Little Ice Age thermometers – History and Reliability

Guest post by TonyB

How reliable are The Little Ice Age thermometers ?

The Little Ice age thermometers project is an attempt to compile instrumental readings from 1660 that predate the era of modern ‘global temperatures’ as recorded by Hadley (1850) and Giss (1880). These datasets are accessed from a graphic through this link;

http://climatereason.com/LittleIceAgeThermometers/

The project will examine the reliability of these historic datasets as a means for climate researchers to gaze into our past to see if there are any lessons for the present. In this respect many of those individual stations found to date will have been included in the Cru datasets that are not readily accessible to those outside selected members of the scientific community. In order to examine these records and place them into context with Hadley/Cru and Giss, the author has produced three separate but interlinked articles as follows;

*This first one deals with activities centred around 1850/1880 a period which has had an important bearing on our current understanding of global temperatures.

*Article two will examine the development of the thermometer and our understanding of climate prior to the 1850/1880 records. In doing so we will pay particular attention to the quality/reliability of the readings used in the project, examine those who carried out the observations, and look at the circumstances under which they were collected. This should answer the question-are the readings a reliable record of the time?

* The third article examines the methods of compiling ‘modern’ records from 1850/1880-to today. In examining whether the historic temperature readings can be viewed as a useful tool for modern climate researchers, we also consider if the modern records are as reliable as they are portrayed.

In order to obtain some context of the period leading up to 1850/1880 some key historic points are outlined here to demonstrate that knowledge of weather, climate and temperatures, is not purely confined to the modern era.

Ancient History

An understanding of the importance of temperatures -and the climate they were a product of- goes back to the ancient Greeks who recognised the expansion of air by heat over two thousand years ago. The earliest writings concerning those phenomena were from the works of Philo of Byzantium (2nd Century B.C.) and Heron of Alexandria, each of whom constructed a crude ‘thermo scope.’

Aristotle subsequently postulated his four qualities, the hot, the cold, the moist, and the dry, and his ideas were adopted by Galen (A.D. 130-200), who was the first man to describe the heat and cold by a number about fifteen hundred years ago.

The rise of Rome coincided with the warm Roman optimum. We are fortunate that we have available the climate references from not only the Western Roman empire, but those of the Byzantine empire (the Eastern Roman empire after the collapse of Rome) approx 380-1453 AD. Collectively, the Egyptian, Roman and Byzantine empires can provide records of some 4000 years of climate change. Geographically this covers a large part of Europe, the Middle East and North Africa. Knowledge of the Vikings enables us to extend that geographic range far to the North. Studies from elsewhere in the world-including the Southern Hemisphere-provide tantalising glimpses of climate change elsewhere.

Some of the Roman climate references are fascinating. This observation from a series of cold winters -after many warm ones- around the 8th century in Byzantium (centred around Modern day Turkey)

“Theophanes’ account recalls how, as a child, the author (or his source’s author) went out on the ice with thirty other children and played on it and that some of his pets and other animals died. It was possible to walk all over the Bosporus around Constantinople and even cross to Asia on the ice. One huge iceberg crushed the wharf at the Acropolis, close to the tip of Constantinople’s peninsula, and another extremely large one hit the city wall, shaking it and the houses on the other side, before breaking into three large pieces; it was higher than the city walls. The terrified Constantinopolitans wondered what it could possibly portend.”

It would be remiss not to connect the Roman warm optimum and the series of savage winters recorded above that afflicted Constantinople, with the great medieval warming of Greenland and the age of the Vikings several hundred years later. This enables us to contemplate the astonishing notion of Romans and Vikings from respective warm periods co-existing in the same era, as Vikings guarded the capital of the Eastern Roman empire.

http://travdyn.wordpress.com/2008/05/21/the-little-known-tale-of-the-byzantine-vikings/

Constantinople was guarded by an elite mercenary squad of Russianized Vikings (who apparently were fond of the Mediterranean climate) named the Varangian Guard. According to a wonderful entry in this History of Warfare blog, (and we pick this story up in medias res)

In early 989 AD a Viking fleet arrived with the promised 6000 Norseman. A few weeks later they crossed the straits of the Golden Horn under the cover of darkness and took up positions a few hundred yards from the rebel camp. At first light they attacked, while a squadron of imperial flame-throwers sprayed the shore with Greek fire. Phocas’s men awoke to the terrifying sight of the Varangians swinging their swords and battleaxes. The result was a massacre. Basil with the aid of the Varangians soon crushed the rebellion entirely.

Figure 1: The Viking Varangian guard

After the rebellion, the Varangians were immediately established as the emperor’s personal bodyguards. Anna Komnena writing in ‘the Alexiad’ claimed that the Guard were far more reliable and trustworthy as bodyguards than native Byzantine troops.”

The Little ice age.

In due course we shall examine the Hadley/Giss data sets from 1850/80 which overlap with the final burst of the LIA. This period commenced around 1350 and is a much misrepresented era;

IPCC FAQ 6.2 Page114 of TAR4.

‘All published reconstructions find that temperatures were warm during medieval times, cooled to low values in the 16th 17th 18th 19th centuries, then warmed rapidly after that.’

Of course there were periods of bitter cold as this traditional view demonstrates

Figure 2 Hunters in the snow-by Flemish painter Pieter Bruegel the elder 1525-1569. Completed in 1565 and said to depict the first of the bitter winters of the Little Ice Age. This helps colour our view that this period was unremittingly cold-a mistake that even the IPCC make.

But these were interspersed with much warmer periods, illustrating that even through this anomalous period the summits and valleys that typify climate patterns through the ages still operated, as observed from the Roman optimum and Medieval warm period to the Modern warm period.

Figure 3 –Little Ice age and warm periods

Some of these temperature summits will be explored through the medium of the LIA thermometers projects in Article two and are even more startling than they appear in this graphic, demonstrating the periods of warmth during the LIA were not that much different to today.

It enables us to refute the comments of the IPCC by paraphrasing Michael Mann and assert that ‘the LIA is an outdated concept’.

Early temperature devices

Although there are many claims as to who built the first thermo scope (a thermo scope does not have a scale, a thermometer does) most authorities attribute its invention to the Italian scientist Galileo (1564–1642), probably in 1592. There are independent reports of air thermo scopes invented by Galileo’s medical disciple, Sanctorius (1561–1636). It appears likely that these were directly derived from Arabic translations of Philo and Heron’s work -compiled in Greek- from millennia before, referenced earlier.

Figure 4. Thermometers (1–5) and a hygrometer (6) of the Accademia del Cimento. (From Middleton [2]).physician and mystical philosopher, Robert Fudd (1574–1651); the clock maker, Cornelius Drebbel (1572–1633); and the engineer, Salomon de Caus [1]. The first thermometers had the common property of being a tube of different construction opened to the atmosphere. They either did not have a scale, or they were crudely graduated with notches. They were usually intended for medical or meteorological purposes. The thermometers built by Evangelista Torricelli (1608–1647) had blown glass bubbles of different weight; the ball that floated determined the particular level of temperature. None of the scales were comparable with other instruments or accurate from one day to the other because of changing barometric conditions. The earliest air thermometer that corrected for air pressure seems to be the one described by Guillaume Amontons (1663–1705) to the Académie des Sciences in 1702 [2].

In January 1660/61-the year the Royal Society was established- English diarist Pepys observed; “It is strange what weather we have had all this winter; no cold at all; but the ways are dusty, and the flyes fly up and down, and the rose-bushes are full of leaves, such a time of the year as was never known in this world before here.”

One of the earliest attempts at calibration and standardisation between thermometers was made in October 1663 in London. The members of the Royal Society of London agreed to use one of several thermometers made by Robert Hooke as the standard, so that the reading of others could be adjusted to it. Thus the reading in one laboratory could compare a temperature to reading in another laboratory through the standard correction.

The Danish astronomer Rømer (1644–1710), discoverer of the finite speed of light, is assumed to be the first to build reproducible thermometers. In 1702 he proposed using two fixed points.

http://chemeducator.org/sbibs/s0005002/spapers/520088jw.htm

The development of stable Fahrenheit thermometers was a watershed point in the development of thermometry. The methods of making scale were in confusion at that time, because the craftsman in different countries used different calibration points (there were 18 scales up to 1841 and around 40 at the start of the 18th century).

Thermometers from their earliest days were precision instruments made with great care and at great cost. Some of the early manufacturers included Dolland and Newman, Adams and GB Fahrenheit. They were used as serious scientific instruments to observe, measure and record, but as ever the rich and powerful adopted the new technology and thermometers became fashionable

Frederik became King of Prussia in 1701 and immediately set up a measuring station that became Berlin Tempelhof, one of our oldest records (and mentioned in LIA thermometers) and this started a rash of similar stations that caused Samuel Horsley to comment in 1774:

‘The practice of keeping meteorological journals is of late years becoming very general’. The weather was seen to be important for our well-being. ‘We shall always search for ways to make observations more exact, both for the sake of agriculture and our health’, said Johann Hemmer in 1780. Records were kept for years in the hope of seeing patterns emerge which could have future use. As the LIA thermometers project illustrates, that is exactly what has happened as we are able to view climatic cycles through the centuries, somewhat disproving the Met Offices observations of limited climatic variability in the past. . http://www.metoffice.gov.uk/climatechange/policymakers/policy/slowdown.html

Extract “Before the twentieth century, when man-made greenhouse gas emissions really took off, there was an underlying stability to global climate. The temperature varied from year to year, or decade to decade, but stayed within a certain range and averaged out to an approximately steady level.”

The 1850/1880 dividing line which this article has taken as its focal point is a useful one, as 1850 is the date from which Phil Jones based his Hadley Cru set.

Figure 5-Global average land temperature from 1850-Crutem3 from Hadley/Cru

1880 is the date from which James Hansen chose to start his global records from.

Figure 6-Global average land temperatures from 1850-Giss

The link below is Hansen’s original 1987 paper where he identified the stations that he felt could be used in his own dataset. Figure 2 sums the numbers up.

http://pubs.giss.nasa.gov/docs/1987/1987_Hansen_Lebedeff.pdf

GS Callendar -who in 1938 published his theory of man made warming caused by Co2- was a respected amateur meteorologist and believed the number of reliable temperature datasets (as opposed to available temperature datsets) numbered only a couple of hundred when he was making his study. Numbers tailed off dramatically as he regressed through each decade, with extremely poor coverage in the Southern Hemisphere throughout the study. It was for the resons of spatial numbers- not measurement quality- that Hansen decided that 1850 was not a practical start date and chose a later one which had slightly better spatial numbers. Even then there were very few thermometers giving consistent readings for anything other than the Northern Hemisphere and those mostly in Europe/North America.

This from Chiefio http://chiefio.wordpress.com/ illustrates the surprisingly small number of thermometers used in the GISS database (from the 1879 decade). (Article three draws much more from the remarkable work of E M Smith.)

The March of the Thermometers

Year, and 20 degree latitude bands, south to north. Thermometer years.
SP – South Pole SC – Southern Cold ST – Southern Temperate
SW – Southern Warm EQ – Equator NW – Northern Warm NT – Northern Temperate NC – Northern Cold NP – North Pole.

(So everything to the left of the EQ column is SH everything to the right of EQ is NH)

SP SC ST SW EQ NW NT NC NP

DecadeLat: 1709 0 0 0 0 0 0 0 1 0

DecadeLat: 1719 0 0 0 0 0 0 0 1 0

DecadeLat: 1729 0 0 0 0 0 0 0 1 0

DecadeLat: 1739 0 0 0 0 0 0 0 2 0

DecadeLat: 1749 0 0 0 0 0 0 1 3 0

DecadeLat: 1759 0 0 0 0 0 0 3 6 0

DecadeLat: 1769 0 0 0 0 0 0 6 10 0

DecadeLat: 1779 0 0 0 0 0 0 9 14 0

DecadeLat: 1789 0 0 0 0 0 0 16 16 0

DecadeLat: 1799 0 0 0 0 0 0 19 16 0

DecadeLat: 1809 0 0 0 0 0 1 24 20 0

DecadeLat: 1819 0 0 0 0 0 1 32 28 0

DecadeLat: 1829 0 0 0 0 0 2 54 48 0

DecadeLat: 1839 0 0 0 0 0 4 74 72 0

DecadeLat: 1849 0 0 1 0 2 6 93 82 1

DecadeLat: 1859 0 0 3 0 2 11 137 92 2

DecadeLat: 1869 0 0 15 0 3 7 173 103 1

DecadeLat: 1879 0 0 27 2 15 20 336 110 2

DecadeLat: 1889 0 0 44 10 18 48 624 184 3

DecadeLat: 1899 0 2 57 26 31 87 1175 309 3

DecadeLat: 1909 0 9 111 61 44 133 1510 382 5

DecadeLat: 1919 0 11 174 124 57 160 1789 479 8

DecadeLat: 1929 0 11 187 145 66 212 1961 545 16

DecadeLat: 1939 0 13 220 180 91 304 2156 713 26

DecadeLat: 1949 0 20 261 259 116 407 2412 887 37

DecadeLat: 1959 9 43 347 453 421 1010 3417 1249 80

DecadeLat: 1969 32 68 466 650 729 1310 4121 1511 105

DecadeLat: 1979 34 85 580 747 661 1269 4204 1511 103

DecadeLat: 1989 25 68 495 605 452 916 3805 1307 82

DecadeLat: 1999 9 32 212 250 224 429 2128 314 27

DecadeLat: 2009 7 20 102 132 159 316 1339 241 17

Figure 7-Thermometers by decade

More specifically in 1880 Africa had 14 thermometers; Asia 26, North America 233, Australia 30 and Europe 118.

