The article states "now" on the weather stations Jan 10 2010 Vancouver Sun.
And this is based on "time", satellites can't look backwards in time so trending is important.
This is from NASA: Saying the last 2 decades have shown us we don't know what were talking about??? or did i read that wrong.
http://science.nasa.gov/newhome/head...d06oct97_1.htmImages of the Earth, such as this one in the infrared, tell us much about the distribution of water vapor. Areas within the Earth's atmosphere that are extremely dry, especially in the tropics, can act as large "chimneys" that allow energy to freely radiate into space, enhancing the cooling of the Earth. The effects of the tropical dry troposphere are poorly understood, and currently are not well-incorporated into computer models of global warming.
Last edited by Thiebear; March-4th-2010 at 07:20 PM.
i just got back from the NASA website: it is VERY cool by the way.
AND they say they have no idea based on the upper atmosphere and the Sun and water vaport etc. etc. etc.
doesnt mean we can't say
Last edited by Thiebear; March-4th-2010 at 07:22 PM.
When talking about UHI affects and surface data, you have to be careful about what surface data set you are talking about because some are INTENTIONALLY not corrected for UHI affects as people like to do things like study UHI affects so it is useful to have a non-corrected data set.
Others, which most non-skeptics talk about when talking about AGW, have a correction for UHI affects and other issues.
The net affect is the adjusted data closely matches the satelite data, which shouldn't suffer from any UHI issues.
(The blue being one of the UHI corrected data sets.)
Different times people do "analysis" showing issues with UHI affects in global temperature analysis, and in every case I've seen, they are starting with a data set that anybody that pays attention KNOWS has UHI issues because nobody is corrected for UHI, but nobody really uses as an argument to support AGW.
As to the other issue, this might help some:
"Why use temperature anomalies (departure from average) and not absolute temperature measurements?
Absolute estimates of global average surface temperature are difficult to compile for several reasons. Some regions have few temperature measurement stations (e.g., the Sahara Desert) and interpolation must be made over large, data-sparse regions. In mountainous areas, most observations come from the inhabited valleys, so the effect of elevation on a region’s average temperature must be considered as well. For example, a summer month over an area may be cooler than average, both at a mountain top and in a nearby valley, but the absolute temperatures will be quite different at the two locations. The use of anomalies in this case will show that temperatures for both locations were below average.
Using reference values computed on smaller [more local] scales over the same time period establishes a baseline from which anomalies are calculated. This effectively normalizes the data so they can be compared and combined to more accurately represent temperature patterns with respect to what is normal for different places within a region.
For these reasons, large-area summaries incorporate anomalies, not the temperature itself. Anomalies more accurately describe climate variability over larger areas than absolute temperatures do, and they give a frame of reference that allows more meaningful comparisons between locations and more accurate calculations of temperature trends."
Last edited by PeterMP; March-4th-2010 at 07:51 PM.
Christy and Spencer spent a better part of a decade claiming that satellite data showed no warming. Finally, somebody else found the error in their calculation and Christy and Spencer have admitted it:
"We agree with C. A. Mears and F. J. Wentz ("The effect of diurnal correction on satellite- derived lower tropospheric temperature," Reports, 2 Sept., p. 1548; published online 11 Aug.) that our University of Alabama in Huntsville (UAH) method of calculating a diurnal correction to our lower tropospheric (LT) temperature data (v5.1) introduced a spurious component. We are grateful that they spotted the error and have made the necessary adjustments. The new UAH LT trend (v5.2, December 1978 to July 2005) is 0.123 K/decade, or 0.035 K/decade warmer than v5.1. This adjustment is within our previously published error margin of ±0.05 K/decade (1)."
Anything prior to the 2005 with respect to the Chirsty and Spencer satellite data needs to be ignored.
Early models also didn't account for "dimming" due to aresoles and so significantly over estimated warming. This hasn't been an issue since about 1997.
Last edited by PeterMP; March-4th-2010 at 08:13 PM.
