UAH and RSS out for Feb 09, but show divergence

The UAH and RSS global temperature anomaly data was posted while Steve and I were at the ICCC in New York. I’m unable to setup a graph for these while I’m on the road, so a short table will have to do:

RSS (Remote Sensing Systems, Santa Rosa)
RSS data here (RSS Data Version 3.2)

RSS Jan09   .322
RSS Feb09   .230

UAH (University of Alabama, Huntsville)
Reference: UAH lower troposphere data

UAH Jan09   .304
UAH Feb09   .350

Oddly, a divergence has developed, and opposite in direction to boot.

I spoke with Dr. Roy Spencer at the ICCC this morning (3/10) and asked him about the data divergence. Dr. Spencer had not yet seen that data, since he has been attending the conference. The data of course has been released by his associates and staff back at UAH. Here is what he had to say:

“I believe it has to do with the differences in how diurnal variation is tracked and adjusted for.” he said. I noted that Feburary was a month with large diurnal variations.

For that reason, UAH has been using data from the AQUA satellite MSU, and RSS to my knowledge does not, and makes an adjustment to account for it. I believe our data [UAH] is probably closer to the true anomaly temperature, and if I’m right, we’ll see the two datasets converge again when the diurnal variations are minimized.”

For layman readers that don’t know what diurnal variation is, it is the daily variation of temperature due to the variation of incoming solar radiation from rotation of the earth on its axis.

It looks like this:



  1. Kenneth Fritsch
    Posted Mar 10, 2009 at 9:27 AM | Permalink

    So, Anthony, would it be fair to say that the differences are like night and day?

  2. BillA
    Posted Mar 10, 2009 at 11:03 AM | Permalink

    May I assume that the units are degrees Centigrade and are temperatures, not anomalies?
    Apologies for my ignorance on this.

    • Ryan O
      Posted Mar 10, 2009 at 11:11 AM | Permalink

      Re: BillA (#4), It’s anomalies, but the unit used for anomalies is degrees C.

    • Jon
      Posted Mar 11, 2009 at 6:25 AM | Permalink

      Re: BillA (#3),


      If I may add something. I’m new to this topic, and,like you, the use of the term “anomalies” has thrown me a few times. It’s not being used in the sense of events that are necessarily unusual, odd, or peculiar. In “climatespeak”, an anomaly seems to be an observation that diverges from a statistical average. Since virtually all observations diverge from a statistical average, the term “anomaly” is effectively synonymous with the term “observation” — at least in the world of climate science. In this case, the “anomalies” or “observations” are temperature measurements.

      Maybe someone could correct me on that.

      Now I just have to get used to the term “forcing”.

      • bender
        Posted Mar 11, 2009 at 11:23 AM | Permalink

        Re: Jon (#8),
        Bill A probably knows what “anomalies” are. What he didn’t realize – and what Ryan O corrected him on – is that anomalies are not unitless. They are expressed in the same units as the observations. A long-term mean of 30°C and an indivdual observation of 24°C implies an anomaly of -6°C. Still, Bill A’s real question is valid. You generally need to know if it is temperature or temperature difference that is being plotted. (In this specific case the point is moot. Because whether you plot hourly temperature or hourly temperature difference relative to some mean, the shape of the curves doesn’t change.)
        One thing that I find really annoying about most plots posted at CA – including many of Steve M’s – is that little attention is given to axis labels and legends. The plots above are an example.
        What’s to get about “forcing”? A variable outside the system (controlled from afar) affects the state of the system. This is called “forcing” because it forces the system to change state. Manmade GHGs are a forcing because it is a variable over which we nominally have some control. Solar is a forcing because it comes in from afar. Volcanoes are a forcing because the patterns of eruption are governed by tectonics, not climate thermodynamics. Variables that are considered internal to the climate system are not “forcings”. Instead, the term “feedback” is used to describe the cause-and-effect relationship amongst endogenous variables.

  3. jae
    Posted Mar 10, 2009 at 11:07 AM | Permalink

    That’s a pretty big divergence, I’d say! Something’s weird.

  4. shs28078
    Posted Mar 10, 2009 at 11:31 AM | Permalink

    Can someone explain to me how the monthly anomaly for Feb is .23 deg. But on their site ( the multi-decadal trend (1979-2/2009) fell from .157 deg/decade to .156 deg/decade from January to February? This has happened consistently over the last several months.

