No trend in global water vapor, another WUWT fail

Forrest M. Mims III Forrest Mims is an interesting character. To quote from the introduction to his Science article on amateur science: "Forrest M. Mims III is a writer, teacher, and amateur scientist. He received a Rolex Award for developing a miniature instrument that measures the ozone layer and has contributed projects to “The Amateur Scientist” column in Scientific American. His scientific publications have appeared in Nature and other scholarly journals."

Anthony Watts just published a guest post by Forrest M. Mims III with the title: "Another IPCC AR5 reviewer speaks out: no trend in global water vapor". I have no special expertise in this area, but I am privileged being able to read the article that is discussed. This is sufficient to see that the article and its post are two different worlds. Update: An earlier draft is available (thanks, michael sweet).

First, note that being a "expert reviewer" does not say much. There are over a thousand reviewers, even Anthony Watts himself is an IPCC "expert reviewer". On the other hand, Mims may be an amateur, but did do valued scientific work on UV measurements.

The trend in global water vapor

The post discusses a paper by Vonder Haar et al. (2012) on the NASA Water Vapor Project (NVAP) dataset. The main piece of information missing from the post is that this dataset is only 22 years long. Almost any climatological measurement will not have a statistically significant trend over such a short period, but the story is even weirder.

Just as in the misleading post on homogenization of climate data earlier this year, Anthony Watts again proofs to have a keen eye in finding the best misinformation.

Mims added a list with all the comments of his review. In this list, Watts found this comment:

This paper concludes,

“Therefore, at this time, we can neither prove nor disprove a robust trend in the global water vapor data.”

Non-specialist readers must be made aware of this finding and that it is at odds with some earlier papers.

The complete citation from the Geophysical Research Letters article is:

"The results of Figures 1 and 4 have not been subjected to detailed global or regional trend analyses, which will be a topic for a forthcoming paper. Such analyses must account for the changes in satellite sampling discussed in the auxiliary material. Therefore, at this time, we can neither prove nor disprove a robust trend in the global water vapor data."

In other words, they cannot say anything about the trend, because they have not even tried to compute it and estimate its uncertainty. Especially estimating the error in the trend will be very difficult as the dataset uses different satellites for different periods of the dataset, which invariably creates jumps in the dataset that should not be mistaken for true climate variability or trends.

The paper is thus not at odds with earlier papers. These earlier papers studied longer periods and probably datasets which were more homogenenous and consequently did find a statistically significant trend. There is thus no contradiction.

Changing the political dynamics of greenhouse gas reductions

Photo by Caveman Chuck Coker, Creative Commons by-nd licence

Another climate conference failed miserably. Maybe we need a completely different system, a system in which forerunners are rewarded and not punished.

A stable, predictable climate is a common good. Climate change is one of the most difficult tragedies of the commons. There are great benefits to using energy and the climate costs are spread almost perfectly to everyone. No single industry or country contributes much to the problem, but some industries and countries do benefit strongly and have a large incentive to halt the negotiations and to spread doubt. This makes greenhouse gas mitigation arguably the most difficult tragedy of the commons.

It is possible to solve such tragedies, the Montreal protocol to curb emissions of chlorofluorocarbons (CFC) to protect the ozone layer seems to work well. The ozone layer is now at its thinnest, but scientists expect that is will start to become thicker soon and return to almost normal levels in several decades. However, in case of the Montreal protocol only the producers of fridges, air conditionings and spray cans were affected. Greenhouse gasses are emitted by the energy, agricultural and building sectors. These are powerful parties and this makes a global treaty difficult. Maybe it is better to solve the tragedy of the commons by allowing countries and regions that want to reduce their CO₂ emissions to protect themselves against unfair competition.

Traditional milk in Germany: raw and hay milk

Photo used with permission from heumlich.at.

