Sunday, 21 August 2016

Naïve empiricism and what theory suggests about errors in observed global warming

In its time it was huge progress that Francis Bacon stressed the importance of observations. Even if he did not do that much science himself, his advocacy for the Baconian (scientific) method, gave him a place as one of the fathers of modern science together with Nicolaus Copernicus and Isaac Newton.

However, you can also become too fundamentalist about empiricism. Modern science is characterized by an intricate interplay of observations and theory. An observation is never free of theory. You may not be aware of it, but you make theoretical assumptions about what you see in any observation. Theory also guides what to observe, what kind of experiments to make.

Charles Darwin often claimed to adhere to Bacon's ideals, but he had another side. University of California professor of biology and philosophy Francisco Ayala writes in Darwin and the scientific method:
“Let theory guide your observations.” Indeed, Darwin had no use for the empiricist claim that a scientist should not have a preconception or hypothesis that would guide his work. Otherwise, as he wrote, one “might as well go into a gravel pit and count the pebbles and describe the colors. How odd it is that anyone should not see that observation must be for or against some view if it is to be of any service”
But his ambivalence is seen in Darwin's advice to a young scientist:
Let theory guide your observations, but till your reputation is well established be sparing in publishing theory. It makes persons doubt your observations.
The same ambivalence is seen in Einstein. Mitigation skeptics like this quote:
No amount of experimentation can ever prove me right; a single experiment can prove me wrong.
The quote this when the observations show less changes than the model. If the observations show more changes than the model/theory the observations, they quickly forget Einstein and the observations are suddenly wrong.

In practice Einstein was more realistic. Prof in molecular physics [[John Rigden]] wrote in his book about Einstein's wonder year 1905: "Einstein saw beyond common sense and, while he respected experimental data, he was not its slave."

That is perfectly reasonable. When theory and observations do not match, the theory can be wrong, the observations can be wrong and the comparison can be wrong. What is called observations is nearly always something that was computed from observations and also that computation can be imperfect. Only when we understand the reason, can we say what it was.

The main blog of the mitigation skeptical movement, WUWT, on the other hand is famous for calling trying to understand the reasons for discrepancies: "excuses".

Global mean temperature

That was a long introduction to get to the graph I wanted to show, where theory suggests the global mean temperature estimates in some periods may have problems.

The graph was compute by Andrew Poppick and colleagues and it looks as if the manuscript is not published yet. They model the temperature for the instrumental period based on the known human forcings — mainly increases in greenhouse gasses and aerosols (particles from combustion floating in the air) — and natural forcings — volcanoes and solar variations. The blue line is the model, the grey line the temperature estimate from NASA GISS (GISTEMP).

The fit is astonishing. There are two periods, however, where the fit could be better: world war II and the first 40 to 50 years. So either the theory (this statistical model) is incomplete or the observations have problems.

It is expected that the observations in the WWII are more uncertain. Especially the sea surface temperature changes are hard to estimate because the type of ships and thus the type of observations changed radically in this period. The HadSST estimate of the measurement methods is shown below. During WWII American war ships dominated and they mainly used Engine Room Intake observations, whereas before and after the war merchant ship would often measure the temperature of a bucket of sea water.

The figure above are the observational methods estimated by the UK Hadley Centre for HadSST. Poppick's manuscript uses GISTEMP. Its sea surface temperature comes from ERSST v4. (The land data of GISTEMP comes from the stations gathered by NOAA (GHCNv3) and additional Antarctic stations).

ERSST estimates the observational methods of ships by comparing the sea surface temperature to the night marine air temperature (NMAT). This relationship is only stable over larger areas and multiple years. They can thus not follow the fast changes in the WWII observational methods well.

Also for HadSST it is not clear whether these corrections are accurate and they are large: in the order of 0.3°C. What makes this assessment more difficult is that in the beginning of WWII there was a strong and long [[El Nino event]]. Thus a bit of a peak is expected, but it is not clear whether the size is right.

I would not mind if a reviewer would request to add a statistical model that includes El Nino as predictor in Poppick's paper. That would reduce the noise further (part of the remaining noise is likely explained by El Nino) and that would make it easier to assess how well the temperature fits in the WWII.

The Southern Oscilation Index (SOI) of the Australian Bureaux of Meteorology (BOM). Zoomed in to show the period around WWII. Values below -7 indicate El Nino events and above +7 La Nina events.

It would be an important question to resolve. The peak in the WWII is a large part of the hiatus (a real one) we see in the period 1940 to 1980. If you think the peak in the 1940s away, this hiatus is a lot smaller. The lack of warming in this period is typically explained with increases in aerosols. It ended when air pollution regulations slowed the growth of aerosols; especially in the industrialised air quality improved a lot. I guess that if this peak is smaller, that would indicate that the influence of aerosols is smaller than we currently think.

While the observations hardly showed any warming the first 40 to 50 years, the statistical model suggests that there should have been some warming. The global climate models also suggest some warming. And also several other climate variables suggest warming: the warming in winter, the time lakes and rives freeze and break up, the retreat of glaciers, temperature reconstructions from proxies, and possibly sea level rise. See for example this graph of the dates rivers and lakes froze up and broke up.

I wrote about these changes in my previous post on "early global warming". Poppick's statistical model adds another piece of evidence and suggests that we should have a look whether we understand the measurement problems in the early data well enough.

By comparing the observations with the statistical model we can see periods in which the fit is bad. Whether the long-term observed trend is right cannot be seen this way because the statistical model would still fit well, just with a different coefficient for the long-term forcings. This relationship is likely biased in a similar way as the simple statistical models used to estimate the equilibrium climate sensitivity from observations. This model, and thus theory, does provide a beautiful sanity check on the quality of the observations and suggests periods which we may need to study better.

Related reading

Falsifiable and falsification in science

Early global warming

On the naive empirical view of Australian politician Malcolm Roberts on science: What Climate Change Skeptics Aren’t Getting About Science

Piers Sellers in The New Yorker: Space, Climate Change, and the Real Meaning of Theory


Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein, 2016: Estimating trends in the global mean temperature record. unpublished manuscript.

* Portrait of Francis Bacon at the top is taken from Wikipedia and is in the public domain.

Monday, 15 August 2016

Downscaling temperature fields with genetic programming

Sierpinski fractal

This blog is not called Variable Variability for nothing. Variability is the most fascinating aspect of the climate system. Like a fractal you can zoom in and out of a temperature signal and keep on finding interesting patterns. The same goes for wind, humidity, precipitation and clouds. This beauty was one of the reasons why I changed from physics to the atmospheric sciences, not being aware at the time that also physicists had started studying complexity.

There is variability on all spatial scales, from clusters of cloud droplets to showers, fronts and depressions. There is variability on all temporal scales. With a fast thermometer you can see temperature fluctuations within a second and the effect of clouds passing by. Temperature has a daily cycle, day to day fluctuations, seasonal fluctuations and year to year fluctuations and so on.

Also the fluctuations fluctuate. Cumulus fields may contain young growing clouds with a lot of variability, older smoother collapsing clouds and a smooth haze in between. Temperature fluctuations are different during the night when the atmosphere is stable, after sun rise when the sun heats the atmosphere from below and the summer afternoon when thermals develop and become larger and larger. The precipitation can come down as a shower or as drizzle.

This makes measuring the atmosphere very challenging. If your instrument is good at measuring details, such as a temperature or cloud water probe on an aircraft, you will have to move it to get a larger spatial overview. The measurement will have to be fast because the atmosphere is changing continually. You can also select an instrument that measures large volumes or areas, such as a satellite, but then you miss out on much of the detail. A satellite looking down on a mountain may measure the brightness of some mixture of the white snow-capped mountains, dark rocks, forests, lush green valleys with agriculture and rushing brooks.