Even if man made global warming is real why should we care? Throughout the history of the planet and man's existence there have been numerous events (volcanoes, meteor impacts, etc...) that have shocked this planet and altered the climate far more rapidly than what is happening right now and you know what, we're still around. This is the main reason I don't care about global warming, there is no basis to the doom and gloom scenarios IMO and most of these have been proven to have been completely fabricated.
So, according to my calculations....this is alexey's third thread he's started with these videos. And the 5th time he's posted them.
Was there anything wrong with the previous threads alex? Seriously asking....
Gavin Schmidt excoriated this claim as follows:D’Aleo is misdirecting through his teeth here. … he also knows that urban heat island effects are corrected for in the surface records, and he also knows that this doesn’t effect ocean temperatures, and that the station dropping out doesn’t affect the trends at all (you can do the same analysis with only stations that remained and it makes no difference). Pure disinformation.Later in the comments (#167), an RC reader inquired about UHI adjustments, noting the lack of discusison of this point as follows:
#167/ In all of the above posts there is no mention of the urban heat island effect, nor of the effect of rural station drop out nor of the effect the GISS data manipulation has on surface temperature. Why is that?To which Gavin replied:
[Response: Because each of these ‘issues' are non-issues, simply brought up to make people like you think there is something wrong. The UHI effect is real enough, but it is corrected for - and in any case cannot effect ocean temperatures, retreating glaciers or phenological changes (all of which confirm significant warming). The station drop out ‘effect' is just fake, and if you don't like GISS, then use another analysis - it doesn't matter. - gavin]Neither CRU nor NOAA have archived any source code for their calculations, so it is impossible to know for sure exactly what they do. However, I am unaware of any published documents by either of these agencies that indicate that they “correct” their temperature index for UHI effect (as Gavin claims here) and so I’m puzzled as to how Gavin expects D’Aleo to be able to “know” that they carry out such corrections. And as to GISS adjustments, as we’ve discussed here in the past (and I’ll review briefly), outside the US, they have the odd situation where “negative UHI adjustments” are as common as “positive UHI adjustments”, raising serious questions about whether the method accomplishes anything at all, as opposed to simply being a Marvelous Toy.
CRU Urban Adjustments?
The most recent exposition of CRU methodology is Brohan et al 2006, which stated in respect to UHI that they included an allowance of 0.1 deg C/century in the uncertainty, but does not describe any “correction” to the reported average temperature:The previous analysis of urbanisation effects in the HadCRUT dataset [Folland et al., 2001] recommended a 1 sigma uncertainty which increased from 0 in 1900 to 0.05 deg C in 1990 (linearly extrapolated after 1990) [Jones et al., 1990]. … To make an urbanisation assessment for all the stations used in the HadCRUT dataset would require suitable meta-data for each station for the whole period since 1850. No such complete meta-data are available, so in this analysis the same value for urbanisation uncertainty is used as in the previous analysis [Folland et al., GRL 2001]; that is, a 1 sigma value of 0.0055 deg C/decade, starting in 1900… The same value is used over the whole land surface, and it is one-sided: recent temperatures may be too high due to urbanisation, but they will not be too low.For greater certainty that CRU makes no “correction” for UHI in the actual temperature (only an allowance in the “uncertainty”), Folland et al (GRL 2001) stated:
We add independent uncertainties due to urbanisation, changing land-based observing practices and SST bias corrections. … The uncertainties given by RSOA due to data gaps and random errors (Figure 1a) were augmented using published estimates of global uncertainties associated with urbanization effects (e.g. Jones et al., 1990),…We assume that the global average LAT uncertainty increased from zero in 1900 to 0.1°C in 1990 (Jones et al, 1990), a value we extrapolate to 0.12°C in 2000 (Figure 1a).Both sources clearly stated that they allow for UHI only by a slight increase in their uncertainty factor. Note that even this estimate relies on Jones et al 1990, a study which has been discussed at CA preciously. After Jones refused for years to identify the stations used in the 1990 study, FOI actions obtained this information. We discussed Jones et al 1990 in a number of posts. We observed here that Jones et al 1990 made untrue claims on the quality control for their Chinese network (the falseness of which would rise to misconduct in many fields). Jones et al 1990 described their QC procedures for Chinese stations as follows:
The stations were selected on the basis of station history; we selected those with few, if any changes in instrumentation, location or observation times.I observed at the time that I had been able to track down third-party documentation on stations used in Jones’ China network and that it was “impossible that Jones et al could have carried out the claimed QC procedures.” Doug Keenan followed up on this with a complaint against Wang. As I recall, part of Wang’s defence was that the station histories consulted in 1990 had now been “lost”. So station histories – documents that had survived World War II, the Communist Revolution, the Great Leap Forward, carefully preserved by diligent clerks – were lost or destroyed by climate scientists under the IPCC regime. Hard to believe.
