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.

Has COST HOME (2007-2011) passed without true impact on practical homogenisation?

Guest post by Peter Domonkos, one of the leading figures in the homogenization of climate data and developer of the homogenization method ACMANT, which is probably the most accurate method currently available.

A recent investigation done in the Centre of Climate Change of University Rovira i Virgili (Spain) showed that the ratio of the practical use of HOME-recommended monthly homogenisation methods is very low, namely it is only 8.4% in the studies published or accepted for publication in 6 leading climatic journals in the first half of 2013.

The six journals examined are the Bulletin of the American Meteorological Society, Climate of the Past, Climatic Change, International Journal of Climatology, Journal of Climate and Theoretical and Applied Climatology. 74 studies were found in which one or more statistical homogenisation methods were applied for monthly temperature or precipitation datasets, the total number of homogenisation exercises in them is 119. A large variety of homogenisation methods was applied: 34 different methods have been used, even without making distinction among different methods labelled by the same name (as it is the case with the procedures of SNHT and RHTest). HOME-recommended methods were applied only in 10 cases (8.4%) and the use of objective or semi-objective multiple break methods was even much rare, 3.4% only.

In the international blind test experiments of HOME, the participating multiple break methods produced the highest efficiency in terms of the residual RMSE and trend bias of homogenised time series. (Note that only methods that detect and correct directly the structures of multiple breaks are considered multiple break methods.) The success of multiple break methods was predictable, since their mathematical structures are more appropriate for treating the multiple break problem than the hierarchic organisation of single break detection and correction.

Highlights EUMETNET Data Management Workshop 2013

The Data Management Workshop (DMW) had four main themes: data rescue, homogenization, quality control and data products. Homogenization was clearly the most important topic with about half of the presentations and was also the main reason I was there. Please find below the highlights I expect to be more interesting. In retrospect this post has quite a focus on organizational matters, mainly because this was most new to me.

The DMW is different from the Budapest homogenization workshops in that it focused more on best practices at weather services and Budapest more on the science and the development of homogenization methods. One idea from the workshop is that it may be worthwhile to have a counterpart to the homogenization workshop in the field of quality control.

BREAKING NEWS: Tamas Szentimrey announced that the 8th Homogenization seminar will be organized together with 3rd interpolation seminar in Budapest on 12-16 May 2014.

UPDATE: The slides of many presentations can now be downloaded.

Weather variability and Data Management Workshop 2013 in San Lorenzo de El Escorial, Spain

This week I will be at the Data Management Workshop (DMW) in San Lorenzo de El Escorial. Three fun filled days about data rescue, homogenization, quality control and data products (database). While it is nice weather outside.

It is organized by EUMETNET, a network of 30 European National Meteorological Services. Thus I will be one of the few from a university, as is typical for homogenization. It is a topic of high interest to the weather services.

Most European experts will be there. The last meeting I was at was great. The program looks good. I am looking forward to it.

My contribution to the workshop will be to present a joint review of what we know about inhomogeneities in daily data. Much of this information stems from parallel measurements, in other words from simultaneous measurements with a modern and a historical set-up. We need to know about non-climatic changes in extremes and weather variability, to be able to assess the climatic changes.

The coming time, I hope to be able to blog about some of the topics of this review. It shows that the homogenization of daily data is a real challenge and that we need much more data from parallel measurements to study the non-climatic changes in the probability distribution of daily datasets. Please find our abstract below.

The slides of the presentation can be downloaded here.

Atmospheric warming hiatus: The peculiar debate about the 2% of the 2%

Dana Nuccitelli recently wrote an article for the Guardian and the introduction read: "The slowed warming is limited to surface temperatures, two percent of overall global warming, and is only temporary". As I have been arguing before, how minute the recent deviation of the predicted warming is, my first response was, good that someone finally computed how small.

However, Dana Nuccitelli followed the line of argumentation of Wotts and argued that the atmosphere is just a small part of the climate system and that you do see the warming continue in the rest, mainly in the oceans. He thus rightly sees focusing on the surface temperatures only as a form of cherry picking. More on that below.

