Estimating the statistical properties of inhomogeneities without homogenization

One way to study inhomogeneities is to homogenize a dataset and study the corrections made. However, that way you only study the inhomogeneities that have been detected. Furthermore, it is always nice to have independent lines of evidence in an observational science. So in this recently published study Ralf Lindau and I (2019) set out to study the statistical properties of inhomogeneities directly from the raw data.

Break frequency and break size

The description of inhomogeneities can be quite complicated.

Observational data contains both break inhomogeneities (jumps due to, for example, a change of instrument or location) and gradual inhomogeneities (for example, due to degradation of the sensor or the instrument screen, growing vegetation or urbanization). The first simplification we make is that we only consider break inhomogeneities. Gradual inhomogeneities are typically homogenized with multiple breaks and they are often quite hard to distinguish from actual multiple breaks in case of noisy data.

When it comes to the year and month of the break we assume every date has the same probability of containing a break. It could be that when there is a break, it is more likely that there is another break, or less likely that there is another break.* It could be that some periods have a higher probability of having a break or the beginning of a series could have a different probability or when there is a break in station X, there could be a larger chance of a break in station Y. However, while some of these possibilities make intuitively sense, we do not know about studies on them, so we assume the simplest case of independent breaks. The frequency of these breaks is a parameter our method will estimate.

* When you study the statistical properties of breaks detected by homogenization methods, you can see that around a break it is less likely for there to be another break. One reason for this is that some homogenization methods explicitly exclude the possibility of two nearby breaks. The methods that do allow for nearby breaks will still often prefer the simpler solution of one big break over two smaller ones.


When it comes to the sizes of the breaks we are reasonably confident that they follow a normal distribution. Our colleagues Menne and Williams (2005) computed the break sizes for all dates where the station history suggested something happened to the measurement that could affect its homogeneity.** They found the break size distribution plotted below. The graph compares the histogram to a normal distribution with an average of zero. Apart from the actual distribution not having a mean of zero (leading to trend biases) it seems to be a decent match and our method will assume that break sizes have a normal distribution.


Figure 1. Histogram of break sizes for breaks known from station histories (metadata).


** When you study the statistical properties of breaks detected by homogenization methods the distribution looks different; the graph plotted below is a typical example. You will not see many small breaks; the middle of the normal distribution is missing. This is because these small breaks are not statistically significant in a noisy time series. Furthermore, you often see some really large breaks. These are likely multiple breaks being detected as one big one. Using breaks known from the metadata, as Menne and Williams (2005) did, avoids or reduces these problems and is thus a better estimate of the distribution of actual breaks in climate data. Although, you can always worry that the breaks not known in the metadata are different. Science never ends.



Figure 2. Histogram of detected break sizes for the lower USA.

Temporal behavior

The break frequency and size is still not a complete description of the break signal, there is also the temporal dependence of the inhomogeneities. In the HOME benchmark I had assumed that every period between two breaks had a shift up or down determined by a random number, what we call “Random Deviation from a baseline” in the new article. To be honest, “assumed” means I had not really thought about it when generating the data. In the same year, NOAA published a benchmark study where they assumed that the jumps up and down (and not the levels) were given by a random number, that is, they assumed the break signal is a random walk. So we have to distinguish between levels and jumps.

This makes quite a difference for the trend errors. In case of Random Deviations, if the first jump goes up it is more likely that the next jump goes down, especially if the first jump goes up a lot. In case of a random walk or Brownian Motion, when the first jump goes up, this does not influence the next jump and it has a 50% probability of also going up. Brownian Motion hence has a tendency to run away, when you insert more breaks, the variance of the break signal keeps going up on average, while Random Deviations are bounded.

The figure from another new paper (Lindau and Venema, 2020) shown below quantifies the big difference this makes for the trend error of a typical 100 years long time series. On the x-axis you see the frequency of the breaks (in breaks per century) and on the y-axis the variance of the trends (in Kelvin² or Celsius² per century²) these breaks produce.

The plus-symbols are for the case of Random Deviations from a baseline. If you have exactly two breaks per time series this gives the largest trend error. However, because the number of breaks varies, an average break frequency of about three breaks per series gives the largest trend error. This makes sense as no breaks would give no trend error, while in case of more and more breaks you average over more and more independent numbers and the trend error becomes smaller and smaller.

The circle-symbols are for Brownian Motion. Here the variance of the trends increases linearly with the number of breaks. For a typical number of breaks of more than five, Brownian Motion produces a much larger trend error than Random Deviations.


Figure 3. Figure from Lindau and Venema (2020) quantifying the trend errors due to break inhomogeneities. The variance of the jump sizes is the same in both cases: 1 °C².

One of our colleagues, Peter Domonkos, also sometimes uses Brownian Motion, but puts a limit on how far it can run away. Furthermore, he is known for the concept of platform-like inhomogeneity pairs, where if the first break goes up, the next one is more likely to go down (or the other way around) thus building a platform.

All of these statistical models can make physical sense. When a measurement error causes the observation to go up (or down), once this problem is discovered it will go down (or up) again, thus creating a platform inhomogeneity pair. When the first break goes up (or down) because of a relocation, this perturbation remains when the the sensor is changed and both remain when the screen is changed, thus creating a random walk. Relocations are a frequent reason for inhomogeneities. When the station Bonn is relocated, the operator will want to keep it in the region, thus searching in a random directions around Bonn, rather than around the previous location. That would create Random Deviations.

In the benchmarking study HOME we looked at the sign of consecutive detected breaks (Venema et al., 2012). In case of Random Deviations, like HOME used for its simulated breaks, you would expect to get platform break pairs (first break up and the second down, or reversed) in 4 of 6 cases (67%). We detected them in 63% of the cases, a bit less, probably showing that platform pairs are a bit harder to detect than two breaks going in the same direction. In case of Brownian Motion you would expect 50% platform break pairs. For the real data in the HOME benchmark the percentage of platforms was 59%. So this does not fit to Brownian Motion, but is lower than you would expect from Random Deviations. Reality seems to be somewhere in the middle.

So for our new study estimating the statistical properties of inhomogeneities we opted for a statistical model where the breaks are described by a Random Deviations (RD) signal added to a Brownian Motion (BM) signal and estimate their parameters to see how large these two components are.

The observations

To estimate the properties of the inhomogeneities we have monthly temperature data from a large number of stations. This data has a regional climate signal, observational and weather noise and inhomogeneities. To separate the noise and the inhomogeneities we can use the fact that they are very different with respect to their temporal correlations. The noise will be mostly independent in time or weakly correlated in as far as measurement errors depend on the weather. The inhomogeneities, on the other hand, have correlations over many years.

However, the regional climate signal also has correlations over many years and is comparable in size to the break signal. So we have opted to work with a difference time series, that is, subtracting the time series of a neighboring station from that of a candidate station. This mostly removes the complicated climate signal and what remains is two times the inhomogeneities and two times the noise. The map below shows the 1459 station pairs we used for the USA.


Figure 4. Map of the lower USA with all the pairs of stations we used in this study.

For estimating the inhomogeneities, the climate signal is noise. By removing it we reduce the noise level and avoid having to make assumptions about the regional climate signal. There are also disadvantages to working with the difference series, inhomogeneities that are in both the candidate and the reference series will be (partially) removed. For example, when there is a jump because of the way the temperature is computed this leads to a change in the entire network***. Such a jump would be mostly invisible in a difference series. Although not fully invisible because the jump size will be different in every station.


*** In the past the temperature was read multiple times a day or a minimum and maximum temperature thermometer was used. With labor-saving automatic weather stations we can now sample the temperature many times a day and changing from one definition to another will give a jump.

Spatiotemporal differences

As test statistic we have chosen the variance of the spatiotemporal differences. The “spatio” part of the differences I already explained, we use the difference between two stations. Temporal differences mean we subtract two numbers separated by a time lag. For all pairs of stations and all possible pairs of values with a certain lag, we compute the variance of all these difference values and do this for lags of zero to 80 years.