There was a big upsurge in stations in N America from around this time and numbers rose to 1446 in 1914. Around 1880 there were a number of US weather related developments.

*General Hazen became the chief officer for the Signal Service (1880), but he was later discredited for allegedly embezzling $237,000 dollars. During his administration, other strife existed as the Army became increasingly unhappy that the enlisted men in the Signal Corps could not be pulled away from their duties and the weather.

More Info

*After General Hazen’s death in 1887, General Greely and President Benjamin Harrison were able to quiet some of the unrest and pushed to have the Signal Corps transferred to the Department of Agriculture on October 1, 1870. On July 1, 1891, all weather instrumentation and staff were transferred from the Signal Corps’ to the Department of Agriculture’s new civilian Weather Bureau.

*State Weather Services were organized starting in 1883 by Lieutenant. H. H. C. Dunwoody. In October 1895 control of the State Weather Services passed to the larger United States Weather Bureau formed in 1891.

http://weather.about.com/od/weatherhistory/tp/Signal_Service.02.htm

Whilst there was a lot happening in the States, they were by no means the prime weather service in the world-the British, Dutch, or Swedes would claim that honour. . There was an upsurge in the numbers of stations precipitated by the demands of the embryonic weather services in various countries, military requirements, and partly because this was the age of mass production and thermometers became cheaper and more readily available. It thus made a final progression from a scientific instrument in the hands of well trained recorders to a more workaday tool, with all that meant in terms of the quality of observations made at the ever expanding station network.

There were other driving forces in the weather world at this time, and the most important of these was the invention of the Stevenson screen by British civil engineer Thomas Stevenson- father of author Robert Louis Stevenson- in 1864

It is this device that tends to be the great dividing line between historic and modern era -in as much instrumental reliability is considered better after its creation than before. This impression is somewhat mistaken as shall be illustrated in article two, as it assumes trained observers- whose life work was often recording weather and temperatures- did not understand how to observe temperatures accurately before the advent of the Stevenson screen. Any potential shortfalls there may have been in the accuracy of the historic records have been corrected by later generations of dedicated researchers. One of the most famous of these is G Manley who spent a lifetime researching British temperature records and published his findings in an article dated 1974. The UK has the longest instrumental records of anywhere in the world, allowing recent climate changes to be judged in the context of the last 250-350 years. The Central England temperature (CET) series starts in 1659 and was enhanced by Parker et al. (1992) who added the daily series back to 1772, it is routinely updated and the monthly data can be found online.

http://www.cru.uea.ac.uk/cru/info/ukweather/

The Stevenson screen

http://en.wikipedia.org/wiki/Stevenson_screen

The Stevenson Screen or thermometer screen is a standard shelter (from rain, snow and high winds, but also leaves and animals) for meteorological instruments, particularly wet and dry bulb thermometers used to record humidity and air temperature.

It is kept 1.25m/4.1ft (UK standard) above the ground by legs to avoid strong temperature gradients at ground level, has louvred sides to encourage the free passage of air, and is painted white to reflect heat radiation, since what is measured is the temperature of the air in the shade, not of the sunshine.

Proper siting and construction are vital if an accurate reading is to be obtained and the early practice of constructing screens of asbestos board, size variations, siting confusion, internal coatings of paint, all had accuracy impacts, many of which persist to this day

http://209.85.229.132/search?q=cache:jH498fHvnIMJ:wattsupwiththat.com/2008/01/14/a-typical-day-in-the-stevenson-screen-paint-test/+invention+of+stevenson+screens&cd=5&hl=en&ct=clnk&gl=uk

and this; http://www.surfacestations.org/

It took some decades before the Stevenson screen became universal. This paper calls on the work of Parker and others in examining the effects of the Stevenson screen and earlier shelters on instrumental readings.

http://www.dvgu.ru/meteo/library/199548746943.pdf

Extract;

“Many early thermometer-stands were open to poleward, allowing reflected solar radiation to affect the thermometers by day, and permitting radiative heat-loss at night. As a result, recorded diurnal ranges were enhanced relative to what would have been measured in Stevenson screens (Figs. 2 and 3). The effect was greatest in summer. There were differences between types of thermometer-stand: for example, the night-time cooling evident in the Glaisher stand (Fig. 2; Margary, 1924) was not found in the French stand (Dettwiller, 1978). Also Young (1920) only found generally small biases in the USA’s fruit-region shelter. His sample, however, was small.

Wild’s apparatus, common in Russia and eastern and central Europe in the late nineteenth century was more complex, consisting of a cylindrical shield inside a

louvred screen. Parker (1994) provides illustrations. However, its biases also enhanced the recorded diurnal ranges (Fig. 4), though with less of an annual cycle than for the open stands. In the tropics, thatched or felted sheds were common until the 1920’s, the thermometers being suspended from the eaves in a cage, or fixed to a trellis in the shed. Receipt of reflected solar and emitted longwave radiation from the ground outside the shed made maxima too high relative to a Stevenson screen, and minima were also too high owing to retention of heat by the roofing material. According to the results of Field (1920) (Fig. 5a) diurnal ranges were slightly reduced on an annual average, but with some seasonal variation; Bamford (1928), however, obtained an enhanced diurnal range throughout the year (Fig 5b). The biases will have been affected by the material used in the shed, by the reflection and emission properties of the ground outside it, and by the radiation and advection (wind)

climate of the site. The results of all instrumental comparisons must be to some extent site-specific.

North-wall screened exposures yield reduced diurnal ranges (Fig. 6; Marriott, 1879). These exposures were common in much of central, northern and eastern Europe in the late nineteenth and early twentieth century, Russia before the 1870’s, USA until 1890, Canada in the late nineteenth century (Parker, 1994).

Hazen (1885) examined unscreened north-wall exposures, which were common in the USA and Prussia until the early 1890’s. His results were for fixed hours (as opposed to maxima and minima) and suggest a slight enhancement of the diurnal range. However, the results will have been dependent on the particular sites used, and generalizations must be made with caution. Some old exposures have never been compared with Stevenson screens. A particular example is the Canadian screen and shed described by Kingston (1878) and illustrated by Parker (1994). Reconstruction of the apparatus, and a series of comparative measurements have been recommended (Parker, 1994).

The above results show that in most, but not all, cases, apparent diurnal ranges were reduced by the introduction of new instrumentation.”

The slow spread of the screen can be seen in this article about the Australian experience;

http://www3.interscience.wiley.com/journal/21922/abstract

Extract;

“There is ample contemporary evidence that most meteorological thermometers in Australia were not exposed in Stevenson screens until very late in the nineteenth century, and in many places not until well into the twentieth century. There is also evidence, from a long-running comparison at Adelaide, that mean temperatures in a Stevenson screen are lower than in an open stand in Australian conditions. Thus, there are strong grounds for expecting that nineteenth century, and some early twentieth century, Australian temperatures are biased warm, relative to modern exposures.”

So if objections are made to the accuracy of pre Stevenson screen temperatures- and bearing in mind the effective date of almost universal introduction is around 1920- then logically some 70 years of Hadley and 40 years of Giss must be discounted.

At this stage three things must be considered -firstly that mercury or alcohol thermometers can not be used to compute fractional temperature differences. At best they may be accurate to 0.5 to 1 degrees in well trained professional hands.

Secondly, in these days of ‘global’ temperatures it is easy to forget that a thermometer was designed to merely record the microclimate immediately around the device, and thirdly that using such devices to record an accurate global temperature takes an extraordinary leap of faith that sufficient numbers of properly sited, consistently monitored and accurate thermometers are used, and that these stations were never moved, deleted or had all sorts of debatable corrections made to them. That the end result can only be approximate is evident –to believe we know global temperatures have risen by a figure as precise as say 0.7c since 1880 is rather fanciful.

Micro climates

The LIA thermometers project makes no claim beyond the recording of the micro climate around the instrument. The global temperatures go much further in gluing together numerous micro climates which often bear little relationship to the original reading as they have been adjusted so comprehensively.

This observation is fundamental to understanding the relative merits and accuracy of the individual LIA thermometers and the global datasets from Hadley and Giss. What a micro climate is, how they are affected by a variety of factors, and what lessons they can teach us in examining the viability of historic or modern records is explained by the following series of article-this first explains the general principles;.

http://www.timesonline.co.uk/tol/news/science/article6797699.ece

This next link is a good document of a practical study of micro climates (Teignmouth on the South coast of England, and Princetown on the uplands of Dartmoor a few miles away ) This also has very good study of London weather stations, amply illustrating micro climates and the impact of UHI (which will figure prominently in article three).

http://www.metoffice.gov.uk/corporate/library/factsheets/factsheet14.pdf

This about world climate zones;

http://www.metoffice.gov.uk/corporate/library/factsheets/factsheet16.pdf

The considerable impacts of land use on local/regional temperatures are discussed here;

http://pielkeclimatesci.files.wordpress.com/2009/08/r-329.pdf

As can be seen microclimates are influenced by many factors, prevailing winds-which can change, the warmth of the sea, humidity, forestation/ground use, relative sunshine, developments, prevailing weather systems etc. These can all alter microclimates characteristics over time so that it becomes warmer, cooler, wetter or dryer relative to another microclimate in which the characteristics –such as a change in the prevailing wind direction-come into play in a different manner.

The emphasis on global temperatures obscures the data the micro climates are providing. One of these is that there are dozens perhaps hundreds of locations from around the world that have been cooling for at least thirty years, some from the 1930’s and even earlier, in contradiction to the IPCC who assert;

“Over 1901 to 2000 as a whole, noting the strong consistency across the land-ocean boundary, most warming is observed over mid- and high latitude Asia and parts of western Canada. The only large areas of observed cooling are just south and east of Greenland and in a few scattered continental regions in the tropics and sub-tropics.”

http://www.ipcc.ch/ipccreports//tar/wg1/057.htm

Global data also obscures the fundamental impact of UHI on many individual locations, and also that some modern micro climate data no longer actually comes from the micro climate it originally began recording. Add to this data that has been adjusted to such a degree that any rational comparison becomes difficult, and the complexity of unravelling what is really happening becomes evident.

1850/1880 marks a watershed in the transition of the recording of micro climates to that of it being used to measure global temperatures. It also marks a fundamental clash in interpretation of history in as much Dr Hansen’s 1987 paper appears to make no acknowledgement that what was being recorded from 1880 was an upswing in temperatures from the lowest point of the climate cycle that heralded the final epoch of the little ice age. Indeed Dr Hansen says that he could find no sign of a cold period around 1880.

Clearly this interpretation is of fundamntal importance as there also appears to be no mention that taking readings from the top of the previous warm cycle would have caused entirely diferent conclusions to be reached than have been, namely that todays temperatures were nothing out of the ordinary when seen in context against the pre 1880 datasets when coupled with contemporary acounts of the times reaching back through the LIA to the MWP and Roman optimum.

These are themes that will be taken up in the next articles.

Acknowledgements and references not carried in the body of the article

http://en.wikipedia.org/wiki/Thermometer

http://www.zytemp.com/tutorial/History_Of_Thermometry.htm

http://www.makingthemodernworld.org.uk/stories/enlightenment_and_measurement/05.ST.05/?scene=5&tv=true

http://books.google.co.uk/books?id=n0ye-tRYTNkC&pg=PA185&lpg=PA185&dq=Works+of+Philo+of+Byzantium+(2nd+Century+B.C.)+and+Heron+of+Alexandria+expasnsion+of+air+by+heat+.&source=bl&ots=Aq5VBnw_-V&sig=W2O30MTo_f48P8nlwknGsuZPgf4&hl=en&ei=REX8StjtFoz24Abhr7jgAw&sa=X&oi=book_result&ct=result&resnum=1&ved=0CAgQ6AEwAA#v=onepage&q=&f=false

(About Greek thermoscopes)

http://weather.about.com/od/weatherhistory/ig/US-Weather-Bureau-History/Early-Weather-Records.htm

Figure 3-. Origin unknown.

click for PDF

I’ll have a lot more on this study later, but for now just a short rebuttal.

