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Wednesday, 30 November 2016

Statistically significant trends - Short-term temperature trend are more uncertain than you probably think

Yellowknife, Canada, where the annual mean temperature is zero degrees Celsius.

In times of publish or perish, it can be tempting to put "hiatus" in your title and publish an average article on climate variability in one of the prestigious Nature journals. But my impression is that this does not explain all of the enthusiasm for short-term trends. Humans are greedy pattern detectors: it is better to see a tiger, a conspiracy or trend change one time too much than one time too little. Thus maybe humans have a tendency to see significant trends where statistics keeps a cooler head.

Whatever the case, I expect that also many scientists will be surprised to see how large the difference in uncertainty is between long-term and short-term trends. However, I will start with the basics, hoping that everyone can understand the argument.

Statistically significant

That something is statistically significant means that it is unlikely to happen due to chance alone. When we call a trend statistically significant, it means that it is unlikely that there was no trend, but that the trend you see is due to chance. Thus to study whether a trend is statistically significant, we need to study how large a trend can be when we draw random numbers.

For each of the four plots below, I drew ten random numbers and then computed the trend. This could be 10 years of the yearly average temperature in [[Yellowknife]]*. Random numbers do not have a trend, but as you can see, a realisation of 10 random numbers appears to have one. These trends may be non-zero, but they are not significant.

If you draw 10 numbers and compute their trends many times, you can see the range of trends that are possible in the left panel below. On average these trends are zero, but a single realisation can easily have a trend of 0.2. Even higher values are possible with a very small probability. The statistical uncertainty is typically expressed as a confidence interval that contains 95% of all points. Thus even when there is no trend, there is a 5% chance that the data has a trend that is wrongly seen as significant.**

If you draw 20 numbers, 20 years of data, the right panel shows that those trends are already quite a lot more accurate, there is much less scatter.

To have a look at the trend errors for a range of different lengths of the series, the above procedure was repeated for lengths between 5 and 140 random numbers (or years) in steps of 5 years. The confidence interval of the trend for each of these lengths is plotted below. For short periods the uncertainty in the trend is enormous. It shoots up.

In fact, the confidence range for short periods shoots up so fast that it is hard to read the plot. Thus let's show the same data with different (double-logarithmic) axis in the graph below. Then the relationship look like a line. That shows that size of the confidence interval is a power law function of the number of years.

The exponent is -1.5. As an example that means that the confidence interval of a ten year trend is 32 (10^1.5) times as large as the one of a hundred year trend.

Some people looking at the global mean temperature increase plotted below claim to see a hiatus between the years 1998 and 2013. A few years ago I could imagine people thinking: that looks funny, let's make a statistical test whether there is a change in the trend. But when the answer then clearly is "No, no way", and the evidence shows it is "mostly just short-term fluctuations from El Nino", I find it hard to understand why people believe in this idea so strongly that they defend it against this evidence.

Especially now it is so clear, without any need for statistics, that there never was anything like an "hiatus". But still some people claim there was one, but it stopped. I have no words. Really, I am not faking this dear colleagues. I am at a loss.

Maybe people look at the graph below and think, well that "hiatus" is ten percent of the data and intuit that the uncertainty of the trend is only 10 times as large, not realising that it is 32 times.

Maybe people use their intuition from computing averages; the uncertainty of a ten year average is only 3 times as large that of a 100 year average. That is a completely different game.

The plots below for the uncertainty in the average are made in the same way as the above plots for the trend uncertainty. Also here more data is better, but the function is much less steep. Plots of power laws always look very similar, you need to compare the axis or the computed exponent, which in this case is only -0.5.

It is typical to use 30 year periods to study the climate. These so-called climate normals were introduced around 1900 in a time the climate was more or less stable and the climate needed to be described for agriculture, geography and the like. Sometimes it is argued that to compute climate trends you need at least 30 years of data, that is not a bad rule of thumb and would avoid a lot of nonsense, but the 30 year periods were not intended as a period on which to compute trends. Given how bad the intuition of people apparently is there seems to be no alternative to formally computing the confidence interval.

