Blog review of the Watts et al. (2012) manuscript on surface temperature trends

[UPDATE: Skeptical Science has written an extensive review of the Watts et al. manuscript: "As it currently stands, the issues we discuss below appear to entirely compromise the conclusions of the paper." They mention all the important issues, except maybe for the selection bias mentioned below. Thus my fast preliminary review below can now be considered outdated. Have fun.]

Anthony Watts put his blog on hold for two days because he had to work on an urgent project.

Something’s happened. From now until Sunday July 29th, around Noon PST, WUWT will be suspending publishing. At that time, there will be a major announcement that I’m sure will attract a broad global interest due to its controversial and unprecedented nature.

What has happened? Anthony Watts, President of IntelliWeather has co-written a manuscript and a press release! As Mr. Watts is a fan of review by bloggers, here is my first reaction after looking through the figures and the abstract.

Investigation of methods for hydroclimatic data homogenization

The self-proclaimed climate sceptics have found an interesting presentation held at the General meeting of the European Geophysical Union.

In the words of Anthony Watts, the "sceptic" with one of the most read blogs, this abstract is a ”new peer reviewed paper recently presented at the European Geosciences Union meeting.” A bit closer to the truth is that this is a conference contribution by Steirou and Koutsoyiannis, based on a graduation thesis (Greek), which was submitted to the EGU session "Climate, Hydrology and Water Infrastructure". An EGU abstract is typically half a page, it is not possible to do a real review of a scientific study based on such a short text. The purpose of an EGU abstract is in practice to decide who gets a talk and who gets a poster, nothing more, everyone is welcome to come to EGU.

What is a change in extreme weather?

What is a change in extreme weather?

The reason for changes in extremes can be divided up into two categories: changes in the mean (see panel a of the figure below) and other changes in the distribution (simplified as a change in the variance in panel b). Mixtures are of course also possible (panel c).

If you are interested in the impacts of climate change, you do not care why the the extremes are changing. If the dikes need to be made stronger or the sewage system needs larger sewers and larger reservoirs, all you need to know is how likely it is that a certain threshold is reached. Much research into changes in extreme weather is climate change impact research and thus does not care much about this distinction.

If you are interested in understanding the climate system, it does matter why the extremes are changing. Changes in the mean state of the climate are relatively well studied. Interesting questions are, for instance, whether a change in the mean changes the distribution via feedback processes or whether the reduced temperature contrasts between the poles and the equator or between day and night cause changes in the distribution.

If you are interested in understanding the climate system also the spatial and temporal averaging scales matter. If rain fronts move slower, they may locally produce more extreme daily precipitation sums, while on a global scale or instantaneously there is no change in the distribution of precipitation.

I hope scientists will distinguish between these two different ways in which extremes may change in future publications and, for example, not only compute the increase in the number of tropical days, but also how many of these days are due to the change in the mean and how many are due to changes in the distribution. I think this would contribute to a better understanding of the climate system.

Figure is taken from Real Climate, which took it from IPCC (2001).

Paleo and fruitarian lifestyles have a lot in common

My new fitness trainer eats a lot of fruit. And she looks darn healthy. Now I know, you should not take weight-training advice from a professional body builder or risk serious overtraining, but still I was intrigued and did some research. The vegan and paleo communities are often not on friendly terms. Thus what struck me most researching fruitarian blogs, was how similar many of the ideas were.

A very strict fruitarian only eats fruits in the common meaning, sweet and juicy fruits from trees or bushes. Others also include vegetable fruits such as avocados, tomatoes and cucumbers, still others also include nuts, many regularly eat salad. To get sufficient calories from fruits, a fruitarian has to eat several kilograms of fruit. Some people calling themselves fruitarians actually get most calories from nuts and avocados. In this post fruitarians are people getting most calories from simple carbohydrates, that is from sweet fruits.