I believe this study is hopelessly flawed due to the fact that the authors take the data from the weather stations at face value without considering bias due to measurement error or siting error, both of which are rampant in the US surface station network.

Read my report at left.

While not all situations with poorly sited weather stations affect trends, a weather station like this one at the University of Arizona’s parking lot in front of the atmospheric science department is represenative of the kinds of problems that would lead to an increased number of new high temperature records set.

Above: official USHCN weather station, in the parking lot, Atmospheric Science Dept. University of Arizona, Tucson. Photo: Warren Meyer

Plus then there’s the error problem. For example we saw this summer that Honolulu set new record highs, but they turned out to be in error. The kicker is that NOAA let the records stand anyway! The problem is that a number of climate stations are at airports. Watch this NWS employee say on record that these airport weather stations are “placed for aviation purposes…not necessarily for  climate purposes.”

So take this NCAR study with a grain of salt, since the authors did not address any of these issues.

From NCAR: Record High Temperatures Far Outpace Record Lows Across U.S.

BOULDER—Spurred by a warming climate, daily record high temperatures occurred twice as often as record lows over the last decade across the continental United States, new research shows. The ratio of record highs to lows is likely to increase dramatically in coming decades if emissions of greenhouse gases continue to climb.

“Climate change is making itself felt in terms of day-to-day weather in the United States,” says Gerald Meehl, the lead author and a senior scientist at the National Center for Atmospheric Research (NCAR). “The ways these records are being broken show how our climate is already shifting.”

This graphic shows the ratio of record daily highs to record daily lows observed at about 1,800 weather stations in the 48 contiguous United States from January 1950 through September 2009.

Each bar shows the proportion of record highs (red) to record lows (blue) for each decade. The 1960s and 1970s saw slightly more record daily lows than highs, but in the last 30 years record highs have increasingly predominated, with the ratio now about two-to-one for the 48 states as a whole. [ENLARGE] (©UCAR, graphic by Mike Shibao.) News media terms of use*

The study, by authors at NCAR, Climate Central, The Weather Channel, and the National Oceanic and Atmospheric Administration (NOAA), has been accepted for publication in Geophysical Research Letters. It was funded by the National Science Foundation, NCAR’s sponsor, the Department of Energy, and Climate Central.

If temperatures were not warming, the number of record daily highs and lows being set each year would be approximately even. Instead, for the period from January 1, 2000, to September 30, 2009, the continental United States set 291,237 record highs and 142,420 record lows, as the country experienced unusually mild winter weather and intense summer heat waves.

A record daily high means that temperatures were warmer on a given day than on that same date throughout a weather station’s history. The authors used a quality control process to ensure the reliability of data from thousands of weather stations across the country, while looking at data over the past six decades to capture longer-term trends.

This decade’s warming was more pronounced in the western United States, where the ratio was more than two to one, than in the eastern United States, where the ratio was about one-and-a-half to one.

The study also found that the two-to-one ratio across the country as a whole could be attributed more to a comparatively small number of record lows than to a large number of record highs. This indicates that much of the nation’s warming is occurring at night, when temperatures are dipping less often to record lows. This finding is consistent with years of climate model research showing that higher overnight lows should be expected with climate change.

In addition to surveying actual temperatures in recent decades, Meehl and his co-authors turned to a sophisticated computer model of global climate to determine how record high and low temperatures are likely to change during the course of this century.

The modeling results indicate that if nations continue to increase their emissions of greenhouse gases in a “business as usual” scenario, the U.S. ratio of daily record high to record low temperatures would increase to about 20-to-1 by mid-century and 50-to-1 by 2100. The mid-century ratio could be much higher if emissions rose at an even greater pace, or it could be about 8-to-1 if emissions were reduced significantly, the model showed.

The authors caution that such predictions are, by their nature, inexact. Climate models are not designed to capture record daily highs and lows with precision, and it remains impossible to know future human actions that will determine the level of future greenhouse gas emissions. The model used for the study, the NCAR-based Community Climate System Model, correctly captured the trend toward warmer average temperatures and the greater warming in the West, but overstated the ratio of record highs to record lows in recent years.

However, the model results are important because they show that, in all likely scenarios of future greenhouse gas emissions, record daily highs should increasingly outpace record lows over time.

“If the climate weren’t changing, you would expect the number of temperature records to diminish significantly over time,” says Claudia Tebaldi, a statistician with Climate Central who is one of the paper’s co-authors. “As you measure the high and low daily temperatures each year, it normally becomes more difficult to break a record after a number of years. But as the average temperatures continue to rise this century, we will keep setting more record highs.”

An expanding ratio

The study team focused on weather stations that have been operating since 1950. They found that the ratio of record daily high to record daily low temperatures slightly exceeded one to one in the 1950s, dipped below that level in the 1960s and 1970s, and has risen since the 1980s. The results reflect changes in U.S. average temperatures, which rose in the 1950s, stabilized in the 1960s, and then began a warming trend in the late 1970s.

Even in the first nine months of this year, when the United States cooled somewhat after a string of unusually warm years, the ratio of record daily high to record daily low temperatures was more than three to two.

Despite the increasing number of record highs, there will still be occasional periods of record cold, Meehl notes.

“One of the messages of this study is that you still get cold days,” Meehl says. “Winter still comes. Even in a much warmer climate, we’re setting record low minimum temperatures on a few days each year. But the odds are shifting so there’s a much better chance of daily record highs instead of lows.”

Millions of readings from weather stations across the country

The study team analyzed several million daily high and low temperature readings taken over the span of six decades at about 1,800 weather stations across the country, thereby ensuring ample data for statistically significant results. The readings, collected at the National Oceanic and Atmospheric Administration’s National Climatic Data Center, undergo a quality control process at the data center that looks for such potential problems as missing data as well as inconsistent readings caused by changes in thermometers, station locations, or other factors.

Meehl and his colleagues then used temperature simulations from the Community Climate System Model to compute daily record highs and lows under current and future atmospheric concentrations of greenhouse gases.

About the article

Title: “The relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S.”

Authors: Gerald A. Meehl, Claudia Tebaldi, Guy Walton, David Easterling, and Larry McDaniel

Publication: Geophysical Research Letters (in press)

Lucia beat me to a post on this, so I’ll give her the honor here. Interesting thing though, the delay of Hadley may have provided a better data presentation. – Anthony

Guest Post by Lucia from The Blackboard

Guess what? The much anticipated Hadley monthly surface temperature anomalies are now available. I always use the NH+SH simple average.

Guess what else? According to this metric, the global surface temperature anomaly September 2009 cooled relative to August 2009 dropping from0.548C to 0.457C. In contrast, GISSTemp, NOAA/NCDC, UAH and RSS all reported distinctly warmer anomalies in September relative to August. This divergence is a pit surprising– though I’d have to plough through numbers to see if this sort of mismatch is unprecedented in the record.

One of the interesting happenings this month was Hadley’s decision to delay processing because they considered the some data they received to be obviously wrong. We don’t have details on precisely what was wrong about it, but I noticed large blanked out areas on their map:

Figure 1: Missing temperatures in Africa.

 

The blanked out areas do seem to be surrounded by warm regions. Maybe the computed value for September’s monthly average will rise when that region reports data Hadley trusts. In the meantime, Hadley’s September temperature is low relative to the other metrics.

Since we anticipate October temperature will be reported soon, and I suspect some revisions for September, I’ll just show the trends based on reported temperatures since both 2000 and 2001, and also compare them anomalies to the multi-model mean anomalies from the AR4 climate models driven by the A1B SRES.

Figure 2: Trends since 2000 and 2001

As you can see, EL Nino has caused temperatures to rise; the anomalies for individual months values are currently approaching the mean value projected by the models. As El Nino warms further, the observations for individual months may finally catch and surpass the models, as the do from time to time. However, it’s going to take sustained warming for the trends since either 2001 or 2000 to catch up with the projections. Will it happen? We’ll wait and see.

Lucia, I don’t think this is anything out of the ordinary to have so may data holes. Look at GISS for September:

Link to original at GISS is here

The trend of missing stations in GHCN continues. It appears that Hadley actually has more stations than GISS. Maybe the delay was to allow more trickle in of late reporters. – Anthony

It has been awhile since we’ve looked at stations in the United States Historical Climatology Network. Last night I was doing quality control and updates to the database and came across this photo by Surfacestations.org volunteer John W. Slayton. He’s been surveying dozens of stations this summer and has been adding quite a number of USHCN surveys to the database. We all thank him.

This photo has been retouched to minimize the sun glare. Find the thermometer.

Official NOAA Climate Station of Record, Fillmore, UT - click to enlarge

Give up? Here’s the photo labeled to point it out:

Official NOAA Climate Station of Record, Fillmore, UT, looking north - click to enlarge

Here’s another view showing the rain gauge:

Fillmore, UT USHCN Climate Station of Record, looking south - Click for larger image

You can see more photos here at the Surfacestations.org gallery server

The tree shade and the junk makes for an interesting combination of exposure factors. This station is in the backyard of a private observer whom I won’t name. Certainly you can’t fault the observer for the measurement environment, people are free to do with and maintain their property however they wish. And as we’ve seen time and again, NOAA/NWS usually does not concern itself with the measurement environment. As long as the station produces data, they are generally satisfied. However, this sort of arrangement doesn’t always yield a controlled measurement environment.

For example the month the photo was taken, the observer missed only 7 days.

July 2009 B91 report for Fillmore, UT USHCN station - click to enlarge

So the question in my mind is: how does the albedo of old tires compare to asphalt?

The surfacestations project is approaching the 90% mark now for the nationwide survey. I’ll post an update in a few days. Papers for peer review are still in process, but my goal is to have the first ready for publication by the end of the year. A combination of illnesses, business duties, and another paper by a co-author has slowed the progress.

By Joseph D’Aleo, AMS Fellow, CCM

In this recent post, we discussed the problems with recent data that showed the argument presented by the EDF’s millionaire lawyer playing clueless environmentalist on Lou Dobbs Tonight that this will be the warmest decade is nonsense. This claim was well refuted and Al Gore’s credibility disassembled by Phelim McAleer, of the new documentary Not Evil, Just Wrong that challenges the lies and exaggerations (totalling 35) in Al Gore scifi horror comedy film, An Inconvenient Truth. 9 were serious enough for a UK judge to require a disclaimer itemizing them be read whenever, the movie was shown in the schools.

The world’s climate data has become increasingly sparse with a big dropoff around 1990. There was also a tenfold increase in missing months around the same time. Stations (90% in the United States which has the Cadillac data system) are poor to very poorly sited and not properly adjusted for urbanization. Numerous peer review papers suggest an exaggeration of the warming by 30%, 50% or even more. The station dropout can be clearly seen in the two maps below with the number of station going from over 6000 to just 1079 from April 1978 to April 2008.

April 1978 GISS global plot - click for larger image

April 2008 GISS global plot - click for larger image

See the big gaps in the recent data in Canada, Greenland, Africa, South America, parts of western Asia, parts of Australia.

SEE FOR YOURSELF

Take this test yourself to see how bad a shape the global data base is.  Look for yourself following these directions using the window into the NOAA GHCN data provided by NASA GISS here.

Point to any location on the world map. You will see a list of stations and approximate populations. Locations with less than 10,000 are assumed to be rural (even though Oke has shown that even a town of 1,000 can have an urban warming of 2.2C).

You will see that the stations have a highly variable range of years with data.

Try and find a few stations with data that extends to 2009. To see how complete the data set is for that station, click in the bottom left of the graph Download monthly data as text.

For many, many stations, you will see the data set in a monthly tabular form has many missing data months mostly after 1990 (designated by 999.9).

See larger image here

This required the data centers to estimate data for the grid box for that location with other stations nearby (homogenization). In the 2008 plot above only 1079 stations were used. NASA went to locations within 250 km (155 miles) to find data for the grid boxes. For grid boxes without stations within 250 km, they are left blank, thus the large gaps.

Most of the stations that dropped out were rural. More of the missing data points are having their missing months filled in with more urban data in the grid boxes.