    • John M
      Posted Mar 10, 2009 at 5:06 PM | Permalink

      Re: shs28078 (#6),

      Those two number are only coincidentally similar in magnitude. One is a single data point and the other is per decade trend determined from almost three decades of data.

      If you scroll down this page, you’ll see that 0.23 is still below the least squares fit, hence the calculated slope falls when the new point is figured in.

  5. Larry Huldén
    Posted Mar 11, 2009 at 9:11 AM | Permalink

    Jon said: “In this case, the “anomalies” or “observations” are temperature measurements.”

    An anomaly is the difference between two measured values. They may represent single measurements or averages or both. A temperature trend can be expressed for example as the difference between the average temperature of one year against an average of ten preceeding years.

  6. BillA
    Posted Mar 11, 2009 at 11:54 AM | Permalink

    Thanks to Ryan O for identifying these as “anomalies”. It would have troubled AGW advocates had they been temperatures.

    Perhaps some one could now help me identify the base line for the UAH / RSS anomalies. If one is to address variations from an average, it would be useful to know what data was used to figure the average. Searches in Google, Wikipedia and NASA deliver a lot of links; none of those I chased defined the base line.

    The graph of Annual Temperature Anomalies ( ) starts around 1979, a cooler time. It then claims a trend. (If I were to run a trend line from the low temperature last night to the high temperature today, we would be extra-crispy by month end.)

    One source gives the date range for their monthly series as 1/1/1958 to 9/?/2005. But this lies outside the range of satellite records. ( )

    • Ryan O
      Posted Mar 11, 2009 at 12:17 PM | Permalink

      Re: BillA (#12), The baseline for both UAH and RSS is 1979-1999. This means they average the temperatures for each month during this period and set that to zero. So a +0.350 for Feb ’09 means that the average temp in Feb ’09 was 0.350 deg C higher than the 1979-1999 February average.

      • BillA
        Posted Mar 11, 2009 at 1:57 PM | Permalink

        Re: Ryan O (#13),
        Ryan O: Thank you for confirming that the range of the average used in figuring the anomaly was the same as that used in the graph. Very helpful! As noted in # 12, there are other averages.

        Re: Scott Lurndal (#15),
        Scott is correct. I was / am interested in the data source and method used to establish the baseline. If it is all satellite data, then perhaps there were several satellites and instruments involved in producing the data. If so, their data must have been reconciled with each other (as were TSI instruments). Also, I understand that absolute accuracy tends to drift as instruments age, and so must be adjusted.

        As a bear of little brain come late to the party, when I am told that something has changed, I become curious about “relative to what?”. Perhaps I should change my handle to “Pooh”.

        • DeWitt Payne
          Posted Mar 11, 2009 at 3:42 PM | Permalink

          Re: BillA (#16),

          If it is all satellite data, then perhaps there were several satellites and instruments involved in producing the data. If so, their data must have been reconciled with each other (as were TSI instruments). Also, I understand that absolute accuracy tends to drift as instruments age, and so must be adjusted.

          The answer to all of your questions is yes. Multiple satellites over time, most of them with sufficient overlap for cross calibration. Accuracy is checked by taking brightness temperature readings of deep space and a temperature controlled plate on the satellite as the detector rotates through a set of fixed observation angles for a two point correction of slope and intercept. The stability of the temperature and emissivity of the temperature plate contributes to the uncertainty budget. The data also have to be corrected for orbital decay, among other things. Orbital decay is less of a problem with the arrival of the AMSU on the Aqua satellite, which has station keeping capability. The detector rotation also means that the surface is observed at different angles at different times. IIRC, UAH uses this information in their t2lt algorithm and RSS doesn’t. RSS and UAH don’t always agree on the cross calibration between different satellites too.

    • bender
      Posted Mar 11, 2009 at 12:26 PM | Permalink

      Re: BillA (#12),
      I didn’t realize you were referring to the data files. I thought you were referring to the graphs. It’s obvious from the range in those data files that these are temperature differences (i.e. anomalies). Why do you need the base average if all you are interested in is “variations from an average”? It’s the same average being subtracted off every observation.

      Jon, the reason the word “anomaly” is used for a temperature deviation from mean is because when you plot these over an area you get a spatial pattern that reflects the atmospheric circulation “anomaly”. And it is fair to say that circulation is always “anomalous” – for the reasons Tom Vonk describes. (To the best of my knowledge the average American is not half-male & half-female. In bistable systems individuals do not regress to the population/ensemble mean.)