The paleolithic lifestyle is inspired by the way people lived before agriculture. As the information from the Paleolithic Age is scarce, in praxis this often means, that existing hunter gatherers and their diets and lifestyles are studied. Such bands often trade with nearby agriculturalists and thus no longer live a true stone-age life. Still as long as they are free from the deceases of civilisation, they provide good role models in my view. Similarly, many paleos also look at other existing cultures that are in good health. In this respect the paleo community is close to the Weston A Price Foundation, who seek guidance with how people lived a few generations ago. The paleo diet is best defined by what it not eaten: processed foods, grains, sugar and refined seed oils.

HUME: Homogenisation, Uncertainty Measures and Extreme weather

Proposal for future research in homogenisation

To keep this post short, a background in homogenisation is assumed and not every argument is fully rigorous.

Aim

This document wants to start a discussion on the research priorities in homogenisation of historical climate data from surface networks. It will argue that with the increased scientific work on changes in extreme weather, the homogenisation community should work more on daily data and especially on quantifying the uncertainties remaining in homogenized data. Comments on these ideas are welcome as well as further thoughts. Hopefully we can reach a consensus on research priorities for the coming years. A common voice will strengthen our voice with research funding agencies.

State-of-the-art

From homogenisation of monthly and yearly data, we have learned that the size of breaks is typically on the order of the climatic changes observed in the 20th century and that period between two detected breaks is around 15 to 20 years. Thus these inhomogeneities are a significant source of error and need to be removed. The benchmark of the Cost Action HOME has shown that these breaks can be removed reliably, that homogenisation improves the usefulness of the temperature and precipitation data to study decadal variability and secular trends. Not all problems are already optimally solved, for instance the solutions for the inhomogeneous reference problem are still quite ad hoc. The HOME benchmark found mixed results for precipitation and the handling of missing data can probably be improved. Furthermore, homogenisation of other climate elements and from different, for example dry, regions should be studied. However, in general, annual and monthly homogenisation can be seen as a mature field. The homogenisation of daily data is still in its infancy. Daily datasets are essential for studying extremes of weather and climate. Here the focus is not on the mean values, but on what happens in the tails of the distributions. Looking at the physical causes of inhomogeneities, one would expect that many of them especially affect the tails of the distributions. Likewise the IPCC AR4 report warns that changes in extremes are often more sensitive to inhomogeneous climate monitoring practices than changes in the mean.

Homogenisation of monthly and annual data from surface stations

To study climate change and variability long instrumental climate records are essential, but are best not used directly. These datasets are essential since they are the basis for assessing century-scale trends or for studying the natural (long-term) variability of climate, amongst others. The value of these datasets, however, strongly depends on the homogeneity of the underlying time series. A homogeneous climate record is one where variations are caused only by variations in weather and climate. In our recent article we wrote: “Long instrumental records are rarely if ever homogeneous”. A non-scientist would simply write: homogeneous long instrumental records do not exist. In practice there are always inhomogeneities due to relocations, changes in the surrounding, instrumentation, shelters, etc. If a climatologist only writes: “the data is thought to be of high quality” and then removes half of the data and does not mention the homogenisation method used, it is wise to assume that the data is not homogeneous.

Results from the homogenisation of instrumental western climate records indicate that detected inhomogeneities in mean temperature series occur at a frequency of roughly 15 to 20 years. It should be kept in mind that most measurements have not been specifically made for climatic purposes, but rather to meet the needs of weather forecasting, agriculture and hydrology (Williams et al., 2012). Moreover the typical size of the breaks is often of the same order as the climatic change signal during the 20^th century (Auer et al., 2007; Menne et al., 2009; Brunetti et al., 2006; Caussinus and Mestre; 2004, Della-Marta et al., 2004). Inhomogeneities are thus a significant source of uncertainty for the estimation of secular trends and decadal-scale variability.

If all inhomogeneities would be purely random perturbations of the climate records, collectively their effect on the mean global climate signal would be negligible. However, certain changes are typical for certain periods and occurred in many stations, these are the most important causes discussed below as they can collectively lead to artificial biases in climate trends across large regions (Menne et al., 2010; Brunetti et al., 2006; Begert et al., 2005).