See larger image here

WUWT Volunteer John Goetz created this video that shows the worldwide dropout of weather stations:

One example of how good or bad this works is from Maine. Volunteers completed surveys of the United States Historic Climate Network (USHCN) temperature stations in Maine for Anthony Watts surface station evaluation project. The survey determined that every one of the stations in Maine was subject to microclimate or urbanization biases. One station especially surprised the surveyors, Ripogenus Dam, a station that was officially closed in 1995.

See larger image here

Despite being closed in 1995, USHCN data for this station is publicly available until 2006! (GISS stopped in 1995)

Part of the USHCN data is created by a computer program called “filnet” which estimates missing values. According to the NOAA, filnet works by using a weighted average of values from neighboring stations. In this example, data was created for a no longer existing station from surrounding stations, which in this case as the same evaluation noted were all subject to microclimate and urban bias, no longer adjusted for. Note the rise in temperatures after this before the best sited truly rural station in Maine was closed. GISS does display this station that did incorporate the “filnet” data input for missing months although as noted they stopped its plot in 1995 which NOAA extended artificially to at least 2006.

How can we trust NOAA/NASA/Hadley assessment of global changes given these and the other data integrity issues?  Given that Hadley has destroyed old original data because they were running out of room in their data cabinet, can we ever hope to reconstruct the real truth?

As one of our scientist readers noted: “Well, the 999.9s you showed me today sure opened my eyes…the ramifications are stunning. I knew about the drop-off of stations before but never that existing station reports are so full of gaps or that they’re getting temperature readings from “ghost” stations. This is, as you have said, GARBAGE. See PDF here.

KNMI has been measuring the wrong temperature for years

WUWT reader Mike writes with this little bombshell on one of the world’s leading meteorological agencies. It seems they can’t get their thermometer siting correct which resulted in a bias to the record. Hmmm. Where have we heard this before? The newspaper “AD” in the Netherlands has picked up the issue with two separate stories.Mike writes:

Dear Anthony,

I left this on the “tips” thread on WUWT, but since it is also relevant to surface stations, I felt you should hear of it directly.  It probably deserves a whole story on WUWT.

As you probably already know, KNMI De Bilt is the only station in the Netherlands used for GISTEMP. The nearest long-term station is in a suburb of Brussels, hence is undoubtably UHI-polluted. De Bilt is the only long record stn in NL & within 150km in any direction would be a useful correction.

Two stories caught my eye in the Dutch papers today about a 0.5-degree error in the De Bilt record which was miraculously corrected this summer with a station move of 200 m without anyone being told of it. Here are the links to and my translations of the articles.

Mike’s translations of the newspaper stories are below, I’ve added relevant graphics. – Anthony

DE BILT – Weather Institute KNMI has been measuring the years incorrect temperatures on its grounds in De Bilt due to an incorrect setup of a thermometer.

The instrument stood too close to a line of trees, due to which on average half a degree (Celsius) too high was measured.

After discovery of the fault the thermometer was moved to an open spot on the measurement field before last summer, the KNMI has confirmed. Due to the change the average measured temperature fell half a degree. This measurement should be reliable.

The mistake resulted in that the KNMI has announced more “official” summery and tropical days than there were in reality. According to the Institute, the defect has not or hardly influenced the scientific discussion on climate change, because researchers use the data from a large number of weather stations.  SUZANNE DOCHTER

Above: GISS Temperature plot for De Bilt KNMI – notice the step function. Click for source data.

Here’s a picture and metadata for De Bilt, direct from KNMI. While I can’t be certain, this photo appears to be after the move:

Checking some nearby stations in GISS, click for source data:

The GISS plot for Maastricht Airport:

One whole data point? Why does GISS keep a station in the database with only one data point?

UPDATE: Well if GISS can’t find the data for Maastricht Airport, everybody else can, and damn quick:

See Weather Underground for current conditions.

And this website, tutiempo.net , has the complete climatic data set back to 1949.

http://www.tutiempo.net/en/Climate/Maastricht_Airport_Zuid_Limburg/63800.htm

Call out to GISS: Hey Gavin, as an American Taxpayer that funds your work, I request you take a moment from moderating realclimate.org and put some work into updating this record.

Here’s the next closest station, Essen,  according to GISS a city of 7.5 million – doesn’t look much like the KNMI record:

2nd story –

KNMI has been deaf to criticism for years

WAGENINGEN – weather Institute KNMI has been deaf to years of criticism from competitor Meteo Consult of its temperature measurements in De Bilt

Weather specialists from the Wageningen-based Meteo Consult have been expressing their distrust for years, because the KNMI figures in De Bilt were always a bit warmer than in Cabau, 16 km away, where there is also a KNMI thermometer. The position of both places could, according to Meteo Consult, not explain the temperature difference of on average half a degree (Celsius). It was also not taken into account that De Bilt is located in a more built-up, and probably therefore warmer, surroundings than Cabau, near IJsselstein.

The meteorologists from Wageningen discovered this summer to their amazement that the temperature difference between both places in the KNMI figures had more or less disappeared. On enquiring of the De Bilt employees, it appeared that the thermometer had been moved. Since the intervention, the measurements from De Bilt show not 1/2°, but on average just 2 hundredths of a degree warmer than Cabauw, according to the spokesman of Meteo Consult.

This summer it appeared that the temperature difference was suddenly resolved. Again discussions blazed between the weather specialists and it was decided to closely compare the measurements between Bilt and Cabauw. “It was thus discovered that last summer in De Bilt was still 1/2 degree warmer and this year there was just a difference of 0.02 degree Celsius”, explained a spokesman of Meteo Consult.

The organisation decided to call the KNMI and heard that the “weather cabin” [translation: Stevenson Screen], in which the thermometer is located, had been moved. According to the KNMI the measuring instrument stood too close to a row of trees. Because the trees continued to get taller, the wind began to influence the temperature measurements too much. Now the “weather cabin” has been moved 200 m away, to a more open spot on the measurement field of De Bilt.  KNMI employee Cees Molenaars cannot say how much influence the old placement of the thermometer has had on weather reports. “We must investigate that. We only regret is that we did not keep Meteo Consult and other parties informed of the movement.”

The thermometer of De Bilt is the official measurement used for determining heatwaves, cold waves, and summery days. To speak of a heatwave it must be at least 25°C released 5 days. Also it must be warm than 30° for 3 days. At 25° one can talk about a summery day.

With a cold wave, freezing temperatures must be measured for 5 adjoining days at De Bilt, with also 3 days with a hard frost. “The differences in minimum temperature between de Bilt and Cabauw were much smaller,” said the spokesman of Meteo Consult. “The chance that a cold wave is missed, is thus smaller.”

The thermometer in De Bilt has less influence on KNMI weather predictions. These are performed on the basis of the data of tens of measurement stations. Further, for scientific purposes, such as climate change research, the central Dutch temperature was brought to life long ago. For this, data from various stations is used [NOT TRUE -- GISTEMP ONLY USES DE BILT!]. Meteo Consult are above all happy that the riddle has been solved. For fun they have also calculated what an extra half degree in De Bilt would have meant for this summer: 5 extra summery days and 2 tropical ones.

====================

Coincidentally, I’ve been conversing with Jos de Laat of KNMI, the Dutch Meteorological Institute who offered some scans of weather station siting specifications from the World Meteorological Institute (WMO)

he writes:
OK then, you can find the first part of the report here (~ 1 Mb):

http://www.knmi.nl/~laatdej/TMP/WMO488.pdf

Especially the beginning of part 3 is relevant, I guess. Because of document size considerations for now I only scanned up to paragraph 3.1.2.1.7 (after paragraph 3.1.2.1.7 the description of requirements for measuring on other locations like sea and the free troposphere starts).

Descriptions of sensor and siting requirements are also available online (see below) …

http://www.wmo.ch/pages/prog/www/IMOP/publications/CIMO-Guide/Draft%207th%20edition/Part1-Ch01FINAL_Corr.pdf

http://www.wmo.ch/pages/prog/www/IMOP/publications/CIMO-Guide/Draft%207th%20edition/Part1-Ch02Final.pdf

… but they are more formal and largely based on WMO report 488, which contains some interesting quotes that are not present in later reports. The online reports also refer to the report below, which unfortunately I was not able to locate either online nor in our library.

World Meteorological Organization, 1993a: Siting and Exposure of Meteorological Instruments (J. Ehinger). Instruments and Observing Methods Report No. 55, WMO/TD-No. 589, Geneva.

These specs are worth a read, because they show that quite a lot of thought and analysis went info choosing the specs.

As for the 100 feet cited by the NWS on this page: http://www.nws.noaa.gov/om/coop/standard.htm

I suspect its a round off of 30.48 m where 30 meters is the minimum distance to an artificial heat source cited for a Class 2 climate site as defined by the specs used in the Climate Reference Network (CRN) which has a French lineage, and likely traces back to WMO.

It seems that no matter where you look, meteorological agencies can’t follow siting specifications.

Steve McIntyre of Climate Audit has more on De Bilt and the adjustments that are being applied there:

http://www.climateaudit.org/?p=1650

How to properly place your outdoor thermometer

04:52 PM PDT on Wednesday, July 29, 2009

By TRAVIS PITTMAN / KING5.com

excerpts:

SEATTLE – With temperatures in the Puget Sound region breaking records this week, many people are playing a watching and waiting game – waiting to see when the thermometer outside their home will reach triple digits.

…

Below is a photo of a thermometer sent to KING 5 News Tuesday afternoon by a viewer in Oso, east of Arlington. It clearly shows the temperature reading 116 degrees. You can also clearly tell the sun is reflecting off it and it’s mounted right next to a building.

source: KING 5 Viewer…

The National Weather Service says this is where you need to place your thermometer to get an accurate reading:

- It must be in a shaded, well-ventilated and open area, 5 feet above ground, give or take a foot.

- Away from sprinkler systems

- No closer than four times the height of any obstruction. For example, if a building is 10 feet tall, it needs to be no closer than 40 feet from that building.

- Located over natural ground such as grass, dirt or sod.

- At least 100 feet from road or concrete.

…

The picture they provide is what the surfacestations project is all about. Note the 100 foot distance from asphalt.

source: National Weather Service

Here is a diagram of how to properly place your thermometer to get an accurate reading.

Full article here h/t to WUWT reader “Ed”

Weather station, Mozal - Aluminum Smelter - Maputo, Mozambique - installed in 1998

More weather station photos from Africa here.

These stations shown and linked above are not GHCN stations as far as I can tell, but the siting was interesting nonetheless.

This new paper by John Christy, who works with Dr. Roy Spencer on the UAH dataset, points out that Tmin seems to have a signal in Africa where Tmax does not. Land use changes and aersols that affect the boundary layer at night are theorized and possible reasons. – Anthony

Surface Temperature Variations in East Africa and Possible Causes
JOHN R. CHRISTY, WILLIAM B. NORRIS, AND RICHARD T. MCNIDER
Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

ABSTRACT
Surface temperatures have been observed in East Africa for more than 100 yr, but heretofore have not been subject to a rigorous climate analysis. To pursue this goal monthly averages of maximum (TMax), minimum (TMin), and mean (TMean) temperatures were obtained for Kenya and Tanzania from several sources. After the data were organized into time series for specific sites (60 in Kenya and 58 in Tanzania), the series were adjusted for break points and merged into individual gridcell squares of 1.258, 2.58, and 5.08.

Results for the most data-rich 58 cell, which includes Nairobi, Mount Kilimanjaro, and Mount Kenya, indicate that since 1905, and even recently, the trend of TMax is not significantly different from zero. However, TMin results suggest an accelerating temperature rise.

Uncertainty estimates indicate that the trend of the difference time series (TMax2 TMin) is significantly less than zero for 1946–2004, the period with the highest density of observations. This trend difference continues in the most recent period (1979–2004), in contrast with findings in recent periods for global datasets, which
generally have sparse coverage of East Africa.

The differences between TMax and TMin trends, especially recently, may reflect a response to complex changes in the boundary layer dynamics; TMax represents the significantly greater daytime vertical connection to the deep atmosphere, whereas TMin often represents only a shallow layer whose temperature is more dependent on the turbulent state than on the temperature aloft.

Because the turbulent state in the stable boundary layer is highly dependent on local land use and perhaps locally produced aerosols, the significant human development of the surface may be responsible for the rising TMin while having little impact on TMax in East Africa. This indicates that time series of TMax and TMin should
become separate variables in the study of long-term changes.

Some excerpts from the paper:

c. Possible causes for TMax and TMin differences The fact that the trends in the two temperature measurements (TMax and TMin) are likely significantly different encourages an examination of the causes for the warming of TMin and the significance of trends in TMin in the context of tracking global climate change.  Given a lack of detail on station siting and uncertainties in specifics on the boundary layer in East Africa, definitive reasons for the trends may not be available.

