4 reasons why climate change is controversial


First published on LinkedIn, 28.07.2015

I am following the ongoing discussion between climate change advocates and skeptics since my studies in geophysics and meteorology at the University of Hamburg where one of the advocate group is located. I will not point fingers to one side or the other because both sides provide useful insights to solve the question whether anthropogenic climate change is real or not.

But since my study years the discussions grew from scientific disputes to religious-like beliefs and the discussions have reached a level of personal opinions rather than facts.

Let me illustrate this on a recently published dispute between Marotzke and Forster [1] as climate change advocates and Nicholas Lewis as climate change skeptic [2]. It contains three reasons for the controversy around climate change. But I start with a fundamental issue, which is only implicit part of the Dispute.

  1. Probable flaw in the main assumption

Correct modelling of the past don’t necessarily mean that the same model yields correct future predictions. Geological processes like plate tectonics, mountain building (orogeny) and meteorological processes like the water (hydrological) cycle happen in the same manner since the beginning of the earth until now and therefore we can predict future behavior by understanding the past. But the Earth‘s climate is a different category. It is known as a chaotic process which means that small changes in one parameter can cause large effects in the result (e.g. butterfly effect). In fact, some of the most popular chaotic figures, the Lorenz attractor, was discovered by Edward N. Lorenz a meteorologist by analyzing meteorological data and the underlying mathematics. He claims in his paper [3] that: “If the [weather] system is stable, its future development will then remain arbitrarily close to its past history, and will be quasi-periodic. …since the atmosphere has not been observed to be periodic…..no forecasting scheme could have given correct result…” In addition to the usual chaotic behavior one large (VEI >= 5; [6]) volcanic eruption will make all existing anthropogenic climate change forecasts obsolete for decades: And in the past 25 years there were 2 eruptions with VEI >= 5; [7].

  1. Simulation driven approach

Most of the anthropogenic climate change forecasts are model based rather than data driven and are built from a positive assumption. I will not go into the mathematics, but I have to show one equation to make my point. The following equation is the main subject of the discussion between Marotzke&Foster and Lewis where an energy balance is described as: ΔT = ΔF / (α + κ) + ε. Don’t worry, it is not necessary to understand the equation in detail. You just need to know that three of the four quantities ΔF, α, κ are parameters modelled from simulations and used to simulate ΔT due to lack of direct measurements. You can see from the quantities‘ names that they have no direct relation to known physical quantities: ΔF = change of effective radiative forcing, α = climate feedback parameter, κ = ratio of change in the heat uptake of a climate system. They are hypothetical constructs. The fourth quantity ε is a computer generated random value.

In short: they use simulation results to simulate something that cannot be measured directly. I am not saying you can’t do such thing, but it becomes more and more difficult to understand the reliability of the results, especially if at least one simulation is nonlinear or even chaotic. It doesn’t help to claim that the prediction is correct because 114 simulations based on the same model / assumptions provide the same results [1]. This is comparable to: 114 translations of Grimm’s fairy tales are similar, so the story must be true.


  1. Opinion and agendas before facts

Science has nothing to do with opinions and agendas. But we scientists, as social beings, have something to do with it and we have to work with that every day. Unfortunately, some scientists become believers of their own opinion instead to discuss issues in a constructive scientific way.

Marotzke is already convinced that the climate change is anthropogenic and he said [4]: “Sceptics who still doubt anthropogenic climate change have now been stripped of one of their last-ditch arguments”. This is an opinion not a fact!

Due to the nature of climate change the chosen approach is an inductive projection which means we don’t know exactly the mechanism behind climate change. We can’t even be sure that anthropogenic climate change exists. Measuring a temperature increase in the last hundred years and a change of our daily weather behavior compared to the last century hasn’t necessary something to do with an increase of CO2 or that it is manmade. There are other explanations that are equally probable. Maybe we have lived in a lucky period where the climate was quasi periodic and this starts to change now, independent of what we are doing?

  1. Missing constructive discussions

The concept of science is to find reliable and reproducible descriptions of observations we make. In other words: Is there something we don’t understand we try to find a scientific explanation. Due to our human nature we get sometimes lost in our hypothesis. Therefore we should embrace questions which contradict our hypothesis and should discuss them constructively.

