EGU 2017: Start of the conference

Here we are. My fourth time at the EGU and as always there is a new record in the number of participants (somewhere around 14,000). The last time I attended this conference was 2014 and so a lot has changed. A symbol for this are the tents on the former meeting area in front of the main building, which are really a pity (I certainly will complain about it a lot this week ;)). My personal contributions will be all on Friday, so I have some days to look around and enjoy some chit-chat in the poster sessions. Continue reading


Statistics is subjective… always!

From time to time you listen to talks and in a moment you do not expect any surprises you hear the argument that statistics is objective. It is often used to strengthen other arguments and tries to prevent doubts in them. Much to often statistics is given a credibility it does not deserve and so the times it gets usefully applied get devalued. With this in mind, one thing have to be clear: Statistics is subjective…. always!

Quite often the words on objectivity are used in haste, often also from scientists who should know it better. Many arguments on the objectivity of statistics comes from the past, where frequentist statistics was the norm and its application for nearly every problem was seen as appropriate and therefore objective. But many forget, that by using frequentist statistics they make a choice. A choice on assumptions many have learnt years ago and are long forgotten.

May it be an assumption on normal distributions, on stationarity or ergodicity. Let’s be honest, those are never fulfilled, but always accepted indirectly by choosing a standard methodology. And when you doubt that you have a choice on your statistical methodology, then the answer is in nearly ever case that there is one. You do not have to go all the way to Bayesian statistics, there are many steps in between. The start is usually to think about the assumptions you are currently making in your methodology and then go the extra step to think what happens when one of these fails.

Most important is that we start to teach the students that statistics is based on assumptions. For myself it is a game of assumptions and usually you have a lot of freedom to make them. That does not mean that standard methods should not be taught, yes, for everyone who works in geoscience a foundation in statistical techniques is necessary. But it is important at the same time that we make clear that each methodology has its disadvantages and that alternatives exist. They are not necessarily easily to calculate, but they are certainly at least some seconds worth to think about.

A look at lecturing: Just before the start

After I had written nearly two months ago how the preparations for the lecture in the new term has started, it is now the time to wrap up the preparations as from next week on the term starts. So what have I achieved up to now? Well, more or less nearly all lectures are prepared, I have one left to do, but this will be done nearer to the actual lecture, because I need one for a bit of wiggle room in the middle (so when I am too slow or I see that students do not get used to my concepts). Also I have managed to have ideas and prepare most of the practical sheets, which the students have to do. So far, I am quite happy with that, but I will only see in the active phase, whether this will really work out as planned. Continue reading

Scientists and holidays

Every worker as a right for holidays. Yes, especially in Europe this promise by the declaration of human rights is seen as very important, while in the US the amount of holidays is generally quite limited. So in Europe most scientists, as most workers, have the right to something around 25 to 30 holidays per calendar year. Many enjoy it, but when you talk to scientist you often hear some form of guilt when they take it. This post should address the reasons for it and are of course only my own observations. Continue reading

A look at lecturing: Preparations months ahead

Part of an academical job is to lecture. Myself am very lucky that this duty is part of my obligations as I really like to do it. In the past I have mainly assisted teaching or did tutoring in various lectures, but next term I will get my own lecture to plan and give in full. I will get important assistance on one or two lectures as my schedule require me to be away for some dates, but apart from that it I will have to fill the four hours a week. The topic will be in a statistical area and so more in my core expertise as my lecturing I did up to now, which was mainly in the physical areas of climate science.

In the upcoming months I will write some posts about this topic, my experience of preparing the lectures and my thoughts about concepts. Of cause I will omit talking about the actual lectures, as students should never fear that they are put on the spot. As the topic of the lecture will be the basis of statistics, it will be not so much about the actual topics, but on how to present them and how to make it an interesting learning experience for the students.

As there are another two month to go I have started to prepare the first lectures. All in all there will be roughly 15 weeks to fill, partly with predefined content and with practicals. The german system sets a fixed numbers of hours the student should work on any lecture and in my case this number can be worked out as 12 hours per week. That is a lot, because even with taking the four hours of presence study not into account, there are eight hours left. So it will be a balance to get enough stuff into the lectures and explaining it in a way that a general unloved topic can be understood. Statistics is for many students like maths and that is in applied physics courses like meteorology/oceanography/geophysics usually not very popular for them. Usually one to two years of mathematical studies, mostly not very connected to the rest of the curriculum, are the beginning of every students life and so the next step with a mostly quite dry topic like statistics is thought to be the same. And unfortunately, therein lies a problem. When you get into statistics too much on the applied side, then you do not give context to the maths lectures given before and it will get harder for the students in the future to get into statistics properly (so not only as an auxiliary subject, but a real tool which is comfortable to handle). On the other side when you do it too mathematically, it is just another hated maths subject. Balancing in the middle of it is certainly an aim, but not really realistic to achieve.