That short-term trends have such a large uncertainty also provides some insight into the importance of homogenisation. The typical time between two inhomogeneities is 15 to 20 years for temperature. The trend over the homogeneous subperiods between two inhomogeneities is thus very uncertain and not that important for the long-term trend. What counts is the trend of the averages of the homogeneous subperiods.

That insight makes you want to be sure you do a good job when homogenising your data rather than mindlessly assume everything will be alright and raw data good enough. Neville Nicholls wrote about how he started working on homogenisation:

When this work began 25 years or more ago, not even our scientist colleagues were very interested. At the first seminar I presented about our attempts to identify the biases in Australian weather data, one colleague told me I was wasting my time. He reckoned that the raw weather data were sufficiently accurate for any possible use people might make of them.

Sad.

[UPDATE: In part 2 of this series, I show how these large trend uncertainties in combination with the deceptive strategy of "cherry-picking" a specific period very easily produces a so-called "hiatus".]

Notes

* In Yellowknife the annual mean temperature is about zero degrees Celsius. Locally the standard deviation of annual temperatures is about 1°C. Thus I could conveniently use the normal distribution with zero mean and standard deviation one. The global mean temperature has a much smaller standard deviation of its fluctuations around the long-term trend.
** Rather than calling something statistically significant and thus only communicating whether the probability was below 5% or not, it fortunately becomes more common to simply give the probability (p-value). In the past this was hard to compute and people compared their computation to the 5% levels given in statistical tables in books. With modern numerical software it is easy to compute the p-value itself.
*** Here is the cleaned R code to generated the plots of this post.

The photo of YellowKnife at the top is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.

Sunday, 21 August 2016

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

[[Francis Bacon]]

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.

[UPDATE. I finally found the Darwin quote I had wanted to use below. It is:

About thirty years ago there was much talk that geologists ought only to observe and not theorise; and I well remember some one saying that at this rate a man might as well go into a gravel-pit and count the pebbles and describe the colours. How odd it is that anyone should not see that all observation must be for or against some view if it is to be of any service! ]

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.

They 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 computed by Andrew Poppick and colleagues[, now published in Advances in Statistical Climatology, Meteorology and Oceanography] ~~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 (small airborne particles from combustion) — 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.

References

Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein, 2016: Estimating trends in the global mean temperature record. Unpublished manuscript. Now published in Advances in Statistical Climatology, Meteorology and Oceanography

* 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.

References

Poli, R., W.B. Langdon and N. F. McPhee, 2016: A field guide to genetic programming. Published via Lulu.com (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.

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.

Notes

* 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.

References

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.

Sunday, 1 May 2016

Christy and McNider: Time Series Construction of Summer Surface Temperatures for Alabama

John Christy and Richard McNider have a new paper in the AMS Journal of Applied Meteorology and Climatology called "Time Series Construction of Summer Surface Temperatures for Alabama, 1883–2014, and Comparisons with Tropospheric Temperature and Climate Model Simulations". Link: Christy and McNider (2016).

This post gives just few quick notes on the methodological aspects of the paper.
1. They select data with a weak climatic temperature trend.
2. They select data with a large cooling bias due to improvements in radiation protection of thermometers.
3. They developed a new homogenization method using an outdated design and did not test it.

Weak climatic trend

Christy and McNider wrote: "This is important because the tropospheric layer represents a region where responses to forcing (i.e., enhanced greenhouse concentrations) should be most easily detected relative to the natural background."

The trend in the troposphere should a few percent stronger than at the surface; mainly in the tropics. However, it is mainly interesting that they see a strong trend as a reason to prefer tropospheric temperatures, because when it comes to the surface they select the period and temperature with the smallest temperature trend: the daily maximum temperatures in summer.

The trend in winter due to global warming should be 1.5 times the trend in summer and the trend in the night time minimum temperatures is stronger than the trend in the day time maximum temperatures, as discussed here. Thus Christy and McNider select the data with the smallest trend for the surface. Using their reasoning for the tropospheric temperatures they should prefer night time winter temperatures.

(And their claim on the tropospheric temperatures is not right because whether a trend can be detected does not only depend on the signal, but also on the noise. The weather noise due to El Nino is much stronger in the troposphere and the instrumental uncertainties are also much larger. Thus the signal to noise ratio is smaller for the tropospheric temperatures, even if the signal were as long as the surface observations.

