a filter, it does not guarantee that an article is right. Many peer-reviewed articles contain errors, many ideas outside of the peer-reviewed literature are worthwhile. However, on average the quality of peer-reviewed work is better. Thus peer-reviewed work is more likely worthy of your attention. If you are a scientist and an idea/study is about something you are knowledgeable about there is no reason to limit yourself exclusively to peer-reviewed articles, but it is smart to prefer them.

Given that an important function of peer review is to give the article credibility, it is also logical that reviewers pay extra attention if an article makes strong claims, that is claims that clearly deviate from our current understanding. In an ideal world, without any time pressures, peer review would be perfect every instance. However, a run of the mill article by a well-known author is much less likely to contain problems.

I would argue that that should be no problem for the people making strong claims. Every claim should hold up the scrutiny of the reviewers any way. That may be more work, but such an article also brings much more acclaim and is thus worth some work. As quoted above, sometimes reviewers will block your beautiful manuscript. That has also happened to me and probably to any active scientist. Then you just go to another journal. If the idea is valid, you will find a place for it.

That peer review is not perfect may be more of a problem for seasoned scientists. I especially notice that well-written article, by native speakers, are more likely to contain small errors. The smooth language seems to make the reviewers less critical and the author is punished by publishing articles with embarrassing errors.

Interesting what the interesting Judith Curry finds interesting

I am a little late with this, I just came across the Week in review by Judith Curry. If there is one thing that annoys me about the way Curry communicates, it is her suggestive, but completely non-committal language. The exact opposite of what I am used to among scientists. The word "interesting" is one of her favorite suggestive words.

Here is a quote illustrating the problem. Curry writes:

Freezing is the new warming

RealClearPolitics has an interesting article Freezing is the new warming, that summarizes the current state of the public debate on climate change.  Excerpts:

Or try refuting global warming. Temperatures have stopped warming for more than a decade? That’s just a temporary “pause” in the warming that we just know is going to come roaring back any day now. Antarctic ice is growing? That’s actually caused by the melting of ice, don’t you know. A vicious cold snap that sets record low temperatures? That’s just because the North Pole is actually warming. So if the winter is warm, that’s global warming, but if the winter is cold, that’s global warming, too. If sea ice is disappearing, that’s global warming, but if sea ice is increasing, that’s global warming.

Now we can see what they mean when the warmthers say that global warming is supported by an ironclad scientific consensus. The theory is so irrefutable that it’s unfalsifiable!

Which is to say that it has become a cognitive spaghetti bowl full of ad hoc rationalizations, rather than a genuine scientific hypothesis. 

This is pure and utter nonsense. Let me just discuss the main point, all the other denier memes are debunked at Skeptical Science. It is very easy to falsify the theory of global warming by greenhouse gasses.

If there would be an unexplained temperature drop of one degree and it would stay there for a decade, the theory is completely dead. If the same thing happens to the ocean heat content, the theory is dead within a year.

RealClearPolitics is right in suggesting that anything that will actually happen is very unlikely to refute the theory. Quite of lot of basic science would need to be wrong. And RealClearPolitics is right in suggesting that it is not sufficient for something to happen what feelies intuitively feel should not happen in a warmer climate. You do need some actual proof that the phenomenon should behave that way. His blog is at least honestly called a political blog.

Judith Curry is very intelligent and has much experience as scientist. She naturally knows that this quote was nonsense, but also that her audience likes it. Thus she non-noncommittally calls it interesting.

My wish for 2014 is that Curry comes back to the scientific community and stops using the word "interesting" so much. The scientific way of trying to understand why there is a difference of understanding is making it clearer what you mean.

[UPDATE: The blog Klimaatverandering just has listed 10 ways to "falsify AGW". Worth reading.]

[UPDATE: This post and the ensuing discussion made me think that a long post on the topic may be useful. I would argue that falsifiable is important and that falsification is overrated.]

Climate myths translated into econ talk

Yesterday, I was at an amazing meeting. The three public lectures about climatology were not that eventful, although it was interesting to see how you can present the main climatological findings in a clear way.