In the paper we do all the math to show how the three components (noise, Random Deviation and Brownian Motion) depend on the lag. The noise does not depend on the lag. It is constant. Brownian Motion produces a linear increase of the variance as a function of lag, while the Random Deviations produce a saturating exponential function. How fast the function saturates is a function of the number of breaks per century.

The variance of the spatiotemporal differences for America is shown below. The O-symbols are the variances computed from the data. The other lines are the fits for the various parts of the statistical model. The variance of the noise is about 0.62 Kelvin² or Celsius² and shown as a horizontal line as it does not depend on the lag. The component of the Brownian Motion is the line indicated by BM, while the Random Deviation (RD) component is the curve starting at the origin and growing to about 0.47 K². From how fast this curve growths we estimate that the American data has one RD break every 5.8 years.

The curve for Brownian Motion being a line already suggests that it is not possible to estimate how many BM breaks the time series contains, we only know the total variance, but not whether it is contained in many small ones or one big one.



Figure 5. The variance of the spatiotemporal differences as a function of the time lag for the lower USA.

The situation for Germany is a bit different; see figure below. Here we do not see the continual linear increase in the variance we had above for America. Apparently the break signal in Germany does not have a significant Brownian Motion component and only contains Random Deviation breaks. The number of breaks is also much smaller, the German data only has one break every 24 years. The German weather service seems to give undisturbed climate observations a high priority.

For both countries the size of the RD breaks is about the same and quite small, expressed as typical jump size it would be about 0.5°C.



Figure 6. The variance of the spatiotemporal differences as a function of the time lag L for Germany.

The number of detected breaks

The number of breaks we found for America is a lot larger than the number of breaks detected by statistical homogenization. Typical numbers for detected breaks are one per 15 years for America and one per 20 years for Europe, although it also depends considerably on the homogenization method applied.

I was surprised by the large difference between actual breaks and detected breaks, I thought we would maybe miss 20 to 25% of the breaks. If you look at the histograms of the detected breaks, such as Figure 2 reprinted below, where the middle is missing, it looks as if about 20% is missing in a country with a dense observational network.

But these histograms are not a good way to determine what is missing. Next to the influence of chance, small breaks may be detected because they have a good reference station and other breaks are far away, while relatively big breaks may go undetected because of other nearby breaks. So there is not a clear cut-off and you would have to go far from the middle to find reliably detected breaks, which is where you get into the region where there are too many large breaks because detection algorithms combined two or more breaks into one. In other words, it is hard to estimate how many breaks are missing by fitting a normal distribution to the histogram of the detected breaks.

If you do the math, as we do in Section 6 of the article, it is perfectly possible not to detect half of the breaks even for a dense observational network.


Figure 2. Histogram of detected break sizes for the lower USA.

Final thoughts

This is a new methodology, let’s see how it holds when others look at it, with new methods, other assumptions about the nature of inhomogeneities and other datasets. Separating Random Deviations and Brownian Motion requires long series. We do not have that many long series and you can already see in the figures above that the variance of the spatiotemporal differences for Germany is quite noisy. The method thus requires too much data to apply it to networks all over the world.

In Lindau and Venema (2018) we introduced a method to estimate the break variance and the number of breaks for a single pair of stations (but not BM vs RD). This needed some human inspection to ensure the fits were right, but it does suggest that there may be a middle ground, a new method which can estimate these parameters for smaller amounts of data, which can be applied world wide.

The next blog post will be about the trend errors due to these inhomogeneities. If you have any questions about our work, do leave a comment below.

Other posts in this series

Part 5: Statistical homogenization under-corrects any station network-wide trend biases

Part 4: Break detection is deceptive when the noise is larger than the break signal

Part 3: Correcting inhomogeneities when all breaks are perfectly known

Part 2: Trend errors in raw temperature station data due to inhomogeneities

Part 1: Estimating the statistical properties of inhomogeneities without homogenization

References

Lindau, R, Venema, V., 2020: Random trend errors in climate station data due to inhomogeneities. International Journal Climatology, 40, pp. 2393-2402. Open Access. https://doi.org/10.1002/joc.6340

Lindau, R, Venema, V., 2019: A new method to study inhomogeneities in climate records: Brownian motion or random deviations? International Journal Climatology, 39: p. 4769– 4783. Manuscript. https://eartharxiv.org/vjnbd/ https://doi.org/10.1002/joc.6105

Lindau, R. and Venema, V.K.C., 2018: The joint influence of break and noise variance on the break detection capability in time series homogenization. Advances in Statistical Climatology, Meteorology and Oceanography, 4, p. 1–18. https://doi.org/10.5194/ascmo-4-1-2018

Menne, M.J. and C.N. Williams, 2005: Detection of Undocumented Changepoints Using Multiple Test Statistics and Composite Reference Series. Journal of Climate, 18, 4271–4286. https://doi.org/10.1175/JCLI3524.1

Menne, M.J., C.N. Williams, and R.S. Vose, 2009: The U.S. Historical Climatology Network Monthly Temperature Data, Version 2. Bulletin American Meteorological Society, 90, 993–1008. https://doi.org/10.1175/2008BAMS2613.1

Venema, V., O. Mestre, E. Aguilar, I. Auer, J.A. Guijarro, P. Domonkos, G. Vertacnik, T. Szentimrey, P. Stepanek, P. Zahradnicek, J. Viarre, G. Müller-Westermeier, M. Lakatos, C.N. Williams, M.J. Menne, R. Lindau, D. Rasol, E. Rustemeier, K. Kolokythas, T. Marinova, L. Andresen, F. Acquaotta, S. Fratianni, S. Cheval, M. Klancar, M. Brunetti, Ch. Gruber, M. Prohom Duran, T. Likso, P. Esteban, Th. Brandsma, 2012: Benchmarking homogenization algorithms for monthly data. Climate of the Past, 8, pp. 89-115. https://doi.org/10.5194/cp-8-89-2012

Bernie Sanders is more electable than Joe Biden and will win

Bernie Sanders will become the 46th US president.

After Iowa and so many good New Hampshire polls for Sanders, it is about time to present my prediction for the 2020 presidential election before it is no longer an interesting take. I try to only write about such matters when I think the mainstream opinion is wrong and the published opinion is wrong about Sanders' electability.

Full disclose: I hope Sanders or Warren wins, the biggest problem America faces is crony capitalism. Systemic corruption is the foundation of nearly all US problems, which spill into the world, including insufficient climate action. Given this bias I will try to quantify as much as possible and give my sources.

Unfortunately it is not guaranteed Sanders will win and it is hard to quantify, but to go on the record with a clear prediction, let me state he has a chance of 54% of winning. This is based on a chance to win the primary of 60% and then a chance of 90% to win the general. This makes it a probabilistic prediction, just like "there is a 70% probability it will rain tomorrow", which needs multiple predictions to validate. For validation, you could combine it with my previous political predictions going against the mainstream:

1. I already have my warning for clear and present danger before the 2016 election: "there is now a real possibility Trump could become president". In the post you will find the reasons why the terrible pundits in the US media were wrong.

2. Another prediction was that the UK election in 2017 would be a lot closer than poll whisperer Nate Silver predicted because he ignored comrade trend. (Although he is an incompetent establishment pundit, but really good with numbers, so this was an interesting prediction.)

I am confident that Sanders will win an election against Trump (90%), but even if it looks good now winning the primary (60%) is harder because TV news keeps on repeating that Sanders cannot win the general election, as far as I have seen mostly without arguments and sometimes with very cherry picked or hacky evidence.

The power is shifting from corporate media to social media, independent media and membership supported media. The media and candidates can no longer be sure to get away with misinformation without risking their reputation. Although sometimes they slip into old patterns and claim that they said X in 1976 and I am shouting at my monitor that everyone has seen the video of you saying Y.

The power is also shifting from big donors to crowd funding. Even in the face of rising inequality, technology has made small donations so easy as to be competitive. Fortunately to spread the truth you also need less money than to spread lies and presidential candidates get a lot of free media.