In the ancestral health community raw milk and milk from grass-fed cows is highly praised. See Chris Kresser for an excellent overview of the benefits and the small risks of raw milk. Mark Sisson gives a nice overview of the more healthy fat composition of pastured butter. It took me some time to understand the situation in Germany, until I knew the two magic words: Vorzugsmilch and Heumilch.

Vorzugsmilch

Raw milk is not pasteurised and not homogenized. Pasteurisation is quickly heating and cooling to reduce the bacteria concentration. Milk is white because of all the small fat droplets in the water. In homogenization, milk is pressed through a valve at very high pressures to make the droplets smaller, which prolongs the time until the droplets combine to form cream at the top of the milk.

In Germany, retail of normal raw milk is forbidden, but a farmer is allowed to sell his raw milk directly to consumers. Raw milk sold in shops is called Vorzugsmilch, let's call it merit milk in English, I like alliteration. Cows and farms producing merit milk are inspected regularly and the milk has to get to the consumer within 96 hours. According to Andrea Fink-Keßler (agricultural scientist) the diet of the cows producing Vorzugsmilch is similar as for hay milk; see below.

Radiative transfer and cloud structure

Last month our paper on small-scale cloud structure and radiative transfer using a state-of-the-art 3-dimensional Monte Carlo radiative transfer model was published. It was written together with two radiative transfer specialists: Sebastian Gimeno García and Thomas Trautmann. The paper introduces the new version of this model called MoCaRT, but the interesting part for this blog on variability are the results on the influence of small-scale variability on radiative transfer. Previously, I have written about cloud structure, whether it is fractal and the processes involved in creating such complicated and beautiful structures. This post will explain, why this structure is important for radiative transfer and thus for remote sensing (for example for weather satellites) and the radiative balance of the earth (determining the surface temperature). I will try to do so also for people not familiar with radiative transfer.

As an aside, the word radiation in this context should not be confused with radioactive radiation. (It is rumored that the Earth Radiation satellite Mission had to be renamed to the EarthCARE to be funded, as the word radiation sounds negative due to its association with radioactivity.)

Radiative transfer

In theory, radiative transfer is well understood. The radiative transfer equation is long know and describes how electromagnetic radiation (intensity) propagates through a medium and is scatter and emitted by it. Climatologically important are solar radiation from the sun and infrared (heat) radiation from the earth's surface and the atmosphere. For remote sensing of the atmosphere also radio waves are important.

In practice, radiative transfer through the atmosphere is difficult to compute. This starts with the fact that the equation is valid for one frequency of the electromagnetic wave only, while the optical properties of the atmosphere can depend strongly on the frequency. To compute the radiative balance of the earth, a large number of frequencies in the solar and infra red regime thus need to be computed (such models are called line-by-line models). More efficient are computations in broader frequency bands, but then approximations need to be made.

Beta version of a new global temperature database released

Today, a first version of the global temperature dataset of the International Surface Temperature Initiative (ISTI) with 39 thousand stations has been released. The aim of the initiative is to provide an open and transparent temperature dataset for climate research.

The database is designed as a climate "sceptic" wet dream: the entire processing of the data will be performed with automatic open software. This includes every processing step from conversion to standard units, to merging stations to longer series, to quality control, homogenisation, gridding and computation of regional and global means. There will thus be no opportunity for evil climate scientists to fudge the data and create an artificially strong temperature trend.

It is planned that in many cases, you can go back to the digital images of the books or cards on which the observer noted down the temperature measurements. This will not be possible for all data. Many records have been keyed directly in the past, without making digital images. Sometimes the original data is lost, for instance in case of Austria, where the original daily observation have been lost in the Second World War and only the monthly means are still available from annual reports.

The ISTS also has a group devoted to data rescue to encourage people to go into the archives, image and key in the observations and upload this information to the database.