The same problem happens when you model the atmosphere. A typical global atmospheric oceanic climate model has a resolution of about 50 km. Those beautiful snow-capped mountains outside are smoothed to fit into the model and may have no snow any more. If you want to study how mountain glaciers and snow cover feed the rivers you can thus not use the simulation of such a global climate model directly. You need a method to generate a high resolution field from the low resolution climate model fields. This is called downscaling, a beautiful topic for fans of variability.

Deterministic and stochastic downscaling

For the above mountain snow problem, a simple downscaling method would take a high-resolution height dataset of the mountain and make the higher parts colder and the lower parts warmer. How much exactly, you can estimate from a large number of temperature measurements with weather balloons. However, it is not always colder at the top. On cloud-free nights, the surface rapidly cools and in turn cools the air above. This cold air flows down the mountain and fills the valleys with cold air. Thus the next step is to make such a downscaling method weather dependent.

Such direct relationships between height and temperature are not always enough. This is best seen for precipitation. When the climate model computes that it will rain 1 mm per hour, it makes a huge difference whether this is drizzle everywhere or a shower in a small part of the 50 times 50 km box. The drizzle will be intercepted by the trees and a large part will evaporate quickly again. The drizzle that lands on the ground is taken up and can feed the vegetation. Only a small part of the heavy shower will be intercepted by trees, most of it will land on the ground, which can only absorb a small part fast enough and the rest runs over the land towards brooks and rivers. Much of the vegetation in this box did not get any water and the rivers swell much faster.

In the precipitation example, it is not enough to give certain regions more and others less precipitation, the downscaling needs to add random variability. How much variability needs to be added depends on the weather. On a dreary winters day the rain will be quite uniform, while on a sultry summer evening the rain more likely comes down as a strong shower.

Genetic Programming

There are many downscaling methods. This is because the aims of the downscaling depend on the application. Sometimes making accurate predictions is important; sometimes it is important to get the long-term statistics right; sometimes the bias in the mean is important; sometimes the extremes. For some applications it is enough to have data that is locally realistic, sometimes also the spatial patterns are important. Even if the aim is the same, downscaling precipitation is very different in the moderate European climate than it is in the tropical simmering pot.

With all these different aims and climates, it is a lot of work to develop and test downscaling methods. We hope that we can automate a large part of this work using machine learning: Ideally we only set the aims and the computer develops the downscaling method.

We do this with a method called "Genetic Programming", which uses a computational approach that is inspired by the evolution of species (Poli and colleagues, 2016). Every downscaling rule is a small computer program represented by a tree structure.

The main difference from most other optimization approaches is that GP uses a population. Every downscaling rule is a member of this population. The best members of the population have the highest chance to reproduce. When they cross-breed, two branches of the tree are exchanged. When they mutate, an old branch is substituted by a new random branch. It is a cartoonish version of evolution, but it works.

We have multiple aims, we would like the solution to be accurate, we would like the variability to be realistic and we would like the downscaling rule to be small. You can try to combine all these aims into one number and then optimize that number. This is not easy because the aims can conflict.
1. A more accurate solution is often a larger solution.
2. Typically only a part of the small-scale variability can be predicted. A method that only adds this predictable part of the variability, would add too little variability. If you would add noise to such a solution, its accuracy goes down again.

Instead of combining all aims into one number we have used the so-called “Pareto approach”. What a Pareto optimal solution is is best explained visually with two aims, see the graphic below. The square boxes are the Pareto optimal solutions. The dots are not Pareto optimal because there are solutions that are better for both aims. The solutions that are not optimal are not excluded: We work with two populations: a population of Pareto optimal solutions and a population of non-optimal solutions. The non-optimal solutions are naturally less likely to reproduce.

Example of a Pareto optimization with two aims. The squares are the Pareto optimal solutions, the circles the non-optimal solutions. Figure after Zitzler and Thiele (1999).

Coupling atmospheric and surface models

We have the impression that this Pareto approach has made it possible to solve a quite complicated problem. Our problem was to downscale the fields near the surface of an atmospheric model before they are passed to a model for the surface (Zerenner and colleagues, 2016; Schomburg and colleagues, 2010). These were, for instance, fields of temperature, wind speed.

The atmospheric model we used is the weather prediction model of the German weather service. It has a horizontal resolution of 2.8 km and computes the state of the atmosphere every few seconds. We run the surface model TERRA at 400 m resolution. Below every atmospheric column of 2.8x2.8 km, there are 7x7 surface pixels.

The spatial variability of the land surface can be huge; there can be large differences in height, vegetation, soil type and humidity. It is also easier to run a surface model at a higher spatial resolution because it does not need to be computed so often, the variations in time are smaller.

To be able to make downscaling rules, we needed to know how much variability the 400x400 m atmospheric fields should have. We study this using a so-called training dataset, which was made by making atmospheric model runs with 400 m resolution for a smaller than usual area for a number of days. This would be too much computer power for a daily weather prediction for all of Germany, but a few days on a smaller region are okay. An additional number of 400 m model runs was made to be able to validate how well the downscaling rules work on an independent dataset.

The figure below shows an example for temperature during the day. The panel to the left shows the coarse temperature field after smoothing it with a spline, which preserves the coarse scale mean. The panel in the middle shows the temperature field after downscaling with an example downscaling rule. This can be compared to the 400 m atmospheric field the coarse field was originally computed from on the right. During the day, the downscaling of temperature works very well.

The figure below is the temperature field at night during a clear sky night. This is a difficult case. On cloud-free nights the air close to the ground cools and gathers in the valleys. These flows are quite close to the ground, but a good rule was to take the temperature gradient in the lower model layers and multiply it with the height anomalies (height differences from spline-smoothed coarse field).

Having a population of Pareto optimal solutions is one advantage of our approach. There is normally a trade of between the size of the solution and its performance and having multiple solutions means that you can study this and then chose a reasonable compromise.

Contrary to working with artificial neural networks as machine learning method, the GP solution is a piece of code, which you can understand. You can thus select a solution that makes sense physically and thus more likely works as well in situation that are not in the training dataset. You can study the solutions that seem strange and try to understand why they work and gain insight into your problem.

This statistical downscaling as an interface between two physical models is a beautiful synergy of statistics and physics. Physics and statistics are often presented at antagonists, but they actually strength each other. Physics should inform your statistical analysis and the above is an example where statistics makes a physical model more realistic (not performing a downscaling is also a statistical assumption, just less visible and less physical).

I would even argue that the most interesting current research in the atmospheric sciences merges statistics and physics: ensemble weather prediction and decadal climate prediction, bias corrections of such ensembles, model output statistics, climate model emulators, particle assimilation methods, downscaling global climate models using regional climate models and statistical downscaling, statistically selecting representative weather conditions for downscaling with regional climate models and multivariate interpolation. My work on adaptive parameterisation combining the strengths of more statistical parameterisations with more physical parameterisations is also an example.

Related reading

On cloud structure

An idea to combat bloat in genetic programming


Poli, R., W.B. Langdon and N. F. McPhee, 2016: A field guide to genetic programming. Published via (With contributions by J. R. Koza).

Schomburg, A., V.K.C. Venema, R. Lindau, F. Ament and C. Simmer, 2010: A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models. Tellus B, doi: 10.1111/j.1600-0889.2010.00466.x, 62, no. 4, pp. 242-258.

Zerenner, Tanja, Victor Venema, Petra Friederichs and Clemens Simmer, 2016: Downscaling near-surface atmospheric fields with multi-objective Genetic Programming. Environmental Modelling & Software, in press.