The atmospheric warming hiatus is a minor deviation

There is another two percent. Just look at the graph below of the global mean temperature since increases in greenhouse gasses became important.


The anomalies of the global mean temperature of the Global Historical Climate Network dataset versions 3 (GHCNv3) of NOAA. The anomalies are computed of the temperature by subtracting the mean temperature from 1880 to 1899.

The temperature increase we have seen since the beginning of 1900 is about 31 degree years (the sum of the anomalies over all years). You can easily compute that this is about right because the triangle below the temperature curve, with a horizontal base of about 100 years and a vertical size (temperature increase) of about 0.8°C: 0.5*100*0.8=40 degree years; the large green triangle in the figure below. For the modest aims of this post 31 and 40 degree years are both fine values.

The hiatus, the temperature deviation the climate ostriches are getting crazy about, has lasted at best 15 years and has a size of about 0.1°C. Thus using the same triangular approach we can compute that this is 0.5*15*0.1=0.75 degree years; this is the small blue triangle in the figure below.

The atmospheric warming hiatus is thus only 100% * 0.75 / 31 = 2.4% of the total warming since 1900. This is naturally just a coarse estimate of the order of magnitude of the effect, almost any value below 5% would be achievable with other reasonable assumptions. I admit having tried a few combinations before getting the nice matching value for the title.

How to talk to uncle Bob, the climate ostrich

Willard of Never Ending Audit recommended a video on climate communication in the comments at HotWhopper that deserves more attention. In the video George Marshall of talkingclimate.org explains how to talk to climate ostriches, for example by being respectful to climate ostriches. In the video, he is not talking to the fringe ostriches, however, but to the mainstream. Talking about the ostriches the way one talks when they are not in the room, makes the climate ostriches go mad in the comments. I would advice any ostrich with blood pressure problems not to watch this video.

The video is called "How to talk to a climate change denier" and halfway Marshall explains that that term is best avoided in a productive conversation. Furthermore, it is not about the oddish "debate" in the blogosphere, it is about talking to someone you know about climate change. This is why I rename the video to "How to talk to uncle Bob, the climate ostrich". Marshall offers the term "climate dissenter", I personally like "ostrich", it fits to these people denying that climate change is a problem in a wide variety of (conflicting) ways.

The conversations with family and friends are probably the most important discussions, while you might have the tendency to avoid them. You are more likely to convince your uncle Bob as someone who build his internet identity around refuting that climate change is a problem.

Quotes from the video

Are you struggling to find ways of talking to people who simply do not believe that climate change is happening. ... Do I fight it out with them? ... You just laugh it out and let it go. Actually that conversation is really important. The fast majority of people form their views from the social interactions with people around them, with their peers.

Common ground

Do not go into an argument, seek common ground. ... When you think of the times when you have been challenged. And someone has changed your views. Was that an argument that was won or lost? Did you ever at the end of a conversation with someone say: you know what, you were right all along, silly me, I was wrong all over, you are right.

Respect

Okay, I know it is hard to feel much respect for people that ...

They are forming their own views, they are expressing their own views. ... The easy option is to say. Climate change is a huge problem and then not to think about not, not to do anything about it. At least they are engaging. ... Let's respect that quality that they make up their own mind.

Hold your views

It is important that you own your views. Don't make it an argument about an "it". ... Don't seek to undermine their sources of information. That is another discussion about the "it". ... This is again going head to head about the "it".

Your journey

People form their opinions over time, it is a steady process of negotiation. ... So it is important you tell them about the process how you came to your views.

Fits worldview

There is no reason why climate change cannot be a matter of deep concern for conservative people, to traditional people, to old people. ... Try to find ways to talk about it in a way that it concerns about them.

Offer rewards

People who do not believe in climate change nonetheless have very strong values in other areas. They are people who have very strong interest in their community, in their family, in a social life. They'd love to be quite traditional in the sense that they have a very strong sense of identity. ... These are values to speak to.

Many (new) open-access journals in meteorology and climatology

9-minute video by PhD Comics explaining open access from WikiMedia.

