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Satellite measurements of warming in the troposphere

What the science says...

Select a level... Basic Intermediate Advanced

Satellite measurements do show warming in the Tropsphere when a cooling bias from the Stratosphere is removed. Warming trends agree well with surface temperatures and model predictions except near the Poles. Differences between various analyses are largely due to analysis techniques and compensations for satellite data issues.

Climate Myth...

Satellites show no warming in the troposphere
"Satellite measurements indicate an absence of significant global warming since 1979, the very period that human carbon dioxide emissions have been increasing rapidly. The satellite data signal not only the absence of substantial human-induced warming but also provide an empirical test of the greenhouse hypothesis - a test that the hypothesis fails." (Bob Carter)

The History of Tropospheric Temperature Measurement by Satellite

NASA has been building and launching the Tiros series (Television Infrared Observation Satellite) weather satellites since 1961. The satellites’ design has evolved over time. And after launch they are often operated by other agencies. The ones used for Tropospheric temperature measurement are operated by NOAA (National Oceanographic and Atmospheric Administration) beginning with Tiros N in Oct 1978 then NOAA 6 in June 1979 through to NOAA 19 Feb 2009. These satellites are designed principally as Weather Satellites; their use in Climatology is a secondary role. Also used in temperature measurement is the NASA AQUA satellite launched in May 2002 as a research Satellite, part of the NASA A-Train.

Tiros

A general overview of the spacecraft and their equipment and roles can be found here

What the satellites do

These satellites have Microwave Sounding Units (MSU) that read the Brightness Temperature of microwave signals from below in 4 separate frequencies radiated by Oxygen molecules. These frequencies tend to originate at different altitudes in the air column below the satellite and reflect the temperature at that altitude. I say tend because this isn’t exactly true and will matter later in the discussion. Using microwave signals associated with Oxygen has the advantage that microwaves are not substantially blocked by the atmosphere and Oxygen is evenly distributed throughout the atmosphere so its concentration only varies by negligible amounts. So the temperature signal from Oxygen is easy to detect, and isn’t going to be distorted by concentration changes. 

As the satellite orbits the Earth the MSU continually scans a swathe below the spacecraft, at nadir (looking straight down) and to the limits of the instrument on each side. Also on each scan the MSU calibrates its readings by taking readings from 2 other sources – cold deep-space, and an on-board, instrumented, hot reference source.

The satellites are Polar Orbiting & Sun Synchronous. Each makes around 14 orbits a day. Their orbit takes them nearly over the poles and the plane of the orbit lines up with the Sun. This is important because it ensures that each point on the Earth is always measured at the same time of day – Solar noon and Solar midnight.

On satellites up to NOAA-14, MSU’s were used. On later satellites, including AQUA, Advanced Microwave Sounding Units (AMSU) were fitted. These are more advanced designs that scan in more detail and over more frequencies, but the basics of how they work are the same. In this discussion I will refer mainly to MSU’s. The same concepts apply to the AMSU’s. 

Some Science

 This paper Grody 1983 (section 2) contains a discussion of the science of Microwave Sounding, and in particular the existence of Weighting Functions derived from solving the Radiative Transfer Equation. “…the temperature weighting function…defines the contribution of temperature at different altitudes to the brightness temperature.”. These functions are produced by summing the contribution at each frequency of microwave emissions from multiple levels in the atmosphere, taking into account the radiating behaviour of the atmosphere, pressure, temperature, path length etc.  Weighting Functions

Grody 1983 section 2

Here, as is common with atmospheric measurements, altitudes are given as pressures rather than kilometres. The dotted lines are the weighting functions for the extreme side parts of the scans while the solid lines are for the nadir view. The fact that the weighting functions at different frequencies have very different profiles wrt altitude is what allows us to measure temperatures at these different altitudes. Each frequency obtains most of its signal from a band of altitude. This altitude behaviour adds a major complication to measurement however; More on this later… 

The signal received by the MSU in its target frequency is made up of three components: Signals from the atmosphere radiated up to the satellite, signals from the atmosphere radiated down and reflected off the Earth’s surface, and signals emitted by the Earth itself.