Furthermore, I am somewhat amused that there are still people interested in the question whether global warming can be detected.)

[UPDATE. Tamino shows that within the USA, Alabama happens to be the region with the least warming. The more so for the maximum temperature. The more so for the summer temperature.]

Cooling bias

Then they used data with a very large cooling bias due to improvements in the protection of the thermometer for (solar and infra-red) radiation. Early thermometers were not protected as well against solar radiation and typically record too high temperatures. Early thermometers also recorded too cool minimum temperatures; the thermometer should not see the cold sky, otherwise it radiates out to it and cools. The warming bias in the maximum temperature is larger than the cooling bias in the minimum temperature, thus the mean temperature still has some bias, but less than the maximum temperature.

Due to this reduction in the radiation error summer temperatures have a stronger cooling bias than winter temperatures.

The warming effect of early measurements on the annual means is probably about 0.2 to 0.3°C. In the maximum temperature is will be a lot higher and in the summer temperature it will again be a lot higher.

That is why most climatologists use the annual means. Homogenization can improve climate data, but it cannot remove all biases. Thus it is good to start with data that has least bias. Much better than starting with a highly biased dataset like Christy and McNider did.

Statistical homogenization removes biases by comparing a candidate station to its neighbour. The stations need to be close enough together so that the regional climate can be assumed to be similar in both stations. The difference between two stations is then weather noise and inhomogeneities (non-climatic changes due to changes in the way temperature was measured).

If you want to be able to see the inhomogeneities you thus need to have well correlated neighbors that have as little weather noise as possible. By using only the maximum temperature, rather than the mean temperature, you increase the weather noise. But using the monthly means in summer, rather than the annual means or at the very least the summer means, you increase the weather noise. By going back in time more than a century you increase the noise because we had less stations to compare with at the time.

They keyed part of the the data themselves mainly for the period before 1900 from the paper records. It sounds as if they performed no quality control of these values (to detect measurement errors). This will also increase the noise.

With such a low signal to noise ratio (inhomogeneities that are small relative to the weather noise in the difference time series), the estimated date of the breaks they still found will have a large uncertainty. It is thus a pity that they purposefully did not use information from station histories (metadata) to get the date of the breaks right.

Homogenization method

They developed their own homogenization method and only tested it on a noise signal with one break in the middle. Real series have multiple breaks; in the USA typically every 15 years. Furthermore also the reference series has breaks.

The method uses the detection equation from the Standard Normal Homogeneity Test (SNHT), but then starts using different significance levels. Furthermore for some reason it does not use the hierarchical splitting of SNHT to deal with multiple breaks, but it detects on a window, in which it is assumed there is only one break. However, if you select the window too long it will contain more than one break and if you select the window too short the method will have no detection power. You would thus theoretically expect the use of a window for detection to perform very badly and this is also what we found in a numerical validation study.

I see no real excuse not to use better homogenization methods (ACMANT, PRODIGE, HOMER, MASH, Craddock). These are build to take into account that also the reference station has breaks and that a series will have multiple breaks; no need for ad-hoc windows.

If you design your own homogenization method, it is good scientific practice to test it first, to study whether it does what you hope it does. There is, for example, the validation dataset of the COST Action HOME. Using that immediately allows you to compare your skill to the other methods. Given the outdated design principles, I am not hopeful the Christy and McNider homogenization method would score above average.

Conclusions

These are my first impressions on the homogenization method used. Unfortunately I do not have the time at the moment to comment on the non-methodological parts of the paper.

If there are no knowledgeable reviewers available in the USA, it would be nice if the AMS would ask European researchers, rather than some old professor who in the 1960s once removed an inhomogeneity from his dataset. Homogenization is a specialization, it is not trivial to make data better and it really would not hurt if the AMS would ask for expertise from Europe when American experts are busy.

Hitler is gone. The EGU general assembly has a session on homogenization, the AGU does not. The EMS has a session on homogenization, the AMS does not. EUMETNET organizes data management workshops, a large part of which is about homogenization; I do not know of an American equivalent. And we naturally have the Budapest seminars on homogenization and quality control. Not Budapest, Georgia, nor Budapest, Missouri, but Budapest, Hungary, Europe.

