Well, there is a new paper in press in the Journal of Climate:

Improved methods for PCA-based reconstructions: case study using the Steig et al. 2009 Antarctic temperature reconstruction by Ryan O’Donnell, Nicholas Lewis, Steve McIntyre, Jeff Condon

The abstract states:

A detailed analysis is presented of a recently published Antarctic temperature reconstruction that combines satellite and ground information using a regularized expectation-maximization algorithm. Though the general reconstruction concept has merit, it is susceptible to spurious results for both temperature trends and patterns. The deficiencies include: (a) improper calibration of satellite data; (b) improper determination of spatial structure during infilling; and (c) suboptimal determination of regularization parameters, particularly with respect to satellite principal component retention. We propose two methods to resolve these issues. One utilizes temporal relationships between the satellite and ground data; the other combines ground data with only the spatial component of the satellite data. Both improved methods yield similar results that disagree with the previous method in several aspects. Rather than finding warming concentrated in West Antarctica, we find warming over the period of 1957–2006 to be concentrated in the Peninsula (≈0.35°C decade−1). We also show average trends for the continent, East Antarctica, and West Antarctica that are half or less than that found using the unimproved method. Notably, though we find warming in West Antarctica to be smaller in magnitude, we find that statistically significant warming extends at least as far as Marie Byrd Land. We also find differences in the seasonal patterns of temperature change, with winter and fall showing the largest differences and spring and summer showing negligible differences outside of the Peninsula.

Meanwhile, Ross McKitrick writes:

“NEW PAPER ON CONTAMINATED SURFACE TEMPERATURE DATA: In 2007 I published a paper with Pat Michaels showing evidence that CRU global surface temperature data used by the IPCC are likely contaminated due to socioeconomic development and variations in data quality. In 2009 Gavin Schmidt published a paper in the International Journal of Climatology claiming our results, as well as those of de Laat and Maurellis who independently found the same things we did, were spurious. My rebuttal, coauthored with Nicolas Nierenberg, has been accepted at The Journal of Economic and Social Measurement.

McKitrick, Ross R. and Nicolas Nierenberg (2010) Socioeconomic Patterns in Climate Data. Journal of Economic and Social Measurement, forthcoming.

Data/Code archive here. The paper provides a complete and thorough refutation of Schmidt’s critique. Why JESM? First, because it is a journal that focuses on the critical evaluation of policy-relevant databases, and its editors and reviewers have considerable econometric depth, and this paper is fundamentally an application of econometrics to the evaluation of data quality. Second, we submitted the paper to the IJOC in April 2009, on the assumption that, having published Schmidt’s paper, they were interested in the topic. Evidently their interest only extends to analyses that support IPCC views. After 10 months we found out that IJOC was rejecting our paper on the basis of some inane referee reports to which Nico and I were not given a chance to reply. We did anyway, and if anyone thinks the rejection by IJOC amounts to a knock against our paper, please read our response letter for some perspective. Whether or not the IJOC editors read it, they refused to reconsider our paper. Interestingly, we learned from the Climategate release that Schmidt’s paper, which focuses on defending Phil Jones’ CRU data against its various critics, was sent by the IJOC Editors to be reviewed by Phil Jones of the CRU. As you can imagine his review was shallow and uncritical, but evidently impressed the editors of IJOC. They didn’t ask deLaat or me to supply a review, nor did they invite us to contribute a response. Every interaction I have had over the years with the IJOC has left me very unimpressed.”