The 4 frequencies are designated MSU Channels 1 (50.30 GHz), 2 (53.74 GHz), 3 (54.96 GHz), and 4 (57.95 GHz). Because the peak of the weighting function for Channel 1 is so close to the surface, this has a rather high component being emitted from the surface and is not very useful for Tropospheric temperature measurement because of this surface ‘contamination’. For the other channels, the surface component is much smaller but still needs to be allowed for. And this differs over land and sea. 

Some Nomenclature

A range of terms are used in the following discussion so I will summarise their meaning here:

Terminology

Meaning

T1, MSU Channel 1 Real channel peaking near ground level. Seldom used

T2, MSU Channel 2, TMT

Real channel peaking in mid to lower Troposphere. Stratospheric bias not removed
T3 MSU Channel 3, TTS, TUT Real channel peaking in mid to upper Troposphere. Stratospheric bias not removed

T4, MSU Channel 4, TLS

Real channel peaking in lower Stratosphere.
TLT Synthetic channel derived from T2, peaking in lower Troposphere, Stratospheric bias is removed

 

 

 

 

 

 

 

Who Analyses the data

Data from the various instruments onboard the NOAA satellites are distributed to a wide range of organisations for various purposes. For Climatological temperature measurement the two main groups performing this regular analysis of the data from the MSU’s and providing temperature products are at the University of Alabama, Huntsville (UAH) & at Remote Sensing Systems in California (RSS). A number of other research groups have also done analyses of the data, but to investigate the methodology, not to produce regular temperature series products. To produce a long term temperature series from the satellite data these groups need to address a number of issues: 

Satellite Problems:

NOAA-B, 1980 failed to achieve orbit. NOAA-13 had a catastrophic power failure 2 weeks after launch. NOAA-9 only had a relatively short overlap (3 months) with its follow-on satellite NOAA-10. Questions have been raised about the calibration of NOAA-16.

Overlap between satellites.

Each satellite has slightly different calibrations, orbits etc. To get a long term temperature series, you need to ‘splice’ together the data from various satellites, launched and de-activated at different times. You need enough overlap between the operating lives of each satellite to compare their results to establish a common baseline. Many of the satellites have had quite long lives so another factor is degradation of the equipment and ‘drift’ in their calibrations. There is then the question of whether to use a new satellite’s data with its overlap issues or continue using an older satellite with its ageing issues. Some commentators have suggested that a major part of the discrepancy between the UAH & RSS products is due to the different methods they have used to handle the limited overlap of NOAA-9 & NOAA-10, perhaps as high as 65% of the difference. The following graph shows the difference between UAH & RSS temperature series for channel T2. The divergence is noticeable from around 1987 when NOAA-9 & NOAA-10 had their limited overlap period. The upper line on each graph is the difference between RSS & UAH. These graphs only go to 2004.

RSS vs UAH 

Switching from MSU to AMSU:

Since the AMSU has a different number of channels at slightly different frequencies, this makes ‘splicing’ their data to that of the older MSU’s more complex.  

Orbital Decay:

The NOAA satellites do not have propulsion systems to correct for decay in their orbit due to friction from the very top of the atmosphere. So their altitude slowly drops over time. This has an effect on the readings; in much the same way as changing the scan angle alters the weighting function. This must be compensated for. Orbital decay is not always even. Changes in solar activity cause the Earths atmosphere to bulge & contract, changing the decay rates. However, the AQUA satellite does have propulsion so does not suffer as much from these problems. 

Instrument Body Effect:

This is the problem of the satellite experiencing varying heating and cooling as it travels around its orbit. The hot target is meant to be fixed to a single temperature but actually they experience some change over the life of the satellite. Also the body of the MSU warms and cools and this can affect the readings it takes. Some of this IBE can be adjusted for after launch by analyses that compare between satellites. Also, over long periods these variations will tend to average out. But not all errors can be removed. 