Be that as it may, Brohan et al 2006 does not say that they make any “correction” to their records for UHI, only that they make a slight increase in “uncertainty” – a completely different thing even in Gavin-World.
NOAA UHI Adjustments
The homepage for the NOAA temperature index is here. It cites Smith and Reynolds (2005) as authority. Smith and Reynolds, in turn, state that they use the identical procedure as CRU, i.e. they make an allowance in uncertainty, but do not correct the temperature index itself.For LST [land surface temperatures] , bias errors may be caused by urbanization over the twentieth century, and uncertainty due to the use of nonstandard thermometer shelters before 1950 (Jones et al. 1990; Parker 1994; Folland et al. 2001). Here we use the LST bias uncertainty estimates of Folland et al. (2001).GISS U.S. Adjustments
Unlike CRU and NOAA, GISS makes a decent effort to adjust for UHI in the U.S. (outside the USA, its efforts are risible.) A few days ago, I showed the notable difference between the GISS (UHI-adjusted) version in the US and the NOAA unadjusted version, where the difference is much more than 0.1 deg C/century asserted by CRU/NOAA.
surfacestations.org has made a concerted effort to identify high-quality stations within the USHCN network (CRN1-2 stations) and preliminary indications are that the GISS U.S. estimate will not differ greatly from results from the “best” stations (though there will probably be a little bias.)
This does not prove that CRU and NOAA estimates are any good. Quite the contrary. It shows that the CRU and NOAA failures to make UHI adjustments along the lines of GISS are introducing a substantial bias in these records.
GISS ROW Adjustments
Last year, I reviewed GISS adjustments outside the US in a series of posts. These adjustments are pig’s breakfast. In many cases, GISS makes UHI adjustments the “wrong” way” i.e. their adjustments presume a UHI cooling effect. These goofy results are mentioned passim by Hansen as “false local adjustments”. At the end of the day, there is no evidence that Hansen’s “UHI” adjustments outside the U.S. even begin to deal with the problem. Posts were here here here here here here.
The difference between the US and ROW arises because the US has a fairly unique backbone of long relatively rural stations (the USHCN network), where, despite all the barbecues and air conditioners and parking lots, an attempt has been made at having weather stations located at non-airport non-urban locations. GISS uses nightlights information to subset this data and to choose a subset as a trend reference. There’s lots to dislike in the execution, but the intent makes sense.
Outside the US, there is no corresponding network. A lot of the stations are in cities and virtually all of the recent data (post-1990) is from airports. GISS uses hopelessly obsolete population meta-data to supposedly identify “rural” stations, but GISS “rural” is all too often small city (or even large city). Unlike the US, GISS methods don’t find sure ground and thus their adjustments end up being essentially random, mostly reflecting random site relocations and having nothing to do with UHI adjustment. They may say that they adjust for UHI, but this cannot be demonstrated in their actual adjustment, which throws up nonsensical wrong-way adjustments. Even Hansen acknowledges the wrong-way adjustments as being a problem:it is difficult to have confidence in the use of urban records for estimating climate change…some urban stations show little or no warming, even a slight cooling relative to rural neighbors. Such results can be a real systematic effect e.g. cooling by planted vegetation or the movement of a thermometer away from the urban center or a random effect of unforced regional variability and measurement errors. Another consideration is that even rural locations may contain some anthropogenic warming.And CRU and NOAA don’t even bother.
