Scientific literature – A jungle full of obstacles

Within a scientific cycle, there is one part, which is quite traditional and the foundation of scientific work. It is the reading of the current literature on the topic the scientist is working on. Even when earth sciences are mainly experimental driven, either in the field or with a computer, it is essential to know what others have written about your topic and methods. In the following I would like to take a look on the background of the literature in science, its role and what consequences this brings for the scientists and others. Continue reading

The role of statistics in science

Traditionally within the different disciplines of earth science the scientists are divided into two groups: modelers and observationalists. In this view the modellers are those who do theory, possibly with pen and paper alone, and the observationalist go into the field and get dirty hands. That this view is a little bit outdated, won’t be anything new. In my opinion, it really started with the establishment of remote sensing that this division reunited (Yes, reunite, because in the old days, there were a lot of scientists who did everything). As I am a learned meteorologist, from my view it is quite common that this division is not really existent anymore. Both types of scientists sit in front of their computer, both are programming and both have to write papers with a lot of mathematical equations. In other fields, the division might be still more obvious (e.g. Geology), but for many its only the type of data someone is working with, which classify someone as observationalist or modeller. Continue reading

Let’s play: HadCRUT 4

Playing around with data can be quite funny and sometimes deliver some interesting results. I had done this a lot in the past, which was mainly a necessity coming from my PhD. Therein I had developed some methods for quality assurance of data, which needed of cause some interesting applications. So every time a nice dataset got to live, I had run them through my methods and usually the results were quite boring. Main reason for this is that these methods are designed to identify inhomogeneities and a lot of the published data nowadays is already quality controlled (homogenised), which makes it quite hard to identify new properties within the dataset. Especially model data is often quite smoothed so that it is necessary to look at quite old data to find something really interesting. Continue reading

The sampling issue

Observations are generally a tricky thing. Not only are they a special kind of model, which tries to cover a sometimes very complicate laboratory experiment. Additionally they are also representing the truth, as far as we are able to measure it. As a consequence they play a really important part in science, but are in some fields hard to generate.

During the PALSEA2 meeting a question has come up in the context of the generation of paleo-climatic sea-level observations.

Assumed your ressources allow only two measurements, is it better when they be near towards each other or should they be far away.

In the heat of the discussion both sides were taken, but in the end the conclusion was the typical answer for such kind of questions: “it depends on what you want to measure”. Continue reading