Diurnal Drift:

Earlier I mentioned that the satellites are in Sun Synchronous orbits and are meant to stay aligned with the Sun so that they always cross the equator at the same time – the Local Equator Crossing Time (LECT). If they don’t then the normal daily temperature cycles below (the Diurnal cycle) will start to add a false bias to the data. To stay Sun Synchronous the satellite’s orbit has a small precession, just less than one degree per day. However, this precession isn’t perfectly accurate and small drifts in this can introduce a ‘diurnal drift’ for each satellite, slowly changing it’s LECT. Drifts of up to 0.5 hr/year have been observed. So a Diurnal Drift correction is needed for each satellite. The two groups – UAH & RSS have used different methods to achieve this.  

UAH use data from view angles to left & right of nadir at fixed times in the orbit to look at different times of day below. This data allows a calculation that can remove the effect of the drift rate. The approach is simple but the calculations can magnify the effects of other uncertainties. 

RSS take the approach of using a high resolution climate model to simulate the expected daily variations beneath the satellite and use this to remove the diurnal drift. The modelled simulation is validated against the actual daily temperature ranges observed by the satellite. This method uses a simulation but has much less sampling noise compared to the UAH method. 

And as the following graph shows, the net effect is that the UAH method has added a cooling bias over time while RSS’s adds a warming bias to the raw temperature data.

 RSS vs UAH DD Correction


Figure 2: Diurnal drift corrections by UAH and RSS. UAH corrections add an overall cooling effect. RSS corrections added an overall warming effect. Both teams show strongest corrections in the tropics but in opposite directions.

Sea Ice & Summer Melt Pools

A complicating factor in the Polar Regions is surface emissions from ice. These make up the normal surface emissions that have to be allowed for in calculating temperature but with the large seasonal variations in sea ice extent, no single surface factor for these regions can be used. Similarly the appearance of melt pools on the ice in summer confuses the picture since these emit like water not ice. For this reason the temperature products don’t go all the way to the poles.

Stratospheric Biasing: Why T2 isn’t what it seems.

Twice I have mentioned that the way the microwave signal is generated at different altitudes in the atmosphere is important. Go back and look at the first figure, of weighting functions. The horizontal line at 200 mbar marks the approximate starting height of the Stratosphere. (this actually varies from 1l km near the pole to 17 km at the equator). Look at how much of each curve is above this line. And recall that one of the major effects of AGW is a cooling of the Stratosphere. So Stratospheric cooling adds a cooling bias to the microwave signals. The signal the satellite measures underestimates the Tropospheric temperature. This is most an issue with channels T2 & T3. For T2, around 15% of the signal originates in the Stratosphere and since the Stratosphere has cooled much more than the Troposphere has warmed the effect of this is more than15% of the reading. T3 is split almost 50/50 between the 2 layers. T4 on the other hand gets most of its signal from the Stratosphere with very little from the Troposphere. As a result T2 & T3 significantly underestimate the warming that has occurred in their nominal altitude band. Without some form of correction, they are almost useless.  

T2 vs. TLT

The problems with Stratospheric cool biasing were recognised early and in 1992, Spencer & Christy at UAH introduced a new temperature product to remove the Stratospheric bias and focus more on the lower Troposphere. This removed most of the bias by mathematically combining readings from multiple view angles on the same scan to produce a reading weighted more strongly to the lower Troposphere. This was originally called MSU2LT and later with the addition of AMSU readings MSUTLT. This method produces the lower Troposphere weighting expected but is vulnerable to significantly increased sampling errors – essentially taking the difference between two samples will magnify the sample errors. Also, by looking at an East/West swathe, they are sensitive to temperature variations across the swathe. They are also more sensitive to direct surface emissions since they are magnifying the lower level signal.  

However, this approach was a significant advance in reading lower Tropospheric temperatures. In 2005 RSS also introduced a TLT product using the same nadir/side scan approach and in this work they introduced the different Diurnal Drift compensation described above. 