Zitzler, Eckart and Lothar Thiele, 1999: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation 3.4, pp. 257-271, 10.1109/4235.797969.

* Sierpinski fractal at the top was generated by Nol Aders and is used under a GNU Free Documentation License.

* Photo of mountain with clouds all around it (Cloud shroud) by Zoltán Vörös and is used under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.

Wednesday, 3 August 2016

Climate model ensembles of opportunity and tuning

Listen to grumpy old men.

As a young cloud researcher at a large conference, enthusiastic about almost any topic, I went to a town-hall meeting on using a large number of climate model runs to study how well we know what we know. Or as scientists call this: using a climate model ensemble to study confidence/uncertainty intervals.

Using ensembles was still quite new. Climate Prediction dot Net had just started asking citizens to run climate models on their Personal Computers (old big iPads) to get the computer power to create large ensembles. Studies using just one climate model run were still very common. The weather predictions on the evening television news were still based on one weather prediction model run; they still showed highs, lows and fronts on static "weather maps".

During the questions, a grumpy old men spoke up. He was far from enthusiastic about his new stuff. I see a Statler or Waldorf angrily swing his wooden walking stick in the air. He urged everyone, everyone to be very careful and not to equate the ensemble with a sample from a probability distribution. The experts dutifully swore they were fully aware of this.

They likely were and still are. But now everyone uses ensembles. Often using them as if they sample the probability distribution.

Before I wrote about the problems confusing model spread and uncertainty made in the now mostly dead "hiatus" debate. That debate remains important: after the hiatus debate is before the hiatus debate. The new hiatus is already 4 month old.* And there are so many datasets to select a "hiatus" from.

Fyfe et al. (2013) compared the temperature trend from the CMIP ensemble (grey histogram) to observations (red something) implicitly assuming that the model spread is the uncertainty. While the estimated trend is near the model spread, it is well within the uncertainty. The right panel is for a 20 year period: 1993–2012. The left panel starts in the cherry picked large El Nino year: 1998–2012.

This time I would like to explain better why the ensemble model spread is typically smaller than the confidence interval. These reasons suggest other questions where we need to pay attention: It is also important for comparing long-term historical model runs with observations and could affect some climate change impact studies. For long-term projections and decadal climate prediction it is likely less relevant.

Reasons why model spread is not uncertainty

One climate model run is just one realisation. Reality has the same problem. But you can run a model multiple times. If you change the model fields you begin with just a little bit, due to the chaotic nature of atmospheric and oceanic flows a second run will show a different realisation. The highs, lows and fronts will move differently, the ocean surface is consequently warmed and cooled at different times and places, internal modes such as El Nino will appear at different times. This chaotic behaviour is mainly found at the short time scales and is one reason for the spread of an ensemble. And it is one reason to expect that model spread is not uncertainty because models focus on getting the long term trend right and differ strongly when it comes to the internal variability.

But that is just reason one. The modules of a climate model that simulate specific physical processes have parameters that are based on measurements or more detailed models. We only know these parameters within some confidence interval. A normal climate model takes the best estimate of these parameters, but they could be anywhere within the confidence interval. To study how important these parameters are special "perturbed physics" ensembles are created where every model run has parameters that vary within the confidence interval.

Creating a such an ensemble is difficult. Depending on the reason for the uncertainty in the parameter, it could make sense to keep its value constant or to continually change it within its confidence interval and anything in between. It could make sense to keep the value constant over the entire Earth or to change it spatially and again anything in between. The parameter or how much it can fluctuate may dependent on the local weather or climate. It could be that parameter X is high also parameter Y is high (or low); these dependencies should also be taken into account. Finally, also the distributions of the parameters needs to be realistic. Doing all of this for the large number of parameters in a climate model is a lot of work, typically only the most important ones are perturbed.

You can generate an ensemble that has too much spread by perturbing the parameters too strongly (and by making the perturbations too persistent). If you do it optimally, the ensemble would still show too little spread because not all physical processes are modelled because they are thought not to be important enough to justify the work and the computational resources. Part of this spread can be studied by making ensembles using many different models (multi-model ensemble), which are developed by different groups with different research questions and different ideas what is important.

That is where the title comes in: ensembles of opportunity. These are ensembles of existing model runs that were not created to be an ensemble. The most important example is the ensemble of the Coupled Models Intercomparison Project (CMIP). This group coordinates the creating of a set of climate model runs for similar scenarios, so that the results of these models can be compared with each other. This ensemble will automatically sample the chaotic flows and it is a multi-model ensemble, but it is not a perturbed physics ensemble; these model runs are model aiming at the best possible reproduction of what happened. For this reason alone the spread of the CMIP ensemble is expected to be too low.

The term "ensembles of opportunity" is another example the tendency of natural scientists to select neutral or generous terms to describe the work of colleagues. The term "makeshift ensemble" may be clearer.

Climate model tuning

The CMIP ensemble also has too little spread when it comes to the global mean temperature because the model are partially tuned to it. There is just an interesting readable article out on climate model tuning in BAMS**, which is intended for a general audience. Tuning has a large number of objectives, from getting the mean temperature right to the relationship between humidity and precipitation. There is also a section on tuning to the magnitude of warming the last century. It states about the historical runs:
The amplitude of the 20th century warming depends primarily on the magnitude of the radiative forcing, the climate sensitivity, as well as the efficiency of ocean heat uptake. ...

Some modeling groups claim not to tune their models against 20th century warming, however, even for model developers it is difficult to ensure that this is absolutely true in practice because of the complexity and historical dimension of model development. ...

There is a broad spectrum of methods to improve model match to 20th century warming, ranging from simply choosing to no longer modify the value of a sensitive parameter when a match is already good for a given model, or selecting physical parameterizations that improve the match, to explicitly tuning either forcing or feedback both of which are uncertain and depend critically on tunable parameters (Murphy et al. 2004; Golaz et al. 2013). Model selection could, for instance, consist of choosing to include or leave out new processes, such as aerosol cloud interactions, to help the model better match the historical warming, or choosing to work on or replace a parameterization that is suspected of causing a perceived unrealistically low or high forcing or climate sensitivity.
Due to tuning models that have a low climate sensitivity tend to have stronger forcings over the last century and model with a high climate sensitivity a weaker forcing. The forcing due to greenhouse gasses does not vary much, that part is easy. The forcings due to small particles in the air (aerosols) that like CO2 stem from the burning of fossil fuels and are quite uncertain and Kiehl (2007) showed that high sensitivity models tend to have more cooling due to aerosols. For a more nuanced updated story see Knutti et al. (2008) and Forster et al. (2013).

Kiehl (2007) found an inverse correlation between forcing and climate sensitivity. The main reason for the differences in forcing was the cooling by aerosols.
This "tuning" initially was not an explicit tuning of model parameters, but mostly because modellers keep working until the results look good. Look good compared to observations. Bjorn Stevens talks about this in an otherwise also recommendable Forecast episode.

Nowadays the tuning is often performed more formally and an important part of studying the climate models and understanding their uncertainties. The BAMS article proposes to collect information on tuning for the upcoming CMIP. In principle a good idea, but I do not think that that is enough. In a simple example of climate sensitivity and aerosol forcing, the groups with low sensitivity and forcing and the ones with high sensitivity and forcing are happy with their temperature trend and will report not to have tuned. But that choice also leads to too little ensemble spread, just like the groups that did need to tune. Tuning makes it complicated to interpret the ensemble, it is no problem for a specific model run.