However, general aspects of boundary layer behavior may provide some guide for interpreting the trends.Thus, the following should be viewed as a context and hypothesis for the trend differences that deserve discussion and further attention.

…

From the conclusion:

For the 100-yr period from 1905 to 2004 in this grid cell, the trends were near zero for both TMax and TMin, but confidence in these results is low because of the relatively sparse data in the years before 1946. Beginning with 1946 and ending in 2004, near-zero trends were found for TMax. The TMin trends were more positive, and significantly so based on both measurement error and temporal sampling error. It is difficult to assess the
measurement error of these trends, but using the spread of 20 realizations in which the construction parameters
were varied, the range of 60.108C decade21 is plausible. The fact that the difference in trends in TMax and TMin continues, and in fact accelerates, in the period of 1979–2004 in East Africa may be important in interpreting the results of Vose et al. (2005).

While it is possible that East Africa difference trends are indeed different than that of the globe as provided by Vose et al. (2005), there is concern that the reduced number of stations in the 1979–2004 GHCN dataset may not be sampling many of the areas of the globe that are behaving like East Africa. Thus, it is important that the GHCN dataset be expanded to include more stations distributed around the globe.
The noticeable difference in trends of TMax and TMin implies that daytime and nighttime temperatures are responding differently to environmental factors. Changes in the surface characteristics and the boundary layer atmospheric constituents may be responsible for the relatively recent and rapid rise in TMin. There appears to be little change in East Africa’s TMax, and if TMax is a suitable proxy for climate changes affecting the deep atmosphere, there has been little impact in the past half-century.

The investigation of the surface temperature record as an indicator of human-induced climate change involves understanding the complex behavior of boundary layer processes (where surface temperatures are actually measured) and how temperatures within it are affected by the numerous changes that occur. This is an area of research open for considerable inquiry because it raises new questions concerning the types of
data indices now used to detect climate change.

At the least, the time series of both TMax and TMin should become separate variables to be studied for long-term changes.

Full paper is available here as a PDF 1.9 MB

I’m pleased to announce that the surfacestations.org project has now surveyed over 1000 of the 1221 USHCN stations in the USA, putting the percentage of the survey at over 82% now.My sincere thanks to the many volunteers who stepped up recently to survey additional stations in Texas, New Mexico, Arizona, Colorado, and many other states.

Here is what the coverage looks like as of 7-14-09

Here is the breakdown by state. Note that 5 states are now 100% completed.

Here is a chart to show the table data above:

Note the states that are lacking the most in coverage are Virgina, Missouri, Texas, South Dakota, Maryland, Wisconsin, West Virginia, Nebraska, Pennesylvania, Iowa, and Idaho. These states are all below 80%. The states I would most like to get more surveys from are Missouri, Virginia, and Upstate New York which has a cluster of stations untouched. Arrangements have been made to get three stations in Texas this coming weekend: Alice, Falfurrias, and Rio Grande City, so anyone who is considering Texas can cross those off the list.

unsurveyed USHCN stations - click for larger image

For those that wish to help getting the final stations here is a Google Earth KML file that will help you locate the remaining stations yet to be surveyed in the USA.

Download Google Earth KML file here (sincere thanks to Gary Boden for his preparation of this)

Just download it (right click, save as) and then drag and drop to Google Earth, which can be downloaded free here.

The coordinates are mostly accurate, but it is always a good idea to get station location descriptions from NCDC’s MMS database also. Often they can be located easier by description than by coordinates. Note the the KML file has descriptions also along with the COOP ID number to help you get a match with NCDC’s database.

If you wish to help in surveying the remaining stations, go to the surfacestations.org project and complete the signup process.

I know that many people have been waiting for an analysis of the data. That is in process right now and a paper suitable for peer review is being prepared. I’ll answer the most obvious question ahead of time, and that is: no I will not be posting the results here first. After the paper has been set for publication, and within the rules of the journal, the paper, all of the data, methods, and results will be made public for anyone who wishes to replicate the work or to challenge it. There will be no hidden folders marked “censored” or incomplete MATLAB code. I’ll post the Full Monty once cleared by the journal.

That being said, given the tasks ahead for me, I’ll be posting far less frequently on WUWT. In the meantime, please be patient and let me finish this up with my co-authors.

Some people have wondered why I have taken two years now before going into data analysis mode. There are a couple of reasons.

1- Getting the best stations. The number of well sited stations are so few, getting enough to do a valid comparison to the poorly sited stations was a challenge.This is why I’ll continue to ask for additional surveys until we reach a publication deadline. There are so few “best” stations that even adding a handful more will be statistically significant. So please, keep up the surveys.

2- Coverage. I wanted to be absolutely certain that I had an undisputably large enough sample both in percentage volume and in spatial distribution. There were some folks who did some analysis using data from early in the project, such as John V at about 30% (with very poor spatial distribution) and the recent NCDC Talking points memo at 43%  NCDC “thought” they had the most current data, but they don’t have it, nor did they ask before attempting that analysis. That was an error on their part, and they are aware of it now. I’ve been in touch with the principal investigators at NCDC.

3- Patience is a virtue. If I had done analysis at 30 or 40%, as many suggested I do, and the analyzed results suggested that “siting mattered significantly” to the accuracy of the US Temperature record, I would be immediately vilified for having an inadequate sample, and rightly so. Interestingly, no such criticisms have been levied at NCDC by the AGW blog community for their results in the “talking points memo” at 43%, or at John V at 30%. Yet those results are being held up as examples of valid results by some. A double standard for statistical significance is something we’ve seen before in examples demonstrated by Steve McIntyre and others. Yet even without the statistical analysis, it is clear that the USHCN has not been well maintained. NOAA/NWS has closed many stations that we have highlighted, and even some we haven’t. Most recently Telluride, CO which is another story. If nothing else, this project is helping to get the USHCN network cleaned up. NOAA agrees in practice, as does NCDC, otherwise the US Climate Reference Network (USCRN) would not have been created nor would there be an HCN modernization program if the USHCN was in an acceptable condition.

I wish to thank everyone who has helped in making this project continue to the level of coverage it has. Regardless of the outcome of the analysis, whether it shows that siting matters or it does not, one thing can always be said with pride: this survey is a one of a kind volunteer accomplishment that NOAA couldn’t do themselves.

It has been a long road, fraught with roadblocks, frustration, and criticisms.  I appreciate everyone who has helped me along the road.

Guest post by John Goetz

As noted in the previous post, GISS has released their monthly global temperature summary for June, 2009. This month’s whopping anomaly of 0.63C is once again much higher than that of RSS, UAH, and even NOAA, which is the source of the GISS temperature data. Not only is the anomaly higher than the other metrics, but it is trending in the opposite direction.

Temperature data from 1079 stations worldwide contributed to the analysis, 134 of them being located in the 50 US states. Data from essentially the same few stations have been used for the past twenty-four months. Many, many hundreds of stations that have historically been included in the record and still collect data today continue to be ignored by GISS in global temperature calculations.

Once again, the bulk of temperatures comprising the present-day worldwide GISS average come from airports – in this case 554 airports, according to the NOAA metadata from the V2 station inventory. In the US, the ratio of airports to total stations continues to run very high, with 121 out of the 134 reporting stations being located at airports.

Why worry about airports? Aside from recent posts on this site documenting problems with airport ASOS equipment in the US, WUWT has also documented a number of equipment siting problems, notably the typical close proximity of the equipment to a tarmac heat sink. Airports can introduce a mini-UHI effect where one would otherwise not be found.

The NOAA metadata is not entirely accurate, and several stations located at airports are not noted as such. Some examples include Londrina and Brasilia in Brazil, Ely / Yelland in Nevada, and Broome in Austrailia. Those stations were easy to find because they had “airport” (or some variant) in the station name. A check of coordinates using Google Earth confirmed the airport locations.

Let’s examine the metadata a little further, shall we?

NOAA says that 345 of the stations it passes on to GISS are rural and presumably free of UHI influence. Fifteen of those stations are located in the US. However, only 201 of those rural stations are not located at an airport, and therefore presumably free of UHI effects (including tarmac heat sinks). In the US, only one of the fifteen stations is listed as both rural, and not located at an airport: Ely / Yelland in Nevada.

Doh!!! As noted above, that station is located at an airport – confirmed not just by Google Earth, but also by NOAA’s NCDC website as well! This means that all of the US temperatures – including those for Alaska and Hawaii – were collected from either an airport (the bulk of the data) or an urban location.

As for the rest of the world, some of the stations listed as being rural and not at an airport have metadata indicating they are located in an area of “dim” or “bright” lights. Filtering those out, we find a total of 128 stations that are rural, not at an airport, and “dark”.

Why are “dark” stations important? Recall that GISS uses dark stations to adjust for UHI in the urban stations. With only 128 dark stations available, none being in the US, it would seem this is an impossible task.

Fortunately, GISS adjustment rules allow old data to be used in adjusting new data. The older “non-reporting” rural weather stations continue to adjust reporting urban stations, even though the most recent two years of overlap is missing.

Thankfully, the algorithms are robust enough to calculate adjustments to the 100th of a degree even when data is missing.

That windchill is vicious, be sure to dress warmly going outside at Key West. Cold kills. Actually the new record low was colder than that shown above. It hit -27F earlier. See the complete NOAA report here (PDF)

OK fun aside, this is obviously another ASOS thermohygrometer malfunction, but one in the opposite direction that we usually see. But, there’s an interesting twist here that will provide a useful test of the integrity of data handling policy within NOAA/NWS. Please read on.

Here is what our offending ASOS in Key West looks like. It was recently surveyed on 6/1/2009 and was the last USHCN station surveyed in Florida to complete the USHCN state survey.

Key West airport ASOS with maintenance technician at ready - click for image gallery

Early in June, there was an incident in Honolulu International Airport where the ASOS station there malfunctioned and it set a string of new high temperature records for Honolulu.

Those records still stand for Honolulu despite protest even though it was clear that fixing the ASOS sensor dropped the temperature dramatically and immediately. I did an analysis at the time comparing PHNL to another COOP station just four miles away. The differences were obvious.

Graph of PHNL and PTWC station data for June 2009 – click for larger image

So now the question is, we have another obvious malfunction, but in the opposite direction.

Will NOAA keep this new “record low” which like the Honolulu record highs a fault of a ASOS equipment failure? Or, will they throw it out?

To be consistent with the Honolulu decision they would naturally keep it, though in both cases, logic dictates the data should be thrown out.

The other question is: How long will it take them to detect and fix this ASOS station? As of midnight on 7/11/2009 it was still reporting -13F

Here is the URL to watch for yourself to see when NOAA fixes the problem:

http://www.srh.noaa.gov/data/obhistory/KEYW.html

A big WUWT hat tip to Corky Boyd for this one.

UPDATE: Either the sensor has started working again on its own, or has been repaired. However there’s something still not quite right as it is now apparently snowing at 9:53 AM in Key West.

People send me stuff. My Inbox bursts daily with ideas, suggestions, papers, and photos.

Here is a climate monitoring station in Tremonton Utah. Notice anything peculiar about the placement of the temperature sensor? It is the white “bee hive” on the pole on the asphalt.

Tremonton UT COOP-A Climate station looking south - click for larger image

Note the conduit for the cable to the MMTS. This underscores something I’ve been saying about the MMTS installation for some time. The COOP managers that install these aren’t given the tools or time to get past obstructions like asphalt and concrete, thus the MMTS ends up closer to buildings than the “wireless” Stevenson Screen.

Randy Julander writes in:

Randy Julander here, snow survey supervisor, NRCS, USDA. Here are a few pics from the Tremonton Utah MMTS site which is right outside our NRCS field office in Tremonton. Normally there is a large truck parked right next to the sensor. As you can see, next to the building, next to the air conditioner, asphalt everywhere. Nice placement.

I’ll say, right on the pavement, 10 feet from the building. Randy mentions a truck being parked by the sensor. It shows up nicely on the Google aerial view:

Tremonton UT aerial view from Google Earth - click for larger image

A live Google maps interactive view is here

Temperature measurement issues aside, I wonder what the wind tunnel between the vehicles does for the accuracy of the rain gauge?

Here is the view looking north showing the parking lot that doubles as a climate monitoring station.

Tremonton UT COOP-A station looking north - click for larger image

In the photo below, notice how the rain gauge and MMTS have been placed in parallel with the diagonal parking. This is not accidental.

Tremonton UT COOP-A looking northwest - click for larger image

While we have many people who are actively surveying the USHCN network, there are still hundreds of other NOAA/NWS COOP stations that are not part of the special USHCN subset of stations.