Fu et al 2004, 2005

In 2004/2005 Qiang Fu, Celeste Johanson et al published an alternative method for removing the Stratospheric bias from the T2 signal. Since the T4 channel is predominantly Stratospheric in origin they removed a proportion of the T4 signal from the T2 signal to remove the Stratospheric bias. In order to determine how much to remove, they used radiosonde data to establish a vertical temperature profile for the atmosphere. Then they determine by a least squares regression technique the appropriate weight to give to T2 & T4. They performed this on global, hemispheric and tropical zones on both the UAH & RSS data, to calculate a temperature series for each between 850 & 300 hPa, producing the following trend values:    Fu et al 2004 Trends

Note that these values were produced in 2004, before RSS had added their Diurnal Drift compensation. And the following shows the modified weighting function from Fu et al: Fu et al Weighting Function

Their weighting function has a broader weighting over the entire Troposphere than the TLT products so is likely to be more representative of the overall Troposphere. NOAA maintains a comparison temperature record for UAH, RSS & the Fu et al adjustments to them here.

The technique of Fu et al has limitations. It depends on an independent source for the vertical temperature profile it uses – the Radiosonde record. This record suffers from limited geographic coverage and has its own issues with data quality. Also the profile may alter over time. And since it uses profiles averaged over large regions, it is not useful for estimating regional trends other than very approximately. However it provides an important validation of the broad results from the TLT products.

In further work here Fu et al used a similar technique to their 2004 study but instead of using radiosonde data they performed a correlation directly between T2 and T4 directly to produce a mid Troposphere result TTT and between T2 and the less frequently used T3 channel to produce a lower Troposphere series TTLT, removing the Stratospheric bias from both and showing results for the tropics. The resulting trends are: Fu et al 2005

The data for this only covers 1987 to 2003 since the T3 Channels on earlier satellites were unreliable prior to 1987. They also critique the UAH data, suggesting that their results are un-physical. However, since their data only goes to 2003, it does not include more recent corrections by UAH. 

Vinnikov & Grody 

In 2005 Vinnikov & Grody et al published another analysis of MSU data trends. Based on their previous work, it used a quite different, frequency & statistically based method to determine the underlying trends for the MSU measurements. They also consider additional issues related to calibration errors. Instead of assuming that there is a linear calibration error associated with the hot target calibration, they allow for this calibration varying over the satellites orbit due to external factors. They show that they can calculate this effect based just on latitude/longitude variation of the reading without needing to look at any time dependency.  

In their earlier work they had put a figure on trends of 0.22 to 0.26 C/Decade. In this work they are estimating 0.20 C/Decade. The following graphs show measured Surface, and their calculated TMT trends vs. latitude, and the same values calculated by climate models. The key discrepancies are at the poles with the modelled Northern Surface temps being much higher than measured Northern Surface temps, but modelled and measured Northern Troposphere values agreeing well. Surface temperature products don’t cover the Northern polar region or extrapolate from lower latitude measurements. Southern polar values also disagree but this is commonly ascribed to the effects of the Ozone hole which climate models do not include yet. There is significant agreement at mid and tropical latitudes.

 V&G Measured trends

  V&G Modelled Trends

Zou et al

In 2010 Zou et al published a new analysis method to produce low level MSU data for T2. This deals with removing many of the other calibration and inter-satellite correlation issues. Their method uses Synchronous Nadir Overpasses – points in time where two satellites are able to observe the same point below. This happens more commonly at high latitudes. Using this they are able to evaluate most of the onboard inter-satellite calibration issues since the satellites are receiving the same signal from below. The main outstanding areas that their analysis does not address are Diurnal Drift and Stratospheric cooling bias. They are not really trying to do this, instead producing a lower level data set to which others could apply further work. Their results are shown below. The data from their analysis can be obtained here.

 Trends from Zou et al

Monthly anomaly time series and trends for the global mean TMT, TUT and TLS, where TMT,TUT, and TLS represent deep-layer temperatures at mid-Troposphere, upper-Troposphere, and lower-Stratosphere.

So who is right?