The amazing part was the Q&A afterwards. I was already surprised to see that I was one of the youngest ones, but had not anticipated that most of these people were engineers and economists, that is climate ostriches. As far as I remember, not one public question was interesting! All were trivially nonsense, I am sorry to have to write.

One of the ostriches showed me some graphs from a book by Fred Singer. Maybe I should go to an economics conference and cite some mercantile theorems of Colbert. I wonder how they would respond.

Afterwards I wondered whether translating their "arguments" against climatology to economy would help non-climatologists to see the weakness of the simplistic arguments. This post is a first attempt.

Seven translations

#1. That there is and always have been natural variability is not an argument again anthropogenic warming just like the pork cycle does not preclude economic growth.

#2. One of our economist ostriches thought that there was no climate change in Germany because one mountain station shows cooling. That is about as stupid as claiming that there is no economic growth because one of your uncles had a decline in his salary.

#3. The claim that the temperature did not increase or that it was even cooling in the last century, that it is all a hoax of climatologists (read the evil Phil Jones) can be compared to a claim that the world did not get wealthier in the last century and that all statistics showing otherwise are a government cover-up. In both cases there are so many independent lines of research showing increases.

#4. The idea that CO₂ is not a greenhouse gas and that increases in CO₂ cannot warm the atmosphere is comparable to people claiming that their car does not need energy and that they will not drive less if gasoline becomes more expensive. Okay maybe this is not the best example, most readers will likely claim that gas prices have no influence on them, they have no choice and have to drive, but I would hope that economists know better. The strength of both effects needs study, but to suggest that there is no effect is beyond reason.

#5. Which climate change are you talking about, it stopped in 1998. That would be similar to the claim that since the banking crisis in 2008 markets are no longer efficient. Both arguments ignore the previous increases and deny the existence of variability.

#6. The science isn't settled. Both science have foundations that are broadly accepted in the profession (consensus) and problems that are not clear yet and that are a topic of research.

#7. The curve fitting exercises without any physics by the ostriches are similar to "technical analyses" of stock ratings.

[UPDATE. Inspired by a comment of David in the comments of Judith Curry on Climate Change (EconTalk)
#8 The year 1998 was a strong El Nino year and way above trend, well above nearby years. Choosing that window is similar to saying that stocks are a horrible investment because the market collapsed during the Great Depression.]

[UPDATE. Found a nice one.
Daniel Barkalow writes:

Looking at the global average surface temperature (which is what those graphs tend to show), is a bit like looking at someone's bank account. It's a pretty good approximation of how much money they have, but there's going to be a lot of variability, based on not knowing what outstanding bills the person has, and the person is presumably earning income continuously, but only getting paychecks at particular times. This mostly averages out, but there's the risk in looking at any particular moment that it's a really uncharacteristic moment.

In particular, it seems to me that the "pause" idea is based on the fact that 1998 was warmer than nearly every year since, while neglecting that 1998 was warmer than 1997 or any previous year by more than 15 years of predicted warming. If this were someone's bank account, we'd guess that it reflected an event like having their home purchase fall through after selling their old home: some huge asset not usually included ended up in their bank account for a certain period before going back to wherever it was. You wouldn't then think the person had stopped saving, just because they hadn't saved up to a level that matches when their house money was in their bank account. You'd say that there was weird accounting in 1998, rather than an incredible gain followed by a mysterious loss.

]

One interesting question

The engineers and economists were wearing suits and the scientists were dressed more casually. Thus it was easy to find each other. One had an interesting challenge, which was at least new to me, he argued that the Fahrenheit scale, which was used a lot in the past is uncertain because it depends on the melting point of brine and the amount of salt put in the brine will vary.

One would have to make quite an error with the brine to get rid of global warming, however. Furthermore, everyone would have had to make the same error, because a random errors would average out. And if there were a bias, this would be reduced by homogenization. And almost all of the anthropogenic warming was after the 1950-ies, where this problem no longer existed.