      • Scott Lurndal
        Posted Mar 11, 2009 at 12:53 PM | Permalink

        Re: bender (#14),

        I suspect that BillA is interested more in _how_ the baseline is determined than the actual baseline value

      • Jon
        Posted Mar 11, 2009 at 3:15 PM | Permalink

        Re: bender (#14),

        Ok. The term “forcing” seemed to be close what I was thinking of as an independent variable. Using my own words, a forcing is a causal factor in a climate system. A forcing has some natural variability but it can’t really be independently varied or controlled. At least not in a practical sense.

        In any case, I appreciate your explanation of these terms, as well as the other responses. I’ll go back to lurking now. Thanks. 🙂

  7. Posted Mar 11, 2009 at 3:49 PM | Permalink

    Re: Ryan O #13 (baseline):

    At least for RSS, the baseline is 1979-1998 (not 1999).

    Anomalies are computed by subtracting the mean monthly value (averaged from 1979 through 1998 for each channel) from the average brightness temperature for each month.”

    By the way, the RSS web page has much information to answer at least some of Bill A’s questions about baseline calculation, merging of data from different satellites and so on (and references to answer the rest).

    Re: shs28078 (#6),

    Can someone explain to me how the monthly anomaly for Feb is .23 deg. But on their site ( the multi-decadal trend (1979-2/2009) fell from .157 deg/decade to .156 deg/decade from January to February? This has happened consistently over the last several months.

    As noted above, the baseline is 1979-1998, so the trend line probably crosses 0 deg C anomaly around 1989, almost two decades ago. That means the trend line is now at about 0.3 deg C and any reading below that will cause the calculated trend to fall.

    Regarding “diurnal variation”, this explanation from the UAH README my be helpful:

    Update 3 Jan 2008
    We now have data from AQUA added to the time series beginning with day 221 of 2002. AQUA is a spacecraft with on-board propulsion and thus has stable station-keeping. Thus, AQUA’s AMSU will not be subject to diurnal temperature drifts. Upon comparison with NOAA-15’s AMSU, wefind only minor differences for their 5+ year overlap, with NOAA-15 being slightly warmer near the end of the time series for LT and MT. The error values for NOAA-15 are much smaller than what we indicated below.

    For previous satellites, the AMSU record has to be corrected for orbital decay, and resultant “diurnal drift.”

    Currently, to my knowledge, RSS uses NOAA-15 and not AQUA. UAH uses only AQUA.

    Having said all that, I believe this is not the only explanation for the differences noted above. For one thing, the UAH team has stated that NOAA-15 currently has a slight warming bias, yet the UAH February anomaly is higher than RSS. I’ll be posting a comment on this soon.

  8. Posted Mar 11, 2009 at 4:04 PM | Permalink

    By the way, here is the URL for RSS (sorry about that).

    Re: DeWitt Payne (#18),

    I think we’re pretty much on the same page here, except this part, where you wrote:

    The detector rotation also means that the surface is observed at different angles at different times. IIRC, UAH uses this information in their t2lt algorithm and RSS doesn’t.

    RSS explains the TLT algorithm:

    The brightness temperature for each channel corresponds to an average temperature of the atmosphere averaged over that channel’s weighting function. In the case of channel TMT, most of the signal is from a thick layer in the middle troposphere at altitudes from 4 to 7 km, with smaller contributions from both the surface and the stratosphere. Channel TLT uses a weighted average between the near-limb and nadir views to extrapolate the data to lower altitude, thus removing almost all of the stratospheric influence. For each channel, the brightness temperature can be thought of as the averaged temperature over a thick atmospheric layer.

    So both RSS and UAH use angle information in the respective “lower troposphere” temperature analyses, but in different ways.

    • Geoff Sherrington
      Posted Mar 11, 2009 at 6:38 PM | Permalink

      Re: Deep Climate (#20),

      Is it possible to give a quick statement about how satellite reflectance values are converted to temperature in degrees? There is much confusion about ground truthing; and if it was used, whose recored was used – I mostly see people writing of CRU-Hadley because of their sea coverage, but I do not know the pedigree of the comments.