Friday, 17 July 2015

Lakes are warming at a surprisingly fast rate

Map with lake temperature trends. As so often the trend is strongest in the mid-latitudes of the Northern Hemisphere. Two seasons are used to minimize cloud blockage: JAS (July, August & September) and JFM (January, February & March) for the dry season.

Many changes in the climate system go faster than expected, which fits to my hunch that the station temperature trend may have a cooling bias. The coming time I would like to blog about a few example changes. This first post is about lakes and rivers, their temperature changes and changes in the date they freeze and the date the ice breaks up. In this case it is hard to say whether this goes faster than expected, because there is not much research on this, but the temperature changes sure are surprisingly fast.

We will see more research on this in future. There is now a Global Lake Temperature Collaboration (GLTC), which is collecting and analyzing lake temperatures. It is easy to complain about the weather services and how they keep on making changes to the meteorological networks and fail to share many important observational datasets (and I will keep complaining), but at least they have systematically made such historical observations. For other observations, such a lake temperatures and especially ecological datasets it is much harder to obtain long and stable observations lacking institutional support. Now going into the century of climate change this institutional failure becomes even more problematic. I feel it should be part of the international climate change treaties to set up organizations that can provide long term, well-documented and climate quality stable measurements of a large range of environmental systems.

One of the reasons to found the Global Lake Temperature Collaboration was a scientific article by Schneider and Hook in 2010. It analyzed the temperature trends of lakes using thermal infra-red satellite images ([[AVHRR]] and [[ATSR]]). Between 1985 and 2009 the satellite lake temperatures increased by 1.13°C, which they report is more than the regional air temperature increases. For comparison this amounts to 4.5°C per century; see graph below. This is stronger than the land temperatures, although one would expect less warming of the lakes. For the same period the Northern Hemisphere temperature of Berkeley Earth increased by 3.9°C per century.

Trend in lake temperature anomalies. This is an average over all 113 water bodies that were large enough and had at least 15 years of data. The trend over the period 1985 to 2009 is 4.5±1.1°C per century.

Satellite data is great for their global overview (see graph at the top of this post), but are tricky when it comes to trends. Trend estimates from satellite data are difficult due to degradation of the instruments in a harsh space environment, changes in the orbit and height of the satellite, while at the same time the limited life span of satellites means that the instruments are regularly replaced and technological improvement often lead to new designs. All this happens while the small number of satellites means that there is minimal redundancy to study such data problems. Schneider and Hook (2010) did their best to study such data problems, used data from seasons with few clouds, so that the satellite can see the lakes more often. They compared their data with surface observations and found only small biases between the satellites and no indications of trend biases. And they used night-time observations to reduce the influence of orbital drift.

Still an astonishing outlier trend from satellite data calls for ground validation (in situ measurements). The GLTC now provides a dataset with both satellite and in-situ lake temperature measurements. The paper describing the dataset is out now (Sharma et al., 2015). The paper analyzing the trends has still to be published, but Philipp Schneider of the GLTC wrote to me that the in-situ trend is similar. Further papers explaining the trends are in preparation.

At the moment it is not clear yet what is the reason for the stronger increase. Many lakes are close to the Arctic, thus it could be ice albedo feedback for Northern lakes. The summer surface temperature of Lake Superior over the interval 1979 - 2006 has, for example, increased by 11±6°C per century, faster than regional atmospheric warming. This is thought to be due a reduction in the albedo of the lakes due to a reduction in ice cover (Austin and Colman, 2007). The air in the Arctic is also warming faster than elsewhere. Changes in the observations are naturally also possible — as always — and the land surface temperature trend might also be underestimated.

Stronger insolation may especially heat lakes, which typically reflect less solar radiation than the land surface. This may especially be important for the recent decades in the industrialised countries, where air pollution has been reduced considerably.

Surface temperatures may change due to less mixing with deep cold water: as the top temperatures warm more the warm surface water mixes less well with the deep cold and dense water and reductions in the wind speed can reduce mixing (Butcher et al., 2015). Also changes in the transparency of the water can influence where the solar warming ends up (Butcher et al., 2015).