As moving target it is hard to say how much difference this power shift makes in 2020, we can be sure the donor class and the media will throw the kitchen sink at Sanders. They hurt themselves doing so, but they despise him from their corporate core to their high-dollar hosts and guests. So I am not as confident about my primary prediction, not knowing how this will play out.

Sanders Beats Trump

The media is sure Sanders cannot win because Republicans would call him a socialist. One often has the impression that they and the Democratic leadership think you are not allowed to reply when Republicans say something. At every primary debate Sanders thus gets his socialism question, gives a strong answer, which the journalists apparently have forgotten again in the next debate. Maybe they are trying to train us into also thinking that resistance replying is futile.

Democratic leadership would like Sanders to cower, just like them, to be weak, to defend themselves against the unfair accusation of being a socialist with some soft spoken words. But if you are defending you are losing. It is much stronger to accept the label and fill it with content.

Is there a better campaign than replying and telling the American people about the high quality of living in social democratic countries, about the higher salaries for workers, about their vibrant market economies, about their high ranking in global indices for entrepreneurship and freedom, about their well-trained competitive work forces, about being treated with respect, about a government that works for all and not just for the donors? Even Danish politicians have started helping:



So what is the quantitative evidence whether Sanders or Biden is the stronger candidate?

Policies

1. The policies of Sanders are the most popular ones. This is already clear by most presidential candidates adopting or claiming to adopt the most popular Sanders policies. To be fair the difference with Biden, on average over all policies, is just one percent, but does not go in the direction the pundits would like you to think:

"Senator Bernie Sanders of Vermont edges out his Democratic opponents on health care, immigration, the environment and the economy, according to a Reuters/Ipsos poll. ... For health care, arguably Sanders' staple issue, the Senator claims 27.1 percent support, eclipsing Biden and Warren by 9 percent and 14.6 percent, respectively. On the environment, Sanders again edges out Biden by 9.7 percent and Warren by 8.2 percent. He also comes out ahead on the economy and jobs."

This week's Quinnipiac University poll asked Democrat and Democrat-leaning voters: "Regardless of how you intend to vote in the Democratic primary for president, which candidate do you think - has the best policy ideas?" Sanders was the choice of 27%, Warren of 16% and Biden of 14%. The voting intentions from the same poll, are 25%, 14% and 17%, respectively, which are higher for Biden than the policy support and lower for Sanders. This suggests that many people unfortunately plan on voting for a candidate they agree with less because they believe the media on electability.

Money and enthusiasm

2. Biden is losing the Money Primary. In the fourth quarter of 2019 he was 3rd with respect to donations. (In the 3rd quarter he was only 4th.)

In the fourth quarter Sanders had 1.8 million individual donors, while Biden had only half a million donors. This is a sign of enthusiasm. Just as the 10 million calls to voters made by Sanders volunteers.


The number of donors. Sanders is leading in 46 states. Graphic: The New York Times.

Electability according to the markets

3. The betting market PredictIt finds it most likely that Sanders will win the primary. The graph below shows the price of shares for Sanders winning, which are equal to the predicted probability he will win in percent. Sanders has a probability of 45% of winning the primary and another betting market gives him a 29% probability of winning the presidency.


The betting market PredictIt for the Democratic primary over last 90 days. The price of stocks in cent is the percentage change a candidate will win the primary.


Following Bayesian statistics, the probability of winning the presidency, P(presidency), is the probability of winning the primary, P(primary), times the probability of winning the presidency after having won the primary, P(presidency|primary). As an equation this reads:

P(presidency) = P(primary) x P(presidency|primary)

From this is follows that:

P(presidency|primary) = P(presidency) / P(primary)

The numbers for Sanders are:

P(presidency) / P(primary) = 29% / 45% = 64%

The probability of winning presidency if the nominee is thus 64% for Sanders. The same numbers for Biden are:

P(presidency|primary) = P(presidency) / P(primary) = 5% / 12% = 42%

So people willing to put money on their political assessment do not agree with the pundit class and see Sanders as 50% more electable than Biden.

To be fair, like the pundits, I also disagree with the betting market. They have a 54% chance of Trump winning. That is preposterous for a historically unpopular president, but betting against Big Money is a loosing strategy on the short term. One would have to hold the bet until election day to win and the chance of Trump winning is unfortunately not zero.

National head to head polling 2020

4. There is the simple polling of head to head races. According to a recent Survey USA poll, this is evidence favoring for Sanders.

The poll found that 52 percent of voters would choose Sanders and 43 percent Trump, giving the veteran senator a nine-point lead. Next was former vice president Joe Biden at 50 percent to Trump's 43 percent, a seven-point lead.

Looking back at older similar polls, the situation can also be reversed. On average I see no difference between the two candidates.

I personally do not like these head to head polls at this stage. Some candidates do quite poorly in head to head polls against Trump. If you look in detail, you will find that Trump gets about the same percentage against all candidates. What varies is how many people prefer the Democratic candidate or are undecided. My impression is that this is mostly measuring name recognition.

National head to head polling 2016

5. It is hard to imagine being in the situation of having the chose between X and Trump, the election is almost a year out and part of the supporters of candidate Y will say they do not know or would vote Trump during the primary, but in the end vote for their party.

However, for 2016 we have similar polling closer to the date of the election. Biden is naturally not Clinton, but in May 2016 PolitiFact found that Sanders beat Trump more easily, by 3 to 12 percent points more than Clinton.

Just a few days before the election a Gravis poll showed that Clinton would beat Trump by 2%, while Sanders would beat Trump by 10%. Caveat: the questions seem neutral, but the poll was commissioned by a politician who endorsed Sanders.

While both Trump and Clinton had net negative favorability values, Sanders net favorability grew during the campaign as people got more familiar with his ideas and ended on plus 17% favorability.

Michael Bloomberg acknowledged these facts right after the 2016 election: “Bernie Sanders would have beaten Donald Trump. Polls show he would have walked away with it. But Hillary Clinton got the nomination.”

Head to head polling swing states

6. Swing states show another picture than the national polls. What the swing states are will depend on the candidate, but to avoid cherry picking, let's take the ones from the Cook Report. Their toss ups for the Electoral College are: Arizona, Florida, North Carolina, Pennsylvania and Wisconsin.

Unfortunately all the head to head state polling we have are the averages of Real Clear Politics, which does not take the quality of the polls into account like 538 usually does. This makes manipulating the public opinion with bad polls easier.

	Biden vs Trump			Sanders vs Trump
State	Trump	Biden	Net	Trump	Sanders	Net
Arizona	47.0	47.3	+0.3	48.5	43.5	-5.0
Florida	45.3	48.0	+2.7	47.0	47.0	Tie
North Carolina	44.8	48.2	+3.4	46.0	47.0	+1.0
Pennsylvania	43.3	50.3	+7.0	44.3	48.0	+3.7
Wisconsin	43.3	47.0	+3.7	44.7	46.7	+2.0
Average	44.7	48.2	+3.5	46.1	46.4	+0.3

Here Biden has a small advantage. Also Sanders would win most swing states, but with less of a margin according to these polls.

Personality

7. Sanders is personally very popular with Democrats and Americans. For example asking "which candidate do you think - cares the most about people like you?" 24% reply Sanders and 19% Biden, in a Quinnipiac University poll.

Asking which candidate is more honest in the same poll, 25% reply Sanders and 14% Biden. Thus Americans do not agree with political insiders and TV pundits who clearly dislike Sanders. Their dislike has a good reason, he would upend their corrupt self-dealing system.

Summary of the evidence

These are the more or less objective pieces of data we have, the rest is more political judgement. So let's summarize the evidence.

When it comes to policies Sanders is more popular. The money primary shows the money and enthusiasm is with Sanders. Looking at what betting markets expect to happen Sanders is more electable. And Americans see Sanders as some who cares about them and is honest. Biden also has good numbers, but not as good.