Summary of McKitrick & Nierenberg (2010):

To generate a climate data set, temperature data collected at the Earth’s surface must be adjusted to remove non-climatic effects such as urbanization and measurement discontinuities. Some studies have shown that the post-1980 spatial pattern of temperature trends over land in prominent climate data sets is strongly correlated with the spatial pattern of socioeconomic development, implying that the adjustments are inadequate, leaving a residual warm bias. This evidence has been disputed on three grounds: spatial autocorrelation of the temperature field undermines significance of test results; counterfactual experiments using model generated data suggest such correlations have an innocuous interpretation; and different satellite covariates yield unstable results. Somewhat surprisingly, these claims have not been put into a coherent framework for the purpose of statistical testing. We combine economic and climatological data sets from various teams with trend estimates from global climate models and we use spatial regressions to test the competing hypotheses. Overall we find that the evidence for contamination of climatic data is robust across numerous data sets, it is not undermined by controlling for spatial autocorrelation, and the patterns are not explained by climate models. Consequently we conclude that important data products used for the analysis of climate change over global land surfaces may be contaminated with socioeconomic patterns related to urbanization and other socioeconomic processes.

Records kept over the years give us historical figures to make comparisons between average temperatures then and now. This sounds simple, but the very concept of an average temperature has no simple definition. First, we have to realise that temperature is what is known as an intensive property of matter. This simply means that it does not depend on the nature or size of the material for which it is measured.

So, for example, air and a body of water may have the same measured temperature at a particular moment, but their behaviour is very different. Air has a low thermal capacity (it take little heat to change its temperature), while water has a high thermal capacity and its temperature changes relatively slowly. In the present long cold spell in western Europe, ponds and lakes need a period of consistently sub-zero temperatures before they begin to freeze. Equally, as air temperatures rise, the ice may take many days to melt. A given volume of water has a very different thermal energy content than the same volume of air. This can be easily quantified and, in contrast to temperature, is an extensive property.

When trying to average temperatures, the first obvious rule is that the measurements must all be of the same material: you cannot average air and water temperatures, for example, and get a meaningful answer. This in itself is pretty obvious and, in discussing climate change, air and water temperatures are considered separately. However, the difficulties with averaging do not stop there.

Even if temperatures are measured under carefully controlled conditions as expected for official records, they will fluctuate quite rapidly depending on wind direction and strength, cloud cover, time of day etc. The convention is to measure a maximum and minimum shade temperature each day. These readings can then be used to provide average maxima and minima per month or year, or combined to give an overall ‘average temperature’. And the figures for individual stations can themselves be combined to give national, regional and global averages.

These figures tell us something, of course, but the desire to quantify also obscures the detail. Say, for example, that place X has an average maximum temperature of +15°C and an average minimum of +5° and place Y registers +25° and -5°. Both have an overall average of +10°, but the actual climate experienced would be quite different. In a similar way, measured air temperatures in the shade bear little relationship to the apparent temperature in the sun. Although the measured shade air temperature might be the same whether or not the sun is shining, the effect on the Earth’s surface of the sunlight is significant and, once the ground has been warmed, it will release its heat at night to keep the air somewhat warmer, at least temporarily.

Simple averaging can be deceptive in other ways as well. Depending on the weather conditions or time of year, either the maximum or minimum temperature might be more typical of the day as a whole, yet both are implicitly given equal weight. Nevertheless, it is arguable that such issues are not important when comparing time series of measured temperatures. For example, the Central England Temperature record (CET) is the longest continual record available, with monthly means being recorded from 1659 and daily means logged from 1722. Looking at this it is easy to see the recorded range and note that temperatures do indeed appear to have been higher in the latter part of the 20th Century, although they have dipped again since 2000. It is the changes which are significant rather than the absolute values, provided that all measurements are strictly comparable.

This, of course, introduces yet another concern. The same instruments would not have been used in the 17th Century as 300 years later and, with the best will in the world, it is difficult to guarantee that no artefacts have been introduced. Equally, it is hardly conceivable that the surroundings of the measuring stations will be unchanged over this period (although hopefully none of the weather stations is now in an urban area, on tarmac or near heat sources as some have been found to be in other countries).

A final problem to bring up with averages is that, to avoid giving a misleading picture, data should be taken from stations spread evenly over the Earth’s surface. This is certainly not the case. In particular, there are large areas of the Arctic and Antarctic with no data being collected. The same is true for the open oceans, where collecting surface water temperatures reliably is enough of a challenge, without trying to measure air temperatures.