Future research in homogenisation of climate data – EMS2012 in Poland

By Enric Aguilar and Victor Venema

The future of research and training in homogenisation of climate data was discussed at the European Meteorological Society in Lodz by 21 experts. Homogenisation of monthly temperature data has improved much in the last years, as seen in the results of the COST-HOME project. On the other hand the homogenization of daily and subdaily data is still in its infancy and this data is used frequently to analyse changes in extreme weather. It is expected that inhomogeneities in the tails of the distribution are stronger than in the means. To make such analyses on extremes more reliable, more work on daily homogenisation is urgently needed. This does not mean than homogenisation at the monthly scale is already optimal, much can still be improved.

Parallel measurements

Parallel measurements with multiple measurement set-ups were seen as an important way to study the nature of inhomogeneities in daily and sub-daily data. It would be good to have a large international database with such measurements. The regional climate centres (RCC) could host such a dataset. Numerous groups are working on this topic, but more collaboration is needed. Also more experiments would be valuable.

When gathering parallel measurements the metadata is very important. INSPIRE (an EU Directive) has a standard format for metadata, which could be used.

It may be difficult to produce an open database with parallel measurements as European national meteorological and hydrological services are often forced to sell their data for profit.(Ironically, in the Land the Free (markets), climate data is available freely, the public already paid for it with their tax money after all.) Political pressure to free climate data is needed. Finland is setting a good example and will free its data in 2013.

The paleo culture

A volunteer of the Ancestral Health Symposium 2012 has criticized the culture of the paleo movement. Richard Nikoley apparently felt attacked and as a prolific blogger immediately wrote a hot tempered post in defence. (In the meantime, the blog with the criticism has been deleted due to the personal attacks and threats.) Richards defensive post focused on the few lines that went over the top.

The demographic at this event was almost all white, child bearing age, healthy, wealthy, highly educated, libertarian, racist, sexist and bigoted.

I presume these lines were more provoked by a life of discrimination as by a single symposium.

It is normal to be defensive while receiving criticism. The day after, one often notices that honest feedback is actually very valuable, that it gives rare and precious insight into how one is seen from the outside. The valuable points of the criticism were (i) that she did not feel welcome, as a not wealthy person and also as an older woman. Furthermore, there were (ii) many crackpots at the symposium.

Demographics

I must admit that I also sometimes find the paleo culture to be rather off putting. The reason I stay is because many good ideas from the paleo community have helped improve my health enormously. The main bloggers are friendly and many focus just on science, which is neutral, but you are often just one click away from the National Rife Association. The community has a strong focus on the health effects of nature, but I never saw a link to a nature conservation group. Paleo is inspired by the life style of hunter-gatherers, but I had to hear about Survival International, an organisation that helps indigenous peoples protect themselves, on the German radio. There is lots of talk about expensive food, supplements and gear, but not about anti-hierarchical strategies used by hunter-gather groups to keep their band egalitarian and strong. Much of the advice is focused on males and it may, for example, well be that the standard routines for intermittent fasting are too heavy for woman.

Statistical homogenisation for dummies

The self-proclaimed climate sceptics keep on spreading fairy tales that homogenisation is smoothing climate data and leads to adjustments of good stations to make them into bad stations. Quite some controversy for such an innocent method to reduce non-climatic influences from the climate record.

In this post, I will explain how homogenisation really works using a simple example with only three stations. Figure 1 shows these three nearby stations. Statistical homogenisation exploits the fact that these three time series are very similar (are highly correlated) as they measure almost the same regional climate. Changes that happen at only one of the stations are assumed to be non-climatic. The aim of homogenisation is to remove such non-climatic changes in the data.

Figure 1. The annual mean temperature data of three hypothetical stations in one climate region.

(In case colleagues of mine are reading this and are wondering about my craftsmanship: I do know who to operate scientific plotting software, but some “sceptics” make fun of people who have no experience with Excel. I just wanted to show off with being able to use a spreadsheet.)

For the example, I have added a break inhomogeneity in the middle with a typical size of 0.8 °C (1.5 °F) to the data for station A; see Figure 2.