So which teams analysis method is correct? Throughout the history of Tropospheric temperature measurement, the UAH analysis has always been lower than RSS for all temperature products. However, as time has gone by they have been drawing closer together. Currently their TLT trends are RSS 0.147 °C/decade and UAH 0.138 °C/decade which are down from earlier trends due to the slow down in warming in recent years. The convergence of their results may be due to the diminishing impact of the overlap problems between NOAA-9/NOAA-10. By comparison, the Fu et al method applied to RSS TMT & UAH TMT give RSS/FU 0.153  °C/decade , UAH/FU 0.112 °C/decade. Vinnikov & Grody have given around 0.20 °C/decade while Zou et al give 0.137 °C/decade; both without Stratospheric bias adjustment. 

Which of the techniques of UAH or RSS are correct? Both have weaknesses – UAH use comparisons between different view angles from one scan in two different parts of their analysis, magnifying the sensitivity to errors. RSS use a short term climate model rather than just data. Commentators seem to prefer the RSS analysis. Neither applies the lat/long dependent analysis of hot source calibration used by V&G so this could well increase their trends somewhat. And applying the Fu et al technique to V&G or Zou may give more divergent results again.  

Perhaps what can be said is that the UAH/RSS approach probably straddles the result their methods would find. Other methods suggest higher values. So a reasonable estimate at this point is that warming lies somewhere between the mid estimate of UAH/RSS and the figures that would be produced by V&G & Zou if Stratospheric cool biasing were removed. This suggests a long term trend of around 0.15 to 0.18 °C/decade for the lower Troposphere, much in line with the surface trends. And similar or higher for the mid-Troposphere based on the fact that Fu et al is looking at the entire Troposphere and V&G are showing higher Tropospheric than surface warming through the mid and tropical latitudes. 

So these various analyses clearly show that the Troposphere IS warming, as determined from multiple sources. And if anyone quotes satellite temperature data to make a point with you, make sure you ask them which series they are referring to. If they simply say ‘the satellite data from UAH’, they may not know what they are talking about.

Further reading:

The IPCC had this to say about the satellite record (section 3.4.1.2).

And Scott Church tells you even more than that up to 2005.

Addendum

This post also has relevance to the ‘There's no Tropospheric hot spot’ argument. Look at some of the graphs above. Fu et al 2004 and 2005 showed greater warming in the Troposphere than the surface for the Tropics & Southern Hemisphere for their adjustments to the RSS data. And Vinnikov & Grody also show greater warming in the Troposphere compared to surface records and also in agreement with models in the Tropics & Southern Hemisphere. Whereas the analyses by UAH, RSS & Zou are not able to show reliably what has happened in the mid & upper Troposphere

Last updated on 24 March 2011 by Glenn Tamblyn.

Printable Version  |  Offline PDF Version  |  Link to this page

Further reading

Where Bob Carter got his data

When I emailed Bob Carter querying about his data in the article above, this was his reply (28 Jun 2007):

"By mistake the graph that was reproduced in the Telegraph article was for the middle troposphere. Though it does not materially affect the argument or conclusions, I am embarrassed by it because it can be made to look as if I was pulling a swiftie - which I wasn't (intending to)."

I'll take Carter at his word that it was an honest mistake, although I've noticed he continues to state satellites show little to no tropospheric warming.

Comments

Comments 1 to 27:

  1. John, note that Christy (with Spencer) pulled a bait and switch on his CCSP report co-authors after they'd gone to all that trouble avoiding trashing his work in the report. I don't think they were amused. Also FYI, Roy Spencer has become Rush Limbaugh's in-house climatologist.
  2. Why start the trend line at 1982(?) Why not 1980? Or 1995? Wouldn't the trend line be pointing downwards then? Isn't playing with statistics fun? ;-)
  3. From none of those years would the trend line point downward. The most playng with statistics I've seen so far is from all those funny people who want to look at temp from 1998...
  4. Use raw data without corrections and see what they say.
  5. The current UAH satellite numerical data (lower atmospheric temperature differences from the 1979 thru 1998 average) is at http://vortex.nsstc.uah.edu/data/msu/t2lt/uahncdc.lt .
    According to this data, the AVERAGE GLOBAL TEMPERATURE for the first 8 months of 2008 is LOWER than the average from 2000 thru 2007 by an amount equal to 46.7% of the total linearized increase (NOAA data) during the 20th century. Since 2000, the CARBON DIOXIDE LEVEL HAS INCREASED by 13.6% of the total increase since the start of the Industrial Revolution.
  6. Looking at a graph generated from the Global data column it appears to trundle along with more or less equal + and - shifts until 1998 when there is a large rise of around +0.5C which declines to zero (2000)
    From then on there is a further large rise peaking in 2005 at +0.3C and then falling back to +0.1C