There is also the COOP-A network, which is used in climate and the is reported to NCDC, just like USHCN.

Tremonton NCDC MMS record - click for larger image

Click for larger image

Most importantly, these stations are used by NCDC’s FILNET program, which will “fill in” missing data for a single station.

Here’s more on FILNET from NCDC:

FILNET (Fill Missing Original Data in the Network)

Estimates for missing data are provided using a procedure similar to that used in SHAP. This adjustment uses the debiased data from the SHAP and fills in missing original data when needed (i.e. calculates estimated data) based on a “network” of the best correlated nearby stations. The FILNET program also completed the data adjustment process for stations that moved too often for SHAP to estimate the adjustments needed to debias the data.

The B91 for Marysville is shown at left. Note the significant amount of missing data.

This happened because the volunteer observer was the fire station office manager, who didn’t work weekends or holidays, and had vacation throughout the year. Even though the fire station was manned 24/7, unfortunately the firefighters on-duty did not participate in keeping the records.

See a B91 form page from the Marysville USHCN record at left and note the missing data.

COOP-A stations like Tremonton Utah one are used by NCDC’s FILNET to interpolate missing data for nearby USHCN stations. Thus, it is just as important that they also be properly sited and maintained.

It appears though that they may suffer from the same sort of maintenance and siting issues that the USHCN does. After all, other than being a special subset of the COOP-A network, chosen for continuity of records over a long period and a minimum of site moves, there really is no other difference between USHCN and COOP-A stations.

They are all part of the same group and are maintained by the same people using the same tools and methods.

It seems that the sot of problems we see at Tremonton UT are widespread in the entire COOP-A network as well as USHCN. I’ll have more examples in future posts.

Roger A. Pielke Sr. Comments On The NCDC Talking Point Response To The Report “Is The U.S. Surface Temperature Record Reliable?” By Anthony Watts

The National Climate Data Center (NCDC) has responded to the excellent report

Watts, A. 2009: Is the U.S. Surface Temperature Record Reliable? 28 pages, March 2009 The Heartland Institute [hard copies available from The Heartland Institute 19 South LaSalle Street #903 Chicago Illinois 60603]

which I weblogged on at “Is The U.S. Surface Temperature Record Reliable?” By Anthony Watts.

The NCDC “Talking Points” released on June 9, 2009  are available at

Talking Points related to: Is the U.S. Temperature Record Reliable?

Unfortunately, the author of the NCDC Talking Points cavalierly and poorly responded to Anthony Watts report. They did not even have the courtesy to cite the report! {UPDATE 7/3/09: They have now cited Anthony’s report, but retained the original date of the Talking Points of June 9 2009).

Below, I comment on their response.

NCDC Talking Point #1

Q. Do many U.S. stations have poor siting by being placed inappropriately close to trees, buildings, parking lots, etc.?

A. Yes. The National Weather Service has station siting criteria, but they were not always followed. That is one reason why NOAA created the Climate Reference Network, with excellent siting and redundant sensors. It is a network designed specifically for assessing climate change. http://www.ncdc.noaa.gov/oa/climate/uscrn/. Additionally, an effort is underway to modernize the Historical Climatology Network, though funds are currently available only to modernize and maintain stations in the Southwest. Managers of both of these networks work diligently to put their stations in locations not only with excellent current siting, but also where the site characteristics are unlikely to change very much over the coming decades.

Climate Science Response

Their answer confirms what Anthony Watts and colleagues have carefully documented.  An obvious question is why did not NCDC elevate this as a priority sooner? Moreover, if the current sites can be “adjusted” to be regionally representative, why does NOAA even need the new Climate Reference Network? The answer to that is that they have recognized for years that there is a problem with the siting of the surface stations, but deliberately attempted to bury this issue until Anthony Watts and colleagues confronted NCDC with the issue.

NCDC Talking Point #2

Q. How has the poor siting biased local temperatures trends?

A. At the present time (June 2009), to the best of our knowledge, there has only been one published peer-reviewed study that specifically quantified the potential bias in trends caused by poor station siting: Peterson, Thomas C., 2006: Examination of Potential Biases in Air Temperature Caused by Poor Station Locations. Bulletin of the American Meteorological Society, 87, 1073-1080. Written by a NOAA National Climatic Data Center scientist, it examined only a small subset of stations – all that had their siting checked at that time – and found no bias in long-term trends. The linear trend in adjusted temperature series over the period examined was nearly identical between the stations with good siting and the stations with poor siting, with the stations having poor siting showing slightly less warming. The following questions address implications from that paper.

Climate Science Response

This is blatantly untrue and the author of these talking points know that. Tom Peterson, for example, was even a reviewer of the Pielke 2007a and 2007b papers, and was aware of the Pielke et al 2002 paper.

Pielke Sr., R.A., T. Stohlgren, L. Schell, W. Parton, N. Doesken, K. Redmond, J. Moeny, T. McKee, and T.G.F. Kittel, 2002: Problems in evaluating regional and local trends in temperature: An example from eastern Colorado, USA. Int. J. Climatol., 22, 421-434.

Pielke Sr., R.A. J. Nielsen-Gammon, C. Davey, J. Angel, O. Bliss, N. Doesken, M. Cai., S.  Fall, D. Niyogi, K. Gallo, R. Hale, K.G. Hubbard, X. Lin, H. Li, and S. Raman, 2007a: Documentation of uncertainties and biases associated with surface temperature measurement sites for climate change assessment. Bull. Amer. Meteor. Soc., 88:6, 913-928.

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007b: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229.

In the second paper, we wrote

“Peterson’s approach and conclusions, therefore, provide a false sense of confidence with these data for temperature change studies by seeming to indicate that the errors can be corrected.”

The decision of the NCDC Talking Points to ignore these papers illustrates the state that NCDC is in with respect to Climate Science. NCDC, as led by Tom Karl, is not interested in an inclusive assessment of climate science issues (in this case the multi-decadal surface temperature trends), but are only interested in promoting their particular agenda and in protecting their particular data set.

NCDC Talking Point #3

Q. Does a station with poor siting read warmer than a station with good siting?

Not necessarily. A station too close to a parking lot would be expected to read warmer than a station situated over grass far from any human influence other natural obstructions. But a station too close to a large tree to the west, so that the station was shaded in the afternoon, would be expected to make the afternoon maximum temperature read a bit cooler than a station in full sunlight. Many local factors influence the observed temperature: whether a station is in a valley with cold air drainage, whether the station is a liquid-in-glass thermometer in a standard wooden shelter or an electronic thermometer in the new smaller and more open plastic shelters, whether the station reads and resets its maximum and minimum thermometers in the coolest time of the day in early morning or in the warmest time of the day in the afternoon, etc. But for detecting climate change, the concern is not the absolute temperature – whether a station is reading warmer or cooler than a nearby station over grass – but how that temperature changes over time.

Climate Science Response

The answer correctly reports on the variety of issues that affect surface temperatures. However, where we disagree is that the multi-decadal surface temperature trends and anomalies also depend on the details of the observing sites and how these details change over time.

This can be illustrated from our 2007 BAMS paper, where the set of relatively closely spaced stations shown in Figure 10 (reproduced belw) have significantly different long term trends, as summarized in Table 5 (reproduced below) from that paper. Despite being relatively close together, the variations in both the local enviroment and the station exposure result in distinctly different trends [Using the categories in the Watts, 2009 report, the stations had the following Trinidad (3); Cheyenne Wells (1); Las Animas (5); Eads (4) and Lamar (4)].

Even sites that are locally in a category 1 class, such as Cheyenne Wells, however, also have issues with the landscape in their local surroundings, as we documented for locations in northeastern Colorado in Figures 5, 7, 9, 10 and 12 of

Hanamean, J.R. Jr., R.A. Pielke Sr., C.L. Castro, D.S. Ojima, B.C. Reed, and Z. Gao, 2003: Vegetation impacts on maximum and minimum temperatures in northeast Colorado. Meteorological Applications, 10, 203-215.

Depending on wind direction, the air that reaches the observing site can have a different temperature. Changes in the wind directions over time can result in temperature trends that are due to this effect alone.

This local landscape variation as a function of azimith can be seen in the photographs for the Cheyenne Wells site in

Davey, C.A., and R.A. Pielke Sr., 2005: Microclimate exposures of surface-based weather stations – implications for the assessment of long-term temperature trends. Bull. Amer. Meteor. Soc., Vol. 86, No. 4, 497–504,

where depending on the wind direction and time of year, the air that the temperature sensor monitors may transit a dirt road, crops, or other land surface varations, each with a different surface heat budget., before reaching the temperature observing site.

The NCDC Talking Points ignore informing us why all of these local landscape effects on multi-decadal surface temperature trends would be random and average out.

NCDC Talking Point #4

Q. So a station moving from a location with good siting to a location with poor siting could cause a bias in the temperature record. Can that bias be adjusted out of the record?

A. A great dealof work has gone into efforts to account for a wide variety of biases in the climate record, both in NOAA and at sister agencies around the world. Since the 1980s, scientists at NOAA’s NationalClimatic Data Center are at the forefront of this effort developing techniques to detect and quantify biases in station time series. When a bias associated with any change is detected, it is removed so that the time series is homogeneous with respect to its current instrumentation and siting. The latest peer-reviewed paper which provides an overview the sources of bias and their removal (Menne et al., 2009 in press), including urbanization and nonstandard siting. At the time that paper was written, station site evaluations were too incomplete to conduct a thorough investigation (that analysis is forthcoming). However, they could evaluate urban bias and found that once the data were fully adjusted the 30% most urban stations had about the same trend as the remaining more rural stations.

Climate Science Response

The failure of NCDC to correct for all of the recognized biases has been documented in

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229;

a paper NCDC has chosen to ignore [another surface temperature analysis group has been open to scientific debate, however; see].

NCDC has also ignored

Lin, X., R.A. Pielke Sr., K.G. Hubbard, K.C. Crawford, M. A. Shafer, and T. Matsui, 2007: An examination of 1997-2007 surface layer temperature trends at two heights in Oklahoma. Geophys. Res. Letts., 34, L24705, doi:10.1029/2007GL031652,

where we document a bias in the use of a single level surface temperature (the minimum temperature, in particular) to monitor multi-decadal surface temperature trends.

The NCDC talking points also mention the Menne et al (2009) paper, which, unfortunately, perpetuates the NCDC failure to adequately consider all of the biases and uncertainties in the surface temperature record. The Menne et al paper was weblogged in

Comments On The New Paper “The United States Historical Climatology Network Monthly Temperature Data – Version 2 By Menne Et Al 2009

Finally, we have several other papers in the review process, and look forward to communicating them to you when accepted for publication.

NCDC Talking Point #5

Q. What can we say about poor siting’s impact on national temperature trends?

A. We are limited in what we can say due to limited information about station siting. Surfacestations.org has examined about 70% of the 1221 stations in NOAA’s Historical Climatology Network (USHCN). According to their web site of early June 2009, they classified 70 USHCN version 2 stations as good or best (class 1 or 2). The criteria used to make that classification is based on NOAA’s Climate Reference Network Site Handbook so the criteria are clear. But, as many different individuals participated in the site evaluations, with varying levels of expertise, the degree of standardization and reproducibility of this process is unknown.

However, at the present time this is the only large scale site evaluation information available so we conducted a preliminary analysis.

Two national time series were made using the same gridding and area averaging technique. One analysis was for the full data set. The other used only the 70 stations that surfacestations.org classified as good or best. We would expect some differences simply due to the different area covered: the 70 stations only covered 43% of the country with no stations in, for example, New Mexico, Kansas, Nebraska, Iowa, Illinois, Ohio, West Virginia, Kentucky, Tennessee or North Carolina. Yetthe two time series, shown below as both annual data and smooth data, are remarkably similar. Clearly there is no indication for this analysis that poor current siting is imparting a bias in the U.S. temperature trends.

Climate Science Response

This is a cavalier response.  In order to show that there is little effect on surface temperature anomalies due to station siting, they need to assess the anomalies over time in the same region for each category of station siting. A national average which includes includes large regional variations (e.g. see Figure 20a in Pielke et al 2007a ) tells us little about the quality of the data.

Q. Is there any question that surface temperatures in the United States have been rising rapidly during the last 50 years?

A. None at all. Even if NOAA did not have weather observing stations across the length and breadth of the United States the impacts of the warming are unmistakable. For example, lake and river ice is melting earlier in the spring and forming later in the fall. Plants are blooming earlier
in the spring. Mountain glaciers are melting. And a multitude of species of birds, fish, mammals and plants are extending their ranges northward and, in mountainous areas, upward as well.