“urban heat island effects are corrected for in the surface records”
Contrary to Gavin’s assertion, there is no evidence that CRU or NOAA correct their records for urban heat island effects. They make a very slight allowance in their “uncertainty” for UHI relying ultimately on an estimate made in Jones et al 1990, a study which made untrue (and impossible) claims about quality control steps.
The only network where a plausible adjustment is made is the GISS US network (representing less than 2% of the world’s surface, as NASA GISS reminds us.) While GISS US results are plausible, outside the US, the GISS adjustment is a pig’s breakfast and no sane person can claim that they live up to the warranty. What makes this frustrating is that the US temperature history (GISS version) had 1934 as a record year – a result that was at variance with the other indices and other parts of the world. Is this because this is the only network/country combination with an effective UHI adjustment or because of a unique “regional” climate history in the US?
Whether or not urban heat islands have a material impact on the surface records is a different question. The difference between GISS US results and NOAA US results is strong evidence that there is a noticeable impact – one which needs to be addressed by CRU and NOAA and by GISS outside the US. In my opinion, Gavin’s own statement that “urban heat island effects are corrected for in the surface records” is, to borrow a phrase from realclimate, “disinformation”.
For the record, I think that Gavin was entitled to complain about the lack of balance or representativeness in the Lou Dobbs panel: whether D’Aleo, Lehr and Wissner-Gross are right or wrong about their points, they are completely unrepresentative of the mainstream climate community, which is surely entitled to complain on that count. My not discussing their solar views here doesn’t mean that I endorse them – Gavin Schmidt and his colleagues spend time deconstructing such analyses; solar proponents should pay attention to criticism regardless of the quarter from which it originates; given that others do such analyses, I think that my time is better spent on issues not covered elsewhere. The fact that there is a legitimate complaint against the construction of the Lou Dobbs panel doesn’t mean that Schmidt should make untrue claims about what CRU and NOAA do in their construction of surface records.
Joe D’Aleo responds to realclimate here, referring, inter alia, to some CA analyses.
Smith, T. M., and R. W. Reynolds (2005), A global merged land air and sea surface temperature reconstruction based on historical observations (1880-1997), J. Climate, 18, 2021-2036.
Folland, C. K., N. A. Rayner, S. J. Brown, T. M. Smith, S. S P. Shen, D. E. Parker, I. Macadam, P. D. Jones, R. N. Jones, N. Nichols and D. M. H. Sexton (2001), Global temperature change and its uncertainties since 1861, G.R.L, 28, 13, 2621–24, (2001GL012877).
Brohan et al 2006,…
Last edited by alexey; March-4th-2010 at 11:37 PM.
Urban Heat IslandWe summarized the Urban Heat Island (UHI) effect and the treatment of the UHI in assessing the climate trends by Hadley, NOAA and the IPCC in this pdf. We will summarize some of the points and then talk about GISS’s (Goddard Institute for Space Studies) so called UHI adjustments.Oke found that the warming can be directly tied to population. Oke (1973) found evidence that the UHI (in oC) increases according to the formula
There is no real dispute that weather data from cities, as collected by meteorological stations, is contaminated by urban heat island (UHI) bias, and that this has to be removed to identify climatic trends. Disputes center on whether corrections applied by the researchers on whom the IPCC relies for generating its climatic data are adequate for removing contamination.
And the UHI effect occurs not only for big cities but also for smaller towns that grow. For example, Oke (1973) and Hoyt (2002) have shown a town of 1000 could see a warming of 2C or 3F) especially in winter. Hinkel et al (2003) showed even the village of Barrow, Alaska with a population of 4600 has shown a warming of 2.2C (3.4F) in winter over surrounding rural areas.