What we are left with then is an incomplete record of imperfect data, from which conclusions about climate change are drawn. This is the basis of the ‘global warming’ message. But actually the concept of global average temperature is again a little misleading, since the summary of the IPCC Fourth Assessment Report shows that the warming pattern is regional rather than global. Warming over the 20th Century was recorded on all continents apart from Antarctica, but was considerably greater in the northern than the southern hemisphere. Given the greater proportion of ocean in the south, this is not surprising.

But global averages are still the main measure and this is the time of year when preliminary conclusions are drawn about the current year, as the annual meeting of the UN Convention on Climate Change takes place. So far, the message being put out by the World Meteorological Organization is that 2010 is likely to be among the warmest three on record. Based on the temperature record, this is doubtless correct, but how meaningful is this?

The WMO points towards record high temperatures in Russia, China and Greenland to support its case. Meanwhile anyone mentioning record lows and pointing out that new records are set nearly every day somewhere in the world is told that this means nothing. In practical terms, life has to go on and adapt to whatever climatic conditions turn out to be. Measuring temperatures remains a useful thing to do, but we must be careful not to read too much into the average figures. And we should never forget that, whatever the temperature is, we still have only a hazy idea about what controls it.

The Scientific Alliance
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In an escalating row dubbed ‘Satellitegate’ further evidence proves NOAA knew of these faults for years. World’s top climate scientists and even prior governmental reports cite underfunding and misallocation as the trigger for spiraling satellite data calamities. Key flaws with five satellites undermines global data.

Most disturbing of all is that it took publication of my article last week to persuade the authorities to withdraw the errant NOAA-16 satellite from service. But as Dr. John Christy indicates, the real Satellitegate is not about one satellite. The scandal is endemic with comparable flaws across the entire network; the scandal is also that it took a tip off from a member of the public and the widespread broadcast of my article before one of the offending junk boxes, NOAA-16, got taken down.

Readers who missed the details when this sensational story first broke can see here at Climatedepot.com.

Read more: Canada Free Press: Top Climate Scientists Speak out on the Satellitegate Scandal

There was wind of the suspicion that NASA’s Satellite temperature maps may be nonsense in 2009:

Pool: The satellite temperature maps from NASA may all be nonsense. Here is why. from Peter Ravenscroft, 20.12.09

The number of weather stations providing data to GHCN plunged in 1990 and again in 2005. The sample size has fallen by over 75% from its peak in the early 1970s, and is now smaller than at any time since 1919. The collapse in sample size has increased the relative fraction of data coming from airports to about 50 percent (up from about 30 percent in the 1970s). It has also reduced the average latitude of source data and removed relatively more high-altitude monitoring sites.

Oceanic data are based on sea surface temperature (SST) rather than marine air temperature (MAT). All three global products rely on SST series derived from the ICOADS archive. ICOADS observations were primarily obtained from ships that voluntarily monitored SST. Prior to the post-war era, coverage of the southern oceans and polar regions was very thin. Coverage has improved partly due to deployment of buoys, as well as use of satellites to support extrapolation. Ship-based readings changed over the 20th century from bucket-and-thermometer to engine-intake methods, leading to a warm bias as the new readings displaced the old. Until recently it was assumed that bucket methods disappeared after 1941, but this is now believed not to be the case, which may necessitate a major revision to the 20th century ocean record. There is evidence that SST trends overstate nearby MAT trends.

The quality of data over land, namely the raw temperature data in GHCN, depends on the validity of adjustments for known problems due to urbanization and land-use change. The adequacy of these adjustments has been tested in three different ways, with two of the three finding evidence that they do not suffice to remove warming biases.

The overall conclusion of this report is that there are serious quality problems in the surface temperature data sets that call into question whether the global temperature history, especially over land, can be considered both continuous and precise. Users should be aware of these limitations, especially in policy-sensitive applications.