In other words, no observation is ever completely straight forward and the air temperature is just one factor influencing lake water temperatures. Just like with station measurements, you always need to study which part of the changes in the raw observations is the part you are interested in.

On the other hand, because more of the additional greenhouse warming goes into evaporation rather than to warming, the warming of the air over land is expected to be stronger than the lake water temperatures. Butcher and colleagues (2015) estimate that the surface water temperature increases are only about 77% of increase in average air temperature change. Previously Schmidt and colleagues (2014) estimated this to be between 70 to 85%.

Freezing and melting of lakes and rivers

The lake temperature observations are unfortunately not very long. To put their warming into perspective there are also observations of the freezing and breakup dates of lakes and rivers. These sometimes go back many centuries. Magnuson and colleagues (2000) have gathered 39 observational dataset on lakes and rivers with more than 150 years of data. They found that all but one of them showed later freezing dates and earlier breakup dates. The freeze dates are 5.8 days per century later and the breakup dates are 6.5 days per century earlier. This is comparable to a warming of the regional air temperature of about 1.2°C (2°F) per century, but with a large confidence interval. For comparison, the Berkeley Earth dataset shows a warming of the NH land temperatures for the same period between 1846 and 1995 of 0.67°C per century. For this comparison it should be reminded that these rivers and lakes are in high latitude regions that warm more.

Time series of freeze and breakup dates from selected Northern Hemisphere lakes and rivers (1846 to 1995). Data were smoothed with a 10-year moving average. Figure 1 from Magnuson (2002).

For one dataset they mention a small non-climatic influence (a power plant) and one dataset (a harbor) is excluded because of its much stronger trend. Magnuson and colleagues seem confident that warm waste water is not the reason for the trends in the other series, but do not explicitly write about that in their 4-page Science article.

Melting lakes showing a clear contrast between ice and water. This makes is relatively easy to use historical aircraft and satellite observations. Figure 5 from Duguay et al. (2003).

The advantage of such datasets is that they provide yearly information, that the observations are often long and that they are relatively precise. For the recent decades, the observations can be made for a large number of lakes from space. These freezing and breakup dates are also nicely determined by the temperature averaged over a longer period, which removes a lot of variability: Freezing is determined by the temperature in the last two months before the event.

What complicates matters is that snow isolates the ice and slows down freezing and thawing. Thus the ice breakup of lakes is also influenced by snow on top of the ice. Also the depth of the lake is important; deeper lakes thaw later (Duguay and colleagues, 2003). The breakup date of rivers is also determined by the timing and amount of spring river run-off.

Concluding. Lake temperature are rising fast over the last decades, likely faster than the air temperatures, while one would expect that lakes warm slower because more heat goes into evaporation. This could be in part due to an albedo feedback or due to more sunshine from reductions in air pollution during these decades. Furthermore, also the length of the period that lakes and rivers are frozen decreases rapidly since 1850. Converting this in a temperature signal introduces a large uncertainty, but also this temperature increase seems to be faster than the current estimates of land surface temperature increases.

Now if I were a political activist, I would call climate science a hoax or claim that all scientists are stupid. Just listen to lonely brilliant Galileo me, forget science, the oceans will boil soon.

But, well, I am sorry for being such a scientist, I would just say, that we found something very interesting. Apparent discrepancies help one to understand a problem better. Only once we understand the warming of the lakes better can we know whether climate science was wrong.

As will be the case for most of this blog series on faster changes, the topic of this post goes beyond my expertise. Thus if anyone knows of good studies on this topic, I would be very grateful if you could leave a comment or would write me. Especially if anyone knows of comparisons between air and water temperature trends or between models and observations for lake and river temperatures or their ice cover. There are a number of interesting papers coming up, thus I will probably have to write an update in a few months.

Related resources

Climate Central reports on a new study (December 2015) on global lake temperature changes.

John Lenters (coordinator of the GLTC) at Nature's Scientific Data blog: Author’s Corner: Are lakes warming?

Global Lake Temperature Collaboration

Why raw temperatures show too little global warming

A recent study shows that since 1970 the ocean heat content of the upper 700m has increased 15% more than climate models have predicted.

Chris Mooney has an interesting piece in the Washington Post on related snow cover observations: Northern Hemisphere snow cover is near record lows. Here’s why that should worry you.