UHI= 0.73 log10 POP
wherepop denotes population. This means that a village with a population of 10 has a warm bias of 0.73C, a village with 100 has a warm bias of 1.46 C, a town with a population of 1000 people has a warm bias of 2.2 C, and a large city with a million people has a warm bias of 4.4 C (Oke, 1973).
Tom Karl et al 1988employed a similar scheme for the first USHCN data base (probably the best data set available at any time). He noted that at that time the national climate network was predominantly rural or small towns population below 25,000 (as of 1980 census) and that a UHI effect was clearly evident.
He noted that the UHI warming was clearly greatest with respect to minimum temperatures with a slight cooling shown for maxima.
From: Karl, T.R., H.F. Diaz, and G. Kukla, 1988: Urbanization: its detection and effect in the United States climate record, J. Climate,1, 1099-1123.
He did note that because of the bias towards smaller town or rural stations, the net contamination by UHI on the regional or national scale was still relatively small but that significant anomalies showed up in rapidly growing population centers (and thus should be addressed which USHCNv1 did).
In 2007, NCDC in its version 2 of USHCN inexplicably removed the Karl UHI adjustment and substituted a CHANGE POINT ALGORITHM that looks for sudden shifts (discontinuities). It is suited for finding site moves or local land use changes (putting a paved road or building next to sensors or shelters) but not the slow ramp up characteristic of a growing town or city.
Note how this change introduced a warming in the early 1900s cold period, a warming in the middle century cold period and significant boost in the late 1990s and first half of this decade. The net result is to make the recent warm cycle max more important relative to the early century max in the 1930s. The change can be seen clearly in this animation. (courtesy of climate skeptic). This serves to virtually eliminate the inconvenient finding that the 1930s was as warm or warmer than the recent warmth much as Mann’s (Gavin’s partner in crime at RC) hockey stick conveniently removed the hard to explain away Medieval Warm Period, shown clearly by Mcintyre and Mckitrick and then Wegman backed up by good detective work by the Idsos at CO2 Science here to be very real.
THE GLOBAL DATA BASESJones et al 1990 (Hadley) had concluded that UHI bias in gridded data could be capped at 0.05 deg C (not per decade, per century). Peterson et al 1999 agreed with the conclusions of Jones et al. (1990) and Easterling et al. (1997) that urban effects on 20th century globally and hemispherically averaged land air temperature time-series do not exceed about 0.05°C over the period 1900 to 1990. Peterson (2003) and Parker (2004) argue urban adjustment thus is not necessary.
NCDC for GHCN regarded urban areas as those with populations exceeding 10,000. Remember, Oke, the 2008 winner of the AMS Helmut Landsberg Award for his work on UHI showed warming for even 1,000 and smaller population towns.
All these ignore the findings of the more than half a dozen peer reviewed papers in this pdf that the lack of adequate UHI and local land use change adjustment could account for up to 50% of the warming since 1900 (the exaggeration I talked about with Lou Dobbs)
NASA GISSIs NASA better? Steve McIntyre has taken an in depth look at the data adjustments made in the NASA GISS data set and the findings are summarized very well in the following pdf Correct the Correction by Ken Gregory..
NASA’s Goddard Institute of Space Studies (GISS) publishes a global temperature index.
The temperature record is contaminated by the effects of urban development and land use changes. NASA applies an "urbanization adjustment" to adjust the temperature histories to eliminate these effects. The resulting GISS temperature index is supposed to represent what the temperatures would have been in the absence of urbanization and land use changes. Most scientists assume that these adjustments are done correctly.
An audit by researcher Steve McIntyre reveals that NASA has made urban adjustments of temperature data in its GISS temperature record in the wrong direction, with almost as many urban areas adjusted to show more warming as less warming.
The audit shows that 74% of the USA stations are adjusted, but only 37% of the rest of the world stations are adjusted. There are almost as many negative adjustments as positive adjustments in the rest of world stations. The contiguous USA land area is only about 7% of the world surface area, so the other stations have a much larger effect on the global temperature index.