      • Posted Mar 11, 2009 at 9:00 PM | Permalink

        Re: Geoff Sherrington (#22),

        Here’s an excerpt from the 2007 paper by Mears “Constructing Climate Quality Atmospheric Temperatures from Satellite Microwave Measurements”:

        “[AMSU and MSU] instruments measure the thermal emission from oxygen molecules, thus inferring the average temperature of a thick layer of the atmosphere. The height of the measured layer is determined by the opacity of the atmosphere for the microwaves being observed, and thus can be chosen by adjusting the microwave frequency. The instruments make measurements over a swath several hundred kilometers wide by scanning from side to side to measure the upwelling microwave radiation on either side of the satellite’s orbital track.”

        But I’m not sure that paper is available for free on the internet.

        If you want more detail, I’d try the RSS team paper by Schabel, Mears and Wentz, “Stable Long-Term Retrieval of Tropospheric Temperature Time Series from the Microwave Sounding Unit”.

        This and other RSS team references are in the analysis section of the RSS MSU web page.

    • BillA
      Posted Mar 11, 2009 at 6:52 PM | Permalink

      Re: Deep Climate (#20),
      You wrote: “By the way, here is the URL for RSS (sorry about that).”
      Thank you for the link to RSS / MSU. This helped me a lot. As I understand (or maybe do not understand), microwave soundings / readings on O2 absorption channels can be / are calibrated to atmospheric temperatures. These can be had at several layers of the atmosphere. (I am unclear about the distinction between “temperature” and “brightness temperature”.)

      I appreciate their straightforward point-of-fact that inter-calibration is needed for a climate quality dataset.

      Again, thank you for the reference. Now, if I could only find as good for UAH.

      • Posted Mar 11, 2009 at 9:07 PM | Permalink

        Re: BillA (#23),

        If anyone out there has a “one stop” URL for the UAH team analysis I’d be interested in it too. Spencer and Christy were the pioneers of these techniques, so obviously their papers are seminal.

        But my impression is that the info is spread around – e.g. at NASA, UAH and Spencer’s website – and not all the pages are maintained as far as I can tell.

    • Geoff Sherrington
      Posted Mar 16, 2009 at 1:15 AM | Permalink

      Re: Deep Climate (#19),

      Thank you for the lead-in reference. I’m digging deeper and becoming more fascinated by the capacity of the early satellites to produce large errors, mainly because there are sometimes many indirect steps in some procedures and the errors from each are not always combined. I’ll give examples later this month.

      OTOH, they did some magnificent work for their primary missions.

  9. Posted Mar 11, 2009 at 4:09 PM | Permalink

    … and anyone interested in my URL, it’s now corrected (sheesh).

  10. Posted Mar 11, 2009 at 10:03 PM | Permalink

    UAH uses the Aqua satellite, among others. Aqua has a controlled orbit, so that (oversimplifying) it always measures the temperature of the same places at the same time of day. Other satellites drift – so when you measure the temperature of a particular location, the time of measurement drifts. You have to make a “correction” for that – in other words, pull a number out [snip] to compensate for drift, make a [snip] guess as to how much error the drift is causing.

    Such [snip] guesses may be influenced by politics, and by the possibility that if your numbers differ from that of your colleagues, your colleagues will get cross with you.

    RomanM: Inappropriate language.

  11. Eric Anderson
    Posted Mar 12, 2009 at 7:48 AM | Permalink

    Deep Climate, wow, that’s quite an agenda!

    “In the coming weeks and months, I’ll be looking at the organizations that propagate climate science disinformation and the public relations professionals who have worked behind the scenes to ensure maximum impact of that disinformation. I intend both to “follow the money” (flowing primarily from special interests opposed to regulation or taxation of greenhouse gas emissions) and to “follow the science” (by exposing the most egregious flaws in the “evidence” against the attribution of contemporary climate change primarily to human causes). From time to time, I’ll also “follow the politics” and examine the various ties between the “skeptic” movement and the Conservative Party of Canada.”

    • Willem Kernkamp
      Posted Mar 13, 2009 at 11:05 AM | Permalink

      Re: Eric Anderson (#27),


      Why not stick with just

      following the science

      No amount of money or political connectedness can ultimately triumph over the science. To me, (an individual not connected to anything), it is disturbing that there are serious and traceable flaws in the established science that drives the political discussion. The most glaring one being the absence of the medieval warm period and little ice age from the famous hockey stick temperature reconstruction. This does not mean that we should not take the issues seriously. However, the climate science community must do some vetting of it’s knowledge base. Currently, this vetting is not happening for fear of loosing public support for the measures that are thought necessary to avert climate change. However, this is extremely unwise. The measures necessary to change the energy track of the world have a large impact. We are not going to convince the Chinese unless we have science that can be audited in the way that Steve McIntyre is trying to do here.