The mixed evidence comes from head to head polling. In swing states the Real Clear Politics polls give Biden an advantage, nationally the polling suggests that Sanders would beat Trump in 2020 and would have obliterated Trump in 2016.

Hope and change

On to the more subjective political assessment.

All the polling indicates that Americans are not happy and want change. Obama successfully campaigned on hope and change. That was not how he governed, but it was how he won elections as a skilful campaigner.

Biden runs on nothing will fundamentally change like Clinton ran on "America is already great". In 2016 Clinton won with the people who thought their candidates "Cares about people like me", "Has the right experience" or "Has good judgment", but Trump won the "Can bring needed change" with 83%, according to exit polling.

NYT and Trump endorsements

Intriguingly Biden did not even get the NYT endorsement, in fact he was not even in their top four, although they are his people. The NYT endorsement went to Warren and Klobuchar.

In public Trump may ignore Sanders, so much that I have the impression he deeply fears Sanders. But in private, in a secret recoding by his Ukrainian friend in crime, Lev Parnas, Trump admits that he fears Sanders the most. He may be an incompetent lazy fool, but he does know marketing.

Socialism

In the introduction I already argued that the Republicans making the same-old empty attacks by calling Sanders a socialist is welcome. There is also polling on this question. Data For Progress polled people whether they preferred Trump or Sanders with three different formulations:

• No information: “If the 2020 U.S. Presidential election was held today, who would you vote for if the candidates were Bernie Sanders and Donald Trump?”
• Partisan cues: “If the 2020 U.S. Presidential election was held today, who would you vote for if the candidates were Democrat Bernie Sanders and Republican Donald Trump?”
• Socialists and billionaires: “If the 2020 U.S. Presidential election was held today, who would you vote for if the candidates were Democrat Bernie Sanders, who wants to tax the billionaire class to help the working class and Republican Donald Trump, who says Sanders is a socialist who supports a government takeover of healthcare and open borders?”

Calling Sanders a socialist did not hurt him. The only thing that ironically hurts a little is being called a Democrat.

Political record and campaign

A debate between Biden and Trump would look like the fight between Konstantin Chernenko and Ronald Reagan in Two Tribes Go To War. Biden runs on his record. He is thus vulnerable to what Trump does best and enjoys the most in life: denigrating other people in the media.


Frankie Goes To Hollywood - Two Tribes

Sanders runs on a policy platform and is thus less vulnerable to personal attacks. A platform with many policies Trump ran on in 2016, but did not execute because he campaigned as a populist, but governs as an establishment Republican plus hatred.

In times where people identify as Republican because they hate Democrats and identify as Democrat because they hate Republicans it is difficult to win elections by advocating for the policies of the other side. There are naturally policies that appeal to large majorities, that may thus also convince people from the other side.

That such policies are not implemented yet is because of the corrupting influence of money in politics and media. A politician who is free from such influences can make a highly attractive policy platform. A politician who floated up due to their support for the donor class and corporations is restricted. Corporations are not charities, they expect a return on investment. The donor class has different interests and world views than the rest of us. A policy package designed for them will be less attractive for voters.

The upside is the money, which clearly helps the campaign, as we can see in billionaire Bloomberg buying a preposterous vote share. In the past voters may have naively expected that the money did not have much influence and it also took time for the political class to become corrupted by it. But the distance between Washington DC and America has grown together with the length of the list of popular policies that have no chance of passing Congress.

Even if Biden would promise the same policies in the primary as Sanders, people by now expect a general election pivot and a cabinet full of people from the short lists of the donors. Consequently, there is now a much larger bonus for a reputation of honesty and consistency. Thus a people-power campaign needs less money in 2020.

Imagine Trump would legalize marijuana and remove American troops from Iraq. That would sink a Biden general election campaign. Biden not only voted for the Iraq war, already 5 years before the Iraq war, in 1998 Biden was making the case for a ground war.

There is a lot in Biden's record that can be used by the Trump campaign to suppress the Democratic turnout using targetted social media ads. Workers will get ads about Biden's position on the Permanent Normal Trade Relations with China and NAFTA. Poor and old people about Biden trying to reduce Social Security, Medicare and Medicaid.

Joe Biden lied about participating in the Civil Rights movement, admitted as much in 1987, but in this campaign he again started lying about it. Trump will not care about the hypocrisy of him pointing to such problems given his own abysmal record. His authoritarian followers will not see the targetted ads and would also not really care.

Sanders can hammer Trump on the promises Trump made and broke. Trump's budgets reduced Social Security, Medicaid and Medicare, which he had promised to protect. Trump's trade deals are almost the same and were negotiated with corporations at the table. Trump promised that his healthcare plan would cover everyone and would be cheaper, while millions lost their health insurance.

Project fear of the Democratic establishment likes to name drop candidates like George McGovern, but somehow do not mention Hillary Clinton, John Kerry or Al Gore. They especially do not mention Franklin D. Roosevelt, who won the presidency four times and whose New Deal has much in common with Sanders' platform. Also on the Republican side it seems to be hard to make the case that Republicans won who agreed with Democrats, while those who fought Democrats lost. Quite the opposite.

Winning the primary election

Polling aggregator 538 converts the polling information into a probability of winning the nomination by winning the majority of the delegates. The methodology seems to be sound and is likely the best estimate we have.



Nate Silver of 538 seemed a bit dismayed at how much the prediction changed after Iowa. The model gives a bonus for winning Iowa, which traditionally helps candidates in future races. Silver wondered whether the bonus was too large given that Sanders and Buttigieg are tied for one winning metric (the delegate equivalents). The bonus is to take the positive media coverage into account, but the media put much emphasis on the tie and less on Sanders winning the popular vote (in the first and final round), while Silver's model gave all three metrics equal weight.

My impression is that the jump was mostly so large because Biden lost so enormously and is on track to also losing the next two primaries. At the same time the competitors of Biden do not have much chance of winning. Buttigieg may do well today in New Hampshire, but hardly has any staff in subsequent states and nearly no support among non-white voters. Amy Klobuchar is rising, but still polling badly nationally.

In the betting market billionaire Bloomberg is the runner up after Sanders. He has spend $200 million on ads and bought himself 12% in national polling. This is still rising and it thus makes sense that a market would give him a bonus over polling. But as soon as he becomes a serious candidate people will bring up his atrocious record, today #BloombergIsARacist is trending as an appetiser. The media will be nice to him, Bloomberg is expected to spend a billion in ads and every media outlet wants to get some of that. But I expect that social media will keep him small.

Thus I do not see Buttigieg, Klobuchar or Bloomberg winning, but they all have a chance of succeeding Biden and will likely stay in the race a long time, splitting and wasting establishment votes.

Biden's campaign runs on money, which he only gets when he will likely win; the donors want a return on investment. So he may be forced out of the race, although I see him as the only serious competitor to Sanders. Warren might see it coming that she will stay below the 15% threshold for most primaries and chose to combine her campaign with Sanders'. However, she could also wait for Biden dropping out and may then have a chance.

[ Update after the New Hampshire primary. 538 now has "no one" as the most likely winner of the primary, but with Sanders as close second.

Sanders is the clear frontrunner in the current crowded field, but tends to get only a quarter of the vote. So it remains interesting what would happen when the field winnows. Some pundits simply add up all the other "moderate" candidates; that is not how it works.

A recent YouGov/Yahoo head to head poll of the main primary candidates suggests that Sanders would also win in that situation. Sanders would beat Klobuchar by 21 points, Bloomberg by 15 points, but also Biden by 4 points and Warren by 2 points. Also Warren would beat all the other candidates. Life-long Republican Bloomberg would loose against all other candidates. Biden got some hits, but of the "moderates" he is still the strongest competition against Sanders. ]

Nate Silver gives Sanders a chance of 46% of winning. Silver's model has an additional chance of 27% that no one will directly win a majority. If Sanders does have a clear plurality, it would be handing the presidency to Trump to nominate someone else. So also in case of a contested convention Sanders has a good chance of winning. Furthermore, polling for Sanders tends to go up in the weeks before primaries. That is the moment people start paying attention and talking to each other. So I feel my prediction of 60% chance of Sanders winning the primary is reasonable.