More scientific articles on lake ice by the group of John Magnuson.

The river and like ice phenology database: Benson, B. and J. Magnuson. 2000, updated 2012. Global lake and river ice phenology database. Boulder, Colorado USA: National Snow and Ice Data Center. doi: 10.7265/N5W66HP8.

References

Austin, J. A. and S. M. Colman, 2007: Lake Superior summer water temperatures are increasing more rapidly than regional air temperatures: A positive ice-albedo feedback, Geophysical Research Letters, 34, art. no. L06604, doi: 10.1029/2006GL029021.

Butcher, Jonathan B., Daniel Nover, Thomas E. Johnson, and Christopher M. Clark, 2015: Sensitivity of lake thermal and mixing dynamics to climate change. Climatic Change, March 2015, 129, Issue 1-2, pp 295-305, doi: 10.1007/s10584-015-1326-1.

Duguay, Claude R., Greg M. Flato, Martin O. Jeffries, Patrick Ménard, Kim Morris, and Wayne R. Rouse, 2003: Ice-cover variability on shallow lakes at high latitudes: model simulations and observations. Hydrological Processes, 17, pp. 3465-3483, doi: 10.1002/hyp.1394.

Magnuson, John J., Dale M. Robertson, Barbara J. Benson, Randolf H. Wynne, David M. Livingstone, Tadashi Arai, Raymond A. Assel, Roger B. Barry, Virginia Card, Esko Kuusisto, Nick G. Granin, Terry D. Prowse, Kenton M. Stewart, and Valery S. Vuglinski, 2000: Historical trends in lake and river ice cover in the Northern Hemisphere. Science, 289, pp. 1743-1746, doi: 10.1126/science.289.5485.1743.

Schmid, Martin, Stefan Hunziker, and Alfred Wüest , 2014: Lake surface temperatures in a changing climate: a global sensitivity analysis. Climatic Change, 124, pp. 301–315, doi: 10.1007/s10584-014-1087-2.

Schneider, Philipp, and Simon J. Hook, 2010: Space observations of inland water bodies show rapid surface warming since 1985. Geophysical Research Letters, 37, art. no. L22405, doi: 10.1029/2010GL045059.

Schneider, Philipp and Simon J. Hook, 2012: Global Trends of Lake Surface Temperatures Observed From Space. Geophysical Research Abstracts, 14, EGU2012-2858, EGU General Assembly 2012.

Schneider, Philipp, Simon J. Hook, Derek K. Gray, Jordan S. Read, Stephanie E. Hampton, Catherine M. O’Reilly, Sapna Sharma, and John D. Lenters, 2013: Global lake warming trends derived from satellite and in situ observations. Geophysical Research Abstracts, 15, EGU2013-2235, EGU General Assembly 2013.

Thursday, 12 February 2015

Just the facts, homogenization adjustments reduce global warming

Climatologists make adjustments to climate data to remove non-climatic changes (homogenization). This fact is used to accuse them of fiddling with temperature data to create or exaggerate global warming. This is often done by showing for a small piece of the data and suggesting it is typical. Often mentioned is the USA, where the raw data only show half the warming of the adjusted data. However, the USA is big, but still only 2% of the Earth's surface.

In recent weeks we had a similar case in The Telegraph about Paraguay. Last year we had similar misleading stories about two stations in Australia and the stations in New Zealand.

Global temperature collections contain thousands of stations. CRUTEM contains 4,842 quality stations and Berkeley Earth collected 39,000 unique stations. No wonder some are strongly adjusted up, just as some happen to be strongly adjusted down. In fact it would be easy to present a station where the raw data shows a cooling trend of several degrees being adjusted to a warming trend. However, then the reader might start to think if the raw data is really better.

The information on small regions or a few stations is normally not put into perspective: the average trend over all stations is only adjusted upwards slightly.

It is normally not explained why these adjustments are made nor how these adjustments are made.

Zeke Hausfather, an independent researcher that is working with Berkeley Earth, made a beautiful series of plots to show the size of the adjustments.

The first plot is for the land surface temperature from climate stations. The data is from the Global Historical Climate Dataset (GHCNv3) of NOAA (USA). Their method to remove non-climatic effects (homogenization) is well validated and recommended by the homogenization community.