A related problem is that the definition of the Fahrenheit scale has changed and also that there are many temperature scales and in old documents it is not always clear which unit was used. Wikipedia lists these scales: Celsius, Delisle, Fahrenheit, Kelvin, Newton, Rankine, Réaumur and Rømer. Such questions are interesting to get the last decimal right, but no reason to become an ostrich.

Disturbing

I find it a bit disturbing that so many economists come up with so simple counter "arguments". They basically assume that climatologists are stupid or are conspiring against humanity. Expecting that for anther field of study makes one wonder where they got that expectation from and shines a bad light on economics.

This was just a quick post, I would welcome ideas for improvements and additions in the comments. Did I miss any interesting analogies?

Announcement of the 8th Seminar for Homogenization in Budapest in May 2014

The first announcement has been published of the 8th Seminar for Homogenization. This is the main meeting of the homogenization community. It was announced on the homogenization distribution list. Anyone working on homogenization is welcome to join this list.

This time it will be organized together with the 3rd conference on spatial interpolation techniques in climatology and meteorology. As always it will be held in Budapest, Hungary. It will take place from the 12th to the 16 May 2014. The pre-registration and abstract submission deadline is 30 March 2014.

[UPDATE: There is now a homepage with all the details about the seminar.]

The announcement is available as PDF. Some excerpts:

Background

At present we plan to organize the Homogenization Seminar and the Interpolation Conference together considering certain theoretical and practical aspects. Theoretically there is a strong connection between these topics since the homogenization and quality control procedures need spatial statistics and interpolation techniques for spatial comparison of data. On the other hand the spatial interpolation procedures (e.g. gridding) need homogeneous, high quality data series to obtain good results, as it was performed in the Climate of Carpathian Region project led by OMSZ and supported by JRC. The main purpose of the project was to produce a gridded database for the Carpathian region based on homogenized data series. The experiences of this project may be useful for the implementation of gridded databases.

On the importance of changes in weather variability for changes in extremes

This is part 2 of the series on weather variability.

A more extreme climate is often interpreted in terms of weather variability. In the media weather variability and extreme weather are typically even used as synonyms. However, extremes may also change due to changes in the mean state of the atmosphere (Rhines and Huybers, 2013) and it is in general difficult to decipher the true cause.

Katz and Brown theorem

Changes in mean and variability are dislike quantities. Thus comparing them is like comparing apples and oranges. Still Katz and Brown (1992) found one interesting general result: the more extreme the event, the more important a change in the variability is relative to the mean (Figure 1). Thus if there is a change in variability, it is most important for the most extreme events. If the change is small, these extreme events may have to be extremely extreme.

Given this importance of variability they state:

"[Changes in the variability of climate] need to be addressed before impact assessments for greenhouse gas-induced climate change can be expected to gain much credibility."

The relative sensitivity of an extreme to changes in the mean (dashed line) and in the standard deviation (solid line) for a certain temperature threshold (x-axis). The relative sensitivity of the mean (standard deviation) is the change in probability of an extreme event to a change in the mean (or standard deviation) divided by its probability. From Katz and Brown (1992). It is common in the climatological literature to also denote events that happen relatively regularly with the term extreme. For example, the 90 and 99 percentiles are often called extremes even if such exceedances will occur a few times a month or year. Following the common parlance, we will denote such distribution descriptions as moderate extremes, to distinguish them from extreme extremes. (Also the terms soft and hard extremes are used.) Based on the theory of Katz and Brown, the rest of this section will be ordered from moderate to extreme extremes.

Examples from scientific literature

We start with the variance, which is a direct measure of variability and strongly related to the bulk of the distribution. Della-Marta et al. (2007) studied trends in station data over the last century of the daily summer maximum temperature (DSMT). They found that the increase in DSMT variance over Western Europe and central Western Europe is, respectively, responsible for approximately 25% and 40% of the increase in hot days in these regions.

They also studied trends in the 90th, 95th and 98th percentiles. For these trends variability was found to be important: If only changes in the mean had been taken into account these estimates would have been between 14 and 60% lower.

Also in climate projections for Europe, variability is considered to be important. Fischer and Schär (2009) found in the PRUDENCE dataset (a European downscaling project) that for the coming century the strongest increases in the 95th percentile are in regions where variability increases most (France) and not in regions where the mean warming is largest (Iberian Peninsula).