  12. Jaye Bass
    Posted Mar 12, 2009 at 11:38 AM | Permalink

    From time to time, I’ll also “follow the politics” and examine the various ties between the “skeptic” movement and the Conservative Party of Canada.”

    Maybe you can sick one of your inhuman rights tribunals on them…free speech…yea right.

  13. BillA
    Posted Mar 12, 2009 at 1:40 PM | Permalink

    This is about the best I can find from a Google search on “UAH roy spencer AQUA satellite design”

    UAH is a compilation of temperature records from satellite data, most recently from the instruments aboard the Aqua (and Terra) satellites.

    Taking Earth’s temperature
    Describes Advanced Microwave Scanning Radiometer (AMSU) aboard NASA’s Aqua satellite. References microwave sensor aboard TIROS satellite (1978). MSR apparently looks at the intensity of microwaves emitted by oxygen molecules. In space, each microwave sounding unit self-calibrates every cycle. Describes daily calibration and steps to remove four sources of error.

    Spencer, Roy, PhD, and Dr. John R. Christy. “Taking Earth’s temperature .” Educational. UAH News: Your Official UAH News Source.

    Science Writers’ Guide To Aqua
    Aqua’s Instruments (duplicated on TERRA)
    The Aqua satellite has six instruments onboard to measure and monitor Earth’s hydrologic cycle.
    These include (pages 3-4):
    • AIRS (Atmospheric Infrared Sounder)
    • AMSR-E (Advanced Microwave Scanning Radiometer – EOS)
    • AMSU (Advanced Microwave Sounding Unit)
    • CERES (Clouds and the Earth’s Radiant Energy System)
    • HSB (Humidity Sounder for Brazil)
    • MODIS (Moderate-Resolution Imaging Spectroradiometer)

    Measurements (Page 5):
    Note: ” | ” represents columns in the document’s table. Far left column represents “region” of interest.
    Region | Measurement | Instrument(s) Used
    Atmosphere | Aerosol Properties | MODIS, CERES
    | (Composition, Size, Distribution)
    | Atmospheric Humidity | AIRS, AMSR-E, AMSU, HSB, MODIS
    | Atmospheric Temperature | AIRS, AMSU, MODIS
    | Cloud Properties | MODIS, CERES
    | Greenhouse Gases | AIRS
    | Precipitation | AIRS, AMSR-E, HSB
    | Radiative Energy Fluxes | AIRS, AMSR-E, CERES,
    | (Emitted Thermal and Reflected Solar Radiation) | MODIS
    Cryosphere | Sea Ice | AMSR-E, MODIS
    | Snow Cover and Depth | AMSR-E, MODIS
    Land | Fire Occurrence | MODIS
    | Land Cover and Land Use Change | MODIS
    | Surface Temperature | AIRS, AMSR-E, MODIS
    | Surface Wetness | AMSR-E
    | Volcanic Effects | MODIS
    Ocean | Ocean Color | MODIS
    | Phytoplankton and Dissolved Organic Matter | MODIS
    | Sea Surface Temperature | AIRS, AMSR-E, MODIS
    | Sea Surface Wind Speed | AMSR-E

    Gutro, Rob, Krishna Ramanujan, and Jim Closs. “Science Writers’ Guide To Aqua.” NASA Goddard Space Flight Center, March 31, 2002.

  14. Posted Mar 13, 2009 at 9:20 AM | Permalink

    GISTEMP Feb ’09 is out and also reports a 0.1C cooling from January to February at 0.41C.

  15. Posted Mar 14, 2009 at 4:29 PM | Permalink

    As a side effect of looking at MM07 and S09 I noticed some interesting things about surface temperatures versus tropospheric temperatures as measured by satellite. I posted on it here.

  16. Deep Climate
    Posted Mar 14, 2009 at 11:11 PM | Permalink

    Getting back to the topic at hand, here is my first post on seasonal divergence in UAH trends from last week.

    I’m working on a second post focusing on monthly trends. The UAH trends vary much more than RSS and the surface data sets. Here’s a preview:

    In recent years, UAH has shown much higher anomalies in February than in later months (e.g. May), leading to a wide divergence of monthly and seasonal trends.