    From 1998 on the graph seems to include two events which have pushed the GMT well above mean.
    The CO2 record shows a ggod correlation to GMT fluctuations AND a spike in 1998 and subsequent spikes at the 'right' places...but the scale effect is wrong.
    1998 shows +0.5C when the CO2 rise was 1.0ppm (97-98)
    Later CO2 increases of the same magnitude are not matched by similar Temp rises.
    It seems some other factors are at work..??
  7. Have just graphed CO2 and GMT records from 1978 to 2007.
    By using the year on year change in CO2 plotted against GMT yearly change you get a remarkable match...
    I then factored the CO2 to get the 1998 peaks to align and then ALMOST ALL the peaks/troughs from 1978 onwards line up.
    Unfortunately can't find a way to put the graph on here!
    But you can always do it yourself ( unless someone tells me how to post it)
  8. OK, now extend the GISS/UAH comparison to 2008, and try to tell me we're recording more tropospheric warming than surface warming.

    http://wattsupwiththat.com/2009/01/18/giss-divergence-with-satellite-temperatures-since-the-start-of-2003/
  9. UAH data, (Christy,Spencer) have seen correction after correction, nearly all in the upward direction, and many errors discovered by others. I don't particularly trust their data.
  10. I think this website is naive.

    Doesn't it bother you, that every bit of new evidence that comes along that goes against the 'global human warming trend', is routinely dismissed with further 'data corrections'. Doesn't human bias mean anything to you?

    As soon as anyone starts making corrections, you can pretty much guarantee that such data becomes contaminated by human bias. Historians know this. Medical researchers know this. No mention of such here. Unbelievably naive.

    Karl Marx in the 19th century scoffed at the idea that factory managers running the economy would be even tempted to corrupt data, or distort process, or exploit the system or the workers. He also said the 'state' would wither away under such a system (note: it got bigger). Christians think everything in the New Testament, after hundreds of years of 'corrections' to the concoted manuscripts, accurately reflects what occurred in the 1st century.

    What a load of mumbo jumbo. I wouldn't trust these 'corrections' with a ten foot pole, and I don't know how someone could be so naive as to think that academic/ bureaucrats can be trusted with such numerous 'corrections'.

    'What the science says'. Communists would have said 'what the Party/Stalin/Our dear Leader says', christians would say 'what the bible says' (even when it doesn't), and this website claims corrections that support global warming by humans is 'what the "science" says'. Or is it what a few academic /bureaucrats want it to say?

    According to the discussion above, corrections by RSS had to be done twice (once by people 'within' RSS, once by RSS-what is the difference??), to get the desired result. This is known as a) cooking and b) re-cooking the cooking. Isn't one cooking enough? What a joke. I'm sure Stalin would have only needed one cooking, or the cookers above would have all been Gulaged.

    If there really was issues with splicing and diurnal drifts etc, then discrepancies should go both ways. They don't-they nearly always get cooked upwards. If you cook the data long enough, you get chocolate coated global warming.

    A telling statement concludes at the end "this error is most likely due to data errors", referring to discrepancies between satellite measurements and model predictions in the tropics. There are at least 3 errors and/or bias evident in this statement.
    1) it is not an 'error', it is a difference between a model and a prediction, to say it is an error is to assume the data is wrong, not the model. The statements preceeding it say no such thing, they say the issue is still open, NOT that there is an error.You have pre-assumed a conclusion, and it is therefore a distorted statement.
    2)The statements previous to this do not say 'it is most likely due to data errors', you yourself have concluded and enhanced this, from the previous paragraphs, and you are also asserting it is what John Christy etc says-he simply says 'the issue is still open', and doesnt mention anything about 'likelihood'-you have added this yourself. This is what is known as cooking and re-cooking, to get a desired result. If the same was done to the data, it would be invalid.
    3) And also, you have repeated the word 'error' twice in this sentence, just to re-cook it, again. You have preassumed a conclusion, and cooked the statement twice. Stalin would not have been impressed, and would have advised that one cooking would have sufficed, or the GULAG.