Given that we know the temperature increase, it is impossible not to get a tuned result. Furthermore, I mention several additional reasons why the model spread is not the uncertainty above that complicate the interpretation of the ensemble in the same way. A solution could be to follow the work in ensemble weather prediction with perturbed-physics ensembles and to tune all models, but to tune them to cover the full range of uncertainties that we estimate from the observations. This should at least cover the the climate sensitivity and ocean heat uptake, but preferably also other climate characteristics that are important for climate impact and climate variability studies. Large modelling centres may be able to create such large ensembles by themselves, the others could coordinate their work in CMIP to make sure the full uncertainty range is covered.

Historical climate runs

Because the physics is not perturbed and especially due to the tuning, you would expect that the CMIP ensemble spread is too low for global mean temperature increase. That the CMIP ensemble average fits well to the observed temperature increase shows that with reasonable physical choices we can understand why the temperature increased. It shows that known processes are sufficient to explain it. That is fits so accurately, does not say much. I liked the title of an article from Reto Knutti (2008): "Why are climate models reproducing the observed global surface warming so well?" Which implies it all.

Much more interesting to study how good models are, are spatial patterns and other observations. New datasets are greeted with much enthusiasm by modellers because they allow for the best comparison and are more likely to show new problems that need fixing and lead to a better understanding. Also model results for the deep past are important tests, which models are not tuned for.

That the CMIP ensemble mean fits to the observations is no reason to expect that the observations are reliable

When the observations peak out of this too narrow CMIP ensemble spread that is to be expected. If you want to make a case that our understanding does not fit to the observations, you have to take the uncertainties into account, not the spread.

Similarly, that the CMIP ensemble mean fits to the observations is no reason to expect that the observations are reliable. Because of the overconfidence in the data quality also many scientists took the recent minimal deviations from the trend line too seriously. This finally stimulated more research into the accuracy of temperature trends, into inhomogeneities in the ERSST sea surface temperatures, into the effect of coverage and how we blend sea, land and ice temperatures together. There are some more improvements under way.

Compared to the global warming of about 1°C up to now, these recent and upcoming corrections are large. Many of the problem could have been found long ago. It is 2016. It is about time to study this. If funding is an issue we could maybe sacrifice some climate change impact studies for wine. Or for truffles. Or caviar. The quality of our data is the foundation of our science.

That the comparison of the CMIP ensemble average with the instrumental observation is so central to the public climate "debate" is rather ironic. Please take a walk in the forest. Look at all the different changes. The ones that go slower as well as the many that go faster than expected.

Maybe it is good to emphasise that for the attribution of climate change to human activities, the size of the historical temperature increase is not used. The attribution is made via correlations with the 3-dimensional spatial patterns between observations and models. By using the correlations (rather than root mean square errors), the magnitude of the change in either the models or the observations is no longer important. Ribes (2016) is working on using the magnitude of the changes as well. This is difficult because of inevitable tuning, which makes specifying the uncertainties very difficult.

Climate change impact studies

Studying the impacts of climate change is hard. Whether dikes break depends not only on sea level rise, but also on the changes in storms. The maintenance of the dikes and the tides are important. It matters whether you have a functioning government that also takes care of problems that only become apparent when the catastrophe happens. I would not sleep well if I lived in an area where civil servants are not allowed to talk about climate change. Because of the additional unnecessary climate dangers, but especially because that is a clear sign of a dysfunctional government that does not prioritise protecting its people.

The too narrow CMIP ensemble spread can lead to underestimates of the climate change impacts because typically the higher damages from stronger than expected changes are larger than the reduced damages from smaller changes. The uncertainty monster is not our friend. Admittedly, the effect of the uncertainties is rather modest. This this is only important for those impacts we understand reasonably well already. The lack of variability can be partially solved in the statistical post-processing (bias correction and downscaling). This is not common yet, but Grenier et al. (2015) proposed a statistical method to make the natural variability more realistic.

This problem will hopefully soon be solved when the research programs on decadal climate prediction mature. The changes over a decade due to greenhouse warming are modest, for decadal prediction we thus especially need to accurately predict the natural variability of the climate system. An important part of these studies is assessing whether and which changes can be predicted. As a consequence there is a strong focus on situation specific uncertainties and statistical post-processing to correct biases of the model ensemble in the means and in the uncertainties.

In the tropics decadal climate prediction works reasonably well and helps farmers and governments in their planning.

In the mid-latitudes, where most of the researchers live, it is frustratingly difficult to make decadal predictions. Still even in that case, we would still have an ensemble where the ensemble can be used as a sample of the probability distribution. That is important progress.

When a lack of ensemble spread is a problem for historical runs, you might expect it to be a problem for projecting for the rest of the century. This is probably not the case. The problem of tuning would be much reduced because the influence of aerosols will be much smaller as the signal of greenhouse gasses becomes much more dominant. For long term projections the main factor is that the climate sensitivity of the models needs to fit to our understanding of the climate sensitivity from all studies. This fit is reasonable for the best estimate of the climate sensitivity, which we expect to be 3°C for a doubling of the CO2 concentration. I do not know how well the fit is for the spread in the climate sensitivity.

However, for long-term projections even the climate sensitivity is not that important. For the magnitude of the climatic changes in 2100 and for the impact of climate change in 2100, the main source of uncertainty is what we will do. As you can see in the figure below the difference between a business as usual scenario and strong climate policies is 3 °C (6 °F). The uncertainties within these scenario's is relatively small. Thus the main question is whether and how aggressively we will act to combat climate change.

Related information

Discussion paper suggesting a path to solving the difference between model spread and uncertainty by James Annan and Julia Hargreaves: On the meaning of independence in climate science.

Is it time to freak out about the climate sensitivity estimates from energy budget models?

Fans of Judith Curry: the uncertainty monster is not your friend

Are climate models running hot or observations running cold?

Forecast: Gavin Schmidt on the evolution, testing and discussion of climate models

Forecast: Bjorn Stevens on the philosophy of climate modeling

The Guardian: In a blind test, economists reject the notion of a global warming pause


Forster, P.M., T. Andrews, P. Good, J.M. Gregory, L.S. Jackson, and M. Zelinka, 2013: Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. Journal of Geophysical Research, 118, 1139–1150, doi: 10.1002/jgrd.50174.

Fyfe, John C., Nathan P. Gillett and Francis W. Zwiers, 2013: Overestimated global warming over the past 20 years. Nature Climate Change, 3, pp. 767–769, doi: 10.1038/nclimate1972.

Golaz, J.-C., J.-C. Golaz, and H. Levy, 2013: Cloud tuning in a coupled climate model: Impact on 20th century warming. Geophysical Research Letters, 40, pp. 2246–2251, doi: 10.1002/grl.50232.

Grenier, Patrick, Diane Chaumont and Ramón de Elía, 2015: Statistical adjustment of simulated inter-annual variability in an investigation of short-term temperature trend distributions over Canada. EGU general meeting, Vienna, Austria.

Hourdin, Frederic, Thorsten Mauritsen, Andrew Gettelman, Jean-Christophe Golaz, Venkatramani Balaji, Qingyun Duan, Doris Folini, Duoying Ji, Daniel Klocke, Yun Qian, Florian Rauser, Cathrine Rio, Lorenzo Tomassini, Masahiro Watanabe, and Daniel Williamson, 2016: The art and science of climate model tuning. Bulletin of the American Meteorological Society, published online, doi: 10.1175/BAMS-D-15-00135.1.

Kiehl, J.T., 2007: Twentieth century climate model response and climate sensitivity. Geophysical Research Letters, 34, L22710, doi: 10.1029/2007GL031383.

Knutti, R., 2008: Why are climate models reproducing the observed global surface warming so well? Geophysical Research Letters, 35, L18704, doi: 10.1029/2008GL034932.

Murphy, J.M., D.M.H. Sexton, D.N. Barnett, G.S. Jones, M.J. Webb, M. Collins and D.A. Stainforth, 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430, pp. 768–772, doi: 10.1038/nature02771.