They adjust the trend upwards. In the raw data the trend is 0.6°C per century since 1880 while after removal of non-climatic effects it becomes 0.8°C per century. See the graph below. But it is far from changing a cooling trend into strong warming. (A small part of the GHCNv3 raw data was already homogenized before they received it, but this will not change the story much.)

Not many people know, however, that the sea surface temperature trend is adjusted downward. These downward adjustments happen to be about the same size, but go into the other direction. See below the sea surface temperature of the Hadley Centre (HadSST3) of the UK MetOffice.

Being land creatures people do not always realise how big the ocean is, but 71% of the Earth is ocean. Thus if you combine these two temperature signals taking the area of the land and the ocean into account you get the result below. The net effect of the adjustments is a reduction of global warming.

It is pure coincidence that this happens, the reasons for the adjustments are fully different.

The land surface temperature trend has to be adjusted up because old temperatures were often too high due to insufficient protection against warming by the sun, possibly because the siting of the stations improved and there are likely more reasons.

The old sea surface temperature are adjusted downward because old measurements were made by taking a bucket of water out of the ocean and the water cooled by evaporation during the measurement. Furthermore, modern measurements are made at the water inlet of the engine and the hull of the ship warms the water a little before it is measured.

But while it is a pure coincidence and while other datasets may show somewhat different numbers (the BEST adjustments are smaller), the downward adjustment does clearly show that climatologists do not have an agenda to exaggerate global warming. That would still be true if the adjustments had happened to go upward.

Tuesday, 10 February 2015

Climatologists have manipulated data to REDUCE global warming

Climatologists are continually accused of fiddling with the data to make global warming stronger for political purposes by political activists.

A typical scam is to show a few stations that have been adjusted upwards and act as if that is typical. For example, recently The Telegraph article, "The fiddling with temperature data is the biggest science scandal ever", wrote about someone comparing

temperature graphs for three weather stations in Paraguay against the temperatures that had originally been recorded. In each instance, the actual trend of 60 years of data had been dramatically reversed, so that a cooling trend was changed to one that showed a marked warming.

Three, I repeat: 3 stations. For comparison, global temperature collections contain thousands of stations. CRUTEM contains 4,842 quality stations and Berkeley Earth collected 39,000 unique stations. No wonder some are strongly adjusted up, just as some happen to be strongly adjusted down. In fact it would be easy to present a station where the raw data shows a decreasing trend of several degrees being adjusted upwards, but then the reader might start to think if the raw data is really better.

What these people do not tell their readers is that the average trend over all station is only adjusted upwards slightly. That would put things too much in perspective. What these people do not tell their readers is why these adjustments are made. That might make some think that it may make sense. What these people normally do not tell their readers is how these adjustments are made. That would not sound sufficiently arbitrary and conspirational.

Last year we had similar scams about two stations in Australia and the stations in New Zealand.

In an internet poll, 88% of the readers of the abysmal Telegraph piece agree with the question: "Has global warming been exaggerated by scientists?"

I hope that after reading this post, these 88% will agree that they have been conned by The Telegraph. That scientists have actually made global warming smaller.

Zeke Hausfather, an independent researcher that is working with Berkeley Earth, made a beautiful series of plots to show the size of the adjustments.

The first plot is for the land surface temperature from climate stations. The data is from the Global Historical Climate Dataset (GHCNv3) of NOAA (USA). Their method to remove non-climatic effects (homogenization) is well validated and recommended by the homogenization community.

They adjust the trend upwards. In the raw data the trend is 0.6°C per century since 1880 while after removal of non-climatic effects it becomes 0.8°C per century. See below. But it is far from changing a cooling trend into strong warming.

(In case you believe many national weather services are also in the conspiracy: a small part of the GHCNv3 raw data was already homogenized before they received it.)

Not many people know, however, that the sea surface temperature trend is adjusted downward. That does not fit the narrative of WUWT & Co. It sounds like even many scientists did not know that. These downward adjustments happen to be about the same size, but go into the other direction. See below the sea surface temperature of the Hadley Centre (HadSST3) of the UK MetOffice.