If we combine that with a 90% chance of winning the general election, the chance of stopping the class warfare against us is 54%. Let's hope for the best.

Related reading

Sunrise Movement endorses Bernie Sanders for President: "Senator Sanders has made it clear throughout his political career and in this campaign that he grasps the scale of the climate crisis, the urgency with which we must act to address it, and the opportunity we have in coming together to do so."

USA Today: Moderate Democrats have a duty to consider Sanders. He has a clear path to beating Trump. "This senator isn’t even my favorite senator running for the nomination. Yet one reason I have to seriously consider Sanders is that he has the clearest path to uniting the Democratic Party and ousting the evil clown in the Oval Office."

538: You’ll Never Know Which Candidate Is Electable

MostElectable.com

Bernie Sanders leads Donald Trump in polls, even when you remind people he’s a socialist. Socialism is unpopular, but America’s leading socialist isn’t.

Shaun King: 2 truths and 31 lies Joe Biden has told about his work in the Civil Rights Movement

Leftism Isn’t Very Appealing to Nonvoters. But Bernie Sanders Is.

Take the Money and Run. The 2020 Democratic primary has been as much about how candidates raise money as what they want to do once in office.

538: What Fourth-Quarter Fundraising Can Tell Us About 2020

Because it does not fit the stereotype: Bernie Sanders Leads Trump in Donations From Active-Duty Troops

If you want a counter-view there is this terrible piece by Jonathan Chait, be warned that it is filled to the brim with misinformation: Running Bernie Sanders Against Trump Would Be an Act of Insanity. He is also the author of "Liberals Should Support a Trump Republican Nomination". Countering all misinformation would be another blog post, luckily Jacobin did a part: "Jonathan Chait Is Wrong About Everything, Including Sanders' Electability."

USA Today: Trump loses almost every matchup with top 2020 Democrats in Florida, Wisconsin and Michigan, polls find

European Meteorological Society Meeting highlights on station data quality and communication #EMS2019

Last week I was at the Annual Meeting of the European Meteorological Society in Copenhagen, Denmark. Here are the highlights for station data (quality) and communication.

Warming in Svalbard

Øyvind Nordli and colleagues estimated the warming on the Arctic island of Svalbard/Spitsbergen; see figure below. They use the linear red line to estimate the total warming and claim 3.8°C of warming. I would say it warmed a whooping 6°C (11°F). The graph already mostly shows that such a linear trend based estimate will underestimate the total warming.

The monthly data was already published in 2014. At that time I would have called it 5°C of warming; recent years were very warm.

They put a lot of work in the homogenization; even made modern parallel measurements to estimate the effect of past relocations of the station. The next, almost published, paper is on the daily data, so that we can study changes in the number of growing, freezing or melting days.

Warming in the tropical hot spot

There is a small region high up in the air in the tropics that is dear to many climate "skeptics", the tropical hot spot. It is one of the coldest places on Earth which warms strongly when the world is warming (for any reason). Because some observations do not show as much warming there, climate "skeptics" have declared this region to be the arbiter of climate truth, these observations and satellite estimates to the be best we have and most informative for the changes of our climate.


The warming for a GISS model equilibrium run for a 2% increase in solar forcing showing a maximum around 20N to 20S around 300mb (10 km).

Back to reality, it is really hard to make good measurements of such a cold place starting at such a tropically warm place. The thermometer needs to be reliable over about 100°C of range. That is a lot. It is not that easy to launch a weather balloon up to such heights and colds; the balloon will expand enormously. While the countries making these measurements are among the poorest on Earth.

What I had not realized is how few weather balloon make it to such heights. A poster by Souleymane Sy showed this; see Figure below. For trend estimates the sharp drop off above the pressure level of 300mb is especially very worrying. Changes in this drop off level due to changes in equipment can easily lead to changes in the estimated temperature. There is a part of the tropical hot spot below 300mb; that would be the part I would prioritize in trend estimates.


Number of radiosonde stations recording at least a given percentage of temperature and relative humidity monthly data at mandatory pressure levels since 1978 to present time for the Tropics (20° North to 20° South).

Weather forecasts in America and Europe

Communication at the EMS mostly means presenting the daily TV weather forecasts. There was a lovely difference between American and European presenters. The Americans were explaining how to dumb down your forecast as much as possible. A study found that most high school students in Alabama could not find their county on a map of Alabama; so the advice is to put a city name on every number on the map. The Europeans presented their educational work.

Our Irish friends had made three one-hour shows about the weather on consecutive days between 7 and 8pm when normally the soaps are running; light information in a botanical garden with a a small audience.

German weather presenter Karsten Schwanke got a price for his educational weather forecasts, which add information on climate change; for example in case of Dorian show the increase in the sea surface temperature. For Schwanke providing context is the main task of TV weather, the local numbers are available from a weather app.


Karsten Schwanke explains the relationship between the jet stream, wild fires and the drought in Europe. In German.

An increasing problem is fake weather predictions. Amateurs who can make a decent map are often seen as reliable sources, which can be dangerous in case of severe weather.

American weather caster Jay Trobec reported that it is common to have weather information three times during a news block, before, in the middle and at the end. In Europe you just get weather at the end. In America the weather is live, a presenter explaining everyone should leave the disaster area they went to to make this live broadcast. In Europe typically reported and the weather shown in videos. Trobec stated that during severe weather people watch TV rather than use the internet.


Live hurricane weather. :-)

The difference is likely that there is not that much severe weather in Europe, you normally watch the weather to see if you have to take an umbrella with you, rarely to see whether your house will soon be destroyed. Live weather would be looking at a weather presenter slowly getting wet in the drizzle. In addition, European public media have an educational mandate, they are paid by the public to make society better, while in America media is commercial and will do whatever makes money.

In the harbor of Copenhagen is the famous little mermaid. Tourists ships went to see it, had to keep quite a distance and could only show her back. Typically the boats only waited a few seconds because there was nothing to see. But due to commercial pressure they had to have the little mermaid on their tour schedule. They follow demand, whether the outcome is good or not.

Short hits communication

• When asked what 30% probability of rain means for a weather prediction most people gave the wrong answer: that 30% of the region would experience rain. The formally correct answer is that 30% of the cases this prediction is made you will experience rain. To be fair to the people, I often explain the need to give such a percentage by saying that in case of showers we cannot tell whether it rains in Bonn or Cologne. I feel this is quite common explanation and the main effect. The German weather service is working on providing more detailed probabilistic information to weather brigades. That seems to be appreciated (and they answered the question mostly right).
• Amanda Ruggeri won the journalism award for her story on sea level rise in Miami, which was reviewed by ClimateFeedback who found its scientific credibility to be "very high". Recommended read.
• EUMETSAT operates the European satellites once in space. They also make MOOCs ([[Massive Open Online  Courses]]). They have one on the oceans and one on the atmosphere. They are a great way to introduce these topics to new people and in future they plan to do more live. 
• Climate change is seen as the Top Global Threat according to global polling by the Pew Institute. In 2018 67 percent of the world sees climate change as a major threat to their country.  
• During a Q&A someone remarked that it would be good to talk about the history of climatology more because people are spreading the rumor that climatology is a new field of science trying to make it sound less solid.
• In case I have any Finnish speaking readers, Finland has a two-yearly bulletin on weather and climate, recently revamped.
• Copernicus has a "new" journal on statistical climatology, ideally suited for homogenization studies: Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO). It does not have an Impact Factor yet, but seeing the editorial team and reading a few articles it is clearly a serious journal and likely will get one soon. It is worth building up such a journal to have an outlet for statistical/methodological studies on climate. We already published there once; post upcoming.
• Did you know about STATMOS, an American Research Network for Statistical Methods for Atmospheric and Oceanic Sciences?