Menne, Matthew J., Claude N. Williams, Jr. and Russell S. Vose, 2009: The United States Historical Climatology Network Monthly Temperature Data – Version 2. Bulletin of the American Meteorological Society, in press.

Peterson, Thomas C., 2006: Examination of Potential Biases in Air Temperature Caused by Poor Station Locations. Bulletin of the American Meteorological Society, 87, 1073-1080. It is available from http://ams.allenpress.com/archive/1520-0477/87/8/pdf/i1520-0477-87-8-1073.pdf.

Climate Science Response

Their claim that temperatures have been “rising rapidly” over the past 50 years is based on the surface temperature record in which there are reported warm biases; e.g. see

Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229.

NCDC also is misinformed with respect to the other climate metrics. For example, they write

“Plants are blooming earlier in the spring.”

However, a new paper in press (see)

White, M.A., K.M. de Beurs, K. Didan, D.W. Inouye, A.D. Richardson, O.P. Jensen, J. O’Keefe, G. Zhang, R.R. Nemani, W.J.D. van Leeuwen, J.F. Brown, A. de Wit, M. Schaepman, X. Lin, M. Dettinger, A. Bailey, J. Kimball, M.D. Schwartz, D.D. Baldocchi, J.T. Lee, W.K. Lauenroth. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006. Global Change Biology (in press),

writes

“Trend estimates from the SOS [Start of Spring] methods as well as measured and modeled plant phenologystrongly suggest either no or very geographically limited trends towards earlier spring arrival, although we caution that, for an event such as SOS with high interannual variability, a 25-year SOS record is short for detecting robust trends.”

IN CONCLUSION

NCDC would be a much more valuable resource in the climate community if they worked to be inclusive in presenting all peer reviewed perspectives in climate science. Currently, they are only reporting on information that supports their agenda and not communicating real world observational data that conflicts with that agenda. The fault for this failure in leadership is with Tom Karl who is Director of NCDC.

The Hillsboro, Ohio USHCN climate station of record, measuring rainfall and the “surface” temperature. Note the MMTS temperature sensor laying flat on the ground.

To be fair, apparently the station is in the process of being moved. The new addition to the home (seen below) required it to be moved around to the side.

Surfacestations.org volunteer Ed Fix writes in the station survey:

Private residence, second longest continuously used Coop. site in Ohio.  It has been at the present position since 1959, and in use in the area since 1893.

The previous owners of the house Marie and Thomas Knott, now deceased, collected the data from 1959 until Marie’s death in 2007.  Current resident is continuing the observation duties.

MMTS temp, manual rain gauge.  Rain gauge has been in use since at least 1959. The instrumentation is in the process of being moved.  The previous location is on the south end of a deck (removed 3 weeks ago), approximately 14′ from the metal-sided south wall of the house, 40′ west of the new location.  It was approx 42′ from an air conditioner at the SW corner of the house.  The station is to be set back up in the very near future.

The proposed new location is SE of the SE corner of the house, approx 21′.  This is 32′ from the corner of the house next door, with an air conditioner condenser.

While one would think that maintaining a continuity of records for such a station would be of prime importance, it lays idle as the photography shows.

The first point here is that backyards are dynamic places, prone to land use changes and biases that result from the changing/evolving lifestyle of the owner of the home, which makes them less than desirable for gathering scientific data.  The second point is that the gap in the record could easily be avoided if the local NWS COOP manager had worked to help the homeowner get the equipment operational again.

The USHCN is far from a homogeneous measurement system.

Addendum:

I should add that the many problems we have seen in the USHCN are not the fault of the volunteer observers. These people give their valuable time and dedicate their lives to doing a mostly thankless job. They do it mostly for their own satisfaction and interest in providing something useful that can be part of the permanent record of our country.

We should never forget that. They are to be congratulated for their service.

The responsibility for the errors in siting is with NOAA, as they are in charge of doing these installations and ensuring a modicum of quality control based on their own 100 foot rule.

http://www.nws.noaa.gov/om/coop/standard.htm

Readers may find the title familiar, that’s because Basil Copeland and I also did a paper looking at solar signatures in climatic data, which has received a lot of criticism because we made an analytical error in our attempt. But errors are useful, teachable moments, even if they are embarrassing, and our second attempt though, titled,

Evidence of a Lunisolar Influence on Decadal and Bidecadal Oscillations In Globally Averaged Temperature Trends

hasn’t been significantly challenged yet that I am aware of. Basil and I welcome any comments or suggestions on that work.

In our work, we used Hodrick-Prescott filtering to extract the solar cycle signal from the HadCRUT temperature dataset. In this paper the data are extracted from the ECA&ECD database (available via http://eca.knmi.nl ).  According to the paper, they are “using a nonlinear technique of analysis developed for time series whose complexity arises from interactions between different sources over different time scales”. Read more about it in the paper. In both our paper, and in this one, a solar signature is evident in the temperature data.  – Anthony

Evidence for a solar signature in 20th-century temperature

By Jean-Louis Le Mouel, Vincent Courtillot, Elena Blanter, Mikhail Shnirman (PDF available here)

J.-L. Le Mouël et al., Evidence for a solar signature in 20th-century temperature data from the USA and Europe, C. R. Geoscience (2008), doi:10.1016/j.crte.2008.06.001

Click for a larger image - Comparison of the mean squared interannual variation (left column) and lifetime (right column) of the overall minimum temperature data from the US (153 stations), Australia (preliminary, 5 stations) and Europe (44 stations). Europe (bottom row) is shown for the two types of calculation for quick comparison (green curves), and also the magnetic index representing solar activity (blue curve).

Abstract

We analyze temperature data from meteorological stations in the USA (six climatic regions, 153 stations), Europe (44 stations, considered as one climatic region) and Australia (preliminary, five stations). We select stations with long, homogeneous series of daily minimum temperatures (covering most of the 20th century, with few or no gaps).We find that station data are well correlated over distances in the order of a thousand kilometres. When an average is calculated for each climatic region, we find well characterized mean curves with strong variability in the 3–15-year period range and a superimposed decadal to centennial (or ‘secular’) trend consisting of a small number of linear segments separated by rather sharp changes in slope.

Our overall curve for the USA rises sharply from 1910 to 1940, then decreases until 1980 and rises sharply again since then. The minima around 1920 and 1980 have similar values, and so do the maxima around 1935 and 2000; the range between minima and maxima is 1.3 °C. The European mean curve is quite different, and can be described as a step-like function with zero slope and a ~1 8°C jump occurring in less than two years around 1987. Also notable is a strong (cold) minimum in 1940. Both the USA and the European mean curves are rather different from the corresponding curves illustrated in the 2007 IPCC report.We then estimate the long-term behaviour of the higher frequencies (disturbances) of the temperature series by calculating the mean-squared interannual variations or the ‘lifetime’ (i.e. the mean duration of temperature disturbances) of the data series.We find that the resulting curves correlate remarkably well at the longer periods, within and between regions. The secular trend of all of these curves is similar (an S-shaped pattern), with a rise from 1900 to 1950, a decrease from 1950 to 1975, and a subsequent (small) increase. This trend is the same as that found for a number of solar indices, such as sunspot number or magnetic field components in any observatory. We conclude that significant solar forcing is present in temperature disturbances in the areas we analyzed and conjecture that this should be a global feature.

…

We find that station data are well correlated over distances in the order of a thousand kilometres. When an average is calculated for each climatic region, we find well characterized mean curves with strong variability in the 3-15-year period range and a superimposed decadal to centennial or ‘secular’ trend consisting of a small number of linear segments separated by rather sharp changes in slope. Our overall curve for the USA rises sharply from 1910 to 1940, then decreases until 1980 and rises sharply again since then. The minima around 1920 and 1980 have similar values, and so do the maxima around 1935 and 2000; the range between minima and maxima is 1.38C. The European mean curve is quite different, and can be described as a step-like function with zero slope and a 1.8C jump occurring in less than two years around 1987. Also notable is a strong (cold) minimum in 1940. Both the USA and the European mean curves are rather different from the corresponding curves illustrated in the 2007 IPCC report.

…

We then estimate the long-term behaviour of the higher frequencies (disturbances) of the temperature series by calculating the mean-squared interannual variations or the ‘lifetime’ (i.e. the mean duration of temperature disturbances) of the data series. We find that the resulting curves correlate remarkably well at the longer periods, within and between regions. The secular trend of all of these curves is similar (an S-shaped pattern), with a rise from 1900 to 1950, a decrease from 1950 to 1975, and a subsequent (small) increase. This trend is the same as that found for a number of solar indices, such as sunspot number or magnetic field components in any observatory.

…

We conclude that significant solar forcing is present in temperature disturbances in the areas we analyzed and conjecture that this should be a global feature.

We have also shown that solar activity, as characterized by the mean-squared daily variation of a geomagnetic component (but equally by sunspot numbers or sunspot surface) modulates major features of climate. And this modulation is strong, much stronger than the one per mil variation in total solar irradiance in the 1- to 11-year range: the interannual variation, which does amount to energy content, varies by a factor of two in Europe, the USA and Australia. This result could well be valid at the full continental scale if not worldwide. We have calculated the evolution of temperature disturbances, using either the mean-squared annual variation or the lifetime. When 22-year averaged variations are compared, the same features emerge, particularly a characteristic centennial trend (an S-shaped curve) consisting of a rise from 1920 to 1950, a decrease from 1950 to 1975 and a rise since. A very similar trend is found for solar indices. Both these longer-term variations, and decadal and sub-decadal, well-correlated features in lifetime result from the persistence of higher frequency phenomena that appear to be influenced by the Sun. The present preliminary study of course needs confirmation by including regions that have not yet been analyzed.

Orland CA and the New Adjustments

In my last post, I observed that NOAA’s Talking Points applied their new “adjustments” to supposedly prove that NOAA’s negligent administration of the USHCN network did not “matter”.

In order to illustrate the effect of the new methods in this post, I’ll compare the new adjustments (post-TOBS) to the old adjustments (post-TOBS) on a “good” station – Orland CA, a prototype “good” station, discussed at the outset of surfacestations.org, discussed at WUWT here and CA here in early 2007.

The station history for Orland (at CDIAC) says that it has been in its present location for (at least) most of the 20th century and has had minimal changes during that time, other than perhaps time-of-observation (TOBS). The TOBS adjustment is carried forward into USHCN-v2. As I understand it, NOAA’s New Adjustment Method replaces station-history based adjustments for instrumentation changes and station location (the latter formerly done in FILNET).

As a benchmark, here is the difference between FILNET (adjusted) and TOBS for Orland in the “old” USHCN. Adjustments in the 20th century are negligible – in keeping with station history information that indicates no changes in location.

Figure 1. “Old” USHCN Adjustments for Station Location and Instrument Changes

Now here is the net adjustment in the “New” USHCN.

Two points jump out. Look first at the monthly adjustments at the right hand side. In the “old” method, there weren’t any adjustments to recent data – where metadata did not indicate any relevant change. In the “new” method, there are all sorts of jittery little adjustments. They seem to average out, but why introduce these jitters in the first place? It’s starting to look like a pointless Hansen-esque (ROW-style) adjustment that simply distorts the underlying data.

On a larger scale, the new adjustment noticeably increases the 20th century trend at Orland.

These graphics strongly indicate to me that the effect of the algorithm – regardless of whatever good intentions may underlie it – is that data from lower quality stations is being blended into the presently archived Orland data. I presume that something similar is happening to other “good” stations (though I’ve only examined one example so far.) (Note that Orland is a CRN3 station. However, its excellent continuity makes it a pretty attractive station for benchmarking and visually it doesn’t look a “bad” CRN3 station).

Based on this example, it looks like NOAA’s Talking Points comparison is between the overall average and 70 “adjusted” stations – AFTER the good stations have been adjusted.

Foreword: I give thanks to Steve McIntyre for this analysis. Steve came to a conclusion similar to what I alluded to in my initial rebuttal where I said:

“For all I know, they could be comparing homogenized data from CRN1 and 2 (best stations) to homogenized data from CRN 345 (the worst stations), which of course would show nearly no difference.“

Steve does a superb job of deconstructing the memo’s undocumented results. Perhaps someday Dr. Thomas Peterson of NCDC will tell us how he did his analysis and show supporting data and methods. – Anthony

The Talking Points Memo

The NOAA Talking Points memo falls well short of a “full, true and plain disclosure” standard – aside from the failure to appropriately credit Watts (2009).