There are some truly stunning comments from climate scientist Peter Webster, who was allowed access to CRU temperature station data despite the fact that CRU claimed it was unable to release the data as it was bound by confidentiality agreements:

While amateur climatologist McIntyre spent years begging in vain for the raw data, Webster eventually managed to convince Jones to send them to him. He is the only scientist to date who has been given access to the data. “To be honest, I’m shocked by the sloppy documentation,” Webster told SPIEGEL.

Curious Inconsistencies

Webster doesn’t believe that inconsistencies like these will invalidate the Jones curve altogether. “But we would like to know, of course, what’s behind all of these phenomena.” If a natural mechanism were at least partly to blame for the rise in temperatures, it would decrease the share of human influence in current global warming.

In the end the paper was accepted for publication, but not in a climatology journal. From my perspective the episode has some comic value, but I can afford to laugh about it since I am an economist, not a climatologist, and my career doesn’t depend on getting published in climatology journals. If I was a young climatologist I would have learned that my career prospects would be much better if I never write papers that question the IPCC.

I am taking this story public because of what it reveals about the journal peer review process in the field of climatology. Whether climatologists like it or not, the general public has taken a large and legitimate interest in how the peer review process for climatology journals works, because they have been told for years that they will have to face lots of new taxes and charges and fees and regulations because of what has been printed in climatology journals. Because of the policy stakes, a bent peer review process is no longer a private matter to be sorted out among academic specialists. And to the extent the specialists are unable or unwilling to fix the process, they cannot complain that the public credibility of their discipline suffers.

Read the entire essay: Circling the Bandwagons: My Adventures Correcting the IPCC by Ross McKitrick, Professor of Economics, University of Guelph, March 2010.

Roger Pielke Jr empathises with Ross McKitrick here.

In its last assessment the Intergovernmental Panel on Climate Change (IPCC) said the evidence that the world was warming was “unequivocal”.

It warned that greenhouse gases had already heated the world by 0.7C and that there could be 5C-6C more warming by 2100, with devastating impacts on humanity and wildlife. However, new research, including work by British scientists, is casting doubt on such claims. Some even suggest the world may not be warming much at all.

“The temperature records cannot be relied on as indicators of global change,” said John Christy, professor of atmospheric science at the University of Alabama in Huntsville, a former lead author on the IPCC.

The doubts of Christy and a number of other researchers focus on the thousands of weather stations around the world, which have been used to collect temperature data over the past 150 years.

These stations, they believe, have been seriously compromised by factors such as urbanisation, changes in land use and, in many cases, being moved from site to site.

Christy has published research papers looking at these effects in three different regions: east Africa, and the American states of California and Alabama.

“The story is the same for each one,” he said. “The popular data sets show a lot of warming but the apparent temperature rise was actually caused by local factors affecting the weather stations, such as land development.”

The IPCC faces similar criticisms from Ross McKitrick, professor of economics at the University of Guelph, Canada, who was invited by the panel to review its last report.

The experience turned him into a strong critic and he has since published a research paper questioning its methods.

“We concluded, with overwhelming statistical significance, that the IPCC’s climate data are contaminated with surface effects from industrialisation and data quality problems. These add up to a large warming bias,” he said.

Such warnings are supported by a study of US weather stations co-written by Anthony Watts, an American meteorologist and climate change sceptic.

Read more in The Sunday Times: World may not be warming, say scientists

Read the entire article by Ken Stewart: GISS manipulates climate data in Mackay (2nd Edition)

A Guardian investigation of thousands of emails and documents apparently hacked from the University of East Anglia’s climatic research unit has found evidence that a series of measurements from Chinese weather stations were seriously flawed and that documents relating to them could not be produced.

Jones and a collaborator have been accused by a climate change sceptic and researcher of scientific fraud for attempting to suppress data that could cast doubt on a key 1990 study on the effect of cities on warming – a hotly contested issue.

Today the Guardian reveals how Jones withheld the information requested under freedom of information laws. Subsequently a senior colleague told him he feared that Jones’s collaborator, Wei-­Chyung Wang of the University at Albany, had “screwed up”.