Ribes, A., 2016: Multi-model detection and attribution without linear regression. 13th International Meeting on Statistical Climatology, Canmore, Canada. Abstract below.

Rowlands, Daniel J., David J. Frame, Duncan Ackerley, Tolu Aina, Ben B. B. Booth, Carl Christensen, Matthew Collins, Nicholas Faull, Chris E. Forest, Benjamin S. Grandey, Edward Gryspeerdt, Eleanor J. Highwood, William J. Ingram, Sylvia Knight, Ana Lopez, Neil Massey, Frances McNamara, Nicolai Meinshausen, Claudio Piani, Suzanne M. Rosier, Benjamin M. Sanderson, Leonard A. Smith, Dáithí A. Stone, Milo Thurston, Kuniko Yamazaki, Y. Hiro Yamazaki & Myles R. Allen, 2012: Broad range of 2050 warming from an observationally constrained large climate model ensemble. Nature Geoscience, 5, pp. 256–260, doi: 10.1038/ngeo1430 (manuscript).

Aurélien Ribes
Abstract. Conventional D&A statistical methods involve linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. As an alternative to this approach, we propose a new statistical model for detection and attribution based only on the additivity assumption. We introduce estimation and testing procedures based on likelihood maximization. As the possibility of misrepresented response magnitudes is removed in this revised statistical framework, it is important to take the climate modelling uncertainty into account. In this way, modelling uncertainty in the response magnitude and the response pattern is treated consistently. We show that climate modelling uncertainty can be accounted for easily in our approach. We then provide some discussion on how to practically estimate this source of uncertainty, and on the future challenges related to multi-model D&A in the framework of CMIP6/DAMIP.

* Because this is the internet, let me say that "The new hiatus is already 4 month old." is a joke.

** The BAMS article calls any way to estimate a parameter "tuning". I would personally only call it tuning if you optimize for emerging properties of the climate model. If you estimate a parameter based on observations or a specialized model, I would not call this tuning, but simply parameter estimation or parameterization development. Radiative transfer schemes use the assumption that adjacent layers of clouds are maximally overlapped and that if there is a clear layer between two cloud layers that they are random overlapped. You could introduce two parameters that vary between maximum and random for these two cases, but that is not done. You could call that an implicit parameter, which shows that distinguishing between parameter estimation and parameterization development is hard.

*** Photo at the top: Grumpy Tortoise Face by Eric Kilby, used under a Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0) license.

Thursday, 14 July 2016

Do not ban homoeopathy in the name of Science

The BBC reports on a petition to ban homoeopathy for pets, which is already singed by a 1000 British vets. The justification for this is that homoeopathy does not work. The vet who started the petition said: "It's been shown that homeopathy doesn't work, so it probably shouldn't be offered any more even if it is offered with good intentions."

Classical homoeopathy naturally does not work. It works with strong dilutions, some are actually so strongly diluted that you can show that most bottles do not contain anything.

Furthermore, homoeopathy is taken as pills or drops. This means that you can make a double blind randomized trail. You randomly give half the people the "medicine" and half a fake medicine (placebo). The patient and doctor both do not know who got what (double blind). At the end you analyse whether there was a difference between the medicine and the placebo. If you pass this trial, your medicine works, no matter how it was made, whether is was conventional or homoeopathic. Also for many traditional medicines it is not known (well) how they work, but the double-blind randomized trail shows that it does. Classical homoeopathic medicine has failed this foundational test.

However, that homoeopathy does not work is not sufficient reason to ban it. It also does no harm and in doubt we should always chose the side of freedom. Government action is there to fix important wrongs, not for stuff that just makes us uncomfortable.

Just sleep

Even if it does not work, homoeopathy may still do some good. Also placebos work. Patients heal faster with a placebo than without treatment. Medical science should work much more on how to optimize the placebo effect. The number of times a day you take the pill, its colour, taste, size, ...

In The Netherlands doctors often tell their patients to simply go home, sleep and if the symptoms do not go away come back in a week. I like that, I am Dutch. Isn't it a relief when the doctor tells you it is not something serious and you should just go to bed? Sounds fine to me.

Foreigners mostly hate it. If they do not get a pill they do not feel taken seriously and they can even get quite aggressive. I would not be surprised if their anger leads to an anti-placebo effect. Why not give them something homoeopathic? That is better than unnecessary medicine, which may do harm, and lot better than over-prescribing on antibiotics.

Homoeopathy or classical homoeopathy?

The vet in the BBC article gives the impression he really believes in homoeopathy. That could be problematic. I would avoid such doctors because they clearly do not understand how to evaluate evidence. But a doctor who pragmatically prescribes it in case someone should just go to bed would be fine.

It could also be that the medicines this vet prescribes are not classical homoeopathy, but herbal medicine, which is often marketed as homoeopathic. Those could naturally work, many conventional medicines come from substances that were used in traditional cures in healing traditions from all over the world. The active ingredient of aspirin famously comes from willow bark.

That is why I do not like intolerance directed against traditional medicine; we should study it, see if it works, try to understand why it works, how to reduce side effects and how to improve their effectiveness. In many cases we do not have clear evidence against it, like we have for classical homoeopathy, and even if the "explanation" of how the cures work makes no sense we should keep an open mind that the actual treatment does bring benefits.

If a "homoeopathic" herbal medicine would pass the double blind trial, you could sell it as a normal medicine if it works better than existing medicines. Thus for me it is a bad sign when a pill is marketed as homoeopathic. Although, I do not know whether it is true, but I once heard an ironic story about a pill that went through all the tests and was approved as conventional medicine, but then the marketing department decided it was better to sell it as a herbal cure.

Science and politics

There is a parallel to the climate debate. Science can tell you some of the consequences of continued fossil fuel use. Science can tell you classical homoeopathy does not work. Science cannot tell society how to respond to climate change. Science cannot tell whether we should ban homoeopathy.

As a citizen I feel we should transition to renewable energy. As a citizen I feel we can be tolerant towards homoeopathy.

Related reading

BBC News, Science & Environment: Vets: Ban the use of homeopathy in animals

Steven Novella, MD, academic clinical neurologist at Yale University School of Medicine and president and co-founder of the New England Skeptical Society want to ban homoeopathy for pets: Should We Ban Homeopathy for Animals?

Quark Soup by David Appell: The Homeopathic Patient :-)

* Top photo: Cats by Abdullah AlBargan, used with a Creative Commons Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0) license.
* Middle photo: Cat, MacDuff the cat by Kevin Dooley, used with a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license.
* This post was a wonderful excuse to finally write a popular post with cute cat photos.

Thursday, 7 July 2016

Is it time to freak out about the climate sensitivity estimates from energy budget models?

Estimates of climate sensitivity using simple energy budget models tended to produce lower values than many other methods. Consequently they were loved by the mitigation sceptical movement, who seemed to regard these as the most robust of all methods. Part of their argument is the claim that these are “empirical” estimates, conveniently forgetting the simple statistical model the method uses, that they still require information from physical global climate models for the forcings, and that global climate models output also fit the “empirical” temperature change (and many other observed changes).

Before the last IPCC report the estimate for equilibrium climate sensitivity was between 2°C and 4.5°C with a best estimate of 3°C. I do not know of any explicit statement, but I have the feeling that the new studies with low estimates from energy budget models were the reason why the last IPCC report reduced the lower bound to 1.5°C. Since the reasons for the discrepancies were not understood the last IPCC report no longer gave a best estimate for equilibrium climate sensitivity.

The equilibrium climate sensitivity is defined as the equilibrium change in global mean near-surface air temperature after doubling the atmospheric concentration of carbon dioxide.