Being land creatures people do not always realise how big the ocean is. Thus if you combine these two temperature signals taking the area of the land and the ocean into account you get the result below. The net effect of the adjustments is a reduction of global warming.

It is pure coincidence that this happens, the reasons for the adjustments are fully different.

The land surface temperature trend has to be adjusted up because old temperatures were often too high due to insufficient protection against warming by the sun and possibly because the siting of the stations improved. There are likely more reasons.

The sea surface temperature are adjusted downward because old measurements were made by taking a bucket of water out of the ocean and the water cooled by evaporation during the temperature measurement. Furthermore, modern measurements are made at the water inlet of the engine and the hull of the ship warms the water a little before it is measured.

But while it is a pure coincidence and while other datasets may show somewhat different numbers (the BEST adjustments are smaller), the downward adjustment does clearly show that climatologists do not have an agenda to exaggerate global warming. Like all reasonable people already knew. That would still be true if the adjustments had happened to go upward.

[UPDATE:

Small networks

The smaller the networks, the larger the size of the non-climatic changes typically is.

A recent paper about the US mountain network (SNOWTEL) explained that their mountain stations showed more warming than the lower lying USHCN stations. This could be a snow-albedo feedback (that the warming reduces the white snow and reveals the dark surface leading to more warming. However they found it was a non-climatic change in the temperature due to in the installation of new equipment. The new instruments recorded about 1.5°C higher minimum temperatures; an extraordinary large change (the maximum temperature was hardly affected). Accurate data is not just important for trends, but also for physics (snow-albedo feedback).

That is another case of climatologists reducing warming and a feedback.

But what did a well-know blog of the mitigation sceptics, WUWT, write? They headlined: "Another bias in temperature measurements discovered" and opened: "From the “temperature bias only goes one way department”".

The second comment is by "cg": "Lying in Weather Reporting is common place and shamelessly just like the Global Financiers want it. Pure Evil."
Brute: "You sound insane."
KaiserDerden: "no more insane than you do claiming CO2 controls the weather/climate… actually less so in fact ...
Brute: "I have never said a single word regarding how “CO2 controls the weather/climate”. It is curious how much paranoia one finds around here... just about as much as one finds among the warmist cults...."
Sun Spot: "@Brute, you sound sanctimonious"
Ofay Cat: "CG ... you have it right ... those others are uninformed or misinformed. Which means Liberal."
cg: "Thanks"
]

Let's end on a depressive note. Rob Honeycutt says:

Take note. Proving the conspiracy wrong is sure to be taken as proof you’re part of the conspiracy.

It would be interesting to track, but I somehow doubt the number of “skeptic” posts with accusations of fraud is going to change. And I think this is merely because the source of the “skepticism” isn’t rooted in true scientific skepticism. It’s formed on an ideological basis. So, asking them to accept the data as correct is the same, from their standpoint, as asking them to change their ideology.

End of rant. Sorry for the tone. One sometimes gets the impression that WUWT & Co. select the most stupid memes possible to produce the largest antagonistic effect possible. It would be too easy to talk about the real caveats, the ones also mentioned by the enemy in the IPCC reports. For example, that assessing the impacts of climate change is enormously difficult because it involves ecosystems and humans. For example, that estimating trends in extreme weather is very challenging and very much current research; also partially due to non-climatic changes in the daily data.

[UPDATE. This version got a bit snarkier than usual, which maybe warranted in talking to hardcore mitigation sceptics. To link to in discussions with people who might be open for debate, I have written a second matter-of-fact version: Just the facts, homogenization adjustments reduce global warming. In case of doubt, when you do not know people well, that is probably also the best version.]

Pages

Wednesday, 30 November 2016

Statistically significant

Related reading

Notes

Sunday, 21 August 2016

Global mean temperature

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References

Monday, 15 August 2016

Deterministic and stochastic downscaling

Genetic Programming

Coupling atmospheric and surface models

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References

Thursday, 7 July 2016

Notes

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References

Sunday, 1 May 2016

Weak climatic trend

Cooling bias

Homogenization method

Conclusions

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Friday, 17 July 2015

Freezing and melting of lakes and rivers

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References

Thursday, 12 February 2015

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Tuesday, 10 February 2015

Small networks

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