    Then again, with this naivety, I could always send this website details of how they have access to millions of dollars in Nigerian oil money...
  11. Thingadonta: Frankly, your post gets closer to Truth than anything else I've read here. It is a history proven fact that humans, are...well human...and will do much to dig-in on a vested position to avoid 1) loss of credibility, 2) loss of real or perceived power, and the age old 3) loss of $$ (i.e funding).

    Real science has *never* been based on consensus -- it has been driven by those that are usually well outside the consensus. Otherwise, we'd all believe that the earth was the center of our universe.

    The lack of real, public debate on what may be the most important issue of our time does not exist. That is frightening.
  12. Refer to

    http://sciencespeak.com/MissingSignature.pdf

    This is a discussion by David Evans of the radiosonde data 1879-1999 which appears to show that there is no tropical hotspot. Since a tropical hotspot is a feature of the positive feedback due to water vapour, assumed by IPCC models, is this not a clear disproof of predictions of significant AGW?
  13. This just got used by someone in a Deltoid thread, claiming it's the state of contemporary science:

    http://spacescience.spaceref.com/newhome/headlines/essd06oct97_1.htm

    The page begins:
    Accurate "Thermometers" in Space
    The State of Climate Measurement Science


    October 2, 1997

    Just how accurate are space-based measurements of the temperature of the Earth's atmosphere? In a recent edition of Nature, scientists Dr. John Christy of the University of Alabama in Huntsville, and Dr. Roy Spencer of NASA/Marshall describe in detail just how reliable these measurements are....
  14. hank,
    point those guy to this post. It stars exactly with the issue of flawed UAH data analisys; the webpage they link is old, well before the flaw was corrected.
  15. One thing I don't get about the skeptical argument:

    Suppose that the tropospheric warming relative to the surface warming were less than the models predict.

    Won't that end up making the climate sensitivity bigger, not smaller?

    The troposphere is where heat is radiated back into space, so if it's responding less than expected to a forcing, then the result is less heat radiated from the earth = more heat retained = more temperature increase?
  16. Re #15 suckfish, what matters is the distribution of the tropospheric warming. If it is concentrated then there will be more radiated heat and less retained. If it is diffuse then less radiated and more retained. The average increase or decrease doesn't tell us that and is not as important.
  17. So rate of tropospheric warming is less than expectation.

    How was the expectation derived?

    Did it include, for example, heat transfer to oceans?

    Did it include increased rates of evaporation (due to surface warmth) and consequently of condensation/precipitation ("Water cycle"). Water molecules absorb sensible energy at the surface when they evaporate, and re-release that energy at whatever tropospheric height when they re-condense. That is, intensification of the water cycle results in energy "bypassing" tropospheric greenhouse gases as it departs earth.

    (Also, surface layers of ocean are now rather less opaque than previously thought (lower densities of phytoplankton), then insolation is penetrating to greater depths. This would tend to decrease surface temperatures because energy is not being absorbed at surface.)
  18. If anyone's interested, the amsutemps applet at uah.edu is up and running again, although it looks like Dr. Spencer hasn't yet had time to update the numbers with the days since the tornadoes (or perhaps that will run automatically with the next day's updates).
  19. Is there a prize for the finest examples of cherry picking?

    I just had a "skeptic" tell me that the satellite data is biased because they were launched in 1978 which was "one of the coldest periods on record". This was his "proof", a post from Steven Goddard:

    http://www.real-science.com/dessler-partially-correct

    The temperature data comes from a *single* location in Greenland (Nuuk)!
  20. Could you please add a direct rebuttal to the following article by Singer?
    Lack of consistency between modeled and observed temperature trends

    It attacks CCSP's modeling as being "an artifact."
    Response:

    [DB] Please, no All-Caps.