A Nature News and Views by Kyle Armour (2016) showed this week that three assumptions made in the simple energy budget models lead to strong biases.

1. This week Mark Richardson and colleagues (2016) showed that the temperature change is underestimated because we have few measurements in regions where the change is large, especially the Arctic. This masking problem creates a bias of 15%.

Furthermore, over the ocean, empirical estimates do not use the air temperature, but use the sea surface temperature instead; the water temperature is a much smoother field and can thus be estimated using many fewer samples, which is good because observations over the oceans are sparse. Above sea ice the air temperature is used. Thus this also means that the decrease in the ice cover need to be taken into account. The temperature trend of the air temperature over the ocean is also higher than the trend of the sea surface temperature. Both effects make the "observed" trend 9% smaller.*

2. Climate change is mainly due to increases in carbon dioxide concentrations, but also warming due to increases in methane concentrations, cooling due to increases in aerosols (small airborne particles) and changing due to land use changes. Half a year ago Kate Marvel and colleagues showed that these forcings do not have the same global effect as carbon dioxide and that, as a consequence, the energy balance models are biased low. Marvel and colleagues estimate that this makes the estimates of energy balance models 30% too low.

3. Kyle Armour and colleagues (2013) previous work showed that in the early warming phase climate sensitivity appears smaller than the true value you would get if you would wait till the system has returned to equilibrium. This leads to an underestimate of 25%.

Taking all three biases into account the best estimate from the energy balance models from around 2°C estimate becomes 4.6°C**; see Figure 1b of Armour (2016) reproduced below.

Climate sensitivity estimated from observations1 (black), and its revision following Richardson et al. (blue) then following Marvel et al. (green), and in red the revision for the time dependence (Armour). The grey histogram shows climate model values.

The equilibrium climate sensitivity from global climate models is about 3.5°C***, which is close to the best estimate from all lines of evidence of about 3°C. The "empirical" estimate of 4.6°C is now thus clearly larger than the ones of the global climate models.

Is that a reason to freak out? Have we severely underestimated the severity of the problem?

Probably not, there are many different lines of evidence that support an equilibrium climate sensitivity around 3, with a likely range from around 2 to about 4.5. That the simple energy balance models might now suggest a best estimate of around 4.6°C does not really influence this overall assessment. It is just one line of evidence.

That the energy balance climate sensitivity is minimally above the upper bound does not change this. These energy balance models have not been studied much and the biases are so large that the correction need to very accurate, while they are currently mostly based on single studies. It is quite likely that this value will still change the coming years. If this value still holds after a dozen more studies you may want to consider freaking out a little. How uncertain this bias corrected climate sensitivity is is illustrated by its wide distribution in the above graph with a 95% uncertainty range of 2.5-12.8°C.

[UPDATE. Gavin Schmidt mentions on twitter that it should also be studied whether these three factors are fully independent. While they seem to relate to different aspects there could be a link because spatial patterns and forcing efficacy are strongly related. Thus it would be valuable to make a study that considers all three biases in combination.]

The promotion of the cherry picked climate sensitivity of 2°C, or lower, was disingenuous. A similar promotion of a value of 4.6°C would be no better. (Someone promoting a climate sensitivity of 12.8°C deserves a place in statistical Purgatory.)

There are many other lines of evidence for an equilibrium climate sensitivity around 3, from basic physics, to global climate models, various climatic changes in the deep past and the climate response to volcanoes. Before accepting values far away from 3 we would need to understand the physics of the feedbacks that produce such deviations.

Figure 1 Ranges and best estimates of ECS based on different lines of evidence. Bars show 5-95% uncertainty ranges with the best estimates marked by dots. Dashed lines give alternative estimates within one study. The grey shaded range marks the likely 1.5°C to 4.5°C range as reported in AR5, and the grey solid line the extremely unlikely less than 1°C, the grey dashed line the very unlikely greater than 6°C. Figure taken from figure 1 of Box 12.2 in the IPCC 5th assessment report (AR5). Unlabeled ranges refer to studies cited in AR4. The figure in the review article by Knutti and Hegerl (2008) presented by Skeptical Science is also a very insightful overview.

The likely range of possible climate sensitivity values has been between 1.5°C and 4.5°C since the 1979. That does not sound like much progress. However, we now have many more lines of evidence and those lines have been much better vetted. Thus we can be more sure nowadays that this range is about right. A large part of the uncertainty comes from cloud and vegetation feedbacks. Having worked on clouds myself, I know that these are very difficult problems. Thus I am not hopeful that the uncertainty range will strongly decrease the coming decade or maybe even decades.

We will have to make decisions in the face of this uncertainty. Like any decision in a complex world.


* The temperature trend of the air temperature over the ocean is 9% higher than the trend of the sea surface temperature in the CMIP5 models. For most models the top layer is 10 m deep. For those models with a higher vertical resolution the trend is only 8% higher. The difference is small and not statistically significant, but the effective resolution of numerical models is normally larger than the nominal resolution, thus I would not be surprised if studies with dedicated high resolution models may lead to estimates that are a few percent points lower.

** If we simply combine all these biases: 1.24 (Richardson) * 1.30 (Marvel) * 1.25 (Armour) we get that the simple energy balance models are biased by as much as a factor 2. Taking this into account could suggest increasing the best estimate from the energy balance models from around 2oC to around 4oC. Because of the uncertainty around the estimates and the thick tails, the estimate becomes 4.6°C. See Figure 1b of Armour (2016).

*** The ensemble of global climate models of the CMIP5 project have an average climate sensitivity of 3.5°C with a 95% uncertainty range of 2.0-5.6°C (Geoffroy, et al. 2013).

**** Many thanks to Kyle Armour and And Then There’s Physics for many helpful hints and comments. Any errors are naturally mine.

Related reading

An oldie from Science in 2004: Three Degrees of Consensus explains the various ways to estimate climate sensitivity and why it may have been more luck than wisdom that the first estimate of the range of the climate sensitivity still holds.

Skeptical Science: How sensitive is our climate?

Climate dialogue: Climate Sensitivity and Transient Climate Response

Fans of Judith Curry: the uncertainty monster is not your friend

Tough, but interesting for scientists: Andrew Dessler talk at Ringberg15 on why the equilibrium climate sensitivity exceeds 2°C.


Armour, Kyle C., 2016: Projection and prediction: Climate sensitivity on the rise. Nature Climate Change, News and Views, doi: 10.1038/nclimate3079.

Armour, Kyle C., Cecilia M. Bitz and Gerard H. Roe, 2013: Time-Varying Climate Sensitivity from Regional Feedbacks. Journal of Climate, doi: 10.1175/JCLI-D-12-00544.1

Geoffroy, O., D. Saint-Martin, G. Bellon, A. Voldoire, D.J.L. Olivié and S. Tytéca, 2013: Transient Climate Response in a Two-Layer Energy-Balance Model. Part II: Representation of the Efficacy of Deep-Ocean Heat Uptake and Validation for CMIP5 AOGCMs. Journal of Climate, 26, pp. 1859- 1876, doi: 10.1175/JCLI-D-12-00196.1.

Marvel, K., G.A. Schmidt, R.L. Miller and L.S. Nazarenko, 2015: Implications for climate sensitivity from the response to individual forcings, Nature Climate Change, 6, pp. 386-389. 10.1038/nclimate2888.

Richardson, Mark, Kevin Cowtan, Ed Hawkins and Martin B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change, doi: 10.1038/nclimate3066. If you cannot read this article at Nature, you can go there via The Guardian, which has a special link that allows everyone to read (not download) the article. See also the News and Views on this article by Kyle Armour.