  17. Posted Mar 14, 2009 at 11:13 PM | Permalink

    Here is the correct link for the above chart if you want to see it full screen.

  18. Robinedwards
    Posted Mar 16, 2009 at 5:12 PM | Permalink

    Like several others I’ve downloaded the most recent RSS and UAH data, but unlike most other posters I’ve been looking at the full data set, from Dec 1978. First, I must say that the technical aspects of the measurements are beyond my ability to comment upon. I simply note that other posters warn of possible difficulties, which I shall perforce ignore, and I’ll accept, for present purposes, that the data are “reasonable”.

    The very strong signal from these data sets is of a highly significant positive coefficient, which we all can derive via standard or exotic (R!) software. The UAH and RSS sets are very similar, which is reassuring.

    However, as I have written in other postings, I am of the opinion that fitting a simple linear model to long and possibly complex data sets such as these and expecting to derive something really sensible and insightful is very optimistic. Of course, the highly suggestive coefficients generate a sense of security. There can be no doubt whatsoever as to their statistical significance. Whether or not they are of real practical value or import is, I suggest, open to question.

    What I advocate is a careful appraisal of the data to see whether a simple linear model over the whole time span of the data is indeed a sensible approach. In the current case I am sure that it is not.

    What I’d recommend to those who like to dissect data is that you do exactly that, and fit a three part composite linear fit. Here are some recommendations: Fit the Global UAH data from 1978 to 1993 to a linear model, and repeat the process for 1994 to 1997 and then for 1998 to Feb 2009. What you will find is that the time coefficient for each of these periods cannot be distinguished from zero, but that the mean values are very different. What has happened is that the data underwent remarkably clear step changes. Next, repeat the process for the RSS data (I worked with data averaged over the full set of locations to cut down the amount of work). You will find the same step change scenario.

    So, what has been going on? I have no idea, unfortunately, but the evidence for these abrupt changes is really quite strong. Something must have caused them. The possibility of instrument problems is remote, since UAH and RSS work independently. The remarkable agreement between the two sets is strongly suggestive of two very reliable technologies both detecting rapid changes in the signals they are receiving, which occur virtually at the same time.

    If you are wary about choosing “arbitrary” subsets of the data, just consider what observers carrying out analyses in “real time” would have found. They would have been unable to find significant changes over the time periods I’ve suggested (or similar time periods), but if their data sets included the points of abrupt change they would have found substantial (positive) coefficients. This is exactly what fitting a long term straight line does.

    I suggest that these ideas are worth following up by other readers of this blog, and I look forward to reading what you have made of it.

    What I’ve found is evidence for the following abrupt changes (approximate values):

    _____________UAH/Global ____ RSS/Average

    Late__ 1993 _____+ 0.09 _____0.13
    End___ 1997 ____ + 0.18 _____ 0.20

    Some formatting difficulties -sorry

    This is the briefest summary I can think of. I can supply the rest of the stats stuff if anyone wants it.


    • See - owe to Rich
      Posted Mar 22, 2009 at 12:03 PM | Permalink

      Re: Robinedwards (#36), I argued on something very similar with Tamino at RealClimate around August 2007. The data I was looking at were HadCRUT3 1986-1996 and 1996-2006, and I claimed a step change in 1996 and otherwise non-significant increases.

      However, if one simply invalidates the years 1993 and 1994 for Pinatubo and 1998 for super El Nino, which is not a totally unreasonable thing to do, then a single linear trend fits better than a step change, as I recall.

      So, the data are not that robust against censoring of extreme residuals known/believed to be connected to a physical process.

      I suspect you may be seeing the same sort of thing with UAH/RSS.


  19. Johan i Kanada
    Posted Mar 22, 2009 at 1:17 AM | Permalink

    UAH and RSS both attempt to measure the same thing, i.e. a global monthly average temperature anomaly.

    Now, the difference between the two is 0.13 C (for Feb 09), i.e. a difference of more than a tenth of a degree C. Hence, why would they claim results with a precision of one thousands of a degree?

    More appropriate statements would be:
    – UAH: Global average temperature anomaly for Feb 09 is approx 0.4, +/-0.2 (or more)
    – RSS: Global average temperature anomaly for Feb 09 is approx 0.2, +/-0.2 (or more)

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