The journal of the German language meteorological organizations, Meteorologische Zeitschrift, has just announced it will move to full open-access publishing in 2014.

[The] editorial board and editor-in-chief of Meteorologische Zeitschrift (MetZet) are pleased to announce that MetZet will be published as full open access journal from the beginning of the year 2014. All contents of this journal from then on will be freely available to readers. Authors are free to non commercially distribute their articles and to post them on their home pages. MetZet follows with this change the requests of many authors, institutions, and funding organizations.

This was a long term request of mine. MetZet has very high standards and publishes good work. In that respect it would be an honour to publish there. In the past it was even one of the main journals in the field. It published the first climate classification by Köppen. It has articles by Hann, Bjeknes, Angström, Flohn and Ertel. However, I did not publish in MetZet up to now because almost nobody has a subscription to it. Thus after getting through the tough review, who would read the articles had I published there? Now this problem is solved.

Other less well known "national" open journals are Időjárás - Quarterly Journal of the Hungarian Meteorological Service (OMSZ) and the Journal of the Catalan Association of Meteorology Tethys. Also Tellus A: Dynamic Meteorology and Oceanography and Tellus B: Chemical and Physical Meteorology have changed to open-access in 2012.

Then we have the IOP journal Environmental Research Letters and the new Elsevier journal Weather and Climate Extremes. Copernicus, the publisher of the European Geophysical Union, has many more open access journals. The most important ones for meteorologists and climatologists are likely:

• Climate of the Past (CP)
• Atmospheric Chemistry and Physics (ACP)
• Nonlinear Processes in Geophysics (NPG)

[UPDATE. O. Bothe has written a more up to date list with open-access journals (October 2014)]

Bad journals

Not all open-access journals are good. Jeffrey Beale even keeps a list of predatory publishers and journals.

Does a body composition scale provide independent information? Two experiments: weight and age

Since the beginning of the year I have gained about 8 kg. Was that fat, muscle, water? Probably some of all. My body fat scale estimates that my body fat percentage has increased by about 3%, which would be 2 to 3 kg of fat.

The problem

My problem? My scale knows my weight gain and likely uses my weight to estimate my body fat percentage. If it would not use my weight to estimate body fat, why would it want to know my height (body mass index, BMI), age and sex? Part of the information on body fat likely comes from the currents send through my body by the scale, but another part from my weight or BMI. Thus my problem is, does the increase in body fat percentage tell me more than what I already knew, that I have gained 8 kg?

The experiment

Now luckily, my fitness studio has another instrument to measure body fat. It is attached to a computer and also wants to know my weight. But here, you have to type in. Thus with this instrument you can experiment.

The instrument has a infra-red sensor that has to be placed on your biceps. To compute your body fat, and a colorful page full of other probably highly reliable information, it uses your weight, height, age and fitness.

We made one measurement stating my real current weight and one stating my weight at the beginning of the year. The difference in computed body fat percentage: 3,3%! Almost to similar to the increase estimated by my scale to be true.

The interpretation

It is possible, I gained some fat and my waist did increase some. It is also possible that nothing happened fatwise, other places look more muscular nowadays. I guess all I know is that I gained 8 kg.

I guess this does not surprise scientists and engineers working on these measurement devises. The German Wikipedia even mentions this effect qualitatively. But I am not sure if all users are aware of this problem and we can now put a number on it. If your fat gain is less than a third of your weight gain, the measurement is too uncertain to determine whether there really was a fat gain.

Post Scriptum. We forgot one experiment. I would love to know what the estimate for my body fat percentage would be if I tell the computer I am a young man. I expect some age discrimination by the multiple linear regression equations used.

Post Post Scriptum. Thanks to Caro of Stangenliebe (Pole art fitness Bonn) for the measurement.

UPDATE. I realised I could do the experiment with my age using my own scale. My Soehnle scale has two presets. I set person 1 to my age (rounded to 42) and person 2 to an Adonis of 18 years. In all four measurements the Adonis has exactly 3.7% fat less. If only my age group would not be so overweight, my readings would be a lot better.