I am looking forward to this experience, but am also aware that all my planning and thoughts might not work out as planned and it ends up it a struggle for the students and myself. That is a challenge and I like challenges.

Interdisciplinarity in science: Chances and Problems

Climate science has a huge advantage, as it covers so many disciplines, which all have to interact to yield a proper result. May it be meteorology, oceanography, glaciology and geophysics as the main physical subjects working on the issue, but are just the first layer. Below it you find maths, physics, chemistry and biology as the fundamental sciences on which these subjects base. And then there are all the auxiliary subjects like geology or computer science, which are highly interacting with climate science community. But this advantage has also its weak spots, as working with many disciplines lead also to many problems in the daily work.

One point are the different vocabularies these fields use. For the same thing or methodology, each discipline has its own description. This is also true for subjects which are very similar, like meteorology and oceanography, which share a lot of common ground, but have developed in the past decades in different directions. To illustrate that I usually illustrate this with a saying: “When you have a three year interdisciplinary project, you spend the first two years with writing a dictionary to communicate and in the third you can start to work.” This is of cause a massive exaggeration, but it shows that even after a long time working together it happens that the researcher from different fields still are not used to the others researchers views and language. And once again, for outsiders these fields are hardly different disciplines, we are not talking about interaction between e.g. social science and physics, where these problems might be much more severe.

To address this many study programmes have moved in the past years from a pure subject-based courses to interdisciplinary ones. The idea is that when students learn the different subjects from the start, the interdisciplinary science gets easier for the next generation. Because let’s face it, the future will be interdisciplinary, all the funding agencies require it and the pure subject based research seems to be mostly covered in the past. But there will be also the problems coming up from this approach. Coming from only one subject in the undergrad and graduate programme gives you a real expertise in this one subject. Having that background with some effort it is possible to translate the approaches of the other disciplines into your own language and then you are able to work with them like they are your own. It needs time, but it is at least possible. When the interdisciplinary study programmes fail to cover the whole extend of a single subject, but just parts of it in several disciplines, it might lead to problems in this approach. Yes, you might be fit in the basics of several fields, but when something unknown comes along the risk is there that you cannot bring it back to your own turf to work with it. It does not mean that it has to be this way, but it requires a lot of care when study programs are developed for the future.

So what is my own way? Well, I still went to a one subject study course (meteorology), sure you had your minors, but in the end it was more or less a training for translation of other fields into your own. My graduate project was together with computer scientists, my first postgrad with geodesist and geologist. Nowadays I work mainly with oceanographers. Of cause I encounter some problems within communication, because in some cases I miss some basics, but on the other side I do exactly the above described translation on a daily basis. As everyone, I am a bit biased towards the way I took myself, so I see the risks as well as the chances for the new approaches. We will see in the next decade, whether they will be successful and bring the science effectively forward, because that is in the end the only thing that counts.

Two new papers out

The new year has started and in the recent weeks two new papers with myself in the author list have been published. Both are covering a wide spectrum and my contribution was in both cases more something I would classify as statistical assistance. Therfore, I will keep my comments brief at this place and just quickly introduce the topics.

Speleothem evidence for MIS 5c and 5a sea level above modern level at Bermuda

This paper is about the sea-level height at Bermuda at roughy 70,000 years back. It is mainly a geological paper and focusses on the evidence from speleotherms, that indicate that sea-level was positive compared to today at that time. That is important, because the rest of the world has in many places lower than modern sea-level at that time. A plot in the later part of the paper shows, that the difference at different locations in the carribean can be up to 30-40m. Explained can this be with GIA modelling and the paper is therefore a good help to better calibrate those models.

Wainer, K. A. I.; Rowe, M. P.; Thomas, A. L.; Mason, A. J.; Williams, B.; Tamisiea, M. E.; Williams, F. H.; Düsterhus, A.; Henderson, G. M. (2017): Speleothem evidence for MIS 5c and 5a sea level above modern level at Bermuda, Earth and Planetary Science Letters, 457, 325-334

Hindcast skill for the Atlantic meridional overturning circulation at 26.5°N within two MPI-ESM decadal climate prediction systems

The second paper focusses on the hindcast skill of two decadal forecasting systems of the Atlantic meridional overturning circulation (AMOC). It shows that both system have significant hindcast skill in predicting the AMOC for up to five years in advance, while an uninitialised model run has not. The time series for evaluationg the systems are still quite short, but the extensive statistics in the paper allows to transparently follow the argument, why the system do have this capability.

Müller V.; Pohlmann, H.; Düsterhus, A.; Matei, D.; Marotzke; J; Müller, W. A.; Zeller, M.; Baehr, J.: Hindcast skill for the Atlantic meridional overturning circulation at 26.5°N within two MPI-ESM decadal climate prediction systems, Climate Dynamics