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.

IPCC videos on the working groups The Physical Science Basis and Extremes

The IPCC has just released a video with main points from the IPCC report on the physical basis. Hat tip Klimazwiebel. It is beautifully made and I did not notice any obvious errors, which you can normally not say for journalistic works, unfortunately.

For the typical reader of this blog it may be a bit to superficial. And the people who do not read this blog will likely never see it. Thus I do wonder for whom the video is made. :-)

Another nice video by the IPCC is the one on the special report on Extremes (SREX) published last year. Same caveat.

On the reactions to the doubling of the recent temperature trend by Curry, Watts and Lucia

The recent Cowtan and Way study, coverage bias in the HadCRUT4 temperature record, in the QJRMS showed that the temperature trend over the last 15 years is more than twice as strong as previously thought. [UPDATE: The paper can be read here it is now Open Access]

This created quite a splash in the blog-o-sphere; see my last post. This is probably no wonder. The strange idea that the global warming has stopped is one of the main memes of the climate ostriches and in the USA even of the main stream media. A recent media analysis showed that half of the reporting of the recent publication of the IPCC report pertained this meme.

This reporting is in stark contrast to the the IPCC having almost forgotten to write about it as it has little climatological significance. Also after the Cowtan and Way (2013) paper, the global temperature trend between 1880 and now is still about 0.8 degrees per century.

The global warming of the entire climate system is continuing without pause in the warming of the oceans. While the oceans are the main absorber of energy in the climate system. The atmospheric temperature increase only accounts for about 2 percent of the total. Because the last 15 years also just account for a short part of the anthropogenic warming period, one can estimate that the discussion is about less than one thousandths of the warming.

Reactions

The study was positively received by amongst others the Klimalounge (in German), RealClimate, Skeptical Science, Carbon Brief, QuakeRattled, WottsUpWithThatBlog, OurChangingClimate, Moyhu (Nick Stockes) and Planet 3.0. It is also discussed in the press: Sueddeutsche Zeitung, TAZ, Spiegel Online (three leading newspapers in Germany, in German), The Independent (4 articles), Mother Jones, Hürriyet (a large newspaper in Turkey) and Science Daily.

Lucia at The Blackboard wrote in her first post Cotwan and Way: Have they killed the pause? and stated: "Right now, I’m mostly liking the paper. The issues I note above are questions, but they do do quite a bit of checking". And Lucia wrote in her second post: "The paper is solid."

Furthermore, Steve Mosher writes: "I know robert [Way] does first rate work because we’ve been comparing notes and methods and code for well over a year. At one point we spent about 3 months looking at labrador data from enviroment canada and BEST. ... Of course, folks should double and triple check, but he’s pretty damn solid."

The main serious critical voice seems to be Judith Curry at Climate Etc. Her comments have been taken up by numerous climate ostrich blogs. This post discusses Curry's comments, which were also taken up by Lucia. And I will also include some erroneous additions by Antony Watts. And it will discuss one one additional point raised by Lucia.

1. Interpolation
2. UAH satellite analyses
3. Reanalyses
4. No contribution
5. Model validation
6. A hiatus in the satellite datasets (Black Board)

Temperature trend over last 15 years is twice as large as previously thought

UPDATED: Now with my response to Juddith Curry's comments and an interesting comment by Peter Thorne.

Yesterday a study appeared in the Quarterly Journal of the Royal Meteorological Society that suggests that the temperature trend over the last 15 years is about twice a large as previously thought. This study [UPDATE: Now Open Access] is by Kevin Cowtan and Robert G. Way and is called: "Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends".

The reason for the bias is that in the HadCRUT dataset, there is a gap in the Arctic and the study shows that it is likely that there was strong warming in this missing data region (h/t Stefan Rahmstorf at Klimalounge in German; the comments and answers by Rahmstorf there are also interesting and refreshingly civilized; might be worth reading the "translation"). In the HadCRUT4 dataset the temperature trend over the period 1997-2012 is only 0.05°C per decade. After filling the gap in the Arctic, the trend is 0.12 °C per decade.

The study starts with the observation that over the period 1997 to 2012 "GISTEMP, UAH and NCEP/NCAR [which have (nearly) complete global coverage and no large gap at the Arctic, VV] all show faster warming in the Arctic than over the planet as a whole, and GISTEMP and NCEP/NCAR also show faster warming in the Antarctic. Both of these regions are largely missing in the HadCRUT4 data. If the other datasets are right, this should lead to a cool bias due to coverage in the HadCRUT4 temperature series.".

Datasets

All datasets have their own strengths and weaknesses. The nice thing about this paper is how they combine the datasets and use the strengths and mitigate their weaknesses.

Surface data. Direct (in-situ) measurements of temperature (used in HadCRU and GISTEMP) are very important. Because they lend themselves well to homogenization, station data is temporal consistent and its trend are thus most reliable. Problems are that most observations were not performed with climate change in mind and the spatial gaps that are so important for this study.

Satellite data. Satellites perform indirect measurements of the temperature (UAH and RSS). Their main strengths are the global coverage and spatial detail. A problem for satellite datasets are that the computation of physical parameters (retrievals) needs simplified assumptions and that other (partially unknown) factors can influence the result. The temperature retrieval needs information on the surface, which is especially important in the Arctic. Another satellite temperature dataset by RSS therefore omits the Arctic from their dataset. UAH is also expected to have biases in the Arctic, but does provide data.