Otto, A., F.E.L. Otto, O. Boucher, J. Church, G. Hegerl, P.M. Forster, N.P. Gillett, J. Gregory, G.C. Johnson, R. Knutti, N. Lewis, U. Lohmann, J. Marotzke, G. Myhre, D. Shindell, B. Stevens, and M.R. Allen, 2013: Energy budget constraints on climate response", Nature Geoscience, 6, pp. 415-416. 10.1038/ngeo1836.

Thursday, 30 June 2016

The EU, refugees and migration

An alternative to Brexit that helps refugees and workers

Summary. This post proposes an alternative to Brexit that makes all EU citizens better off and helps refugees better. Let's add to the refugee convention the condition that if you help refugees in the region, you do not have to house them at home. The real problem of migration is not the migration itself, but the reduction in bargaining power of the average worked. Rather than restrict the freedom of EU citizens to work elsewhere, we could also improve the bargaining power of the workers in other ways. If we then also stop the neo-liberal projects TTIP, CETA and Euro, the EU becomes an attractive way to collaborate for all European citizens.

Brexit, Geert Wilders, Nigel Farage, Marine Le Pen and thousands of refugees drowning in the Mediterranean. We should talk about the EU, refugees and migration.


The European Union started as a peace project, as a collaboration based on two industries that were crucial for war: coal and iron. It is often still sold as a peace project. That is certainly an aspect, but I think that this part is oversold when people point to Europe's violent past. The EU sure helps. However, also outside of the EU the frequency of international conflicts is decreasing. The benefits of war have decreased, most capital is nowadays in humans and organisation and cannot be easily plundered. The costs of war have also increased with nuclear and chemical weapons. The spread of democracy and the absence of war itself makes war less likely.

If leaders and countries acted rationally the EU might no longer be necessary for peace in Europe. Marine Le Pen in France, Geert Wilders in The Netherlands, Nigel Farage in England and Donald Trump in the USA make clear that we should not count on every leader making a cost benefit analysis. The wars in Yugoslavia and Ukraine also warn us that war is possible in Europe. Peace is one of the benefits of the EU and one reason why right-wing extremists do not like it.

The main benefit of the EU is that is allows the citizens of Europe to collaborate and stand up against economic powers. Environmental problems belong to this category. Where powerful companies pollute to make more money, while people with less power have to deal with the consequences. This power abuse increases inequality. On a national level, it is the role of the government to solve such problems. The polluter can, however, threaten to go to another country. International collaboration by setting environmental standards make such threats less credible and makes it easier for governments to serve their populations.

Many environmental problems are naturally also international, for instance, pollution of large rivers and acid rain, and natural candidates for collaboration to avoid international conflicts. A study just out this week tried to estimate the impact of European political measures to reduce air pollutants. It found that:
The reduction in PM2.5 concentrations [very small particles in the air] is calculated to have prevented 80 000 (37 000–116 000, at 95% confidence intervals) premature deaths annually across the European Union, resulting in a perceived financial benefit to society of US$232 billion annually (1.4% of 2010 EU GDP).
Those 80 thousand bodies and 1.4% of GDP is for small particles alone. Add to this all the other pollutants, workers rights and consumer protection. National laws would on average be less strict because firms would nationally have a stronger negotiation position. More people would die, more economic damage would be done. Socializing losses is a money making machine. In this case avoid investments in cleaner technology or selling more cheaper lower quality products increase the private gains at our costs.

People need to collaborate to reduce tax competition between countries. The rich and especially their money are more mobile and they can threaten democracies to pay their taxes elsewhere if the rates for the rich and large companies do not go down. That means that the lower 99%, you and me, will have to pay more, which is why incomes did not increase for most groups in the last decades, while most of the new wealth went to the super rich. The EU should coordinate taxes much more and especially get rid of national tax tricks to allow foreigners to pay less taxes than local people. The EU does this too little, but without the EU we can stop dreaming of achieving more justice here.

What amazes me most about the Leave campaign in the UK is that they managed to portrait the EU as the establishment and themselves as the defenders of the normal man. In reality both campaigns had their elites behind them and leaving the EU would make the UK establishment more powerful. Rupert Murdoch supported the Leave campaign because politicians in London do what he tells them to do. Last time I looked Rupert Murdoch was member of the establishment and not a working man trying to get by.

Rupert Murdoch supported the Leave campaign because politicians in London do what he tells them to do

Yes, the EU also does terrible things. A democratic institution will not always follow your preference; if you like that, try to become a dictator. The establishment naturally also sees that the EU is their main opponent and lobbies to make the EU do what they would like. This is facilitated by the fact that the media does not report much on the EU and much can thus be done behind the back of the people, which means that politicians do not have to fear losing their jobs for doing the bidding of the establishment. We should pay more attention and organize to make sure that our lobbies are also in Brussels.

Well know examples of terrible neo-liberal EU projects are the trade agreements TTIP and CETA and the Euro. Europe is not a banana republic, our courts do their job well and there is no need for special TTIP private courts so that corporations can threaten governments who want to improve living conditions. It is an assault on our democracies. If the EU presses through TTIP or CETA, I will stop being reasonable and from then on I will be anti-EU.

The Euro is a mess, it has many, many problems. We should slowly move out of it.


The increase in the number of refugees is not just an EU problem. Improved information and travel possibilities means that more people are travelling further to find a save home.

It is not just an EU problem, but currently the consequences of the Bush-Blair war against Iraq are producing large refugee streams into the EU and tensions within the union. A closer EU foreign policy could have prevented this mess. (The misinformation campaign for the Iraq war is comparable with the Brexit campaign; in both cases the Anglo-American population did not do their due diligence.)

Most countries on Earth have signed the [[Geneva Convention relating to the Status of Refugees]], which obliges them to act humanely and accept refugees. The duty to protect refugees is international law.

While most people we have an empathetic side that wants to help people in need, we also have a tribal side and many do not like too many people from other groups coming to stay with us. If you put yourself in the position of a native American that instinct can make sense. In retrospect it was a monumental mistake to let Columbus and Co. get away alive.

In response to the growing numbers, the refugee convention has been hollowed out by making it very difficult to enter a country and ask for asylum, as well as by the principle of secure third countries and send refugees away without investigating their case. As a consequence many thousands of people drown in the Mediterranean trying to reach Europe and Australia has set up an disgusting system of lawless concentration camps.

I would propose to add two principles to the convention:
1. That when a country helps refugees in the region they come from, they are no longer obliged to house them in their own country. But only then.
2. That refugees can also send a request for asylum by mail, so that people are no longer dying trying to cross the border.

The current refugee crisis started with insufficient aid for the refugee camps along the Syrian border where people were literally hungering. This new legal principle would make such cases of neglect less likely, because it would have consequences. If we do our part to help locally, it should be no problem that asylum can be requested by mail, because they could then be rejected.

Helping refugees locally is also better for them. We may be rich, but a refugee who is used to a normal culture will have a hard time accustoming to the cold and impersonal European societies. Next to all the other culture shocks. Even without considering cultural differences, staying in the region makes it easier to maintain social ties and to go back home when the problems are over.

Staying in the region makes it easier to maintain social ties and to go back home

We will not be able to help everyone locally, especially in case of small groups or individuals. A gay man who is threatened in Russia is helped most easily by granting him asylum in Europe.

These two new principles would strengthen the right of asylum, help refugees better and reduce the number of refugees coming to Europe. Racists will not like this solution, but for the majority who experience a mix of empathy and concern, this should be a good solution. For people in favour of a multi-cultural society this is a good solution because it helps refugees better (and there will be enough diversity left).