They presented the following graphic that purported to show that NOAA’s negligent administration of the USHCN station network did not “matter”, describing the stations as follows:

Two national time series were made using the same gridding and area averaging technique. One analysis was for the full data set. The other used only the 70 stations that surfacestations.org classified as good or best… the two time series, shown below as both annual data and smooth data, are remarkably similar. Clearly there is no indication for this analysis that poor current siting is imparting a bias in the U.S. temperature trends.

Figure 1. From Talking Points Memo.

Beyond the above sentence, there was no further information on the provenance of the two data sets. NOAA did not archive either data set nor provide source code for reconciliation.

The red graphic for the “full data set” had, using the preferred terminology of climate science, a “remarkable similarity” to the NOAA 48 data set that I’d previously compared to the corresponding GISS data set here (which showed a strong trend of NOAA relative to GISS). Here’s a replot of that data – there are some key telltales evidencing that this has a common provenance to the red series in the Talking Points graphic.

Figure 2. Plot of US data from www1.ncdc.noaa.gov/pub/data/cirs/drd964x.tmpst.txt

An obvious question is whether the Talking Points starting point of 1950 is relevant. Here’s the corresponding graphic with the 1895 starting point used in USHCN v2. Has the truncation of the graphic start at 1950 “enhanced” the visual impression of an increasing trend? I think so.

Figure 3. As Figure 2, but to USHCN v2 start

The Talking Points’ main point is its purported demonstration that UHI-type impacts don’t “matter”. To show one flaw in their arm-waving, here is a comparison of the NOAA U.S. temperature data set and the NASA GISS US temperature data set over the same period – a comparison that I’ve made on several occasions, including most recently here. NASA GISS adjusts US temperatures for UHI using nightlights information, coercing the low-frequency data to the higher-quality stations. The trend difference between NOAA and NASA GISS is approximately 0.7 deg F/century in the 1950-2008 period in question: obviously not a small proportion of the total reported increase.

Figure 4. Difference between NOAA and NASA in the 1950-2008 period. In def F following NOAA (rather than deg C)

As has been discussed at considerable length, the NASA GISS adjusted version runs fairly close to “good” CRN1-2 stations – a point which Team superfans have used in a bait-and-switch to supposedly vindicate entirely different NASA GISS adjustments in the ROW, (adjustments which appear to me to be no more than random permutations of the data, a point discussed at considerable length on other occasions.)

For present purposes, we need only focus on the observation that there is a substantial trend difference between NOAA and GISS trends.

Given that, when NOAA’s Talking Points claim that there is a supposedly negligible difference between the average of their “good” stations and the NOAA average (which we know to run hot relative to GISS), then arguably this raises issues about the new USHCN procedures.

Y’see, while NOAA doesn’t actually bother saying how it did the calculations, here’s my guess as to what they did. The new USHCN data sets (as I’ll discuss in a future post) ONLY show adjusted data. No more inconvenient data trails with unadjusted and TOBS versions.

When I looked at SHAP and FILNET adjustments a couple of years ago, one of my principal objections to these methods was that they adjusted “good” stations. After FILNET adjustment, stations looked a lot more similar than they did before. I’ll bet that the new USHCN adjustments have a similar effect and that the Talking Points memo compares adjusted versions of “good” stations to the overall average.

So what they are probably saying is this: after the new USHCN “adjustments” (about which little is known as the ink is barely dry on the journal article describing the new method and code for which is unavailable), there isn’t much difference between the average of good stations and the average of all stations.

If the NASA GISS adjustment procedure in the US is justified (and most Team advocates have supported the NASA GISS adjustment in the US), then the Talking Points memo merely demonstrates that there is something wrong with the new USHCN adjustments.

click for larger image

From environmentalist Jennifer Marohasy’s blog in Australia, please pay her a visit here – Anthony

There has been criticism of the potential for official weather stations in the USA to record artificially high temperatures because of the changing environments in which they exist, for example, new asphalt, new building or new air conditioning outlets.   Meteorologist, Anthony Watts, has documented evidence of the problem and Canadian academic, Ross McKitrick, has attempted to calculate just how artificially elevated temperatures might be as a consequence.

A reader of this blog, Michael Hammer, recently studied the official data from the US official weather stations and in particular how it is adjusted after it has been collected.   Mr Hammer concludes that the temperature rise profile claimed by the US government is largely if not entirely an artefact of the adjustments applied after the raw data is collected from the weather stations.

Does the US Temperature Record Support Global Warming?
By Michael Hammer

IN the US, the National Oceanic and Atmospheric Administration (NOAA) collects, analyses and publishes temperature data for the United States.   As part of the analysis process, NOAA applies several adjustments to the raw data.

If we consider, the above graph, which shows, their plot of the raw data  (dark pink) and the adjusted data (pale pink), it is obvious that the adjustments have little impact on data from early in the 20th century but adjust later temperature readings upwards by an increasing amount.  This means that the adjustments will create an apparent warming trend over the 20th century.  [Click on the above chart for a better larger view, this chart can also be viewed at http://cdiac.ornl.gov/epubs/ndp/ushcn/ndp019.html .]

NOAA state that they adjust the raw data for five factors.  The magnitude of the adjustments are shown in Figure 2.

Figure 2.  Form of individual corrections applied by NOAA. The black line is the adjustment for time of observation.  The red line is for a change in maximum/minimum thermometers used.  The yellow line is for changes in station siting. The pale blue line is for filling in missing data from individual station records. The purple line is for UHI effects (this correction is now removed).  [Click on the chart for a better larger view or visit the same website as for Figure 1.]

It is obvious that the only adjustment which reduces the reported warming is UHI which is a linear correction of 0.1F or about 0.06C per century, Figure 2.  Note also that the latest indications are that even this minimal UHI adjustment has now been removed in the latest round of revisions to the historical record.  To put this in perspective, in my previous article on this site I presented bureau of meteorology data which shows that the UHI impact for Melbourne Australia was 1.5C over the last 40 years equivalent to 3.75C per century and highly non linear.

Compare the treatment of UHI with the adjustments made for measuring stations that have moved out of the city centre, typically to the airport.  These show lower temperatures at their new location and the later readings have been adjusted upwards so as to match the earlier readings.  The airport readings are lower because the station has moved away from the city UHI.  Raising the airport readings, while not adding downwards compensation for UHI, results in an overstatement of the amount of warming. This would seem to be clear evidence of bias.  It would be more accurate to lower the earlier city readings to match the airport readings rather than vice versa.

Note also the similarity between the shape of the time of observation adjustment and the claimed global warming record over the 20th century especially the steep rise since 1970.  This is even more pronounced if one looks at the total adjustment shown in Figure 3 (again from the same site as Figure 1).  As a comparison, a recent version of the claimed 20th century global temperature record downloaded from  www.giss.nasa.gov is shown in Figure 4.

Figure 3.  Magnitude of the total correction applied by NOAA

[Click on the charts for a larger/better view.]

Figure 4.  Temperature anomaly profile from NASA GISS

Since the total corrections for the US look so similar to the claimed temperature anomaly, it begs the questions as to what the raw data looks like without any corrections.  Does it show the claimed rapidly accelerating warming trend claimed by the AGW advocates?  To determine this I took the raw data from the USHCN graph shown in Figure 1 and plotted this using  a 5 year mean (blue trace), matching the smoothing in the NASA GISS profile shown in Figure 4.  The result is shown in Figure 5.  Please note that while the plot is one that I generated, the data comes directly from the raw data from Figure 1 published by NOAA.

Figure 5  Plot of raw temperature data versus time (from fig 1) 5 point smoothing. Vertical axis degrees Fahrenheit.  Red line is a linear trend line. Green line is a 2nd order (parabolic) trend line.

Clearly the shape of this graph bears no similarity at all to the graph shown in Figure 4.  The graph does not even remotely correlate to the shape of the CO2 versus time graph.  The warming was greatest in the 1930’s before CO2 started to rise rapidly.  The rate of rise in 1920, the early 1930’s and the early 1950’s is significantly greater than anything in the last 30 years.  Despite the rapid rise in CO2 since 1960, the 1970’s to early 1980’s was the time of the global cooling scare and looking at the graph in Figure 5 one can see why (almost 2F cooling over 50 years).

A linear least squares trend line, created using the Excel trend line function (Red trace)  shows a small temperature rise of 0.09C per century which is far less than the rise claimed by AGW supporters and clearly of no concern.  However, the data shown in figure 5 bears little if any resemblance to a linear function.  One can always fit a linear trend line to any data but that does not mean the fitted line has any significance.  For example, if instead I fit a second order trend line (a parabolic) the result is extremely different.  That suggests a temperature peak around 1950 with an underlying cooling trend since.  Which trend line is the more significant one?  If there was really a strong underlying linear rise over the time period it should have shown up in the 2nd order trend line as well.  This suggests that it is questionable whether any relevant underlying trend can be determined from the data.

It would appear that the temperature rise profile claimed by the adjusted data is largely if not entirely an artefact arising from the adjustments applied (as shown in Figure 3), not from the experimental data record.  In fact, the raw data does not in any way support the AGW theory.

Based on this data, the US temperature data does not correlate with carbon dioxide levels.  The warming over the last 3 decades is completely unremarkable and if present at all is significantly less than occurred in the 1930’s.  It is questionable whether any long term temperature rise over the 20th century can be inferred from the data but if there is any it is far less than claimed by the AGW proponents.

The corrected data from NOAA has been used as evidence of anthropogenic global warming yet it would appear that the rising trend over the 20th century is largely if not entirely an artefact arising from the “corrections” applied to the experimental data, at least in the US, and is not visible in the uncorrected experimental data record.

This is an extremely serious issue.  It is completely unacceptable, and scientifically meaningless, to claim experimental confirmation of a theory when the confirmation arises from the “corrections” to the raw data rather than from the raw data itself.  This is even more the case if the organisation carrying out the corrections has published material indicating that it supports the theory under discussion.  In any other branch of science that would be treated with profound scepticism if not indeed rejected outright.  I believe the same standards should be applied in this case.

*********************

Notes and Links

Interestingly, there was an earlier version of the NASA GISS data shown in Figure 4 which was originally published at http://www.giss.nasa.gov/data/update/gistemp/graphs/FigD.txt While this site has now been taken down the data was apparently archived by John Daly and available at his website http://www.john-daly.com/usatemps.006.  The data is presented in tabular form rather than graphical form but appears to be either identical or extremely similar to that shown in my Figure 5.

Other contributions from Michael Hammer can be read here: http://jennifermarohasy.com/blog/author/michael-hammer/
[scroll down, click on the title for the full article]

Ross McKitrick, Ph.D. – Quantifying the Influence of Anthropogenic Surface Processes on Gridded Global Climate Data
http://www.heartland.org/events/NewYork08/newyork2008-video.html

Anthony Watts – http://wattsupwiththat.com/

By Joseph D’Aleo, AMS Fellow, CCM

The average arctic temperature is still not above (take your pick) 32°F 0°C 273.15°K–this the latest date in fifty years of record keeping that this has happened. Usually it is beginning to level off now and if it does so, it will stay near freezing on average in the arctic leading to still less melting than last summer which saw a 9% increase in arctic ice than in 2007.  H/T to FredM and MarcM

Data from DMI (Danish Meteorological Institute)

See larger image here. Compare with DMI charts in other years here.

[NOTE: as a second source to Joe's article I've added this weather station data from the "North Pole Cam" operated by NOAA. Link is here: http://www.arctic.noaa.gov/gallery_np.html

There is a webcam at the "North Pole" (at least it starts out very near there) that reports via satellite data uplink at regular intervals. They also have a weather station with a once weekly data plot.  Note it is still below zero centigrade there.

Latest data (updated approximately weekly) Readers should note that the station really isn't at the north pole anymore due to significant ice drift.  - Anthony ]

The AMSR-E shows the ice situation on June 23rd:

See where we stand relative to recent years in terms of total extent here. We are using JAXA-IJIS AMSR-E data to track ice as NSIDC is using older satellites and the new director Mark Serreze has proven untrustworthy. The next two months will be interesting. Temperatures usually begin flatlining in late June which would suggest less ice loss, although the water temperature beneath plays a key role and all of the warm water that entered the Arctic when the Atlantic was very warm in the middle 2000s (now is nearer normal) may not have circulated out yet.

The other question is what effect the early spring Mt. Redoubt eruptions may be having. Are the sulfate aerosols trapped in the arctic stratosphere reflecting back some of what sunlight reaches the high latitudes?

Along the edge of the arctic, Ross Hays who worked for CNN and then NASA who last year posted from Antartica sent this note to me “They have me working in arctic Sweden until mid July. One of the Esrange staff members told me that so far Kiruna had had the coldest June in 150 years!”

See PDF here.