In this post I will introduce a number of typical causes for inhomogeneities and methods to remove them from the data.

New article: Benchmarking homogenisation algorithms for monthly data

The main paper of the COST Action HOME on homogenisation of climate data has been published today in Climate of the Past. This post describes shortly the problem of inhomogeneities in climate data and how such data problems are corrected by homogenisation. The main part explains the topic of the paper, a new blind validation study of homogenisation algorithms for monthly temperature and precipitation data. All the most used and best algorithms participated.

Inhomogeneities

To study climatic variability the original observations are indispensable, but not directly usable. Next to real climate signals they may also contain non-climatic changes. Corrections to the data are needed to remove these non-climatic influences, this is called homogenisation. The best known non-climatic change is the urban heat island effect. The temperature in cities can be warmer than on the surrounding country side, especially at night. Thus as cities grow, one may expect that temperatures measured in cities become higher. On the other hand, many stations have been relocated from cities to nearby, typically cooler, airports. Other non-climatic changes can be caused by changes in measurement methods. Meteorological instruments are typically installed in a screen to protect them from direct sun and wetting. In the 19th century it was common to use a metal screen on a North facing wall. However, the building may warm the screen leading to higher temperature measurements. When this problem was realised the so-called Stevenson screen was introduced, typically installed in gardens, away from buildings. This is still the most typical weather screen with its typical double-louvre door and walls. Nowadays automatic weather stations, which reduce labor costs, are becoming more common; they protect the thermometer by a number of white plastic cones. This necessitated changes from manually recorded liquid and glass thermometers to automated electrical resistance thermometers, which reduces the recorded temperature values.

One way to study the influence of changes in measurement techniques is by making simultaneous measurements with historical and current instruments, procedures or screens. This picture shows three meteorological shelters next to each other in Murcia (Spain). The rightmost shelter is a replica of the Montsouri screen, in use in Spain and many European countries in the late 19th century and early 20th century. In the middle, Stevenson screen equipped with automatic sensors. Leftmost, Stevenson screen equipped with conventional meteorological instruments. Picture: Project SCREEN, Center for Climate Change, Universitat Rovira i Virgili, Spain.

A further example for a change in the measurement method is that the precipitation amounts observed in the early instrumental period (about before 1900) are biased and are 10% lower than nowadays because the measurements were often made on a roof. At the time, instruments were installed on rooftops to ensure that the instrument is never shielded from the rain, but it was found later that due to the turbulent flow of the wind on roofs, some rain droplets and especially snow flakes did not fall into the opening. Consequently measurements are nowadays performed closer to the ground.

What distinguishes a benchmark?

Benchmarking is a community effort

Science has many terms for studying the validity or performance of scientific methods: testing, validation, intercomparison, verification, evaluation, and benchmarking. Every term has a different, sometimes subtly different, meaning. Initially I had wanted to compare all these terms with each other, but that would have become a very long post, especially as the meaning for every term is different in business, engineering, computation and science. Therefore, this post will only propose a definition for benchmarking in science and what distinguishes it from other approaches, casually called other validation studies from now on.

In my view benchmarking has three distinguishing features.
1. The methods are tested blind.
2. The problem is realistic.
3. Benchmarking is a community effort.
The term benchmark has become fashionable lately. It is also used, however, for validation studies that do not display these three features. This is not wrong, as there is no generally accepted definition of benchmarking. In fact in an important article on benchmarking by Sim et al. (2003) defines "a benchmark as a test or set of tests used to compare the performance of alternative tools or techniques." which would include any validation study. Then they limit the topic of their article, however, to interesting benchmarks, which are "created and used by a technical research community." However, if benchmarking is used for any type of validation study, there would not be any added value to the word. Thus I hope this post can be a starting point for a generally accepted and a more restrictive definition.