Refugees and migrants are often seen as one category, but they are fundamentally different. A refugee needs our help. To allow the partner and children of a refugee to live together would be migration, but seems to be a no-brainer as well for people with some empathy.

Economic migration is a different case. Even when it is good for a country, it may not be good for all segments of society. One reason we have democracy is to make sure that all interests are represented.

For the elite migration is mostly nice. By definition the migrants see migration as a benefit. And the elite has other options, thus if they migrate that is normally because they see clear benefits. Even before EU citizens could work everywhere in Europe it was normally possible for scientists to work elsewhere because of the importance of migration for science. Science is highly specialized; there is no labour market in Germany for my specialization.

Many other professions are similarly specialised and professionals with high salaries were normally allowed to work in another country. Also a sufficiently wealthy pensioner will be happy to be allowed to migrate to another (warmer) country. Living in another country a few years can be very enriching.

If you are less well off, the possibility of migration of cheap labour can be used by firms to reduce your bargaining power and you may end up with an even lower salary or without a job. The region the migrant comes from looses a valuable labourer. Migration can thus be used to increase inequality even more. Even for scientists from wealthy countries migration makes the negotiation positions weaker and thus labour conditions worse. But it is good for science and for scientists from poorer countries.

Salaries are determined by bargaining power, not by productivity, which is undefined for individuals in nonlinear production processes

Especially within the EU, I would be in favour of allowing everyone to work where they would like to. Freedom should be our default. In return for this benefit, the elite should compensate the disadvantages for the rest of society by improving the negotiation position of workers. One may think of migration restrictions for some professions, stronger unions, redistribution of wealth, programs for retraining, job guarantees for the unemployed and humane treatment of unemployed people.

A new European Union

There are many benefits of collaboration. The people of Europe need to collaborate to have the power to stand up against ever larger economic powers. There is no reason why this collaboration needs to be so intensive that the EU would become a nation itself. People's interests and customs differ and power is best exercised close to the people. We should only collaborate on large scales where this has a clear benefit.

The Euro makes inequality worse and it creates a lot of negative political energy in the EU that avoids other positive changes. Let's get rid of it. Now that the worst financial crisis is over, this is a good time to start a slow transition.

If we change the refugee convention to alternatively help refugees in the region where they come from, we can help them better than now, less will die on their way to Europe and another problem that creates bad blood in Europe would be gone.

Because refugees and migration are often seen as one problem, a reduction in the number of refugees may also reduce problems people have with migration. Still we should not be blind to the large difference in interests between the elites and the rest of society when it comes to migration. A compromise between the groups may be improving the negotiation position of workers.

Overarching above it all: You are not more pro Europe the more you would like the EU to replace the old nations. When Juncker sees the Brexit as a great opportunity to build a European nation and force the fast introduction of the Euro in every country, he is pro Europe. When I reject that uncreative vision of Europe and see the EU as a way for the people of Europe to collaborate, I am also pro Europe. Just like people argue nationally what the role of government is, we should have an open discussion in the EU about where collaboration is fruitful and possible given our differences.

For me, the most valuable innovation of the EU is actually that it is a twitter. That is the reason why nations all over the world are building similar regional collaborations. They would not if the aim would the end of their nations and only building a larger more anonymous nation. Europe should be proud of its queer identity.

Related reading

Brexit is great news for the rest of the EU. Britain has not yet come to terms with its own irrelevance, and would only have got in the way of plans to create a more democratic pooling of sovereignty.

* Top photo: EU Grunge Flag, Attribution 2.0 Generic (CC BY 2.0).

Photo Auschwitz: Arbeit Macht Frei, Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0)

Map of Regional Organizations: CC BY-SA 3.0.

Thursday, 23 June 2016

Four wonderful climate science podcasts you need to know

[UPDATE: How is this Buzzfeed headline?]

For some years I was a regular listener of EconTalk, where the economist Russ Roberts would interview a colleague, typically about a recent book or article. Roberts is a staunch libertarian and the interviews with fellow libertarians are worse than listening in on drunk men agreeing with each other in a bar at 4am, but many other interviews with economists who went out in the world and had studied reality were wonderful. I learned a lot about the world view of this special tribe and something about the limits on how we organize society.

Podcasts are a nice way to learn. The debate makes otherwise maybe more boring engaging and you can listen to it while commuting, walking or doing the household chores.

I tried to get a few people enthusiastic about doing such a podcast for climate science and even in the end considered doing it myself. But there is no need any more. Suddenly a wealth of really good climate science podcasts has sprung up.

Warm Regards

The newest podcast is by Eric Holthaus, journalist at Slate. It is called Warm Regards. He has as co-hosts climate scientist and Ice Age ecologist Jacquelyn Gill and New York Times science blogger Andrew Revkin. They are so new, in fact, that I could not listen to their first podcast yet: "How Do We Talk About Climate Change?". They are now also on iTunes.

[UPDATE. While preparing my diner, I listened to the podcast. Really enjoyed it. Good voices. Good sound. Professionally made. They introduced themselves, what they work on and why they care about climate change. The main topic was science communication and they emphasised that a good relationship with the listener is much more important than details that are quickly forgotten. Revkin liked talking to mitigation sceptics, the other two take the more productive route of trying to talk as much as possible to people who are wiling to listen and consider the arguments. The app Block Together is a good way to keep lines of communication open with decent people on twitter by very efficiently blocking harassing accounts. I also use it and can highly recommend it.

Personally I would add that we should not overestimate the importance of science communication. Outside of Anglo-America scientists are much less active in communicating climate change, but we have nearly no problems with mitigation sceptical movements. The difference is a working political system and better press. Talking about climate and science is what I do best, but if you have the option, it is probably better to invest your time in getting money out of US politics and building up a free and democratic press, for example by supporting membership supported media channels.]

Climate History Podcast

The Climate History Podcast is hosted by Dr. Dagomar Degroot, the founder of and co-founder of the Climate History Network. Even if society has changed a lot, we can learn from how humans have responded to the small climatic changes in the past. This initiative is just three podcasts old and the one I listened to, on the little ice age, is really interesting. First three titles are:

1. Climate Change and Crisis: Lessons from the Past
2. The History of Climate Change with Professor Sam White
3. Archaeology in the Arctic: Reconstructing the Consequences of Climate Change in the Far North

You can listen to it on iTunes project and download and listen to the podcast at SoundCloud.

Mostly Weather

The UK MetOffice produces the podcasts Mostly Weather. So officially it is about weather, but most topics are actually dual-use science, important for both weather and climate. (Mitigation sceptics often do not seem to know that meteorology is bigger than climatology.) It is made with love by climate scientists Doug McNeall, Niall Robinson and Claire Witham.

It is aimed at a general audience, but also a scientist can still learn something. I learned from their first podcasts on the history of weather forecasting that this started over the ocean: there it is most important and easier to do. Other podcasts were on the elements: clouds, snow, lightning and about the structure of atmosphere and they just had a series on weather forecasting.


For me as a scientist, the clear favorite is Forecast. It is made by Michael White, editor for climate topics at the scientific journal Nature. He makes it as a private project, but naturally he has access to the best and the brightest as editor and a good understanding of the climate system. This makes for in depth interviews on the science, but he also talks a lot about the serendipitous personal and scientific histories of the scientists. I have the feeling, many non-scientists will be able to understand the interviews, but admit I am not a very good judge of this.

The last interview was with Gabi Hegerl, the woman who discovered climate change (the first to do an attribution study). Other names people may recognize are: Reto Knutti, astronaut Piers Sellers, Chris Field, Bjorn Stevens, Kim Cobb, Mat Collins. Oh and a modeller from NASA GISS. Gavin Schmidt.

Have fun listening. Let me know if I missed something and which podcasts you like most.