Short hits observations

• I had seen people use measurements of cosmic rays to estimate the soil moisture between the surface and the probe, but it was new to me to use it to measure the amount of snow on top of a glacier.
• Michal Zak of the Czech Hydrometeorological Institute and colleagues had an interesting way to estimate how urban a station is. They computed the absolute day to day differences of the maximum and of the minimum temperature and subtracted them from each other. If the maximum temperature varies more a station is likely urban, if the minimum varies more it is likely rural. For Prague and its surrounding the differences between stations were not particularly large and smaller than its seasonal cycle, but it could be a useful check. This could also be a measure that could help one to selected climatologically similar pairs of stations in relative statistical homogenization.
• The Homogenization Seminar in Budapest will be from 18 to 21 of May 2020. Announcements will follow, e.g., on the homogenization list. (I should write less mails to the homogenization list; at EMS someone asked to be added to the homogenization newsletter.) 
• Carla Mateus studied Data Rescue (DARE) as a scientific problem. By creating one really high quality transcribed dataset as a benchmark, she studied how accurately various groups transcribed historical observations. Volunteers of the Irish meteorological society were an order of magnitude more accurate (0.3% errors) than students (3.3%). Great talk.
• Our colleagues from Catalonia studied the influence of the time of observation. Manual observations tend to be made at 8am, while automatic measurements often use a normal calendar day. This naturally mattered most for the minimum temperature. With statistical homogenization the small breaks are hard to find, to formulate it diplomatically.
• Monika Lakato has ambitious plans to study changes in hourly precipitation in Hungary motivated by increases in rain intensity (precipitation amount on rainy days).
• Peter Domonkos studied how well network-wide trends are corrected in the new MULTITEST benchmark dataset (the presentation as pptx file). He found that his method (ACMANTv4) was able to reduce this error by about 30% and others were worse. It would be interesting to study what is different in the MULTITEST dataset or this analysis because the results of Williams et al. (2012) are much more optimistic; here 50 to 90% of the trend error is removed for similarly dense networks.
• ACMANTv4 is on GitHub and about to be published. Some colleagues already used it. 

Meteorological Glossaries

Miloslav Müller gave a talk on the new Slovak meteorological glossary, listing many other glossaries. So I now have a bookmark folder full of glossaries.

• WMO Meteorological Glossary (1992), old book
• New digital glossary: WMO MeteoTerm
(you have to bookmark this page, the page it links to, requires a login if you come from elsewhere, from your bookmark)
• American Meteorological Society Glossary of Meteorology
• US National Weather Service Glossary
• Wikipedia has Glossaries of Meteorology and of Climate Change
• Electronic Slovak meteorological glossary (which seems to be hosted in the Check Republic), the glossary is in English, some of the homepage is not

To finish with a great audience comment on the last day, not directly weather related: "In Russian education everything is explained, you do not have to remember or study." I loved that expression. That is the reason I studied physics, I also loved biology, but you have to remember so much and my memory is very poor for random stuff like names of organisms. When you understand something, you (I?) automatically remember it, it does not even feel like learning.

Related reading

The IPCC underestimates global warming. This post explains why using linear regression underestimates total warming

Annual Meeting of the European Meteorological Society

The World Meteorological Organisation will build the greatest global climate change network

“Having left a legacy of a changing climate, this [reference climate network] is the very least successive generations can expect from us in order to enable them to more precisely determine how the climate has changed.”

Thorne et al. (2018)

 

Never trust a headline. The WMO cannot build the network. But the highest body of the World Meteorological Organisation (WMO) has approved our plans for a Global Climate Reference Station Network. Its Congress with the leaders of all member organisations meets every two years in neutral Geneva, Switzerland, and has approved the report on a Global Surface Reference Network of the Global Climate Observing System (GCOS) Task Team on a reference network. The WMO is the oldest international organisation and coordinates the works of its members, mostly national weather services. So the WMO will not build the network itself; we are now looking for volunteers.

(Disclosure: I am a member of the Task Team.^* Funny: in a team with big name climatologists I am somehow the "Climate scientist representative".)

Humanity is performing the greatest experiment in its history. We better measure it accurately. For humanity and for science.

Never trust a headline. What the heck does “greatest” mean? As someone trying to estimate how much the climate has changed, I would have been so happy if people had continued the really poor measurement methods they used in the 19th century. Mercury thermometers were placed in the North (pole) facing window of an unheated room. Being so close to the building is not good for ventilation, the sun could get on the sensor or heat the wall beneath. I would have lost that fight. Mercury thermometers are now forbidden. Weather prediction models would be better than this observation. The finance minister would have forced us to switch to automatic measurements. We may think that how we measure today is good enough, but people in 2100 will likely disagree.

At least following the biggest technological steps will be unavoidable. If that happens we will make long comparisons with the old and new set-up; estimating differences in the averages is not enough, also the variability is affected, which is hard to estimate. The reasons for measurement errors will change and thus its dependence on the weather.

Any data processing, if only averaging or applying a calibration factor, that is performed today, will be performed on hardware and software that is not available in 2100. Any instrument we would buy off the shelf will not be available in 2100; the upper air reference network is being forced to change their instruments because Vaisala will soon no longer sell them. So best means that we have open hardware and open software so that we can keep on building the instrument, can redo the data processing from scratch and can recreate the exact same processing on newer computers or whatever we use after the Butlerian Jihad.


A station of the US Climate Reference Network.

Never trust a headline. What does measuring climate mean? I work on improving trend estimates based on historical measurements made in many different ways by comparing neighbouring stations with each other (statistical homogenisation). This makes me acutely aware that there is only so much you can do with statistical homogenisation, a considerable error remains. It works relatively well for annual average temperatures because the correlations between stations are high. Much harder are estimates of the changes of the variability around the means, which are important for changes in extreme weather. Especially estimates of changes in precipitation, humidity, insolation, cloud cover, snow depth, etc. have wide confidence intervals because statistical homogenisation is very hard. For these other observations having reference data that does not need to be statistically homogenised is crucial. These other variables are very important for climate change impacts and understanding how the climate is changing. Reference networks can not only help in quantifying these confidence intervals, but as an independent line of evidence also provide confidence the confidence intervals are right.

The preliminary proposal for variables to observe in reference quality is:

• Air temperature
• Precipitation
• Pressure
• Wind speed and direction (10 m)
• Relative humidity
• Surface radiation (down and up)
• Land Surface Temperature
• Soil moisture
• Soil temperature
• Snow/ice (Snow Water Equivalent)
• Albedo

If you disagree or have additional ideas please contact us.


Tiered system of systems approach.

Never trust a headline. By itself this network will not be the best to study climate change. We also need the other stations. The reference network will be the stable backbone of the entire climate observation system. The part which is best at estimating the long term trends, while we need the other stations to reduce sampling errors and study spatial patterns.

Maintaining a reference station will be clearly more expensive than a standard climate station. Thus the number of stations will be limited. For the long term warming we expect to need about 200 stations well spread over the world. This takes into account that even if we select locations where we expect nothing will happen in the next century, we will still loose some stations due to conflict or "progress".

At a reference station (or nearby) preferably also measurements with the locally standard set-up are made, so that they can be compared with each other and provide information on any measurement problems. This will improve the quality of the entire network. A network with 200 reference stations would on average have about 1 station per country. For the comparison with the national networks having at least one station per country would also be desirable, but large countries will need multiple stations and it is also more efficient when countries with a reference station have multiple stations because a large part of the costs are overheads (well-trained operators and well-instrumented laboratories).


A society grows great when old men plant trees whose shade they know they will never sit in - Greek proverb (I did not check the provenance, experience tells me, the source of such quotes is always wrong, but do leave a comment).

Never trust a headline. The reference network is not only interesting for studying climate change. If it were we would need to wait many decades before it becomes useful. In this age that would likely mean that it would not be funded. Due to the metrological [sic] standards for computing confidence intervals and the traceability back to SI standards, the measurements will be comparable all over the world within specific confidence intervals for the absolute values, not just the (e.g., temperature) anomalies mostly used to study climate change. Together with the representativeness of the stations for the region this makes the network useful for the validation of absolute estimates from satellites or atmospheric models.