GISS uses two different methods of categorizing stations as rural or urban. Stations in the USA, southern Canada and northern Mexico are classified based on the amount of night time light measured by satellites from the station locations. Unlit stations are classified as rural stations.
(Note: Anthony Watts and Steve McIntyre have shown some errors in classification using night lights).
Outside of the USA, southern Canada and northern Mexico, GISS uses population data to define rural stations. Hansen et al 1999 provided the following definitions for "rural", "small" and "urban": "We use the definition of Peterson et al 1997 for these categories: that is, rural areas have a recent population of less than 10,000, small towns between 10,000 and 50,000 and urban areas more than 50,000. These populations refer to approximately 1980."
The GISS sites are defined to be "rural" if the town has a population of under 10,000. Unfortunately, the population data utilized by GISS to classify the stations is out of date. Stations at cities with populations greatly exceeding 10,000 are incorrectly classified as rural. For example, in Peru, there are 13 stations classified as rural. Of these, one station is located at a city with a population of 400,000. Five stations are at cities with populations between 50,000 and 135,000.
Steve McIntyre says here, "If the supposedly "rural" comparanda are actually "urban" or "small" within the Hansen definitions, then the GISS "adjustment" ends up being an almost completely meaningless adjustment of one set of urban
values by another set of urban values. No wonder these adjustments seem so random."
Here is an example of an urban negative adjustment from Peru:
Note that the raw data shows no warming, but after applying the GISS urban adjustment, the adjusted data shows a significant warming trend. The adjustments are applied to reduce the past temperatures by up to 3 degrees Celsius. This is a very large adjustment when compared to the total warming of the twentieth century of 0.6 Celsius estimated by the IPCC.
The data shows that the stations classified as rural are almost as likely to have as much a warming trend as urban stations. Why would almost half of the urban stations have lower warming trends than the nearby rural stations? It is very unlikely that heat sources near urban stations were gradually removed.
A population increase of 500 in a town of 2000 people would have a much larger effect on temperature measurements than the same increase in a city of 500,000 people. A city with a growing population generally increases its area. A temperature station inside the city would be little affected by the expansion of the suburbs. However, a temperature station located just outside a city would be greatly affected by the city expanding around the station. This effect is shown in the following diagram.
A hypothetical urban station is shown located in a city and a rural station is located outside the city in the year 1920. By 1960, the city has grown out to the rural station. The city growth has little effect on the urban station, but a much larger affect on the rural station. By 2000, the rural station is completely surrounded by the city, so it has the same temperature as the urban station.
As indicated in the graph, the unadjusted rural temperature trend is much greater than the urban station trend.According to the GISS urban adjustment procedure, the urban station trend is increased to match the rural station trend by reducing the past temperatures.
A proper urban correction algorithm would reduce the warming trends of both stations to make an adjusted temperature record represent what would have happened if nobody lived near the stations.
Ross McKitrick and Patrick Michaels published a paper in December 2007 that shows a strong correlation between urbanization indicators and the "urban adjusted" temperatures, indicating that the adjustments are inadequate. The conclusion is: Fully correcting the surface temperature data for "non-climatic effects reduce the estimated 1980-2002 global average temperature trend over land by about half."
Dutch meteorologists, Jos de Laat and Ahilleas Maurellis, showed (2006) that climate models predict there should be no correlation between the spatial pattern of warming in climate data and the spatial pattern of industrial development. But they found this correlation exists, and is statistically significant. They also concluded it adds a large upward bias to the measured global warming trend.
These studies convincingly show that the urban corrections fail to correct for the effects of urbanization, but do not indicate why the corrections fail. The audit of GISS urban adjustments by Steve McIntyre, shows why the corrections failed. Governments around the world intend to spend billions of dollars based on the belief that the temperature indexes are properly corrected for the effects of urbanizationNASA GISS should be commended for attempting to correct for urbanization, but their efforts may be best described as a Chinese fire drill with lots of movement but no resulting impact.
"Global warming", "climategate" and now just... "climate change"?
Jesus. Get real and stop arguing. You'll never get to the end of the semantics, let alone the science.
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