    [JH]CCSP = US Climate Change Science Program.
  21. It is not within my sphere of expertise to post a formal rebuttal, but I note two telling points:
    1) It is an opinion piece, an article, not a peer-reviewed scientific paper;
    2) Note the provenance: it was published in ENERGY & ENVIRONMENT VOLUME 22 No. 4 2011, which is an organ of the fossil fuel industry, not a scientific journal.

    So, the article fails the test of credibility right at the outset. Prudence dictates it should be regarded as a suspect source unless and until it is published in the formal scientific manner.

    I note that Singer has authored work which has been debunked here and elsewhere, but I am not dismissing this article on that ground.
  22. Sorry, meant to add this link to Sks debunking Singer:
    Fred Singer Denies Global Warming
  23. scienceteacher, you refer to CCSP modeling being an artifact, but actually Singer's claim is that Santer et al 2008's finding of consistency between modelled and observed results is "an artifact"... CCSP 2006 is mentioned in that section only as sharing some of the same authors as the Santer study.

    Singer makes a number of claims about the Santer study, but the primary one is that the modeled results do not match UAH satellite observations. This is essentially the same song and dance we've been hearing from Spencer & Christy (the developers of the UAH record) and debunked here.

    Finally, his history should be considered. This is Fred Singer. Name a major instance of 'scientific' denial in the past 40 years and he has been part of it. Tobacco doesn't cause cancer, asbestos is safe, acid rain doesn't exist, there is no danger from ozone depletion, et cetera... he was involved in all of them. No, his track record does not prove that he is wrong about AGW too... but it certainly shouldn't inspire confidence.
  24. Roy Spencer's latest claim that the tropospheric temperature proves the models wrong, has been aptly shot full of holes by Glenn Tamblyn and others.  For example, Spencer compared temperature observations from only the tropics, to model results between 20 and 20 latitudes.  Spencer compared model results for the surface, to observations of the "middle troposphere."  Spencer's observations of the "middle troposphere" actually are 1/4 from the stratosphere, which of course cools as a consequence of increased greenhouse gases.

  25. The question WHY the trend line was from 1982 to ~2009 intrigues me because it is said that climatology deals in 30 year intervals or more.  So 1979 to 2009 would be a "better" line.  But it looks like the chart would show nearly the same result.

  26. knaugle - The reason for starting in 1982 was the 60-month averaging Spencer used, which leads directly to some of the distortions in the graphing. The satellite temps are more sensitive (higher swings) to ENSO and yearly variation than the surface temperatures, and the 5-year average for 1982 (1979-1984) is one of the highest peaks above trend for the UAH record. 

    'Baselining' to a single point of the 60-month average, as Spencer did, gives an artificial negative offset to the UAH record, as below:

    60-month Temperatures and Offsets

    [Source]

    Blue: HadCRUT4 60 month average, Red: UAH 60 month average baselined to the common average 1980-1999, and Green: UAH using Spencers 60-month average and a single point (mis)alignment. 

    As you can clearly see, the 60-month UAH alignment shifts the entire record down, artificially offsetting the satellite temps and making surface and model temperatures seem much higher. When in reality UAH swings both above and below the surface temps, as per the graphs baselined for comparison over a period longer than their internal variability

    I don't know whether Drs Spencer and Christy did this alignment out of an error of confirmation bias or as a deliberate 'trick', but either way the graphs they presented with that 60-month baselining are deceptive. 

  27. knauble - Here's the same graph along with trends, and you can clearly see that the 60-month single point offset Spencer/Christy used results in the UAH trend sharing no common period with HadCRUT4 whatsoever. 

    60 Month averages with trends

    [Source]

    If trends don't cross in the baseline period, you cannot reasonably judge any divergence - this is a clear error on their part. And quite deceptive when their claim is that similarly baselined models are running too high - as they've artificially introduced an offset. 

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