Also the comparison of the reference measurements with the national networks will produce valuable information within the first decade. For example, the American Climate Reference Network shows that the warming estimates of the national network are reliable and if anything underestimates the warming in America; the reference network has the larger trend.


The US Climate Reference Network (USCRN; red line) is below the normal national station network (ClimDiv; green line) in beginning and above it at the end. The trend of the reference network is thus larger. (The values themselves are quite noisy because America is just a small part of the Earth and trends over such short periods do not contain information on long-term warming.)

Never trust a headline. We are land animals and it is thus come natural to us to see climate stations as prototypical for climate observations, but the climate system is much richer. There is already a network for reference upper air measurements (GRUAN) made with weather balloons (radiosondes). The high metrological quality of the ARGO network probably also makes them a reference network. They measure ocean temperature profiles to estimate the ocean heat content.

Both the upper air and the oceans are wonderfully uniform media to measure; characterising the influence of the surroundings and preventing changes therein will be the main additional challenge of a land station network.

Studying climatic changes in urban regions is also important. Here it would be even more important to accurately describe the surrounding because changes will happen. Thus urban regions would need their own reference network.

We hope that our reference network will stimulate the founding of further reference networks. The cryosphere (the part of the Earth which is frozen) needs specialised observations. Hydrological and marine surface observations in reference quality would be very valuable; we should never forget that 70% of the Earth is water. Observations of tiny airborne particles (aerosols) and clouds could be made in reference quality.

In other news. The WMO Congress has also decided to make & share more real-time observations for weather predictions. The norms for quality & quantity will become more strict & are monitored.

20-25% of WMO members is already compliant.

25-30% would be compliant if they would share their data internationally. Many of these countries are big, so they represent a larger part of the world.

The rest will need international support to build the capacity to extend their measurement program and share the data.

Hopefully, the Green Climate Fund can help. The 24/7 monitoring by the WMO will give feedback to the funders on the value of their investment.

Climatology has the advantage that national weather services perform observations operationally. This institutional support has produced the long series we can use to study climate change. We currently see huge changes in the biosphere. Insects seem to be vanishing, but this is really hard to study without long-term observations. The ecological long-term observational programs need institutional support.

Where possible these reference networks should aim to use the same locations, so that the observations can support each other, as well as to reduce costs. It may be easier to obtain funding for reference networks in a large coalition than for every network separately. So I hope that these other communities will develop similar plans. If you know of anyone in these communities, please point them to this post or our report.

We estimate that this reference land station network will cost a few million dollars per year. Thus running this network for a decade would still cost much less than a single satellite missions, which measures far fewer climate variances and has much less accuracy and less confidence in its accuracy. If you know someone at Lockheed Martin or Airbus who may be interested in building a space-grade reference network and has the right lobbyists, please tell them of this initiative.

Coming back the first paragraph: we need volunteers. We need weather services interested in setting up reference stations and we need ones interested in becoming a Lead Centre. A Lead Centre would coordinate the network, organise joint calibrations and comparison campaigns, lead the drawing up of measurement requirements, etc. To spread the work load it could be an idea to one Lead Centre to one instrument or observation type. Please talk about this with your colleagues and spread this post.

UPDATE November 2020. The World Meteorological Organization Commission for Observation, infrastructure and information system (INFCOM) has approved the plan. The climate reference network implementation plan is now part of the WMO Infrastructure Commission workplan, which includes in its outputs and deliverables the establishment of a GSRN, identifying candidate stations and the call for the Lead Centre. Based on this and on the recommendation from the report of the GSRN task team, published in February 2019 (GCOS-226),  a new task team has been established to develop (i) a draft implementation plan for the GSRN, (ii) a proposal for management and governance structures of the GSRN, and (iii) a process for nominating and approving stations contributing to the GSRN.

^* The opinions in the post are mine, the report represents the opinion of the Task Team.

Further reading

Thorne P.W., H.J. Diamond, B. Goodison, S. Harrigan, Z. Hausfather, N.B. Ingleby, P.D. Jones, J.H. Lawrimore, D.H. Lister, A. Merlone, T. Oakley, M. Palecki, T.C. Peterson, M. de Podesta, C. Tassone, V. Venema and K.M. Willett, 2018: Towards a global land surface climate fiducial reference measurements network. Int J Climatol., 38, pp. 2760–2774. https://doi.org/10.1002/joc.5458

The report of the GCOS Task Team: GCOS Surface Reference Network (GSRN): Justification, requirements, siting and instrumentation options

GCOS, 2017: Report of the 1st Meeting of the GCOS Surface Reference Network (GSRN) Task Team
Maynooth, Ireland, 1-3 November 2017.

My first post trying to get the discussion going in October 2016: A stable global climate reference network

January 2018 GCOS Newsletter on designing a GCOS Surface Reference Network

A historic climate election in Germany

It is really late, but I have to report on a historical European election night in Germany. The government parties lost bigly, while the Greens won enormously. It is not the only reason, but a main reason for these changes was a lack of government action on climate change. The leaders of the main parties agreed with his assessment.

The difference between recent polling and the results suggest that also the YouTube video "The Destruction of the CDU" mattered. The CDU is the governing conservative party led by Angel Merkel. This video was watched more than 10 million times, which is more than 1 in 10 Germans.

Before we begin some background most non-Germans will need. Germany is currently governed by a coalition of Christian Democrats (not purposefully nasty Conservatives) and Social Democrats (Labour). These used to be huge parties, people's parties, which officially cater to all demographics. Before the 2017 general election they were also in power and already got a beating. Even together they now only have a modest majority. However, no other coalition could be found, the classical liberals broke up an earlier coalition attempt, and they were forced to govern together again.

So they were already vulnerable. Then they put the brake on the energy transition by strongly reducing its funding and got ahead slowly with building the new stronger power grid, they agreed on a very late closing date for the lignite coal power plants and it became clear that they will miss the CO2 emissions reduction goals they set themselves for 2020.

Sunday for Future

The climate strikes initiated by Greta Thunberg have become an enormous movement in Germany, still attracting many students after many months of strikes. This Friday, just before the European elections, there was an international strike day. An estimated 222 places in Germany held protests; this time there were not just students. And a week ago the video "The Destruction of the CDU" dropped, followed by a petition of a large part of the German YouTube scene not to vote for the governing parties, nor the far right.


Friday for Future rally in Stockholm with Greta Thunberg on stage.

As a consequence climate change was a big topic in the campaigns. All main parties promised they would work on it. Polls show that 81% of Germans demand better action on climate policy and environmental protection.

In the exit polls they always ask what the main themes are that decided one's vote. For the first time the most mentioned theme was: "Climate & environmental protection". Fortyeight percent gave this answer (you can give multiple answers).

The table below gives the results of the European election in Germany, together with the previous European election five years ago, the general election two years ago and recent polling.

Compared to the previous European election the Christian Democrats lost 6.5% and the Social Democrats lost 11.7% of the votes. The Greens on the other hand gained 10% and are now the second largest party with 20.7% of the votes. The Greens were already polling at this level since October last year.

I have chosen not to show the joy of the Greens when the results came in. In case any CEOs or climate science deniers are reading this post, I do not want to cause any heart problems.

However, in the European polling data the Greens were lower just before the election. Probably because in European elections there are many small parties to compete with. I would say that the Greens won 2 to 3% more than expected and the Christian Democrats and Social Democrats lost a percent more than expected. That is some evidence that the YouTube video made a difference.

In my last post I wrote that I would not be surprised if polling were off more than usual because of the large turnout, the large changes and the events of the last week. But they were within the normal uncertainties. Chapeaux!!