The revelations on the inadequacies of the 1990 paper do not undermine the case that humans are causing climate change, and other studies have produced similar findings. But they do call into question the probity of some climate change science. (CRN comment: No mention here of other studies that seriously question claims that the Urban Heat Island Effect is small – the warm bias in the surface temperature trends could be as much as 50%)

The apparent attempts to cover up problems with temperature data from the Chinese weather stations provide the first link between the email scandal and the UN’s embattled climate science body, the Intergovernmental Panel on Climate Change, as a paper based on the measurements was used to bolster IPCC statements about rapid global warming in recent decades.

Wang was cleared of scientific fraud by his university, but new information brought to light today indicates at least one senior colleague had serious concerns about the affair.

It also emerges that documents which Wang claimed would exonerate him and Jones did not exist.

Read the entire front page article in The Guardian: Leaked climate change emails scientist ‘hid’ data flaws

A pdf of the pre-print is available here.

Atmospheric Circulations do not Explain the Temperature-Industrialization Correlation*

Abstract:

Gridded land surface temperature data products are used in climatology on the assumption that contaminating effects from urbanization, land-use change and related socioeconomic processes have been identified and filtered out, leaving behind a “pure” record of climatic change. But several studies have shown a correlation between the spatial pattern of warming trends in climatic data products and the spatial pattern of industrialization, indicating that local non-climatic effects may still be present. This, in turn, could bias measurements of the amount of global warming and its attribution to greenhouse gases. The 2007 report of the Intergovernmental Panel on Climate Change (IPCC) set aside those concerns with the claim that the temperature-industrialization correlation becomes statistically insignificant if certain atmospheric circulation patterns, also called oscillations, are taken into account. But this claim has never been tested and the IPCC provided no evidence for its assertion. I estimate two spatial models that simultaneously control for the major atmospheric oscillations and the distribution of socioeconomic activity. The correlations between warming patterns and patterns of socioeconomic development remain large and significant in the presence of controls for atmospheric oscillations, contradicting the IPCC claim. Tests for outlier influence, spatial autocorrelation, endogeneity bias, residual nonlinearity and other problems are discussed.

Key words: global warming, data quality, industrialization, spatial regression

Conclusions:

Regional patterns of industrialization, land-use change and variations in the quality of temperature monitoring have been shown by several groups of authors to leave significant imprints on climate data, adding up to a widespread net warming bias that may account for as much as half the post 1980 warming over land. The Fourth Assessment Report of the IPCC dismissed this evidence with the claim that “the correlation of warming with industrial and socioeconomic development ceases to be statistically significant” upon controlling for atmospheric circulation patterns. This claim was presented without any supporting statistical evidence. The models in this paper implement a reasonable way of augmenting the original regressions with the relevant oscillation data, and the results contradict the IPCC claim. The temperature-industrialization correlations in question are quite robust to the inclusion of standard measures of the effects of atmospheric circulation patterns on temperatures, confirming the presence of significant extraneous signals in surface climate data on a scale that may account for about half the observed upward trend over land since 1980.

As discussed in the underlying papers by deLaat and Maurellis and McKitrick and Michaels, socioeconomic activity can lead to purely local atmospheric modifications (such as changes in water vapour and fine particle levels), which, along with other land-surface modifications and data inhomogeneities, can cause apparent trends in temperature data that are not attributable to general climatic changes. As was noted half a century ago by J. Murray Mitchell Jr., referring to the use of temperature observations for measuring climatic trends, “The problem remains one of determining what part of a given temperature trend is climatically real and what part the result of observational difficulties and of artificial modification of the local environment.” (Mitchell Jr., 1953). The results herein show that this concern is still valid, and the conjecture invoked by the IPCC to dismiss it is not supported by the data. A substantial fraction of the post-1980 trends in gridded climate data over land are likely not “climatically real” but arise from measurement quality problems and local environmental modifications.