The 2003 heat wave is a clear example of an extreme extreme, where one would thus expect that variability is important. Schär et al. (2004) indeed report that the 2003 heat wave is extremely unlikely given a change in the mean only. They show that a recent increase in variability would be able to explain the heat wave. An alternative explanation could also be that the temperature does not follow the normal distribution.

Are break inhomogeneities a random walk or a noise?

Tomorrow is the next conference call of the benchmarking and assessment working group (BAWG) of the International Surface Temperature Initiative (ISTI; Thorne et al., 2011). The BAWG will create a dataset to benchmark (validate) homogenization algorithm. It will mimic the real mean temperature data of the ISTI, but will include know inhomogeneities, so that we can assess how well the homogenization algorithms remove them. We are almost finished discussing how the benchmark dataset should be developed, but still need to fix some details. Such as the question: Are break inhomogeneities a random walk or a noise?

Previous studies

The benchmark dataset of the ISTI will be global and is also intended to be used to estimate uncertainties in the climate signal due to remaining inhomogeneities. These are the two main improvements over previous validation studies.

Williams, Menne, and Thorne (2012) validated the pairwise homogenization algorithm of NOAA on a dataset mimicking the US Historical Climate Network. The paper focusses on how well large-scale biases can be removed.

The COST Action HOME has performed a benchmarking of several small networks (5 to 19 stations) realistically mimicking European climate networks (Venema et al., 2012). It main aim was to intercompare homogenization algorithms, the small networks allowed HOME to also test manual homogenization methods.

These two studies were blind, in other words the scientists homogenizing the data did not know where the inhomogeneities were. An interesting coincidence is that the people who generated the blind benchmarking data were outsiders at the time: Peter Thorne for NOAA and me for HOME. This probably explains why we both made an error, which we should not repeat in the ISTI.

Introduction to series on weather variability and extreme events

This is the introduction to a series on changes in the daily weather and extreme weather. The series discusses how much we know about whether and to what extent the climate system experiences changes in the variability of the weather. Variability here denotes the the changes of the shape of probability distribution around the mean. The most basic variable to denote variability would be the variance, but many other measures could be used.

Dimensions of variability

Studying weather variability adds more dimensions to our apprehension of climate change and also complexities. This series is mainly aimed at other scientists, but I hope it will be clear enough for everyone interested. If not, just complain and I will try to explain it better. At least if that is possible, we do not have much solid results on changes in the weather variability yet.

The quantification of weather variability requires the specification of the length of periods and the size of regions considered (extent, the scope or domain of the data). Different from studying averages is that the consideration of variability adds the dimension of the spatial and temporal averaging scale (grain, the minimum spatial resolution of the data); thus variability requires the definition of an upper and lower scale. This is important in climate and weather as specific climatic mechanisms may influence variability at certain scale ranges. For instance, observations suggest that near-surface temperature variability is decreasing in the range between 1 year and decades, while its variability in the range of days to months is likely increasing.

Similar to extremes, which can be studied on a range from moderate (soft) extremes to extreme (hard) extremes, variability can be analysed by measures which range from describing the bulk of the probability distribution to ones that focus more on the tails. Considering the complete probability distribution adds another dimension to anthropogenic climate change. Such a soft measure of variability could be the variance, or the interquartile range. A harder measure of variability could be the kurtosis (4th moment) or the distance between the first and the 99th percentile. A hard variability measure would be the difference between the maximum and minimum 10-year return periods.

Another complexity to the problem is added by the data: climate models and observations typically have very different averaging scales. Thus any comparisons require upscaling (averaging) or downscaling, which in turn needs a thorough understanding of variability at all involved scales.

A final complexity is added by the need to distinguish between the variability of the weather and the variability added due to measurement and modelling uncertainties, sampling and errors. This can even affect trend estimates of the observed weather variability because improvements in climate observations have likely caused apparent, but non-climatic, reductions in the weather variability. As a consequence, data homogenization is central in the analysis of observed changes in weather variability.