Unfortunately, the opposite is quite often the case. I think Lewis provided a good mathematical explanation why the results from Marotzke and Forster don’t work. But he finished his analysis with following sentences: “The statistical methods used in the paper are so bad as to merit use in a class on how not to do applied statistics.” and “All this paper demonstrates is that climate scientists should take some basic courses in statistics and Nature should get some competent referees.” Writing these statements doesn’t help to convince the other side. But this shows how overheated the discussion already is and that the sceptics feel not been taken seriously.

Unfortunately, Marotzke and Forster are not taking Lewis analysis serious. They basically repeat in their answer what they already have published in Nature [5]. And the main reason for why Lewis wrote his analysis was rejected with following statement: “It has been alleged that in [1] we applied circular logic. This allegation is incorrect.” This shows the narrow-mindedness and arrogance of the advocates of climate change. To have the majority doesn’t mean they own the truth.

In order to solve the climate change issue advocates and sceptics should work together as equal partners in the same group or directly within the IPCC. Only if both sides are satisfied with the results we might be getting closer to the truth.

[1] Jochem Marotzke & Piers M. Forster. Forcing, feedback and internal variability in global temperature trends. Nature, 517, 565–570 (2015)

[2] http://climateaudit.org/2015/02/05/marotzke-and-forsters-circular-attribution-of-cmip5-intermodel-warming-differences/

[3] http://dx.doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2

[4] http://www.mpg.de/8925360/climate-change-global-warming-slowdown

[5] http://www.skepticalscience.com/marotzke-foster-respond-to-lewis.html

[6] https://en.wikipedia.org/wiki/Volcanic_explosivity_index

[7] https://en.wikipedia.org/wiki/List_of_large_volcanic_eruptions_of_the_20th_century


Are computer simulations useful in all fields of science?

Computer simulations have a long history as scientific tool and started basically in meteorology and nuclear physics in the late 1940s. Since then, it has become essential in nearly all scientific fields. However, the greatest success computer simulations achieve is in engineering, due to the well-established theoretical knowledge and solvable differential equations. For example: new cars are already simulated up to 100% before production in order to eliminate flaws and to comprehend problems like material stress between component interfaces, optimal design to reduce aerodynamic drag and the interaction between engine and driving comfort.

 Successful tool in all science areas?

Computer simulations are a very successful tool for engineers where everyone can experience the results in daily life. But, how about computer simulations in science areas where we do not have all theoretical knowledge we need? In other words: would you buy a car based on unreliable simulation results?

There is a saying in the academic world:

A simulation is only as good as the theoretical knowledge from the real world.

But what does that really mean? Some questions rise immediately, that need to be answered:

  • How good is good?
  • How can one evaluate the reliability of the simulation output?
  • How good are the mathematical equations or the mathematical understanding?
  • How about limited or missing Information?
  • How stable is the simulation if one change some parameter values?
  • How can one be sure that all necessary theories are included or what are the minimum conditions we need?
  • How many simulation parameters do one need and are all simulation parameters tied together correctly?
  • How about cross effects between simulation parameters and the linked differential equations?

You might think: “That’s simple: Just compare the simulation output with real world measurements!” Only to realize after a short while: “Wait a moment, what if we can’t set some simulation parameters due to missing values or differential equations? Or what if we don’t understand the real world in the needed detail? Or how can we predict future behavior if we don’t really understand the past.”

And suddenly, we are back where we started: A simulation is only as good as the theoretical knowledge. Nevertheless, they have become popular in more and more science areas. In such a way that one might wonder: “Is a computer simulation useful in all fields of science?” My answer is:

Yes, but…”

In my experience, many simulations suffer from limited thoughts being put into answering the questions above. Although a number of philosophers discuss how to answer these questions (e.g. epistemology), this is not so often in the science community; at least not communicated. I don’t really know, why. Answering these questions is essential and should be part of each simulation result discussion. Answering these questions is often quite difficult and sometimes challenging, but nonetheless necessary! If we can’t answer those questions we should not rely on simulations.

I believe that simulations are a good scientific tool to estimate how well we understand the real world processes. From simulation results we can tell what kind of experiments and investigations we should undertake next to close knowledge gaps. By comparing simulation output with real world measurements we can establish a feedback loop which improves the simulation results and decrease the difficulty to answer the questions. After some cycles the simulation results might even be used to predict future real world behavior.

My take home message is:

We should not rely on simulation results only!