Name	Ideology	EU 2019	EU 2014	General 2017	Polling
CDU/CSU	Conservative	28.9%	35.3%	32.9%	28%
SPD	Social Democrat	15.8%	27.3%	20.5%	17%
Grünen	Green	20.5%	10.7%	08.9%	18%
Linke	Democratic Socialist	05.5%	07.4%	09.2%	07%
FDP	(Classical) Liberal	05.4%	03.4%	10.7%	06%
AfD	Far Right Mix	11.0%	07.1%	12.6%	12%

Sources: Preliminary official results. Previous results European election and general election from Wahlrecht.de. Polling is the average of the two most recent polls of the most reliable polling agencies in Germany: Forschungsgruppe Wahlen and Infratest Dimap.

Some non-climate notes. The Christian Democrats did relatively well because their front man, Manfred Weber, is running to be the next head of the EU Commission. (The Dutch Social Democrat running for the same post, Frans Timmermans, also did well very well in The Netherlands.)

It is good to see that the AfD, a party which rejects European moral values went down compared to two years ago. It did gain compared to the last European election by 4 percent. This increase is worse than it sounds because five years ago the party was still mostly an anti-Euro party and not yet so radicalised.

The turnout was very high for European elections: 61.4%. This 13% higher than in 2014, when it was only 48.1%. In all of Europe the turnout was relatively high with 50.5%.

Age

There are clear differences between young and old voters. The greens won in all age categories, but were especially strong with younger voters.



While young people watch YouTube more, this is likely not just the video. The younger you are the more climate change will impact your life. Young people also more often vote for the first time and change allegiances faster; so partially it may be a question of time. Furthermore, younger people were already politicised because the Conservatives were hurting the internet, #article13. And the denigrating way they were treated then and now on climate change was a good motivation to show up and vote. The biggest campaign helpers of the Greens were the Conservatives, especially my local MEP Alex Voss representing Bonn and leading the effort for internet upload filters.

Especially spectacular are the results if only people below 30 could vote. The Greens would be way ahead of all other parties with 33 percent of the vote, the Conservatives are a distance second with 13%.


Who did people under 30 vote for?

Political responses

Andrea Nahles (chair of the Social Democrats):

"Climate protection has been a voting issue for many voters. The difference between us and the Greens is not the question whether we want to achieve the Paris climate goals without ifs and buts, but how. And we will also discuss this issue actively in the next few weeks and we will act. With the socially compatible brown coal exit, we have managed what [an in 2017 explored coalition of Conservatives, Greens and Liberals] did not do. Now we take the next step, this year we want to bring a climate protection law for our whole national economy on the way."

German original: "Klimaschutz ist für viele Wählerinnen und Wähler ein Wahlentscheidendes Thema gewesen. Zwischen uns und den Grünen steht nicht die Frage im Raum ob wir die Pariser Klimazielen ohne wenn und aber erreichen wollen sondern wie. Und diese Frage werden wir auch offensiv discutieren in den nächsten Wochen und wir werden handeln. Mit den socialverträglichen Braunköhleausstieg haben wir es geschafft, was Jamaika nicht geschafft hat. jetzt drehen wir das nächste Rad, wir wollen noch diesem Jahr ein Klimaschutzgesetz für unse ganz Volkwirtschaft auf dem Weg bringen."

Katarina Barley (campaign leader of the Social Democrats):

"The topic of climate protection has played a huge role in the last few days and actually the whole election campaign and obviously we are not well enough prepared yet."

German original: "Das Thema Klimaschutz hat den letzten Tagen und eigentlich schon den ganzen Wahlkampf eine riesige Rolle gespielt und da sind wir offensichtlich noch nicht gut genug aufgestellt."

Annegret Kramp-Karrenbauer aka AKK (chair of Conservatives):

"We certainly have the result that in the government we were not very credible how we will protect the climate. And as a party we did not develop our platform enough. We have the ambition to say, we are firmly convinced that one can protect the climate and achieve a good economy and social balance. That we can present concepts that are convincing. This is exactly the work, one could say, that we have as CDU. And that's why we will certainly already starting with the internal party meeting next week, will very intensively care especially about this topic in the coming weeks and months."

German original: "Wir haben sicherlich den Befund, dass wir weder in der Regierung sehr glaubwurdig vertreten wie wir die Klimaschutz erreichen. Und als Partei sind wir programmatisch von den Antworten noch nicht so weit, dass wir das was wir selbst als anspruch haben, namelich zu sagen, wir sind die fest Überzeugung, dass man Klimaschutzen kann und eine gute Wirtschaft und sociale Ausgewogenheit erreichen kann. Das wir dazu die Konzept vorleggen die überzeugen. Das ist genau die Baustelle die wir, wenn Sie so wollen, die wir als CDU haben. Und deswegen werden wir uns sicherlich auch schon beginnend mit der Klausur nächste Woche, sehr intensive vor alem Dingen um diesem Thema in den kommenden Wochen und Monate kummern."

Manfred Weber (leader of the German and EU Christian Democratic campaigns) did not say anything about climate in his official response.

Markus Söder, the leader of the Bavarian Christian Democrats (CSU):

"The big challenge of the future is the intense confrontation with the Greens ... Old standards, as we had them so far, no longer apply. ... As Christian Democrats, we have to work together to become younger, cooler, more open. We have to handle topics and communication such that we do not look like a party from yesterday."

German original: "Die große Herausforderung der Zukunft ist die intensive Auseinandersetzung mit den Grünen ... Alte Maßstäbe, wie wir sie bislang hatten, gelten nicht mehr. ... Wir müssen als Union insgesamt daran arbeiten, wieder jünger, cooler, offener zu werden. Wir müssen mit den Themen und der Kommunikation so agieren, dass wir nicht von gestern wirken."

Annalena Baerbock (Chair of the German Green party):

"This election was a climate change election. This election was an election for democracy. For human rights, for a cosmopolitan Europe. That's why the votes make us happy, they are not just Green votes. These are votes for climate protection. These are votes for democracy. These are votes against right-wing populists. These are votes for human rights throughout Europe. We have not achieved that alone. We achieved that because many people took to the streets for climate protection. Because many young people, in schools, in universities, in sports halls were ready to fight for climate protection."

German original: "Diese Wahl, diese Wahl war ein Klimaschutzwahl. Diese Wahl war eine Wahl für Demokratie. Für Menschenrechten, für ein Weltoffenes Europa. Deswegen sind die Stimmen die uns glücklich machen, nicht nur Grünen Stimmen. Das sind Stimmen für den Klimaschutz. Das sind Stimmen für die Demokratie. Das sind Stimmen gegen Rechtspopulisten. Das sind Stimmen für die Menschenrechten in ganz Europa. Das haben wir nicht nur alleine erreicht. Das haben wir erreicht weil viele viele Menschen für den Klimaschutz auf die Straße gegangen sind. Weil viele junge Leute in den Schulen, in den Unis, in Turnhallen bereit waren für Klimaschutz zu kämpfen."

Jörg Hubert Meuthen (Campaign leader of the far-right AfD):

"Certainly, the topic of climate policy and the hysteria around this topic was something that did not help us. The topic was hyped up. This appealed to people in large numbers."

German original: "Sicherlich war das Thema Klimapolitik und die hier verbreitete Hysterie um dieses Thema etwas was uns nicht in die Karten gespielt hat. Das Thema wurde nach oben gehypt. Die Menschen wurden damit in großer Zahl erreicht."

The press spokesman of the AfD even denies the greenhouse effect itself. Although it is possible that he is too stupid to understand the difference between the natural greenhouse effect and human activities making it stronger leading to global warming. Naturally such extremist irrational positions are problematic trying to gain votes from a well-informed critical electorate that knows the misery right-wing extremism has produced before very well.

Related reading

The leader of the Bavarian Christian Democrats (CSU) declares the Greens to be the main competitor: Söder erklärt Grüne zur Hauptkonkurrenz der Union.

Preliminary official results

Ireland is the place to be for climate reporting in English: Green wave hits Germany with doubling of support. Shock result for Germany’s ruling parties, with worst-ever election for Merkel’s CDU.

AP: Europe wakes up to climate concerns after green wave in vote.